Zero To Production In Rust

Luca Palmieri

Zero To Production In Rust

1. Foreword

1. What Is This Book About

1. Cloud-native applications

2. Working in a team

2. Who Is This Book For

2. 1 Getting Started

1. 1.1 Installing The Rust Toolchain

1. 1.1.1 Compilation Targets

2. 1.1.2 Release Channels

3. 1.1.3 What Toolchains Do We Need?

2. 1.2 Project Setup

3. 1.3 IDEs

1. 1.3.1 Rust-analyzer

2. 1.3.2 IntelliJ Rust

3. 1.3.3 What Should I Use?

4. 1.4 Continuous Integration

1. 1.4.1 CI Steps

2. 1.4.2 Ready-to-go CI Pipelines

3. 2 Building An Email Newsletter

1. 2.1 Our Driving Example

1. 2.1.1 Problem-based Learning

2. 2.1.2 Course-correcting

2. 2.2 What Should Our Newsletter Do?

1. 2.2.1 Capturing Requirements: User Stories

3. 2.3 Working In Iterations

1. 2.3.1 Coming Up

4. 3 Sign Up A New Subscriber

1. 3.1 Our Strategy

2. 3.2 Choosing A Web Framework

3. 3.3 Our First Endpoint: A Basic Health Check

1. 3.3.1 Wiring Up actix-web

2. 3.3.2 Anatomy Of An actix-web Application

3. 3.3.3 Implementing The Health Check Handler

4. 3.4 Our First Integration Test

1. 3.4.1 How Do You Test An Endpoint?

2. 3.4.2 Where Should I Put My Tests?

3. 3.4.3 Changing Our Project Structure For Easier Testing

5. 3.5 Implementing Our First Integration Test

1. 3.5.1 Polishing

6. 3.6 Refocus

7. 3.7 Working With HTML Forms

1. 3.7.1 Refining Our Requirements

2. 3.7.2 Capturing Our Requirements As Tests

3. 3.7.3 Parsing Form Data From A POST Request

8. 3.8 Storing Data: Databases

1. 3.8.1 Choosing A Database

2. 3.8.2 Choosing A Database Crate

3. 3.8.3 Integration Testing With Side-effects

4. 3.8.4 Database Setup

5. 3.8.5 Writing Our First Query

6. 3.8.6 Application State In actix-web

7. 3.8.7 actix-web Workers

8. 3.8.8 The Data Extractor

9. 3.8.9 The INSERT Query

9. 3.9 Updating Our Tests

1. 3.9.1 Test Isolation

10. 3.10 Summary

5. 4 Telemetry

1. 4.1 Unknown Unknowns

2. 4.2 Observability

3. 4.3 Logging

1. 4.3.1 The log Crate

2. 4.3.2 actix-web’s Logger Middleware

3. 4.3.3 The Facade Pattern

4. 4.4 Instrumenting POST /subscriptions

1. 4.4.1 Interactions With External Systems

2. 4.4.2 Think Like A User

3. 4.4.3 Logs Must Be Easy To Correlate

5. 4.5 Structured Logging

1. 4.5.1 The tracing Crate

2. 4.5.2 Migrating From log To tracing

3. 4.5.3 tracing’s Span

4. 4.5.4 tracing-futures

5. 4.5.5 tracing’s Subscriber

6. 4.5.6 tracing-subscriber

7. 4.5.7 tracing-bunyan-formatter

8. 4.5.8 tracing-log

9. 4.5.9 Removing Unused Dependencies

10. 4.5.10 Cleaning Up Initialisation

11. 4.5.11 Logs For Integration Tests

12. 4.5.12 Cleaning Up Instrumentation Code - tracing::instrument

13. 4.5.13 Request Id

14. 4.5.14 Leveraging The tracing Ecosystem

6. 4.6 Summary

6. 5 Going Live

1. 5.1 We Must Talk About Deployments

2. 5.2 Choosing Our Tools

1. 5.2.1 Virtualisation: Docker

2. 5.2.2 Hosting: DigitalOcean

3. 5.3 A Dockerfile For Our Application

1. 5.3.1 Dockerfiles

2. 5.3.2 Build Context

3. 5.3.3 Sqlx Offline Mode

4. 5.3.4 Running An Image

5. 5.3.5 Networking

6. 5.3.6 Hierarchical Configuration

7. 5.3.7 Database Connectivity

8. 5.3.8 Optimising Our Docker Image

4. 5.4 Deploy To DigitalOcean Apps Platform

1. 5.4.1 Setup

2. 5.4.2 App Specification

3. 5.4.3 How To Inject Secrets Using Environment Variables

4. 5.4.4 Connecting To Digital Ocean’s Postgres Instance

5. 5.4.5 Environment Variables In The App Spec

6. 5.4.6 One Last Push

7. 6 Reject Invalid Subscribers #1

1. 6.1 Requirements

1. 6.1.1 Domain Constraints

2. 6.1.2 Security Constraints

2. 6.2 First Implementation

3. 6.3 Validation Is A Leaky Cauldron

4. 6.4 Type-Driven Development

5. 6.5 Ownership Meets Invariants

1. 6.5.1 AsRef

6. 6.6 Panics

7. 6.7 Error As Values - Result

1. 6.7.1 Converting parse To Return Result

8. 6.8 Insightful Assertion Errors: claim

9. 6.9 Unit Tests

10. 6.10 Handling A Result

1. 6.10.1 match

2. 6.10.2 The ? Operator

3. 6.10.3 400 Bad Request

11. 6.11 The Email Format

12. 6.12 The SubscriberEmail Type

1. 6.12.1 Breaking The Domain Sub-Module

2. 6.12.2 Skeleton Of A New Type

13. 6.13 Property-based Testing

1. 6.13.1 How To Generate Random Test Data With fake

2. 6.13.2 quickcheck Vs proptest

3. 6.13.3 Getting Started With quickcheck

4. 6.13.4 Implementing The Arbitrary Trait

14. 6.14 Payload Validation

1. 6.14.1 Refactoring With TryInto

15. 6.15 Summary

8. 7 Reject Invalid Subscribers #2

1. 7.1 Confirmation Emails

1. 7.1.1 Subscriber Consent

2. 7.1.2 The Confirmation User Journey

3. 7.1.3 The Implementation Strategy

2. 7.2 EmailClient, Our Email Delivery Component

1. 7.2.1 How To Send An Email

2. 7.2.2 How To Write A REST Client Using reqwest

3. 7.2.3 How To Test A REST Client

4. 7.2.4 First Sketch Of EmailClient::send_email

5. 7.2.5 Tightening Our Happy Path Test

6. 7.2.6 Dealing With Failures

3. 7.3 Skeleton And Principles For A Maintainable Test Suite

1. 7.3.1 Why Do We Write Tests?

2. 7.3.2 Why Don’t We Write Tests?

3. 7.3.3 Test Code Is Still Code

4. 7.3.4 Our Test Suite

5. 7.3.5 Test Discovery

6. 7.3.6 One Test File, One Crate

7. 7.3.7 Sharing Test Helpers

8. 7.3.8 Sharing Startup Logic

9. 7.3.9 Build An API Client

10. 7.3.10 Summary

4. 7.4 Refocus

5. 7.5 Zero Downtime Deployments

1. 7.5.1 Reliability

2. 7.5.2 Deployment Strategies

6. 7.6 Database Migrations

1. 7.6.1 State Is Kept Outside The Application

2. 7.6.2 Deployments And Migrations

3. 7.6.3 Multi-step Migrations

4. 7.6.4 A New Mandatory Column

5. 7.6.5 A New Table

7. 7.7 Sending A Confirmation Email

1. 7.7.1 A Static Email

2. 7.7.2 A Static Confirmation Link

3. 7.7.3 Pending Confirmation

4. 7.7.4 Skeleton of GET /subscriptions/confirm

5. 7.7.5 Connecting The Dots

6. 7.7.6 Subscription Tokens

8. 7.8 Database Transactions

1. 7.8.1 All Or Nothing

2. 7.8.2 Transactions In Postgres

3. 7.8.3 Transactions In Sqlx

9. 7.9 Summary

9. 8 Error Handling

1. 8.1 What Is The Purpose Of Errors?

1. 8.1.1 Internal Errors

2. 8.1.2 Errors At The Edge

3. 8.1.3 Summary

2. 8.2 Error Reporting For Operators

1. 8.2.1 Keeping Track Of The Error Root Cause

2. 8.2.2 The Error Trait

3. 8.3 Errors For Control Flow

1. 8.3.1 Layering

2. 8.3.2 Modelling Errors as Enums

3. 8.3.3 The Error Type Is Not Enough

4. 8.3.4 Removing The Boilerplate With thiserror

4. 8.4 Avoid “Ball Of Mud” Error Enums

1. 8.4.1 Using anyhow As Opaque Error Type

2. 8.4.2 anyhow Or thiserror?

5. 8.5 Who Should Log Errors?

6. 8.6 Summary

1. Cover

2. Table of contents

Foreword

What Is This Book About

The world of backend development is vast.

The socio-technical context you operate into has a huge impact on the optimal tools and practices to tackle the problem you are working on.

For example, trunk-based development works extremely well to write software that is continuously deployed in a Cloud environment.
The very same approach might fit poorly the business model and the challenges faced by a team that sells software that is hosted and run on-premise by their customers - they are more likely to benefit from a Gitflow approach.
If you are working alone, you can just push straight to main.

There are few absolutes in the field of software development and I feel it’s beneficial to clarify your point of view when evaluating the pros and cons of any technique or approach.

Zero To Production will focus on the challenges of writing Cloud-native applications in a team of four or five engineers with different levels of experience and proficiency.

Cloud-native applications

Defining what Cloud-native application means is, by itself, a topic for a whole new book[1]. Instead of prescribing what Cloud-native applications should look like, we can lay down what we expect them to do.
Paraphrasing Cornelia Davis, we expect Cloud-native applications:

- To achieve high-availability while running in fault-prone environments;

- To allow us to continuously release new versions with zero downtime;

- To handle dynamic workloads (e.g. request volumes).

These requirements have a deep impact on the viable solution space for the architecture of our software.

High availability implies that our application should be able to serve requests with no downtime even if one or more of our machines suddenly starts failing (a common occurrence in a Cloud environment[2]). This forces our application to be distributed - there should be multiple instances of it running on multiple machines.
The same is true if we want to be able to handle dynamic workloads - we should be able to measure if our system is under load and throw more compute at the problem by spinning up new instances of the application. This also requires our infrastructure to be elastic to avoid overprovisioning and its associated costs.
Running a replicated application influences our approach to data persistence - we will avoid using the local filesystem as our primary storage solution, relying instead on databases for our persistence needs.

Zero To Production will thus extensively cover topics that might seem tangential to pure backend application development. But Cloud-native software is all about rainbows and DevOps, therefore we will be spending plenty of time on topics traditionally associated with the craft of operating systems.

We will cover how to instrument your Rust application to collect logs, traces and metrics to be able to observe our system.

We will cover how to set up and evolve your database schema via migrations.

We will cover all the material required to use Rust to tackle both day one and day two concerns of a Cloud-native API.

Working in a team

The impact of those three requirements goes beyond the technical characteristics of our system: it influences how we build our software.

To be able to quickly release a new version of our application to our users we need to be sure that our application works.
If you are working on a solo project you can rely on your thorough understanding of the whole system: you wrote it, it might be small enough to fit entirely in your head at any point in time.[3]
If you are working in a team on a commercial project, you will be very often working on code that was neither written or reviewed by you. The original authors might not be around anymore.

You will end up being paralysed by fear every time you are about to introduce changes if you are relying on your comprehensive understanding of what the code does to prevent it from breaking.

You want automated tests.
Running on every commit. On every branch. Keeping main healthy.

You want to leverage the type system to make undesirable states difficult or impossible to represent.

You want to use every tool at your disposal to empower each member of the team to evolve that piece of software. To contribute fully to the development process even if they might not be as experienced as you or equally familiar with the codebase or the technologies you are using.

Zero To Production will therefore put a strong emphasis on test-driven development and continuous integration from the get-go - we will have a CI pipeline set up before we even have a web server up and running!

We will be covering techniques such as black-box testing for APIs and HTTP mocking - not wildly popular or well documented in the Rust community yet extremely powerful.

We will also borrow terminology and techniques from the Domain Driven Design world, combining them with type-driven design to ensure the correctness of our systems.

Our main focus is enterprise software: correct code which is expressive enough to model the domain and supple enough to support its evolution over time.

We will thus have a bias for boring and correct solutions, even if they incur a performance overhead that could be optimised away with a more careful and chiseled approach.
Get it to run first, optimise it later (if needed).

Who Is This Book For

The initial response to a random tweet about the idea of spinning up this project has been overwhelming.
Hundreds of subscribers to a rushed email newsletter.
Several emails and private messages detailing this or that specific setup and the challenges they are currently facing.

I have written and re-written the table of contents more than a couple of times trying to zero in on what I want to cover. I realised by the second version of it that I can’t satisfy many of those emails and DMs.

The Rust ecosystem has had a remarkable focus on smashing adoption barriers with amazing material geared towards beginners and newcomers, a relentless effort that goes from documentation to the continuous polishing of the compiler diagnostics.
There is value in serving the largest possible audience.
At the same time, trying to always speak to everybody can have harmful side-effects: material that would be relevant to intermediate and advanced users but definitely too much too soon for beginners ends up being neglected.

I struggled with it first-hand when I started to play around with async/await.
There was a significant gap between the knowledge I needed to be productive and the knowledge I had built reading The Rust Book or working in the Rust numerical ecosystem.
I wanted to get an answer to a straight-forward question: > Can Rust be a productive language for API development?

Yes.

But it can take some time to figure out how.

That’s why I am writing this book.

I am writing this book for the seasoned backend developers who have read The Rust Book and are now trying to port over a couple of simple systems.

I am writing this book for the new engineers on my team, a trail to help them make sense of the codebases they will contribute to over the coming weeks and months.

I am writing this book for a niche whose needs I believe are currently underserved by the articles and resources available in the Rust ecosystem.

I am writing this book for myself a year ago.
To socialise the knowledge gained during the journey: what does your toolbox look like if you are using Rust for backend development in 2020? What are the design patterns? Where are the pitfalls?

If you do not fit this description but you are working towards it I will do my best to help you on the journey: while we won’t be covering a lot of material directly (e.g. most Rust language features) I will try to provide references and links where needed to help you pick up/brush off those concepts along the way.

Let’s get started.

1. Like the excellent Cloud-native patterns by Cornelia Davis!↩

2. For example, many companies run their software on AWS Spot Instances to reduce their infrastructure bills. The price of Spot instances is the result of a continuous auction and it can be substantially cheaper than the corresponding full price for On Demand instances (up to 90% cheaper!).
There is one gotcha: AWS can decommission your Spot instances at any point in time. Your software must be fault-tolerant to leverage this opportunity.↩

3. Assuming you wrote it recently.
Your past self from one year ago counts as a stranger for all intents and purposes in the world of software development. Pray that your past self wrote comments for your present self if you are about to pick up again an old project of yours.↩

1 Getting Started

There is more to a programming language than the language itself: tooling is a key element of the experience of using the language.
The same applies to many other technologies (e.g. RPC frameworks like gRPC or Apache Avro) and it often has a disproportionate impact on the uptake (or the demise) of the technology itself.

Tooling should therefore be treated as a first-class concern both when designing and teaching the language itself.

The Rust community has put tooling at the forefront since its early days: it shows.
We are now going to take a brief tour of a set of tools and utilities that are going to be useful in our journey. Some of them are officially supported by the Rust organisation, others are built and maintained by the community.

1.1 Installing The Rust Toolchain

There are various ways to install Rust on your system, but we are going to focus on the recommended path: via rustup.

Instructions on how to install rustup itself can be found at https://rustup.rs.

rustup is more than a Rust installer - its main value proposition is toolchain management.

A toolchain is the combination of a compilation target and a release channel.

1.1.1 Compilation Targets

The main purpose of the Rust compiler is to convert Rust code into machine code - a set of instructions that your CPU and operating system can understand and execute.
Therefore you need a different backend of the Rust compiler for each compilation target, i.e. for each platform (e.g. 64-bit Linux or 64-bit OSX) you want to produce a running executable for.
The Rust project strives to support a broad range of compilation targets with various level of guarantees. Targets are split into tiers, from “guaranteed-to-work” Tier 1 to “best-effort” Tier 3.

An exhaustive and up-to-date list can be found here.

1.1.2 Release Channels

The Rust compiler itself is a living piece of software: it continuously evolves and improves with the daily contributions of hundreds of volunteers.

The Rust project strives for stability without stagnation. Quoting from Rust’s documentation:

[..] you should never have to fear upgrading to a new version of stable Rust. Each upgrade should be painless, but should also bring you new features, fewer bugs, and faster compile times.

That is why, for application development, you should generally rely on the latest released version of the compiler to run, build and test your software - the so-called stable channel.
A new version of the compiler is released on the stable channel every six weeks[1] - the latest version at the time of writing is v1.43.1[2].

There are two other release channels:

- beta, the candidate for the next release;

- nightly, built from the master branch of rust-lang/rust every night, thus the name.

Testing your software using the beta compiler is one of the many ways to support the Rust project - it helps catching bugs before the release date[3].

nightly serves a different purpose: it gives early adopters access to unfinished features[4] before they are released (or even on track to be stabilised!).
I would invite you to think twice if you are planning to run production software on top of the nightly compiler: it’s called unstable for a reason.

1.1.3 What Toolchains Do We Need?

Installing rustup will give you out of the box the latest stable compiler with your host platform as a target.

Some of the tools we will be using on our development machine (e.g macro expansion) will rely on the nightly compiler. While nightly is discouraged for production workloads it is not a big deal if something fails on our local machine - we can live with that.

You can install the nightly compiler by running

rustup toolchain install nightly --allow-downgrade

Some components of the bundle installed by rustup might be broken/missing on the latest nightly release: --allow-downgrade tells rustup to find and install the latest nightly where all the needed components are available.

We are only specifying the release channel, nightly - rustup uses our host platform as default for the target. On my system, if I wanted to be explicit, I would have to use

rustup toolchain install \
nightly-x86_64-unknown-linux-gnu \
--allow-downgrade

You can update your toolchains with rustup update, while rustup toolchain list will give you an overview of what is installed on your system.

We will not need (or perform) any cross-compiling - our production workloads will be running in containers, hence we do not need to cross-compile from our development machine to the target host used in our production environment.

1.2 Project Setup

A toolchain installation via rustup bundles together various components.
One of them is the Rust compiler itself, rustc. You can check it out with

rustc --version

You will not be spending a lot of quality time working directly with rustc - your main interface for building and testing Rust applications will be cargo, Rust’s build tool.
You can double-check everything is up and running with

cargo --version

Let’s use cargo to create the skeleton of the project we will be working on for the whole book:

cargo new zero2prod

You should have a new zero2prod folder, with the following file structure:

zero2prod
├── Cargo.toml
├── .gitignore
├── .git
└── src
└── main.rs

The project is already a git repository, out of the box.
If you are planning on hosting the project on GitHub, you just need to create a new empty repository and run

cd zero2prod
git add .
git commit -am "Project skeleton"
git remote add origin git@github.com:YourGitHubNickName/zero2prod.git
git push -u origin main

We will be using GitHub as a reference given its popularity and the recently released GitHub Actions feature for CI pipelines, but you are of course free to choose any other git hosting solution (or none at all).

1.3 IDEs

The project skeleton is ready, it is now time to fire up your favourite editor so that we can start messing around with it.
Different people have different preferences but I would argue that the bare minimum you want to have, especially if you are starting out with a new programming language, is a setup that supports syntax highlighting, code navigation and code completion.

Syntax highlighting gives you immediate feedback on glaring syntax errors, while code navigation and code completion enable “exploratory” programming: jumping in and out of the source of your dependencies, quick access to the available methods on a struct or an enum you imported from a crate without having to continuously switch between your editor and docs.rs.

You have two main options for your IDE setup: rust-analyzer and IntelliJ Rust.

1.3.1 Rust-analyzer

rust-analyzer[5] is an implementation of the Language Server Protocol for Rust.
The Language Server Protocol makes it easy to leverage rust-analyzer in many different editors, including but not limited to VS Code, Emacs, Vim/NeoVim and Sublime Text 3.

Editor-specific setup instructions can be found here.

1.3.2 IntelliJ Rust

IntelliJ Rust provides Rust support to the suite of editors developed by JetBrains.

If you don’t have a JetBrains license[6], IntelliJ IDEA is available for free and supports IntelliJ Rust.
If you have a JetBrains license, CLion is your go-to editor for Rust in JetBrains’ IDE suite.

1.3.3 What Should I Use?

As of May 2020, IntelliJ Rust should be preferred.
Although rust-analyzer is promising and has shown incredible progress over the last year, it is still quite far from delivering an IDE experience on par with what IntelliJ Rust offers today.

On the other hand, IntelliJ Rust forces you to work with a JetBrains’ IDE, which you might or might not be willing to. If you’d like to stick to your editor of choice look for its rust-analyzer integration/plugin.

It is worth mentioning that rust-analyzer is part of a larger library-ification effort taking place within the Rust compiler: there is overlap between rust-analyzer and rustc, with a lot of duplicated effort.
Evolving the compiler’s codebase into a set of re-usable modules will allow rust-analyzer to leverage an increasingly larger subset of the compiler codebase, unlocking the on-demand analysis capabilities required to offer a top-notch IDE experience.
An interesting space to keep an eye on in the future[7].

1.4 Continuous Integration

Toolchain, installed.
Project skeleton, done.
IDE, ready.

One last thing to look at before we get into the details of what we will be building: our Continuous Integration (CI) pipeline.

In trunk-based development we should be able to deploy our main branch at any point in time.
Every member of the team can branch off from main, develop a small feature or fix a bug, merge back into main and release to our users.

Continuous Integration empowers each member of the team to integrate their changes into the main branch multiple times a day.

This has powerful ripple effects.
Some are tangible and easy to spot: it reduces the chances of having to sort out messy merge conflicts due to long-lived branches. Nobody likes merge conflicts.
Some are subtler: Continuous Integration tightens the feedback loop. You are less likely to go off on your own and develop for days or weeks just to find out that the approach you have chosen is not endorsed by the rest of the team or it would not integrate well with the rest of the project.
It forces you to engage with your teammates earlier than when it feels comfortable, course-correcting if necessary when it is still easy to do so (and nobody is likely to get offended).

How do we make it possible?

With a collection of automated checks running on every commit - our CI pipeline.
If one of the checks fails you cannot merge to main - as simple as that.

CI pipelines often go beyond ensuring code health: they are a good place to perform a series of additional important checks - e.g. scanning our dependency tree for known vulnerabilities, linting, formatting, etc.

We will run through the different checks that you might want to run as part of the CI pipeline of your Rust projects, introducing the associated tools as we go along.
We will then provide a set of ready-made CI pipelines for some of the major CI providers.

1.4.1 CI Steps

1.4.1.1 Tests

If your CI pipeline had a single step, it should be testing.
Tests are a first-class concept in the Rust ecosystem and you can leverage cargo to run your unit and integration tests:

cargo test

cargo test also takes care of building the project before running tests, hence you do not need to run cargo build beforehand (even though most pipelines will invoke cargo build before running tests to cache dependencies).

1.4.1.2 Code Coverage

Many articles have been written on the pros and cons of measuring code coverage.
While using code coverage as a quality check has several drawbacks I do argue that it is a quick way to collect information and spot if some portions of the codebase have been overlooked over time and are indeed poorly tested.

The easiest way to measure code coverage of a Rust project is via cargo tarpaulin, a cargo subcommand developed by xd009642. You can install tarpaulin with

# At the time of writing tarpaulin only supports
# x86_64 CPU architectures running Linux.
cargo install cargo-tarpaulin

while

cargo tarpaulin --ignore-tests

will compute code coverage for you application code, ignoring your test functions.

tarpaulin can be used to upload code coverage metrics to popular services like Codecov or Coveralls - instructions can be found in tarpaulin’s README.

1.4.1.3 Linting

Writing idiomatic code in any programming language requires time and practice.
It is easy at the beginning of your learning journey to end up with fairly convoluted solutions to problems that could otherwise be tackled with a much simpler approach.

Static analysis can help: in the same way a compiler steps through your code to ensure it conforms to the language rules and constraints, a linter will try to spot unidiomatic code, overly-complex constructs and common mistakes/inefficiencies.

The Rust team maintains clippy, the official Rust linter[8].
clippy is included in the set of components installed by rustup if you are using the default profile. Often CI environments use rustup’s minimal profile, which does not include clippy.
You can easily install it with

rustup component add clippy

If it is already installed the command is a no-op.

You can run clippy on your project with

cargo clippy

In our CI pipeline we would like to fail the linter check if clippy emits any warnings.
We can achieve it with

cargo clippy -- -D warnings

Static analysis is not infallible: from time to time clippy might suggest changes that you do not believe to be either correct or desirable.
You can mute a warning using the #[allow(clippy::lint_name)] attribute on the affected code block or disable the noisy lint altogether for the whole project with a configuration line in clippy.toml or a project-level #![allow(clippy::lint_name)] directive.
Details on the available lints and how to tune them for your specific purposes can be found in clippy’s README.

1.4.1.4 Formatting

Most organizations have more than one line of defence for the main branch: one is provided by the CI pipeline checks, the other is often a pull request review.

A lot can be said on what distinguishes a value-adding PR review process from a soul-sucking one - no need to re-open the whole debate here.
I know for sure what should not be the focus of a good PR review: formatting nitpicks - e.g. Can you add a newline here?, I think we have a trailing whitespace there!, etc.

Let machines deal with formatting while reviewers focus on architecture, testing thoroughness, reliability, observability. Automated formatting removes a distraction from the complex equation of the PR review process. You might dislike this or that formatting choice, but the complete erasure of formatting bikeshedding is worth the minor discomfort.

rustfmt is the official Rust formatter.
Just like clippy, rustfmt is included in the set of default components installed by rustup. If missing, you can easily install it with

rustup component add rustfmt

You can format your whole project with

cargo fmt

In our CI pipeline we will add a formatting step

cargo fmt -- --check

It will fail when a commit contains unformatted code, printing the difference to the console.[9]

You can tune rustfmt for a project with a configuration file, rustfmt.toml. Details can be found in rustfmt’s README.

1.4.1.5 Security Vulnerabilities

cargo makes it very easy to leverage existing crates in the ecosystem to solve the problem at hand.
On the flip side, each of those crates might hide an exploitable vulnerability that could compromise the security posture of your software.

The Rust Secure Code working group maintains an Advisory Database - an up-to-date collection of reported vulnerabilities for crates published on crates.io.

They also provide cargo-audit[10], a convenient cargo sub-command to check if vulnerabilities have been reported for any of the crates in the dependency tree of your project.
You can install it with

cargo install cargo-audit

Once installed, run

cargo audit

to scan your dependency tree.

We will be running cargo-audit as part of our CI pipeline, on every commit.
We will also run it on a daily schedule to stay on top of new vulnerabilities for dependencies of projects that we might not be actively working on at the moment but are still running in our production environment!

1.4.2 Ready-to-go CI Pipelines

Give a man a fish, and you feed him for a day. Teach a man to fish, and you feed him for a lifetime.

Hopefully I have taught you enough to go out there and stitch together a solid CI pipeline for your Rust projects.
We should also be honest and admit that it can take multiple hours of fidgeting around to learn how to use the specific flavour of configuration language used by a CI provider and the debugging experience can often be quite painful, with long feedback cycles.
I have thus decided to collect a set of ready-made configuration files for the most popular CI providers - the exact steps we just described, ready to be embedded in your project repository:

- GitHub Actions;

- CircleCI;

- GitLab CI;

- Travis.

It is often much easier to tweak an existing setup to suit your specific needs than to write a new one from scratch.
Feel free to get in touch if you would like to provide a template for a CI provider that is currently not covered in the list above.

1. More details on the release schedule can be found here.↩

2. You can check the next version and its release date at Rust forge.↩

3. It’s fairly rare for beta releases to contain issues thanks to the CI/CD setup of the Rust project. One of its most interesting components is crater, a tool designed to scrape crates.io and GitHub for Rust projects to build them and run their test suites to identify potential regressions. Pietro Albini gave an awesome overview of the Rust release process in his Shipping a compiler every six weeks talk at RustFest 2019.↩

4. You can check the list of feature flags available on nightly in The Unstable Book. Spoiler: there are loads.↩

5. rust-analyzer is not the first attempt to implement the LSP for Rust: RLS was its predecessor. RLS took a batch-processing approach: every little change to any of the files in a project would trigger re-compilation of the whole project. This strategy was fundamentally limited and it led to poor performance and responsiveness. RFC2912 formalised the “retirement” of RLS as the blessed LSP implementation for Rust in favour of rust-analyzer.↩

6. Students and teachers can claim a free JetBrains educational license.↩

7. Check their Next Few Years blog post for more details on rust-analyzer’s roadmap and main concerns going forward.↩

8. Yes, clippy is named after the (in)famous paperclip-shaped Microsoft Word assistance.↩

9. It can be annoying to get a fail in CI for a formatting issue. Most IDEs support a “format on save” feature to make the process smoother. Alternatively, you can use a git pre-push hook.↩

10. cargo-deny, developed by Embark Studios, is another cargo sub-command that supports vulnerability scanning of your dependency tree. It also bundles additional checks you might want to perform on your dependencies - it helps you identify unmaintained crates, define rules to restrict the set of allowed software licenses and spot when you have multiple versions of the same crate in your lock file (wasted compilation cycles!). It requires a bit of upfront effort in configuration, but it can be a powerful addition to your CI toolbox.↩

2 Building An Email Newsletter

2.1 Our Driving Example

The Foreword stated that

Zero To Production will focus on the challenges of writing cloud-native applications in a team of four or five engineers with different levels of experience and proficiency.

How? Well, by actually building one!

2.1.1 Problem-based Learning

Choose a problem you want to solve.
Let the problem drive the introduction of new concepts and techniques.

It flips the hierarchy you are used to: the material you are studying is not relevant because somebody claims it is, it is relevant because it is useful to get closer to a solution.
You learn new techniques and when it makes sense to reach for them.

The devil is in the details: a problem-based learning path can be delightful, yet it is painfully easy to misjudge how challenging each step of the journey is going to be.
Our driving example needs to be:

- small enough for us to tackle in a book without cutting corners;

- complex enough to surface most of the key themes that come up in bigger systems;

- interesting enough to keep readers engaged as they progress.

We will go for an email newsletter - the next section will detail the functionality we plan to cover[1].

2.1.2 Course-correcting

Problem-based learning works best in an interactive environment: the teacher acts as a facilitator, providing more or less support based on the behavioural cues and reactions of the participants.
A book, published on a website, does not give me the same chance.
I truly appreciate feedback on the material - please reach out to contact@lpalmieri.com or send me a DM on Twitter.
Providing feedback is, at this stage, a tangible way to contribute to Zero To Production.

2.2 What Should Our Newsletter Do?

There are dozens of companies providing services that include or are centered around the idea of managing a list of email addresses.

While they all share a set of core functionalities (i.e. sending emails), their services are tailored to specific use-cases: UI, marketing spin and pricing will differ significantly between a product targeted at big companies managing hundreds of thousands of addresses with strict security and compliance requirements compared to a SaaS offering geared to indie content creators running their own blogs or small online stores.

Now, we have no ambition to build the next MailChimp or ConvertKit - the scope would definitely be too broad for us to cover over the course of a book. Furthermore, several features would require applying the same concepts and techniques over and over again - it gets tedious to read after a while.

We will try to build an email newsletter service that supports what you need to get off the ground if you are willing to add an email subscription page to your blog - nothing more, nothing less[2].

2.2.1 Capturing Requirements: User Stories

The product brief above leaves some room for interpretation - to better scope what our service should support we will leverage user stories.
The format is fairly simple:

As a …,
I want to …,
So that …

A user story helps us to capture who we are building for (as a), the actions they want to perform (want to) as well as their motives (so that).

We will fulfill three user stories:

- As a blog visitor,
I want to subscribe to the newsletter,
So that I can receive email updates when new content is published on the blog;

- As the blog author,
I want to send an email to all my subscribers,
So that I can notify them when new content is published;

- As a subscriber,
I want to be able to unsubscribe from the newsletter,
So that I can stop receiving email updates from the blog.

We will not add features to

- manage multiple newsletters;

- segment subscribers in multiple audiences;

- track opening and click rates.

As said, pretty barebone - nonetheless, enough to satisfy the requirements of most blog authors. It would certainly satisfy mine for Zero To Production itself.

2.3 Working In Iterations

Let’s zoom on one of those user stories:

As the blog author,
I want to send an email to all my subscribers,
So that I can notify them when new content is published.

What does this mean in practice? What do we need to build?
As soon as you start looking closer at the problem tons of questions pop up - e.g. how do we ensure that the caller is indeed the blog author? Do we need to introduce an authentication mechanism? Do we support HTML in emails or do we stick to plain text? What about emojis 😱?

We could easily spend months implementing an extremely polished email delivery system without having even a basic subscribe/unsubscribe functionality in place.
We might become the best at sending emails, but nobody is going to use our email newsletter service - it does not cover the full journey.

Instead of going deep on one story, we will try to build enough functionality to satisfy, to an extent, the requirements of all of our stories in our first release.
We will then go back and improve: add fault-tolerance and retries for email delivery, add a confirmation email for new subscribers, etc.

We will work in iterations: each iteration takes a fixed amount of time and gives us a slightly better version of the product, improving the experience of our users.

Worth stressing that we are iterating on product features, not engineering quality: the code produced in each iteration will be tested and properly documented even if it only delivers a tiny, fully functional feature.
Our code is going to production at the end of each iteration - it needs to be production-quality.

2.3.1 Coming Up

Strategy is clear, we can finally get started: the next chapter will focus on the subscription functionality.
Getting off the ground will require some initial heavy-lifting: choosing a web framework, setting up the infrastructure for managing database migrations, putting together our application scaffolding as well as our setup for integration testing.
Expect to spend way more time pair programming with the compiler going forward!

1. Who knows, I might end up using our home-grown newsletter application to release the final chapter - it would definitely provide me with a sense of closure.↩

2. Make no mistake: when buying a SaaS product it is often not the software itself that you are paying for - you are paying for the peace of mind of knowing that there is an engineering team working full time to keep the service up and running, for their legal and compliance expertise, for their security team. We (developers) often underestimate how much time (and headaches) that saves us over time.↩

3 Sign Up A New Subscriber

We spent the whole previous chapter defining what we will be building (an email newsletter!), narrowing down a precise set of requirements. It is now time to roll up our sleeves and get started with it.

This chapter will take a first stab at implementing this user story:

As a blog visitor,
I want to subscribe to the newsletter,
So that I can receive email updates when new content is published on the blog.

We expect our blog visitors to input their email address in a form embedded on a web page.
The form will trigger an API call to a backend server that will actually process the information, store it and send back a response.
This chapter will focus on that backend server - we will implement the /subscriptions POST endpoint.

3.1 Our Strategy

We are starting a new project from scratch - there is a fair amount of upfront heavy-lifting we need to take care of:

- choose a web framework and get familiar with it;

- define our testing strategy;

- choose a crate to interact with our database (we will have to save those emails somewhere!);

- define how we want to manage changes to our database schemas over time (a.k.a. migrations);

- actually write some queries.

That is a lot and jumping in head-first might be overwhelming.
We will add a stepping stone to make the journey more approachable: before tackling /subscriptions we will implement a /health_check endpoint. No business logic, but a good opportunity to become friends with our web framework and get an understanding of all its different moving parts.

We will be relying on our Continuous Integration pipeline to keep us in check throughout the process - if you have not set it up yet, have a quick look at Chapter 1 (or grab one of the ready-made templates).

3.2 Choosing A Web Framework

What web framework should we use to write our Rust API?
This was supposed to be a section on the pros and cons of the Rust web frameworks currently available. It eventually grew to be so long that it did not make sense to embed it here and I published it as a spin-off article: check out Choosing a Rust web framework, 2020 edition for a deep-dive on actix-web, rocket, tide and warp.

TL;DR: as of February 2021, actix-web should be your go-to web framework when it comes to Rust APIs aimed for production usage - it has seen extensive usage in the past couple of years, it has a large and healthy community behind it and it runs on tokio, therefore minimising the likelihood of having to deal with incompatibilities/interop between different async runtimes.
It will thus be our choice for Zero To Production.

Nonetheless tide, rocket and warp have huge potential and we might end up making a different decision later in 2021 - if you are following along Zero To Production using a different framework I’d be delighted to have a look at your code! Please shoot me an email at contact@lpalmieri.com

Throughout this chapter and beyond I suggest you to keep a couple of extra browser tabs open: actix-web’s website, actix-web’s documentation and actix-web’s examples collection.

3.3 Our First Endpoint: A Basic Health Check

Let’s try to get off the ground by implementing a health-check endpoint: when we receive a GET request for /health_check we want to return a 200 OK response with no body.

We can use /health_check to verify that the application is up and ready to accept incoming requests.
Combine it with a SaaS service like pingdom.com and you can be alerted when your API goes dark - quite a good baseline for an email newsletter that you are running on the side.

A health-check endpoint can also be handy if you are using a container orchestrator to juggle your application (e.g. Kubernetes or Nomad): the orchestrator can call /health_check to detect if the API has become unresponsive and trigger a restart.

3.3.1 Wiring Up actix-web

Our starting point will be the Hello World! example on actix-web’s homepage:

use actix_web::{web, App, HttpRequest, HttpServer, Responder};

async fn greet(req: HttpRequest) -> impl Responder {
let name = req.match_info().get("name").unwrap_or("World");
format!("Hello {}!", &name)
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

Let’s paste it in our main.rs file.
A quick cargo check[1]:

error[E0432]: unresolved import `actix_web`
--> src/main.rs:1:5
|
1 | use actix_web::{web, App, HttpRequest, HttpServer, Responder};
|     ^^^^^^^^^ use of undeclared type or module `actix_web`

error[E0433]: failed to resolve:
use of undeclared type or module `actix_web`
--> src/main.rs:8:3
|
8 | #[actix_web::main]
|   ^^^^^^^^ use of undeclared type or module `actix_web`

error: aborting due to 2 previous errors

We have not added actix-web to our list of dependencies, therefore the compiler cannot resolve what we imported.
We can either fix the situation manually, by adding

#! Cargo.toml
# [...]

[dependencies]
# We are using the latest beta release of actix-web
# that relies on tokio 1.x.x
# There is _some_ turbulence when working with betas,
# we are pinning a few other supporting packages to ensure
# compatibility.
actix-web = "=4.0.0-beta.8"
actix-http = "=3.0.0-beta.8"

under [dependencies] in our Cargo.toml or we can use cargo add to quickly add the latest version of both crates as a dependency of our project:

cargo add actix-web --vers 4.0.0-beta.8

cargo add is not a default cargo command: it is provided by cargo-edit, a community-maintained[2] cargo extension. You can install it with:

cargo install cargo-edit

If you run cargo check again there should be no errors.
You can now launch the application with cargo run and perform a quick manual test:

curl http://127.0.0.1:8000

Hello World!

Cool, it’s alive!
You can gracefully shut down the web server with Ctrl+C if you want to.

3.3.2 Anatomy Of An actix-web Application

Let’s go back now to have a closer look at what we have just copy-pasted in our main.rs file.

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

3.3.2.1 Server - HttpServer

HttpServer is the backbone supporting our application. It takes care of things like:

- where should the application be listening for incoming requests? A TCP socket (e.g. 127.0.0.1:8000)? A Unix domain socket?

- what is the maximum number of concurrent connections that we should allow? How many new connections per unit of time?

- should we enable transport level security (TLS)?

- etc.

HttpServer, in other words, handles all transport level concerns.
What happens afterwards? What does HttpServer do when it has established a new connection with a client of our API and we need to start handling their requests?
That is where App comes into play!

3.3.2.2 Application - App

App is where all your application logic lives: routing, middlewares, request handlers, etc.
App is the component whose job is to take an incoming request as input and spit out a response.
Let’s zoom in on our code snippet:

App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))

App is a practical example of the builder pattern: new() gives us a clean slate to which we can add, one bit at a time, new behaviour using a fluent API (i.e. chaining method calls one after the other).
We will cover the majority of App’s API surface on a need-to-know basis over the course of the whole book: by the end of our journey you should have touched most of its methods at least once.

3.3.2.3 Endpoint - Route

How do we add a new endpoint to our App?
The route method is probably the simplest way to go about doing it - it is used in an Hello World! example after all!

route takes two parameters:

- path, a string, possibly templated (e.g. "/{name}") to accommodate dynamic path segments;

- route, an instance of the Route struct.

Route combines a handler with a set of guards.
Guards specify conditions that a request must satisfy in order to “match” and be passed over to the handler. From an implementation standpoint guards are implementors of the Guard trait: Guard::check is where the magic happens.

In our snippet we have

.route("/", web::get().to(greet))

"/" will match all requests without any segment following the base path - i.e. http://localhost:8000/.
web::get() is a short-cut for Route::new().guard(guard::Get()) a.k.a. the request should be passed to the handler if and only if its HTTP method is GET.

You can start to picture what happens when a new request comes in: App iterates over all registered endpoints until it finds a matching one (both path template and guards are satisfied) and passes over the request object to the handler.
This is not 100% accurate but it is a good enough mental model for the time being.

What does a handler look like instead? What is its function signature?
We only have one example at the moment, greet:

async fn greet(req: HttpRequest) -> impl Responder {
[...]
}

greet is an asynchronous function that takes an HttpRequest as input and returns something that implements the Responder trait[3]. A type implements the Responder trait if it can be converted into a HttpResponse - it is implemented off the shelf for a variety of common types (e.g. strings, status codes, bytes, HttpResponse, etc.) and we can roll our own implementations if needed.

Do all our handlers need to have the same function signature of greet?
No! actix-web, channelling some forbidden trait black magic, allows a wide range of different function signatures for handlers, especially when it comes to input arguments. We will get back to it soon enough.

3.3.2.4 Runtime - actix_rt

We drilled down from the whole HttpServer to a Route. Let’s look again at the whole main function:

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

What is #[actix_web::main] doing here? Well, let’s remove it and see what happens! cargo check screams at us with these errors:

error[E0277]: `main` has invalid return type `impl std::future::Future`
--> src/main.rs:8:20
|
8 | async fn main() -> std::io::Result<()> {
|                    ^^^^^^^^^^^^^^^^^^^
| `main` can only return types that implement `std::process::Termination`
|
= help: consider using `()`, or a `Result`

error[E0752]: `main` function is not allowed to be `async`
--> src/main.rs:8:1
|
8 | async fn main() -> std::io::Result<()> {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| `main` function is not allowed to be `async`

error: aborting due to 2 previous errors

We need main to be asynchronous because HttpServer::run is an asynchronous method but main, the entrypoint of our binary, cannot be an asynchronous function. Why is that?

Asynchronous programming in Rust is built on top of the Future trait: a future stands for a value that may not be there yet. All futures expose a poll method which has to be called to allow the future to make progress and eventually resolve to its final value. You can think of Rust’s futures as lazy: unless polled, there is no guarantee that they will execute to completion. This has often been described as a pull model compared to the push model adopted by other languages[4].

Rust’s standard library, by design, does not include an asynchronous runtime: you are supposed to bring one into your project as a dependency, one more crate under [dependencies] in your Cargo.toml. This approach is extremely versatile: you are free to implement your own runtime, optimised to cater for the specific requirements of your usecase (see the Fuchsia project or bastion’s actor framework).

This explains why main cannot be an asynchronous function: who is in charge to call poll on it?
There is no special configuration syntax that tells the Rust compiler that one of your dependencies is an asynchronous runtime (e.g. as we do for allocators) and, to be fair, there is not even a standardised definition of what a runtime is (e.g. an Executor trait).
You are therefore expected to launch your asynchronous runtime at the top of your main function and then use it to drive your futures to completion.
You might have guessed by now what is the purpose of #[actix_web::main], but guesses are not enough to satisfy us: we want to see it.

How?
actix_web::main is a procedural macro and this is a great opportunity to introduce cargo expand, an awesome addition to our Swiss army knife for Rust development:

cargo install cargo-expand

Rust macros operate at the token level: they take in a stream of symbols (e.g. in our case, the whole main function) and output a stream of new symbols which then gets passed to the compiler. In other words, the main purpose of Rust macros is code generation.
How do we debug or inspect what is happening with a particular macro? You inspect the tokens it outputs!

That is exactly where cargo expand shines: it expands all macros in your code without passing the output to the compiler, allowing you to step through it and understand what is going on.
Let’s use cargo expand to demistify #[actix_web::main]:

cargo expand

/// [...]

fn main() -> std::io::Result<()> {
actix_web::rt::System::new("main").block_on(async move {
{
HttpServer::new(|| {
App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))
})
.bind("127.0.0.1:8000")?
.run()
.await
}
})
}

The main function that gets passed to the Rust compiler after #[actix_web::main] has been expanded is indeed synchronous, which explain why it compiles without any issue.
The key line is this:

actix_web::rt::System::new("main").block_on(async move { ... })

We are starting actix’s async runtime (rt = runtime) and we are using it to drive the future returned by HttpServer::run to completion.
In other words, the job of #[actix_web::main] is to give us the illusion of being able to define an asynchronous main while, under the hood, it just takes our main asynchronous body and writes the necessary boilerplate to make it run on top of actix’s runtime.

actix’s runtime is built on top of tokio’s: we can therefore leverage the whole tokio ecosystem when building our applications.

3.3.3 Implementing The Health Check Handler

We have reviewed all the moving pieces in actix_web’s Hello World! example: HttpServer, App, route and actix_web::main.
We definitely know enough to modify the example to get our health check working as we expect: return a 200 OK response with no body when we receive a GET request at /health_check.

Let’s look again at our starting point:

//! src/main.rs
use actix_web::{web, App, HttpRequest, HttpServer, Responder};

async fn greet(req: HttpRequest) -> impl Responder {
let name = req.match_info().get("name").unwrap_or("World");
format!("Hello {}!", &name)
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/", web::get().to(greet))
.route("/{name}", web::get().to(greet))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

First of all we need a request handler. Mimicking greet we can start with this signature:

async fn health_check(req: HttpRequest) -> impl Responder {
todo!()
}

We said that Responder is nothing more than a conversion trait into a HttpResponse. Returning an instance of HttpResponse directly should work then!
Looking at its documentation we can use HttpResponse::Ok to get a HttpResponseBuilder primed with a 200 status code. HttpResponseBuilder exposes a rich fluent API to progressively build out a HttpResponse response, but we do not need it here: we can get a HttpResponse with an empty body by calling finish on the builder.
Gluing everything together:

async fn health_check(req: HttpRequest) -> impl Responder {
HttpResponse::Ok().finish()
}

A quick cargo check confirms that our handler is not doing anything weird. A closer look at HttpResponseBuilder unveils that it implements Responder as well - we can therefore omit our call to finish and shorten our handler to:

async fn health_check(req: HttpRequest) -> impl Responder {
HttpResponse::Ok()
}

The next step is handler registration - we need to add it to our App via route:

App::new()
.route("/health_check", web::get().to(health_check))

Let’s look at the full picture:

//! src/main.rs

use actix_web::{web, App, HttpRequest, HttpResponse, HttpServer, Responder};

async fn health_check(req: HttpRequest) -> impl Responder {
HttpResponse::Ok()
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

cargo check runs smoothly although it raises one warning:

warning: unused variable: `req`
--> src/main.rs:3:23
|
3 | async fn health_check(req: HttpRequest) -> impl Responder {
|                       ^^^
| help: if this is intentional, prefix it with an underscore: `_req`
|
= note: `#[warn(unused_variables)]` on by default

Our health check response is indeed static and does not use any of the data bundled with the incoming HTTP request (routing aside). We could follow the compiler’s advice and prefix req with an underscore… or we could remove that input argument entirely from health_check:

async fn health_check() -> impl Responder {
HttpResponse::Ok()
}

Surprise surprise, it compiles! actix-web has some pretty advanced type magic going on behind the scenes and it accepts a broad range of signatures as request handlers - more on that later.

What is left to do?
Well, a little test!

# Launch the application first in another terminal with `cargo run`
curl -v http://127.0.0.1:8000/health_check

*   Trying 127.0.0.1...
* TCP_NODELAY set
* Connected to localhost (127.0.0.1) port 8000 (#0)
> GET /health_check HTTP/1.1
> Host: localhost:8000
> User-Agent: curl/7.61.0
> Accept: */*
>
< HTTP/1.1 200 OK
< content-length: 0
< date: Wed, 05 Aug 2020 22:11:52 GMT

Congrats, you have just implemented your first working actix_web endpoint!

3.4 Our First Integration Test

/health_check was our first endpoint and we verified everything was working as expected by launching the application and testing it manually via curl.

Manual testing though is time consuming: as our application gets bigger, it gets more and more expensive to manually check that all our assumptions on its behaviour are still valid every time we perform some changes.
We’d like to automate as much as possible: those checks should be run in our CI pipeline every time we are committing a change in order to prevent regressions.

While the behaviour of our health check might not evolve much over the course of our journey, it is a good starting point to get our testing scaffolding properly set up.

3.4.1 How Do You Test An Endpoint?

An API is a means to an end: a tool exposed to the outside world to perform some kind of task (e.g. store a document, publish an email, etc.).
The endpoints we expose in our API define the contract between us and our clients: a shared agreement about the inputs and the outputs of the system, its interface.

The contract might evolve over time and we can roughly picture two scenarios: - backwards-compatible changes (e.g. adding a new endpoint); - breaking changes (e.g. removing an endpoint or dropping a field from the schema of its output).

In the first case, existing API clients will keep working as they are. In the second case, existing integrations are likely to break if they relied on the violated portion of the contract.

While we might intentionally deploy breaking changes to our API contract, it is critical that we do not break it accidentally.

What is the most reliable way to check that we have not introduced a user-visible regression?
Testing the API by interacting with it in the same exact way a user would: performing HTTP requests against it and verifying our assumptions on the responses we receive.

This is often referred to as black box testing: we verify the behaviour of a system by examining its output given a set of inputs without having access to the details of its internal implementation.

Following this principle, we won’t be satisfied by tests that call into handler functions directly - for example:

#[cfg(test)]
mod tests {
use crate::health_check;

#[actix_rt::test]
async fn health_check_succeeds() {
let response = health_check().await;
// This requires changing the return type of `health_check`
// from `impl Responder` to `HttpResponse` to compile
// You also need to import it with `use actix_web::HttpResponse`!
assert!(response.status().is_success())
}
}

We have not checked that the handler is invoked on GET requests.
We have not checked that the handler is invoked with /health_check as the path.

Changing any of these two properties would break our API contract, but our test would still pass - not good enough.

actix-web provides some conveniences to interact with an App without skipping the routing logic, but there are severe shortcomings to its approach:

- migrating to another web framework would force us to rewrite our whole integration test suite. As much as possible, we’d like our integration tests to be highly decoupled from the technology underpinning our API implementation (e.g. having framework-agnostic integration tests is life-saving when you are going through a large rewrite or refactoring!);

- due to some actix-web’s limitations[5], we wouldn’t be able to share our App startup logic between our production code and our testing code, therefore undermining our trust in the guarantees provided by our test suite due to the risk of divergence over time.

We will opt for a fully black-box solution: we will launch our application at the beginning of each test and interact with it using an off-the-shelf HTTP client (e.g. reqwest).

3.4.2 Where Should I Put My Tests?

Rust gives you three options when it comes to writing tests:

- next to your code in an embedded test module, e.g.

// Some code I want to test

#[cfg(test)]
mod tests {
// Import the code I want to test
use super::*;

// My tests
}

- in an external tests folder, i.e.

> ls

src/
tests/
Cargo.toml
Cargo.lock
...

- as part of your public documentation (doc tests), e.g.

/// Check if a number is even.
/// ```rust
/// use zero2prod::is_even;
///
/// assert!(is_even(2));
/// assert!(!is_even(1));
/// ```
pub fn is_even(x: u64) -> bool {
x % 2 == 0
}

What is the difference?
An embedded test module is part of your project, just hidden behind a configuration conditional check, #[cfg(test)]. Anything under the tests folder and your documentation tests, instead, are compiled in their own separate binaries.
This has consequences when it comes to visibility rules.

An embedded test module has privileged access to the code living next to it: it can interact with structs, methods, fields and functions that have not been marked as public and would normally not be available to a user of our code if they were to import it as a dependency of their own project.
Embedded test modules are quite useful for what I call iceberg projects, i.e. the exposed surface is very limited (e.g. a couple of public functions), but the underlying machinery is much larger and fairly complicated (e.g. tens of routines). It might not be straight-forward to exercise all the possible edge cases via the exposed functions - you can then leverage embedded test modules to write unit tests for private sub-components to increase your overall confidence in the correctness of the whole project.

Tests in the external tests folder and doc tests, instead, have exactly the same level of access to your code that you would get if you were to add your crate as a dependency in another project. They are therefore used mostly for integration testing, i.e. testing your code by calling it in the same exact way a user would.

Our email newsletter is not a library, therefore the line is a bit blurry - we are not exposing it to the world as a Rust crate, we are putting it out there as an API accessible over the network.
Nonetheless we are going to use the tests folder for our API integration tests - it is more clearly separated and it is easier to manage test helpers as sub-modules of an external test binary.

3.4.3 Changing Our Project Structure For Easier Testing

We have a bit of housekeeping to do before we can actually write our first test under /tests.
As we said, anything under tests ends up being compiled in its own binary - all our code under test is imported as a crate. But our project, at the moment, is a binary: it is meant to be executed, not to be shared. Therefore we can’t import our main function in our tests as it is right now.

If you won’t take my word for it, we can run a quick experiment:

# Create the tests folder
mkdir -p tests

Create a new tests/health_check.rs file with

//! tests/health_check.rs

use zero2prod::main;

#[test]
fn dummy_test() {
main()
}

cargo test should fail with something similar to

error[E0432]: unresolved import `zero2prod`
--> tests/health_check.rs:1:5
|
1 | use zero2prod::main;
|     ^^^^^^^^^ use of undeclared type or module `zero2prod`

error: aborting due to previous error

For more information about this error, try `rustc --explain E0432`.
error: could not compile `zero2prod`.

We need to refactor our project into a library and a binary: all our logic will live in the library crate while the binary itself will be just an entrypoint with a very slim main function.
First step: we need to change our Cargo.toml.
It currently looks something like this:

[package]
name = "zero2prod"
version = "0.1.0"
authors = ["Luca Palmieri <contact@lpalmieri.com>"]
edition = "2018"

[dependencies]
# [...]

We are relying on cargo’s default behaviour: unless something is spelled out, it will look for a src/main.rs file as the binary entrypoint and use the package.name field as the binary name.
Looking at the manifest target specification, we need to add a lib section to add a library to our project:

[package]
name = "zero2prod"
version = "0.1.0"
authors = ["Luca Palmieri <contact@lpalmieri.com>"]
edition = "2018"

[lib]
# We could use any path here, but we are following the community convention
# We could specify a library name using the `name` field. If unspecified,
# cargo will default to `package.name`, which is what we want.
path = "src/lib.rs"

[dependencies]
# [...]

The lib.rs file does not exist yet and cargo won’t create it for us:

cargo check

error: couldn't read src/lib.rs: No such file or directory (os error 2)

error: aborting due to previous error

error: could not compile `zero2prod`

Let’s add it then - it can be empty for now.

touch src/lib.rs

Everything should be working now: cargo check passes and cargo run still launches our application.
Although it is working, our Cargo.toml file now does not give you at a glance the full picture: you see a library, but you don’t see our binary there. Even if not strictly necessary, I prefer to have everything spelled out as soon as we move out of the auto-generated vanilla configuration:

[package]
name = "zero2prod"
version = "0.1.0"
authors = ["Luca Palmieri <contact@lpalmieri.com>"]
edition = "2018"

[lib]
path = "src/lib.rs"

# Notice the double square brackets: it's an array in TOML's syntax.
# We can only have one library in a project, but we can have multiple binaries!
# If you want to manage multiple libraries in the same repository
# have a look at the workspace feature - we'll cover it later on.
[[bin]]
path = "src/main.rs"
name = "zero2prod"

[dependencies]
# [...]

Feeling nice and clean, let’s move forward.
For the time being we can move our main function, as it is, to our library (named run to avoid clashes):

//! main.rs

use zero2prod::run;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
run().await
}

//! lib.rs

use actix_web::{web, App, HttpResponse, HttpServer};

async fn health_check() -> HttpResponse {
HttpResponse::Ok().finish()
}

// We need to mark `run` as public.
// It is no longer a binary entrypoint, therefore we can mark it as async
// without having to use any proc-macro incantation.
pub async fn run() -> std::io::Result<()> {
HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
})
.bind("127.0.0.1:8000")?
.run()
.await
}

Alright, we are ready to write some juicy integration tests!

3.5 Implementing Our First Integration Test

Our spec for the health check endpoint was:

When we receive a GET request for /health_check we return a 200 OK response with no body.

Let’s translate that into a test, filling in as much of it as we can:

//! tests/health_check.rs

// `actix_rt::test` is the testing equivalent of `actix_web::main`.
// It also spares you from having to specify the `#[test]` attribute.
//
// Use `cargo add actix-rt --dev --vers 2` to add `actix-rt`
// under `[dev-dependencies]` in Cargo.toml
//
// You can inspect what code gets generated using
// `cargo expand --test health_check` (<- name of the test file)
#[actix_rt::test]
async fn health_check_works() {
// Arrange
spawn_app().await.expect("Failed to spawn our app.");
// We need to bring in `reqwest`
// to perform HTTP requests against our application.
//
// Use `cargo add reqwest --dev --vers 0.11` to add
// it under `[dev-dependencies]` in Cargo.toml
let client = reqwest::Client::new();

// Act
let response = client
.get("http://127.0.0.1:8000/health_check")
.send()
.await
.expect("Failed to execute request.");

// Assert
assert!(response.status().is_success());
assert_eq!(Some(0), response.content_length());
}

// Launch our application in the background ~somehow~
async fn spawn_app() -> std::io::Result<()> {
todo!()
}

Take a second to really look at this test case.
spawn_app is the only piece that will, reasonably, depend on our application code.
Everything else is entirely decoupled from the underlying implementation details - if tomorrow we decide to ditch Rust and rewrite out application in Ruby on Rails we can still use the same test suite to check for regressions in our new stack as long as spawn_app gets replaced with the appropriate trigger (e.g. a bash command to launch the Rails app).

The test also covers the full range of properties we are interested to check:

- the health check is exposed at /health_check;

- the health check is behind a GET method;

- the health check always returns a 200;

- the health check’s response has no body.

If this passes we are done.

The test as it is crashes before doing anything useful: we are missing spawn_app, the last piece of the integration testing puzzle.
Why don’t we just call run in there? I.e.

//! tests/health_check.rs
// [...]

async fn spawn_app() -> std::io::Result<()> {
zero2prod::run().await
}

Let’s try it out!

cargo test

Running target/debug/deps/health_check-fc74836458377166

running 1 test
test health_check_works ...
test health_check_works has been running for over 60 seconds

No matter how long you wait, test execution will never terminate. What is going on?

In zero2prod::run we invoke (and await) HttpServer::run. HttpServer::run returns an instance of Server - when we call .await it starts listening on the address we specified indefinitely: it will handle incoming requests as they arrive, but it will never shutdown or “complete” on its own.
This implies that spawn_app never returns and our test logic never gets executed.

We need to run our application as a background task.
tokio::spawn[6] comes quite handy here: tokio::spawn takes a future and hands it over to the runtime for polling, without waiting for its completion; it therefore runs concurrently with downstream futures and tasks (e.g. our test logic).

Let’s refactor zero2prod::run to return a Server without awaiting it:

//! src/lib.rs

use actix_web::{web, App, HttpResponse, HttpServer};
use actix_web::dev::Server;

async fn health_check() -> HttpResponse {
HttpResponse::Ok().finish()
}

// Notice the different signature!
// We return `Server` on the happy path and we dropped the `async` keyword
// We have no .await call, so it is not needed anymore.
pub fn run() -> Result<Server, std::io::Error> {
let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
})
.bind("127.0.0.1:8000")?
.run();
// No .await here!
Ok(server)
}

We need to amend our main.rs accordingly:

//! src/main.rs

use zero2prod::run;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// Bubble up the io::Error if we failed to bind the address
// Otherwise call .await on our Server
run()?.await
}

A quick cargo check should reassure us that everything is in order.
We can now write spawn_app:

//! tests/health_check.rs
// [...]

// No .await call, therefore no need for `spawn_app` to be async now.
// We are also running tests, so it is not worth it to propagate errors:
// if we fail to perform the required setup we can just panic and crash
// all the things.
fn spawn_app() {
let server = zero2prod::run().expect("Failed to bind address");
// Launch the server as a background task
// tokio::spawn returns a handle to the spawned future,
// but we have no use for it here, hence the non-binding let
//
// New dev dependency - let's add tokio to the party with
// `cargo add tokio --dev --vers 1`
let _ = tokio::spawn(server);
}

Quick adjustment to our test to accommodate the changes in spawn_app’s signature:

//! tests/health_check.rs
// [...]

#[actix_rt::test]
async fn health_check_works() {
// No .await, no .expect
spawn_app();
// [...]
}

It’s time, let’s run that cargo test command!

cargo test

Running target/debug/deps/health_check-a1d027e9ac92cd64

running 1 test
test health_check_works ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

Yay! Our first integration test is green!
Give yourself a pat on the back on my behalf for the second major milestone in the span of a single chapter.

3.5.1 Polishing

We got it working, now we need to have a second look and improve it, if needed or possible.

3.5.1.1 Clean Up

What happens to our app running in the background when the test run ends? Does it shut down? Does it linger as a zombie somewhere?

Well, running cargo test multiple times in a row always succeeds - a strong hint that our 8000 port is getting released at the end of each run, therefore implying that the application is correctly shut down.
A second look at tokio::spawn’s documentation supports our hypothesis: when a tokio runtime is shut down all tasks spawned on it are dropped. actix_rt::test spins up a new runtime at the beginning of each test case and they shut down at the end of each test case.
In other words, good news - no need to implement any clean up logic to avoid leaking resources between test runs.

3.5.1.2 Choosing A Random Port

spawn_app will always try to run our application on port 8000 - not ideal: - if port 8000 is being used by another program on our machine (e.g. our own application!), tests will fail; - if we try to run two or more tests in parallel only one of them will manage to bind the port, all others will fail.

We can do better: tests should run their background application on a random available port.
First of all we need to change our run function - it should take the application address as an argument instead of relying on a hard-coded value:

//! src/lib.rs
// [...]

pub fn run(address: &str) -> Result<Server, std::io::Error> {
let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
})
.bind(address)?
.run();
Ok(server)
}

All zero2prod::run() invocations must then be changed to zero2prod::run("127.0.0.1:8000") to preserve the same behaviour and get the project to compile again.

How do we find a random available port for our tests?
The operating system comes to the rescue: we will be using port 0.
Port 0 is special-cased at the OS level: trying to bind port 0 will trigger an OS scan for an available port which will then be bound to the application.

It is therefore enough to change spawn_app to

//! tests/health_check.rs
// [...]

fn spawn_app() {
let server = zero2prod::run("127.0.0.1:0").expect("Failed to bind address");
let _ = tokio::spawn(server);
}

Done - the background app now runs on a random port every time we launch cargo test!
There is only a small issue… our test is failing[7]!

running 1 test
test health_check_works ... FAILED

failures:

---- health_check_works stdout ----
thread 'health_check_works' panicked at
'Failed to execute request.:
reqwest::Error { kind: Request, url: "http://localhost:8000/health_check",
source: hyper::Error(
Connect,
ConnectError(
"tcp connect error",
Os {
code: 111,
kind: ConnectionRefused,
message: "Connection refused"
}
)
)
}', tests/health_check.rs:10:20
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Panic in Arbiter thread.

failures:
health_check_works

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out

Our HTTP client is still calling 127.0.0.1:8000 and we really don’t know what to put there now: the application port is determined at runtime, we cannot hard code it there.
We need, somehow, to find out what port the OS has gifted our application and return it from spawn_app.

There are a few ways to go about it - we will use a std::net::TcpListener.
Our HttpServer right now is doing double duty: given an address, it will bind it and then start the application. We can take over the first step: we will bind the port on our own with TcpListener and then hand that over to the HttpServer using listen.

What is the upside?
TcpListener::local_addr returns a SocketAddr which exposes the actual port we bound via .port().

Let’s begin with our run function:

//! src/lib.rs

use actix_web::dev::Server;
use actix_web::{web, App, HttpResponse, HttpServer};
use std::net::TcpListener;

// [...]

pub fn run(listener: TcpListener) -> Result<Server, std::io::Error> {
let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
})
.listen(listener)?
.run();
Ok(server)
}

The change broke both our main and our spawn_app function. I’ll leave main to you, let’s focus on spawn_app:

//! tests/health_check.rs
// [...]

fn spawn_app() -> String {
let listener = TcpListener::bind("127.0.0.1:0")
.expect("Failed to bind random port");
// We retrieve the port assigned to us by the OS
let port = listener.local_addr().unwrap().port();
let server = zero2prod::run(listener).expect("Failed to bind address");
let _ = tokio::spawn(server);
// We return the application address to the caller!
format!("http://127.0.0.1:{}", port)
}

We can now leverage the application address in our test to point our reqwest::Client:

//! tests/health_check.rs
// [...]

#[actix_rt::test]
async fn health_check_works() {
// Arrange
let address = spawn_app();
let client = reqwest::Client::new();

// Act
let response = client
// Use the returned application address
.get(&format!("{}/health_check", &address))
.send()
.await
.expect("Failed to execute request.");

// Assert
assert!(response.status().is_success());
assert_eq!(Some(0), response.content_length());
}

All is good - cargo test comes out green. Our setup is much more robust now!

3.6 Refocus

Let’s take a small break to look back, we covered a fair amount of ground!
We set out to implement a /health_check endpoint and that gave us the opportunity to learn more about the fundamentals of our web framework, actix-web, as well as the basics of (integration) testing for Rust APIs.

It is now time to capitalise on what we learned to finally fulfill the first user story of our email newsletter project:

As a blog visitor,
I want to subscribe to the newsletter,
So that I can receive email updates when new content is published on the blog.

We expect our blog visitors to input their email address in a form embedded on a web page.
The form will trigger a POST /subscriptions call to our backend API that will actually process the information, store it and send back a response.

We will have to dig into:

- how to read data collected in a HTML form in actix-web (i.e. how do I parse the request body of a POST?);

- what libraries are available to work with a PostgreSQL database in Rust (diesel vs sqlx vs tokio-postgres);

- how to setup and manage migrations for our database;

- how to get our hands on a database connection in our API request handlers;

- how to test for side-effects (a.k.a. stored data) in our integration tests;

- how to avoid weird interactions between tests when working with a database.

Let’s get started!

3.7 Working With HTML Forms

3.7.1 Refining Our Requirements

What information should we collect from a visitor in order to enroll them as a subscriber of our email newsletter?

Well, we certainly need their email addresses (it is an email newsletter after all).
What else?

This would usually spark a conversation among the engineers on the team as well as the product manager in your typical business setup. In this case, we are both the technical leads and the product owners so we get to call the shots!

Speaking from personal experience, people generally use throwaway or masked emails when subscribing to newsletters (or, at least, most of you did when subscribing to Zero To Production!).
It would thus be nice to collect a name that we could use for our email greetings (the infamous Hey {{subscriber.name}}!) as well as to spot mutuals or people we know in the list of subscribers.
We are not cops, we have no interest in the name field being authentic - we will let people input whatever they feel like using as their identifier in our newsletter system: DenverCoder9, we welcome you.

It is settled then: we want an email address and a name for all new subscribers.

Given that the data is collected via a HTML form, it will be passed to our backend API in the body of a POST request. How is the body going to be encoded?
There are a few options available when using HTML forms: application/x-www-form-urlencoded is the most suitable to our usecase.
Quoting MDN web docs, with application/x-www-form-urlencoded

the keys and values [in our form] are encoded in key-value tuples separated by ‘&’, with a ‘=’ between the key and the value. Non-alphanumeric characters in both keys and values are percent encoded.

For example: if the name is Le Guin and the email is ursula_le_guin@gmail.com the POST request body should be name=le%20guin&email=ursula_le_guin%40gmail.com (spaces are replaced by %20 while @ becomes %40 - a reference conversion table can be found here).

To summarise:

- if a valid pair of name and email is supplied using the application/x-www-form-urlencoded format the backend should return a 200 OK;

- if either name or email are missing the backend should return a 400 BAD REQUEST.

3.7.2 Capturing Our Requirements As Tests

Now that we understand better what needs to happen, let’s encode our expectations in a couple of integration tests.

Let’s add the new tests to the existing tests/health_check.rs file - we will re-organise our test suite folder structure afterwards.

//! tests/health_check.rs
use std::net::TcpListener;

/// Spin up an instance of our application
/// and returns its address (i.e. http://localhost:XXXX)
fn spawn_app() -> String {
[...]
}

#[actix_rt::test]
async fn health_check_works() {
[...]
}

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app_address = spawn_app();
let client = reqwest::Client::new();
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = client
.post(&format!("{}/subscriptions", &app_address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(200, response.status().as_u16());
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_data_is_missing() {
// Arrange
let app_address = spawn_app();
let client = reqwest::Client::new();
let test_cases = vec![
("name=le%20guin", "missing the email"),
("email=ursula_le_guin%40gmail.com", "missing the name"),
("", "missing both name and email")
];

for (invalid_body, error_message) in test_cases {
// Act
let response = client
.post(&format!("{}/subscriptions", &app_address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(invalid_body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(
400,
response.status().as_u16(),
// Additional customised error message on test failure
"The API did not fail with 400 Bad Request when the payload was {}.",
error_message
);
}
}

subscribe_returns_a_400_when_data_is_missing is an example of table-driven test also known as parametrised test.
It is particularly helpful when dealing with bad inputs - instead of duplicating test logic several times we can simply run the same assertion against a collection of known invalid bodies that we expect to fail in the same way.
With parametrised tests it is important to have good error messages on failures: assertion failed on line XYZ is not great if you cannot tell which specific input is broken! On the flip side, that parametrised test is covering a lot of ground so it makes sense to invest a bit more time in generating a nice failure message.
Test frameworks in other languages sometimes have native support for this testing style (e.g. parametrised tests in pytest or InlineData in xUnit for C#) - there are a few crates in the Rust ecosystem that extend the basic test framework with similar features, but unfortunately they do not interop very well with the #[actix_rt::test] macro that we need to write asynchronous tests idiomatically (see rstest or test-case).

Let’s run our test suite now:

---- health_check::subscribe_returns_a_200_for_valid_form_data stdout ----
thread 'health_check::subscribe_returns_a_200_for_valid_form_data'
panicked at 'assertion failed: `(left == right)`
left: `200`,
right: `404`:

---- health_check::subscribe_returns_a_400_when_data_is_missing stdout ----
thread 'health_check::subscribe_returns_a_400_when_data_is_missing'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `404`:
The API did not fail with 400 Bad Request when the payload was missing the email.'

As expected, all our new tests are failing.
You can immediately spot a limitation of “roll-your-own” parametrised tests: as soon as one test case fails, the execution stops and we do not know the outcome for the following tests cases.

Let’s get started on the implementation.

3.7.3 Parsing Form Data From A POST Request

All tests are failing because the application returns a 404 NOT FOUND for POST requests hitting /subscriptions. Legitimate behaviour: we do not have a handler registered for that path.
Let’s fix it by adding a matching route to our App in src/lib.rs:

//! src/lib.rs
use actix_web::dev::Server;
use actix_web::{web, App, HttpResponse, HttpServer};
use std::net::TcpListener;

// We were returning `impl Responder` at the very beginning.
// We are now spelling out the type explicitly given that we have
// become more familiar with `actix-web`.
// There is no performance difference! Just a stylistic choice :)
async fn health_check() -> HttpResponse {
HttpResponse::Ok().finish()
}

// Let's start simple: we always return a 200 OK
async fn subscribe() -> HttpResponse {
HttpResponse::Ok().finish()
}

pub fn run(listener: TcpListener) -> Result<Server, std::io::Error> {
let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
// A new entry in our routing table for POST /subscriptions requests
.route("/subscriptions", web::post().to(subscribe))
})
.listen(listener)?
.run();
Ok(server)
}

Running our test suite again:

running 3 tests
test health_check::health_check_works ... ok
test health_check::subscribe_returns_a_200_for_valid_form_data ... ok
test health_check::subscribe_returns_a_400_when_data_is_missing ... FAILED

failures:

---- health_check::subscribe_returns_a_400_when_data_is_missing stdout ----
thread 'health_check::subscribe_returns_a_400_when_data_is_missing'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `200`:
The API did not fail with 400 Bad Request when the payload was missing the email.'

failures:
health_check::subscribe_returns_a_400_when_data_is_missing

test result: FAILED. 2 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out

subscribe_returns_a_200_for_valid_form_data now passes: well, our handler accepts all incoming data as valid, no surprises there.
subscribe_returns_a_400_when_data_is_missing, instead, is still red.
Time to do some real parsing on that request body. What does actix-web offer us?

3.7.3.1 Extractors

Quite prominent on actix-web’s User Guide is the Extractors’ section.
Extractors are used, as the name implies, to tell the framework to extract certain pieces of information from an incoming request.
actix-web provides several extractors out of the box to cater for the most common usecases:

- Path to get dynamic path segments from a request’s path;

- Query for query parameters;

- Json to parse a JSON-encoded request body;

- etc.

Luckily enough, there is an extractor that serves exactly our usecase: Form.
Reading straight from its documentation:

Form data helper (application/x-www-form-urlencoded).
Can be used to extract url-encoded data from the request body, or send url-encoded data as the response.

That’s music to my ears.
How do we use it?

Looking at actix-web’s User Guide:

An extractor can be accessed as an argument to a handler function. Actix-web supports up to 10 extractors per handler function. Argument position does not matter.

Example:

use actix_web::web;

#[derive(serde::Deserialize)]
struct FormData {
username: String,
}

/// Extract form data using serde.
/// This handler get called only if content type is *x-www-form-urlencoded*
/// and content of the request could be deserialized to a `FormData` struct
fn index(form: web::Form<FormData>) -> String {
format!("Welcome {}!", form.username)
}

So, basically… you just slap it there as an argument of your handler and actix-web, when a request comes in, will somehow do the heavy-lifting for you. Let’s ride along for now and we will circle back later to understand what is happening under the hood.

Our subscribe handler currently looks like this:

//! src/lib.rs
// Let's start simple: we always return a 200 OK
async fn subscribe() -> HttpResponse {
HttpResponse::Ok().finish()
}

Using the example as a blueprint, we probably want something along these lines:

//! src/lib.rs
// [...]

#[derive(serde::Deserialize)]
struct FormData {
email: String,
name: String
}

async fn subscribe(_form: web::Form<FormData>) -> HttpResponse {
HttpResponse::Ok().finish()
}

cargo check is not happy:

error[E0433]: failed to resolve: use of undeclared type or module `serde`
--> src/lib.rs:9:10
|
9 | #[derive(serde::Deserialize)]
|          ^^^^^ use of undeclared type or module `serde`

Fair enough: we need to add serde to our dependencies. Let’s add a new line to our Cargo.toml:

[dependencies]
# We need the optional `derive` feature to use `serde`'s procedural macros:
# `#[derive(Serialize)]` and `#[derive(Deserialize)]`.
# The feature is not enabled by default to avoid pulling in
# unnecessary dependencies for projects that do not need it.
serde = { version = "1", features = ["derive"]}

cargo check should succeed now. What about cargo test?

running 3 tests
test health_check_works ... ok
test subscribe_returns_a_200_for_valid_form_data ... ok
test subscribe_returns_a_400_when_data_is_missing ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

They are all green!

But why?

3.7.3.2 Form And FromRequest

Let’s go straight to the source: what does Form look like?
You can find its source code here.

The definition seems fairly innocent:

#[derive(PartialEq, Eq, PartialOrd, Ord)]
pub struct Form<T>(pub T);

It is nothing more than a wrapper: it is generic over a type T which is then used to populate Form’s only field.
Not much to see here.
Where does the extraction magic take place?

An extractor is a type that implements the FromRequest trait.
FromRequest’s definition is a bit noisy because Rust does not yet support async fn in trait definitions. Reworking it slightly, it boils down to something that looks more or less like this:

/// Trait implemented by types that can be extracted from request.
///
/// Types that implement this trait can be used with `Route` handlers.
pub trait FromRequest: Sized {
type Error = Into<actix_web::Error>;

async fn from_request(
req: &HttpRequest,
payload: &mut Payload
) -> Result<Self, Self::Error>;

/// Omitting some ancillary methods that actix-web implements
/// out of the box for you and supporting associated types
///  [...]
}

from_request takes as inputs the head of the incoming HTTP request (i.e. HttpRequest) and the bytes of its payload (i.e. Payload). It then returns Self, if the extraction succeeds, or an error type that can be converted into actix_web::Error.
All arguments in the signature of a route handler must implement the FromRequest trait: actix-web will invoke from_request for each argument and, if the extraction succeeds for all of them, it will then run the actual handler function.
If one of the extractions fails, the corresponding error is returned to the caller and the handler is never invoked (actix_web::Error can be converted to a HttpResponse).

This is extremely convenient: your handler does not have to deal with the raw incoming request and can instead work directly with strongly-typed information, significantly simplifying the code that you need to write to handle a request.

Let’s look at Form’s FromRequest implementation: what does it do?
Once again, I slightly reshaped the actual code to highlight the key elements and ignore the nitty-gritty implementation details.

impl<T> FromRequest for Form<T>
where
T: DeserializeOwned + 'static,
{
type Error = actix_web::Error;

async fn from_request(
req: &HttpRequest,
payload: &mut Payload
) -> Result<Self, Self::Error> {
// Omitted stuff around extractor configuration (e.g. payload size limits)

match UrlEncoded::new(req, payload).await {
Ok(item) => Ok(Form(item)),
// The error handler can be customised.
// The default one will return a 400, which is what we want.
Err(e) => Err(error_handler(e))
}
}
}

All the heavy-lifting seems to be happening inside that UrlEncoded struct.
UrlEncoded does a lot: it transparently handles compressed and uncompressed payloads, it deals with the fact that the request body arrives a chunk at a time as a stream of bytes, etc.
The key passage, after all those things have been taken care of, is:

serde_urlencoded::from_bytes::<T>(&body).map_err(|_| UrlencodedError::Parse)

serde_urlencoded provides (de)serialisation support for the application/x-www-form-urlencoded data format.
from_bytes takes as input a contiguous slice of bytes and it deserialises an instance of type T from it according to the rules of the URL-encoded format: the keys and values are encoded in key-value tuples separated by &, with a = between the key and the value; non-alphanumeric characters in both keys and values are percent encoded.

How does it know how to do it for a generic type T?
It is because T implements the DeserializedOwned trait from serde:

impl<T> FromRequest for Form<T>
where
T: DeserializeOwned + 'static,
{
// [...]
}

To understand what is actually going under the hood we need to take a closer look at serde itself.

The next section on serde touches on a couple of advanced Rust topics.
It’s fine if not everything falls into place the first time you read it!
Come back to it once you have played with Rust and serde a bit more to deep-dive on the toughest bits of it.

3.7.3.3 Serialisation In Rust: serde

Why do we need serde? What does serde actually do for us?
Quoting from its guide:

Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.

3.7.3.3.1 Generically

serde does not, by itself, provide support for (de)serialisation from/to any specific data format: you will not find any code inside serde that deals with the specifics of JSON, Avro or MessagePack. If you need support for a specific data format, you need to pull in another crate (e.g. serde_json for JSON or avro-rs for Avro).
serde defines a set of interfaces or, as they themselves call it, a data model.

If you want to implement a library to support serialisation for a new data format, you have to provide an implementation of the Serializer trait.
Each method on the Serializer trait corresponds to one of the 29 types that form serde’s data model - your implementation of Serializer specifies how each of those types maps to your specific data format.
For example, if you were adding support for JSON serialisation, your serialize_seq implementation would output an opening square bracket [ and return a type which can be used to serialize sequence elements.[8]

On the other side, you have the Serialize trait: your implementation of Serialize::serialize for a Rust type is meant to specify how to decompose it according to serde’s data model using the methods available on the Serializer trait.
Using again the sequence example, this how Serialize is implemented for a Rust vector:

use serde::ser::{Serialize, Serializer, SerializeSeq};

impl<T> Serialize for Vec<T>
where
T: Serialize,
{
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
let mut seq = serializer.serialize_seq(Some(self.len()))?;
for element in self {
seq.serialize_element(element)?;
}
seq.end()
}
}

That is what allows serde to be agnostic with respect to data formats: once your type implements Serialize, you are then free to use any concrete implementation of Serializer to actually perform the serialisation step - i.e. you can serialize your type to any format for which there is an available Serializer implementation on crates.io (spoiler: almost all commonly used data formats).
The same is true for deserialisation, via Deserialize and Deserializer, with a few additional details around lifetimes to support zero-copy deserialisation.

3.7.3.3.2 Efficiently

What about speed?
Is serde slower due to the fact that it is generic over the underlying data formats?

No, thanks to a process called monomorphization.
Every time a generic function is called with a concrete set of types, the Rust compiler will create a copy of the function body replacing the generic type parameters with the concrete types. This allows the compiler to optimize each instance of the function body with respect to the concrete types involved: the result is no different from what we would have achieved writing down separate functions for each type, without using generics or traits. In other words, we do not pay any runtime costs for using generics[9].

This concept is extremely powerful and it’s often referred to as zero-cost abstraction: using higher-level language constructs results in the same machine code you would have obtained with uglier/more “hand-rolled” implementations. We can therefore write code that is easier to read for a human (as it’s supposed be!) without having to compromise on the quality of the final artifact.

serde is also extremely careful when it comes to memory usage: the intermediate data model that we spoke about is implicitly defined via trait methods, there is no real intermediate serialised struct. If you want to learn more about it, Josh Mcguigan wrote an amazing deep-dive titled Understanding Serde.

It is also worth pointing out that all information required to (de)serialize a specific type for a specific data format are available at compile-time, there is no runtime overhead.
(De)serializers in other languages often leverage runtime reflection to fetch information about the type you want to (de)serialize (e.g. the list of their field names). Rust does not provide runtime reflection and everything has to be specified upfront.

3.7.3.3.3 Conveniently

This is where #[derive(Serialize)] and #[derive(Deserialize)] come into the picture.
You really do not want to spell out, manually, how serialisation should be performed for every single type defined in your project. It is tedious, error-prone and it takes time away from the application-specific logic that you are supposed to be focused on.

Those two procedural macros, bundled with serde behind the derive feature flag, will parse the definition of your type and automatically generate for you the right Serialize/Deserialize implementation.

3.7.3.4 Putting Everything Together

Given everything we learned so far, let’s take a second look at our subscribe handler:

#[derive(serde::Deserialize)]
pub struct FormData {
email: String,
name: String,
}

// Let's start simple: we always return a 200 OK
async fn subscribe(_form: web::Form<FormData>) -> HttpResponse {
HttpResponse::Ok().finish()
}

We now have a good picture of what is happening:

- before calling subscribe actix-web invokes the from_request method for all subscribe’s input arguments: in our case, Form::from_request;

- Form::from_request tries to deserialise the body into FormData according to the rules of URL-encoding leveraging serde_urlencoded and the Deserialize implementation of FormData, automatically generated for us by #[derive(serde::Deserialize)];

- if Form::from_request fails, a 400 BAD REQUEST is returned to the caller. If it succeeds, subscribe is invoked and we return a 200 OK.

Take a moment to be amazed: it looks so deceptively simple, yet there is so much going on in there - we are leaning heavily on Rust’s strength as well as some of the most polished crates in its ecosystem.

3.8 Storing Data: Databases

Our POST /subscriptions endpoint passes our tests but its usefulness is fairly limited: we are not storing valid emails and names anywhere.
There is no permanent record of the information that we collected from our HTML form.
How do we fix it?

When we defined what Cloud-native stands for we listed some of the emergent behaviour that we expect to see in our system: in particular, we want to achieve high-availability while running in a fault-prone environment.
Our application is therefore forced to be distributed - there should be multiple instances of it running on multiple machines in order to survive hardware failures.

This has consequences when it comes to data persistence: we cannot rely on the filesystem of our host as a storage layer for incoming data.
Anything that we save on disk would only be available to one of the many replicas of our application[10]. Furthermore, it would probably disappear if the underlying host crashed.

This explains why Cloud-native applications are usually stateless: their persistence needs are delegated to specialised external systems - databases.

3.8.1 Choosing A Database

What database should we use for our newsletter project?

I will lay down my personal rule-of-thumb, which might sound controversial:

If you are uncertain about your persistence requirements, use a relational database.
If you have no reason to expect massive scale, use PostgreSQL.

The offering when it comes to databases has exploded in the last twenty years.
From a data-model perspective, the NoSQL movement has brought us document-stores (e.g. MongoDB), key-value stores (e.g. AWS DynamoDB), graph databases (e.g. Neo4J), etc.
We have databases that use RAM as their primary storage (e.g. Redis).
We have databases that are optimised for analytical queries via columnar storage (e.g. AWS RedShift).

There is a world of possibilities and you should definitely leverage this richness when designing systems.
Nonetheless, it is much easier to design yourself into a corner by using a specialised data storage solution when you still do not have a clear picture of the data access patterns used by your application.
Relational databases are reasonably good as jack-of-all-trades: they will often be a good choice when building the first version of your application, supporting you along the way while you explore the constraints of your domain[11].

Even when it comes to relational databases there is plenty of choice.
Alongside classics like PostgreSQL and MySQL you will find some exciting new entries like AWS Aurora, Google Spanner and CockroachDB.
What do they all have in common?
They are built to scale. Way beyond what traditional SQL databases were supposed to be able to handle.
If scale is a problem of yours, by all means, take a look there. If it isn’t, you do not need to take onboard the additional complexity.

This is how we end up with PostgreSQL: a battle-tested piece of technology, widely supported across all cloud providers if you need a managed offering, opensource, exhaustive documentation, easy to run locally and in CI via Docker, well-supported within the Rust ecosystem.

3.8.2 Choosing A Database Crate

As of August 2020, there are three top-of-mind options when it comes to interacting with PostgreSQL in a Rust project:

- tokio-postgres;

- sqlx;

- diesel.

All three are massively popular projects that have seen significant adoption with a fair share of production usage. How do you pick one?

It boils down to how you feel about three topics:

- compile-time safety;

- SQL-first vs a DSL for query building;

- async vs sync interface.

3.8.2.1 Compile-time Safety

When interacting with a relational database it is fairly easy to make mistakes - we might, for example,

- have a typo in the name of a column or a table mentioned in our query;

- try to perform operations that are rejected by the database engine (e.g. summing a string and a number or joining two tables on the wrong column);

- expect to have a certain field in the returned data that is actually not there.

The key question is: when do we realise we made a mistake?

In most programming languages, it will be at runtime: when we try to execute our query the database will reject it and we will get an error or an exception. This is what happens when using tokio-postgres.

diesel and sqlx try to speed up the feedback cycle by detecting at compile-time most of these mistakes.
diesel leverages its CLI to generate a representation of the database schema as Rust code, which is then used to check assumptions on all of your queries.
sqlx, instead, uses procedural macros to connect to a database at compile-time and check if the provided query is indeed sound[12].

3.8.2.2 Query Interface

Both tokio-postgres and sqlx expect you to use SQL directly to write your queries.

diesel, instead, provides its own query builder: queries are represented as Rust types and you add filters, perform joins and similar operations by calling methods on them. This is often referred to with the name of Domain Specific Language (DSL).

Which one is better?
As always, it depends.

SQL is extremely portable - you can use it in any project where you have to interact with a relational database, regardless of the programming language or the framework the application is written with.
diesel’s DSL, instead, is only relevant when working with diesel: you have to pay an upfront learning cost to become fluent with it and it only pays off if you stick to diesel for your current and future projects. It is also worth pointing out that expressing complex queries using diesel’s DSL can be difficult and you might end up having to write raw SQL anyway.

On the flip side, diesel’s DSL makes it easier to write reusable components: you can split your complex queries into smaller units and leverage them in multiple places, as you would do with a normal Rust function.

3.8.2.3 Async Support

I remember reading somewhere a killer explanation of async IO that more or less sounded like this:

Threads are for working in parallel, async is for waiting in parallel.

Your database is not sitting next to your application on the same physical machine host: to run queries you have to perform network calls.
An asynchronous database driver will not reduce how long it takes to process a single query, but it will enable your application to leverage all CPU cores to perform other meaningful work (e.g. serve another HTTP request) while waiting for the database to return results.

Is this a significant enough benefit to take onboard the additional complexity introduced by asynchronous code?
It depends on the performance requirements of your application.
Generally speaking, running queries on a separate threadpool should be more than enough for most usecases. At the same time, if your web framework is already asynchronous, using an asynchronous database driver will actually give you less headaches[13].

Both sqlx and tokio-postgres provide an asynchronous interface, while diesel is synchronous and does not plan to roll out async support in the near future.

It is also worth mentioning that tokio-postgres is, at the moment, the only crate that supports query pipelining. The feature is still at the design stage for sqlx while I could not find it mentioned anywhere in diesel’s docs or issue tracker.

3.8.2.4 Summary

Let’s summarise everything we covered in a comparison matrix:

Crate

Compile-time safety

Query interface

Async

tokio-postgres

No

SQL

Yes

sqlx

Yes

SQL

Yes

diesel

Yes

DSL

No

3.8.2.5 Our Pick: sqlx

For Zero To Production we will use sqlx: its asynchronous support simplifies the integration with actix-web without forcing us to compromise on compile-time guarantees. It also limits the API surface that we have to cover and become proficient with thanks to its usage of raw SQL for queries.

3.8.3 Integration Testing With Side-effects

What do we want to accomplish?
Let’s look again at our “happy case” test:

//! tests/health_check.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app_address = spawn_app();
let client = reqwest::Client::new();
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = client
.post(&format!("{}/subscriptions", &app_address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(200, response.status().as_u16());
}

The assertion we have there is not enough.
We have no way to tell, just by looking at the API response, if the desired business outcome has been achieved - we are interested to know if a side-effect has taken place, i.e. data storage.

We want to check if the details of our new subscriber have actually been persisted.
How do we go about it?

We have two options:

1. leverage another endpoint of our public API to inspect the application state;

2. query directly the database in our test case.

Option 1 should be your go-to when possible: your tests remain oblivious to the implementation details of the API (e.g. the underlying database technology and its schema) and are therefore less likely to be disrupted by future refactorings.

Unfortunately we do not have any public endpoint on our API that allows us to verify if a subscriber exists.
We could add a GET /subscriptions endpoint to fetch the list of existing subscribers, but we would then have to worry about securing it: we do not want to have the names and emails of our subscribers exposed on the public internet without any form of authentication.
We will probably end up writing a GET /subscriptions endpoint down the line (i.e. we do not want to log into our production database to check the list of our subscribers), but we should not start writing a new feature just to test the one we are working on.

Let’s bite the bullet and write a small query in our test. We will remove it down the line when a better testing strategy becomes available.

3.8.4 Database Setup

To run queries in our test suite we need:

- a running Postgres instance[14];

- a table to store our subscribers data.

3.8.4.1 Docker

To run Postgres we will use Docker - before launching our test suite we will launch a new Docker container using Postgres’ official Docker image.
You can follow the instructions on Docker’s website to install it on your machine.

Let’s create a small bash script for it, scripts/init_db.sh, with a few knobs to customise Postgres’ default settings:

#!/usr/bin/env bash
set -x
set -eo pipefail

# Check if a custom user has been set, otherwise default to 'postgres'
DB_USER=${POSTGRES_USER:=postgres}
# Check if a custom password has been set, otherwise default to 'password'
DB_PASSWORD="${POSTGRES_PASSWORD:=password}"
# Check if a custom database name has been set, otherwise default to 'newsletter'
DB_NAME="${POSTGRES_DB:=newsletter}"
# Check if a custom port has been set, otherwise default to '5432'
DB_PORT="${POSTGRES_PORT:=5432}"

# Launch postgres using Docker
docker run \
-e POSTGRES_USER=${DB_USER} \
-e POSTGRES_PASSWORD=${DB_PASSWORD} \
-e POSTGRES_DB=${DB_NAME} \
-p "${DB_PORT}":5432 \
-d postgres \
postgres -N 1000
# ^ Increased maximum number of connections for testing purposes

Let’s make it executable:

chmod +x scripts/init_db.sh

We can then launch Postgres with

./scripts/init_db.sh

If you run docker ps you should see something along the lines of

IMAGE            PORTS                   STATUS
postgres   126.0.0.1:5432->5432/tcp   Up 12 seconds   [...]

N.B. - the port mapping bit could be slightly different if you are not using Linux!

3.8.4.2 Database Migrations

To store our subscribers details we need to create our first table.
To add a new table to our database we need to change its schema - this is commonly referred to as a database migration.

3.8.4.2.1 sqlx-cli

sqlx provides a command-line interface, sqlx-cli, to manage database migrations.

We can install the CLI with

cargo install --version=0.5.5 sqlx-cli --no-default-features --features postgres

Run sqlx --help to check that everything is working as expected.

3.8.4.2.2 Database Creation

The first command we will usually want to run is sqlx database create. According to the help docs:

sqlx-database-create
Creates the database specified in your DATABASE_URL

USAGE:
sqlx database create

FLAGS:
-h, --help       Prints help information
-V, --version    Prints version information

In our case, this is not strictly necessary: our Postgres Docker instance already comes with a default database named newsletter, thanks to the settings we specified when launching it using environment variables. Nonetheless, you will have to go through the creation step in your CI pipeline and in your production environment, so worth covering it anyway.

As the help docs imply, sqlx database create relies on the DATABASE_URL environment variable to know what to do.
DATABASE_URL is expected to be a valid Postgres connection string - the format is as follows:

postgres://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:${DB_PORT}/${DB_NAME}

We can therefore add a couple more lines to our scripts/init_db.sh script[15]:

# [...]

export DATABASE_URL=postgres://${DB_USER}:${DB_PASSWORD}@localhost:${DB_PORT}/${DB_NAME}
sqlx database create

You might run into an annoying issue from time to time: the Postgres container will not be ready to accept connections when we try to run sqlx database create.
It happened to me often enough to look for a workaround: we need to wait for Postgres to be healthy before starting to run commands against it. Let’s update our script to:

#!/usr/bin/env bash
set -x
set -eo pipefail

DB_USER=${POSTGRES_USER:=postgres}
DB_PASSWORD="${POSTGRES_PASSWORD:=password}"
DB_NAME="${POSTGRES_DB:=newsletter}"
DB_PORT="${POSTGRES_PORT:=5432}"

docker run \
-e POSTGRES_USER=${DB_USER} \
-e POSTGRES_PASSWORD=${DB_PASSWORD} \
-e POSTGRES_DB=${DB_NAME} \
-p "${DB_PORT}":5432 \
-d postgres \
postgres -N 1000

# Keep pinging Postgres until it's ready to accept commands
export PGPASSWORD="${DB_PASSWORD}"
until psql -h "localhost" -U "${DB_USER}" -p "${DB_PORT}" -d "postgres" -c '\q'; do
>&2 echo "Postgres is still unavailable - sleeping"
sleep 1
done

>&2 echo "Postgres is up and running on port ${DB_PORT}!"

export DATABASE_URL=postgres://${DB_USER}:${DB_PASSWORD}@localhost:${DB_PORT}/${DB_NAME}
sqlx database create

Problem solved!
The health check uses psql, the command line client for Postgres. Check these instructions on how to install it on your OS.

Scripts do not come bundled with a manifest to declare their dependencies: it’s unfortunately very common to launch a script without having installed all the prerequisites. This will usually result in the script crashing mid-execution, sometimes leaving stuff in our system in a half-broken state.
We can do better in our initialization script: let’s check that both psql and sqlx-cli are installed at the very beginning.

set -x
set -eo pipefail

if ! [ -x "$(command -v psql)" ]; then
echo >&2 "Error: `psql` is not installed."
exit 1
fi

if ! [ -x "$(command -v sqlx)" ]; then
echo >&2 "Error: `sqlx` is not installed."
echo >&2 "Use:"
echo >&2 "    cargo install --version=0.5.5 sqlx-cli --no-default-features --features postgres"
echo >&2 "to install it."
exit 1
fi

# The rest of the script

3.8.4.2.3 Adding A Migration

Let’s create our first migration now with

# Assuming you used the default parameters to launch Postgres in Docker!
export DATABASE_URL=postgres://postgres:password@localhost:5432/newsletter
sqlx migrate add create_subscriptions_table

A new top-level directory should have now appeared in your project - migrations. This is where all migrations for our project will be stored by sqlx’s CLI.
Under migrations you should already have one file called {timestamp}_create_subscriptions_table.sql - this is where we have to write the SQL code for our first migration.

Let’s quickly sketch the query we need:

-- migrations/{timestamp}_create_subscriptions_table.sql
-- Create Subscriptions Table
CREATE TABLE subscriptions(
id uuid NOT NULL,
PRIMARY KEY (id),
email TEXT NOT NULL UNIQUE,
name TEXT NOT NULL,
subscribed_at timestamptz NOT NULL
);

There is a endless debate when it comes to primary keys: some people prefer to use columns with a business meaning (e.g. email, a natural key), others feel safer with a synthetic key without any business meaning (e.g. id, a randomly generated UUID, a surrogate key).
I generally default to a synthetic identifier unless I have a very compelling reason not to - feel free to disagree with me here.

A couple of other things to make a note of:

- we are keeping track of when a subscription is created with subscribed_at (timestamptz is a time-zone aware date and time type);

- we are enforcing email uniqueness at the database-level with a UNIQUE constraint;

- we are enforcing that all fields should be populated with a NOT NULL constraint on each column;

- we are using TEXT for email and name because we do not have any restriction on their maximum lengths.

Database constraints are useful as a last line of defence from application bugs but they come at a cost - the database has to ensure all checks pass before writing new data into the table. Therefore constraints impact our write-throughput, i.e. the number of rows we can INSERT/UPDATE per unit of time in a table.
UNIQUE, in particular, introduces an additional B-tree index on our email column: the index has to be updated on every INSERT/UPDATE/DELETE query and it takes space on disk.

In our specific case, I would not be too worried: our mailing list would have to be incredibly popular for us to encounter issues with our write throughput. Definitely a good problem to have, if it comes to that.

3.8.4.2.4 Running Migrations

We can run migrations against our database with

sqlx migrate run

It has the same behaviour of sqlx database create - it will look at the DATABASE_URL environment variable to understand what database needs to be migrated.

Let’s add it to our scripts/init_db.sh script:

#!/usr/bin/env bash
set -x
set -eo pipefail

if ! [ -x "$(command -v psql)" ]; then
echo >&2 "Error: `psql` is not installed."
exit 1
fi

if ! [ -x "$(command -v sqlx)" ]; then
echo >&2 "Error: `sqlx` is not installed."
echo >&2 "Use:"
echo >&2 "    cargo install --version=0.5.5 sqlx-cli --no-default-features --features postgres"
echo >&2 "to install it."
exit 1
fi

DB_USER=${POSTGRES_USER:=postgres}
DB_PASSWORD="${POSTGRES_PASSWORD:=password}"
DB_NAME="${POSTGRES_DB:=newsletter}"
DB_PORT="${POSTGRES_PORT:=5432}"

# Allow to skip Docker if a dockerized Postgres database is already running
if [[ -z "${SKIP_DOCKER}" ]]
then
docker run \
-e POSTGRES_USER=${DB_USER} \
-e POSTGRES_PASSWORD=${DB_PASSWORD} \
-e POSTGRES_DB=${DB_NAME} \
-p "${DB_PORT}":5432 \
-d postgres \
postgres -N 1000
fi

export PGPASSWORD="${DB_PASSWORD}"
until psql -h "localhost" -U "${DB_USER}" -p "${DB_PORT}" -d "postgres" -c '\q'; do
>&2 echo "Postgres is still unavailable - sleeping"
sleep 1
done

>&2 echo "Postgres is up and running on port ${DB_PORT} - running migrations now!"

export DATABASE_URL=postgres://${DB_USER}:${DB_PASSWORD}@localhost:${DB_PORT}/${DB_NAME}
sqlx database create
sqlx migrate run

>&2 echo "Postgres has been migrated, ready to go!"

We have put the docker run command behind a SKIP_DOCKER flag to make it easy to run migrations against an existing Postgres instance without having to tear it down manually and re-create it with scripts/init_db.sh. It will also be useful in CI, if Postgres is not spun up by our script.

We can now migrate the database with

SKIP_DOCKER=true ./scripts/init_db.sh

You should be able to spot, in the output, something like

+ sqlx migrate run
20200823135036/migrate create subscriptions table (7.563944ms)

If you check your database using your favourite graphic interface for Postgres you will now see a subscriptions table alongside a brand new _sqlx_migrations table: this is where sqlx keeps track of what migrations have been run against your database - it should contain a single row now for our create_subscriptions_table migration.

3.8.5 Writing Our First Query

We have a migrated database up and running. How do we talk to it?

3.8.5.1 Sqlx Feature Flags

We installed sqlx-cli, but we have actually not yet added sqlx itself as a dependency of our application.
Let’s append a new line to our Cargo.toml:

[dependencies]
# [...]

# Using table-like toml syntax to avoid a super-long line!
[dependencies.sqlx]
version = "0.5.5"
default-features = false
features = [
"runtime-actix-rustls",
"macros",
"postgres",
"uuid",
"chrono",
"migrate"
]

Yeah, there are a lot of feature flags. Let’s go through all of them one by one:

- runtime-actix-rustls tells sqlx to use the actix runtime for its futures and rustls as TLS backend;

- macros gives us access to sqlx::query! and sqlx::query_as!, which we will be using extensively;

- postgres unlocks Postgres-specific functionality (e.g. non-standard SQL types);

- uuid adds support for mapping SQL UUIDs to the Uuid type from the uuid crate. We need it to work with our id column;

- chrono adds support for mapping SQL timestamptz to the DateTime<T> type from the chrono crate. We need it to work with our subscribed_at column;

- migrate gives us access to the same functions used under the hood by sqlx-cli to manage migrations. It will turn out to be useful for our test suite.

These should be enough for what we need to do in this chapter.

3.8.5.2 Configuration Management

The simplest entrypoint to connect to a Postgres database is PgConnection.
PgConnection implements the Connection trait which provides us with a connect method: it takes as input a connection string and returns us, asynchronously, a Result<PostgresConnection, sqlx::Error>.

Where do we get a connection string?

We could hard-code one in our application and then use it for our tests as well.
Or we could choose to introduce immediately some basic mechanism of configuration management.

It is simpler than it sounds and it will save us the cost of tracking down a bunch of hard-coded values across the whole application.
The config crate is Rust’s swiss-army knife when it comes to configuration: it supports multiple file formats and it lets you combine different sources hierarchically (e.g. environment variables, configuration files, etc.) to easily customise the behaviour of your application for each deployment environment.

We do not need anything fancy for the time being: a single configuration file will do.

3.8.5.2.1 Making Space

Right now all our application code lives in a single file, lib.rs.
Let’s quickly split it into multiple sub-modules to avoid chaos now that we are adding new functionality. We want to land on this folder structure:

src/
configuration.rs
lib.rs
main.rs
routes/
mod.rs
health_check.rs
subscriptions.rs
startup.rs

Our lib.rs file becomes

//! src/lib.rs
pub mod configuration;
pub mod routes;
pub mod startup;

startup.rs will host our run function, health_check goes into routes/health_check.rs, subscribe and FormData into routes/subscriptions.rs, configuration.rs starts empty. Both handlers are re-exported in routes/mod.rs:

//! src/routes/mod.rs
mod health_check;
mod subscriptions;

pub use health_check::*;
pub use subscriptions::*;

You might have to add a few pub visibility modifiers here and there, as well as performing a few corrections to use statements in main.rs and tests/health_check.rs.

Make sure cargo test comes out green before moving forward.

3.8.5.2.2 Reading A Configuration File

To manage configuration with config we must represent our application settings as a Rust type that implements serde’s Deserialize trait.
Let’s create a new Settings struct:

//! src/configuration.rs
#[derive(serde::Deserialize)]
pub struct Settings {}

We have two groups of configuration values at the moment:

- the application port, where actix-web is listening for incoming requests (currently hard-coded to 8000 in main.rs);

- the database connection parameters.

Let’s add a field for each of them to Settings:

//! src/configuration.rs
#[derive(serde::Deserialize)]
pub struct Settings {
pub database: DatabaseSettings,
pub application_port: u16
}

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
pub port: u16,
pub host: String,
pub database_name: String,
}

We need #[derive(serde::Deserialize)] on top of DatabaseSettings otherwise the compiler will complain with

error[E0277]: the trait bound
`configuration::DatabaseSettings: configuration::_::_serde::Deserialize<'_>`
is not satisfied
--> src/configuration.rs:3:5
|
3 |     pub database: DatabaseSettings,
|     ^^^ the trait `configuration::_::_serde::Deserialize<'_>`
|         is not implemented for `configuration::DatabaseSettings`
|
= note: required by `configuration::_::_serde::de::SeqAccess::next_element`

It makes sense: all fields in a type have to be deserialisable in order for the type as a whole to be deserialisable.

We have our configuration type, what now?

First of all, let’s add config to our dependencies with

cargo add config

We want to read our application settings from a configuration file named configuration:

//! src/configuration.rs
// [...]

pub fn get_configuration() -> Result<Settings, config::ConfigError> {
// Initialise our configuration reader
let mut settings = config::Config::default();

// Add configuration values from a file named `configuration`.
// It will look for any top-level file with an extension
// that `config` knows how to parse: yaml, json, etc.
settings.merge(config::File::with_name("configuration"))?;

// Try to convert the configuration values it read into
// our Settings type
settings.try_into()
}

Let’s modify our main function to read configuration as its first step:

//! src/main.rs
use std::net::TcpListener;
use zero2prod::startup::run;
use zero2prod::configuration::get_configuration;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// Panic if we can't read configuration
let configuration = get_configuration().expect("Failed to read configuration.");
// We have removed the hard-coded `8000` - it's now coming from our settings!
let address = format!("127.0.0.1:{}", configuration.application_port);
let listener = TcpListener::bind(address)?;
run(listener)?.await
}

If you try to launch the application with cargo run it should crash:

Running `target/debug/zero2prod`

thread 'main' panicked at 'Failed to read configuration.:
configuration file "configuration" not found', src/main.rs:7:25

note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Panic in Arbiter thread.

Let’s fix it by adding a configuration file.
We can use any file format for it, as long as config knows how to deal with it: we will go for YAML.

# configuration.yaml
application_port: 8000
database:
host: "localhost"
port: 5432
username: "postgres"
password: "password"
database_name: "newsletter"

cargo run should now execute smoothly.

3.8.5.3 Connecting To Postgres

PgConnection::connect wants a single connection string as input, while DatabaseSettings provides us with granular access to all the connection parameters. Let’s add a convenient connection_string method to do it:

//! src/configuration.rs
// [...]
impl DatabaseSettings {
pub fn connection_string(&self) -> String {
format!(
"postgres://{}:{}@{}:{}/{}",
self.username, self.password, self.host, self.port, self.database_name
)
}
}

We are finally ready to connect!
Let’s tweak our happy case test:

//! tests/health_check.rs
use sqlx::{PgConnection, Connection};
use zero2prod::configuration::get_configuration;
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app_address = spawn_app();
let configuration = get_configuration().expect("Failed to read configuration");
let connection_string = configuration.database.connection_string();
let connection = PgConnection::connect(&connection_string)
.await
.expect("Failed to connect to Postgres.");
let client = reqwest::Client::new();
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = client
.post(&format!("{}/subscriptions", &app_address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(200, response.status().as_u16());
}

And… cargo test works!
We just confirmed that we can successfully connect to Postgres from our tests!
A small step for the world, a huge leap forward for us.

3.8.5.4 Our Test Assertion

Now that we are connected, we can finally write the test assertions we have been dreaming about for the past 10 pages.
We will use sqlx’s query! macro:

//! tests/health_check.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// [...]
// The connection has to be marked as mutable!
let mut connection = ...

// Assert
assert_eq!(200, response.status().as_u16());

let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
.fetch_one(&mut connection)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
}

What is the type of saved? The query! macro returns an anonymous record type: a struct definition is generated at compile-time after having verified that the query is valid, with a member for each column on the result (i.e. save.email for the email column).

If we try to run cargo test we will get an error:

error: `DATABASE_URL` must be set to use query macros
--> tests/health_check.rs:59:17
|
59 |     let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
|                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
= note: this error originates in a macro (in Nightly builds,
run with -Z macro-backtrace for more info)

As we discussed before, sqlx reaches out to Postgres at compile-time to check that queries are well-formed. Just like sqlx-cli commands, it relies on the DATABASE_URL environment variable to know where to find the database.
We could export DATABASE_URL manually, but we would then run in the same issue every time we boot our machine and start working on this project. Let’s take the advice of sqlx’s authors - we’ll add a top-level .env file

DATABASE_URL="postgres://postgres:password@localhost:5432/newsletter"

sqlx will read DATABASE_URL from it and save us the hassle of re-exporting the environment variable every single time.
It feels a bit dirty to have the database connection parameters in two places (.env and configuration.yaml), but it is not a major problem: configuration.yaml can be used to alter the runtime behaviour of the application after it has been compiled, while .env is only relevant for our development process, build and test steps.
Commit the .env file to version control - we will need it in CI soon enough!

Let’s try to run cargo test again:

running 3 tests
test health_check_works ... ok
test subscribe_returns_a_400_when_data_is_missing ... ok
test subscribe_returns_a_200_for_valid_form_data ... FAILED

failures:

---- subscribe_returns_a_200_for_valid_form_data stdout ----
thread 'subscribe_returns_a_200_for_valid_form_data' panicked at
'Failed to fetch saved subscription.: RowNotFound', tests/health_check.rs:59:17

failures:
subscribe_returns_a_200_for_valid_form_data

It failed, which is exactly what we wanted!
We can now focus on patching the application to turn it green.

3.8.5.5 Updating Our CI Pipeline

If you check on it, you will notice that your CI pipeline is now failing to perform most of the checks we introduced at the beginning of our journey.
Our tests now rely on a running Postgres database to be executed properly. All our build commands (cargo check, cargo lint, cargo build), due to sqlx’s compile-time checks, need an up-and-running database!

We do not want to venture further with a broken CI.
You can find an updated version of the GitHub Actions setup here. Only general.yml needs to be updated.

Just as we wrote a SELECT query to inspect what subscriptions had been persisted to the database in our test, we now need to write an INSERT query to actually store the details of a new subscriber when we receive a valid POST /subscriptions request.

Let’s have a look at our request handler:

//! src/routes/subscriptions.rs
use actix_web::{web, HttpResponse};

#[derive(serde::Deserialize)]
pub struct FormData {
email: String,
name: String,
}

// Let's start simple: we always return a 200 OK
pub async fn subscribe(_form: web::Form<FormData>) -> HttpResponse {
HttpResponse::Ok().finish()
}

To execute a query within subscribe we need to get our hands on a database connection.
Let’s figure out how to get one.

3.8.6 Application State In actix-web

So far our application has been entirely stateless: our handlers work solely with the data from the incoming request.
actix-web gives us the possibility to attach to the application other pieces of data that are not related to the lifecycle of a single incoming request - the so-called application state.

You can add information to the application state using the app_data method on App.

Let’s try to use app_data to register a PgConnection as part of our application state. We need to modify our run method to accept a PgConnection alongside the TcpListener:

//! src/startup.rs

use crate::routes::{health_check, subscribe};
use actix_web::dev::Server;
use actix_web::{web, App, HttpServer};
use sqlx::PgConnection;
use std::net::TcpListener;

pub fn run(
listener: TcpListener,
// New parameter!
connection: PgConnection
) -> Result<Server, std::io::Error> {
let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
// Register the connection as part of the application state
.app_data(connection)
})
.listen(listener)?
.run();
Ok(server)
}

cargo check is screaming at us:

error[E0277]: the trait bound `PgConnection: std::clone::Clone`
is not satisfied in `[closure@src/startup.rs:8:34: 13:6 PgConnection]`
--> src/startup.rs:8:18
|
8  |       let server = HttpServer::new(|| {
|  __________________^^^^^^^^^^^^^^^_-
| |                  |
| |                  within `[closure@src/startup.rs:8:34: 13:6 PgConnection]`,
| |                  the trait `std::clone::Clone` is not implemented
| |                  for `PgConnection`
9  | |         App::new()
10 | |             .route("/health_check", web::get().to(health_check))
11 | |             .route("/subscriptions", web::post().to(subscribe))
12 | |             .app_data(connection)
13 | |     })
| |_____- within this `[closure@src/startup.rs:8:34: 13:6 PgConnection]`
|
= note: required because it appears within the type
`[closure@src/startup.rs:8:34: 13:6 PgConnection]`
= note: required by `actix_web::server::HttpServer::<F, I, S, B>::new`

error[E0277]: the trait bound `PgConnection: std::clone::Clone`
is not satisfied in `[closure@src/startup.rs:8:34: 13:6 PgConnection]`
--> src/startup.rs:8:18
|
8  |        let server = HttpServer::new(|| {
|   __________________^_______________-
|  |__________________|
| ||
9  | ||         App::new()
10 | ||             .route("/health_check", web::get().to(health_check))
11 | ||             .route("/subscriptions", web::post().to(subscribe))
12 | ||             .app_data(connection)
13 | ||     })
| ||_____- within this `[closure@src/startup.rs:8:34: 13:6 ::PgConnection]`
14 | |      .listen(listener)?
| |_______________________^
|    within `[closure@src/startup.rs:8:34: 13:6 ::PgConnection]`,
|    the trait `std::clone::Clone` is not implemented for `PgConnection`
|
|
56 |        F: Fn() -> I + Send + Clone + 'static,
|                              -----
|    required by this bound in `actix_web::server::HttpServer`
|
= note: required because it appears within the type
`[closure@src/startup.rs:8:34: 13:6 PgConnection]`

HttpServer expects PgConnection to be cloneable, which unfortunately is not the case.
Why does it need to implement Clone in the first place though?

3.8.7 actix-web Workers

Let’s zoom in on our invocation of HttpServer::new:

let server = HttpServer::new(|| {
App::new()
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
})

HttpServer::new does not take App as argument - it wants a closure that returns an App struct.
This is to support actix-web’s runtime model: actix-web will spin up a worker process for each available core on your machine.
Each worker runs its own copy of the application built by HttpServer calling the very same closure that HttpServer::new takes as argument.

That is why connection has to be cloneable - we need to have one for every copy of App.
But, as we said, PgConnection does not implement Clone because it sits on top of a non-cloneable system resource, a TCP connection with Postgres. What do we do?

We can use web::Data, another actix-web extractor.
web::Data wraps our connection in an Atomic Reference Counted pointer, an Arc: each instance of the application, instead of getting a raw copy of a PgConnection, will get a pointer to one.
Arc<T> is always cloneable, no matter who T is: cloning an Arc increments the number of active references and hands over a new copy of the memory address of the wrapped value.

Handlers can then access the application state using the same extractor.

Let’s give it a try:

//! src/startup.rs
use crate::routes::{health_check, subscribe};
use actix_web::dev::Server;
use actix_web::{web, App, HttpServer};
use sqlx::PgConnection;
use std::net::TcpListener;

pub fn run(
listener: TcpListener,
connection: PgConnection
) -> Result<Server, std::io::Error> {
// Wrap the connection in a smart pointer
let connection = web::Data::new(connection);
// Capture `connection` from the surrounding environment
let server = HttpServer::new(move || {
App::new()
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
// Get a pointer copy and attach it to the application state
.app_data(connection.clone())
})
.listen(listener)?
.run();
Ok(server)
}

It doesn’t compile yet, but we just need to do a bit of house-keeping:

error[E0061]: this function takes 2 arguments but 1 argument was supplied
--> src/main.rs:11:5
|
11 |     run(listener)?.await
|     ^^^ -------- supplied 1 argument
|     |
|     expected 2 arguments

Let’s fix the issue real quick:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::{Connection, PgConnection};
use std::net::TcpListener;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let configuration = get_configuration().expect("Failed to read configuration.");
let connection = PgConnection::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");
let address = format!("127.0.0.1:{}", configuration.application_port);
let listener = TcpListener::bind(address)?;
run(listener, connection)?.await
}

Perfect, it compiles.

3.8.8 The Data Extractor

We can now get our hands on an Arc<PgConnection> in our request handler, subscribe, using the web::Data extractor:

//! src/routes/subscriptions.rs
use sqlx::PgConnection;
// [...]

pub async fn subscribe(
_form: web::Form<FormData>,
// Retrieving a connection from the application state!
_connection: web::Data<PgConnection>,
) -> HttpResponse {
HttpResponse::Ok().finish()
}

We called Data an extractor, but what is it extracting a PgConnection from?
actix-web uses a type-map to represent its application state: a HashMap that stores arbitrary data (using the Any type) against their unique type identifier (obtained via TypeId::of).

web::Data, when a new request comes in, computes the TypeId of the type you specified in the signature (in our case PgConnection) and checks if there is a record corresponding to it in the type-map. If there is one, it casts the retrieved Any value to the type you specified (TypeId is unique, nothing to worry about) and passes it to your handler.
It is an interesting technique to perform what in other language ecosystems might be referred to as dependency injection.

3.8.9 The INSERT Query

We finally have a connection in subscribe: let’s try to persist the details of our new subscriber.
We will use again the query! macro that we leveraged in our happy-case test.

//! src/routes/subscriptions.rs
use chrono::Utc;
use uuid::Uuid;
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
connection: web::Data<PgConnection>,
) -> HttpResponse {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
form.email,
form.name,
Utc::now()
)
// We use `get_ref` to get an immutable reference to the `PgConnection`
// wrapped by `web::Data`.
.execute(connection.get_ref())
.await;
HttpResponse::Ok().finish()
}

Let’s unpack what is happening:

- we are binding dynamic data to our INSERT query. $1 refers to the first argument passed to query! after the query itself, $2 to the second and so forth. query! verifies at compile-time that the provided number of arguments matches what the query expects as well as that their types are compatible (e.g. you can’t pass a number as id);

- we are generating a random Uuid for id;

- we are using the current timestamp in the Utc timezone for subscribed_at.

We have to add two new dependencies as well to our Cargo.toml to fix the obvious compiler errors:

[dependencies]
# [...]
uuid = { version = "0.8.1", features = ["v4"] }
chrono = "0.4.15"

What happens if we try to compile it again?

error[E0277]: the trait bound `&PgConnection: sqlx_core::executor::Executor<'_>`
is not satisfied
--> src/routes/subscriptions.rs:29:14
|
29 |     .execute(connection.get_ref().deref())
|              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|              the trait `sqlx_core::executor::Executor<'_>`
|              is not implemented for `&PgConnection`
|
= help: the following implementations were found:
<&'c mut PgConnection as sqlx_core::executor::Executor<'c>>
= note: `sqlx_core::executor::Executor<'_>` is implemented for
`&mut PgConnection`, but not for `&PgConnection`

error: aborting due to previous error

execute wants an argument that implements sqlx’s Executor trait and it turns out, as we should have probably remembered from the query we wrote in our test, that &PgConnection does not implement Executor - only &mut PgConnection does.
Why is that the case?

sqlx has an asynchronous interface, but it does not allow you to run multiple queries concurrently over the same database connection.
Requiring a mutable reference allows them to enforce this guarantee in their API. You can think of a mutable reference as a unique reference: the compiler guarantees to execute that they have indeed exclusive access to that PgConnection because there cannot be two active mutable references to the same value at the same time in the whole program. Quite neat.

Nonetheless it might look like we designed ourselves into a corner: web::Data will never give us mutable access to the application state.
We could leverage interior mutability - e.g. putting our PgConnection behind a lock (e.g. a Mutex) would allow us to synchronise access to the underlying TCP socket and get a mutable reference to the wrapped connection once the lock has been acquired.
We could make it work, but it would not be ideal: we would be basically be constrained to run at most one query at a time. Not great.

Let’s take a second look at the documentation for sqlx’s Executor trait: what else implements Executor apart from &mut PgConnection?
Bingo: a shared reference to PgPool.

PgPool is a pool of connections to a Postgres database. How does it bypass the concurrency issue that we just discussed for PgConnection?
There is still interior mutability at play, but of a different kind: when you run a query against a &PgPool, sqlx will borrow a PgConnection from the pool and use it to execute the query; if no connection is available, it will create a new one or wait until one frees up.
This increases the number of concurrent queries that our application can run and it also improves its resiliency: a single slow query will not impact the performance of all incoming requests by creating contention on the connection lock.

Let’s refactor run, main and subscribe to work with a PgPool instead of a single PgConnection:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::PgPool;
use std::net::TcpListener;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let configuration = get_configuration().expect("Failed to read configuration.");
// Renamed!
let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");
let address = format!("127.0.0.1:{}", configuration.application_port);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool)?.await
}

//! src/startup.rs
use crate::routes::{health_check, subscribe};
use actix_web::dev::Server;
use actix_web::{web, App, HttpServer};
use sqlx::PgPool;
use std::net::TcpListener;

pub fn run(listener: TcpListener, db_pool: PgPool) -> Result<Server, std::io::Error> {
// Wrap the pool using web::Data, which boils down to an Arc smart pointer
let db_pool = web::Data::new(db_pool);
let server = HttpServer::new(move || {
App::new()
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
})
.listen(listener)?
.run();
Ok(server)
}

//! src/routes/subscriptions.rs
// No longer importing PgConnection!
use sqlx::PgPool;
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>, // Renamed!
) -> HttpResponse {
sqlx::query!(/* */)
// Using the pool as a drop-in replacement
.execute(pool.get_ref())
.await;
HttpResponse::Ok().finish()
}

The compiler is almost happy: cargo check has a warning for us.

warning: unused `Result` that must be used
--> src/routes/subscriptions.rs:13:5
|
13 | /     sqlx::query!(
14 | |         r#"
15 | |     INSERT INTO subscriptions (id, email, name, subscribed_at)
16 | |     VALUES ($1, $2, $3, $4)
...  |
23 | |     .execute(pool.as_ref())
24 | |     .await;
| |___________^
|
= note: `#[warn(unused_must_use)]` on by default
= note: this `Result` may be an `Err` variant, which should be handled

sqlx::query may fail - it returns a Result, Rust’s way to model fallible functions.
The compiler is reminding us to handle the error case - let’s follow the advice:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
// `Result` has two variants: `Ok` and `Err`.
// The first for successes, the second for failures.
// We use a `match` statement to choose what to do based
// on the outcome.
// We will talk more about `Result` going forward!
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => HttpResponse::Ok().finish(),
Err(e) => {
println!("Failed to execute query: {}", e);
HttpResponse::InternalServerError().finish()
}
}
}

cargo check is satisfied, but the same cannot be said for cargo test:

error[E0061]: this function takes 2 arguments but 1 argument was supplied
--> tests/health_check.rs:10:18
|
10 |     let server = run(listener).expect("Failed to bind address");
|                  ^^^ -------- supplied 1 argument
|                  |
|                  expected 2 arguments

error: aborting due to previous error

3.9 Updating Our Tests

The error is in our spawn_app helper function:

//! tests/health_check.rs
use zero2prod::startup::run;
use std::net::TcpListener;
// [...]

fn spawn_app() -> String {
let listener = TcpListener::bind("127.0.0.1:0")
.expect("Failed to bind random port");
// We retrieve the port assigned to us by the OS
let port = listener.local_addr().unwrap().port();
let server = run(listener).expect("Failed to bind address");
let _ = tokio::spawn(server);
// We return the application address to the caller!
format!("http://127.0.0.1:{}", port)
}

We need to pass a connection pool to run.
Given that we are then going to need that very same connection pool in subscribe_returns_a_200_for_valid_form_data to perform our SELECT query, it makes sense to generalise spawn_app: instead of returning a raw String, we will give the caller a struct, TestApp. TestApp will hold both the address of our test application instance and a handle to the connection pool, simplifying the arrange steps in our test cases.

//! tests/health_check.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::PgPool;
use std::net::TcpListener;

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

// The function is asynchronous now!
async fn spawn_app() -> TestApp {
let listener = TcpListener::bind("127.0.0.1:0")
.expect("Failed to bind random port");
let port = listener.local_addr().unwrap().port();
let address = format!("http://127.0.0.1:{}", port);

let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

let server = run(listener, connection_pool.clone())
.expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

All test cases have then to be updated accordingly - an off-screen exercise that I leave to you, my dear reader.
Let’s just have a look together at what subscribe_returns_a_200_for_valid_form_data looks like after the required changes:

//! tests/health_check.rs
// [...]
#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app = spawn_app().await;
let client = reqwest::Client::new();
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = client
.post(&format!("{}/subscriptions", &app.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(200, response.status().as_u16());

let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
}

The test intent is much clearer now that we got rid of most of the boilerplate related to establishing the connection with the database.
TestApp is foundation we will be building on going forward to pull out supporting functionality that is useful to most of our integration tests.

The moment of truth has finally come: is our updated subscribe implementation enough to turn subscribe_returns_a_200_for_valid_form_data green?

running 3 tests
test health_check_works ... ok
test subscribe_returns_a_400_when_data_is_missing ... ok
test subscribe_returns_a_200_for_valid_form_data ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

Yesssssssss!
Success!

Let’s run it again to bathe in the light of this glorious moment!

cargo test

running 3 tests
test health_check_works ... ok
Failed to execute query: error returned from database:
duplicate key value violates unique constraint "subscriptions_email_key"
thread 'subscribe_returns_a_200_for_valid_form_data'
panicked at 'assertion failed: `(left == right)`
left: `200`,
right: `500`', tests/health_check.rs:66:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Panic in Arbiter thread.
test subscribe_returns_a_400_when_data_is_missing ... ok
test subscribe_returns_a_200_for_valid_form_data ... FAILED

failures:

failures:
subscribe_returns_a_200_for_valid_form_data

test result: FAILED. 2 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out

Wait, no, what the fuck! Don’t do this to us!

Ok, I lied - I knew this was going to happen.
I am sorry, I let you taste the sweet flavour of victory and then I threw you back into the mud.
There is an important lesson to be learned here, trust me.

3.9.1 Test Isolation

Your database is a gigantic global variable: all your tests are interacting with it and whatever they leave behind will be available to other tests in the suite as well as to the following test runs.
This is precisely what happened to us a moment ago: our first test run commanded our application to register a new subscriber with ursula_le_guin@gmail.com as their email; the application obliged.
When we re-ran our test suite we tried again to perform another INSERT using the same email, but our UNIQUE constraint on the email column raised a unique key violation and rejected the query, forcing the application to return us a 500 INTERNAL_SERVER_ERROR.

You really do not want to have any kind of interaction between your tests: it makes your test runs non-deterministic and it leads down the line to spurious test failures that are extremely tricky to hunt down and fix.

There are two techniques I am aware of to ensure test isolation when interacting with a relational database in a test:

- wrap the whole test in a SQL transaction and rollback at the end of it;

- spin up a brand-new logical database for each integration test.

The first is clever and will generally be faster: rolling back a SQL transaction takes less time than spinning up a new logical database. It works quite well when writing unit tests for your queries but it is tricky to pull off in an integration test like ours: our application will borrow a PgConnection from a PgPool and we have no way to “capture” that connection in a SQL transaction context.
Which leads us to the second option: potentially slower, yet much easier to implement.

How?
Before each test run, we want to:

- create a new logical database with a unique name;

- run database migrations on it.

The best place to do this is spawn_app, before launching our actix-web test application.
Let’s look at it again:

//! tests/health_check.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::PgPool;
use std::net::TcpListener;
use uuid::Uuid;

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

// The function is asynchronous now!
async fn spawn_app() -> TestApp {
let listener = TcpListener::bind("127.0.0.1:0")
.expect("Failed to bind random port");
let port = listener.local_addr().unwrap().port();
let address = format!("http://127.0.0.1:{}", port);

let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

let server = run(listener, connection_pool.clone())
.expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

// [...]

configuration.database.connection_string() uses the database_name specified in our configuration.yaml file - the same for all tests.
Let’s randomise it with

let mut configuration = get_configuration().expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();

let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

cargo test will fail: there is no database ready to accept connections using the name we generated.
Let’s add a connection_string_without_db method to our DatabaseSettings:

//! src/configuration.rs
// [...]

impl DatabaseSettings {
pub fn connection_string(&self) -> String {
format!(
"postgres://{}:{}@{}:{}/{}",
self.username, self.password, self.host, self.port, self.database_name
)
}

pub fn connection_string_without_db(&self) -> String {
format!(
"postgres://{}:{}@{}:{}",
self.username, self.password, self.host, self.port
)
}
}

Omitting the database name we connect to the Postgres instance, not a specific logical database.
We can now use that connection to create the database we need and run migrations on it:

//! tests/health_check.rs
// [...]
use sqlx::{Connection, Executor, PgConnection, PgPool};
use zero2prod::configuration::{get_configuration, DatabaseSettings};

async fn spawn_app() -> TestApp {
// [...]
let mut configuration = get_configuration().expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();
let connection_pool = configure_database(&configuration.database).await;
// [...]
}

pub async fn configure_database(config: &DatabaseSettings) -> PgPool {
// Create database
let mut connection = PgConnection::connect(&config.connection_string_without_db())
.await
.expect("Failed to connect to Postgres");
connection
.execute(&*format!(r#"CREATE DATABASE "{}";"#, config.database_name))
.await
.expect("Failed to create database.");

// Migrate database
let connection_pool = PgPool::connect(&config.connection_string())
.await
.expect("Failed to connect to Postgres.");
sqlx::migrate!("./migrations")
.run(&connection_pool)
.await
.expect("Failed to migrate the database");

connection_pool
}

sqlx::migrate! is the same macro used by sqlx-cli when executing sqlx migrate run - no need to throw bash scripts into the mix to achieve the same result.

Let’s try again to run cargo test:

running 3 tests
test subscribe_returns_a_200_for_valid_form_data ... ok
test subscribe_returns_a_400_when_data_is_missing ... ok
test health_check_works ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

It works, this time for good. Try

repeat 100 { cargo test }

if you are skeptical.

You might have noticed that we do not perform any clean-up step at the end of our tests - the logical databases we create are not being deleted. This is intentional: we could add a clean-up step, but our Postgres instance is used only for test purposes and it’s easy enough to restart it if, after hundreds of test runs, performance starts to suffer due to the number of lingering (almost empty) databases.

3.10 Summary

We covered a large number of topics in this chapter: actix-web extractors and HTML forms, (de)serialisation with serde, an overview of the available database crates in the Rust ecosystem, the fundamentals of sqlx as well as basic techniques to ensure test isolation when dealing with databases.
Take your time to digest the material and go back to review individual sections if necessary.

1. During our development process we are not always interested in producing a runnable binary: we often just want to know if our code compiles or not. cargo check was born to serve exactly this usecase: it runs the same checks that are run by cargo build, but it does not bother to perform any machine code generation. It is therefore much faster and provides us with a tighter feedback loop. See link for more details.↩

2. cargo follows the same philosophy of Rust’s standard library: where possible, the addition of new functionality is explored via third-party crates and then upstreamed where it makes sense to do so (e.g. cargo-vendor).↩

3. impl Responder is using the impl Trait syntax introduced in Rust 1.26 - you can find more details here.↩

4. Check out the release notes of async/await for more details. The talk by withoutboats at Rust LATAM 2019 is another excellent reference on the topic. If you prefer books to talks, check out Futures Explained in 200 Lines of Rust.↩

5. App is a generic struct and some of the types used to parametrise it are private to the actix_web project. It is therefore impossible (or, at least, so cumbersome that I have never succeeded at it) to write a function that returns an instance of App.↩

6. A reminder that actix_rt’s runtime is layered on top of tokio’s, hence all tokio primitives will work like a charm because they are being invoked and executed in the context of a running tokio runtime.↩

7. There is a remote chance that the OS ended up picking 8000 as random port and everything worked out smoothly. Cheers to you lucky reader!↩

8. You can look at serde_json’s serialize_seq implementation for confirmation: here. There is an optimisation for empty sequences (you immediately output []), but that is pretty much what is happening.↩

9. At the same time, it must be said that writing a serializer that is specialised for a single data format and a single usecase (e.g. batch-serialisation) might give you a chance to leverage algorithmic choices that are not compatible with the structure of serde’s data model, meant to support several formats for a variety of usecases. An example in this vein would be simd-json.↩

10. Unless we implement some kind of synchronisation protocol between our replicas, which would quickly turn into a badly-written poor-man-copy of a database.↩

11. Relational databases provide you with transactions - a powerful mechanism to handle partial failures and manage concurrent access to shared data. We will discuss transactions in greater detail in Chapter 7.↩

12. Performing IO in a procedural macro is somewhat controversial and forces you to always have a database up and running when working on a sqlx project; sqlx is adding support for “offline” builds by caching the retrieved query metadata in its upcoming 0.4.0 release.↩

13. Async runtimes are based around the assumptions that futures, when polled, will yield control back to the executor “very quickly”. If you run blocking IO code by mistake on the same threadpool used by the runtime to poll asynchronous tasks you get yourself in troubles - e.g. your application might mysteriously hang under load. You have to be careful and always make sure that blocking IO is performed on a separate threadpool using functions like tokio::spawn_blocking or async_std::spawn_blocking.↩

14. I do not belong to the “in-memory test database” school of thought: whenever possible you should strive to use the same database both for your tests and your production environment. I have been burned one time too many by differences between the in-memory stub and the real database engine to believe it provides any kind of benefit over using “the real thing”.↩

15. If you run the script again now it will fail because there is a Docker container with same name already running! You have to stop/kill it before running the updated version of the script. ## Persisting A New Subscriber↩

4 Telemetry

In Chapter 3 we managed to put together a first implementation of POST /subscriptions to fulfill one of the user stories of our email newsletter project:

As a blog visitor,
I want to subscribe to the newsletter,
So that I can receive email updates when new content is published on the blog.

We have not yet created a web page with a HTML form to actually test the end-to-end flow, but we have a few black-box integration tests that cover the two basic scenarios we care about at this stage:

- if valid form data is submitted (i.e. both name and email have been provided), the data is saved in our database;

- if the submitted form is incomplete (e.g. the email is missing, the name is missing or both), the API returns a 400.

Should we be satisfied and rush to deploy the first version of our application on the coolest cloud provider out there?
Not yet - we are not yet equipped to properly run our software in a production environment.
We are blind: the application is not instrumented yet and we are not collecting any telemetry data, making us vulnerable to unknown unknowns.

If most of the previous sentence makes little to no sense to you, do not worry: getting to the bottom of it is going to be the main focus of this chapter.

4.1 Unknown Unknowns

We have a few tests. Tests are good, they make us more confident in our software, in its correctness.
Nonetheless, a test suite is not proof of the correctness of our application. We would have to explore significantly different approaches to prove that something is correct (e.g. formal methods).
At runtime we will encounter scenarios that we have not tested for or even thought about when designing the application in the first place.

I can point at a few blind spots based on the work we have done so far and my past experiences:

- what happens if we lose connection to the database? Does sqlx::PgPool try to automatically recover or will all database interactions fail from that point onwards until we restart the application?

- what happens if an attacker tries to pass malicious payloads in the body of the POST /subscriptions request (i.e. extremely large payloads, attempts to perform SQL injection, etc.)?

These are often referred to as known unknowns: shortcomings that we are aware of and we have not yet managed to investigate or we have deemed to be not relevant enough to spend time on.
Given enough time and effort, we could get rid of most known unknowns.

Unfortunately there are issues that we have not seen before and we are not expecting, unknown unknowns.

Sometimes experience is enough to transform an unknown unknown into a known unknown: if you had never worked with a database before you might have not thought about what happens when we lose connection; once you have seen it happen once, it becomes a familiar failure mode to look out for.

More often than not, unknown unknowns are peculiar failure modes of the specific system we are working on.
They are problems at the crossroads between our software components, the underlying operating systems, the hardware we are using, our development process peculiarities and that huge source of randomness known as “the outside world”.
They might emerge when:

- the system is pushed outside of its usual operating conditions (e.g. an unusual spike of traffic);

- multiple components experience failures at the same time (e.g. a SQL transaction is left hanging while the database is going through a master-replica failover);

- a change is introduced that moves the system equilibrium (e.g. tuning a retry policy);

- no changes have been introduced for a long time (e.g. applications have not been restarted for weeks and you start to see all sorts of memory leaks);

- etc.

All these scenarios share one key similarity: they are often impossible to reproduce outside of the live environment.
What can we do to prepare ourselves to deal with an outage or a bug caused by an unknown unknown?

4.2 Observability

We must assume that we will not be there when an unknown unknown issue arises: it might be late at night, we might be working on something else, etc.
Even if were paying attention at the very same moment something starts to go wrong, it often isn’t possible or practical to attach a debugger to a process running in production (assuming you even know in the first place which process you should be looking at) and the degradation might affect multiple systems at once.
The only thing we can rely on to understand and debug an unknown unknown is telemetry data: information about our running applications that is collected automatically and can be later inspected to answer questions about the state of the system at a certain point in time.

What questions?
Well, if it is an unknown unknown we do not really know in advance what questions we might need to ask to isolate its root cause - that’s the whole point.
The goal is to have an observable application.
Quoting from Honeycomb’s observability guide

Observability is about being able to ask arbitrary questions about your environment without — and this is the key part — having to know ahead of time what you wanted to ask.

“arbitrary” is a strong word - as all absolute statements, it might require an unreasonable investment of both time and money if we are to interpret it literally.
In practice we will also happily settle for an application that is sufficiently observable to enable us to deliver the level of service we promised to our users.

In a nutshell, to build an observable system we need:

- to instrument our application to collect high-quality telemetry data;

- access to tools and systems to efficiently slice, dice and manipulate the data to find answers to our questions.

We will touch upon some of the options available to fulfill the second point, but an exhaustive discussion is outside of the scope of this book.
Let’s focus on the first for the rest of this chapter.

4.3 Logging

Logs are the most common type of telemetry data.
Even developers who have never heard of observability have an intuitive understanding of the usefulness of logs: logs are what you look at when stuff goes south to understand what is happening, crossing your fingers extra hard hoping you captured enough information to troubleshoot effectively.

What are logs though?
The format varies, depending on the epoch, the platform and the technologies you are using.
Nowadays a log record is usually a bunch of text data, with a line break to separate the current record from the next one. For example

The application is starting on port 8080
Handling a request to /index
Handling a request to /index
Returned a 200 OK

are four perfectly valid log records for a web server.

What does the Rust ecosystem have to offer us when it comes to logging?

4.3.1 The log Crate

The go-to crate for logging in Rust is log.

log provides five macros: trace, debug, info, warn and error.
They all do the same thing - emit a log a record - but each of them uses a different log level, as the naming implies.
trace is the lowest level: trace-level logs are often extremely verbose and have a low signal-to-noise ratio (e.g. emit a trace-level log record every time a TCP packet is received by a web server).
We then have, in increasing order of severity, debug, info, warn and error.
Error-level logs are used to report serious failures that might have user impact (e.g. we failed to handle an incoming request or a query to the database timed out).

Let’s look at a quick usage example:

fn fallible_operation() -> Result<String, String> { ... }

pub fn main() {
match fallible_operation() {
Ok(success) => {
log::info!("Operation succeeded: {}", success);
}
Err(err) => {
log::error!("Operation failed: {}", err);
}
}
}

We are trying to perform an operation that might fail.
If it succeeds, we emit an info-level log record.
If it doesn’t, we emit an error-level log record.

Notice as well how log’s macros support the same interpolation syntax provided by println/print in the standard library.

We can use log’s macros to instrument our codebase.
Choosing what information should be logged about the execution of a particular function is often a local decision: it is enough to look at the function to decide what deserves to be captured in a log record. This enables libraries to be instrumented effectively, extending the reach of our telemetry outside the boundaries of the code we have written first-hand.

4.3.2 actix-web’s Logger Middleware

actix_web provides a Logger middleware. It emits a log record for every incoming request.
Let’s add it to our application.

//! src/startup.rs
use crate::routes::{health_check, subscribe};
use actix_web::dev::Server;
use actix_web::web::Data;
use actix_web::{web, App, HttpServer};
use actix_web::middleware::Logger;
use sqlx::PgPool;
use std::net::TcpListener;

pub fn run(listener: TcpListener, db_pool: PgPool) -> Result<Server, std::io::Error> {
let db_pool = Data::new(db_pool);
let server = HttpServer::new(move || {
App::new()
// Middlewares are added using the `wrap` method on `App`
.wrap(Logger::default())
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
})
.listen(listener)?
.run();
Ok(server)
}

We can now launch the application using cargo run and fire a quick request with curl http://127.0.0.1:8000/health_check -v.
The request comes back with a 200 but… nothing happens on the terminal we used to launch our application.
No logs. Nothing. Blank screen.

4.3.3 The Facade Pattern

We said that instrumentation is a local decision.
There is instead a global decision that applications are uniquely positioned to do: what are we supposed to do with all those log records?
Should we append them to a file? Should we print them to the terminal? Should we send them to a remote system over HTTP (e.g. ElasticSearch)?

The log crate leverages the facade pattern to handle this duality.
It gives you the tools you need to emit log records, but it does not prescribe how those log records should be processed. It provides, instead, a Log trait:

//! From `log`'s source code - src/lib.rs

/// A trait encapsulating the operations required of a logger.
pub trait Log: Sync + Send {
/// Determines if a log message with the specified metadata would be
/// logged.
///
/// This is used by the `log_enabled!` macro to allow callers to avoid
/// expensive computation of log message arguments if the message would be
/// discarded anyway.
fn enabled(&self, metadata: &Metadata) -> bool;

/// Logs the `Record`.
///
/// Note that `enabled` is *not* necessarily called before this method.
/// Implementations of `log` should perform all necessary filtering
/// internally.
fn log(&self, record: &Record);

/// Flushes any buffered records.
fn flush(&self);
}

At the beginning of your main function you can call the set_logger function and pass an implementation of the Log trait: every time a log record is emitted Log::log will be called on the logger you provided, therefore making it possible to perform whatever form of processing of log records you deem necessary.

If you do not call set_logger, then all log records will simply be discarded. Exactly what happened to our application.
Let’s initialise our logger this time.

There are a few Log implementations available on crates.io - the most popular options are listed in the documentation of log itself.
We will use env_logger - it works nicely if, as in our case, the main goal is printing all logs records to the terminal.

Let’s add it as a dependency with

cargo add env_logger

env_logger::Logger prints log records to the terminal, using the following format:

[<timestamp> <level> <module path>] <log message>

It looks at the RUST_LOG environment variable to determine what logs should be printed and what logs should be filtered out.
RUST_LOG=debug cargo run, for example, will surface all logs at debug-level or higher emitted by our application or the crates we are using. RUST_LOG=zero2prod, instead, would filter out all records emitted by our dependencies.

Let’s modify our main.rs file as required:

// [...]
use env_logger::Env;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// `init` does call `set_logger`, so this is all we need to do.
// We are falling back to printing all logs at info-level or above
// if the RUST_LOG environment variable has not been set.
env_logger::Builder::from_env(Env::default().default_filter_or("info")).init();

// [...]
}

Let’s try to launch the application again using cargo run (equivalent to RUST_LOG=info cargo run given our defaulting logic). Two log records should show up on your terminal (using a new line break with indentation to make them fit within the page margins)

[2020-09-21T21:28:40Z INFO  actix_server::builder] Starting 12 workers
[2020-09-21T21:28:40Z INFO  actix_server::builder] Starting
"actix-web-service-127.0.0.1:8000" service on 127.0.0.1:8000

If we make a request with curl http://127.0.0.1:8000/health_check you should see another log record, emitted by the Logger middleware we added a few paragraphs ago

[2020-09-21T21:28:43Z INFO  actix_web::middleware::logger] 127.0.0.1:47244
"GET /health_check HTTP/1.1" 200 0 "-" "curl/7.61.0" 0.000225

Logs are also an awesome tool to explore how the software we are using works.
Try setting RUST_LOG to trace and launching the application again.
You should see a bunch of registering with poller log records coming from mio, a low-level library for non-blocking IO, as well as a couple of startup log records for each worker spawned up by actix-web (one for each logical core available on your machine!).
Insightful things can be learned by playing around with trace-level logs.

4.4 Instrumenting POST /subscriptions

Let’s use what we learned about log to instrument our handler for POST /subscriptions requests. It currently looks like this:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => HttpResponse::Ok().finish(),
Err(e) => {
// Using `println!` to capture information about the error
// in case things don't work out as expected
println!("Failed to execute query: {}", e);
HttpResponse::InternalServerError().finish()
}
}
}

Let’s add log as a dependency:

cargo add log

What should we capture in log records?

4.4.1 Interactions With External Systems

Let’s start with a tried-and-tested rule of thumb: any interaction with external systems over the network should be closely monitored. We might experience networking issues, the database might be unavailable, queries might get slower over time as the subscribers table gets longer, etc.
Let’s add two logs records: one before query execution starts and one immediately after its completion.

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
log::info!("Saving new subscriber details in the database");
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => {
log::info!("New subscriber details have been saved");
HttpResponse::Ok().finish()
},
Err(e) => {
println!("Failed to execute query: {}", e);
HttpResponse::InternalServerError().finish()
}
}
}

As it stands, we would only be emitting a log record when the query succeeds. To capture failures we need to convert that println statement into an error-level log:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
log::info!("Saving new subscriber details in the database");
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => {
log::info!("New subscriber details have been saved");
HttpResponse::Ok().finish()
},
Err(e) => {
log::error!("Failed to execute query: {:?}", e);
HttpResponse::InternalServerError().finish()
}
}
}

Much better - we have that query somewhat covered now.

Pay attention to a small but crucial detail: we are using {:?}, the std::fmt::Debug format, to capture the query error.
Operators are the main audience of logs - we should extract as much information as possible about whatever malfunction occurred to ease troubleshooting. Debug gives us that raw view, while std::fmt::Display ({}) will return a nicer error message that is more suitable to be shown directly to our end users.

4.4.2 Think Like A User

What else should we capture?

Previously we stated that

We will happily settle for an application that is sufficiently observable to enable us to deliver the level of service we promised to our users.

What does this mean in practice?

We need to change our reference system.
Forget, for a second, that we are the authors of this piece of software.
Put yourself in the shoes of one of your users, a person landing on your website that is interested in the content you publish and wants to subscribe to your newsletter.

What does a failure look like for them?
The story might play out like this:

Hey!
I tried subscribing to your newsletter using my main email address, thomas_mann@hotmail.com, but the website failed with a weird error. Any chance you could look into what happened?

Best,
Tom

P.S. Keep it up, your blog rocks!

Tom landed on our website and received “a weird error” when he pressed the Submit button.

Our application is sufficiently observable if we can triage the issue from the breadcrumbs of information he has provided us - i.e. the email address he entered.

Can we do it?

Let’s, first of all, confirm the issue: is Tom registered as a subscriber?
We can connect to the database and run a quick query to double-check that there is no record with thomas_mann@hotmail.com as email in our subscribers table.
The issue is confirmed. What now?

None of our logs include the subscriber email address, so we cannot search for it. Dead end.
We could ask Tom to provide additional information: all our logs record have a timestamp, maybe if he remembers around what time he tried to subscribe we can dig something out?

This is a clear indication that our current logs are not good enough.
Let’s improve them:

//! src/routes/subscriptions.rs
//! ..

pub async fn subscribe(/* */) -> HttpResponse {
// We are using the same interpolation syntax of `println`/`print` here!
log::info!(
"Adding '{}' '{}' as a new subscriber.",
form.email,
form.name
);
log::info!("Saving new subscriber details in the database");
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => {
log::info!("New subscriber details have been saved");
HttpResponse::Ok().finish()
},
Err(e) => {
log::error!("Failed to execute query: {:?}", e);
HttpResponse::InternalServerError().finish()
}
}
}

Much better - we now have a log line that is capturing both name and email.[1].
Is it enough to troubleshoot Tom’s issue?

4.4.3 Logs Must Be Easy To Correlate

Going forward I will omit logs emitted by sqlx from the reported terminal output to keep the examples concise. sqlx’s logs are very verbose and should not be at INFO level - see this open PR.

If we had a single copy of our web server running at any point in time and that copy was only capable of handling a single request at a time, we might imagine logs showing up in our terminal more or less like this:

# First request
[.. INFO zero2prod] Adding 'thomas_mann@hotmail.com' 'Tom' as a new subscriber
[.. INFO zero2prod] Saving new subscriber details in the database
[.. INFO zero2prod] New subscriber details have been saved
[.. INFO actix_web] .. "POST /subscriptions HTTP/1.1" 200 ..
# Second request
[.. INFO zero2prod] Adding 's_erikson@malazan.io' 'Steven' as a new subscriber
[.. ERROR zero2prod] Failed to execute query: connection error with the database
[.. ERROR actix_web] .. "POST /subscriptions HTTP/1.1" 500 ..

You can clearly see where a single request begins, what happened while we tried to fulfill it, what we returned as a response, where the next request begins, etc.
It is easy to follow.
But this is not what it looks like when you are handling multiple requests concurrently:

[.. INFO zero2prod] Receiving request for POST /subscriptions
[.. INFO zero2prod] Receiving request for POST /subscriptions
[.. INFO zero2prod] Adding 'thomas_mann@hotmail.com' 'Tom' as a new subscriber
[.. INFO zero2prod] Adding 's_erikson@malazan.io' 'Steven' as a new subscriber
[.. INFO zero2prod] Saving new subscriber details in the database
[.. ERROR zero2prod] Failed to execute query: connection error with the database
[.. ERROR actix_web] .. "POST /subscriptions HTTP/1.1" 500 ..
[.. INFO zero2prod] Saving new subscriber details in the database
[.. INFO zero2prod] New subscriber details have been saved
[.. INFO actix_web] .. "POST /subscriptions HTTP/1.1" 200 ..

What details did we fail to save though? thomas_mann@hotmail.com or s_erikson@malazan.io?
Impossible to say from the logs.

We need a way to correlate all logs related to the same request.
This is usually achieved using a request id (also known as correlation id): when we start to process an incoming request we generate a random identifier (e.g. a UUID) which is then associated to all logs concerning the fulfilling of that specific request.

Let’s add one to our handler:

//! src/routes/subscriptions.rs
//! ..

pub async fn subscribe(/* */) -> HttpResponse {
// Let's generate a random unique identifier
let request_id = Uuid::new_v4();
log::info!(
"request_id {} - Adding '{}' '{}' as a new subscriber.",
request_id,
form.email,
form.name
);
log::info!(
"request_id {} - Saving new subscriber details in the database",
request_id
);
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => {
log::info!(
"request_id {} - New subscriber details have been saved",
request_id
);
HttpResponse::Ok().finish()
},
Err(e) => {
log::error!(
"request_id {} - Failed to execute query: {:?}",
request_id,
e
);
HttpResponse::InternalServerError().finish()
}
}
}

Logs for an incoming request will now look like this:

curl -i -X POST -d 'email=thomas_mann@hotmail.com&name=Tom' \
http://127.0.0.1:8000/subscriptions

[.. INFO  zero2prod] request_id 9ebde7e9-1efe-40b9-ab65-86ab422e6b87 - Adding
'thomas_mann@hotmail.com' 'Tom' as a new subscriber.
[.. INFO  zero2prod] request_id 9ebde7e9-1efe-40b9-ab65-86ab422e6b87 - Saving
new subscriber details in the database
[.. INFO  zero2prod] request_id 9ebde7e9-1efe-40b9-ab65-86ab422e6b87 - New
subscriber details have been saved
[.. INFO  actix_web] .. "POST /subscriptions HTTP/1.1" 200 ..

We can now search for thomas_mann@hotmail.com in our logs, find the first record, grab the request_id and then pull down all the other log records associated with that request.
Well, almost all the logs: request_id is created in our subscribe handler, therefore actix_web’s Logger middleware is completely unaware of it.
That means that we will not know what status code our application has returned to the user when they tried to subscribe to our newsletter.

What should we do?
We could bite the bullet, remove actix_web’s Logger, write a middleware to generate a random request identifier for every incoming request and then write our own logging middleware that is aware of the identifier and includes it in all log lines.

Could it work? Yes.
Should we do it? Probably not.

4.5 Structured Logging

To ensure that request_id is included in all log records we would have to:

- rewrite all upstream components in the request processing pipeline (e.g. actix-web’s Logger);

- change the signature of all downstream functions we are calling from the subscribe handler; if they are emitting a log statement, they need to include the request_id, which therefore needs to be passed down as an argument.

What about log records emitted by the crates we are importing into our project? Should we rewrite those as well?
It is clear that this approach cannot scale.

Let’s take a step back: what does our code look like?
We have an over-arching task (a HTTP request), which is broken down in a set of sub-tasks (e.g. parse input, make a query, etc.), which might in turn be broken down in smaller sub-routines recursively.
Each of those units of work has a duration (i.e. a beginning and an end).
Each of those units of work has a context associated to it (e.g. name and email of a new subscriber, request_id) that is naturally shared by all its sub-units of work.

No doubt we are struggling: log statements are isolated events happening at a defined moment in time that we are stubbornly trying to use to represent a tree-like processing pipeline.
Logs are the wrong abstraction.

What should we use then?

4.5.1 The tracing Crate

The tracing crate comes to the rescue:

tracing expands upon logging-style diagnostics by allowing libraries and applications to record structured events with additional information about temporality and causality — unlike a log message, a span in tracing has a beginning and end time, may be entered and exited by the flow of execution, and may exist within a nested tree of similar spans.

That is music to our ears.
What does it look like in practice?

4.5.2 Migrating From log To tracing

There is only one way to find out - let’s convert our subscribe handler to use tracing instead of log for instrumentation. Let’s add tracing to our dependencies:

#! Cargo.toml

[dependencies]
tracing = { version = "0.1", features = ["log"] }
# [...]

The first migration step is as straight-forward as it gets: search and replace all occurrences of the log string in our function body with tracing.

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
let request_id = Uuid::new_v4();
tracing::info!(
"request_id {} - Adding '{}' '{}' as a new subscriber.",
request_id,
form.email,
form.name
);
tracing::info!(
"request_id {} - Saving new subscriber details in the database",
request_id
);
match sqlx::query!(/* */)
.execute(pool.as_ref())
.await
{
Ok(_) => {
tracing::info!(
"request_id {} - New subscriber details have been saved",
request_id
);
HttpResponse::Ok().finish()
},
Err(e) => {
tracing::error!(
"request_id {} - Failed to execute query: {:?}",
request_id,
e
);
HttpResponse::InternalServerError().finish()
}
}
}

That’s it.
If you run the application and fire a POST /subscriptions request you will see exactly the same logs in your console. Identical.
Pretty cool, isn’t it?

This works thanks to tracing’s log feature flag, which we enabled in Cargo.toml. It ensures that every time an event or a span are created using tracing’s macros a corresponding log event is emitted, allowing log’s loggers to pick up on it (env_logger, in our case).

4.5.3 tracing’s Span

We can now start to leverage tracing’s Span to better capture the structure of our program.
We want to create a span that represents the whole HTTP request:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
let request_id = Uuid::new_v4();
// Spans, like logs, have an associated level
// `info_span` creates a span at the info-level
let request_span = tracing::info_span!(
"Adding a new subscriber.",
%request_id,
subscriber_email = %form.email,
subscriber_name= %form.name
);
// Using `enter` in an async function is a recipe for disaster!
// Bear with me for now, but don't do this at home.
// See the following section on `tracing-futures`
let _request_span_guard = request_span.enter();

// [...]
// `_request_span_guard` is dropped at the end of `subscribe`
// That's when we "exit" the span
}

There is a lot going on here - let’s break it down.

We are using the info_span! macro to create a new span and attach some values to its context: request_id, form.email and form.name.
We are not using string interpolation anymore: tracing allows us to associate structured information to our spans as a collection of key-value pairs[2]. We can explicitly name them (e.g. subscriber_email for form.email) or implicitly use the variable name as key (e.g. the isolated request_id is equivalent to request_id = request_id).
Notice that we prefixed all of them with a % symbol: we are telling tracing to use their Display implementation for logging purposes. You can find more details on the other available options in their documentation.

info_span returns the newly created span, but we have to explicit step into it using the .enter() method to activate it.

.enter() returns an instance of Entered, a guard: as long the guard variable is not dropped all downstream spans and log events will be registered as children of the entered span. This is a typical Rust pattern, often referred to as Resource Acquisition Is Initilization (RAII): the compiler keeps track of the lifetime of all variables and when they go out of scope it inserts a call to their destructor, Drop::drop.
The default implementation of the Drop trait simply takes care of releasing the resources owned by that variable. We can, though, specify a custom Drop implementation to perform other cleanup operations on drop - e.g. exiting from a span when the Entered guard gets dropped:

//! `tracing`'s source code

impl<'a> Drop for Entered<'a> {
#[inline]
fn drop(&mut self) {
// Dropping the guard exits the span.
//
// Running this behaviour on drop rather than with an explicit function
// call means that spans may still be exited when unwinding.
if let Some(inner) = self.span.inner.as_ref() {
inner.subscriber.exit(&inner.id);
}

if_log_enabled! {{
if let Some(ref meta) = self.span.meta {
self.span.log(
ACTIVITY_LOG_TARGET,
log::Level::Trace,
format_args!("<- {}", meta.name())
);
}
}}
}
}

Inspecting the source code of your dependencies can often expose some gold nuggets - we just found out that if the log feature flag is enabled tracing will emit a trace-level log when a span exits.
Let’s give it a go immediately:

RUST_LOG=trace cargo run

[.. INFO  zero2prod] Adding a new subscriber.; request_id=f349b0fe..
subscriber_email=ursulale_guin@gmail.com subscriber_name=le guin
[.. TRACE zero2prod] -> Adding a new subscriber.
[.. INFO  zero2prod] request_id f349b0fe.. - Saving new subscriber details
in the database
[.. INFO  zero2prod] request_id f349b0fe.. - New subscriber details have
been saved
[.. TRACE zero2prod] <- Adding a new subscriber.
[.. TRACE zero2prod] -- Adding a new subscriber.
[.. INFO  actix_web] .. "POST /subscriptions HTTP/1.1" 200 ..

First of all, notice how all the information we captured in the span’s context is reported in the emitted log line.
We can closely follow the lifetime of our span using the emitted logs:

- Adding a new subscriber is logged when the span is created;

- We enter the span (->);

- We execute the INSERT query;

- We exit the span (<-);

- We finally close the span (--).

Wait, what is the difference between exiting and closing a span?
Glad you asked!

You can enter (and exit) a span multiple times. Closing, instead, is final: it happens when the span itself is dropped.
This comes pretty handy when you have a unit of work that can be paused and then resumed - e.g. an asynchronous task!

4.5.4 tracing-futures

Let’s use our database query as an example.
The executor might have to poll its future more than once to drive it to completion - while that future is idle, we are going to make progress on other futures.

This can clearly cause issues: how do we make sure we don’t mix their respective spans?
The best way would be to closely mimic the future’s lifecycle: we should enter into the span associated to our future every time it is polled by the executor and exit every time it gets parked.

That’s where tracing-futures comes into the picture.

cargo add tracing-futures

It provides an extension trait for futures and other asynchronous types, Instrument. Instrument::instrument does exactly what we want: enters the span we pass as argument every time self, the future, is polled; it exits the span every time the future is parked.

Let’s try it out on our query:

//! src/routes/subscriptions.rs
use tracing_futures::Instrument;
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
let request_id = Uuid::new_v4();
let request_span = tracing::info_span!(
"Adding a new subscriber.",
%request_id,
subscriber_email = %form.email,
subscriber_name= %form.name
);
let _request_span_guard = request_span.enter();

// We do not call `.enter` on query_span!
// `.instrument` takes care of it at the right moments
// in the query future lifetime
let query_span = tracing::info_span!(
"Saving new subscriber details in the database"
);
match sqlx::query!(/* */)
.execute(pool.as_ref())
// First we attach the instrumentation, then we `.await` it
.instrument(query_span)
.await
{
Ok(_) => {
HttpResponse::Ok().finish()
},
Err(e) => {
// Yes, this error log falls outside of `query_span`
// We'll rectify it later, pinky swear!
tracing::error!("Failed to execute query: {:?}", e);
HttpResponse::InternalServerError().finish()
}
}
}

If we launch the application again with RUST_LOG=trace and try a POST /subscriptions request we will see logs that look somewhat similar to these:

[.. INFO  zero2prod] Adding a new subscriber.; request_id=f349b0fe..
subscriber_email=ursulale_guin@gmail.com subscriber_name=le guin
[.. TRACE zero2prod] -> Adding a new subscriber.
[.. INFO  zero2prod] Saving new subscriber details in the database
[.. TRACE zero2prod] -> Saving new subscriber details in the database
[.. TRACE zero2prod] <- Saving new subscriber details in the database
[.. TRACE zero2prod] -> Saving new subscriber details in the database
[.. TRACE zero2prod] <- Saving new subscriber details in the database
[.. TRACE zero2prod] -> Saving new subscriber details in the database
[.. TRACE zero2prod] <- Saving new subscriber details in the database
[.. TRACE zero2prod] -> Saving new subscriber details in the database
[.. TRACE zero2prod] -> Saving new subscriber details in the database
[.. TRACE zero2prod] <- Saving new subscriber details in the database
[.. TRACE zero2prod] -- Saving new subscriber details in the database
[.. TRACE zero2prod] <- Adding a new subscriber.
[.. TRACE zero2prod] -- Adding a new subscriber.
[.. INFO  actix_web] .. "POST /subscriptions HTTP/1.1" 200 ..

We can clearly see how many times the query future has been polled by the executor before completing. How cool is that!?

4.5.5 tracing’s Subscriber

We embarked in this migration from log to tracing because we needed a better abstraction to instrument our code effectively. We wanted, in particular, to attach request_id to all logs associated to the same incoming HTTP request.
Although I promised tracing was going to solve our problem, look at those logs: request_id is only printed on the very first log statement where we attach it explicitly to the span context.
Why is that?

Well, we haven’t completed our migration yet.
Although we moved all our instrumentation code from log to tracing we are still using env_logger to process everything!

//! src/main.rs
//! [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
env_logger::from_env(Env::default().default_filter_or("info")).init();
// [...]
}

env_logger’s logger implements log’s Log trait - it knows nothing about the rich structure exposed by tracing’s Span!
tracing’s compatibility with log was great to get off the ground, but it is now time to replace env_logger with a tracing-native solution.

The tracing crate follows the same facade pattern used by log - you can freely use its macros to instrument your code, but applications are in charge to spell out how that span telemetry data should be processed.
Subscriber is the tracing counterpart of log’s Log: an implementation of the Subscriber trait exposes a variety of methods to manage every stage of the lifecycle of a Span - creation, enter/exit, closure, etc.

//! `tracing`'s source code

pub trait Subscriber: 'static {
fn new_span(&self, span: &span::Attributes<'_>) -> span::Id;
fn event(&self, event: &Event<'_>);
fn enter(&self, span: &span::Id);
fn exit(&self, span: &span::Id);
fn clone_span(&self, id: &span::Id) -> span::Id;
// [...]
}

The quality of tracing’s documentation is breath-taking - I strongly invite you to have a look for yourself at Subscriber’s docs to properly understand what each of those methods does.

4.5.6 tracing-subscriber

tracing does not provide any subscriber out of the box.
We need to look into tracing-subscriber, another crate maintained in-tree by the tracing project, to find a few basic subscribers to get off the ground. Let’s add it to our dependencies:

[dependencies]
# ...
tracing-subscriber = { version = "0.2.12", features = ["registry", "env-filter"] }

tracing-subscriber does much more than providing us with a few handy subscribers.
It introduces another key trait into the picture, Layer.
Layer makes it possible to build a processing pipeline for spans data: we are not forced to provide an all-encompassing subscriber that does everything we want; we can instead combine multiple smaller layers to obtain the processing pipeline we need.
This substantially reduces duplication across in tracing ecosystem: people are focused on adding new capabilities by churning out new layers rather than trying to build the best-possible-batteries-included subscriber.

The cornerstone of the layering approach is Registry.
Registry implements the Subscriber trait and takes care of all the difficult stuff:

Registry does not actually record traces itself: instead, it collects and stores span data that is exposed to any layer wrapping it […]. The Registry is responsible for storing span metadata, recording relationships between spans, and tracking which spans are active and which are closed.

Downstream layers can piggy-back on Registry’s functionality and focus on their purpose: filtering what spans should be processed, formatting span data, shipping span data to remote systems, etc.

4.5.7 tracing-bunyan-formatter

We’d like to put together a subscriber that has feature-parity with the good old env_logger.
We will get there by combining three layers[3]:

- tracing_subscriber::filter::EnvFilter discards spans based on their log levels and their origins, just as we did in env_logger via the RUST_LOG environment variable;

- tracing_bunyan_formatter::JsonStorageLayer processes spans data and stores the associated metadata in an easy-to-consume JSON format for downstream layers. It does, in particular, propagate context from parent spans to their children;

- tracing_bunyan_formatter::BunyanFormatterLayer builds on top of JsonStorageLayer and outputs log records in bunyan-compatible JSON format.

Let’s add tracing_bunyan_formatter to our dependencies[4]:

cargo add tracing-bunyan-formatter

We can now tie everything together in our main function:

//! src/main.rs
//! [...]
use tracing::subscriber::set_global_default;
use tracing_bunyan_formatter::{BunyanFormattingLayer, JsonStorageLayer};
use tracing_subscriber::{layer::SubscriberExt, EnvFilter, Registry};

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// We removed the `env_logger` line we had before!

// We are falling back to printing all spans at info-level or above
// if the RUST_LOG environment variable has not been set.
let env_filter = EnvFilter::try_from_default_env()
.unwrap_or_else(|_| EnvFilter::new("info"));
let formatting_layer = BunyanFormattingLayer::new(
"zero2prod".into(),
// Output the formatted spans to stdout.
std::io::stdout
);
// The `with` method is provided by `SubscriberExt`, an extension
// trait for `Subscriber` exposed by `tracing_subscriber`
let subscriber = Registry::default()
.with(env_filter)
.with(JsonStorageLayer)
.with(formatting_layer);
// `set_global_default` can be used by applications to specify
// what subscriber should be used to process spans.
set_global_default(subscriber).expect("Failed to set subscriber");

// [...]
}

If you launch the application with cargo run and fire a request you’ll see these logs (pretty-printed here to be easier on the eye):

{
"msg": "[ADDING A NEW SUBSCRIBER - START]",
"subscriber_name": "le guin",
"request_id": "30f8cce1-f587-4104-92f2-5448e1cc21f6",
"subscriber_email": "ursula_le_guin@gmail.com"
...
}
{
"msg": "[SAVING NEW SUBSCRIBER DETAILS IN THE DATABASE - START]",
"subscriber_name": "le guin",
"request_id": "30f8cce1-f587-4104-92f2-5448e1cc21f6",
"subscriber_email": "ursula_le_guin@gmail.com"
...
}
{
"msg": "[SAVING NEW SUBSCRIBER DETAILS IN THE DATABASE - END]",
"elapsed_milliseconds": 4,
"subscriber_name": "le guin",
"request_id": "30f8cce1-f587-4104-92f2-5448e1cc21f6",
"subscriber_email": "ursula_le_guin@gmail.com"
...
}
{
"msg": "[ADDING A NEW SUBSCRIBER - END]",
"elapsed_milliseconds": 5
"subscriber_name": "le guin",
"request_id": "30f8cce1-f587-4104-92f2-5448e1cc21f6",
"subscriber_email": "ursula_le_guin@gmail.com",
...
}

We made it: everything we attached to the original context has been propagated to all its sub-spans.
tracing-bunyan-formatter also provides duration out-of-the-box: every time a span is closed a JSON message is printed to the console with an elapsed_millisecond property attached to it.
The JSON format is extremely friendly when it comes to searching: an engine like ElasticSearch can easily ingest all these records, infer a schema and index the request_id, name and email fields. It unlocks the full power of a querying engine to sift through our logs!
This is exponentially better than we had before: to perform complex searches we would have had to use custom-built regexes, therefore limiting considerably the range of questions that we could easily ask to our logs.

4.5.8 tracing-log

If you take a closer look you will realise we lost something along the way: our terminal is only showing logs that were directly emitted by our application. What happened to actix-web’s log records?

tracing’s log feature flag ensures that a log record is emitted every time a tracing event happens, allowing log’s loggers to pick them up.
The opposite does not hold true: log does not emit tracing events out of the box and does not provide a feature flag to enable this behaviour.

If we want it, we need to explicitly register a logger implementation to redirect logs to our tracing subscriber for processing.

We can use LogTracer, provided by the tracing-log crate.

cargo add tracing-log

Let’s edit our main as required:

//! src/main.rs
//! [...]
use tracing::subscriber::set_global_default;
use tracing_bunyan_formatter::{BunyanFormattingLayer, JsonStorageLayer};
use tracing_subscriber::{layer::SubscriberExt, EnvFilter, Registry};
use tracing_log::LogTracer;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// Redirect all `log`'s events to our subscriber
LogTracer::init().expect("Failed to set logger");

let env_filter = EnvFilter::try_from_default_env()
.unwrap_or_else(|_| EnvFilter::new("info"));
let formatting_layer = BunyanFormattingLayer::new(
"zero2prod".into(),
std::io::stdout
);
let subscriber = Registry::default()
.with(env_filter)
.with(JsonStorageLayer)
.with(formatting_layer);
set_global_default(subscriber).expect("Failed to set subscriber");

// [...]
}

All actix-web’s logs should once again be available in our console.

4.5.9 Removing Unused Dependencies

If you quickly scan through all our files you will realise that we are not using log or env_logger anywhere at this point. We should remove them from our Cargo.toml file.

In a large project it is very difficult to spot that a dependency has become unused after a refactoring.
Luckily enough, tooling comes to the rescue once again - let’s install cargo-udeps (unused dependencies):

cargo install cargo-udeps

cargo-udeps scans your Cargo.toml file and checks if all the crates listed under [dependencies] have actually been used in the project. Check cargo-deps’ trophy case for a long list of popular Rust projects where cargo-udeps was able to spot unused dependencies and cut down build times.

Let’s run it on our project!

# cargo-udeps requires the nightly compiler.
# We add +nightly to our cargo invocation
# to tell cargo explicitly what toolchain we want to use.
cargo +nightly udeps

The output should be

zero2prod
dependencies
"env-logger"

Unfortunately it does not pick up log.
Let’s strike both out of our Cargo.toml file.

4.5.10 Cleaning Up Initialisation

We relentlessly pushed forward to improve the observability postured of our application.
Let’s now take a step back and look at the code we wrote to see if we can improve in any meaningful way.

Let’s start from our main function:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::postgres::PgPool;
use std::net::TcpListener;
use tracing::subscriber::set_global_default;
use tracing_bunyan_formatter::{BunyanFormattingLayer, JsonStorageLayer};
use tracing_log::LogTracer;
use tracing_subscriber::{layer::SubscriberExt, EnvFilter, Registry};

#[actix_web::main]
async fn main() -> std::io::Result<()> {
LogTracer::init().expect("Failed to set logger");

let env_filter = EnvFilter::try_from_default_env()
.unwrap_or(EnvFilter::new("info"));
let formatting_layer = BunyanFormattingLayer::new(
"zero2prod".into(),
std::io::stdout
);
let subscriber = Registry::default()
.with(env_filter)
.with(JsonStorageLayer)
.with(formatting_layer);
set_global_default(subscriber).expect("Failed to set subscriber");

let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

let address = format!("127.0.0.1:{}", configuration.application_port);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool)?.await?;
Ok(())
}

There is a lot going on in that main function right now.
Let’s break it down a bit:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use sqlx::postgres::PgPool;
use std::net::TcpListener;
use tracing::{Subscriber, subscriber::set_global_default};
use tracing_bunyan_formatter::{BunyanFormattingLayer, JsonStorageLayer};
use tracing_log::LogTracer;
use tracing_subscriber::{layer::SubscriberExt, EnvFilter, Registry};

/// Compose multiple layers into a `tracing`'s subscriber.
///
/// # Implementation Notes
///
/// We are using `impl Subscriber` as return type to avoid having to
/// spell out the actual type of the returned subscriber, which is
/// indeed quite complex.
/// We need to explicitly call out that the returned subscriber is
/// `Send` and `Sync` to make it possible to pass it to `init_subscriber`
/// later on.
pub fn get_subscriber(
name: String,
env_filter: String
) -> impl Subscriber + Send + Sync {
let env_filter = EnvFilter::try_from_default_env()
.unwrap_or_else(|_| EnvFilter::new(env_filter));
let formatting_layer = BunyanFormattingLayer::new(
name,
std::io::stdout
);
Registry::default()
.with(env_filter)
.with(JsonStorageLayer)
.with(formatting_layer)
}

/// Register a subscriber as global default to process span data.
///
/// It should only be called once!
pub fn init_subscriber(subscriber: impl Subscriber + Send + Sync) {
LogTracer::init().expect("Failed to set logger");
set_global_default(subscriber).expect("Failed to set subscriber");
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into());
init_subscriber(subscriber);

// [...]
}

We can now move get_subscriber and init_subscriber to a module within our zero2prod library, telemetry.

//! src/lib.rs
#![allow(clippy::toplevel_ref_arg)]
pub mod configuration;
pub mod routes;
pub mod startup;
pub mod telemetry;

//! src/telemetry.rs
use tracing::subscriber::set_global_default;
use tracing::Subscriber;
use tracing_bunyan_formatter::{BunyanFormattingLayer, JsonStorageLayer};
use tracing_log::LogTracer;
use tracing_subscriber::{layer::SubscriberExt, EnvFilter, Registry};

pub fn get_subscriber(
name: String,
env_filter: String
) -> impl Subscriber + Sync + Send {
// [...]
}

pub fn init_subscriber(subscriber: impl Subscriber + Sync + Send) {
// [...]
}

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use zero2prod::telemetry::{get_subscriber, init_subscriber};
use sqlx::postgres::PgPool;
use std::net::TcpListener;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into());
init_subscriber(subscriber);

// [...]
}

Awesome.

4.5.11 Logs For Integration Tests

We are not just cleaning up for aesthetic/readability reasons - we are moving those two functions to the zero2prod library to make them available to our test suite!

As a rule of thumb, everything we use in our application should be reflected in our integration tests.
Structured logging, in particular, can significantly speed up our debugging when an integration test fails: we might not have to attach a debugger, more often than not the logs can tell us where something went wrong. It is also a good benchmark: if you cannot debug it from logs, imagine how difficult would it be to debug in production!

Let’s change our spawn_app helper function to take care of initialising our tracing stack:

//! tests/health_check.rs

use zero2prod::configuration::{get_configuration, DatabaseSettings};
use zero2prod::startup::run;
use zero2prod::telemetry::{get_subscriber, init_subscriber};
use sqlx::{Connection, Executor, PgConnection, PgPool};
use std::net::TcpListener;
use uuid::Uuid;

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

async fn spawn_app() -> TestApp {
let subscriber = get_subscriber("test".into(), "debug".into());
init_subscriber(subscriber);

let listener = TcpListener::bind("127.0.0.1:0").expect("Failed to bind random port");
let port = listener.local_addr().unwrap().port();
let address = format!("http://127.0.0.1:{}", port);

let mut configuration = get_configuration().expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();
let connection_pool = configure_database(&configuration.database).await;

let server = run(listener, connection_pool.clone()).expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

// [...]

If you try to run cargo test you will be greeted by one success and a long series of test failures:

failures:
---- subscribe_returns_a_400_when_data_is_missing stdout ----
thread 'subscribe_returns_a_400_when_data_is_missing' panicked at
'Failed to set logger: SetLoggerError(())'
Panic in Arbiter thread.

---- subscribe_returns_a_200_for_valid_form_data stdout ----
thread 'subscribe_returns_a_200_for_valid_form_data' panicked at
'Failed to set logger: SetLoggerError(())'
Panic in Arbiter thread.

failures:
subscribe_returns_a_200_for_valid_form_data
subscribe_returns_a_400_when_data_is_missing

init_subscriber should only be called once, but it is being invoked by all our tests.
We can use once_cell to rectify it:

cargo add once_cell --dev

//! tests/health_check.rs
// [...]
use once_cell::sync::Lazy;

// Ensure that the `tracing` stack is only initialised once using `once_cell`
static TRACING: Lazy<()> = Lazy::new(|| {
let subscriber = get_subscriber("test".into(), "debug".into());
init_subscriber(subscriber);
});

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

async fn spawn_app() -> TestApp {
// The first time `initialize` is invoked the code in `TRACING` is executed.
// All other invocations will instead skip execution.
Lazy::force(&TRACING);

// [...]
}

// [...]

cargo test is green again.
The output, though, is very noisy: we have several log lines coming out of each test case.
We want our tracing instrumentation to be exercised in every test, but we do not want to look at those logs every time we run our test suite.

cargo test solves the very same problem for println/print statements. By default, it swallows everything that is printed to console. You can explicitly opt in to look at those print statements using cargo test -- --nocapture.

We need an equivalent strategy for our tracing instrumentation.
Let’s add a new parameter to get_subscriber to allow customisation of what sink logs should be written to:

//! src/telemetry.rs
use tracing_subscriber::fmt::MakeWriter;
// [...]

pub fn get_subscriber(
name: String,
env_filter: String,
// A function that returns a sink - a place we can write log to
sink: impl MakeWriter + Send + Sync + 'static,
) -> impl Subscriber + Sync + Send {
// [...]
let formatting_layer = BunyanFormattingLayer::new(name, sink);
// [...]
}

We can then adjust our main function to use stdout:

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into(), std::io::stdout);

// [...]
}

In our test suite we will choose the sink dynamically according to an environment variable, TEST_LOG. If TEST_LOG is set, we use std::io::stdout.
If TEST_LOG is not set, we send all logs into the void using std::io::sink.
Our own home-made version of the --nocapture flag.

//! tests/health_check.rs
//! ...

// Ensure that the `tracing` stack is only initialised once using `once_cell`
static TRACING: Lazy<()> = Lazy::new(|| {
let default_filter_level = "info".to_string();
let subscriber_name = "test".to_string();
// We cannot assign the output of `get_subscriber` to a variable based on the value of `TEST_LOG`
// because the sink is part of the type returned by `get_subscriber`, therefore they are not the
// same type. We could work around it, but this is the most straight-forward way of moving forward.
if std::env::var("TEST_LOG").is_ok() {
let subscriber = get_subscriber(subscriber_name, default_filter_level, std::io::stdout);
init_subscriber(subscriber);
} else {
let subscriber = get_subscriber(subscriber_name, default_filter_level, std::io::sink);
init_subscriber(subscriber);
};
});

// [...]

When you want to see all logs coming out of a certain test case to debug it you can run

# We are using the `bunyan` CLI to prettify the outputted logs
# The original `bunyan` requires NPM, but you can install a Rust-port with
# `cargo install bunyan`
TEST_LOG=true cargo test health_check_works | bunyan

and sift through the output to understand what is going on.
Neat, isn’t it?

4.5.12 Cleaning Up Instrumentation Code - tracing::instrument

We refactored our initialisation logic. Let’s have a look at our instrumentation code now.
Time to bring subscribe back once again.

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let request_id = Uuid::new_v4();
let request_span = tracing::info_span!(
"Adding a new subscriber",
%request_id,
subscriber_email = %form.email,
subscriber_name= %form.name
);
let _request_span_guard = request_span.enter();
let query_span = tracing::info_span!(
"Saving new subscriber details in the database"
);
match sqlx::query!(/* */)
.execute(pool.as_ref())
// First we attach the instrumentation, then we `.await` it
.instrument(query_span)
.await
{
Ok(_) => HttpResponse::Ok().finish(),
Err(e) => {
tracing::error!("Failed to execute query: {:?}", e);
HttpResponse::InternalServerError().finish()
}
}
}

It is fair to say logging has added some noise to our subscribe function.
Let’s see if we can cut it down a bit.

We will start with request_span: we’d like all operations within subscribe to happen within the context of request_span.
In other words, we’d like to wrap the subscribe function in a span.
This requirement is fairly common: extracting each sub-task in its own function is a common way to structure routines to improve readability and make it easier to write tests; therefore we will often want to attach a span to a function declaration.

tracing caters for this specific usecase with its tracing::instrument procedural macro. Let’s see it in action:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool),
fields(
request_id = %Uuid::new_v4(),
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let query_span = tracing::info_span!(
"Saving new subscriber details in the database"
);
match sqlx::query!(/* */)
.execute(pool.as_ref())
.instrument(query_span)
.await
{
Ok(_) => HttpResponse::Ok().finish(),
Err(e) => {
tracing::error!("Failed to execute query: {:?}", e);
HttpResponse::InternalServerError().finish()
}
}
}

#[tracing_instrument] create a span at the beginning of the function invocation.

#[tracing_instrument] automatically attaches all arguments passed to the function to the context of the span - in our case, form and pool. Often function arguments won’t be displayable on log records (e.g. pool) or we’d like to specify more explicitly what should/how they should be captured (e.g. naming each field of form) - we can explicitly tell tracing to ignore them using the skip directive.

name can be used to specify the message associated to the function span - if omitted, it defaults to the function name.

We can also enrich the span’s context using the fields directive. It leverages the same syntax we have already seen for the info_span! macro.

The result is quite nice: all instrumentation concerns are visually separated by execution concerns - the first are dealt with in a procedural macro that “decorates” the function declaration, while the function body focuses on the actual business logic.

It is important to point out that tracing::instrument takes care as well to use tracing-futures if it is applied to an asynchronous function.

Let’s extract the query in its own function and use tracing::instrument to get rid of query_span and the call to the .instrument method:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool),
fields(
request_id = %Uuid::new_v4(),
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
match insert_subscriber(&pool, &form).await
{
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish()
}
}

#[tracing::instrument(
name = "Saving new subscriber details in the database",
skip(form, pool)
)]
pub async fn insert_subscriber(
pool: &PgPool,
form: &FormData,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
form.email,
form.name,
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
// Using the `?` operator to return early
// if the function failed, returning a sqlx::Error
// We will talk about error handling in depth later!
})?;
Ok(())
}

The error event does now fall within the query span and we have a better separation of concerns:

- insert_subscriber takes care of the database logic and it has no awareness of the surrounding web framework - i.e. we are not passing web::Form or web::Data wrappers as input types;

- subscribe orchestrates the work to be done by calling the required routines and translates their outcome into the proper response according to the rules and conventions of the HTTP protocol.

I must confess my unbounded love for tracing::instrument: it significantly lowers the effort required to instrument your code.
It pushes you in the pit of success: the right thing to do is the easiest thing to do.

4.5.13 Request Id

We have one last job to do: ensure all logs for a particular request, in particular the record with the returned status code, are enriched with a request_id property. How?

If our goal is to avoid touching actix_web::Logger the easiest solution is adding another middleware, RequestIdMiddleware, that is in charge of:

- generating a unique request identifier;

- creating a new span with the request identifier attached as context;

- wrapping the rest of the middleware chain in the newly created span.

We would be leaving a lot on the table though: actix_web::Logger does not give us access to its rich information (status code, processing time, caller IP, etc.) in the same structured JSON format we are getting from other logs - we would have to parse all that information out of its message string.
We are better off, in this case, by bringing in a solution that is tracing-aware.

Let’s add tracing-actix-web as one of our dependencies[5]:

cargo add tracing-actix-web --vers 0.4.0-beta.8

It is designed as a drop-in replacement of actix-web’s Logger, just based on tracing instead of log:

//! src/startup.rs
use crate::routes::{health_check, subscribe};
use actix_web::dev::Server;
use actix_web::web::Data;
use actix_web::{web, App, HttpServer};
use sqlx::PgPool;
use std::net::TcpListener;
use tracing_actix_web::TracingLogger;

pub fn run(listener: TcpListener, db_pool: PgPool) -> Result<Server, std::io::Error> {
let db_pool = Data::new(db_pool);
let server = HttpServer::new(move || {
App::new()
// Instead of `Logger::default`
.wrap(TracingLogger::default())
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
})
.listen(listener)?
.run();
Ok(server)
}

If you launch the application and fire a request you should see a request_id on all logs as well as request_path and a few other useful bits of information.

We are almost done - there is one outstanding issue we need to take care of.
Let’s take a closer look at the emitted log records for a POST /subscriptions request:

{
"msg": "[REQUEST - START]",
"request_id": "21fec996-ace2-4000-b301-263e319a04c5",
...
}
{
"msg": "[ADDING A NEW SUBSCRIBER - START]",
"request_id":"aaccef45-5a13-4693-9a69-5",
...
}

We have two different request_id for the same request!
The bug can be traced back to the #[tracing::instrument] annotation on our subscribe function:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool),
fields(
request_id = %Uuid::new_v4(),
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
// [...]
}

// [...]

We are still generating a request_id at the function-level which overrides the request_id coming from TracingLogger.
Let’s get rid of it to fix the issue:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool),
fields(
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
// [...]
}

// [...]

All good now - we have one consistent request_id for each endpoint of our application.

4.5.14 Leveraging The tracing Ecosystem

We covered a lot of what tracing has to offer - it has significantly improved the quality of the telemetry data we are collecting as well as the clarity of our instrumentation code.
At the same time, we have barely touched upon the richness of the whole tracing ecosystem when it comes to subscriber layers.
Just to mention a few more of those readily available:

- tracing-actix-web is OpenTelemetry-compatible. If you plug-in tracing-opentelemetry you can ship spans to an OpenTelemetry-compatible service (e.g. Jaeger or Honeycomb.io) for further analysis;

- tracing-error enriches our error types with a SpanTrace to ease troubleshooting.

It is not an exaggeration to state that tracing is a foundational crate in the Rust ecosystem. While log is the minimum common denominator, tracing is now established as the modern backbone of the whole diagnostics and instrumentation ecosystem.

4.6 Summary

We started from a completely silent actix-web application and we ended up with high-quality telemetry data. It is now time to take this newsletter API live!

In the next chapter we will build a basic deployment pipeline for our Rust project.

1. Should we log names and emails? If you are operating in Europe, they generally qualify as Personal Identifiable Information (PII) and their processing must obey the principles and rules laid out in the General Data Protection Regulation (GDPR). We should have tight controls around who can access that information, how long we are planning to store it for, procedures to delete it if the user asks to be forgotten, etc. Generally speaking, there are many types of information that would be useful for debugging purposes but cannot be logged freely (e.g. passwords) - you will either have to do without them or rely on obfuscation (e.g. tokenization/pseudonymisation) to strike a balance between security, privacy and usefulness.↩

2. The capability of capturing contextual information as a collection of key-value pairs has recently been explored in the log crate as well - see the unstable kv feature. At the time of writing though, none of the mainstream Log implementation supports structured logging as far as I can see.↩

3. We are using tracing-bunyan-formatter instead of the formatting layer provided by tracing-subscriber because the latter does not implement metadata inheritance: it would therefore fail to meet our requirements.↩

4. Full disclosure - I am the author of tracing-bunyan-formatter.↩

5. Full disclosure - I am the author of tracing-actix-web.↩

5 Going Live

We have a working prototype of our newsletter API - it is now time to take it live.
We will learn how to package our Rust application as a Docker container to deploy it on DigitalOcean’s App Platform.
At the end of the chapter we will have a Continuous Deployment (CD) pipeline: every commit to the main branch will automatically trigger the deployment of the latest version of the application to our users.

5.1 We Must Talk About Deployments

Everybody loves to talk about how important it is to deploy software to production as often as possible (and I put myself in that bunch!).

“Get customer feedback early!”
“Ship often and iterate on the product!”

But nobody shows you how.

Pick a random book on web development or an introduction to framework XYZ.
Most will not dedicate more than a paragraph to the topic of deployments.
A few will have a chapter about it - usually towards the end of the book, the part you never get to actually read.
A handful actually give it the space it deserves, as early as they reasonably can.

Why?

Because deployments are (still) a messy business.
There are many vendors, most are not straight-forward to use and what is considered state-of-art or best-practice tends to change really quickly[1].
That is why most authors steer away from the topic: it takes many pages and it is painful to write something down to realise, one or two years later, that it is already out of date.

Nonetheless deployments are a prominent concern in the daily life of a software engineer - e.g. it is difficult to talk about database schema migrations, domain validation and API evolution without taking into account your deployment process.
We simply cannot ignore the topic in a book called Zero To Production.

5.2 Choosing Our Tools

The purpose of this chapter is to get you to experience, first hand, what it means to actually deploy on every commit to your main branch.
That is why we are talking about deployment as early as chapter five: to give you the chance to practice this muscle for the rest of the book, as you would actually be doing if this was a real commercial project.
We are particularly interested, in fact, on how the engineering practice of continuous deployment influences our design choices and development habits.

At the same time, building the perfect continuous deployment pipeline is not the focus of the book - it deserves a book on its own, probably a whole company.
We have to be pragmatic and strike a balance between intrinsic usefulness (i.e. learn a tool that is valued in the industry) and developer experience.
And even if we spent the time to hack together the “best” setup, you are still likely to end up choosing different tools and different vendors due to the specific constraints of your organisation.

What matters is the underlying philosophy and getting you to try continuous deployment as a practice.

5.2.1 Virtualisation: Docker

Our local development environment and our production environment serve two very different purposes.
Browsers, IDEs, our music playlists - they can co-exist on our local machine. It is a multi-purpose workstation.
Production environments, instead, have a much narrower focus: running our software to make it available to our users. Anything that is not strictly related to that goal is either a waste of resources, at best, or a security liability, at worst.

This discrepancy has historically made deployments fairly troublesome, leading to the now meme-fied complaint “It works on my machine!”.
It is not enough to copy the source code to our production servers. Our software is likely to make assumptions on the capabilities exposed by the underlying operating system (e.g. a native Windows application will not run on Linux), on the availability of other software on the same machine (e.g. a certain version of the Python interpreter) or on its configuration (e.g. do I have root permissions?).
Even if we started with two identical environments we would, over time, run into troubles as versions drift and subtle inconsistencies come up to haunt our nights and weekends.

The easiest way to ensure that our software runs correctly is to tightly control the environment it is being executed into.

This is the fundamental idea behind virtualisation technology: what if, instead of shipping code to production, you could ship a self-contained environment that included your application?!
It would work great for both sides: less Friday-night surprises for you, the developer; a consistent abstraction to build on top of for those in charge of the production infrastructure.
Bonus points if the environment itself can be specified as code to ensure reproducibility.

The nice thing about virtualisation is that it exists and it has been mainstream for almost a decade now.
As for most things in technology, you have a few options to choose from depending on your needs: virtual machines, containers (e.g. Docker) and a few others (e.g. Firecracker).

We will go with the mainstream and ubiquitous option - Docker containers.

5.2.2 Hosting: DigitalOcean

AWS, Google Cloud, Azure, Digital Ocean, Clever Cloud, Heroku, Qovery…
The list of vendors you can pick from to host your software goes on and on.
People have made a successful business out of recommending the best cloud tailored to your specific needs and usecases - not my job (yet) or the purpose of this book.

We are looking for something that is easy to use (great developer experience, minimal unnecessary complexity) and fairly established.
In November 2020, the intersection of those two requirements seems to be Digital Ocean, in particular their newly launched App Platform proposition.

Disclaimer: Digital Ocean is not paying me to promote their services here.

5.3 A Dockerfile For Our Application

DigitalOcean’s App Platform has native support for deploying containerised applications.
This is going to be our first task: we have to write a Dockerfile to build and execute our application as a Docker container.

5.3.1 Dockerfiles

A Dockerfile is a recipe for your application environment.
They are organised in layers: you start from a base image (usually an OS enriched with a programming language toolchain) and execute a series of commands (COPY, RUN, etc.), one after the other, to build the environment you need.

Let’s have a look at the simplest possible Dockerfile for a Rust project:

# We use the latest Rust stable release as base image
FROM rust:1.53.0

# Let's switch our working directory to `app` (equivalent to `cd app`)
# The `app` folder will be created for us by Docker in case it does not
# exist already.
WORKDIR /app
# Copy all files from our working environment to our Docker image
COPY . .
# Let's build our binary!
# We'll use the release profile to make it faaaast
RUN cargo build --release
# When `docker run` is executed, launch the binary!
ENTRYPOINT ["./target/release/zero2prod"]

Save it in a file named Dockerfile in the root directory of our git repository:

zero2prod/
.github/
migrations/
scripts/
src/
tests/
.gitignore
Cargo.lock
Cargo.toml
configuration.yaml
Dockerfile

The process of executing those commands to get an image is called building.
Using the Docker CLI:

# Build a docker image tagged as "zero2prod" according to the recipe
# specified in `Dockerfile`
docker build --tag zero2prod --file Dockerfile .

What does the . at the end of the command stand for?

5.3.2 Build Context

docker build generates an image starting from a recipe (the Dockerfile) and a build context.
You can picture the Docker image you are building as its own fully isolated environment.
The only point of contact between the image and your local machine are commands like COPY or ADD[2]: the build context determines what files on your host machine are visible inside the Docker container to COPY and its friends.

Using . we are telling Docker to use the current directory as the build context for this image; COPY . app will therefore copy all files from the current directory (including our source code!) into the app directory of our Docker image.
Using . as build context implies, for example, that Docker will not allow COPY to see files from the parent directory or from arbitrary paths on your machine into the image.

You could use a different path or even a URL (!) as build context depending on your needs.

5.3.3 Sqlx Offline Mode

If you were eager enough, you might have already launched the build command… just to realise it doesn’t work!

docker build --tag zero2prod --file Dockerfile .

# [...]
Step 4/5 : RUN cargo build --release
# [...]
error: error communicating with the server:
Cannot assign requested address (os error 99)
--> src/routes/subscriptions.rs:35:5
|
35 | /     sqlx::query!(
36 | |         r#"
37 | |     INSERT INTO subscriptions (id, email, name, subscribed_at)
38 | |     VALUES ($1, $2, $3, $4)
...  |
43 | |         Utc::now()
44 | |     )
| |_____^
|
= note: this error originates in a macro

What is going on?
sqlx calls into our database at compile-time to ensure that all queries can be successfully executed considering the schemas of our tables.
When running cargo build inside our Docker image, though, sqlx fails to establish a connection with the database that the DATABASE_URL environment variable in the .env file points to.

How do we fix it?

We could allow our image to talk to a database running on our local machine at build time using the --network flag. This is the strategy we follow in our CI pipeline given that we need the database anyway to run our integration tests.
Unfortunately it is somewhat troublesome to pull off for Docker builds due to how Docker networking is implemented on different operating systems (e.g. MacOS) and would significantly compromise how reproducible our builds are.

A better option is to use the newly-introduced offline mode for sqlx.
Let’s add the offline feature to sqlx in our Cargo.toml:

#! Cargo.toml
# [...]

# Using table-like toml syntax to avoid a super-long line!
[dependencies.sqlx]
version = "0.5.5"
default-features = false
features = [
"runtime-actix-rustls",
"macros",
"postgres",
"uuid",
"chrono",
"migrate",
"offline"
]

The next step relies on sqlx’s CLI. The command we are looking for is sqlx prepare. Let’s look at its help message:

sqlx prepare --help

sqlx-prepare
Generate query metadata to support offline compile-time verification.

Saves metadata for all invocations of `query!` and related macros to
`sqlx-data.json` in the current directory, overwriting if needed.

During project compilation, the absence of the `DATABASE_URL` environment
variable or the presence of `SQLX_OFFLINE` will constrain the compile-time
verification to only read from the cached query metadata.

USAGE:
sqlx prepare [FLAGS] [-- <args>...]

ARGS:
<args>...
Arguments to be passed to `cargo rustc ...`

FLAGS:
--check
Run in 'check' mode. Exits with 0 if the query metadata is up-to-date.
Exits with 1 if the query metadata needs updating

In other words, prepare performs the same work that is usually done when cargo build is invoked but it saves the outcome of those queries to a metadata file (sqlx-data.json) which can later be detected by sqlx itself and used to skip the queries altogether and perform an offline build.

Let’s invoke it!

# It must be invoked as a cargo subcommand
# All options after `--` are passed to cargo itself
# We need to point it to our binary using --bin
cargo sqlx prepare -- --bin zero2prod

query data written to `sqlx-data.json` in the current directory;
please check this into version control

We will indeed commit the file to version control, as the command output suggests.
Let’s set the SQLX_OFFLINE environment variable to true in our Dockerfile to force sqlx to look at the saved metadata instead of trying to query a live database:

FROM rust:1.53.0

WORKDIR /app
COPY . .
ENV SQLX_OFFLINE true
RUN cargo build --release
ENTRYPOINT ["./target/release/zero2prod"]

Let’s try again to build our Docker container:

docker build --tag zero2prod --file Dockerfile .

There should be no errors this time!
We have a problem though: how do we ensure that sqlx-data.json does not go out of sync (e.g. when the schema of our database changes or when we add new queries)?

We can use the --check flag in our CI pipeline to ensure that it stays up-to-date:

# .github/workflows/general.yml
# [...]

jobs:
test:
name: Test
runs-on: ubuntu-latest
services:
postgres:
image: postgres
ports:
- 5432:5432
steps:
- uses: actions/checkout@v2
- uses: actions-rs/toolchain@v1
with:
profile: minimal
toolchain: stable
override: true
- name: Migrate database
run: |
sudo apt-get install libpq-dev -y
cargo install --version=0.5.5 sqlx-cli --no-default-features --features postgres
SKIP_DOCKER=true ./scripts/init_db.sh
- name: Check sqlx metadata file
# New step!
run: cargo sqlx prepare --check -- --bin zero2prod
- uses: actions-rs/cargo@v1
with:
command: test

# [...]

5.3.4 Running An Image

When building our image we attached a tag to it, zero2prod:

docker build --tag zero2prod --file Dockerfile .

We can use the tag to refer to the image in other commands. In particular, to run it:

docker run zero2prod

docker run will trigger the execution of the command we specified in our ENTRYPOINT statement:

ENTRYPOINT ["./target/release/zero2prod"]

In our case, it will execute our binary therefore launching our API.
Let’s launch our image then!

You should immediately see a couple of log lines. Let’s open another terminal and try to make a request to our health check endpoint:

curl http://127.0.0.1:8000/health_check

curl: (7) Failed to connect to 127.0.0.1 port 8000: Connection refused

Not great.

5.3.5 Networking

By default, Docker images do not expose their ports to the underlying host machine. We need to do it explicitly using the -p flag.
Let’s kill our running image to launch it again using:

docker run -p 8000:8000 zero2prod

Trying to hit the health check endpoint will trigger the same error message.
We need to dig into our main.rs file to understand why:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::run;
use zero2prod::telemetry::{get_subscriber, init_subscriber};
use sqlx::postgres::PgPool;
use std::net::TcpListener;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into());
init_subscriber(subscriber);

let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

let address = format!("127.0.0.1:{}", configuration.application_port);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool)?.await?;
Ok(())
}

We are using 127.0.0.1 as our host in address - we are instructing our application to only accept connections coming from the same machine.
However, we are firing a GET request to /health_check from the host machine, which is not seen as local by our Docker image, therefore triggering the Connection refused error we have just seen.

We need to use 0.0.0.0 as host to instruct our application to accept connections from any network interface, not just the local one.
We should be careful though: using 0.0.0.0 significantly increases the “audience” of our application, with some security implications.
The best way forward is to make the host portion of our address configurable - we will keep using 127.0.0.1 for our local development and set it to 0.0.0.0 in our Docker images.

5.3.6 Hierarchical Configuration

Our Settings struct currently looks like this:

//! src/configuration.rs
#[derive(serde::Deserialize)]
pub struct Settings {
pub database: DatabaseSettings,
pub application_port: u16,
}

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
pub port: u16,
pub host: String,
pub database_name: String,
}

// [...]

Let’s introduce another struct, ApplicationSettings, to group together all configuration values related to our application address:

#[derive(serde::Deserialize)]
pub struct Settings {
pub database: DatabaseSettings,
pub application: ApplicationSettings,
}

#[derive(serde::Deserialize)]
pub struct ApplicationSettings {
pub port: u16,
pub host: String,
}

// [...]

We need to update our configuration.yml file to match the new structure:

#! configuration.yml
application:
port: 8000
host: 127.0.0.1
database:
# [...]

as well as our main.rs, where we will leverage the new configurable host field:

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
// [...]
}

The host is now read from configuration, but how do we use a different value for different environments?
We need to make our configuration hierarchical.

Let’s have a look at get_configuration, the function in charge of loading our Settings struct:

//! src/configuration.rs
// [...]

pub fn get_configuration() -> Result<Settings, config::ConfigError> {
let mut settings = config::Config::default();

settings.merge(config::File::with_name("configuration"))?;

settings.try_into()
}

We are reading from a file named configuration to populate Settings’s fields. There is no further room for tuning the values specified in our configuration.yaml.
Let’s take a more refined approach. We will have:

- A base configuration file, for values that are shared across our local and production environment (e.g. database name);

- A collection of environment-specific configuration files, specifying values for fields that require customisation on a per-environment basis (e.g. host);

- An environment variable, APP_ENVIRONMENT, to determine the running environment (e.g. production or local).

All configuration files will live in the same top-level directory, configuration.

The good news is that config, the crate we are using, supports all the above out of the box!
Let’s put it together:

//! src/configuration.rs
use std::convert::{TryFrom, TryInto};
// [...]

pub fn get_configuration() -> Result<Settings, config::ConfigError> {
let mut settings = config::Config::default();
let base_path = std::env::current_dir().expect("Failed to determine the current directory");
let configuration_directory = base_path.join("configuration");

// Read the "default" configuration file
settings.merge(config::File::from(configuration_directory.join("base")).required(true))?;

// Detect the running environment.
// Default to `local` if unspecified.
let environment: Environment = std::env::var("APP_ENVIRONMENT")
.unwrap_or_else(|_| "local".into())
.try_into()
.expect("Failed to parse APP_ENVIRONMENT.");

// Layer on the environment-specific values.
settings.merge(
config::File::from(configuration_directory.join(environment.as_str())).required(true),
)?;

// Add in settings from environment variables (with a prefix of APP and '__' as separator)
// E.g. `APP_APPLICATION__PORT=5001 would set `Settings.application.port`
settings.merge(config::Environment::with_prefix("app").separator("__"))?;

settings.try_into()
}

/// The possible runtime environment for our application.
pub enum Environment {
Local,
Production,
}

impl Environment {
pub fn as_str(&self) -> &'static str {
match self {
Environment::Local => "local",
Environment::Production => "production",
}
}
}

impl TryFrom<String> for Environment {
type Error = String;

fn try_from(s: String) -> Result<Self, Self::Error> {
match s.to_lowercase().as_str() {
"local" => Ok(Self::Local),
"production" => Ok(Self::Production),
other => Err(format!(
"{} is not a supported environment. Use either `local` or `production`.",
other
)),
}
}
}

Let’s refactor our configuration file to match the new structure.
We have to get rid of configuration.yaml and create a new configuration directory with base.yaml, local.yaml and production.yaml inside.

#! configuration/base.yaml
application:
port: 8000
database:
host: "localhost"
port: 5432
username: "postgres"
password: "password"
database_name: "newsletter"

#! configuration/local.yaml
application:
host: 127.0.0.1

#! configuration/production.yaml
application:
host: 0.0.0.0

We can now instruct the binary in our Docker image to use the production configuration by setting the APP_ENVIRONMENT environment variable with an ENV instruction:

FROM rust:1.53.0
WORKDIR /app
COPY . .
ENV SQLX_OFFLINE true
RUN cargo build --release
ENV APP_ENVIRONMENT production
ENTRYPOINT ["./target/release/zero2prod"]

Let’s rebuild our image and launch it again:

docker build --tag zero2prod --file Dockerfile .
docker run -p 8000:8000 zero2prod

One of the first log lines should be something like

{
"name":"zero2prod",
"msg":"Starting \"actix-web-service-0.0.0.0:8000\" service on 0.0.0.0:8000",
...
}

If it is, good news - our configuration works as expected!

Let’s try again to hit the health check endpoint:

curl -v http://127.0.0.1:8000/health_check

curl -v http://127.0.0.1:8000/health_check

> GET /health_check HTTP/1.1
> Host: 127.0.0.1:8000
> User-Agent: curl/7.61.0
> Accept: */*
>
< HTTP/1.1 200 OK
< content-length: 0
< date: Sun, 01 Nov 2020 17:32:19 GMT

It works, awesome!

5.3.7 Database Connectivity

What about POST /subscriptions?

curl --request POST --data 'name=le%20guin&email=ursula_le_guin%40gmail.com' 127.0.0.1:8000/subscriptions --verbose

A long wait, then a 500!
Let’s look at the application logs (useful, aren’t they?)

{
"msg": "[SAVING NEW SUBSCRIBER DETAILS IN THE DATABASE - EVENT] \
Failed to execute query: PoolTimedOut",
...
}

Our Dockerised application does not have access to a database yet.
It is actually quite surprising that it starts at all without a database considering we have this line in our main.rs file:

let connection_pool = PgPool::connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");

We are using connect instead of connect_lazy, so it would be reasonable for that line to trigger a panic. It seems though that this is not the current behaviour (see open issue on sqlx’s repository).

It took us half a minute to see a 500 coming back - that is because 30 seconds is the default timeout to acquire a connection from the pool in sqlx.
Let’s fail a little faster by using a shorter timeout:

//! src/main.rs
use sqlx::postgres::PgPoolOptions;
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect(&configuration.database.connection_string())
.await
.expect("Failed to connect to Postgres.");
// [...]
}

We could spin up a database locally (e.g. via our init_db script, tweaking some network configuration, or using Docker Compose) but it would not get us any closer to having a working database connection when deployed on Digital Ocean. We will therefore let it be for now.

5.3.8 Optimising Our Docker Image

As far as our Docker image is concerned, it seems to work as expected - time to deploy it!
Well, not yet.

There are two optimisations we can make to our Dockerfile to make our life easier going forward:

- smaller image size for faster usage;

- Docker layer caching for faster builds.

5.3.8.1 Docker Image Size

We will not be running docker build on the machines hosting our application. They will be using docker pull to download our Docker image without going through the process of building it from scratch.

This is extremely convenient: it can take quite a long time to build our image (and it certainly does in Rust!) and we only need to pay that cost once.
To actually use the image we only need to pay for its download cost which is directly related to its size.

How big is our image?

We can find out using

docker images zero2prod

REPOSITORY          TAG             SIZE
zero2prod           latest          2.31GB

Is that big or small?
Well, our final image cannot be any smaller than the image we used as base - rust:1.53.0. How big is that?

docker images rust:1.53.0

REPOSITORY          TAG             SIZE
rust                1.53.0            1.28GB

Ok, our final image is almost twice as heavy as our base image.
We can do much better than that!

Our first line of attack is reducing the size of the Docker build context by excluding files that are not needed to build our image.
Docker looks for a specific file in our project to determine what should be ignored - .dockerignore
Let’s create one in the root directory with the following content:

.env
target/
tests/
Dockerfile
scripts/
migrations/

All files that match the patterns specified in .dockerignore are not sent by Docker as part of the build context to the image, which means they will not be in scope for COPY instructions.
This will massively speed up our builds (and reduce the size of the final image) if we get to ignore heavy directories (e.g. the target folder for Rust projects).

The next optimisation, instead, leverages one of Rust’s unique strengths.
Rust’s binaries are statically linked[3] - we do not need to keep the source code or intermediate compilation artifacts around to run the binary, it is entirely self-contained.
This plays nicely with multi-stage builds, a useful Docker feature. We can split our build in two stages:

- a builder stage, to generate a compiled binary;

- a runtime stage, to run the binary.

The modified Dockerfile looks like this:

# Builder stage
FROM rust:1.53.0 AS builder

WORKDIR /app
COPY . .
ENV SQLX_OFFLINE true
RUN cargo build --release

# Runtime stage
FROM rust:1.53.0 AS runtime

WORKDIR /app
# Copy the compiled binary from the builder environment
# to our runtime environment
COPY --from=builder /app/target/release/zero2prod zero2prod
# We need the configuration file at runtime!
COPY configuration configuration
ENV APP_ENVIRONMENT production
ENTRYPOINT ["./zero2prod"]

runtime is our final image.
The builder stage does not contribute to its size - it is an intermediate step and it is discarded at the end of the build. The only piece of the builder stage that is found in the final artifact is what we explicitly copy over - the compiled binary!

What is the image size using the above Dockerfile?

docker images zero2prod

REPOSITORY          TAG             SIZE
zero2prod           latest          1.3GB

Just 20 MBs bigger than the size of our base image, much better!
We can go one step further: instead of using rust:1.53.0 for our runtime stage we can switch to rust:1.53.0-slim, a smaller image using the same underlying OS.

# [...]
# Runtime stage
FROM rust:1.53.0-slim AS runtime
# [...]

docker images zero2prod

REPOSITORY          TAG             SIZE
zero2prod           latest          681MB

That is 4x smaller than what we had at the beginning - not bad at all!
We can go even smaller by shaving off the weight of the whole Rust toolchain and machinery (i.e. rustc, cargo, etc) - none of that is needed to run our binary.
We can use the bare operating system as base image (debian:buster-slim) for our runtime stage:

# [...]
# Runtime stage
FROM debian:buster-slim AS runtime
WORKDIR /app
# Install OpenSSL - it is dynamically linked by some of our dependencies
RUN apt-get update -y \
&& apt-get install -y --no-install-recommends openssl \
# Clean up
&& apt-get autoremove -y \
&& apt-get clean -y \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/target/release/zero2prod zero2prod
COPY configuration configuration
ENV APP_ENVIRONMENT production
ENTRYPOINT ["./zero2prod"]

docker images zero2prod

REPOSITORY          TAG             SIZE
zero2prod           latest          88.1MB

Less than a 100 MBs - ~25x smaller than our initial attempt[4].
We could go even smaller by using rust:1.53.0-alpine, but we would have to cross-compile to the linux-musl target - out of scope for now. Check out rust-musl-builder if you are interested in generating tiny Docker images.
Another option to reduce the size of our binary further is stripping symbols from it - you can find more information about it here.

5.3.8.2 Caching For Rust Docker Builds

Rust shines at runtime, consistently delivering great performance, but it comes at a cost: compilation times. They have been consistently among the top answers in the Rust annual survey when it comes to the biggest challenges or problems for the Rust project.

Optimised builds (--release), in particular, can be gruesome - up to 15/20 minutes on medium projects with several dependencies. Quite common on web development projects like ours that are pulling in many foundational crates from the async ecosystem (tokio, actix-web, sqlx, etc.).

Unfortunately, --release is what we use in our Dockerfile to get top-performance in our production environment. How can we mitigate the pain?

We can leverage another Docker feature: layer caching.

Each RUN, COPY and ADD instruction in a Dockerfile creates a layer: a diff between the previous state (the layer above) and the current state after having executed the specified command.
Layers are cached: if the starting point of an operation has not changed (e.g. the base image) and the command itself has not changed (e.g. the checksum of the files copied by COPY) Docker does not perform any computation and directly retrieves a copy of the result from the local cache.

Docker layer caching is fast and can be leveraged to massively speed up Docker builds.
The trick is optimising the order of operations in your Dockerfile: anything that refers to files that are changing often (e.g. source code) should appear as late as possible, therefore maximising the likelihood of the previous step being unchanged and allowing Docker to retrieve the result straight from the cache.

The expensive step is usually compilation.
Most programming languages follow the same playbook: you COPY a lock-file of some kind first, build your dependencies, COPY over the rest of your source code and then build your project.
This guarantees that most of the work is cached as long as your dependency tree does not change between one build and the next.

In a Python project, for example, you might have something along these lines:

FROM python:3
COPY requirements.txt
RUN pip install -r requirements.txt
COPY src/ /app
WORKDIR /app
ENTRYPOINT ["python", "app"]

cargo, unfortunately, does not provide a mechanism to build your project dependencies starting from its Cargo.lock file (e.g. cargo build --only-deps).
Once again, we can rely on a community project to expand cargo’s default capability: cargo-chef[5].

Let’s modify our Dockerfile as suggested in cargo-chef’s README:

FROM lukemathwalker/cargo-chef:latest-rust-1.53.0 as planner
WORKDIR /app
COPY . .
# Compute a lock-like file for our project
RUN cargo chef prepare  --recipe-path recipe.json

FROM lukemathwalker/cargo-chef:latest-rust-1.53.0 as cacher
WORKDIR /app
COPY --from=planner /app/recipe.json recipe.json
# Build our project dependencies, not our application!
RUN cargo chef cook --release --recipe-path recipe.json

FROM rust:1.53.0 AS builder
WORKDIR /app
# Copy over the cached dependencies
COPY --from=cacher /app/target target
COPY --from=cacher /usr/local/cargo /usr/local/cargo
COPY . .
ENV SQLX_OFFLINE true
# Build our application, leveraging the cached deps!
RUN cargo build --release --bin zero2prod

FROM debian:buster-slim AS runtime
WORKDIR /app
# Install OpenSSL - it is dynamically linked by some of our dependencies
RUN apt-get update -y \
&& apt-get install -y --no-install-recommends openssl \
# Clean up
&& apt-get autoremove -y \
&& apt-get clean -y \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/target/release/zero2prod zero2prod
COPY configuration configuration
ENV APP_ENVIRONMENT production
ENTRYPOINT ["./zero2prod"]

We are using four stages: the first computes the recipe file, the second caches our dependencies, the third builds the binary and the fourth is our runtime environment. As long as our dependencies do not change the recipe.json file will stay the same, therefore the outcome of cargo chef cook --release --recipe-path recipe.json will be cached, massively speeding up our builds.

We are taking advantage of how Docker layer caching interacts with multi-stage builds: the COPY . . statement in the planner stage will invalidate the cache for the planner container, but it will not invalidate the cache for the cacher container as long as the checksum of the recipe.json returned by cargo chef prepare does not change.
You can think of each stage as its own Docker image with its own caching - they only interact with each other when using the COPY --from statement.

This will save us a massive amount of time in the next section.

5.4 Deploy To DigitalOcean Apps Platform

We have built a (damn good) containerised version of our application. Let’s deploy it now!

5.4.1 Setup

First of all, you will have to sign up on Digital Ocean’s website.
Once you have an account install doctl, Digital Ocean’s CLI - you can find instructions here.

Hosting on Digital Ocean’s App Platform is not free - keeping our app and its associated database up and running costs roughly 20.00 USD/month.
I suggest you to destroy the app at the end of each session - it should keep your spend way below 1.00 USD. I spent 0.20 USD while playing around with it to write this chapter!

5.4.2 App Specification

Digital Ocean’s App Platform uses a declarative configuration file to let us specify what our application deployment should look like - they call it App Spec.
Looking at the reference documentation, as well as some of their examples, we can piece together a first draft of what our App Spec looks like.

Let’s put this manifest, spec.yaml, at the root of our project directory.

#! spec.yaml
name: zero2prod
# Check https://www.digitalocean.com/docs/app-platform/#regional-availability
# for a list of all the available options.
# You can get region slugs from
# https://www.digitalocean.com/docs/platform/availability-matrix/
# They must specified lowercased.
# `fra` stands for Frankfurt (Germany - EU)
region: fra
services:
- name: zero2prod
# Relative to the repository root
dockerfile_path: Dockerfile
source_dir: .
github:
# Depending on when you created the repository,
# the default branch on GitHub might have been named `master`
branch: main
# Deploy a new version on every commit to `main`!
# Continuous Deployment, here we come!
deploy_on_push: true
# !!! Fill in with your details
# e.g. LukeMathWalker/zero-to-production
repo: <YOUR USERNAME>/<YOUR REPOSITORY NAME>
# Active probe used by DigitalOcean's to ensure our application is healthy
health_check:
# The path to our health check endpoint!
# It turned out to be useful in the end!
http_path: /health_check
# The port the application will be listening on for incoming requests
# It should match what we specified in our configuration/production.yaml file!
http_port: 8000
# For production workloads we'd go for at least two!
# But let's try to keep the bill under control for now...
instance_count: 1
instance_size_slug: basic-xxs
# All incoming requests should be routed to our app
routes:
- path: /

Take your time to go through all the specified values and understand what they are used for.
We can use their CLI, doctl, to create the application for the first time:

doctl apps create --spec spec.yaml

Error: Unable to initialize DigitalOcean API client: access token is required.
(hint: run 'doctl auth init')

Well, we have to authenticate first.
Let’s follow their suggestion:

doctl auth init

Please authenticate doctl for use with your DigitalOcean account.
You can generate a token in the control panel at
https://cloud.digitalocean.com/account/api/tokens

Once you have provided your token we can try again:

doctl apps create --spec spec.yaml

Error: POST
https://api.digitalocean.com/v2/apps: 400 GitHub user not
authenticated

OK, follow their instructions to link your GitHub account.
Third time’s a charm, let’s try again!

doctl apps create --spec spec.yaml

Notice: App created
ID        Spec Name    Default Ingress    Active Deployment ID    In Progress Deployment ID
e80...    zero2prod

It worked!
You can check your app status with

doctl apps list

or by looking at DigitalOcean’s dashboard.

Although the app has been successfully created it is not running yet!
Check the Deployment tab on their dashboard - it is probably building the Docker image.
Looking at a few recent issues on their bug tracker it might take a while - more than a few people have reported they experienced slow builds. Digital Ocean’s support engineers suggested to leverage Docker layer caching to mitigate the issue - we already covered all the bases there!

Wait for these lines to show up in their dashboard build logs:

zero2prod | 00:00:20 => Uploaded the built image to the container registry
zero2prod | 00:00:20 => Build complete

Deployed successfully!
You should be able to see the health check logs coming in every ten seconds or so when Digital Ocean’s platform pings our application to ensure it is running.

With

doctl apps list

you can retrieve the public facing URI of your application. Something along the lines of

https://zero2prod-aaaaa.ondigitalocean.app

Try firing off a health check request now, it should come back with a 200 OK!
Notice that DigitalOcean took care for us to set up HTTPS by provisioning a certificate and redirecting HTTPS traffic to the port we specified in the application specification. One less thing to worry about.

The POST /subscriptions endpoint is still failing, in the very same way it did locally: we do not have a live database backing our application in our production environment.
Let’s provision one.

Add this segment to your spec.yaml file:

databases:
# PG = Postgres
- engine: PG
# Database name
name: newsletter
# Again, let's keep the bill lean
num_nodes: 1
size: db-s-dev-database
# Postgres version - using the latest here
version: "12"

Then update your app specification:

# You can retrieve your app id using `doctl apps list`
doctl apps update YOUR-APP-ID --spec=spec.yaml

It will take some time for DigitalOcean to provision a Postgres instance.

In the meantime we need to figure out how to point our application at the database in production.

5.4.3 How To Inject Secrets Using Environment Variables

The connection string will contain values that we do not want to commit to version control - e.g. the username and the password of our database root user.
Our best option is to use environment variables as a way to inject secrets at runtime into the application environment. DigitalOcean’s apps, for example, can refer to the DATABASE_URL environment variable (or a few others for a more granular view) to get the database connection string at runtime.

We need to upgrade our get_configuration function (again) to fulfill our new requirements.

//! src/configuration.rs
// [...]

pub fn get_configuration() -> Result<Settings, config::ConfigError> {
let mut settings = config::Config::default();
let base_path = std::env::current_dir().expect("Failed to determine the current directory");
let configuration_directory = base_path.join("configuration");
settings.merge(config::File::from(configuration_directory.join("base")).required(true))?;
let environment: Environment = std::env::var("APP_ENVIRONMENT")
.unwrap_or_else(|_| "local".into())
.try_into()
.expect("Failed to parse APP_ENVIRONMENT.");
settings.merge(
config::File::from(configuration_directory.join(environment.as_str())).required(true),
)?;

// Add in settings from environment variables (with a prefix of APP and '__' as separator)
// E.g. `APP_APPLICATION__PORT=5001 would set `Settings.application.port`
settings.merge(config::Environment::with_prefix("app").separator("__"))?;

settings.try_into()
}

This allows us to customize any value in our Settings struct using environment variables, overriding what is specified in our configuration files.

Why is that convenient?
It makes it possible to inject values that are too dynamic (i.e. not known a priori) or too sensitive to be stored in version control.
It also makes it fast to change the behaviour of our application: we do not have to go through a full re-build if we want to tune one of those values (e.g. the database port). For languages like Rust, where a fresh build can take ten minutes or more, this can make the difference between a short outage and a substantial service degradation with customer-visible impact.

Before we move on let’s take care of an annoying detail: environment variables are strings for the config crate and it will fail to pick up integers if using the standard deserialization routine from serde.
Luckily enough, we can specify a custom deserialization function.
Let’s add a new dependency, serde-aux (serde auxiliary):

cargo add serde-aux

and let’s modify both ApplicationSettings and DatabaseSettings

//! src/configuration.rs
// [...]
use serde_aux::field_attributes::deserialize_number_from_string;
// [...]

#[derive(serde::Deserialize)]
pub struct ApplicationSettings {
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
}

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
pub database_name: String,
}

// [...]

5.4.4 Connecting To Digital Ocean’s Postgres Instance

Let’s have a look at the connection string of our database using DigitalOcean’s dashboard (Components -> Database):

postgresql://newsletter:<PASSWORD>@<HOST>:<PORT>/newsletter?sslmode=require

Our current DatabaseSettings does not handle SSL mode - it was not relevant for local development, but it is more than desirable to have transport-level encryption for our client/database communication in production.
Before trying to add new functionality, let’s make room for it by refactoring DatabaseSettings.
The current version looks like this:

//! src/configuration.rs
// [...]

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
pub database_name: String,
}

impl DatabaseSettings {
pub fn connection_string(&self) -> String {
format!(
"postgres://{}:{}@{}:{}/{}",
self.username, self.password, self.host, self.port, self.database_name
)
}

pub fn connection_string_without_db(&self) -> String {
format!(
"postgres://{}:{}@{}:{}",
self.username, self.password, self.host, self.port
)
}
}

We will change its two methods to return a PgConnectOptions instead of a connection string: it will make it easier to manage all these moving parts.

//! src/configuration.rs
use sqlx::postgres::PgConnectOptions;
// [...]

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
pub database_name: String,
}

impl DatabaseSettings {
// Renamed from `connection_string_without_db`
pub fn without_db(&self) -> PgConnectOptions {
PgConnectOptions::new()
.host(&self.host)
.username(&self.username)
.password(&self.password)
.port(self.port)
}

// Renamed from `connection_string`
pub fn with_db(&self) -> PgConnectOptions {
self.without_db().database(&self.database_name)
}
}

We’ll also have to update src/main.rs and tests/health_check.rs:

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]

let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
// `connect_with` instead of `connect`
.connect_with(configuration.database.with_db())
.await
.expect("Failed to connect to Postgres.");

// [...]
}

//! tests/health_check.rs
// [...]

pub async fn configure_database(config: &DatabaseSettings) -> PgPool {
// Create database
let mut connection = PgConnection::connect_with(&config.without_db())
.await
.expect("Failed to connect to Postgres");
connection
.execute(&*format!(r#"CREATE DATABASE "{}";"#, config.database_name))
.await
.expect("Failed to create database.");

// Migrate database
let connection_pool = PgPool::connect_with(config.with_db())
.await
.expect("Failed to connect to Postgres.");
sqlx::migrate!("./migrations")
.run(&connection_pool)
.await
.expect("Failed to migrate the database");

connection_pool
}

Use cargo test to make sure everything is still working as expected.

Let’s now add the require_ssl property we need to DatabaseSettings:

//! src/configuration.rs
use sqlx::postgres::PgSslMode;
// [...]

#[derive(serde::Deserialize)]
pub struct DatabaseSettings {
pub username: String,
pub password: String,
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
pub database_name: String,
// Determine if we demand the connection to be encrypted or not
pub require_ssl: bool,
}

impl DatabaseSettings {
pub fn without_db(&self) -> PgConnectOptions {
let ssl_mode = if self.require_ssl {
PgSslMode::Require
} else {
// Try an encrypted connection, fallback to unencrypted if it fails
PgSslMode::Prefer
};
PgConnectOptions::new()
.host(&self.host)
.username(&self.username)
.password(&self.password)
.port(self.port)
.ssl_mode(ssl_mode)
}

pub fn with_db(&self) -> PgConnectOptions {
self.without_db().database(&self.database_name)
}
}

We want require_ssl to be false when we run the application locally (and for our test suite), but true in our production environment.
Let’s amend our configuration files accordingly:

#! configuration/local.yaml
application:
host: 127.0.0.1
database:
# New entry!
require_ssl: false

#! configuration/production.yaml
application:
host: 0.0.0.0
database:
# New entry!
require_ssl: true

5.4.5 Environment Variables In The App Spec

One last step: we need to amend our spec.yaml manifest to inject the environment variables we need.

#! spec.yaml
name: zero2prod
region: fra
services:
- name: zero2prod
# [...]
envs:
- key: APP_DATABASE__USERNAME
scope: RUN_TIME
value: ${newsletter.USERNAME}
- key: APP_DATABASE__PASSWORD
scope: RUN_TIME
value: ${newsletter.PASSWORD}
- key: APP_DATABASE__HOST
scope: RUN_TIME
value: ${newsletter.HOSTNAME}
- key: APP_DATABASE__PORT
scope: RUN_TIME
value: ${newsletter.PORT}
- key: APP_DATABASE__DATABASE_NAME
scope: RUN_TIME
value: ${newsletter.DATABASE}
databases:
- name: newsletter
# [...]

The scope is set to RUN_TIME to distinguish between environment variables needed during our Docker build process and those needed when the Docker image is launched.
We are populating the values of the environment variables by interpolating what is exposed by the Digital Ocean’s platform (e.g. ${newsletter.PORT}) - refer to their documentation for more details.

5.4.6 One Last Push

Let’s apply the new spec

# You can retrieve your app id using `doctl apps list`
doctl apps update YOUR-APP-ID --spec=spec.yaml

and push our change up to GitHub to trigger a new deployment.

We now need to migrate the database:

DATABASE_URL=YOUR-DIGITAL-OCEAN-DB-CONNECTION-STRING sqlx migrate run

We are ready to go!
Let’s fire off a POST request to /subscriptions:

curl --request POST \
--data 'name=le%20guin&email=ursula_le_guin%40gmail.com' \
https://zero2prod-adqrw.ondigitalocean.app/subscriptions \
--verbose

The server should respond with a 200 OK.
Congrats, you have just deployed your first Rust application!
And Ursula Le Guin just subscribed to your email newsletter (allegedly)!

If you have come this far, I’d love to get a screenshot of your Digital Ocean’s dashboard showing off that running application!
Email it over at rust@lpalmieri.com or share it on Twitter tagging the Zero To Production In Rust account, zero2prod.

1. Kubernetes is six years old, Docker itself is just seven years old!↩

2. Unless you are using --network=host, --ssh or other similar options. You also have volumes as an alternative mechanism to share files at runtime.↩

3. rustc statically links all Rust code but dynamically links libc from the underlying system if you are using the Rust standard library. You can get a fully statically linked binary by targeting linux-musl, see here.↩

4. Credits to Ian Purton and flat_of_angles for pointing out that there was further room for improvement.↩

5. Full disclosure - I am the author of cargo-chef.↩

6 Reject Invalid Subscribers #1

Our newsletter API is live, hosted on a Cloud provider.
We have a basic set of instrumentation to troubleshoot issues that might arise.
There is an exposed endpoint (POST /subscriptions) to subscribe to our content.

We have come a long way!

But we have cut a few corners along the way: POST /subscriptions is fairly… permissive.
Our input validation is extremely limited: we just ensure that both the name and the email fields are provided, nothing else.

We can add a new integration test to probe our API with some “troublesome” inputs:

//! tests/health_check.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_when_fields_are_present_but_empty() {
// Arrange
let app = spawn_app().await;
let client = reqwest::Client::new();
let test_cases = vec![
("name=&email=ursula_le_guin%40gmail.com", "empty name"),
("name=Ursula&email=", "empty email"),
("name=Ursula&email=definitely-not-an-email", "invalid email"),
];

for (body, description) in test_cases {
// Act
let response = client
.post(&format!("{}/subscriptions", &app.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(
200,
response.status().as_u16(),
"The API did not return a 200 OK when the payload was {}.",
description
);
}
}

The new test, unfortunately, passes.
Although all those payloads are clearly invalid, our API is gladly accepting them, returning a 200 OK.
Those troublesome subscriber details end up straight in our database, ready to give us problems down the line when it is time to deliver a newsletter issue.

We are asking for two pieces of information when subscribing to our newsletter: a name and an email.
This chapter will focus on name validation: what should we look out for?

6.1 Requirements

6.1.1 Domain Constraints

It turns out that names are complicated[1].
Trying to nail down what makes a name valid is a fool’s errand. Remember that we chose to collect a name to use it in the opening line of our emails - we do not need it to match the real identity of a person, whatever that means in their geography. It would be totally unnecessary to inflict the pain of incorrect or overly prescriptive validation on our users.

We could thus settle on simply requiring the name field to be non-empty (as in, it must contain at least a non-whitespace character).

6.1.2 Security Constraints

Unfortunately, not all people on the Internet are good people.
Given enough time, especially if our newsletter picks up traction and becomes successful, we are bound to capture the attention of malicious visitors.
Forms and user inputs are a primary attack target - if they are not properly sanitised, they might allow an attacker to mess with our database (SQL injection), execute code on our servers, crash our service and other nasty stuff.
Thanks, but no thanks.

What is likely to happen in our case? What should we brace for in the wild range of possible attacks?[2]
We are building an email newsletter, which leads us to focus on:

- denial of service - e.g. trying to take our service down to prevent other people from signing up. A common threat for basically any online service;

- data theft - e.g. steal a huge list of email addresses;

- phishing - e.g. use our service to send what looks like a legitimate email to a victim to trick them into clicking on some links or perform other actions.

Should we try to tackle all these threats in our validation logic?
Absolutely not!
But it is good practice to have a layered security approach[3]: by having mitigations to reduce the risk for those threats at multiple levels in our stack (e.g. input validation, parametrised queries to avoid SQL injection, escaping parametrised input in emails, etc.) we are less likely to be vulnerable should any of those checks fail us or be removed later down the line.

We should always keep in mind that software is a living artifact: holistic understanding of a system is the first victim of the passage of time.
You have the whole system in your head when writing it down for the first time, but the next developer touching it will not - at least not from the get-go. It is therefore possible for a load-bearing check in an obscure corner of the application to disappear (e.g. HTML escaping) leaving you exposed to a class of attacks (e.g. phishing).
Redundancy reduces risk.

Let’s get to the point - what validation should we perform on names to improve our security posture given the class of threats we identified?
I suggest:

- Enforcing a maximum length. We are using TEXT as type for our email in Postgres, which is virtually unbounded - well, until disk storage starts to run out. Names come in all shapes and forms, but 256 characters should be enough for the greatest majority of our users[4] - if not, we will politely ask them to enter a nickname.

- Reject names containing troublesome characters. /()"<>\{} are fairly common in URLs, SQL queries and HTML fragments - not as much in names[5]. Forbidding them raises the complexity bar for SQL injection and phishing attempts.

6.2 First Implementation

Let’s have a look at our request handler, as it stands right now:

//! src/routes/subscriptions.rs
use actix_web::{web, HttpResponse};
use chrono::Utc;
use sqlx::PgPool;
use uuid::Uuid;

#[derive(serde::Deserialize)]
pub struct FormData {
email: String,
name: String,
}

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool),
fields(
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
match insert_subscriber(&pool, &form).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

// [...]

Where should our new validation live?

A first sketch could look somewhat like this:

//! src/routes/subscriptions.rs

// An extension trait to provide the `graphemes` method
// on `String` and `&str`
use unicode_segmentation::UnicodeSegmentation;
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
if !is_valid_name(&form.name) {
return Err(HttpResponse::BadRequest().finish());
}
match insert_subscriber(&pool, &form).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

/// Returns `true` if the input satisfies all our validation constraints
/// on subscriber names, `false` otherwise.
pub fn is_valid_name(s: &str) -> bool {
// `.trim()` returns a view over the input `s` without trailing
// whitespace-like characters.
// `.is_empty` checks if the view contains any character.
let is_empty_or_whitespace = s.trim().is_empty();

// A grapheme is defined by the Unicode standard as a "user-perceived"
// character: `å` is a single grapheme, but it is composed of two characters
// (`a` and `̊`).
//
// `graphemes` returns an iterator over the graphemes in the input `s`.
// `true` specifies that we want to use the extended grapheme definition set,
// the recommended one.
let is_too_long = s.graphemes(true).count() > 256;

// Iterate over all characters in the input `s` to check if any of them matches
// one of the characters in the forbidden array.
let forbidden_characters = ['/', '(', ')', '"', '<', '>', '\\', '{', '}'];
let contains_forbidden_characters = s.chars().any(|g| forbidden_characters.contains(&g));

// Return `false` if any of our conditions have been violated
!(is_empty_or_whitespace || is_too_long || contains_forbidden_characters)
}

To compile the new function successfully we will have to add the unicode-segmentation crate to our dependencies:

cargo add unicode-segmentation

While it looks like a perfectly fine solution (assuming we add a bunch of tests), functions like is_valid_name give us a false sense of safety.

6.3 Validation Is A Leaky Cauldron

Let’s shift our attention to insert_subscriber.
Let’s imagine, for a second, that it requires form.name to be non-empty otherwise something horrible is going to happen (e.g. a panic!).

Can insert_subscriber safely assume that form.name will be non-empty?
Just by looking at its type, it cannot: form.name is a String. There is no guarantee about its content.
If you were to look at our program in its entirety you might say: we are checking that it is non-empty at the edge, in the request handler, therefore we can safely assume that form.name will be non-empty every time insert_subscriber is invoked.

But we had to shift from a local approach (let’s look at this function’s parameters) to a global approach (let’s scan the whole codebase) to make such a claim.
And while it might be feasible for a small project such as ours, examining all the calling sites of a function (insert_subscriber) to ensure that a certain validation step has been performed beforehand quickly becomes unfeasible on larger projects.

If we are to stick with is_valid_name, the only viable approach is validating again form.name inside insert_subscriber - and every other function that requires our name to be non-empty.
That is the only way we can actually make sure that our invariant is in place where we need it.

What happens if insert_subscriber becomes too big and we have to split it out in multiple sub-functions? If they need the invariant, each of those has to perform validation to be certain it holds.
As you can see, this approach does not scale.

The issue here is that is_valid_name is a validation function: it tells us that, at a certain point in the execution flow of our program, a set of conditions is verified.
But this information about the additional structure in our input data is not stored anywhere. It is immediately lost.
Other parts of our program cannot reuse it effectively - they are forced to perform another point-in-time check leading to a crowded codebase with noisy (and wasteful) input checks at every step.

What we need is a parsing function - a routine that accepts unstructured input and, if a set of conditions holds, returns us a more structured output, an output that structurally guarantees that the invariants we care about hold from that point onwards.
How?

Using types!

6.4 Type-Driven Development

Let’s add a new module to our project, domain, and define a new struct inside it, SubscriberName:

//! src/lib.rs
pub mod configuration;
// New module!
pub mod domain;
pub mod routes;
pub mod startup;
pub mod telemetry;

//! src/domain.rs

pub struct SubscriberName(String);

SubscriberName is a tuple struct - a new type, with a single (unnamed) field of type String.

SubscriberName is a proper new type, not just an alias - it does not inherit any of the methods available on String and trying to assign a String to a variable of type SubscriberName will trigger a compiler error - e.g.:

let name: SubscriberName = "A string".to_string();

error[E0308]: mismatched types
|     let name: SubscriberName = "A string".to_string();
|               --------------   ^^^^^^^^^^^^^^^^^^^^^^
|               |                expected struct `SubscriberName`,
|               |                found struct `std::string::String`
|               |
|               expected due to this

The inner field of SubscriberName, according to our current definition, is private: it can only be accessed from code within our domain module according to Rust’s visibility rules.
As always, trust but verify: what happens if we try to build a SubscriberName in our subscribe request handler?

//! src/routes/subscriptions.rs
/// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let subscriber_name = crate::domain::SubscriberName(form.name.clone());
/// [...]
}

The compiler complains with

error[E0603]: tuple struct constructor `SubscriberName` is private
--> src/routes/subscriptions.rs:25:42
|
25 |     let subscriber_name = crate::domain::SubscriberName(form.name.clone());
|                                          ^^^^^^^^^^^^^^
|                                          private tuple struct constructor
|
::: src/domain.rs:1:27
|
1  | pub struct SubscriberName(String);
|                           ------ a constructor is private if
|                                  any of the fields is private

It is therefore impossible (as it stands now) to build a SubscriberName instance outside of our domain module.
Let’s add a new method to SubscriberName:

//! src/domain.rs
use unicode_segmentation::UnicodeSegmentation;

pub struct SubscriberName(String);

impl SubscriberName {
/// Returns an instance of `SubscriberName` if the input satisfies all
/// our validation constraints on subscriber names.
/// It panics otherwise.
pub fn parse(s: String) -> SubscriberName {
// `.trim()` returns a view over the input `s` without trailing
// whitespace-like characters.
// `.is_empty` checks if the view contains any character.
let is_empty_or_whitespace = s.trim().is_empty();

// A grapheme is defined by the Unicode standard as a "user-perceived"
// character: `å` is a single grapheme, but it is composed of two characters
// (`a` and `̊`).
//
// `graphemes` returns an iterator over the graphemes in the input `s`.
// `true` specifies that we want to use the extended grapheme definition set,
// the recommended one.
let is_too_long = s.graphemes(true).count() > 256;

// Iterate over all characters in the input `s` to check if any of them matches
// one of the characters in the forbidden array.
let forbidden_characters = ['/', '(', ')', '"', '<', '>', '\\', '{', '}'];
let contains_forbidden_characters = s.chars().any(|g| forbidden_characters.contains(&g));

if is_empty_or_whitespace || is_too_long || contains_forbidden_characters {
panic!(format!("{} is not a valid subscriber name.", s))
} else {
Self(s)
}
}
}

Yes, you are right - that is a shameless copy-paste of what we had in is_valid_name.

There is one key difference though: the return type.
While is_valid_name gave us back a boolean, the parse method returns a SubscriberName if all checks are successful.

There is more!
parse is the only way to build an instance of SubscriberName outside of the domain module - we checked this was the case a few paragraphs ago.
We can therefore assert that any instance of SubscriberName will satisfy all our validation constraints.
We have made it impossible for an instance of SubscriberName to violate those constraints.

Let’s define a new struct, NewSubscriber:

//! src/domain.rs
// [...]

pub struct NewSubscriber {
pub email: String,
pub name: SubscriberName,
}

pub struct SubscriberName(String);

// [...]

What happens if we change insert_subscriber to accept an argument of type NewSubscriber instead of FormData?

pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
// [...]
}

With the new signature we can be sure that new_subscriber.name is non-empty - it is impossible to call insert_subscriber passing an empty subscriber name.
And we can draw this conclusion just by looking up the definition of the types of the function arguments - we can once again make a local judgement, no need to go and check all the calling sites of our function.

Take a second to appreciate what just happened: we started with a set of requirements (all subscriber names must verify some constraints), we identified a potential pitfall (we might forget to validate the input before calling insert_subscriber) and we leveraged Rust’s type system to eliminate the pitfall, entirely.
We made an incorrect usage pattern unrepresentable, by construction - it will not compile.

This technique is known as type-driven development[6].
Type-driven development is a powerful approach to encode the constraints of a domain we are trying to model inside the type system, leaning on the compiler to make sure they are enforced.
The more expressive the type system of our programming language is, the tighter we can constrain our code to only be able to represent states that are valid in the domain we are working in.

Rust has not invented type-driven development - it has been around for a while, especially in the functional programming communities (Haskell, F#, OCaml, etc.). Rust “just” provides you with a type-system that is expressive enough to leverage many of the design patterns that have been pioneered in those languages in the past decades. The particular pattern we have just shown is often referred to as the “new-type pattern” in the Rust community.

We will be touching upon type-driven development as we progress in our implementation, but I strongly invite you to check out some of the resources mentioned in the footnotes of this chapter: they are treasure chests for any developer.

6.5 Ownership Meets Invariants

We changed insert_subscriber’s signature, but we have not amended the body to match the new requirements - let’s do it now.

//! src/routes/subscriptions.rs
use crate::domain::{NewSubscriber, SubscriberName};
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
// `web::Form` is a wrapper around `FormData`
// `form.0` gives us access to the underlying `FormData`
let new_subscriber = NewSubscriber {
email: form.0.email,
name: SubscriberName::parse(form.0.name),
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

#[tracing::instrument(
name = "Saving new subscriber details in the database",
skip(new_subscriber, pool)
)]
pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
new_subscriber.email,
new_subscriber.name,
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

Close enough - cargo check fails with:

error[E0308]: mismatched types
--> src/routes/subscriptions.rs:50:9
|
50 |         new_subscriber.name,
|         ^^^^^^^^^^^^^^ expected `&str`,
|         found struct `SubscriberName`

We have an issue here: we do not have any way to actually access the String value encapsulated inside SubscriberName!
We could change SubscriberName’s definition from SubscriberName(String) to SubscriberName(pub String), but we would lose all the nice guarantees we spent the last two sections talking about:

- other developers would be allowed to bypass parse and build a SubscriberName with an arbitrary string

let liar = SubscriberName("".to_string());

- other developers might still choose to build a SubscriberName using parse but they would then have the option to mutate the inner value later to something that does not satisfy anymore the constraints we care about

let mut started_well = SubscriberName::parse("A valid name".to_string());
started_well.0 = "".to_string();

We can do better - this is the perfect place to take advantage of Rust’s ownership system!
Given a field in a struct we can choose to:

- expose it by value, consuming the struct itself:

impl SubscriberName {
pub fn inner(self) -> String {
// The caller gets the inner string,
// but they do not have a SubscriberName anymore!
// That's because `inner` takes `self` by value,
// consuming it according to move semantics
self.0
}
}

- expose a mutable reference:

impl SubscriberName {
pub fn inner_mut(&mut self) -> &mut str {
// The caller gets a mutable reference to the inner string.
// This allows them to perform *arbitrary* changes to
// value itself, potentially breaking our invariants!
&mut self.0
}
}

- expose a shared reference:

impl SubscriberName {
pub fn inner_ref(&self) -> &str {
// The caller gets a shared reference to the inner string.
// This gives the caller **read-only** access,
// they have no way to compromise our invariants!
&self.0
}
}

inner_mut is not what we are looking for here - the loss of control on our invariants would be equivalent to using SubscriberName(pub String).
Both inner and inner_ref would be suitable, but inner_ref communicates better our intent: give the caller a chance to read the value without the power to mutate it.

Let’s add inner_ref to SubscriberName - we can then amend insert_subscriber to use it:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
new_subscriber.email,
// Using `inner_ref`!
new_subscriber.name.inner_ref(),
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

Boom, it compiles!

6.5.1 AsRef

While our inner_ref method gets the job done, I am obliged to point out that Rust’s standard library exposes a trait that is designed exactly for this type of usage - AsRef.

The definition is quite concise:

pub trait AsRef<T: ?Sized> {
/// Performs the conversion.
fn as_ref(&self) -> &T;
}

When should you implement AsRef<T> for a type?
When the type is similar enough to T that we can use a &self to get a reference to T itself!

Does it sound too abstract? Check out the signature of inner_ref again: that is basically AsRef<str> for SubscriberName!

AsRef can be used to improve ergonomics - let’s consider a function with this signature:

pub fn do_something_with_a_string_slice(s: &str) {
// [...]
}

To invoke it with our SubscriberName we would have to first call inner_ref and then call do_something_with_a_string_slice:

let name = SubscriberName::parse("A valid name".to_string());
do_something_with_a_string_slice(name.inner_ref())

Nothing too complicated, but it might take you some time to figure out if SubscriberName can give you a &str as well as how, especially if the type comes from a third-party library.
We can make the experience more seamless by changing do_something_with_a_string_slice’s signature:

// We are constraining T to implement the AsRef<str> trait
// using a trait bound - `T: AsRef<str>`
pub fn do_something_with_a_string_slice<T: AsRef<str>>(s: T) {
let s = s.as_ref();
// [...]
}

We can now write

let name = SubscriberName::parse("A valid name".to_string());
do_something_with_a_string_slice(name)

and it will compile straight-away (assuming SubscriberName implements AsRef<str>).

This pattern is used quite extensively, for example, in the filesystem module in Rust’s standard library - std::fs. Functions like create_dir take an argument of type P constrained to implement AsRef<Path> instead of forcing the user to understand how to convert a String into a Path. Or how to convert a PathBuf into Path. Or an OsString. Or… you got the gist.

There are other little conversion traits like AsRef in that standard library - they provide a shared interface for the whole ecosystem to standardise around. Implementing them for your types suddenly unlocks a great deal of functionality exposed via generic types in the crates already available in the wild.
We will cover some of the other conversion trait later down the line (e.g. From/Into, TryFrom/TryInto).

Let’s remove inner_ref and implement AsRef<str> for SubscriberName:

//! src/domain.rs
// [...]

impl AsRef<str> for SubscriberName {
fn as_ref(&self) -> &str {
&self.0
}
}

We also need to change insert_subscriber:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
new_subscriber.email,
// Using `as_ref` now!
new_subscriber.name.as_ref(),
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

The project compiles…

6.6 Panics

…but our tests are not green:

thread 'actix-rt:worker:0' panicked at
' is not a valid subscriber name.', src/domain.rs:39:13

[...]

---- subscribe_returns_a_200_when_fields_are_present_but_empty stdout ----
thread 'subscribe_returns_a_200_when_fields_are_present_but_empty' panicked at
'Failed to execute request.:
reqwest::Error {
kind: Request,
url: Url {
scheme: "http",
host: Some(Ipv4(127.0.0.1)),
port: Some(40681),
path: "/subscriptions",
query: None,
fragment: None
},
source: hyper::Error(IncompleteMessage)
}',
tests/health_check.rs:164:14
Panic in Arbiter thread.

On the bright side: we are not returning a 200 OK anymore for empty names.
On the not-so-bright side: our API is terminating the request processing abruptly, causing the client to observe an IncompleteMessage error. Not very graceful.

Let’s change the test to reflect our new expectations: we’d like to see a 400 Bad Request response when the payload contains invalid data.

//! tests/health_check.rs
// [...]

#[actix_rt::test]
// Renamed!
async fn subscribe_returns_a_400_when_fields_are_present_but_invalid() {
// [...]

assert_eq!(
// Not 200 anymore!
400,
response.status().as_u16(),
"The API did not return a 400 Bad Request when the payload was {}.",
description
);

// [...]
}

Now, let’s look at the root cause - we chose to panic when validation checks in SubscriberName::parse fail:

//! src/domain.rs
// [...]

impl SubscriberName {
pub fn parse(s: String) -> SubscriberName {
// [...]

if is_empty_or_whitespace || is_too_long || contains_forbidden_characters {
panic!(format!("{} is not a valid subscriber name.", s))
} else {
Self(s)
}
}
}

Panics in Rust are used to deal with unrecoverable errors: failure modes that were not expected or that we have no way to meaningfully recover from. Examples might include the host machine running out of memory or a full disk.
Rust’s panics are not equivalent to exceptions in languages such as Python, C# or Java. Although Rust provides a few utilities to catch (some) panics, it is most definitely not the recommended approach and should be used sparingly.

burntsushi put it down quite neatly in a Reddit thread a few years ago:

[…] If your Rust application panics in response to any user input, then the following should be true: your application has a bug, whether it be in a library or in the primary application code.

Adopting this viewpoint we can understand what is happening: when our request handler panics actix-web assumes that something horrible happened and immediately drops the worker that was dealing with that panicking request.[7]

If panics are not the way to go, what should we use to handle recoverable errors?

6.7 Error As Values - Result

Rust’s primary error handling mechanism is built on top of the Result type:

pub enum Result<T, E> {
Ok(T),
Err(E),
}

Result is used as the return type for fallible operations: if the operation succeeds, Ok(T) is returned; if it fails, you get Err(E).
We have actually already used Result, although we did not stop to discuss its nuances at the time.
Let’s look again at the signature of insert_subscriber:

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
// [...]
}

It tells us that inserting a subscriber in the database is a fallible operation - if all goes as planned, we don’t get anything back (() - the unit type), if something is amiss we will instead receive a sqlx::Error with details about what went wrong (e.g. a connection issue).

Errors as values, combined with Rust’s enums, are awesome building blocks for a robust error handling story.
If you are coming from a language with exception-based error handling, this is likely to be a game changer[8]: everything we need to know about the failure modes of a function is in its signature.

You will not have to dig in the documentation of your dependencies to understand what exceptions a certain function might throw (assuming it is documented in the first place!).
You will not be surprised at runtime by yet another undocumented exception type.
You will not have to insert a catch-all statement “just in case”.

We will cover the basics here and leave the finer details (Error trait) to the next chapter.

6.7.1 Converting parse To Return Result

Let’s refactor our SubscriberName::parse to return a Result instead of panicking on invalid inputs.
We will start by changing the signature, without touching the body:

//! src/domain.rs
// [...]

impl SubscriberName {
pub fn parse(s: String) -> Result<SubscriberName, ???> {
// [...]
}
}

What type should we use as Err variant for our Result?
The simplest option is a String - we just return an error message on failure.

//! src/domain.rs
// [...]

impl SubscriberName {
pub fn parse(s: String) -> Result<SubscriberName, String> {
// [...]
}
}

Running cargo check surfaces two errors from the compiler:

error[E0308]: mismatched types
--> src/routes/subscriptions.rs:27:15
|
27 |         name: SubscriberName::parse(form.0.name),
|               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|               expected struct `SubscriberName`,
|               found enum `Result`

error[E0308]: mismatched types
--> src/domain.rs:41:13
|
14 |     pub fn parse(s: String) -> Result<SubscriberName, String> {
|                                ------------------------------
|                                expected `Result<SubscriberName, String>`
|                                because of return type
...
41 |             Self(s)
|             ^^^^^^^
|             |
|             expected enum `Result`, found struct `SubscriberName`
|             help: try using a variant of the expected enum: `Ok(Self(s))`
|
= note: expected enum `Result<SubscriberName, String>`
found struct `SubscriberName`

Let’s focus on the second error: we cannot return a bare instance of SubscriberName at the end of parse - we need to choose one of the two Result variants.
The compiler understands the issue and suggests the right edit: use Ok(Self(s)) instead of Self(s). Let’s follow its advice:

//! src/domain.rs
// [...]

impl SubscriberName {
pub fn parse(s: String) -> Result<SubscriberName, String> {
// [...]

if is_empty_or_whitespace || is_too_long || contains_forbidden_characters {
panic!(format!("{} is not a valid subscriber name.", s))
} else {
Ok(Self(s))
}
}
}

cargo check should now return a single error:

error[E0308]: mismatched types
--> src/routes/subscriptions.rs:27:15
|
27 |         name: SubscriberName::parse(form.0.name),
|               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|               expected struct `SubscriberName`,
|               found enum `Result`

It is complaining about our invocation of the parse method in subscribe: when parse returned a SubscriberName it was perfectly fine to assign its output directly to Subscriber.name.
We are returning a Result now - Rust’s type system forces us to deal with the unhappy path. We cannot just pretend it won’t happen.
Let’s avoid covering too much ground at once though - for the time being we will just panic if validation fails in order to get the project to compile again as quickly as possible:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let new_subscriber = NewSubscriber {
email: form.0.email,
// Notice the usage of `expect` to specify a meaningful panic message
name: SubscriberName::parse(form.0.name).expect("Name validation failed."),
};
// [...]
}

cargo check should be happy now.
Time to work on tests!

6.8 Insightful Assertion Errors: claim

Most of our assertions will be along the lines of assert!(result.is_ok()) or assert!(result.is_err()).
The error messages returned by cargo test on failure when using these assertions are quite poor. How poor? Let’s run a quick experiment!

If you run cargo test on this dummy test

#[test]
fn dummy_fail() {
let result: Result<&str, &str> = Err("The app crashed due to an IO error");
assert!(result.is_ok());
}

you will get

---- dummy_fail stdout ----
thread 'dummy_fail' panicked at 'assertion failed: result.is_ok()'

We do not get any detail concerning the error itself - it makes for a somewhat painful debugging experience.
We will be using the claim crate to get more informative error messages:

cargo add --dev claim

claim provides a fairly comprehensive range of assertions to work with common Rust types - in particular Option and Result.
If we rewrite our dummy_fail test to use claim

#[test]
fn dummy_fail() {
let result: Result<&str, &str> = Err("The app crashed due to an IO error");
claim::assert_ok!(result);
}

we get

---- dummy_fail stdout ----
thread 'dummy_fail' panicked at 'assertion failed, expected Ok(..),
got Err("The app crashed due to an IO error")'

Much better.

6.9 Unit Tests

We are all geared up - let’s add some unit tests to the domain module to make sure all the code we wrote behaves as expected.

//! src/domain.rs
// [...]

#[cfg(test)]
mod tests {
use crate::domain::SubscriberName;
use claim::{assert_err, assert_ok};

#[test]
fn a_256_grapheme_long_name_is_valid() {
let name = "a̐".repeat(256);
assert_ok!(SubscriberName::parse(name));
}

#[test]
fn a_name_longer_than_256_graphemes_is_rejected() {
let name = "a".repeat(257);
assert_err!(SubscriberName::parse(name));
}

#[test]
fn whitespace_only_names_are_rejected() {
let name = " ".to_string();
assert_err!(SubscriberName::parse(name));
}

#[test]
fn empty_string_is_rejected() {
let name = "".to_string();
assert_err!(SubscriberName::parse(name));
}

#[test]
fn names_containing_an_invalid_character_are_rejected() {
for name in &['/', '(', ')', '"', '<', '>', '\\', '{', '}'] {
let name = name.to_string();
assert_err!(SubscriberName::parse(name));
}
}

#[test]
fn a_valid_name_is_parsed_successfully() {
let name = "Ursula Le Guin".to_string();
assert_ok!(SubscriberName::parse(name));
}
}

Unfortunately, it does not compile - cargo highlights all our usages of assert_ok/assert_err with

66 |         assert_err!(SubscriberName::parse(name));
|         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|         `SubscriberName` cannot be formatted using `{:?}`
|
= help: the trait `std::fmt::Debug` is not implemented for `SubscriberName`
= note: add `#[derive(Debug)]` or manually implement `std::fmt::Debug`
= note: required by `std::fmt::Debug::fmt`

claim needs our type to implement the Debug trait to provide those nice error messages. Let’s add a #[derive(Debug)] attribute on top of SubscriberName:

//! src/domain.rs
// [...]

#[derive(Debug)]
pub struct SubscriberName(String);

The compiler should be happy now. What about tests?

cargo test

failures:
domain::tests::a_name_longer_than_256_graphemes_is_rejected
domain::tests::empty_string_is_rejected
domain::tests::names_containing_an_invalid_character_are_rejected
domain::tests::whitespace_only_names_are_rejected

test result: FAILED. 2 passed; 4 failed; 0 ignored; 0 measured; 0 filtered out

All our unhappy-path tests are failing because we are still panicking if our validation constraints are not satisfied - let’s change it:

//! src/domain.rs
// [...]

impl SubscriberName {
pub fn parse(s: String) -> Result<SubscriberName, String> {
// [...]

if is_empty_or_whitespace || is_too_long || contains_forbidden_characters {
// Replacing `panic!` with `Err(...)`
Err(format!("{} is not a valid subscriber name.", s))
} else {
Ok(Self(s))
}
}
}

All our domain unit tests are now passing - let’s finally address the failing integration test we wrote at the beginning of the chapter.

6.10 Handling A Result

SubscriberName::parse is now returning a Result, but subscribe is calling expect on it, therefore panicking if an Err variant is returned.
The behaviour of the application, as a whole, has not changed at all.

How do we change subscribe to return a 400 Bad Request on validation errors? We can have a look at what we are already doing for our call to insert_subscriber!

6.10.1 match

How do we handle the possibility of a failure on the caller side?

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
// [...]
}

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
// [...]
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

insert_subscriber returns a Result<(), sqlx::Error> while subscribe speaks the language of a REST API - its output must be of type HttpResponse. To return a HttpResponse to the caller in the error case we need to convert sqlx::Error into a representation that makes sense within the technical domain of a REST API - in our case, a 500 Internal Server Error.

That’s where a match comes in handy: we tell the compiler what to do in both scenarios, Ok and Err.

6.10.2 The ? Operator

Speaking of error handling, let’s look again at insert_subscriber:

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber(/* */) -> Result<(), sqlx::Error> {
sqlx::query!(/* */)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

Have you noticed that ?, before Ok(())?

It is the question mark operator, ?.
? was introduced in Rust 1.13 - it is syntactic sugar.
It reduces the amount of visual noise when you are working with fallible functions and you want to “bubble up” failures (e.g. similar enough to re-throwing a caught exception).

The ? in this block

insert_subscriber(&pool, &new_subscriber)
.await
.map_err(|_| HttpResponse::InternalServerError().finish())?;

is equivalent to this control flow block

if let Err(error) = insert_subscriber(&pool, &new_subscriber)
.await
.map_err(|_| HttpResponse::InternalServerError().finish())
{
return Err(error);
}

It allows us to return early when something fails using a single character instead of a multi-line block.

Given that ? triggers an early return using an Err variant, it can only be used within a function that returns a Result. subscribe does not qualify (yet).

6.10.3 400 Bad Request

Let’s handle now the error returned by SubscriberName::parse:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let name = match SubscriberName::parse(form.0.name) {
Ok(name) => name,
// Return early if the name is invalid, with a 400
Err(_) => return HttpResponse::BadRequest().finish(),
};
let new_subscriber = NewSubscriber {
email: form.0.email,
name,
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

cargo test is not green yet, but we are getting a different error:

--- subscribe_returns_a_400_when_fields_are_present_but_invalid stdout ----
thread 'subscribe_returns_a_400_when_fields_are_present_but_invalid'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `200`:
The API did not return a 400 Bad Request when the payload was empty email.',
tests/health_check.rs:167:9

The test case using an empty name is now passing, but we are failing to return a 400 Bad Request when an empty email is provided.
Not unexpected - we have not implemented any kind of email validation yet!

6.11 The Email Format

We are all intuitively familiar with the common structure of an email address - XXX@YYY.ZZZ - but the subject quickly gets more complicated if you desire to be rigorous and avoid bouncing email addresses that are actually valid.

How do we establish if an email address is “valid”?
There are a few Request For Comments (RFC) by the Internet Engineering Task Force (IETF) outlining the expected structure of an email address - RFC 6854, RFC 5322, RFC 2822. We would have to read them, digest the material and then come up with an is_valid_email function that matches the specification.
Unless you have a keen interest in understanding the subtle nuances of the email address format, I would suggest you to take a step back: it is quite messy. So messy that even the HTML specification is willfully non-compliant with the RFCs we just linked.

Our best shot is to look for an existing library that has stared long and hard at the problem to provide us with a plug-and-play solution. Luckily enough, there is at least one in the Rust ecosystem - the validator crate![9]

6.12 The SubscriberEmail Type

We will follow the same strategy we used for name validation - encode our invariant (“this string represents a valid email”) in a new SubscriberEmail type.

6.12.1 Breaking The Domain Sub-Module

Before we get started though, let’s make some space - let’s break our domain sub-module (domain.rs) into multiple smaller files, one for each type, similarly to what we did for routes back in Chapter 3. Our current folder structure (under src) is:

src/
routes/
[...]
domain.rs
[...]

We want to have

src/
routes/
[...]
domain/
mod.rs
subscriber_name.rs
subscriber_email.rs
new_subscriber.rs
[...]

Unit tests should be in the same file of the type they refer to. We will end up with:

//! src/domain/mod.rs

mod subscriber_name;
mod subscriber_email;
mod new_subscriber;

pub use subscriber_name::SubscriberName;
pub use new_subscriber::NewSubscriber;

//! src/domain/subscriber_name.rs

use unicode_segmentation::UnicodeSegmentation;

#[derive(Debug)]
pub struct SubscriberName(String);

impl SubscriberName {
// [...]
}

impl AsRef<str> for SubscriberName {
// [...]
}

#[cfg(test)]
mod tests {
// [...]
}

//! src/domain/subscriber_email.rs

// Still empty, ready for us to get started!

//! src/domain/new_subscriber.rs

use crate::domain::subscriber_name::SubscriberName;

pub struct NewSubscriber {
pub email: String,
pub name: SubscriberName,
}

No changes should be required to other files in our project - the API of our module has not changed thanks to our pub use statements in mod.rs.

6.12.2 Skeleton Of A New Type

Let’s add a barebone SubscriberEmail type: no validation, just a wrapper around a String and a convenient AsRef implementation:

//! src/domain/subscriber_email.rs

#[derive(Debug)]
pub struct SubscriberEmail(String);

impl SubscriberEmail {
pub fn parse(s: String) -> Result<SubscriberEmail, String> {
// TODO: add validation!
Ok(Self(s))
}
}

impl AsRef<str> for SubscriberEmail {
fn as_ref(&self) -> &str {
&self.0
}
}

//! src/domain/mod.rs

mod new_subscriber;
mod subscriber_email;
mod subscriber_name;

pub use new_subscriber::NewSubscriber;
pub use subscriber_email::SubscriberEmail;
pub use subscriber_name::SubscriberName;

We start with tests this time: let’s come up with a few examples of invalid emails that should be rejected.

//! src/domain/subscriber_email.rs

#[derive(Debug)]
pub struct SubscriberEmail(String);

// [...]

#[cfg(test)]
mod tests {
use super::SubscriberEmail;
use claim::assert_err;

#[test]
fn empty_string_is_rejected() {
let email = "".to_string();
assert_err!(SubscriberEmail::parse(email));
}

#[test]
fn email_missing_at_symbol_is_rejected() {
let email = "ursuladomain.com".to_string();
assert_err!(SubscriberEmail::parse(email));
}

#[test]
fn email_missing_subject_is_rejected() {
let email = "@domain.com".to_string();
assert_err!(SubscriberEmail::parse(email));
}
}

Running cargo test domain confirms that all test cases are failing:

failures:
domain::subscriber_email::tests::email_missing_at_symbol_is_rejected
domain::subscriber_email::tests::email_missing_subject_is_rejected
domain::subscriber_email::tests::empty_string_is_rejected

test result: FAILED. 6 passed; 3 failed; 0 ignored; 0 measured; 0 filtered out

Time to bring validator in:

cargo add validator

Our parse method will just delegate all the heavy-lifting to validator::validate_email:

//! src/domain/subscriber_email.rs

use validator::validate_email;

#[derive(Debug)]
pub struct SubscriberEmail(String);

impl SubscriberEmail {
pub fn parse(s: String) -> Result<SubscriberEmail, String> {
if validate_email(&s) {
Ok(Self(s))
} else {
Err(format!("{} is not a valid subscriber email.", s))
}
}
}

// [...]

As simple as that - all our tests are green now!
There is a caveat - all our tests cases are checking for invalid emails. We should also have at least one test checking that valid emails are going through.

We could hard-code a known valid email address in a test and check that it is parsed successfully - e.g. ursula@domain.com.
What value would we get from that test case though? It would only re-assure us that a specific email address is correctly parsed as valid.

6.13 Property-based Testing

We could use another approach to test our parsing logic: instead of verifying that a certain set of inputs is correctly parsed, we could build a random generator that produces valid values and check that our parser does not reject them.
In other words, we verify that our implementation displays a certain property - “No valid email address is rejected”.

This approach is often referred to as property-based testing.

If we were working with time, for example, we could repeatedly sample three random integers

- H, between 0 and 23 (inclusive);

- M, between 0 and 59 (inclusive);

- S, between 0 and 59 (inclusive);

and verify that H:M:S is always correctly parsed.

Property-based testing significantly increases the range of inputs that we are validating, and therefore our confidence in the correctness of our code, but it does not prove that our parser is correct - it does not exhaustively explore the input space (except for tiny ones).

Let’s see what property testing would look like for our SubscriberEmail.

6.13.1 How To Generate Random Test Data With fake

First and foremost, we need a random generator of valid emails.
We could write one, but this a great opportunity to introduce the fake crate.

fake provides generation logic for both primitive data types (integers, floats, strings) and higher-level objects (IP addresses, country codes, etc.) - in particular, emails! Let’s add fake as a development dependency of our project:

# Cargo.toml
# [...]

[dev-dependencies]
# [...]
# We are not using fake >= 2.4 because it relies on rand 0.8
# which has been recently released and it is not yet used by
# quickcheck (solved in its upcoming 1.0 release!)
fake = "~2.3"

Let’s use it in a new test:

//! src/domain/subscriber_email.rs

// [...]

#[cfg(test)]
mod tests {
// We are importing the `SafeEmail` faker!
// We also need the `Fake` trait to get access to the
// `.fake` method on `SafeEmail`
use fake::faker::internet::en::SafeEmail;
use fake::Fake;
// [...]

#[test]
fn valid_emails_are_parsed_successfully() {
let email = SafeEmail().fake();
assert_ok!(SubscriberEmail::parse(email));
}
}

Every time we run our test suite, SafeEmail().fake() generates a new random valid email which we then use to test our parsing logic.
This is already a major improvement compared to a hard-coded valid email, but we would have to run our test suite several times to catch an issue with an edge case. A fast-and-dirty solution would be to add a for loop to the test, but, once again, we can use this as an occasion to delve deeper and explore one of the available testing crates designed around property-based testing.

6.13.2 quickcheck Vs proptest

There are two mainstream options for property-based testing in the Rust ecosystem: quickcheck and proptest.
Their domains overlap, although each shines in its own niche - check their READMEs for all the nitty gritty details.

For our project we will go with quickcheck - it is fairly simple to get started with and it is does not use too many macros, which makes for a pleasant IDE experience.

6.13.3 Getting Started With quickcheck

Let’s have a look at one of their examples to get the gist of how it works:

/// The function we want to test.
fn reverse<T: Clone>(xs: &[T]) -> Vec<T> {
let mut rev = vec!();
for x in xs.iter() {
rev.insert(0, x.clone())
}
rev
}

#[cfg(test)]
mod tests {
#[quickcheck_macros::quickcheck]
fn prop(xs: Vec<u32>) -> bool {
/// A property that is always true, regardless
/// of the vector we are applying the function to:
/// reversing it twice should return the original input.
xs == reverse(&reverse(&xs))
}
}

quickcheck calls prop in a loop with a configurable number of iterations (100 by default): on every iteration, it generates a new Vec<u32> and checks that prop returned true.
If prop returns false, it tries to shrink the generated input to the smallest possible failing example (the shortest failing vector) to help us debug what went wrong.

In our case, we’d like to have something along these lines:

#[quickcheck_macros::quickcheck]
fn valid_emails_are_parsed_successfully(valid_email: String) -> bool {
SubscriberEmail::parse(valid_email).is_ok()
}

Unfortunately, if we ask for a String type as input we are going to get all sorts of garbage which will fail validation.
How do we customise the generation routine?

6.13.4 Implementing The Arbitrary Trait

Let’s go back to the previous example - how does quickcheck know how to generate a Vec<u32>?
Everything is built on top of quickcheck’s Arbitrary trait:

pub trait Arbitrary: Clone + Send + 'static {
fn arbitrary<G: Gen>(g: &mut G) -> Self;

fn shrink(&self) -> Box<dyn Iterator<Item = Self>> {
empty_shrinker()
}
}

We have two methods:

- arbitrary: given a source of randomness (g) it returns an instance of the type;

- shrink: it returns a sequence of progressively “smaller” instances of the type to help quickcheck find the smallest possible failure case.

Vec<u32> implements Arbitrary, therefore quickcheck knows how to generate random u32 vectors.
We need to create our own type, let’s call it ValidEmailFixture, and implement Arbitrary for it.
If you look at Arbitrary’s trait definition, you’ll notice that shrinking is optional: there is a default implementation (using empty_shrinker) which results in quickcheck outputting the first failure encountered, without trying to make it any smaller or nicer. Therefore we only need to provide an implementation of Arbitrary::arbitrary for our ValidEmailFixture.

Let’s add both quickcheck and quickcheck-macros as development dependencies:

#! Cargo.toml
# [...]

[dev-dependencies]
# [...]
quickcheck = "0.9.2"
quickcheck-macros = "0.9.1"

Then

//! src/domain/subscriber_email.rs
// [...]

#[cfg(test)]
mod tests {
// We have removed the `assert_ok` import.
use claim::assert_err;
// [...]

// Both `Clone` and `Debug` are required by `quickcheck`
#[derive(Debug, Clone)]
struct ValidEmailFixture(pub String);

impl quickcheck::Arbitrary for ValidEmailFixture {
fn arbitrary<G: quickcheck::Gen>(g: &mut G) -> Self {
let email = SafeEmail().fake_with_rng(g);
Self(email)
}
}

#[quickcheck_macros::quickcheck]
fn valid_emails_are_parsed_successfully(valid_email: ValidEmailFixture) -> bool {
SubscriberEmail::parse(valid_email.0).is_ok()
}
}

This is an amazing example of the interoperability you gain by sharing key traits across the Rust ecosystem.
How do we get fake and quickcheck to play nicely together?

In Arbitrary::arbitrary we get g as input, an argument of type G.
G is constrained by a trait bound, G: quickcheck::Gen, therefore it must implement the Gen trait in quickcheck, where Gen stands for “generator”.
How is Gen defined?

pub trait Gen: RngCore {
fn size(&self) -> usize;
}

Anything that implements Gen must also implement the RngCore trait from rand-core.

Let’s examine the SafeEmail faker: it implements the Fake trait.
Fake gives us a fake method, which we have already tried out, but it also exposes a fake_with_rng method, where “rng” stands for “random number generator”.
What does fake accept as a valid random number generator?

pub trait Fake: Sized {
//[...]

fn fake_with_rng<U, R>(&self, rng: &mut R) -> U where
R: Rng + ?Sized,
Self: FakeBase<U>;
}

You read that right - any type that implements the Rng trait from rand, which is automatically implemented by all types implementing RngCore!
We can just pass g from Arbitrary::arbitrary as the random number generator for fake_with_rng and everything just works!

Maybe the maintainers of the two crates are aware of each other, maybe they aren’t, but a community-sanctioned set of traits in rand-core gives us painless interoperability. Pretty sweet!

You can now run cargo test domain - it should come out green, re-assuring us that our email validation check is indeed not overly prescriptive.

If you want to see the random inputs that are being generated, add a dbg!(&valid_email.0); statement to the test and run cargo test valid_emails -- --nocapture - tens of valid emails should pop up in your terminal!

6.14 Payload Validation

If you run cargo test, without restricting the set of tests being run to domain, you will see that our integration test with invalid data is still red.

--- subscribe_returns_a_400_when_fields_are_present_but_invalid stdout ----
thread 'subscribe_returns_a_400_when_fields_are_present_but_invalid'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `200`:
The API did not return a 400 Bad Request when the payload was empty email.',
tests/health_check.rs:167:9

Let’s integrate our shiny SubscriberEmail into the application to benefit from its validation in our /subscriptions endpoint.
We need to start from NewSubscriber:

//! src/domain/new_subscriber.rs

use crate::domain::SubscriberName;
use crate::domain::SubscriberEmail;

pub struct NewSubscriber {
// We are not using `String` anymore!
pub email: SubscriberEmail,
pub name: SubscriberName,
}

Hell should break loose if you try to compile the project now.
Let’s start with the first error reported by cargo check:

error[E0308]: mismatched types
--> src/routes/subscriptions.rs:28:16
|
28 |         email: form.0.email,
|                ^^^^^^^^^^^^
|                expected struct `SubscriberEmail`,
|                found struct `std::string::String`

It is referring to a line in our request handler, subscribe:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let name = match SubscriberName::parse(form.0.name) {
Ok(name) => name,
Err(_) => return HttpResponse::BadRequest().finish(),
};
let new_subscriber = NewSubscriber {
// We are trying to assign a string to a field of type SubscriberEmail!
email: form.0.email,
name,
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

We need to mimic what we are already doing for the name field: first we parse form.0.email then we assign the result (if successful) to NewSubscriber.email.

//! src/routes/subscriptions.rs

// We added `SubscriberEmail`!
use crate::domain::{NewSubscriber, SubscriberEmail, SubscriberName};
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let name = match SubscriberName::parse(form.0.name) {
Ok(name) => name,
Err(_) => return HttpResponse::BadRequest().finish(),
};
let email = match SubscriberEmail::parse(form.0.email) {
Ok(email) => email,
Err(_) => return HttpResponse::BadRequest().finish(),
};
let new_subscriber = NewSubscriber { email, name };
// [...]
}

Time to move to the second error:

error[E0308]: mismatched types
--> src/routes/subscriptions.rs:50:9
|
50 |         new_subscriber.email,
|         ^^^^^^^^^^^^^^
|         expected `&str`,
|         found struct `SubscriberEmail`

This is in our insert_subscriber function, where we perform a SQL INSERT query to store the details of the new subscriber:

//! src/routes/subscriptions.rs

// [...]

#[tracing::instrument([...])]
pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
// It expects a `&str` but we are passing it
// a `SubscriberEmail` value
new_subscriber.email,
new_subscriber.name.as_ref(),
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

The solution is right there, on the line below - we just need to borrow the inner field of SubscriberEmail as a string slice using our implementation of AsRef<str>.

//! src/routes/subscriptions.rs

// [...]

#[tracing::instrument([...])]
pub async fn insert_subscriber(
pool: &PgPool,
new_subscriber: &NewSubscriber,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)
"#,
Uuid::new_v4(),
// Using `as_ref` now!
new_subscriber.email.as_ref(),
new_subscriber.name.as_ref(),
Utc::now()
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

That’s it - it compiles now!
What about our integration test?

cargo test

running 4 tests
test subscribe_returns_a_400_when_data_is_missing ... ok
test health_check_works ... ok
test subscribe_returns_a_400_when_fields_are_present_but_invalid ... ok
test subscribe_returns_a_200_for_valid_form_data ... ok

test result: ok. 4 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

All green! We made it!

6.14.1 Refactoring With TryInto

Before we move on let’s spend a few paragraphs to refactor the code we just wrote.
I am referring to our request handler, subscribe:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let name = match SubscriberName::parse(form.0.name) {
Ok(name) => name,
Err(_) => return HttpResponse::BadRequest().finish(),
};
let email = match SubscriberEmail::parse(form.0.email) {
Ok(email) => email,
Err(_) => return HttpResponse::BadRequest().finish(),
};
let new_subscriber = NewSubscriber { email, name };
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

We can extract the first two statements in a parse_subscriber function:

//! src/routes/subscriptions.rs
// [...]

pub fn parse_subscriber(form: FormData) -> Result<NewSubscriber, String> {
let name = SubscriberName::parse(form.name)?;
let email = SubscriberEmail::parse(form.email)?;
Ok(NewSubscriber { email, name })
}

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let new_subscriber = match parse_subscriber(form.0) {
Ok(email) => email,
Err(_) => return HttpResponse::BadRequest().finish(),
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

The refactoring gives us a clearer separation of concerns:

- parse_subscriber takes care of the conversion from our wire format (the url-decoded data collected from a HTML form) to our domain model (NewSubscriber);

- subscribe remains in charge of generating the HTTP response to the incoming HTTP request.

The Rust standard library provides a few traits to deal with conversions in its std::convert sub-module. That is where AsRef comes from!
Is there any trait there that captures what we are trying to do with parse_subscriber?
AsRef is not a good fit for what we are dealing with here: a fallible conversion between two types which consumes the input value.
We need to look at TryInto:

pub trait TryInto<T>: Sized {
/// The type returned in the event of a conversion error.
type Error;

/// Performs the conversion.
fn try_into(self) -> Result<T, Self::Error>;
}

Replace self with FormData, T with NewSubscriber and Self::Error with String - there you have it, the signature of our parse_subscriber function!
Let’s try it out:

//! src/routes/subscriptions.rs
// We need to import the trait to use it!
use std::convert::TryInto;
// [...]

impl TryInto<NewSubscriber> for FormData {
type Error = String;

fn try_into(self) -> Result<NewSubscriber, Self::Error> {
let name = SubscriberName::parse(self.name)?;
let email = SubscriberEmail::parse(self.email)?;
Ok(NewSubscriber { email, name })
}
}

#[tracing::instrument([...])]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let new_subscriber = match form.0.try_into() {
Ok(form) => form,
Err(_) => return HttpResponse::BadRequest().finish(),
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

What do we gain by implementing TryInto?
Nothing shiny, no new functionality - we are “just” making our intent clearer.
We are spelling out “This is a type conversion!”.

Why does it matter? It helps others!
When another developer with some Rust exposure jumps in our codebase they will immediately spot the conversion pattern because we are using a trait that they are already familiar with.

6.15 Summary

Validating that the email in the payload of POST /subscriptions complies with the expected format is good, but it is not enough.
We now have an email that is syntactically valid but we are still uncertain about its existence: does anybody actually use that email address? Is it reachable?
We have no idea and there is only one way to find out: sending an actual email.

Confirmation emails (and how to write a HTTP client!) will be the topic of the next chapter.

1. “Falsehoods programmers believe about names” by patio11 is a great starting point to deconstruct everything you believed to be true about peoples’ names.↩

2. In a more formalised context you would usually go through a threat-modelling exercise.↩

3. It is commonly referred to as defense in depth.↩

4. Hubert B. Wolfe + 666 Sr would have been a victim of our maximum length check.↩

5. Mandatory xkcd comic.↩

6. “Parse, don’t validate” by Alexis King is a great starting point on type-driven development. “Domain Modelling Functional” by Scott Wlaschin is the perfect book to go deeper, with a specific focus around domain modelling - if a book looks like too much material, definitely check out Scott’s talk.↩

7. A panic in a request handler does not crash the whole application. actix-web spins up multiple workers to deal with incoming requests and it is resilient to one or more of them crashing: it will just spawn new ones to replace the ones that failed.↩

8. Checked exceptions in Java are the only example I am aware of in mainstream languages using exceptions that comes close enough to the compile-time safety provided by Result.↩

9. The validator crate follows the HTML specification when it comes to email validation. You can check its source code if you are curious to see how it’s implemented.↩

7 Reject Invalid Subscribers #2

7.1 Confirmation Emails

In the previous chapter we introduced validation for the email addresses of new subscribers - they must comply with the email format.
We now have emails that are syntactically valid but we are still uncertain about their existence: does anybody actually use those email addresses? Are they reachable?
We have no idea and there is only one way to find out: sending out an actual confirmation email.

7.1.1 Subscriber Consent

Your spider-senses should be going off now - do we actually need to know at this stage of the subscriber lifetime? Can’t we just wait for the next newsletter issue to find out if they receive our emails or not?

If performing thorough validation was our only concern, I’d concur: we should wait for the next issue to go out instead of adding more complexity to our POST /subscriptions endpoint.
There is one more thing we are concerned about though, which we cannot postpone: subscriber consent.

An email address is not a password - if you have been on the Internet long enough there is a high chance your email is not so difficult to come by.
Certain types of email addresses (e.g. professional emails) are outright public.

This opens up the possibility of abuse.
A malicious user could start subscribing an email address to all sort of newsletters across the internet, flooding the victim’s inbox with junk.
A shady newsletter owner, instead, could start scraping email addresses from the web and adding them to its newsletter email list.

This is why a request to POST /subscriptions is not enough to say “This person wants to receive my newsletter content!”.
For example, if you are dealing with European citizens, it is a legal requirement to get explicit consent from the user.

This is why it has become common practice to send confirmation emails: after entering your details in the newsletter HTML form you will receive an email in your inbox asking you to confirm that you do indeed want to subscribe to that newsletter.
This works nicely for us - we shield our users from abuse and we get to confirm that the email addresses they provided actually exist before trying to send them a newsletter issue.

7.1.2 The Confirmation User Journey

Let’s look at our confirmation flow from a user perspective.

They will receive an email with a confirmation link.
Once they click on it something happens and they are then redirected to a success page (“You are now a subscriber of our newsletter! Yay!”). From that point onwards, they will receive all newsletter issues in their inbox.

How will the backend work?
We will try to keep it as simple as we can - our version will not perform a redirect on confirmation, we will just return a 200 OK to the browser.

Every time a user wants to subscribe to our newsletter they fire a POST /subscriptions request. Our request handler will:

- add their details to our database in the subscriptions table, with status equal to pending_confirmation;

- generate a (unique) subscription_token;

- store subscription_token in our database against their id in a subscription_tokens table;

- send an email to the new subscriber containing a link structured as https://<our-api-domain>/subscriptions/confirm?token=<subscription_token>;

- return a 200 OK.

Once they click on the link, a browser tab will open up and a GET request will be fired to our GET /subscriptions/confirm endpoint. Our request handler will:

- retrieve subscription_token from the query parameters;

- retrieve the subscriber id associated with subscription_token from the subscription_tokens table;

- update the subscriber status from pending_confirmation to active in the subscriptions table;

- return a 200 OK.

There are a few other possible designs (e.g. use a JWT instead of a unique token) and we have a few corner cases to handle (e.g. what happens if they click on the link twice? What happens if they try to subscribe twice?) - we will discuss both at the most appropriate time as we make progress with the implementation.

7.1.3 The Implementation Strategy

There is a lot to do here, so we will split the work in three conceptual chunks:

- write a module to send an email;

- adapt the logic of our existing POST /subscriptions request handler to match the new specification;

- write a GET /subscriptions/confirm request handler from scratch.

Let’s get started!

7.2 EmailClient, Our Email Delivery Component

7.2.1 How To Send An Email

How do you actually send an email?
How does it work?

You have to look into SMTP - the Simple Mail Transfer Protocol.
It has been around since the early days of the Internet - the first RFC dates back to 1982.

SMTP does for emails what HTTP does for web pages: it is an application-level protocol that ensures that different implementations of email servers and clients can understand each other and exchange messages.

Now, let’s make things clear - we will not build our own private email server, it would take too long and we would not gain much from the effort. We will be leveraging a third-party service.
What do email delivery services expect these days? Do we need to talk SMTP to them?

Not necessarily.
SMTP is a specialised protocol: unless you have been working with emails before, it is unlikely you have direct experience with it. Learning a new protocol takes time and you are bound to make mistakes along the way - that is why most providers expose two interfaces: an SMTP and a REST API.
If you are familiar with the email protocol, or you need some non-conventional configuration, go ahead with the SMTP interface. Otherwise, most developers will get up and running much faster (and more reliably) using a REST API.

As you might have guessed, that is what we will be going for as well - we will write a REST client.

7.2.1.1 Choosing An Email API

There is no shortage of email API providers on the market and you are likely to know the names of the major ones - AWS SES, SendGrid, MailGun, Mailchimp, Postmark.

I was looking for a simple enough API (e.g. how easy is it to literally just send an email?), a smooth onboarding flow and a free plan that does not require entering your credit card details just to test the service out.
That is how I landed on Postmark.

To complete the next sections you will have to sign up to Postmark and, once you are logged into their portal, authorise a single sender email.

Create single sender

Once you are done, we can move forward!

Disclaimer: Postmark is not paying me to promote their services here.

7.2.1.2 The Email Client Interface

There are usually two approaches when it comes to a new piece of functionality: you can do it bottom-up, starting from the implementation details and slowly working your way up, or you can do it top-down, by designing the interface first and then figuring out how the implementation is going to work (to an extent).
In this case, we will go for the second route.

What kind of interface do we want for our email client?
We’d like to have some kind of send_email method. At the moment we just need to send a single email out at a time - we will deal with the complexity of sending emails in batches when we start working on newsletter issues.
What arguments should send_email accept?

We’ll definitely need the recipient email address, the subject line and the email content. We’ll ask for both an HTML and a plain text version of the email content - some email clients are not able to render HTML and some users explicitly disable HTML emails. By sending both versions we err on the safe side.
What about the sender email address?
We’ll assume that all emails sent by an instance of the client are coming from the same address - therefore we do not need it as an argument of send_email, it will be one of the arguments in the constructor of the client itself.

We also expect send_email to be an asynchronous function, given that we will be performing I/O to talk to a remote server.

Stitching everything together, we have something that looks more or less like this:

//! src/email_client.rs

use crate::domain::SubscriberEmail;

pub struct EmailClient {
sender: SubscriberEmail
}

impl EmailClient {
pub async fn send_email(
&self,
recipient: SubscriberEmail,
subject: &str,
html_content: &str,
text_content: &str
) -> Result<(), String> {
todo!()
}
}

//! src/lib.rs

pub mod configuration;
pub mod domain;
// New entry!
pub mod email_client;
pub mod routes;
pub mod startup;
pub mod telemetry;

There is an unresolved question - the return type. We sketched a Result<(), String> which is a way to spell “I’ll think about error handling later”.
Plenty of work left to do, but it is a start - we said we were going to start from the interface, not that we’d nail it down in one sitting!

7.2.2 How To Write A REST Client Using reqwest

To talk with a REST API we need an HTTP client.
There are a few different options in the Rust ecosystem: synchronous vs asynchronous, pure Rust vs bindings to an underlying native library, tied to tokio or async-std, opinionated vs highly customisable, etc.

We will go with the most popular option on crates.io: reqwest.

What to say about reqwest?

- It has been extensively battle-tested (~8.5 million downloads);

- It offers a primarily asynchronous interface, with the option to enable a synchronous one via the blocking feature flag;

- It relies on tokio as its asynchronous executor, matching what we are already using due to actix-web;

- It does not depend on any system library if you choose to use rustls to back the TLS implementation (rustls-tls feature flag instead of default-tls), making it extremely portable.

If you look closely, we are already using reqwest!
It is the HTTP client we used to fire off requests at our API in the integration tests. Let’s lift it from a development dependency to a runtime dependency:

#! Cargo.toml
# [...]

[dependencies]
# [...]
# We need the `json` feature flag to serialize/deserialize JSON payloads
reqwest = { version = "0.11", default-features = false, features = ["json", "rustls-tls"] }

[dev-dependencies]
# Remove `reqwest`'s entry from this list
# [...]

7.2.2.1 reqwest::Client

The main type you will be dealing with when working with reqwest is reqwest::Client - it exposes all the methods we need to perform requests against a REST API.

We can get a new client instance by invoking Client::new or we can go with Client::builder if we need to tune the default configuration.
We will stick to Client::new for the time being.

Let’s add two fields to EmailClient:

- http_client, to store a Client instance;

- base_url, to store the URL of the API we will be making requests to.

//! src/email_client.rs

use crate::domain::SubscriberEmail;
use reqwest::Client;

pub struct EmailClient {
http_client: Client,
base_url: String,
sender: SubscriberEmail
}

impl EmailClient {
pub fn new(base_url: String, sender: SubscriberEmail) -> Self {
Self {
http_client: Client::new(),
base_url,
sender
}
}

// [...]
}

7.2.2.2 Connection Pooling

Before executing an HTTP request against an API hosted on a remote server we need to establish a connection.
It turns out that connecting is a fairly expensive operation, even more so if using HTTPS: creating a brand-new connection every time we need to fire off a request can impact the performance of our application and might lead to a problem known as socket exhaustion under load.

To mitigate the issue, most HTTP clients offer connection pooling: after the first request to a remote server has been completed, they will keep the connection open (for a certain amount of time) and re-use it if we need to fire off another request to the same server, therefore avoiding the need to re-establish a connection from scratch.

reqwest is no different - every time a Client instance is created reqwest initialises a connection pool under the hood.
To leverage this connection pool we need to reuse the same Client across multiple requests.
It is also worth pointing out that Client::clone does not create a new connection pool - we just clone a pointer to the underlying pool.

7.2.2.3 How To Reuse The Same reqwest::Client In actix-web

To re-use the same HTTP client across multiple requests in actix-web we need to store a copy of it in the application context - we will then be able to retrieve a reference to Client in our request handlers using an extractor (e.g. actix_web::web::Data).

How do we pull it off? Let’s look at the code where we build a HttpServer:

//! src/startup.rs
// [...]

pub fn run(listener: TcpListener, db_pool: PgPool) -> Result<Server, std::io::Error> {
let db_pool = Data::new(db_pool);
let server = HttpServer::new(move || {
App::new()
.wrap(TracingLogger)
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
})
.listen(listener)?
.run();
Ok(server)
}

We have two options:

- derive the Clone trait for EmailClient, build an instance of it once and then pass a clone to app_data every time we need to build an App:

//! src/email_client.rs
// [...]

#[derive(Clone)]
pub struct EmailClient {
http_client: Client,
base_url: String,
sender: SubscriberEmail
}

// [...]

//! src/startup.rs
use crate::email_client::EmailClient;
// [...]

pub fn run(
listener: TcpListener,
db_pool: PgPool,
email_client: EmailClient,
) -> Result<Server, std::io::Error> {
let db_pool = Data::new(db_pool);
let server = HttpServer::new(move || {
App::new()
.wrap(TracingLogger)
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
.app_data(email_client.clone())
})
.listen(listener)?
.run();
Ok(server)
}

- wrap EmailClient in actix_web::web::Data (an Arc pointer) and pass a pointer to app_data every time we need to build an App - like we are doing with PgPool:

//! src/startup.rs
use crate::email_client::EmailClient;
// [...]

pub fn run(
listener: TcpListener,
db_pool: PgPool,
email_client: EmailClient,
) -> Result<Server, std::io::Error> {
let db_pool = Data::new(db_pool);
let email_client = Data::new(email_client);
let server = HttpServer::new(move || {
App::new()
.wrap(TracingLogger)
.route("/health_check", web::get().to(health_check))
.route("/subscriptions", web::post().to(subscribe))
.app_data(db_pool.clone())
.app_data(email_client.clone())
})
.listen(listener)?
.run();
Ok(server)
}

Which way is best?
If EmailClient were just a wrapper around a Client instance, the first option would be preferable - we avoid wrapping the connection pool twice with Arc.
This is not the case though: EmailClient has two data fields attached (base_url and sender). The first implementation allocates new memory to hold a copy of that data every time an App instance is created, while the second shares it among all App instances.
That’s why we will be using the second strategy.

Beware though: we are creating an App instance for each thread - the cost of a string allocation (or a pointer clone) is negligible when looking at the bigger picture.
We are going through the decision-making process here as an exercise to understand the tradeoffs - you might have to make a similar call in the future where the cost of the two options is remarkably different.

7.2.2.4 Configuring Our EmailClient

If you run cargo check, you will get an error:

error[E0061]: this function takes 3 arguments but 2 arguments were supplied
--> src/main.rs:24:5
|
24 |     run(listener, connection_pool)?.await?;
|     ^^^ --------  --------------- supplied 2 arguments
|     |
|     expected 3 arguments

error: aborting due to previous error

Let’s fix it!
What do we have in main right now?

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect_with(configuration.database.with_db())
.await
.expect("Failed to connect to Postgres.");

let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool)?.await?;
Ok(())
}

We are building the dependencies of our application using the values specified in the configuration we retrieved via get_configuration.
To build an EmailClient instance we need the base URL of the API we want to fire requests to and the sender email address - let’s add them to our Settings struct:

//! src/configuration.rs
// [...]
use crate::domain::SubscriberEmail;

#[derive(serde::Deserialize)]
pub struct Settings {
pub database: DatabaseSettings,
pub application: ApplicationSettings,
// New field!
pub email_client: EmailClientSettings,
}

#[derive(serde::Deserialize)]
pub struct EmailClientSettings {
pub base_url: String,
pub sender_email: String,
}

impl EmailClientSettings {
pub fn sender(&self) -> Result<SubscriberEmail, String> {
SubscriberEmail::parse(self.sender_email.clone())
}
}

// [...]

We then need to set values for them in our configuration files:

#! configuration/base.yaml

application:
# [...]
database:
# [...]
email_client:
base_url: "localhost"
sender_email: "test@gmail.com"

#! configuration/production.yaml
application:
# [...]
database:
# [...]
email_client:
# Value retrieved from Postmark's API documentation
base_url: "https://api.postmarkapp.com"
# Use the single sender email you authorised on Postmark!
sender_email: "something@gmail.com"

We can now build an EmailClient instance in main and pass it to the run function:

//! src/main.rs
// [...]
use zero2prod::email_client::EmailClient;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect_with(configuration.database.with_db())
.await
.expect("Failed to connect to Postgres.");

// Build an `EmailClient` using `configuration`
let sender_email = configuration.email_client.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email
);

let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
let listener = TcpListener::bind(address)?;
// New argument for `run`, `email_client`
run(listener, connection_pool, email_client)?.await?;
Ok(())
}

cargo check should now pass, although there are a few warnings about unused variables - we will get to those soon enough.
What about our tests?

cargo check --all-targets returns a similar error to the one we were seeing before with cargo check:

error[E0061]: this function takes 3 arguments but 2 arguments were supplied
--> tests/health_check.rs:36:18
|
36 |     let server = run(listener, connection_pool.clone())
|                  ^^^ --------  ----------------------- supplied 2 arguments
|                  |
|                  expected 3 arguments

error: aborting due to previous error

You are right - it is a symptom of code duplication. We will get to refactor the initialisation logic of our integration tests, but not yet.
Let’s patch it quickly to make it compile:

//! tests/health_check.rs

// [...]
use zero2prod::email_client::EmailClient;
// [...]

async fn spawn_app() -> TestApp {
// [...]

let mut configuration = get_configuration()
.expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();
let connection_pool = configure_database(&configuration.database).await;

// Build a new email client
let sender_email = configuration.email_client.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email
);

// Pass the new client to `run`!
let server = run(listener, connection_pool.clone(), email_client)
.expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

// [...]

cargo test should succeed now.

7.2.3 How To Test A REST Client

We have gone through most of the setup steps: we sketched an interface for EmailClient and we wired it up with the application, using a new configuration type - EmailClientSettings.
To stay true to our test-driven development approach, it is now time to write a test!
We could start from our integration tests: change the ones for POST /subscriptions to make sure that the endpoint conforms to our new requirements.
It would take us a long time to turn them green though: apart from sending an email, we need to add logic to generate a unique token and store it.

Let’s start smaller: we will just test our EmailClient component in isolation.
It will boost our confidence that it behaves as expected when tested as a unit, reducing the number of issues we might encounter when integrating it into the larger confirmation email flow.
It will also give us a chance to see if the interface we landed on is ergonomic and easy to test.

What should we actually test though?
The main purpose of our EmailClient::send_email is to perform an HTTP call: how do we know if it happened? How do we check that the body and the headers were populated as we expected?
We need to intercept that HTTP request - time to spin up a mock server!

7.2.3.1 HTTP Mocking With wiremock

Let’s add a new module for tests at the bottom of src/email_client.rs with the skeleton of a new test:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
todo!()
}
}

This will not compile straight-away - we need to add two feature flags to tokio in our Cargo.toml:

#! Cargo.toml
# [...]

[dev-dependencies]
# [...]
tokio = { version = "1", features = ["rt", "macros"] }

We do not know enough about Postmark to make assertions about what we should see in the outgoing HTTP request.
Nonetheless, as the test name says, it is reasonable to expect a request to be fired to the server at EmailClient::base_url!

Let’s add wiremock to our development dependencies:

#! Cargo.toml
# [...]

[dev-dependencies]
# [...]
wiremock = "0.5"

Using wiremock, we can write send_email_fires_a_request_to_base_url as follows:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
use crate::domain::SubscriberEmail;
use crate::email_client::EmailClient;
use fake::faker::internet::en::SafeEmail;
use fake::faker::lorem::en::{Paragraph, Sentence};
use fake::{Fake, Faker};
use wiremock::matchers::any;
use wiremock::{Mock, MockServer, ResponseTemplate};

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
// Arrange
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let email_client = EmailClient::new(mock_server.uri(), sender);

Mock::given(any())
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

// Act
let _ = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

// Assert
}
}

Let’s break down what is happening, step by step.

7.2.3.2 wiremock::MockServer

let mock_server = MockServer::start().await;

wiremock::MockServer is a full-blown HTTP server.
MockServer::start asks the operating system for a random available port and spins up the server on a background thread, ready to listen for incoming requests.

How do we point our email client to our mock server? We can retrieve the address of the mock server using the MockServer::uri method; we can then pass it as base_url to EmailClient::new:

let email_client = EmailClient::new(mock_server.uri(), sender);

7.2.3.3 wiremock::Mock

Out of the box, wiremock::MockServer returns 404 Not Found to all incoming requests.
We can instruct the mock server to behave differently by mounting a Mock.

Mock::given(any())
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

When wiremock::MockServer receives a request, it iterates over all the mounted mocks to check if the request matches their conditions.
The matching conditions for a mock are specified using Mock::given.

We are passing any() to Mock::Given which, according to wiremock’s documentation,

Match all incoming requests, regardless of their method, path, headers or body. You can use it to verify that a request has been fired towards the server, without making any other assertion about it.

Basically, it always matches, regardless of the request - which is what we want here!

When an incoming request matches the conditions of a mounted mock, wiremock::MockServer returns a response following what was specified in respond_with.
We passed ResponseTemplate::new(200) - a 200 OK response without a body.

A wiremock::Mock becomes effective only after it has been mounted on a wiremock::Mockserver - that’s what our call to Mock::mount is about.

7.2.3.4 The Intent Of A Test Should Be Clear

We then have the actual invocation of EmailClient::send_email:

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

// Act
let _ = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

You’ll notice that we are leaning heavily on fake here: we are generating random data for all the inputs to send_email (and sender, in the previous section).
We could have just hard-coded a bunch of values, why did we choose to go all the way and make them random?

A reader, skimming the test code, should be able to identify easily the property that we are trying to test.
Using random data conveys a specific message: do not pay attention to these inputs, their values do not influence the outcome of the test, that’s why they are random!

Hard-coded values, instead, should always give you pause: does it matter that subscriber_email is set to marco@gmail.com? Should the test pass if I set it to another value?
In a test like ours, the answer is obvious. In a more intricate setup, it often isn’t.

7.2.3.5 Mock expectations

The end of the test looks a bit cryptic: there is an // Assert comment… but no assertion afterwards.
Let’s go back to our Mock setup line:

Mock::given(any())
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

What does .expect(1) do?
It sets an expectation on our mock: we are telling the mock server that during this test it should receive exactly one request that matches the conditions set by this mock.
We could also use ranges for our expectations - e.g. expect(1..) if we want to see at least one request, expect(1..=3) if we expect at least one request but no more than three, etc.

Expectations are verified when MockServer goes out of scope - at the end of our test function, indeed!
Before shutting down, MockServer will iterate over all the mounted mocks and check if their expectations have been verified. If the verification step fails, it will trigger a panic (and fail the test).

Let’s run cargo test:

---- email_client::tests::send_email_fires_a_request_to_base_url stdout ----
thread 'email_client::tests::send_email_fires_a_request_to_base_url' panicked at
'not yet implemented', src/email_client.rs:24:9

Ok, we are not even getting to the end of the test yet because we have a placeholder todo!() as the body of send_email.
Let’s replace it with a dummy Ok:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
&self,
recipient: SubscriberEmail,
subject: &str,
html_content: &str,
text_content: &str
) -> Result<(), String> {
// No matter the input
Ok(())
}
}

// [...]

If we run cargo test again, we’ll get to see wiremock in action:

---- email_client::tests::send_email_fires_a_request_to_base_url stdout ----
thread 'email_client::tests::send_email_fires_a_request_to_base_url' panicked at
'Verifications failed:
- Mock #0.
Expected range of matching incoming requests: == 1
Number of matched incoming requests: 0
'

The server expected one request, but it received none - therefore the test failed.

The time has come to properly flesh out EmailClient::send_email.

7.2.4 First Sketch Of EmailClient::send_email

To implement EmailClient::send_email we need to check out the API documentation of Postmark. Let’s start from their “Send a single email” user guide.

Their email sending example looks like this:

curl "https://api.postmarkapp.com/email" \
-X POST \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "X-Postmark-Server-Token: server token" \
-d '{
"From": "sender@example.com",
"To": "receiver@example.com",
"Subject": "Postmark test",
"TextBody": "Hello dear Postmark user.",
"HtmlBody": "<html><body><strong>Hello</strong> dear Postmark user.</body></html>"
}'

Let’s break it down - to send an email we need:

- a POST request to the /email endpoint;

- a JSON body, with fields that map closely to the arguments of send_email. We need to be careful with field names, they must be pascal cased;

- an authorization header, X-Postmark-Server-Token, with a value set to a secret token that we can retrieve from their portal.

If the request succeeds, we get something like this back:

HTTP/1.1 200 OK
Content-Type: application/json

{
"To": "receiver@example.com",
"SubmittedAt": "2021-01-12T07:25:01.4178645-05:00",
"MessageID": "0a129aee-e1cd-480d-b08d-4f48548ff48d",
"ErrorCode": 0,
"Message": "OK"
}

We have enough to implement the happy path!

7.2.4.1 reqwest::Client::post

reqwest::Client exposes a post method - it takes the URL we want to call with a POST request as argument and it returns a RequestBuilder.
RequestBuilder gives us a fluent API to build out the rest of the request we want to send, piece by piece.
Let’s give it a go:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
&self,
recipient: SubscriberEmail,
subject: &str,
html_content: &str,
text_content: &str
) -> Result<(), String> {
// You can do better using `reqwest::Url::join` if you change
// `base_url`'s type from `String` to `reqwest::Url`.
// I'll leave it as an exercise for the reader!
let url = format!("{}/email", self.base_url);
let builder = self.http_client.post(&url);
Ok(())
}
}

// [...]

7.2.4.2 JSON body

We can encode the request body schema as a struct:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
&self,
recipient: SubscriberEmail,
subject: &str,
html_content: &str,
text_content: &str
) -> Result<(), String> {
let url = format!("{}/email", self.base_url);
let request_body = SendEmailRequest {
from: self.sender.as_ref().to_owned(),
to: recipient.as_ref().to_owned(),
subject: subject.to_owned(),
html_body: html_content.to_owned(),
text_body: text_content.to_owned(),
};
let builder = self.http_client.post(&url);
Ok(())
}
}

struct SendEmailRequest {
from: String,
to: String,
subject: String,
html_body: String,
text_body: String,
}

// [...]

If the json feature flag for reqwest is enabled (as we did), builder will expose a json method that we can leverage to set request_body as the JSON body of the request:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
&self,
recipient: SubscriberEmail,
subject: &str,
html_content: &str,
text_content: &str
) -> Result<(), String> {
let url = format!("{}/email", self.base_url);
let request_body = SendEmailRequest {
from: self.sender.as_ref().to_owned(),
to: recipient.as_ref().to_owned(),
subject: subject.to_owned(),
html_body: html_content.to_owned(),
text_body: text_content.to_owned(),
};
let builder = self.http_client.post(&url).json(&request_body);
Ok(())
}
}

It almost works:

error[E0277]: the trait bound `SendEmailRequest: Serialize` is not satisfied
--> src/email_client.rs:34:56
|
34 |         let builder = self.http_client.post(&url).json(&request_body);
|                                                        ^^^^^^^^^^^^^
the trait `Serialize` is not implemented for `SendEmailRequest`

Let’s derive serde::Serialize for SendEmailRequest to make it serializable:

//! src/email_client.rs
// [...]

#[derive(serde::Serialize)]
struct SendEmailRequest {
from: String,
to: String,
subject: String,
html_body: String,
text_body: String,
}

Awesome, it compiles!
The json method goes a bit further than simple serialization: it will also set the Content-Type header to application/json - matching what we saw in the example!

7.2.4.3 Authorization Token

We are almost there - we need to add an authorization header, X-Postmark-Server-Token, to the request.
Just like the sender email address, we want to store the token value as a field in EmailClient.

Let’s amend EmailClient::new and EmailClientSettings:

//! src/email_client.rs

use crate::domain::SubscriberEmail;
use reqwest::Client;

pub struct EmailClient {
http_client: Client,
base_url: String,
sender: SubscriberEmail,
authorization_token: String
}

impl EmailClient {
pub fn new(
base_url: String,
sender: SubscriberEmail,
authorization_token: String
) -> Self {
Self {
http_client: Client::new(),
base_url,
sender,
authorization_token
}
}

// [...]
}

//! src/configuration.rs
// [...]

#[derive(serde::Deserialize)]
pub struct EmailClientSettings {
pub base_url: String,
pub sender_email: String,
// New configuration value!
pub authorization_token: String
}

// [...]

We can then let the compiler tell us what else needs to be modified:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
// New argument!
let email_client = EmailClient::new(mock_server.uri(), sender, Faker.fake());

// [...]
}
}

//! src/main.rs
// [...]

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
// Pass argument from configuration
configuration.email_client.authorization_token,
);
// [...]
}

//! tests/health_check.rs
// [...]

async fn spawn_app() -> TestApp {
// [...]
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
// Pass argument from configuration
configuration.email_client.authorization_token,
);
// [...]
}
// [...]

#! configuration/base.yml
# [...]
email_client:
base_url: "localhost"
sender_email: "test@gmail.com"
# New value!
# We are only setting the development value,
# we'll deal with the production token outside of version control
# (given that it's a sensitive secret!)
authorization_token: "my-secret-token"

We can now use the authorization token in send_email:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
// [...]
) -> Result<(), String> {
// [...]
let builder = self
.http_client
.post(&url)
.header("X-Postmark-Server-Token", &self.authorization_token)
.json(&request_body);
Ok(())
}
}

It compiles straight away.

7.2.4.4 Executing The Request

We have all the ingredients - we just need to fire the request now!
We can use the send method:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
// [...]
) -> Result<(), String> {
// [...]
self
.http_client
.post(&url)
.header("X-Postmark-Server-Token", &self.authorization_token)
.json(&request_body)
.send()
.await?;
Ok(())
}
}

send is asynchronous, therefore we need to await the future it returns.
send is also a fallible operation - e.g. we might fail to establish a connection to the server. We’d like to return an error if send fails - that’s why we use the ? operator.

The compiler, though, is not happy:

error[E0277]: `?` couldn't convert the error to `std::string::String`
--> src/email_client.rs:41:19
|
41 |             .await?;
|                   ^
the trait `From<reqwest::Error>` is not implemented for `std::string::String`

The error variant returned by send is of type reqwest::Error, while our send_email uses String as error type. The compiler has looked for a conversion (an implementation of the From trait), but it could not find any - therefore it errors out.
If you recall, we used String as error variant mostly as a placeholder - let’s change send_email’s signature to return Result<(), reqwest::Error>.

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
// [...]
) -> Result<(), reqwest::Error> {
// [...]
}
}

The error should be gone now!
cargo test should pass too: congrats!

7.2.5 Tightening Our Happy Path Test

Let’s look again at our “happy path” test:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
use crate::domain::SubscriberEmail;
use crate::email_client::EmailClient;
use fake::faker::internet::en::SafeEmail;
use fake::faker::lorem::en::{Paragraph, Sentence};
use fake::{Fake, Faker};
use wiremock::matchers::any;
use wiremock::{Mock, MockServer, ResponseTemplate};

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
// Arrange
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let email_client = EmailClient::new(mock_server.uri(), sender, Faker.fake());

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

Mock::given(any())
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// Act
let _ = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

// Assert
// Mock expectations are checked on drop
}
}

To ease ourselves into the world of wiremock we started with something very basic - we are just asserting that the mock server gets called once. Let’s beef it up to check that the outgoing request looks indeed like we expect it to.

7.2.5.0.1 Headers, Path And Method

any is not the only matcher offered by wiremock out of the box: there are handful available in wiremock’s matchers module.
We can use header_exists to verify that the X-Postmark-Server-Token is set on the request to the server:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]
// We removed `any` from the import list
use wiremock::matchers::header_exists;

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
// [...]

Mock::given(header_exists("X-Postmark-Server-Token"))
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// [...]
}
}

We can chain multiple matchers together using the and method.
Let’s add header to check that the Content-Type is set to the correct value, path to assert on the endpoint being called and method to verify the HTTP verb:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]
use wiremock::matchers::{header, header_exists, path, method};

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
// [...]

Mock::given(header_exists("X-Postmark-Server-Token"))
.and(header("Content-Type", "application/json"))
.and(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// [...]
}
}

7.2.5.0.2 Body

So far, so good: cargo test still passes.
What about the request body?

We could use body_json to match exactly the request body.
We probably do not need to go as far as that - it would be enough to check that the body is valid JSON and it contains the set of field names shown in Postmark’s example.

There is no out-of-the-box matcher that suits our needs - we need to implement our own!
wiremock exposes a Match trait - everything that implements it can be used as a matcher in given and and.
Let’s stub it out:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
use wiremock::Request;
// [...]

struct SendEmailBodyMatcher;

impl wiremock::Match for SendEmailBodyMatcher {
fn matches(&self, request: &Request) -> bool {
unimplemented!()
}
}

// [...]
}

We get the incoming request as input, request, and we need to return a boolean value as output: true, if the mock matched, false otherwise.
We need to deserialize the request body as JSON - let’s add serde-json to the list of our development dependencies:

#! Cargo.toml
# [...]

[dev-dependencies]
# [...]
serde_json = "1"

We can now write matches’ implementation:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

struct SendEmailBodyMatcher;

impl wiremock::Match for SendEmailBodyMatcher {
fn matches(&self, request: &Request) -> bool {
// Try to parse the body as a JSON value
let result: Result<serde_json::Value, _> =
serde_json::from_slice(&request.body);
if let Ok(body) = result {
// Check that all the mandatory fields are populated
// without inspecting the field values
body.get("From").is_some()
&& body.get("To").is_some()
&& body.get("Subject").is_some()
&& body.get("HtmlBody").is_some()
&& body.get("TextBody").is_some()
} else {
// If parsing failed, do not match the request
false
}
}
}

#[tokio::test]
async fn send_email_fires_a_request_to_base_url() {
// [...]

Mock::given(header_exists("X-Postmark-Server-Token"))
.and(header("Content-Type", "application/json"))
.and(path("/email"))
.and(method("POST"))
// Use our custom matcher!
.and(SendEmailBodyMatcher)
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// [...]
}
}

It compiles!
But our tests are failing now…

---- email_client::tests::send_email_fires_a_request_to_base_url stdout ----
thread 'email_client::tests::send_email_fires_a_request_to_base_url' panicked at
'Verifications failed:
- Mock #0.
Expected range of matching incoming requests: == 1
Number of matched incoming requests: 0
'

Why is that?
Let’s add a dbg! statement to our matcher to inspect the incoming request:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

impl wiremock::Match for SendEmailBodyMatcher {
fn matches(&self, request: &Request) -> bool {
// [...]
if let Ok(body) = result {
dbg!(&body);
// [...]
} else {
false
}
}
}
// [...]
}

If you run the test again with cargo test send_email you will get something that looks like this:

--- email_client::tests::send_email_fires_a_request_to_base_url stdout ----
[src/email_client.rs:71] &body = Object({
"from": String("[...]"),
"to": String("[...]"),
"subject": String("[...]"),
"html_body": String("[...]"),
"text_body": String("[...]"),
})
thread 'email_client::tests::send_email_fires_a_request_to_base_url' panicked at '
Verifications failed:
- Mock #0.
Expected range of matching incoming requests: == 1
Number of matched incoming requests: 0
'

It seems we forgot about the casing requirement - field names must be pascal cased!
We can fix it easily by adding an annotation on SendEmailRequest:

//! src/email_client.rs
// [...]

#[derive(serde::Serialize)]
#[serde(rename_all = "PascalCase")]
struct SendEmailRequest {
from: String,
to: String,
subject: String,
html_body: String,
text_body: String,
}

The test should pass now.
Before we move on, let’s rename the test to send_email_sends_the_expected_request - it captures better the test purpose at this point.

7.2.5.1 Refactoring: Avoid Unnecessary Memory Allocations

We focused on getting send_email to work - now we can look at it again to see if there is any room for improvement.
Let’s zoom on the request body:

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
// [...]
) -> Result<(), reqwest::Error> {
// [...]
let request_body = SendEmailRequest {
from: self.sender.as_ref().to_owned(),
to: recipient.as_ref().to_owned(),
subject: subject.to_owned(),
html_body: html_content.to_owned(),
text_body: text_content.to_owned(),
};
// [...]
}
}

#[derive(serde::Serialize)]
#[serde(rename_all = "PascalCase")]
struct SendEmailRequest {
from: String,
to: String,
subject: String,
html_body: String,
text_body: String,
}

For each field we are allocating a bunch of new memory to store a cloned String - it is wasteful. It would be more efficient to reference the existing data without performing any additional allocation.
We can pull it off by restructuring SendEmailRequest: instead of String we have to use a string slice (&str) as type for all fields.
A string slice is a just pointer to a memory buffer owned by somebody else. To store a reference in a struct we need to add a lifetime parameter: it keeps track of how long those references are valid for - it’s the compiler’s job to make sure that references do not stay around longer than the memory buffer they point to!

Let’s do it!

//! src/email_client.rs
// [...]

impl EmailClient {
// [...]

pub async fn send_email(
// [...]
) -> Result<(), reqwest::Error> {
// [...]
// No more `.to_owned`!
let request_body = SendEmailRequest {
from: self.sender.as_ref(),
to: recipient.as_ref(),
subject,
html_body: html_content,
text_body: text_content,
};
// [...]
}
}

#[derive(serde::Serialize)]
#[serde(rename_all = "PascalCase")]
// Lifetime parameters always start with an apostrophe, `'`
struct SendEmailRequest<'a> {
from: &'a str,
to: &'a str,
subject: &'a str,
html_body: &'a str,
text_body: &'a str,
}

That’s it, quick and painless - serde does all the heavy lifting for us and we are left with more performant code!

7.2.6 Dealing With Failures

We have a good grip on the happy path - what happens instead if things don’t go as expected?
We will look at two scenarios:

- non-success status codes (e.g. 4xx, 5xx, etc.);

- slow responses.

7.2.6.1 Error Status Codes

Our current happy path test is only making assertions on the side-effect performed by send_email - we are not actually inspecting the value it returns!
Let’s make sure that it is an Ok(()) if the server returns a 200 OK:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]
use wiremock::matchers::any;
use claim::assert_ok;
// [...]

// New happy-path test!
#[tokio::test]
async fn send_email_succeeds_if_the_server_returns_200() {
// Arrange
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let email_client = EmailClient::new(mock_server.uri(), sender, Faker.fake());

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

// We do not copy in all the matchers we have in the other test.
// The purpose of this test is not to assert on the request we
// are sending out!
// We add the bare minimum needed to trigger the path we want
// to test in `send_email`.
Mock::given(any())
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// Act
let outcome = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

// Assert
assert_ok!(outcome);
}
}

No surprises, the test passes.
Let’s look at the opposite case now - we expect an Err variant if the server returns a 500 Internal Server Error.

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]
use claim::assert_err;
// [...]

#[tokio::test]
async fn send_email_fails_if_the_server_returns_500() {
// Arrange
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let email_client = EmailClient::new(mock_server.uri(), sender, Faker.fake());

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

Mock::given(any())
// Not a 200 anymore!
.respond_with(ResponseTemplate::new(500))
.expect(1)
.mount(&mock_server)
.await;

// Act
let outcome = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

// Assert
assert_err!(outcome);
}
}

We got some work to do here instead:

--- email_client::tests::send_email_fails_if_the_server_returns_500 stdout ----
thread 'email_client::tests::send_email_fails_if_the_server_returns_500' panicked at
'assertion failed, expected Err(..), got Ok(())', src/email_client.rs:163:9

Let’s look again at send_email:

//! src/email_client.rs
// [...]

impl EmailClient {
//[...]
pub async fn send_email(
//[...]
) -> Result<(), reqwest::Error> {
//[...]
self.http_client
.post(&url)
.header("X-Postmark-Server-Token", &self.authorization_token)
.json(&request_body)
.send()
.await?;
Ok(())
}
}
//[...]

The only step that might return an error is send - let’s check reqwest’s docs!

This method fails if there was an error while sending request, redirect loop was detected or redirect limit was exhausted.

Basically, send returns Ok as long as it gets a valid response from the server - no matter the status code!
To get the behaviour we want we need to look at the methods available on reqwest::Response - in particular, error_for_status:

Turn a response into an error if the server returned an error.

It seems to suit our needs, let’s try it out.

//! src/email_client.rs
// [...]

impl EmailClient {
//[...]
pub async fn send_email(
//[...]
) -> Result<(), reqwest::Error> {
//[...]
self.http_client
.post(&url)
.header("X-Postmark-Server-Token", &self.authorization_token)
.json(&request_body)
.send()
.await?
.error_for_status()?;
Ok(())
}
}
//[...]

Awesome, the test passes!

7.2.6.2 Timeouts

What happens instead if the server returns a 200 OK, but it takes ages to send it back?
We can instruct our mock server to wait a configurable amount of time before sending a response back.
Let’s experiment a little with a new integration test - what if the server takes 3 minutes to respond!?

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

#[tokio::test]
async fn send_email_times_out_if_the_server_takes_too_long() {
// Arrange
let mock_server = MockServer::start().await;
let sender = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let email_client = EmailClient::new(mock_server.uri(), sender, Faker.fake());

let subscriber_email = SubscriberEmail::parse(SafeEmail().fake()).unwrap();
let subject: String = Sentence(1..2).fake();
let content: String = Paragraph(1..10).fake();

let response = ResponseTemplate::new(200)
// 3 minutes!
.set_delay(std::time::Duration::from_secs(180));
Mock::given(any())
.respond_with(response)
.expect(1)
.mount(&mock_server)
.await;

// Act
let outcome = email_client
.send_email(subscriber_email, &subject, &content, &content)
.await;

// Assert
assert_err!(outcome);
}
}

After a while, you should see something like this:

test email_client::tests::send_email_times_out_if_the_server_takes_too_long ...
test email_client::tests::send_email_times_out_if_the_server_takes_too_long
has been running for over 60 seconds

This is far from ideal: if the server starts misbehaving we might start to accumulate several “hanging” requests.
We are not hanging up on the server, so the connection is busy: every time we need to send an email we will have to open a new connection. If the server does not recover fast enough, and we do not close any of the open connections, we might end up with socket exhaustion/performance degradation.

As a rule of thumb: every time you are performing an IO operation, always set a timeout!
If the server takes longer than the timeout to respond, we should fail and return an error.

Choosing the right timeout value is often more an art than a science, especially if retries are involved: set it too low and you might overwhelm the server with retried requests; set it too high and you risk again to see degradation on the client side.
Nonetheless, better to have a conservative timeout threshold than to have none.

reqwest gives us two options: we can either add a default timeout on the Client itself, which applies to all outgoing requests, or we can specify a per-request timeout.
Let’s go for a Client-wide timeout: we’ll set it in EmailClient::new.

//! src/email_client.rs
// [...]
impl EmailClient {
pub fn new(
// [...]
) -> Self {
let http_client = Client::builder()
.timeout(std::time::Duration::from_secs(10))
.build()
.unwrap();
Self {
http_client,
base_url,
sender,
authorization_token,
}
}
}
// [...]

If we run the test again, it should pass (after 10 seconds have elapsed).

7.2.6.3 Refactoring: Test Helpers

There is a lot of duplicated code in our four tests for EmailClient - let’s extract the common bits in a set of test helpers.

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

/// Generate a random email subject
fn subject() -> String {
Sentence(1..2).fake()
}

/// Generate a random email content
fn content() -> String {
Paragraph(1..10).fake()
}

/// Generate a random subscriber email
fn email() -> SubscriberEmail {
SubscriberEmail::parse(SafeEmail().fake()).unwrap()
}

/// Get a test instance of `EmailClient`.
fn email_client(base_url: String) -> EmailClient {
EmailClient::new(base_url, email(), Faker.fake())
}

// [...]
}

Let’s use them in send_email_sends_the_expected_request:

//! src/email_client.rs
// [...]

#[cfg(test)]
mod tests {
// [...]

#[tokio::test]
async fn send_email_sends_the_expected_request() {
// Arrange
let mock_server = MockServer::start().await;
let email_client = email_client(mock_server.uri());

Mock::given(header_exists("X-Postmark-Server-Token"))
.and(header("Content-Type", "application/json"))
.and(path("/email"))
.and(method("POST"))
.and(SendEmailBodyMatcher)
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&mock_server)
.await;

// Act
let _ = email_client
.send_email(email(), &subject(), &content(), &content())
.await;

// Assert
}
}

Way less visual noise - the intent of the test is front and center.
Go ahead and refactor the other three!

7.3 Skeleton And Principles For A Maintainable Test Suite

It took us a bit of work, but we now have a pretty decent REST client for Postmark’s API.

EmailClient is just the first ingredient for our confirmation email flow: we have yet to find a way to generate unique confirmation links, which we will then have to embed in the body of the outgoing confirmation emails.
Both tasks will have to wait a bit longer.

We have used a test-driven approach to write all new pieces of functionality throughout the book.
While this strategy has served us well, we have not invested a lot of time into refactoring our test code. As a result, our tests folder is a bit of mess at this point.
Before moving forward, we will restructure our integration test suite to support us as our application grows in complexity and the number of tests increases.

7.3.1 Why Do We Write Tests?

Is writing tests a good use of developers’ time?

A good test suite is, first and foremost, a risk-mitigation measure.
Automated tests reduce the risk associated with changes to an existing codebase - most regressions and bugs are caught in the continuous integration pipeline and never reach users. The team is therefore empowered to iterate faster and release more often.

Tests act as documentation as well.
The test suite is often the best starting point when deep-diving in an unknown code base - it shows you how the code is supposed to behave and what scenarios are considered relevant enough to have dedicated tests for.

“Write a test suite!” should definitely be on your to-do list if you want to make your project more welcoming to new contributors.

There are other positive side-effects often associated with good tests - modularity, decoupling. These are harder to quantify, as we have yet to agree as an industry on what “good code” looks like.

7.3.2 Why Don’t We Write Tests?

Although there are compelling reasons to invest time and effort in writing a good test suite, reality is somewhat messier.

First, the development community did not always believe in the value of testing.
We can find examples of test-driven development throughout the history of the discipline, but it is only with the “Extreme Programming” (XP) book that the practice entered the mainstream debate - in 1999!

Paradigm shifts do not happen overnight - it took years for the test-driven approach to gain traction as a “best practice” within the industry.

If test-driven development has won the minds and hearts of developers, the battle with management is often still undergoing.
Good tests build technical leverage, but writing tests takes time. When a deadline is pressing, testing is often the first to be sacrificed.

As a consequence, most of the material you find around is either an introduction to testing or a guide on how to pitch its value to stakeholders.
There is very little about testing at scale - what happens if you stick to the book and keep writing tests as the codebase grows to tens of thousands of lines, with hundreds of test cases?

7.3.3 Test Code Is Still Code

All test suites start in the same way: an empty file, a world of possibilities.
You go in, you add the first test. Easy, done.
Then the second. Boom.
The third. You just had to copy a few lines from the first, all good.
The fourth…

After a while, test coverage starts to go down: new code is less thoroughly tested than the code you wrote at the very beginning of the project. Have you started to doubt the value of tests?
Absolutely not, tests are great!
Yet, you are writing fewer tests as the project moves forward.
It’s because of friction - it got progressively more cumbersome to write new tests as the codebase evolved.

Test code is still code.

It has to be modular, well-structured, sufficiently documented. It requires maintenance.
If we do not actively invest in the health of our test suite, it will rot over time.
Coverage goes down and soon enough we will find critical paths in our application code that are never exercised by automated tests.

You need to regularly step back to take a look at your test suite as a whole.
Time to look at ours, isn’t it?

7.3.4 Our Test Suite

All our integration tests live within a single file, tests/health_check.rs:

//! tests/health_check.rs
// [...]

// Ensure that the `tracing` stack is only initialised once using `once_cell`
static TRACING: Lazy<()> = Lazy::new(|| {
// [...]
});

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

async fn spawn_app() -> TestApp {
// [...]
}

pub async fn configure_database(config: &DatabaseSettings) -> PgPool {
// [...]
}

#[actix_rt::test]
async fn health_check_works() {
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_data_is_missing() {
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_fields_are_present_but_invalid() {
// [...]
}

7.3.5 Test Discovery

There is only one test dealing with our health check endpoint - health_check_works.
The other three tests are probing our POST /subscriptions endpoint while the rest of the code deals with shared setup steps (spawn_app, TestApp, configure_database, TRACING).

Why have we shoved everything in tests/health_check.rs?
Because it was convenient!
The setup functions were already there - it was easier to add another test case within the same file than figuring out how to share that code properly across multiple test modules.

Our main goal in this refactoring is discoverability:

- given an application endpoint, it should be easy to find the corresponding integration tests within the tests folder;

- when writing a test, it should be easy to find the relevant test helper functions.

We will focus on folder structure, but that is definitely not the only tool available when it comes to test discovery.
Test coverage tools can often tell you which tests triggered the execution of a certain line of application code.
You can rely on techniques such as coverage marks to create an obvious link between test and application code.

As always, a multi-pronged approach is likely to give you the best results as the complexity of your test suite increases.

7.3.6 One Test File, One Crate

Before we start moving things around, let’s nail down a few facts about integration testing in Rust.
The tests folder is somewhat special - cargo knows to look into it searching for integration tests.

Each file within the tests folder gets compiled as its own crate.
We can check this out by running cargo build --tests and then looking under target/debug/deps:

# Build test code, without running tests
cargo build --tests
# Find all files with a name starting with `health_check`
ls target/debug/deps | grep health_check

health_check-fc23645bf877da35
health_check-fc23645bf877da35.d

The trailing hashes will likely be different on your machine, but there should be two entries starting with health_check-*.
What happens if you try to run it?

./target/debug/deps/health_check-fc23645bf877da35

running 4 tests
test health_check_works ... ok
test subscribe_returns_a_400_when_fields_are_present_but_invalid ... ok
test subscribe_returns_a_400_when_data_is_missing ... ok
test subscribe_returns_a_200_for_valid_form_data ... ok

test result: ok. 4 passed; finished in 0.44s

That’s right, it runs our integration tests!
If we had five *.rs files under tests, we’d find five executables in target/debug/deps.

7.3.7 Sharing Test Helpers

If each integration test file is its own executable, how do we share test helpers functions?

The first option is to define a stand-alone module - e.g. tests/helpers/mod.rs[1].
You can add common functions in mod.rs (or define other sub-modules in there) and then refer to helpers in your test file (e.g. tests/health_check.rs) with:

//! tests/health_check.rs
// [...]
mod helpers;

// [...]

helpers is bundled in the health_check test executable as a sub-module and we get access to the functions it exposes in our test cases.
This approach works fairly well to start out, but it leads to annoying function is never used warnings down the line.
The issue is that helpers is bundled as a sub-module, it is not invoked as a third-party crate: cargo compiles each test executable in isolation and warns us if, for a specific test file, one or more public functions in helpers have never been invoked. This is bound to happen as your test suite grows - not all test files will use all your helper methods.

The second option takes full advantage of that each file under tests is its own executable - we can create sub-modules scoped to a single test executable!
Let’s create an api folder under tests, with a single main.rs file inside:

tests/
api/
main.rs
health_check.rs

First, we gain clarity: we are structuring api in the very same way we would structure a binary crate. Less magic - it builds on the same knowledge of the module system you built while working on application code.
If you run cargo build --tests you should be able to spot

Running target/debug/deps/api-0a1bfb817843fdcf

running 0 tests

test result: ok. 0 passed; finished in 0.00s

in the output - cargo compiled api as a test executable, looking for tests cases.
There is no need to define a main function in main.rs - the Rust test framework adds one for us behind the scenes[2].

We can now add sub-modules in main.rs:

//! tests/api/main.rs

mod helpers;
mod health_check;
mod subscriptions;

Add three empty files - tests/api/helpers.rs, tests/api/health_check.rs and tests/api/subscriptions.rs.
Time to delete tests/health_check.rs and re-distribute its content:

//! tests/api/helpers.rs
use sqlx::{Connection, Executor, PgConnection, PgPool};
use std::net::TcpListener;
use uuid::Uuid;
use zero2prod::configuration::{get_configuration, DatabaseSettings};
use zero2prod::email_client::EmailClient;
use zero2prod::startup::run;
use zero2prod::telemetry::{get_subscriber, init_subscriber};

// Ensure that the `tracing` stack is only initialised once using `once_cell`
static TRACING: Lazy<()> = Lazy::new(|| {
// [...]
});

pub struct TestApp {
// [...]
}

// Public!
pub async fn spawn_app() -> TestApp {
// [...]
}

// Not public anymore!
async fn configure_database(config: &DatabaseSettings) -> PgPool {
// [...]
}

//! tests/api/health_check.rs
use crate::helpers::spawn_app;

#[actix_rt::test]
async fn health_check_works() {
// [...]
}

//! tests/api/subscriptions.rs
use crate::helpers::spawn_app;

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_data_is_missing() {
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_fields_are_present_but_invalid() {
// [...]
}

cargo test should succeed, with no warnings.
Congrats, you have broken down your test suite in smaller and more manageable modules!

There are a few positive side-effects to the new structure: - it is recursive.
If tests/api/subscriptions.rs grows too unwieldy, we can turn it into a module, with tests/api/subscriptions/helpers.rs holding subscription-specific test helpers and one or more test files focused on a specific flow or concern; - the implementation details of our helpers function are encapsulated.
It turns out that our tests only need to know about spawn_app and TestApp - no need to expose configure_database or TRACING, we can keep that complexity hidden away in the helpers module; - we have a single test binary.
If you have large test suite with a flat file structure, you’ll soon be building tens of executable every time you run cargo test. While each executable is compiled in parallel, the linking phase is instead entirely sequential! Bundling all your test cases in a single executable reduces the time spent compiling your test suite in CI[3].

If you are running Linux, you might see errors like

thread 'actix-rt:worker' panicked at
'Can not create Runtime: Os { code: 24, kind: Other, message: "Too many open files" }',

when you run cargo test after the refactoring.
This is due to a limit enforced by the operating system on the maximum number of open file descriptors (including sockets) for each process - given that we are now running all tests as part of a single binary, we might be exceeding it. The limit is usually set to 1024, but you can raise it with ulimit -n X (e.g. ulimit -n 10000) to resolve the issue.

7.3.8 Sharing Startup Logic

Now that we have reworked the layout of our test suite it’s time to zoom in on the test logic itself.
We will start with spawn_app:

//! tests/api/helpers.rs
// [...]

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

pub async fn spawn_app() -> TestApp {
Lazy::force(&TRACING);

let listener = TcpListener::bind("127.0.0.1:0").expect("Failed to bind random port");
let port = listener.local_addr().unwrap().port();
let address = format!("http://127.0.0.1:{}", port);

let mut configuration = get_configuration().expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();
let connection_pool = configure_database(&configuration.database).await;

let sender_email = configuration
.email_client
.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
configuration.email_client.authorization_token,
);

let server = run(listener, connection_pool.clone(), email_client)
.expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

// [...]

Most of the code we have here is extremely similar to what we find in our main entrypoint:

//! src/main.rs
use sqlx::postgres::PgPoolOptions;
use std::net::TcpListener;
use zero2prod::configuration::get_configuration;
use zero2prod::email_client::EmailClient;
use zero2prod::startup::run;
use zero2prod::telemetry::{get_subscriber, init_subscriber};

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into());
init_subscriber(subscriber);

let configuration = get_configuration().expect("Failed to read configuration.");
let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect_with(configuration.database.with_db())
.await
.expect("Failed to connect to Postgres.");

let sender_email = configuration
.email_client
.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
configuration.email_client.authorization_token,
);

let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool, email_client)?.await?;
Ok(())
}

Every time we add a dependency or modify the server constructor, we have at least two places to modify - we have recently gone through the motions with EmailClient. It’s mildly annoying.
More importantly though, the startup logic in our application code is never tested.
As the codebase evolves, they might start to diverge subtly, leading to different behaviour in our tests compared to our production environment.

We will first extract the logic out of main and then figure out what hooks we need to leverage the same code paths in our test code.

7.3.8.1 Extracting Our Startup Code

From a structural perspective, our startup logic is a function taking Settings as input and returning an instance of our application as output.
It follows that our main function should look like this:

//! src/main.rs
use zero2prod::configuration::get_configuration;
use zero2prod::startup::build;
use zero2prod::telemetry::{get_subscriber, init_subscriber};

#[actix_web::main]
async fn main() -> std::io::Result<()> {
let subscriber = get_subscriber("zero2prod".into(), "info".into());
init_subscriber(subscriber);

let configuration = get_configuration().expect("Failed to read configuration.");
let server = build(configuration).await?;
server.await?;
Ok(())
}

We first perform some binary-specific logic (i.e. telemetry initialisation), then we build a set of configuration values from the supported sources (files + environment variables) and use it to spin up an application. Linear.
Let’s define that build function then:

//! src/startup.rs
// [...]
// New imports!
use crate::configuration::Settings;
use sqlx::postgres::PgPoolOptions;

pub async fn build(configuration: Settings) -> Result<Server, std::io::Error> {
let connection_pool = PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect_with(configuration.database.with_db())
.await
.expect("Failed to connect to Postgres.");

let sender_email = configuration
.email_client
.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
configuration.email_client.authorization_token,
);

let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
let listener = TcpListener::bind(address)?;
run(listener, connection_pool, email_client)
}

pub fn run(
listener: TcpListener,
db_pool: PgPool,
email_client: EmailClient,
) -> Result<Server, std::io::Error> {
// [...]
}

Nothing too surprising - we have just moved around the code that was previously living in main.
Let’s make it test-friendly now!

7.3.8.2 Testing Hooks In Our Startup Logic

Let’s look at our spawn_app function again:

//! tests/api/helpers.rs
// [...]
use zero2prod::startup::build;
// [...]

pub async fn spawn_app() -> TestApp {
// The first time `initialize` is invoked the code in `TRACING` is executed.
// All other invocations will instead skip execution.
Lazy::force(&TRACING);

let listener = TcpListener::bind("127.0.0.1:0").expect("Failed to bind random port");
// We retrieve the port assigned to us by the OS
let port = listener.local_addr().unwrap().port();
let address = format!("http://127.0.0.1:{}", port);

let mut configuration = get_configuration().expect("Failed to read configuration.");
configuration.database.database_name = Uuid::new_v4().to_string();
let connection_pool = configure_database(&configuration.database).await;

let sender_email = configuration
.email_client
.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
configuration.email_client.authorization_token,
);

let server = run(listener, connection_pool.clone(), email_client)
.expect("Failed to bind address");
let _ = tokio::spawn(server);
TestApp {
address,
db_pool: connection_pool,
}
}

// [...]

At a high-level, we have the following phases:

- Execute test-specific setup (i.e. initialise a tracing subscriber);

- Randomise the configuration to ensure tests do not interfere with each other (i.e. a different logical database for each test case);

- Initialise external resources (e.g. create and migrate the database!);

- Build the application;

- Launch the application as a background task and return a set of resources to interact with it.

Can we just throw build in there and call it a day?
Not really, but let’s try to see where it falls short:

//! tests/api/helpers.rs
// [...]
// New import!
use zero2prod::startup::build;

pub async fn spawn_app() -> TestApp {
Lazy::force(&TRACING);

// Randomise configuration to ensure test isolation
let configuration = {
let mut c = get_configuration().expect("Failed to read configuration.");
// Use a different database for each test case
c.database.database_name = Uuid::new_v4().to_string();
// Use a random OS port
c.application.port = 0;
c
};

// Create and migrate the database
configure_database(&configuration.database).await;

// Launch the application as a background task
let server = build(configuration).await.expect("Failed to build application.");
let _ = tokio::spawn(server);

TestApp {
// How do we get these?
address: todo!(),
db_pool: todo!()
}
}

// [...]

It almost works - the approach falls short at the very end: we have no way to retrieve the random address assigned by the OS to the application and we don’t really know how to build a connection pool to the database, needed to perform assertions on side-effects impacting the persisted state.

Let’s deal with the connection pool first: we can extract the initialisation logic from build into a stand-alone function and invoke it twice.

//! src/startup.rs
// [...]
use crate::configuration::DatabaseSettings;

// We are taking a reference now!
pub async fn build(configuration: &Settings) -> Result<Server, std::io::Error> {
let connection_pool = get_connection_pool(&configuration.database)
.await
.expect("Failed to connect to Postgres.");
// [...]
}

pub async fn get_connection_pool(
configuration: &DatabaseSettings
) -> Result<PgPool, sqlx::Error> {
PgPoolOptions::new()
.connect_timeout(std::time::Duration::from_secs(2))
.connect_with(configuration.with_db())
.await
}

//! tests/api/helpers.rs
// [...]
use zero2prod::startup::{build, get_connection_pool};
// [...]

pub async fn spawn_app() -> TestApp {
// Notice the .clone!
let server = build(configuration.clone())
.await
.expect("Failed to build application.");
// [...]
TestApp {
address: todo!(),
db_pool: get_connection_pool(&configuration.database)
.await
.expect("Failed to connect to the database"),
}
}

// [...]

You’ll have to add a #[derive(Clone)] to all the structs in src/configuration.rs to make the compiler happy, but we are done with the database connection pool.

How do we get the application address instead?
actix_web::dev::Server, the type returned by build, does not allow us to retrieve the application port.
We need to do a bit more legwork in our application code - we will wrap actix_web::dev::Server in a new type that holds on to the information we want.

//! src/startup.rs
// [...]

// A new type to hold the newly built server and its port
pub struct Application {
port: u16,
server: Server,
}

impl Application {
// We have converted the `build` function into a constructor for
// `Application`.
pub async fn build(configuration: Settings) -> Result<Self, std::io::Error> {
let connection_pool = get_connection_pool(&configuration.database)
.await
.expect("Failed to connect to Postgres.");

let sender_email = configuration
.email_client
.sender()
.expect("Invalid sender email address.");
let email_client = EmailClient::new(
configuration.email_client.base_url,
sender_email,
configuration.email_client.authorization_token,
);

let address = format!(
"{}:{}",
configuration.application.host, configuration.application.port
);
let listener = TcpListener::bind(&address)?;
let port = listener.local_addr().unwrap().port();
let server = run(listener, connection_pool, email_client)?;

// We "save" the bound port in one of `Application`'s fields
Ok(Self { port, server })
}

pub fn port(&self) -> u16 {
self.port
}

// A more expressive name that makes it clear that
// this function only returns when the application is stopped.
pub async fn run_until_stopped(self) -> Result<(), std::io::Error> {
self.server.await
}
}

// [...]

//! tests/api/helpers.rs
// [...]
// New import!
use zero2prod::startup::Application;

pub async fn spawn_app() -> TestApp {
// [...]

let application = Application::build(configuration.clone())
.await
.expect("Failed to build application.");
// Get the port before spawning the application
let address = format!("http://127.0.0.1:{}", application.port());
let _ = tokio::spawn(application.run_until_stopped());

TestApp {
address,
db_pool: get_connection_pool(&configuration.database)
.await
.expect("Failed to connect to the database"),
}
}

// [...]

//! src/main.rs
// [...]
// New import!
use zero2prod::startup::Application;

#[actix_web::main]
async fn main() -> std::io::Result<()> {
// [...]
let application = Application::build(configuration).await?;
application.run_until_stopped().await?;
Ok(())
}

It’s done - run cargo test if you want to double-check!

7.3.9 Build An API Client

All of our integration tests are black-box: we launch our application at the beginning of each test and interact with it using an HTTP client (i.e. reqwest).
As we write tests, we necessarily end up implementing a client for our API.

That’s great!
It gives us a prime opportunity to see what it feels like to interact with the API as a user.
We just need to be careful not to spread the client logic all over the test suite - when the API changes, we don’t want to go through tens of tests to remove a trailing s from the path of an endpoint.

Let’s look at our subscriptions tests:

//! tests/api/subscriptions.rs
use crate::helpers::spawn_app;

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app = spawn_app().await;
let client = reqwest::Client::new();
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = client
.post(&format!("{}/subscriptions", &app.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(200, response.status().as_u16());

let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_data_is_missing() {
// Arrange
let app = spawn_app().await;
let client = reqwest::Client::new();
let test_cases = vec![
("name=le%20guin", "missing the email"),
("email=ursula_le_guin%40gmail.com", "missing the name"),
("", "missing both name and email"),
];

for (invalid_body, error_message) in test_cases {
// Act
let response = client
.post(&format!("{}/subscriptions", &app.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(invalid_body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(
400,
response.status().as_u16(),
// Additional customised error message on test failure
"The API did not fail with 400 Bad Request when the payload was {}.",
error_message
);
}
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_fields_are_present_but_invalid() {
// Arrange
let app = spawn_app().await;
let client = reqwest::Client::new();
let test_cases = vec![
("name=&email=ursula_le_guin%40gmail.com", "empty name"),
("name=Ursula&email=", "empty email"),
("name=Ursula&email=definitely-not-an-email", "invalid email"),
];

for (body, description) in test_cases {
// Act
let response = client
.post(&format!("{}/subscriptions", &app.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.");

// Assert
assert_eq!(
400,
response.status().as_u16(),
"The API did not return a 400 Bad Request when the payload was {}.",
description
);
}
}

We have the same calling code in each test - we should pull it out and add a helper method to our TestApp struct:

//! tests/api/helpers.rs
// [...]

pub struct TestApp {
// [...]
}

impl TestApp {
pub async fn post_subscriptions(&self, body: String) -> reqwest::Response {
reqwest::Client::new()
.post(&format!("{}/subscriptions", &self.address))
.header("Content-Type", "application/x-www-form-urlencoded")
.body(body)
.send()
.await
.expect("Failed to execute request.")
}
}

// [...]

//! tests/api/subscriptions.rs
use crate::helpers::spawn_app;

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// [...]
// Act
let response = app.post_subscriptions(body.into()).await;
// [...]
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_data_is_missing() {
// [...]
for (invalid_body, error_message) in test_cases {
let response = app.post_subscriptions(invalid_body.into()).await;
// [...]
}
}

#[actix_rt::test]
async fn subscribe_returns_a_400_when_fields_are_present_but_invalid() {
// [...]
for (body, description) in test_cases {
let response = app.post_subscriptions(body.into()).await;
// [...]
}
}

We could add another method for the health check endpoint, but it’s only used once - there is no need right now.

7.3.10 Summary

We started with a single file test suite, we finished with a modular test suite and a robust set of helpers.
Just like application code, test code is never finished: we will have to keep working on it as the project evolves, but we have laid down solid foundations to keep moving forward without losing momentum.
We are now ready to tackle the remaining pieces of functionality needed to dispatch a confirmation email.

7.4 Refocus

Time to go back to the plan we drafted at the beginning of the chapter:

- write a module to send an email;

- adapt the logic of our existing POST /subscriptions request handler to match the new requirements;

- write a GET /subscriptions/confirm request handler from scratch.

The first item is done, time to move on to the remaining two on the list.
We had a sketch of how the two handlers should work:

POST /subscriptions will:

- add the subscriber details to the database in the subscriptions table, with status equal to pending_confirmation;

- generate a (unique) subscription_token;

- store subscription_token in our database against the subscriber id in a subscription_tokens table;

- send an email to the new subscriber containing a link structured as https://<our-api-domain>/subscriptions/confirm?token=<subscription_token>;

- return a 200 OK.

Once they click on the link, a browser tab will open up and a GET request will be fired to our GET /subscriptions/confirm endpoint. The request handler will:

- retrieve subscription_token from the query parameters;

- retrieve the subscriber id associated with subscription_token from the subscription_tokens table;

- update the subscriber status from pending_confirmation to active in the subscriptions table;

- return a 200 OK.

This gives us a fairly precise picture of how the application is going to work once we are done with the implementation.
It does not help us much to figure out how to get there.

Where should we start from?
Should we immediately tackle the changes to /subscriptions?
Should we get /subscriptions/confirm out of the way?

We need to find an implementation route that can be rolled out with zero downtime.

7.5 Zero Downtime Deployments

7.5.1 Reliability

In Chapter 5 we deployed our application to a public cloud provider.
It is live: we are not sending out newsletter issues yet, but people can subscribe while we figure that part out.

Once an application is serving production traffic, we need to make sure it is reliable.

Reliable means different things in different contexts. If you are selling a data storage solution, for example, it should not lose (or corrupt!) customers’ data.

In a commercial setting, the definition of reliability for your application will often be encoded in a Service Level Agreement (SLA).
An SLA is a contractual obligation: you guarantee a certain level of reliability and commit to compensate your customers (usually with discounts or credits) if your service fails to live up to the expectations.

If you are selling access to an API, for example, you will usually have something related to availability - e.g. the API should successfully respond to at least 99.99% of well-formed incoming requests, often referred to as “four nines of availability”.
Phrased differently (and assuming a uniform distribution of incoming requests over time), you can only afford up to 52 minutes of downtime over a whole year. Achieving four nines of availability is tough.

There is no silver bullet to build a highly available solution: it requires work from the application layer all the way down to the infrastructure layer.
One thing is certain, though: if you want to operate a highly available service, you should master zero downtime deployments - users should be able to use the service before, during and after the rollout of a new version of the application to production.
This is even more important if you are practising continuous deployment: you cannot release multiple times a day if every release triggers a small outage.

7.5.2 Deployment Strategies

7.5.2.1 Naive Deployment

Before diving deeper into zero downtime deployments, let’s have a look at the “naive” approach.
Version A of our service is running in production and we want to roll out version B:

- We switch off all instances of version A running the cluster;

- We spin up new instances of our application running version B;

- We start serving traffic using version B.

There is a non-zero amount of time where there is no application running in the cluster able to serve user traffic - we are experiencing downtime!

To do better we need to take a closer look at how our infrastructure is set up.

7.5.2.2 Load Balancers

Load balancer

We have multiple copies[4] of our application running behind a load balancer.
Each replica of our application is registered with the load balancer as a backend.
Every time somebody sends a request to our API, they hit our load balancer which is then in charge of choosing one of the available backends to fulfill the incoming request.

Load balancers usually support adding (and removing) backends dynamically.
This enables a few interesting patterns.

7.5.2.2.1 Horizontal Scaling

We can add more capacity when experiencing a traffic spike by spinning up more replicas of our application (i.e. horizontal scaling).
It helps to spread the load until the work expected of a single instance becomes manageable.

We will get back to this topic later in the book when discussing metrics and autoscaling.

7.5.2.2.2 Health Checks

We can ask the load balancer to keep an eye on the health of the registered backends.
Oversimplifying, health checking can be:

- Passive - the load balancer looks at the distribution of status codes/latency for each backend to determine if they are healthy or not;

- Active - the load balancer is configured to send a health check request to each backend on a schedule. If a backend fails to respond with a success status code for a long enough time period it is marked as unhealthy and removed.

This is a critical capability to achieve self-healing in a cloud-native environment: the platform can detect if an application is not behaving as expected and automatically remove it from the list of available backends to mitigate or nullify the impact on users[5].

7.5.2.3 Rolling Update Deployments

We can leverage our load balancer to perform zero downtime deployments.

Let’s look at a snapshot of our production environments: we have three replicas of version A of our application registered as backends for our load balancer.
We want to deploy version B.

System before the roll out begins.

We start by spinning up one replica of version B of our application.
When the application is ready to serve traffic (i.e. a few health check requests have succeeded) we register it as a backend with our load balancer.

One operational instance of version B.

We have four replicas of our application now: 3 running version A, 1 running version B. All four are serving live traffic.
If all is well, we switch off one of the replicas running version A.

One instance of version A has been decommissioned.

We follow the same process to replace all replicas running version A until all registered backends are running version B.

This deployment strategy is called rolling update: we run the old and the new version of the application side by side, serving live traffic with both.
Throughout the process we always have three or more healthy backends: users should not experience any kind of service degradation (assuming version B is not buggy).

7.5.2.4 Digital Ocean App Platform

We are running our application on Digital Ocean App Platform.
Their documentation boasts of offering zero downtime deployments out of the box, but they do not provide details on how it is achieved.

A few experiments confirmed that they are indeed relying on a rolling update deployment strategy.

A rolling update is not the only possible strategy for a zero downtime deployment - blue-green and canary deployments are equally popular variations over the same underlying principles.
Choose the most appropriate solution for your application based on the capabilities offered by your platform and your requirements.

7.6 Database Migrations

7.6.1 State Is Kept Outside The Application

Load balancing relies on a strong assumption: no matter which backend is used to serve an incoming request, the outcome will be the same.

This is something we discussed already in Chapter 3: to ensure high availability in a fault-prone environment, cloud-native applications are stateless - they delegate all persistence concerns to external systems (i.e. databases).

That’s why load balancing works: all backends are talking to the same database to query and manipulate the same state.

Think of a database as a single gigantic global variable. Continuously accessed and mutated by all replicas of our application.
State is hard.

7.6.2 Deployments And Migrations

During a rolling update deployment, the old and the new version of the application are both serving live traffic, side by side.
From a different perspective: the old and the new version of the application are using the same database at the same time.

To avoid downtime, we need a database schema that is understood by both versions.
This is not an issue for most of our deployments, but it is a serious constraint when we need to evolve the schema.

Let’s circle back to the job we set out to do, confirmation emails.
To move forward with the implementation strategy we identified, we need to evolve our database schema as follows:

- add a new table, subscription_tokens;

- add a new mandatory column, status, to the existing subscriptions table.

Let’s go over the possible scenarios to convince ourselves that we cannot possibly deploy confirmation emails all at once without incurring downtime.

We could first migrate the database and then deploy the new version.
This implies that the current version is running against the migrated database for some time: our current implementation of POST /subscriptions does not know about status and it tries to insert new rows into subscriptions without populating it. Given that status is constrained to be NOT NULL (i.e. it’s mandatory), all inserts would fail - we would not be able to accept new subscribers until the new version of the application is deployed.
Not good.

We could first deploy the new version and then migrate the database.
We get the opposite scenario: the new version of the application is running against the old database schema. When POST /subscriptions is called, it tries to insert a row into subscriptions with a status field that does not exist - all inserts fail and we cannot accept new subscribers until the database is migrated.
Once again, not good.

7.6.3 Multi-step Migrations

A big bang release won’t cut it - we need to get there in multiple, smaller steps.
The pattern is somewhat similar to what we see in test-driven development: we don’t change code and tests at the same time - one of the two needs to stay still while the other changes.

The same applies to database migrations and deployments: if we want to evolve the database schema we cannot change the application behaviour at the same time.
Think of it as a database refactoring: we are laying down the foundations in order to build the behaviour we need later on.

7.6.4 A New Mandatory Column

Let’s start by looking at the status column.

7.6.4.1 Step 1: Add As Optional

We start by keeping the application code stable.
On the database side, we generate a new migration script:

sqlx migrate add add_status_to_subscriptions

Creating migrations/20210307181858_add_status_to_subscriptions.sql

We can now edit the migration script to add status as an optional column to subscriptions:

ALTER TABLE subscriptions ADD COLUMN status TEXT NULL;

Run the migration against your local database (SKIP_DOCKER=true ./scripts/init_db.sh): we can now run our test suite to make sure that the code works as is even against the new database schema.

It should pass: go ahead and migrate the production database.

7.6.4.2 Step 2: Start Using The New Column

status now exists: we can start using it!
To be precise, we can start writing to it: every time a new subscriber is inserted, we will set status to confirmed.

We just need to change our insertion query from

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber([...]) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"INSERT INTO subscriptions (id, email, name, subscribed_at)
VALUES ($1, $2, $3, $4)"#,
// [...]
)
// [...]
}

to

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber([...]) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"INSERT INTO subscriptions (id, email, name, subscribed_at, status)
VALUES ($1, $2, $3, $4, 'confirmed')"#,
// [...]
)
// [...]
}

Tests should pass - deploy the new version of the application to production.

7.6.4.3 Step 3: Backfill And Mark As NOT NULL

The latest version of the application ensures that status is populated for all new subscribers.
To mark status as NOT NULL we just need to backfill the value for historical records: we’ll then be free to alter the column.

Let’s generate a new migration script:

sqlx migrate add make_status_not_null_in_subscriptions

Creating migrations/20210307184428_make_status_not_null_in_subscriptions.sql

The SQL migration looks like this:

-- We wrap the whole migration in a transaction to make sure
-- it succeeds or fails atomically. We will discuss SQL transactions
-- in more details towards the end of this chapter!
-- `sqlx` does not do it automatically for us.
BEGIN;
-- Backfill `status` for historical entries
UPDATE subscriptions
SET status = 'confirmed'
WHERE status IS NULL;
-- Make `status` mandatory
ALTER TABLE subscriptions ALTER COLUMN status SET NOT NULL;
COMMIT;

We can migrate our local database, run our test suite and then deploy our production database.
We made it, we added status as a new mandatory column!

7.6.5 A New Table

What about subscription_tokens? Do we need three steps there as well?

No, it is much simpler: we add the new table in a migration while the application keeps ignoring it.
We can then deploy a new version of the application that uses it to enable confirmation emails.

Let’s generate a new migration script:

sqlx migrate add create_subscription_tokens_table

Creating migrations/20210307185410_create_subscription_tokens_table.sql

The migration is similar to the very first one we wrote to add subscriptions:

-- Create Subscription Tokens Table
CREATE TABLE subscription_tokens(
subscription_token TEXT NOT NULL,
subscriber_id uuid NOT NULL
REFERENCES subscriptions (id),
PRIMARY KEY (subscription_token)
);

Pay attention to the details here: the subscriber_id column in subscription_tokens is a foreign key.
For each row in subscription_tokens there must exist a row in subscriptions whose id field has the same value of subscriber_id, otherwise the insertion fails. This guarantees that all tokens are attached to a legitimate subscriber.

Migrate the production database again - we are done!

7.7 Sending A Confirmation Email

It took us a while, but the groundwork is done: our production database is ready to accommodate the new feature we want to build, confirmation emails.
Time to focus on the application code.

We will build the whole feature in a proper test-driven fashion: small steps in a tight red-green-refactor loop. Get ready!

7.7.1 A Static Email

We will start simple: we will test that POST /subscriptions is sending out an email.
We will not be looking, at this stage, at the body of the email - in particular, we will not check that it contains a confirmation link.

7.7.1.1 Red test

To write this test we need to enhance our TestApp.

It currently holds our application and a handle to a pool of connections to the database:

//! tests/api/helpers.rs
// [...]

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
}

We need to spin up a mock server to stand in for Postmark’s API and intercept outgoing requests, just like we did when we built the email client.

Let’s edit spawn_app accordingly:

//! tests/api/helpers.rs

// New import!
use wiremock::MockServer;
// [...]

pub struct TestApp {
pub address: String,
pub db_pool: PgPool,
// New field!
pub email_server: MockServer,
}

pub async fn spawn_app() -> TestApp {
// [...]
// Launch a mock server to stand in for Postmark's API
let email_server = MockServer::start().await;

// Randomise configuration to ensure test isolation
let configuration = {
let mut c = get_configuration().expect("Failed to read configuration.");
// [...]
// Use the mock server as email API
c.email_client.base_url = email_server.uri();
c
};

// [...]

TestApp {
// [...],
email_server,
}
}

We can now write the new test case:

//! tests/api/subscriptions.rs
// New imports!
use wiremock::matchers::{method, path};
use wiremock::{Mock, ResponseTemplate};
// [...]

#[actix_rt::test]
async fn subscribe_sends_a_confirmation_email_for_valid_data() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.expect(1)
.mount(&app.email_server)
.await;

// Act
app.post_subscriptions(body.into()).await;

// Assert
// Mock asserts on drop
}

The test, as expected, fails:

failures:

---- subscriptions::subscribe_sends_a_confirmation_email_for_valid_data stdout ----
thread 'subscriptions::subscribe_sends_a_confirmation_email_for_valid_data' panicked at
'Verifications failed:
- Mock #0.
Expected range of matching incoming requests: == 1
Number of matched incoming requests: 0'

Notice that, on failure, wiremock gives us a detailed breakdown of what happened: we expected an incoming request, we received none.
Let’s fix that.

7.7.1.2 Green test

Our handler looks like this right now:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(form: web::Form<FormData>, pool: web::Data<PgPool>) -> HttpResponse {
let new_subscriber = match form.0.try_into() {
Ok(form) => form,
Err(_) => return HttpResponse::BadRequest().finish(),
};
match insert_subscriber(&pool, &new_subscriber).await {
Ok(_) => HttpResponse::Ok().finish(),
Err(_) => HttpResponse::InternalServerError().finish(),
}
}

To send an email we need to get our hands on an instance of EmailClient.
As part of the work we did when writing the module, we also registered it in the application context:

//! src/startup.rs
// [...]

fn run([...]) -> Result<Server, std::io::Error> {
// [...]
let email_client = Data::new(email_client);
let server = HttpServer::new(move || {
App::new()
.wrap(TracingLogger)
// [...]
// Here!
.app_data(email_client.clone())
})
.listen(listener)?
.run();
Ok(server)
}

We can therefore access it in our handler using web::Data, just like we did for pool:

//! src/routes/subscriptions.rs
// New import!
use crate::email_client::EmailClient;
// [...]

#[tracing::instrument(
name = "Adding a new subscriber",
skip(form, pool, email_client),
fields(
subscriber_email = %form.email,
subscriber_name= %form.name
)
)]
pub async fn subscribe(
form: web::Form<FormData>,
pool: web::Data<PgPool>,
// Get the email client from the app context
email_client: web::Data<EmailClient>,
) -> HttpResponse {
// [...]
if insert_subscriber(&pool, &new_subscriber).await.is_err() {
return HttpResponse::InternalServerError().finish();
}
// Send a (useless) email to the new subscriber.
// We are ignoring email delivery errors for now.
if email_client
.send_email(
new_subscriber.email,
"Welcome!",
"Welcome to our newsletter!",
"Welcome to our newsletter!",
)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
HttpResponse::Ok().finish()
}

subscribe_sends_a_confirmation_email_for_valid_data now passes, but subscribe_returns_a_200_for_valid_form_data fails:

thread 'subscriptions::subscribe_returns_a_200_for_valid_form_data' panicked at
'assertion failed: `(left == right)`
left: `200`,
right: `500`'

It is trying to send an email but it is failing because we haven’t setup a mock in that test. Let’s fix it:

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// New section!
Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.mount(&app.email_server)
.await;

// Act
let response = app.post_subscriptions(body.into()).await;

// Assert
assert_eq!(200, response.status().as_u16());
}

All good, the test passes now.
There is not much to refactor at the moment, let’s press forward.

7.7.2 A Static Confirmation Link

Let’s raise the bar a bit - we will scan the body of the email to retrieve a confirmation link.

7.7.2.1 Red Test

We don’t care (yet) about the link being dynamic or actually meaningful - we just want to be sure that there is something in the body that looks like a link.
We should also have the same link in both the plain text and the HTML version of the email body.

How do we get the body of a request intercepted by wiremock::MockServer?
We can use its received_requests method - it returns a vector of all the requests intercepted by the server as long as request recording was enabled (the default).

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_sends_a_confirmation_email_with_a_link() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
// We are not setting an expectation here anymore
// The test is focused on another aspect of the app
// behaviour.
.mount(&app.email_server)
.await;

// Act
app.post_subscriptions(body.into()).await;

// Assert
// Get the first intercepted request
let email_request = &app.email_server.received_requests().await.unwrap()[0];
// Parse the body as JSON, starting from raw bytes
let body: serde_json::Value = serde_json::from_slice(&email_request.body).unwrap();
}

We now need to extract links out of it.
The most obvious way forward would be a regular expression. Let’s face it though: regexes are a messy business and it takes a while to get them right.
Once again, we can leverage the work done by the larger Rust ecosystem - let’s add linkify as a development dependency:

cargo add linkify --vers 0.5.0 --dev

We can use linkify to scan text and return an iterator of extracted links.

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_sends_a_confirmation_email_with_a_link() {
// [...]
let body: serde_json::Value = serde_json::from_slice(&email_request.body).unwrap();

// Extract the link from one of the request fields.
let get_link = |s: &str| {
let links: Vec<_> = linkify::LinkFinder::new()
.links(s)
.filter(|l| *l.kind() == linkify::LinkKind::Url)
.collect();
assert_eq!(links.len(), 1);
links[0].as_str().to_owned()
};

let html_link = get_link(&body["HtmlBody"].as_str().unwrap());
let text_link = get_link(&body["TextBody"].as_str().unwrap());
// The two links should be identical
assert_eq!(html_link, text_link);
}

If we run the test suite, we should see the new test case failing:

failures:

thread 'subscriptions::subscribe_sends_a_confirmation_email_with_a_link'
panicked at 'assertion failed: `(left == right)`
left: `0`,
right: `1`', tests/api/subscriptions.rs:71:9

7.7.2.1.1 Green Test

We need to tweak our request handler again to satisfy the new test case:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(/* */) -> HttpResponse {
// [...]
let confirmation_link =
"https://my-api.com/subscriptions/confirm";
if email_client
.send_email(
new_subscriber.email,
"Welcome!",
&format!(
"Welcome to our newsletter!<br />\
Click <a href=\"{}\">here</a> to confirm your subscription.",
confirmation_link
),
&format!(
"Welcome to our newsletter!\nVisit {} to confirm your subscription.",
confirmation_link
),
)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
HttpResponse::Ok().finish()
}

The test should pass straight away.

7.7.2.2 Refactor

Our request handler is getting a bit busy - there is a lot of code dealing with our confirmation email now.
Let’s extract it into a separate function:

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe(/* */) -> HttpResponse {
let new_subscriber = match form.0.try_into() {
Ok(form) => form,
Err(_) => return HttpResponse::BadRequest().finish(),
};
if insert_subscriber(&pool, &new_subscriber).await.is_err() {
return HttpResponse::InternalServerError().finish();
}
if send_confirmation_email(&email_client, new_subscriber)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
HttpResponse::Ok().finish()
}

#[tracing::instrument(
name = "Send a confirmation email to a new subscriber",
skip(email_client, new_subscriber)
)]
pub async fn send_confirmation_email(
email_client: &EmailClient,
new_subscriber: NewSubscriber,
) -> Result<(), reqwest::Error> {
let confirmation_link = "https://my-api.com/subscriptions/confirm";
let plain_body = format!(
"Welcome to our newsletter!\nVisit {} to confirm your subscription.",
confirmation_link
);
let html_body = format!(
"Welcome to our newsletter!<br />\
Click <a href=\"{}\">here</a> to confirm your subscription.",
confirmation_link
);
email_client
.send_email(
new_subscriber.email,
"Welcome!",
&html_body,
&plain_body,
)
.await
}

subscribe is once again focused on the overall flow, without bothering with details of any of its steps.

7.7.3 Pending Confirmation

Let’s look at the status for a new subscriber now.
We are currently setting their status to confirmed in POST /subscriptions, while it should be pending_confirmation until they click on the confirmation link.

Time to fix it.

7.7.3.1 Red test

We can start by having a second look at our first “happy path” test:

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = app.post_subscriptions(body.into()).await;

// Assert
assert_eq!(200, response.status().as_u16());

let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
}

The name is a bit of a lie - it is checking the status code and performing some assertions against the state stored in the database.
Let’s split it into two separate test cases:

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_returns_a_200_for_valid_form_data() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
let response = app.post_subscriptions(body.into()).await;

// Assert
assert_eq!(200, response.status().as_u16());
}

#[actix_rt::test]
async fn subscribe_persists_the_new_subscriber() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

// Act
app.post_subscriptions(body.into()).await;

// Assert
let saved = sqlx::query!("SELECT email, name FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
}

We can now modify the second test case to check the status as well.

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_persists_the_new_subscriber() {
// [...]

// Assert
let saved = sqlx::query!("SELECT email, name, status FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
assert_eq!(saved.status, "pending_confirmation");
}

The test fails as expected:

failures:

---- subscriptions::subscribe_persists_the_new_subscriber stdout ----
thread 'subscriptions::subscribe_persists_the_new_subscriber'
panicked at 'assertion failed: `(left == right)`
left: `"confirmed"`,
right: `"pending_confirmation"`'

7.7.3.2 Green Test

We can turn it green by touching again our insert query:

//! src/routes/subscriptions.rs

#[tracing::instrument([...])]
pub async fn insert_subscriber([...]) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"INSERT INTO subscriptions (id, email, name, subscribed_at, status)
VALUES ($1, $2, $3, $4, 'confirmed')"#,
// [...]
)
// [...]
}

We just need to change confirmed into pending_confirmation:

//! src/routes/subscriptions.rs

#[tracing::instrument([...])]
pub async fn insert_subscriber([...]) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"INSERT INTO subscriptions (id, email, name, subscribed_at, status)
VALUES ($1, $2, $3, $4, 'pending_confirmation')"#,
// [...]
)
// [...]
}

Tests should be green.

7.7.4 Skeleton of GET /subscriptions/confirm

We have done most of the groundwork on POST /subscriptions - time to shift our focus to the other half of the journey, GET /subscriptions/confirm.
We want to build up the skeleton of the endpoint - we need to register the handler against the path in src/startup.rs and reject incoming requests without the required query parameter, subscription_token.
This will allow us to then build the happy path without having to write a massive amount of code all at once - baby steps!

7.7.4.1 Red Test

Let’s add a new module to our tests project to host all test cases dealing with the confirmation callback.

//! tests/api/main.rs

mod health_check;
mod helpers;
mod subscriptions;
// New module!
mod subscriptions_confirm;

//! tests/api/subscriptions_confirm.rs
use crate::helpers::spawn_app;

#[actix_rt::test]
async fn confirmations_without_token_are_rejected_with_a_400() {
// Arrange
let app = spawn_app().await;

// Act
let response = reqwest::get(&format!("{}/subscriptions/confirm", app.address))
.await
.unwrap();

// Assert
assert_eq!(response.status().as_u16(), 400);
}

Which fails as expected, given that we have no handler yet:

---- subscriptions_confirm::confirmations_without_token_are_rejected_with_a_400 stdout ----
thread 'subscriptions_confirm::confirmations_without_token_are_rejected_with_a_400'
panicked at 'assertion failed: `(left == right)`
left: `404`,
right: `400`'

7.7.4.2 Green Test

Let’s start with a dummy handler that returns 200 OK regardless of the incoming request:

//! src/routes/mod.rs

mod health_check;
mod subscriptions;
// New module!
mod subscriptions_confirm;

pub use health_check::*;
pub use subscriptions::*;
pub use subscriptions_confirm::*;

//! src/routes/subscriptions_confirm.rs

use actix_web::HttpResponse;

#[tracing::instrument(
name = "Confirm a pending subscriber",
)]
pub async fn confirm() -> HttpResponse {
HttpResponse::Ok().finish()
}

//! src/startup.rs
// [...]
use crate::routes::confirm;

fn run([...]) -> Result<Server, std::io::Error> {
// [...]
let server = HttpServer::new(move || {
App::new()
// [...]
.route("/subscriptions/confirm", web::get().to(confirm))
// [...]
})
// [...]
}

We should get a different error now when running cargo test:

---- subscriptions_confirm::confirmations_without_token_are_rejected_with_a_400 stdout ----
thread 'subscriptions_confirm::confirmations_without_token_are_rejected_with_a_400'
panicked at 'assertion failed: `(left == right)`
left: `200`,
right: `400`'

It worked!
Time to turn that 200 OK in a 400 Bad Request.
We want to ensure that there is a subscription_token query parameter: we can rely on another one actix-web’s extractors - Query.

//! src/routes/subscriptions_confirm.rs
use actix_web::{HttpResponse, web};

#[derive(serde::Deserialize)]
pub struct Parameters {
subscription_token: String
}

#[tracing::instrument(
name = "Confirm a pending subscriber",
skip(_parameters)
)]
pub async fn confirm(_parameters: web::Query<Parameters>) -> HttpResponse {
HttpResponse::Ok().finish()
}

The Parameters struct defines all the query parameters that we expect to see in the incoming request. It needs to implement serde::Deserialize to enable actix-web to build it from the incoming request path. It is enough to add a function parameter of type web::Query<Parameter> to confirm to instruct actix-web to only call the handler if the extraction was successful. If the extraction failed a 400 Bad Request is automatically returned to the caller.

Our test should now pass.

7.7.5 Connecting The Dots

Now that we have a GET /subscriptions/confirm handler we can try to perform the fully journey!

7.7.5.1 Red Test

We will behave like a user: we will call POST /subscriptions, we will extract the confirmation link from the outgoing email request (using the linkify machinery we already built) and then call it to confirm our subscription - expecting a 200 OK.
We will not be checking the status from the database (yet) - that is going to be our grand finale.

Let’s write it down:

//! tests/api/subscriptions_confirm.rs
// [...]
use reqwest::Url;
use wiremock::{ResponseTemplate, Mock};
use wiremock::matchers::{path, method};

#[actix_rt::test]
async fn the_link_returned_by_subscribe_returns_a_200_if_called() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.mount(&app.email_server)
.await;

app.post_subscriptions(body.into()).await;
let email_request = &app.email_server.received_requests().await.unwrap()[0];
let body: serde_json::Value = serde_json::from_slice(&email_request.body).unwrap();

// Extract the link from one of the request fields.
let get_link = |s: &str| {
let links: Vec<_> = linkify::LinkFinder::new()
.links(s)
.filter(|l| *l.kind() == linkify::LinkKind::Url)
.collect();
assert_eq!(links.len(), 1);
links[0].as_str().to_owned()
};
let raw_confirmation_link = &get_link(&body["HtmlBody"].as_str().unwrap());
let confirmation_link = Url::parse(raw_confirmation_link).unwrap();
// Let's make sure we don't call random APIs on the web
assert_eq!(confirmation_link.host_str().unwrap(), "127.0.0.1");

// Act
let response = reqwest::get(confirmation_link)
.await
.unwrap();

// Assert
assert_eq!(response.status().as_u16(), 200);
}

It fails with

thread 'subscriptions_confirm::the_link_returned_by_subscribe_returns_a_200_if_called'
panicked at 'assertion failed: `(left == right)`
left: `"my-api.com"`,
right: `"127.0.0.1"`'

There is a fair amount of code duplication going on here, but we will take care of it in due time.
Our primary focus is getting the test to pass now.

7.7.5.2 Green Test

Let’s begin by taking care of that URL issue.
It is currently hard-coded in

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn send_confirmation_email([...]) -> Result<(), reqwest::Error> {
let confirmation_link = "https://my-api.com/subscriptions/confirm";
// [...]
}

The domain and the protocol are going to vary according to the environment the application is running into: it will be http://127.0.0.1 for our tests, it should be a proper DNS record with HTTPS when our application is running in production.
The easiest way to get it right is to pass the domain in as a configuration value.
Let’s add a new field to ApplicationSettings:

//! src/configuration.rs
// [...]

#[derive(serde::Deserialize, Clone)]
pub struct ApplicationSettings {
#[serde(deserialize_with = "deserialize_number_from_string")]
pub port: u16,
pub host: String,
// New field!
pub base_url: String
}

# configuration/local.yaml
application:
base_url: "http://127.0.0.1"
# [...]

#! spec.yaml
# [...]
services:
- name: zero2prod
# [...]
envs:
# We use DO's APP_URL to inject the dynamically
# provisioned base url as an environment variable
- key: APP_APPLICATION__BASE_URL
scope: RUN_TIME
value: ${APP_URL}
# [...]
# [...]

Remember to apply the changes to DigitalOcean every time we touch spec.yaml: grab your app identifier via doctl apps list --format ID and then run doctl apps update $APP_ID --spec spec.yaml.

We now need to register the value in the application context - you should be familiar with the process at this point:

//! src/startup.rs
// [...]

impl Application {
pub async fn build(configuration: Settings) -> Result<Self, std::io::Error> {
// [...]
let server = run(
listener,
connection_pool,
email_client,
// New parameter!
configuration.application.base_url,
)?;

Ok(Self { port, server })
}

// [...]
}

// We need to define a wrapper type in order to retrieve the URL
// in the `subscribe` handler.
// Retrieval from the context, in actix-web, is type-based: using
// a raw `String` would expose us to conflicts.
pub struct ApplicationBaseUrl(pub String);

fn run(
listener: TcpListener,
db_pool: PgPool,
email_client: EmailClient,
// New parameter!
base_url: String,
) -> Result<Server, std::io::Error> {
// [...]
let base_url = Data::new(ApplicationBaseUrl(base_url));
let server = HttpServer::new(move || {
App::new()
// [...]
.app_data(base_url.clone())
})
// [...]
}

We can now access it in the request handler:

//! src/routes/subscriptions.rs
use crate::startup::ApplicationBaseUrl;
// [...]

#[tracing::instrument(
skip(form, pool, email_client, base_url),
[...]
)]
pub async fn subscribe(
// [...]
// New parameter!
base_url: web::Data<ApplicationBaseUrl>,
) -> HttpResponse {
// [...]
// Pass the application url
if send_confirmation_email(
&email_client,
new_subscriber,
&base_url.0
)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
// [...]
}

#[tracing::instrument(
skip(email_client, new_subscriber, base_url)
[...]
)]
pub async fn send_confirmation_email(
// [...]
// New parameter!
base_url: &str,
) -> Result<(), reqwest::Error> {
// Build a confirmation link with a dynamic root
let confirmation_link = format!("{}/subscriptions/confirm", base_url);
// [...]
}

Let’s run the test suite again:

thread 'subscriptions_confirm::the_link_returned_by_subscribe_returns_a_200_if_called'
panicked at 'called `Result::unwrap()` on an `Err` value:
reqwest::Error {
kind: Request,
url: Url {
scheme: "http",
host: Some(Ipv4(127.0.0.1)),
port: None,
path: "/subscriptions/confirm",
query: None,
fragment: None },
source: hyper::Error(
Connect,
ConnectError(
"tcp connect error",
Os {
code: 111,
kind: ConnectionRefused,
message: "Connection refused"
}
)
)
}'

The host is correct, but the reqwest::Client in our test is failing to establish a connection. What is going wrong?
If you look closely, you’ll notice port: None - we are sending our request to http://127.0.0.1/subscriptions/confirm without specifying the port our test server is listening on.
The tricky bit, here, is the sequence of events: we pass in the application_url configuration value before spinning up the server, therefore we do not know what port it is going to listen to (given that the port is randomised using 0!).
This is non-issue for production workloads where the DNS domain is enough - we’ll just patch it in the test.
Let’s store the application port in its own field within TestApp:

//! tests/api/helpers.rs
// [...]

pub struct TestApp {
// New field!
pub port: u16,
// [...]
}

pub async fn spawn_app() -> TestApp {
// [...]

let application = Application::build(configuration.clone())
.await
.expect("Failed to build application.");
let application_port = application.port();
let _ = tokio::spawn(application.run_until_stopped());

TestApp {
address: format!("http://localhost:{}", application_port),
port: application_port,
db_pool: get_connection_pool(&configuration.database)
.await
.expect("Failed to connect to the database"),
email_server,
}
}

We can then use it in the test logic to edit the confirmation link:

//! tests/api/subscriptions_confirm.rs
// [...]

#[actix_rt::test]
async fn the_link_returned_by_subscribe_returns_a_200_if_called() {
// [...]
let mut confirmation_link = Url::parse(raw_confirmation_link).unwrap();
assert_eq!(confirmation_link.host_str().unwrap(), "127.0.0.1");
// Let's rewrite the URL to include the port
confirmation_link.set_port(Some(app.port)).unwrap();

// [...]
}

Not the prettiest, but it gets the job done.
Let’s run the test again:

thread 'subscriptions_confirm::the_link_returned_by_subscribe_returns_a_200_if_called'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `200`'

We get a 400 Bad Request back because our confirmation link does not have a subscription_token query parameter attached.
Let’s fix it by hard-coding one for the time being:

//! src/routes/subscriptions.rs
// [...]

pub async fn send_confirmation_email([...]) -> Result<(), reqwest::Error> {
let confirmation_link = format!(
"{}/subscriptions/confirm?subscription_token=mytoken",
base_url
);
// [...]
}

The test passes!

7.7.5.3 Refactor

The logic to extract the two confirmation links from the outgoing email request is duplicated across two of our tests - we will likely add more that rely on it as we flesh out the remaining bits and pieces of this feature. It makes sense to extract it in its own helper function.

//! tests/api/helpers.rs
// [...]

/// Confirmation links embedded in the request to the email API.
pub struct ConfirmationLinks {
pub html: reqwest::Url,
pub plain_text: reqwest::Url
}

impl TestApp {
// [...]

/// Extract the confirmation links embedded in the request to the email API.
pub fn get_confirmation_links(
&self,
email_request: &wiremock::Request
) -> ConfirmationLinks {
let body: serde_json::Value = serde_json::from_slice(
&email_request.body
).unwrap();

// Extract the link from one of the request fields.
let get_link = |s: &str| {
let links: Vec<_> = linkify::LinkFinder::new()
.links(s)
.filter(|l| *l.kind() == linkify::LinkKind::Url)
.collect();
assert_eq!(links.len(), 1);
let raw_link = links[0].as_str().to_owned();
let mut confirmation_link = reqwest::Url::parse(&raw_link).unwrap();
// Let's make sure we don't call random APIs on the web
assert_eq!(confirmation_link.host_str().unwrap(), "127.0.0.1");
confirmation_link.set_port(Some(self.port)).unwrap();
confirmation_link
};

let html = get_link(&body["HtmlBody"].as_str().unwrap());
let plain_text = get_link(&body["TextBody"].as_str().unwrap());
ConfirmationLinks {
html,
plain_text
}
}
}

We are adding it as a method on TestApp in order to get access to the application port, which we need to inject into the links.
It could as well have been a free function taking both wiremock::Request and TestApp (or u16) as parameters - a matter of taste.

We can now massively simplify our two test cases:

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_sends_a_confirmation_email_with_a_link() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.mount(&app.email_server)
.await;

// Act
app.post_subscriptions(body.into()).await;

// Assert
let email_request = &app.email_server.received_requests().await.unwrap()[0];
let confirmation_links = app.get_confirmation_links(&email_request);

// The two links should be identical
assert_eq!(confirmation_links.html, confirmation_links.plain_text);
}

//! tests/api/subscriptions_confirm.rs
// [...]

#[actix_rt::test]
async fn the_link_returned_by_subscribe_returns_a_200_if_called() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.mount(&app.email_server)
.await;

app.post_subscriptions(body.into()).await;
let email_request = &app.email_server.received_requests().await.unwrap()[0];
let confirmation_links = app.get_confirmation_links(&email_request);

// Act
let response = reqwest::get(confirmation_links.html)
.await
.unwrap();

// Assert
assert_eq!(response.status().as_u16(), 200);
}

The intent of those two test cases is much clearer now.

7.7.6 Subscription Tokens

We are ready to tackle the elephant in the room: we need to start generating subscription tokens.

7.7.6.1 Red Test

We will add a new test case which builds on top of the work we just did: instead of asserting against the returned status code we will check the status of the subscriber stored in the database.

//! tests/api/subscriptions_confirm.rs
// [...]

#[actix_rt::test]
async fn clicking_on_the_confirmation_link_confirms_a_subscriber() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";

Mock::given(path("/email"))
.and(method("POST"))
.respond_with(ResponseTemplate::new(200))
.mount(&app.email_server)
.await;

app.post_subscriptions(body.into()).await;
let email_request = &app.email_server.received_requests().await.unwrap()[0];
let confirmation_links = app.get_confirmation_links(&email_request);

// Act
reqwest::get(confirmation_links.html)
.await
.unwrap()
.error_for_status()
.unwrap();

// Assert
let saved = sqlx::query!("SELECT email, name, status FROM subscriptions",)
.fetch_one(&app.db_pool)
.await
.expect("Failed to fetch saved subscription.");

assert_eq!(saved.email, "ursula_le_guin@gmail.com");
assert_eq!(saved.name, "le guin");
assert_eq!(saved.status, "confirmed");
}

The test fails, as expected:

thread 'subscriptions_confirm::clicking_on_the_confirmation_link_confirms_a_subscriber'
panicked at 'assertion failed: `(left == right)`
left: `"pending_confirmation"`,
right: `"confirmed"`'

7.7.6.2 Green Test

To get the previous test case to pass, we hard-coded a subscription token in the confirmation link:

//! src/routes/subscriptions.rs
// [...]

pub async fn send_confirmation_email([...]) -> Result<(), reqwest::Error> {
let confirmation_link = format!(
"{}/subscriptions/confirm?subscription_token=mytoken",
base_url
);
// [...]
}

Let’s refactor send_confirmation_email to take the token as a parameter - it will make it easier to add the generation logic upstream.

//! src/routes/subscriptions.rs
// [...]

#[tracing::instrument([...])]
pub async fn subscribe([...]) -> HttpResponse {
// [...]
if send_confirmation_email(
&email_client,
new_subscriber,
&base_url.0,
// New parameter!
"mytoken"
)
.await
.is_err() {
return HttpResponse::InternalServerError().finish();
}
// [...]
}

#[tracing::instrument(
name = "Send a confirmation email to a new subscriber",
skip(email_client, new_subscriber, base_url, subscription_token)
)]
pub async fn send_confirmation_email(
email_client: &EmailClient,
new_subscriber: NewSubscriber,
base_url: &str,
// New parameter!
subscription_token: &str
) -> Result<(), reqwest::Error> {
let confirmation_link = format!(
"{}/subscriptions/confirm?subscription_token={}",
base_url,
subscription_token
);
// [...]
}

Our subscription tokens are not passwords: they are single-use and they do not grant access to protected information.[6] We need them to be hard enough to guess while keeping in mind that the worst-case scenario is an unwanted newsletter subscription landing in someone’s inbox.

Given our requirements it should be enough to use a cryptographically secure pseudo-random number generator - a CSPRNG, if you are into obscure acronyms.
Every time we need to generate a subscription token we can sample a sufficiently-long sequence of alphanumeric characters.

To pull it off we need to add rand as a dependency:

#! Cargo.toml
# [...]

[dependencies]
# [...]
# We need the `std_rng` to get access to the PRNG we want
rand = { version = "0.8", features=["std_rng"] }

//! src/routes/subscriptions.rs
use rand::distributions::Alphanumeric;
use rand::{thread_rng, Rng};
// [...]

/// Generate a random 25-characters-long case-sensitive subscription token.
fn generate_subscription_token() -> String {
let mut rng = thread_rng();
std::iter::repeat_with(|| rng.sample(Alphanumeric))
.map(char::from)
.take(25)
.collect()
}

Using 25 characters we get roughly ~10^45 possible tokens - it should be more than enough for our use case.
To check if a token is valid in GET /subscriptions/confirm we need POST /subscriptions to store the newly minted tokens in the database.
The table we added for this purpose, subscription_tokens, has two columns: subscription_token and subscriber_id.

We are currently generating the subscriber identifier in insert_subscriber but we never return it to the caller:

#[tracing::instrument([...])]
pub async fn insert_subscriber([...]) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"[...]"#,
// The subscriber id, never returned or bound to a variable
Uuid::new_v4(),
// [...]
)
// [...]
}

Let’s refactor insert_subscriber to give us back the identifier:

#[tracing::instrument([...])]
pub async fn insert_subscriber([...]) -> Result<Uuid, sqlx::Error> {
let subscriber_id = Uuid::new_v4();
sqlx::query!(
r#"[...]"#,
subscriber_id,
// [...]
)
// [...]
Ok(subscriber_id)
}

We can now tie everything together:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe([...]) -> HttpResponse {
// [...]
let subscriber_id = match insert_subscriber(&mut transaction, &new_subscriber).await {
Ok(subscriber_id) => subscriber_id,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
let subscription_token = generate_subscription_token();
if store_token(&mut transaction, subscriber_id, &subscription_token)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
if send_confirmation_email(
&email_client,
new_subscriber,
&base_url.0,
&subscription_token,
)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
HttpResponse::Ok().finish()
}

#[tracing::instrument(
name = "Store subscription token in the database",
skip(subscription_token, pool)
)]
pub async fn store_token(
pool: &PgPool,
subscriber_id: Uuid,
subscription_token: &str,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"INSERT INTO subscription_tokens (subscription_token, subscriber_id)
VALUES ($1, $2)"#,
subscription_token,
subscriber_id
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

We are done on POST /subscriptions, let’s shift to GET /subscription/confirm:

//! src/routes/subscriptions_confirm.rs
use actix_web::{HttpResponse, web};

#[derive(serde::Deserialize)]
pub struct Parameters {
subscription_token: String
}

#[tracing::instrument(
name = "Confirm a pending subscriber",
skip(_parameters)
)]
pub async fn confirm(_parameters: web::Query<Parameters>) -> HttpResponse {
HttpResponse::Ok().finish()
}

We need to:

- get a reference to the database pool;

- retrieve the subscriber id associated with the token (if one exists);

- change the subscriber status to confirmed.

Nothing we haven’t done before - let’s get cracking!

use actix_web::{web, HttpResponse};
use sqlx::PgPool;
use uuid::Uuid;

#[derive(serde::Deserialize)]
pub struct Parameters {
subscription_token: String,
}

#[tracing::instrument(
name = "Confirm a pending subscriber",
skip(parameters, pool)
)]
pub async fn confirm(
parameters: web::Query<Parameters>,
pool: web::Data<PgPool>,
) -> HttpResponse {
let id = match get_subscriber_id_from_token(&pool, &parameters.subscription_token).await {
Ok(id) => id,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
match id {
// Non-existing token!
None => HttpResponse::Unauthorized().finish(),
Some(subscriber_id) => {
if confirm_subscriber(&pool, subscriber_id).await.is_err() {
return HttpResponse::InternalServerError().finish();
}
HttpResponse::Ok().finish()
}
}
}

#[tracing::instrument(
name = "Mark subscriber as confirmed",
skip(subscriber_id, pool)
)]
pub async fn confirm_subscriber(
pool: &PgPool,
subscriber_id: Uuid
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"UPDATE subscriptions SET status = 'confirmed' WHERE id = $1"#,
subscriber_id,
)
.execute(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

#[tracing::instrument(
name = "Get subscriber_id from token",
skip(subscription_token, pool)
)]
pub async fn get_subscriber_id_from_token(
pool: &PgPool,
subscription_token: &str,
) -> Result<Option<Uuid>, sqlx::Error> {
let result = sqlx::query!(
r#"SELECT subscriber_id FROM subscription_tokens WHERE subscription_token = $1"#,
subscription_token,
)
.fetch_optional(pool)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(result.map(|r| r.subscriber_id))
}

Is it enough? Did we miss anything during the journey?
There is only one way to find out.

cargo test

Running target/debug/deps/api-5a717281b98f7c41
running 10 tests
[...]

test result: ok. 10 passed; 0 failed; finished in 0.92s

Oh, yes! It works!

7.8 Database Transactions

7.8.1 All Or Nothing

It is too soon to declare victory though.
Our POST /subscriptions handler has grown in complexity - we are now performing two INSERT queries against our Postgres database: one to store the details of the new subscriber, one to store the newly generated subscription token.
What happens if the application crashes between those two operations?

The first query might complete successfully, but the second one might never be executed.

There are three possible states for our database after an invocation of POST /subscriptions:

- a new subscriber and its token have been persisted;

- a new subscriber has been persisted, without a token;

- nothing has been persisted.

The more queries you have, the worse it gets to reason about the possible end states of our database.

Relational databases (and a few others) provide a mechanism to mitigate this issue: transactions.

Transactions are a way to group together related operations in a single unit of work.
The database guarantees that all operations within a transaction will succeed or fail together: the database will never be left in a state where the effect of only a subset of the queries in a transaction is visible.

Going back to our example, if we wrap the two INSERT queries in a transaction we now have two possible end states:

- a new subscriber and its token have been persisted;

- nothing has been persisted.

Much easier to deal with.

7.8.2 Transactions In Postgres

To start a transaction in Postgres you use a BEGIN statement. All queries after BEGIN are part of the transaction.
The transaction is then finalised with a COMMIT statement.

We have actually already used a transaction in one of our migration scripts!

BEGIN;
UPDATE subscriptions SET status = 'confirmed' WHERE status IS NULL;
ALTER TABLE subscriptions ALTER COLUMN status SET NOT NULL;
COMMIT;

If any of the queries within a transaction fails the database rolls back: all changes performed by previous queries are reverted, the operation is aborted.
You can also explicitly trigger a rollback with the ROLLBACK statement.

Transactions are a deep topic: they not only provide a way to convert multiple statements into an all-or-nothing operation, they also hide the effect of uncommitted changes from other queries that might be running, concurrently, against the same tables.
As your needs evolves, you will often want to explicitly choose the isolation level of your transactions to fine-tune the concurrency guarantees provided by the database on your operations. Getting a good grip on the different kinds of concurrency-related issues (e.g. dirty reads, phantom reads, etc.) becomes more and more important as your system grows in scale and complexity.

I can’t recommend “Designing Data Intensive Applications” enough if you want to learn more about these topics.

7.8.3 Transactions In Sqlx

Back to the code: how do we leverage transactions in sqlx?

You don’t have to manually write a BEGIN statement: transactions are so central to the usage of relational databases that sqlx provides a dedicated API.
By calling begin on our pool we acquire a connection from the pool and kick off a transaction:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe([...]) -> HttpResponse {
let new_subscriber = // [...]
let mut transaction = match pool.begin().await {
Ok(transaction) => transaction,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
// [...]

begin, if successful, returns a Transaction struct.
A mutable reference to a Transaction implements sqlx’s Executor trait therefore it can be used to run queries. All queries run using a Transaction as executor become of the transaction.

Let’s pass transaction down to insert_subscriber and store_token instead of pool:

//! src/routes/subscriptions.rs
use sqlx::{Postgres, Transaction};
// [...]

#[tracing::instrument([...])]
pub async fn subscribe([...]) -> HttpResponse {
// [...]
let mut transaction = match pool.begin().await {
Ok(transaction) => transaction,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
let subscriber_id = match insert_subscriber(&mut transaction, &new_subscriber).await {
Ok(subscriber_id) => subscriber_id,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
let subscription_token = generate_subscription_token();
if store_token(&mut transaction, subscriber_id, &subscription_token)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
// [...]
}

#[tracing::instrument(
name = "Saving new subscriber details in the database",
skip(new_subscriber, transaction)
)]
pub async fn insert_subscriber(
transaction: &mut Transaction<'_, Postgres>,
new_subscriber: &NewSubscriber,
) -> Result<Uuid, sqlx::Error> {
let subscriber_id = Uuid::new_v4();
sqlx::query!([...])
.execute(transaction)
// [...]
}

#[tracing::instrument(
name = "Store subscription token in the database",
skip(subscription_token, transaction)
)]
pub async fn store_token(
transaction: &mut Transaction<'_, Postgres>,
subscriber_id: Uuid,
subscription_token: &str,
) -> Result<(), sqlx::Error> {
sqlx::query!([..])
.execute(transaction)
// [...]
}

If you run cargo test now you will see something funny: some of our tests are failing!
Why is that happening?

As we discussed, a transaction has to either be committed or rolled back.
Transaction exposes two dedicated methods: Transaction::commit, to persist changes, and Transaction::rollback, to abort the whole operation.
We are not calling either - what happens in that case?

We can look at sqlx’s source code to understand better.
In particular, Transaction’s Drop implementation:

impl<'c, DB> Drop for Transaction<'c, DB>
where
DB: Database,
{
fn drop(&mut self) {
if self.open {
// starts a rollback operation

// what this does depends on the database but generally
// this means we queue a rollback operation that will
// happen on the next asynchronous invocation of the
// underlying connection (including if the connection
// is returned to a pool)
DB::TransactionManager::start_rollback(&mut self.connection);
}
}
}

self.open is an internal boolean flag attached to the connection used to begin the transaction and run the queries attached to it.
When a transaction is created, using begin, it is set to true until either rollback or commit are called:

impl<'c, DB> Transaction<'c, DB>
where
DB: Database,
{
pub(crate) fn begin(
conn: impl Into<MaybePoolConnection<'c, DB>>,
) -> BoxFuture<'c, Result<Self, Error>> {
let mut conn = conn.into();

Box::pin(async move {
DB::TransactionManager::begin(&mut conn).await?;

Ok(Self {
connection: conn,
open: true,
})
})
}

pub async fn commit(mut self) -> Result<(), Error> {
DB::TransactionManager::commit(&mut self.connection).await?;
self.open = false;

Ok(())
}

pub async fn rollback(mut self) -> Result<(), Error> {
DB::TransactionManager::rollback(&mut self.connection).await?;
self.open = false;

Ok(())
}
}

In other words: if commit or rollback have not been called before the Transaction object goes out of scope (i.e. Drop is invoked), a rollback command is queued to be executed as soon as an opportunity arises.[7]
That is why our tests are failing: we are using a transaction but we are not explicitly committing the changes. When the connection goes back into the pool, at the end of our request handler, all changes are rolled back and our test expectations are not met.

We can fix it by adding a one-liner to subscribe:

//! src/routes/subscriptions.rs
use sqlx::{Postgres, Transaction};
// [...]

#[tracing::instrument([...])]
pub async fn subscribe([...]) -> HttpResponse {
// [...]
let mut transaction = match pool.begin().await {
Ok(transaction) => transaction,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
let subscriber_id = match insert_subscriber(&mut transaction, &new_subscriber).await {
Ok(subscriber_id) => subscriber_id,
Err(_) => return HttpResponse::InternalServerError().finish(),
};
let subscription_token = generate_subscription_token();
if store_token(&mut transaction, subscriber_id, &subscription_token)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
if transaction.commit().await.is_err() {
return HttpResponse::InternalServerError().finish();
}
// [...]
}

The test suite should succeed once again.
Go ahead and deploy the application: seeing a feature working in a live environment adds a whole new level of satisfaction!

7.9 Summary

This chapter was a long journey, but you have come a long way as well!
The skeleton of our application has started to shape up, starting with our test suite. Features are moving along as well: we now have a functional subscription flow, with a proper confirmation email.
More importantly: we are getting into the rhythm of writing Rust code.
The very end of the chapter has been a long pair programming session where we have made significant progress without introducing many new concepts.

This is a great moment to go off and explore a bit on your own: improve on what we built so far!
There are plenty of opportunities:

- What happens if a user tries to subscribe twice? Make sure that they receive two confirmation emails;

- What happens if a user clicks on a confirmation link twice?

- What happens if the subscription token is well-formatted but non-existent?

- Add validation on the incoming token, we are currently passing the raw user input straight into a query (thanks sqlx for protecting us from SQL injections <3);

- Use a proper templating solution for our emails (e.g. tera);

- Anything that comes to your mind!

It takes deliberate practice to achieve mastery.

1. Refer to the test organization chapter in the Rust book for more details.↩

2. You can actually override the default test framework and plug your own. Look at libtest-mimic as an example!↩

3. See this article as an example with some numbers (1.9x speedup!). You should always benchmark the approach on your specific codebase before committing.↩

4. You might recall that we set the number of replicas to 1 in chapter 5 to reduce the bill while experimenting. Even if you are running a single replica there is load balancer between your users and your application. Deployments are still performed using a rolling update strategy.↩

5. This is true as long as the platform is also capable of automatically provisioning a new replica of the application when the number of healthy instances falls below a pre-determined threshold.↩

6. You could say that our token is a nonce.↩

7. Rust does not currently support asynchronous destructors, a.k.a. AsyncDrop. There have been some discussions on the topic, but there is no consensus yet. This is a constraint on sqlx: when Transaction goes out of scope it can enqueue a rollback operation, but it cannot execute it immediately! Is it ok? Is it a sound API? There are different views - see diesel’s async issue for an overview. My personal view is that the benefits brought by sqlx to the table offset the risks, but you should make an informed decision taking into account the tradeoffs of your application and use case.↩

8 Error Handling

To send a confirmation email we had to stitch together multiple operations: validation of user input, email dispatch, various database queries.
They all have one thing in common: they may fail.

In Chapter 6 we discussed the building blocks of error handling in Rust - Result and the ? operator.
We left many questions unanswered: how do errors fit within the broader architecture of our application? What does a good error look like? Who are errors for? Should we use a library? Which one?

An in-depth analysis of error handling patterns in Rust will be the sole focus of this chapter.

8.1 What Is The Purpose Of Errors?

Let’s start with an example:

//! src/routes/subscriptions.rs
// [...]

pub async fn store_token(
transaction: &mut Transaction<'_, Postgres>,
subscriber_id: Uuid,
subscription_token: &str,
) -> Result<(), sqlx::Error> {
sqlx::query!(
r#"
INSERT INTO subscription_tokens (subscription_token, subscriber_id)
VALUES ($1, $2)
"#,
subscription_token,
subscriber_id
)
.execute(transaction)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
Ok(())
}

We are trying to insert a row into the subscription_tokens table in order to store a newly-generated token against a subscriber_id.
execute is a fallible operation: we might have a network issue while talking to the database, the row we are trying to insert might violate some table constraints (e.g. uniqueness of the primary key), etc.

8.1.1 Internal Errors

8.1.1.1 Enable The Caller To React

The caller of execute most likely wants to be informed if a failure occurs - they need to react accordingly, e.g. retry the query or propagate the failure upstream using ?, as in our example.

Rust leverages the type system to communicate that an operation may not succeed: the return type of execute is Result, an enum.

pub enum Result<Success, Error> {
Ok(Success),
Err(Error)
}

The caller is then forced by the compiler to express how they plan to handle both scenarios - success and failure.

If our only goal was to communicate to the caller that an error happened, we could use a simpler definition for Result:

pub enum ResultSignal<Success> {
Ok(Success),
Err
}

There would be no need for a generic Error type - we could just check that execute returned the Err variant, e.g.

let outcome = sqlx::query!(/* ... */)
.execute(transaction)
.await;
if outcome == ResultSignal::Err {
// Do something if it failed
}

This works if there is only one failure mode. Truth is, operations can fail in multiple ways and we might want to react differently depending on what happened.
Let’s look at the skeleton of sqlx::Error, the error type for execute:

//! sqlx-core/src/error.rs

pub enum Error {
Configuration(/* */),
Database(/* */),
Io(/* */),
Tls(/* */),
Protocol(/* */),
RowNotFound,
TypeNotFound {/* */},
ColumnIndexOutOfBounds {/* */},
ColumnNotFound(/* */),
ColumnDecode {/* */},
Decode(/* */),
PoolTimedOut,
PoolClosed,
WorkerCrashed,
Migrate(/* */),
}

Quite a list, ain’t it?
sqlx::Error is implemented as an enum to allow users to match on the returned error and behave differently depending on the underlying failure mode. For example, you might want to retry a PoolTimedOut while you will probably give up on a ColumnNotFound.

8.1.1.2 Help An Operator To Troubleshoot

What if an operation has a single failure mode - should we just use () as error type?

Err(()) might be enough for the caller to determine what to do - e.g. return a 500 Internal Server Error to the user.

But control flow is not the only purpose of errors in an application.
We expect errors to carry enough context about the failure to produce a report for an operator (e.g. the developer) that contains enough details to go and troubleshoot the issue.

What do we mean by report?
In a backend API like ours it will usually be a log event.
In a CLI it could be an error message shown in the terminal when a --verbose flag is used.

The implementation details may vary, the purpose stays the same: help a human understand what is going wrong.
That’s exactly what we are doing in the initial code snippet:

//! src/routes/subscriptions.rs
// [...]

pub async fn store_token(/* */) -> Result<(), sqlx::Error> {
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
// [...]
}

If the query fails, we grab the error and emit a log event. We can then go and inspect the error logs when investigating the database issue.

8.1.2 Errors At The Edge

8.1.2.1 Help A User To Troubleshoot

So far we focused on the internals of our API - functions calling other functions and operators trying to make sense of the mess after it happened.
What about users?

Just like operators, users expect the API to signal when a failure mode is encountered.

What does a user of our API see when store_token fails?
We can find out by looking at the request handler:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
// [...]
if store_token(&mut transaction, subscriber_id, &subscription_token)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
// [...]
}

They receive an HTTP response with no body and a 500 Internal Server Error status code.

The status code fulfills the same purpose of the error type in store_token: it is a machine-parsable piece of information that the caller (e.g. the browser) can use to determine what to do next (e.g. retry the request assuming it’s a transient failure).

What about the human behind the browser? What are we telling them?
Not much, the response body is empty.
That is actually a good implementation: the user should not have to care about the internals of the API they are calling - they have no mental model of it and no way to determine why it is failing. That’s the realm of the operator.
We are omitting those details by design.

In other circumstances, instead, we need to convey additional information to the human user. Let’s look at our input validation for the same endpoint:

//! src/routes/subscriptions.rs

#[derive(serde::Deserialize)]
pub struct FormData {
email: String,
name: String,
}

impl TryInto<NewSubscriber> for FormData {
type Error = String;

fn try_into(self) -> Result<NewSubscriber, Self::Error> {
let name = SubscriberName::parse(self.name)?;
let email = SubscriberEmail::parse(self.email)?;
Ok(NewSubscriber { email, name })
}
}

We received an email address and a name as data attached to the form submitted by the user. Both fields are going through an additional round of validation - SubscriberName::parse and SubscriberEmail::parse. Those two methods are fallible - they return a String as error type to explain what has gone wrong:

//! src/domain/subscriber_email.rs
// [...]

impl SubscriberEmail {
pub fn parse(s: String) -> Result<SubscriberEmail, String> {
if validate_email(&s) {
Ok(Self(s))
} else {
Err(format!("{} is not a valid subscriber email.", s))
}
}
}

It is, I must admit, not the most useful error message: we are telling the user that the email address they entered is wrong, but we are not helping them to determine why.
In the end, it doesn’t matter: we are not sending any of that information to the user as part of the response of the API - they are getting a 400 Bad Request with no body.

//! src/routes/subscription.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
let new_subscriber = match form.0.try_into() {
Ok(form) => form,
Err(_) => return HttpResponse::BadRequest().finish(),
};
// [...]

This is a poor error: the user is left in the dark and cannot adapt their behaviour as required.

8.1.3 Summary

Let’s summarise what we uncovered so far.
Errors serve two[1] main purposes:

- Control flow (i.e. determine what do next);

- Reporting (e.g. investigate, after the fact, what went wrong on).

We can also distinguish errors based on their location:

- Internal (i.e. a function calling another function within our application);

- At the edge (i.e. an API request that we failed to fulfill).

Control flow is scripted: all information required to take a decision on what to do next must be accessible to a machine.
We use types (e.g. enum variants), methods and fields for internal errors.
We rely on status codes for errors at the edge.

Error reports, instead, are primarily consumed by humans.
The content has to be tuned depending on the audience.
An operator has access to the internals of the system - they should be provided with as much context as possible on the failure mode.
A user sits outside the boundary of the application[2]: they should only be given the amount of information required to adjust their behaviour if necessary (e.g. fix malformed inputs).

We can visualise this mental model using a 2x2 table with Location as columns and Purpose as rows:

Internal

At the edge

Control Flow

Types, methods, fields

Status codes

Reporting

Logs/traces

Response body

We will spend the rest of the chapter improving our error handling strategy for each of the cells in the table.

8.2 Error Reporting For Operators

Let’s start with error reporting for operators.
Are we doing a good job with logging right now when it comes to errors?

Let’s write a quick test to find out:

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_fails_if_there_is_a_fatal_database_error() {
// Arrange
let app = spawn_app().await;
let body = "name=le%20guin&email=ursula_le_guin%40gmail.com";
// Sabotage the database
sqlx::query!("ALTER TABLE subscription_tokens DROP COLUMN subscription_token;",)
.execute(&app.db_pool)
.await
.unwrap();

// Act
let response =  app.post_subscriptions(body.into()).await;

// Assert
assert_eq!(response.status().as_u16(), 500);
}

The test passes straight away - let’s look at the log emitted by the application[3].

Make sure you are running on tracing-actix-web 0.4.0-beta.4, tracing-bunyan-formatter 0.2.4 and actix-web 4.0.0-beta.5!

# sqlx logs are a bit spammy, cutting them out to reduce noise
export RUST_LOG="sqlx=error,info"
export TEST_LOG=enabled
cargo t subscribe_fails_if_there_is_a_fatal_database_error | bunyan

The output, once you focus on what matters, is the following:

INFO: [HTTP REQUEST - START]
INFO: [ADDING A NEW SUBSCRIBER - START]
INFO: [SAVING NEW SUBSCRIBER DETAILS IN THE DATABASE - START]
INFO: [SAVING NEW SUBSCRIBER DETAILS IN THE DATABASE - END]
INFO: [STORE SUBSCRIPTION TOKEN IN THE DATABASE - START]
ERROR: [STORE SUBSCRIPTION TOKEN IN THE DATABASE - EVENT] Failed to execute query:
Database(PgDatabaseError {
severity: Error,
code: "42703",
message:
"column 'subscription_token' of relation
'subscription_tokens' does not exist",
...
})
target=zero2prod::routes::subscriptions
INFO: [STORE SUBSCRIPTION TOKEN IN THE DATABASE - END]
INFO: [ADDING A NEW SUBSCRIBER - END]
ERROR: [HTTP REQUEST - EVENT] Error encountered while
processing the incoming HTTP request: ""
exception.details="",
exception.message="",
target=tracing_actix_web::middleware
INFO: [HTTP REQUEST - END]
exception.details="",
exception.message="",
target=tracing_actix_web::root_span_builder,
http.status_code=500

How do you read something like this?
Ideally, you start from the outcome: the log record emitted at the end of request processing. In our case, that is:

INFO: [HTTP REQUEST - END]
exception.details="",
exception.message="",
target=tracing_actix_web::root_span_builder,
http.status_code=500

What does that tell us?
The request returned a 500 status code - it failed.
We don’t learn a lot more than that: both exception.details and exception.message are empty.

The situation does not get much better if we look at the next log, emitted by tracing_actix_web:

ERROR: [HTTP REQUEST - EVENT] Error encountered while
processing the incoming HTTP request: ""
exception.details="",
exception.message="",
target=tracing_actix_web::middleware

No actionable information whatsoever. Logging “Oops! Something went wrong!” would have been just as useful.

We need to keep looking, all the way to the last remaining error log:

ERROR: [STORE SUBSCRIPTION TOKEN IN THE DATABASE - EVENT] Failed to execute query:
Database(PgDatabaseError {
severity: Error,
code: "42703",
message:
"column 'subscription_token' of relation
'subscription_tokens' does not exist",
...
})
target=zero2prod::routes::subscriptions

Something went wrong when we tried talking to the database - we were expecting to see a subscription_token column in the subscription_tokens table but, for some reason, it was not there.
This is actually useful!

Is it the cause of the 500 though?
Difficult to say just by looking at the logs - a developer will have to clone the codebase, check where that log line is coming from and make sure that it’s indeed the cause of the issue.
It can be done, but it takes time: it would be much easier if the [HTTP REQUEST - END] log record reported something useful about the underlying root cause in exception.details and exception.message.

8.2.1 Keeping Track Of The Error Root Cause

To understand why the log records coming out tracing_actix_web are so poor we need to inspect (again) our request handler and store_token:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> HttpResponse {
// [...]
if store_token(&mut transaction, subscriber_id, &subscription_token)
.await
.is_err()
{
return HttpResponse::InternalServerError().finish();
}
// [...]
}

pub async fn store_token(/* */) -> Result<(), sqlx::Error> {
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
// [...]
}

The useful error log we found is indeed the one emitted by that tracing::error call - the error message includes the sqlx::Error returned by execute.
We propagate the error upwards using the ? operator, but the chain breaks in subscribe - we discard the error we received from store_token and build a bare 500 response.

HttpResponse::InternalServerError().finish() is the only thing that actix_web and tracing_actix_web::TracingLogger get to access when they are about to emit their respective log records. The error does not contain any context about the underlying root cause, therefore the log records are equally useless.

How do we fix it?

We need to start leveraging the error handling machinery exposed by actix_web - in particular, actix_web::Error. According to the documentation:

actix_web::Error is used to carry errors from std::error through actix_web in a convenient way.

It sounds exactly like what we are looking for. How do we build an instance of actix_web::Error?
The documentation states that

actix_web::Error can be created by converting errors with into().

A bit indirect, but we can figure it out[4].
The only From/Into implementation that we can use, browsing the ones listed in the documentation, seems to be this one:

/// Build an `actix_web::Error` from any error that implements `ResponseError`
impl<T: ResponseError + 'static> From<T> for Error {
fn from(err: T) -> Error {
Error {
cause: Box::new(err),
}
}
}

ResponseError is a trait exposed by actix_web:

/// Errors that can be converted to `Response`.
pub trait ResponseError: fmt::Debug + fmt::Display {
/// Response's status code.
///
/// The default implementation returns an internal server error.
fn status_code(&self) -> StatusCode;

/// Create a response from the error.
///
/// The default implementation returns an internal server error.
fn error_response(&self) -> Response;
}

We just need to implement it for our errors!
actix_web provides a default implementation for both methods that returns a 500 Internal Server Error - exactly what we need. Therefore it’s enough to write:

//! src/routes/subscriptions.rs
use actix_web::ResponseError;
// [...]

impl ResponseError for sqlx::Error {}

The compiler is not happy:

error[E0117]: only traits defined in the current crate
can be implemented for arbitrary types
--> src/routes/subscriptions.rs:162:1
|
162 | impl ResponseError for sqlx::Error {}
| ^^^^^^^^^^^^^^^^^^^^^^^-----------
| |                      |
| |                      `sqlx::Error` is not defined in the current crate
| impl doesn't use only types from inside the current crate
|
= note: define and implement a trait or new type instead

We just bumped into Rust’s orphan rule: it is forbidden to implement a foreign trait for a foreign type, where foreign stands for “from another crate”.
This restriction is meant to preserve coherence: imagine if you added a dependency that defined its own implementation of ResponseError for sqlx::Error - which one should the compiler use when the trait methods are invoked?

Orphan rule aside, it would still be a mistake for us to implement ResponseError for sqlx::Error.
We want to return a 500 Internal Server Error when we run into a sqlx::Error while trying to persist a subscriber token.
In another circumstance we might wish to handle a sqlx::Error differently.

We should follow the compiler’s suggestion: define a new type to wrap sqlx::Error.

//! src/routes/subscriptions.rs
// [...]

//                                    Using the new error type!
pub async fn store_token(/* */) -> Result<(), StoreTokenError> {
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
// [...]
// Wrapping the underlying error
StoreTokenError(e)
})?;
// [...]
}

// A new error type, wrapping a sqlx::Error
pub struct StoreTokenError(sqlx::Error);

impl ResponseError for StoreTokenError {}

It doesn’t work, but for a different reason:

error[E0277]: `StoreTokenError` doesn't implement `std::fmt::Display`
--> src/routes/subscriptions.rs:164:6
|
164 | impl ResponseError for StoreTokenError {}
|      ^^^^^^^^^^^^^
`StoreTokenError` cannot be formatted with the default formatter
|
|
59  | pub trait ResponseError: fmt::Debug + fmt::Display {
|                                       ------------
|           required by this bound in `ResponseError`
|
= help: the trait `std::fmt::Display` is not implemented for `StoreTokenError`

error[E0277]: `StoreTokenError` doesn't implement `std::fmt::Debug`
--> src/routes/subscriptions.rs:164:6
|
164 | impl ResponseError for StoreTokenError {}
|      ^^^^^^^^^^^^^
`StoreTokenError` cannot be formatted using `{:?}`
|
|
59  | pub trait ResponseError: fmt::Debug + fmt::Display {
|                          ----------
required by this bound in `ResponseError`
|
= help: the trait `std::fmt::Debug` is not implemented for `StoreTokenError`
= note: add `#[derive(Debug)]` or manually implement `std::fmt::Debug`

We are missing two trait implementations on StoreTokenError: Debug and Display.
Both traits are concerned with formatting, but they serve a different purpose.
Debug should return a programmer-facing representation, as faithful as possible to the underlying type structure, to help with debugging (as the name implies). Almost all public types should implement Debug.
Display, instead, should return a user-facing representation of the underlying type. Most types do not implement Display and it cannot be automatically implemented with a #[derive(Display)] attribute.

When working with errors, we can reason about the two traits as follows: Debug returns as much information as possible while Display gives us a brief description of the failure we encountered, with the essential amount of context.

Let’s give it a go for StoreTokenError:

//! src/routes/subscriptions.rs
// [...]

// We derive `Debug`, easy and painless.
#[derive(Debug)]
pub struct StoreTokenError(sqlx::Error);

impl std::fmt::Display for StoreTokenError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(
f,
"A database error was encountered while \
trying to store a subscription token."
)
}
}

It compiles!
We can now leverage it in our request handler:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, actix_web::Error> {
// [...]
// The `?` operator transparently invokes the `Into` trait
// on our behalf - we don't need an explicit `map_err` anymore.
store_token(/* */).await?;
// [...]
}

Let’s look at our logs again:

# sqlx logs are a bit spammy, cutting them out to reduce noise
export RUST_LOG="sqlx=error,info"
export TEST_LOG=enabled
cargo t subscribe_fails_if_there_is_a_fatal_database_error | bunyan

...
INFO: [HTTP REQUEST - END]
exception.details= StoreTokenError(
Database(
PgDatabaseError {
severity: Error,
code: "42703",
message:
"column 'subscription_token' of relation
'subscription_tokens' does not exist",
...
}
)
)
exception.message=
"A database failure was encountered while
trying to store a subscription token.",
target=tracing_actix_web::root_span_builder,
http.status_code=500

Much better!
The log record emitted at the end of request processing now contains both an in-depth and brief description of the error that caused the application to return a 500 Internal Server Error to the user.
It is enough to look at this log record to get a pretty accurate picture of everything that matters for this request.

8.2.2 The Error Trait

So far we moved forward by following the compiler suggestions, trying to satisfy the constraints imposed on us by actix-web when it comes to error handling.
Let’s step back to look at the bigger picture: what should an error look like in Rust (not considering the specifics of actix-web)?

Rust’s standard library has a dedicated trait, Error.

pub trait Error: Debug + Display {
/// The lower-level source of this error, if any.
fn source(&self) -> Option<&(dyn Error + 'static)> {
None
}
}

It requires an implementation of Debug and Display, just like ResponseError.
It also gives us the option to implement a source method that returns the underlying cause of the error, if any.

What is the point of implementing the Error trait at all for our error type?
It is not required by Result - any type can be used as error variant there.

pub enum Result<T, E> {
/// Contains the success value
Ok(T),

/// Contains the error value
Err(E),
}

The Error trait is, first and foremost, a way to semantically mark our type as being an error. It helps a reader of our codebase to immediately spot its purpose.
It is also a way for the Rust community to standardise on the minimum requirements for a good error:

- it should provide different representations (Debug and Display), tuned to different audiences;

- it should be possible to look at the underlying cause of the error, if any (source).

This list is still evolving - e.g. there is an unstable backtrace method.
Error handling is an active area of research in the Rust community - if you are interested in staying on top of what is coming next I strongly suggest you to keep an eye on the Rust Error Handling Working Group.

By providing a good implementation of all the optional methods we can fully leverage the error handling ecosystem - functions that have been designed to work with errors, generically. We will be writing one in a couple of sections!

8.2.2.1 Trait Objects

Before we work on implementing source, let’s take a closer look at its return - Option<&(dyn Error + 'static)>.
dyn Error is a trait object[5] - a type that we know nothing about apart from the fact that it implements the Error trait.
Trait objects, just like generic type parameters, are a way to achieve polymorphism in Rust: invoke different implementations of the same interface. Generic types are resolved at compile-time (static dispatch), trait objects incur a runtime cost (dynamic dispatch).

Why does the standard library return a trait object?
It gives developers a way to access the underlying root cause of current error while keeping it opaque.
It does not leak any information about the type of the underlying root cause - you only get access to the methods exposed by the Error trait[6]: different representations (Debug, Display), the chance to go one level deeper in the error chain using source.

8.2.2.2 Error::source

Let’s implement Error for StoreTokenError:

//! src/routes/subscriptions.rs
// [..]

impl std::error::Error for StoreTokenError {
fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
// The compiler transparently casts `&sqlx::Error` into a `&dyn Error`
Some(&self.0)
}
}

source is useful when writing code that needs to handle a variety of errors: it provides a structured way to navigate the error chain without having to know anything about the specific error type you are working with.

If we look at our log record, the causal relationship between StoreTokenError and sqlx::Error is somewhat implicit - we infer one is the cause of the other because it is a part of it.

...
INFO: [HTTP REQUEST - END]
exception.details= StoreTokenError(
Database(
PgDatabaseError {
severity: Error,
code: "42703",
message:
"column 'subscription_token' of relation
'subscription_tokens' does not exist",
...
}
)
)
exception.message=
"A database failure was encountered while
trying to store a subscription token.",
target=tracing_actix_web::root_span_builder,
http.status_code=500

Let’s go for something more explicit:

//! src/routes/subscriptions.rs

// Notice that we have removed `#[derive(Debug)]`
pub struct StoreTokenError(sqlx::Error);

impl std::fmt::Debug for StoreTokenError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "{}\nCaused by:\n\t{}", self, self.0)
}
}

The log record leaves nothing to the imagination now:

...
INFO: [HTTP REQUEST - END]
exception.details=
"A database failure was encountered
while trying to store a subscription token.

Caused by:
error returned from database: column 'subscription_token'
of relation 'subscription_tokens' does not exist"
exception.message=
"A database failure was encountered while
trying to store a subscription token.",
target=tracing_actix_web::root_span_builder,
http.status_code=500

exception.details is easier to read and still conveys all the relevant information we had there before.

Using source we can write a function that provides a similar representation for any type that implements Error:

//! src/routes/subscriptions.rs
// [...]

fn error_chain_fmt(
e: &impl std::error::Error,
f: &mut std::fmt::Formatter<'_>,
) -> std::fmt::Result {
writeln!(f, "{}\n", e)?;
let mut current = e.source();
while let Some(cause) = current {
writeln!(f, "Caused by:\n\t{}", cause)?;
current = cause.source();
}
Ok(())
}

It iterates over the whole chain of errors[7] that led to the failure we are trying to print.
We can then change our implementation of Debug for StoreTokenError to use it:

//! src/routes/subscriptions.rs
// [...]

impl std::fmt::Debug for StoreTokenError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
error_chain_fmt(self, f)
}
}

The result is identical - and we can reuse it when working with other errors if we want a similar Debug representation.

8.3 Errors For Control Flow

8.3.1 Layering

We achieved the outcome we wanted (useful logs), but I am not too fond of the solution: we implemented a trait from our web framework (ResponseError) for an error type returned by an operation that is blissfully unaware of REST or the HTTP protocol, store_token. We could be calling store_token from a different entrypoint (e.g. a CLI) - nothing should have to change in its implementation.
Even assuming we are only ever going to be invoking store_token in the context of a REST API, we might add other endpoints that rely on that routine - they might not want to return a 500 when it fails.

Choosing the appropriate HTTP status code when an error occurs is a concern of the request handler, it should not leak elsewhere.
Let’s delete

//! src/routes/subscriptions.rs
// [...]

// Nuke it!
impl ResponseError for StoreTokenError {}

To enforce a proper separation of concerns we need to introduce another error type, SubscribeError. We will use it as failure variant for subscribe and it will own the HTTP-related logic (ResponseError’s implementation).

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// [...]
}

#[derive(Debug)]
struct SubscribeError {}

impl std::fmt::Display for SubscribeError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(
f,
"Failed to create a new subscriber."
)
}
}

impl std::error::Error for SubscriberError {}

impl ResponseError for SubscribeError {}

If you run cargo check you will see an avalanche of '?' couldn't convert the error to 'SubscribeError' - we need to implement conversions from the error types returned by our functions and SubscribeError.

8.3.2 Modelling Errors as Enums

An enum is the most common approach to work around this issue: a variant for each error type we need to deal with.

//! src/routes/subscriptions.rs
// [...]

#[derive(Debug)]
pub enum SubscribeError {
ValidationError(String),
DatabaseError(sqlx::Error),
StoreTokenError(StoreTokenError),
SendEmailError(reqwest::Error),
}

We can then leverage the ? operator in our handler by providing a From implementation for each of wrapped error types:

//! src/routes/subscriptions.rs
// [...]

impl From<reqwest::Error> for SubscribeError {
fn from(e: reqwest::Error) -> Self {
Self::SendEmailError(e)
}
}

impl From<sqlx::Error> for SubscribeError {
fn from(e: sqlx::Error) -> Self {
Self::DatabaseError(e)
}
}

impl From<StoreTokenError> for SubscribeError {
fn from(e: StoreTokenError) -> Self {
Self::StoreTokenError(e)
}
}

impl From<String> for SubscribeError {
fn from(e: String) -> Self {
Self::ValidationError(e)
}
}

We can now clean up our request handler by removing all those match / if fallible_function().is_err() lines:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
let new_subscriber = form.0.try_into()?;
let mut transaction = pool.begin().await?;
let subscriber_id = insert_subscriber(/* */).await?;
let subscription_token = generate_subscription_token();
store_token(/* */).await?;
transaction.commit().await?;
send_confirmation_email(/* */).await?;
Ok(HttpResponse::Ok().finish())
}

The code compiles, but one of our tests is failing:

thread 'subscriptions::subscribe_returns_a_400_when_fields_are_present_but_invalid'
panicked at 'assertion failed: `(left == right)`
left: `400`,
right: `500`: The API did not return a 400 Bad Request when the payload was empty name.'

We are still using the default implementation of ResponseError - it always returns 500.
This is where enums shine: we can use a match statement for control flow - we behave differently depending on the failure scenario we are dealing with.

//! src/routes/subscriptions.rs
use actix_web::http::StatusCode;
// [...]

impl ResponseError for SubscribeError {
fn status_code(&self) -> StatusCode {
match self {
SubscribeError::ValidationError(_) => StatusCode::BAD_REQUEST,
SubscribeError::DatabaseError(_)
| SubscribeError::StoreTokenError(_)
| SubscribeError::SendEmailError(_) => StatusCode::INTERNAL_SERVER_ERROR,
}
}
}

The test suite should pass again.

8.3.3 The Error Type Is Not Enough

What about our logs?
Let’s look again:

export RUST_LOG="sqlx=error,info"
export TEST_LOG=enabled
cargo t subscribe_fails_if_there_is_a_fatal_database_error | bunyan

...
INFO: [HTTP REQUEST - END]
exception.details="StoreTokenError(
A database failure was encountered while trying to
store a subscription token.

Caused by:
error returned from database: column 'subscription_token'
of relation 'subscription_tokens' does not exist)"
exception.message="Failed to create a new subscriber.",
target=tracing_actix_web::root_span_builder,
http.status_code=500

We are still getting a great representation for the underlying StoreTokenError in exception.details, but it shows that we are now using the derived Debug implementation for SubscribeError. No loss of information though.
The same cannot be said for exception.message - no matter the failure mode, we always get Failed to create a new subscriber. Not very useful.

Let’s refine our Debug and Display implementations:

//! src/routes/subscriptions.rs
// [...]

// Remember to delete `#[derive(Debug)]`!
impl std::fmt::Debug for SubscribeError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
error_chain_fmt(self, f)
}
}

impl std::error::Error for SubscribeError {
fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
match self {
// &str does not implement `Error` - we consider it the root cause
SubscribeError::ValidationError(_) => None,
SubscribeError::DatabaseError(e) => Some(e),
SubscribeError::StoreTokenError(e) => Some(e),
SubscribeError::SendEmailError(e) => Some(e),
}
}
}

impl std::fmt::Display for SubscribeError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match self {
SubscribeError::ValidationError(e) => write!(f, "{}", e),
// What should we do here?
SubscribeError::DatabaseError(_) => write!(f, "???"),
SubscribeError::StoreTokenError(_) => write!(
f,
"Failed to store the confirmation token for a new subscriber."
),
SubscribeError::SendEmailError(_) => {
write!(f, "Failed to send a confirmation email.")
},
}
}
}

Debug is easily sorted: we implemented the Error trait for SubscribeError, including source, and we can use again the helper function we wrote earlier for StoreTokenError.

We have a problem when it comes to Display - the same DatabaseError variant is used for errors encountered when:

- acquiring a new Postgres connection from the pool;

- inserting a subscriber in the subscribers table;

- committing the SQL transaction.

When implementing Display for SubscribeError we have no way to distinguish which of those three cases we are dealing with - the underlying error type is not enough.
Let’s disambiguate by using a different enum variant for each operation:

//! src/routes/subscriptions.rs
// [...]

pub enum SubscribeError {
// [...]
// No more `DatabaseError`
PoolError(sqlx::Error),
InsertSubscriberError(sqlx::Error),
TransactionCommitError(sqlx::Error),
}

impl std::fmt::Display for SubscribeError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match self {
// [...]
SubscribeError::PoolError(_) => {
write!(f, "Failed to acquire a Postgres connection from the pool")
}
SubscribeError::InsertSubscriberError(_) => {
write!(f, "Failed to insert new subscriber in the database.")
}
SubscribeError::TransactionCommitError(_) => {
write!(
f,
"Failed to commit SQL transaction to store a new subscriber."
)
}
}
}
}

impl std::error::Error for SubscribeError {
fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
match self {
//  [...]
// No more DatabaseError
SubscribeError::PoolError(e) => Some(e),
SubscribeError::InsertSubscriberError(e) => Some(e),
SubscribeError::TransactionCommitError(e) => Some(e),
// [...]
}
}
}

impl ResponseError for SubscribeError {
fn status_code(&self) -> StatusCode {
match self {
SubscribeError::ValidationError(_) => StatusCode::BAD_REQUEST,
SubscribeError::PoolError(_)
| SubscribeError::TransactionCommitError(_)
| SubscribeError::InsertSubscriberError(_)
| SubscribeError::StoreTokenError(_)
| SubscribeError::SendEmailError(_) => StatusCode::INTERNAL_SERVER_ERROR,
}
}
}

DatabaseError is used in one more place:

//! src/routes/subscriptions.rs
// [..]

impl From<sqlx::Error> for SubscribeError {
fn from(e: sqlx::Error) -> Self {
Self::DatabaseError(e)
}
}

The type alone is not enough to distinguish which of the new variants should be used; we cannot implement From for sqlx::Error.
We have to use map_err to perform the right conversion in each case.

//! src/routes/subscriptions.rs
// [..]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// [...]
let mut transaction = pool.begin().await.map_err(SubscribeError::PoolError)?;
let subscriber_id = insert_subscriber(&mut transaction, &new_subscriber)
.await
.map_err(SubscribeError::InsertSubscriberError)?;
// [...]
transaction
.commit()
.await
.map_err(SubscribeError::TransactionCommitError)?;
// [...]
}

The code compiles and exception.message is useful again:

...
INFO: [HTTP REQUEST - END]
exception.details="Failed to store the confirmation token
for a new subscriber.

Caused by:
A database failure was encountered while trying to store
a subscription token.
Caused by:
error returned from database: column 'subscription_token'
of relation 'subscription_tokens' does not exist"
exception.message="Failed to store the confirmation token for a new subscriber.",
target=tracing_actix_web::root_span_builder,
http.status_code=500

8.3.4 Removing The Boilerplate With thiserror

It took us roughly 90 lines of code to implement SubscriberError and all the machinery that surrounds it in order to achieve the desired behaviour and get useful diagnostic in our logs.
That is a lot of code, with a ton of boilerplate (e.g. source’s or From implementations).
Can we do better?

Well, I am not sure we can write less code, but we can find a different way out: we can generate all that boilerplate using a macro!

As it happens, there is already a great crate in the ecosystem for this purpose: thiserror. Let’s add it to our dependencies:

#! Cargo.toml

[dependencies]
# [...]
thiserror = "1"

It provides a derive macro to generate most of the code we just wrote by hand.
Let’s see it in action:

//! src/routes/subscriptions.rs
// [...]

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("{0}")]
ValidationError(String),
#[error("Failed to acquire a Postgres connection from the pool")]
PoolError(#[source] sqlx::Error),
#[error("Failed to insert new subscriber in the database.")]
InsertSubscriberError(#[source] sqlx::Error),
#[error("Failed to store the confirmation token for a new subscriber.")]
StoreTokenError(#[from] StoreTokenError),
#[error("Failed to commit SQL transaction to store a new subscriber.")]
TransactionCommitError(#[source] sqlx::Error),
#[error("Failed to send a confirmation email.")]
SendEmailError(#[from] reqwest::Error),
}

// We are still using a bespoke implementation of `Debug`
// to get a nice report using the error source chain
impl std::fmt::Debug for SubscribeError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
error_chain_fmt(self, f)
}
}

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// We no longer have `#[from]` for `ValidationError`, so we need to
// map the error explicitly
let new_subscriber = form.0.try_into().map_err(SubscribeError::ValidationError)?;
// [...]
}

We cut it down to 21 lines - not bad!
Let’s break down what is happening now.

thiserror::Error is a procedural macro used via a #[derive(/* */)] attribute.
We have seen and used these before - e.g. #[derive(Debug)] or #[derive(serde::Serialize)].
The macro receives, at compile-time, the definition of SubscribeError as input and returns another stream of tokens as output - it generates new Rust code, which is then compiled into the final binary.

Within the context of #[derive(thiserror::Error)] we get access to other attributes to achieve the behaviour we are looking for:

- #[error(/* */)] defines the Display representation of the enum variant it is applied to. E.g. Display will return Failed to send a confirmation email. when invoked on an instance of SubscribeError::SendEmailError. You can interpolate values in the final representation - e.g. the {0} in #[error("{0}")] on top of ValidationError is referring to the wrapped String field, mimicking the syntax to access fields on tuple structs (i.e. self.0).

- #[source] is used to denote what should be returned as root cause in Error::source;

- #[from] automatically derives an implementation of From for the type it has been applied to into the top-level error type (e.g. impl From<StoreTokenError> for SubscribeError {/* */}). The field annotated with #[from] is also used as error source, saving us from having to use two annotations on the same field (e.g. #[source] #[from] reqwest::Error).

I want to call your attention on a small detail: we are not using either #[from] or #[source] for the ValidationError variant. That is because String does not implement the Error trait, therefore it cannot be returned in Error::source - the same limitation we encountered before when implementing Error::source manually, which led us to return None in the ValidationError case.

8.4 Avoid “Ball Of Mud” Error Enums

In SubscribeError we are using enum variants for two purposes:

- Determine the response that should be returned to the caller of our API (ResponseError);

- Provide relevant diagnostic (Error::source, Debug, Display).

SubscribeError, as currently defined, exposes a lot of the implementation details of subscribe: we have a variant for every fallible function call we make in the request handler!
It is not a strategy that scales very well.

We need to think in terms of abstraction layers: what does a caller of subscribe need to know?

They should be able to determine what response to return to a user (via ResponseError). That’s it.
The caller of subscribe does not understand the intricacies of the subscription flow: they don’t know enough about the domain to behave differently for a SendEmailError compared to a TransactionCommitError (by design!). subscribe should return an error type that speaks at the right level of abstraction.

The ideal error type would look like this:

//! src/routes/subscriptions.rs

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("{0}")]
ValidationError(String),
#[error(/* */)]
UnexpectedError(/* */),
}

ValidationError maps to a 400 Bad Request, UnexpectedError maps to an opaque 500 Internal Server Error.

What should we store in the UnexpectedError variant?
We need to map multiple error types into it - sqlx::Error, StoreTokenError, reqwest::Error.
We do not want to expose the implementation details of the fallible routines that get mapped to UnexpectedError by subscribe - it must be opaque.

We bumped into a type that fulfills those requirements when looking at the Error trait from Rust’s standard library: Box<dyn std::error::Error>![8]

Let’s give it a go:

//! src/routes/subscriptions.rs

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("{0}")]
ValidationError(String),
// Transparent delegates both `Display`'s and `source`'s implementation
// to the type wrapped by `UnexpectedError`.
#[error(transparent)]
UnexpectedError(#[from] Box<dyn std::error::Error>),
}

We can still generate an accurate response for the caller:

//! src/routes/subscriptions.rs
// [...]

impl ResponseError for SubscribeError {
fn status_code(&self) -> StatusCode {
match self {
SubscribeError::ValidationError(_) => StatusCode::BAD_REQUEST,
SubscribeError::UnexpectedError(_) => StatusCode::INTERNAL_SERVER_ERROR,
}
}
}

We just need to adapt subscribe to properly convert our errors before using the ? operator:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// [...]
let mut transaction = pool
.begin()
.await
.map_err(|e| SubscribeError::UnexpectedError(Box::new(e)))?;
let subscriber_id = insert_subscriber(/* */)
.await
.map_err(|e| SubscribeError::UnexpectedError(Box::new(e)))?;
// [...]
store_token(/* */)
.await
.map_err(|e| SubscribeError::UnexpectedError(Box::new(e)))?;
transaction
.commit()
.await
.map_err(|e| SubscribeError::UnexpectedError(Box::new(e)))?;
send_confirmation_email(/* */)
.await
.map_err(|e| SubscribeError::UnexpectedError(Box::new(e)))?;
// [...]
}

There is some code repetition, but let it be for now.
The code compiles and our tests pass as expected.

Let’s change the test we have used so far to check the quality of our log messages: let’s trigger a failure in insert_subscriber instead of store_token.

//! tests/api/subscriptions.rs
// [...]

#[actix_rt::test]
async fn subscribe_fails_if_there_is_a_fatal_database_error() {
// [...]
// Break `subscriptions` instead of `subscription_tokens`
sqlx::query!("ALTER TABLE subscriptions DROP COLUMN email;",)
.execute(&app.db_pool)
.await
.unwrap();

// [..]
}

The test passes, but we can see that our logs have regressed:

INFO: [HTTP REQUEST - END]
exception.details:
"error returned from database: column 'email' of
relation 'subscriptions' does not exist"
exception.message:
"error returned from database: column 'email' of
relation 'subscriptions' does not exist"

We do not see a cause chain anymore.
We lost the operator-friendly error message that was previously attached to the InsertSubscriberError via thiserror:

//! src/routes/subscriptions.rs
// [...]

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("Failed to insert new subscriber in the database.")]
InsertSubscriberError(#[source] sqlx::Error),
// [...]
}

That is to be expected: we are forwarding the raw error now to Display (via #[error(transparent)]), we are not attaching any additional context to it in subscribe.
We can fix it - let’s add a new String field to UnexpectedError to attach contextual information to the opaque error we are storing:

//! src/routes/subscriptions.rs
// [...]

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("{0}")]
ValidationError(String),
#[error("{1}")]
UnexpectedError(#[source] Box<dyn std::error::Error>, String),
}

impl ResponseError for SubscribeError {
fn status_code(&self) -> StatusCode {
match self {
// [...]
// The variant now has two fields, we need an extra `_`
SubscribeError::UnexpectedError(_, _) => StatusCode::INTERNAL_SERVER_ERROR,
}
}
}

We need to adjust our mapping code in subscribe accordingly - we will reuse the error descriptions we had before refactoring SubscribeError:

//! src/routes/subscriptions.rs
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// [..]
let mut transaction = pool.begin().await.map_err(|e| {
SubscribeError::UnexpectedError(
Box::new(e),
"Failed to acquire a Postgres connection from the pool".into(),
)
})?;
let subscriber_id = insert_subscriber(/* */)
.await
.map_err(|e| {
SubscribeError::UnexpectedError(
Box::new(e),
"Failed to insert new subscriber in the database.".into(),
)
})?;
// [..]
store_token(/* */)
.await
.map_err(|e| {
SubscribeError::UnexpectedError(
Box::new(e),
"Failed to store the confirmation token for a new subscriber.".into(),
)
})?;
transaction.commit().await.map_err(|e| {
SubscribeError::UnexpectedError(
Box::new(e),
"Failed to commit SQL transaction to store a new subscriber.".into(),
)
})?;
send_confirmation_email(/* */)
.await
.map_err(|e| {
SubscribeError::UnexpectedError(
Box::new(e),
"Failed to send a confirmation email.".into()
)
})?;
// [..]
}

It is somewhat ugly, but it works:

INFO: [HTTP REQUEST - END]
exception.details=
"Failed to insert new subscriber in the database.

Caused by:
error returned from database: column 'email' of
relation 'subscriptions' does not exist"
exception.message="Failed to insert new subscriber in the database."

8.4.1 Using anyhow As Opaque Error Type

We could spend more time polishing the machinery we just built, but it turns out it is not necessary: we can lean on the ecosystem, again.
The author of thiserror[9] has another crate for us - anyhow.

#! Cargo.toml

[dependencies]
# [...]
anyhow = "1"

The type we are looking for is anyhow::Error. Quoting the documentation:

anyhow::Error is a wrapper around a dynamic error type. anyhow::Error works a lot like Box<dyn std::error::Error>, but with these differences:

- anyhow::Error requires that the error is Send, Sync, and 'static.

- anyhow::Error guarantees that a backtrace is available, even if the underlying error type does not provide one.

- anyhow::Error is represented as a narrow pointer — exactly one word in size instead of two.

The additional constraints (Send, Sync and 'static) are not an issue for us.
We appreciate the more compact representation and the option to access a backtrace, if we were to be interested in it.

Let’s replace Box<dyn std::error::Error> with anyhow::Error in SubscribeError:

//! src/routes/subscriptions.rs
// [...]

#[derive(thiserror::Error)]
pub enum SubscribeError {
#[error("{0}")]
ValidationError(String),
#[error(transparent)]
UnexpectedError(#[from] anyhow::Error),
}

impl ResponseError for SubscribeError {
fn status_code(&self) -> StatusCode {
match self {
// [...]
// Back to a single field
SubscribeError::UnexpectedError(_) => StatusCode::INTERNAL_SERVER_ERROR,
}
}
}

We got rid of the second String field as well in SubscribeError::UnexpectedError - it is no longer necessary.
anyhow::Error provides the capability to enrich an error with additional context out of the box.

//! src/routes/subscriptions.rs
use anyhow::Context;
// [...]

pub async fn subscribe(/* */) -> Result<HttpResponse, SubscribeError> {
// [...]
let mut transaction = pool
.begin()
.await
.context("Failed to acquire a Postgres connection from the pool")?;
let subscriber_id = insert_subscriber(/* */)
.await
.context("Failed to insert new subscriber in the database.")?;
// [..]
store_token(/* */)
.await
.context("Failed to store the confirmation token for a new subscriber.")?;
transaction
.commit()
.await
.context("Failed to commit SQL transaction to store a new subscriber.")?;
send_confirmation_email(/* */)
.await
.context("Failed to send a confirmation email.")?;
// [...]
}

The context method is performing double duties here:

- it converts the error returned by our methods into an anyhow::Error;

- it enriches it with additional context around the intentions of the caller.

context is provided by the Context trait - anyhow implements it for Result[10], giving us access to a fluent API to easily work with fallible functions of all kinds.

8.4.2 anyhow Or thiserror?

We have covered a lot of ground - time to address a common Rust myth:

anyhow is for applications, thiserror is for libraries.

It is not the right framing to discuss error handling.
You need to reason about intent.

Do you expect the caller to behave differently based on the failure mode they encountered?
Use an error enumeration, empower them to match on the different variants. Bring in thiserror to write less boilerplate.

Do you expect the caller to just give up when a failure occurs? Is their main concern reporting the error to an operator or a user?
Use an opaque error, do not give the caller programmatic access to the error inner details. Use anyhow or eyre if you find their API convenient.

The misunderstanding arises from the observation that most Rust libraries return an error enum instead of Box<dyn std::error::Error> (e.g. sqlx::Error).
Library authors cannot (or do not want to) make assumptions on the intent of their users. They steer away from being opinionated (to an extent) - enums give users more control, if they need it.
Freedom comes at a price - the interface is more complex, users need to sift through 10+ variants trying to figure out which (if any) deserve special handling.
Reason carefully about your usecase and the assumptions you can afford to make in order to design the most appropriate error type - sometimes Box<dyn std::error::Error> or anyhow::Error are the most appropriate choice, even for libraries.

8.5 Who Should Log Errors?

Let’s look again at the logs emitted when a request fails.

# sqlx logs are a bit spammy, cutting them out to reduce noise
export RUST_LOG="sqlx=error,info"
export TEST_LOG=enabled
cargo t subscribe_fails_if_there_is_a_fatal_database_error | bunyan

There are three error-level log records:

- one emitted by our code in insert_subscriber

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber(/* */) -> Result<Uuid, sqlx::Error> {
// [...]
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
// [...]
}

- one emitted by actix_web when converting SubscribeError into an actix_web::Error;

- one emitted by tracing_actix_web::TracingLogger, our telemetry middleware.

We do not need to see the same information three times - we are emitting unnecessary log records which, instead of helping, make it more confusing for operators to understand what is happening (are those logs reporting the same error? Am I dealing with three different errors?).

As a rule of thumb,

errors should be logged when they are handled.

If your function is propagating the error upstream (e.g. using the ? operator), it should not log the error. It can, if it makes sense, add more context to it.
If the error is propagated all the way up to the request handler, delegate logging to a dedicated middleware - tracing_actix_web::TracingLogger in our case.

The log record emitted by actix_web is going to be removed in the next release. Let’s ignore it for now.

Let’s review the tracing::error statements in our own code:

//! src/routes/subscriptions.rs
// [...]

pub async fn insert_subscriber(/* */) -> Result<Uuid, sqlx::Error> {
// [...]
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
// This needs to go, we are propagating the error via `?`
tracing::error!("Failed to execute query: {:?}", e);
e
})?;
// [..]
}

pub async fn store_token(/* */) -> Result<(), StoreTokenError> {
sqlx::query!(/* */)
.execute(transaction)
.await
.map_err(|e| {
// This needs to go, we are propagating the error via `?`
tracing::error!("Failed to execute query: {:?}", e);
StoreTokenError(e)
})?;
Ok(())
}

Check the logs again to confirm they look pristine.

8.6 Summary

We used this chapter to learn error handling patterns “the hard way” - building an ugly but working prototype first, refining it later using popular crates from the ecosystem.
You should now have:

- a solid grasp on the different purposes fulfilled by errors in an application;

- the most appropriate tools to fulfill them.

Internalise the mental model we discussed (Location as columns, Purpose as rows):

Internal

At the edge

Control Flow

Types, methods, fields

Status codes

Reporting

Logs/traces

Response body

Practice what you learned: we worked on the subscribe request handler, tackle confirm as an exercise to verify your understanding of the concepts we covered. Improve the response returned to the user when validation of form data fails.
You can look at the code in the GitHub repository as a reference implementation.

Some of the themes we discussed in this chapter (e.g. layering and abstraction boundaries) will make another appearance when talking about the overall layout and structure of our application. Something to look forward to!

1. We are borrowing the terminology introduced by Jane Lusby in “Error handling Isn’t All About Errors”, a talk from RustConf 2020. If you haven’t watched it yet, close the book and open YouTube - you will not regret it.↩

2. It is good to keep in mind that the line between a user and an operator can be blurry - e.g. a user might have access to the source code or they might be running the software on their own hardware. They might have to wear the operator’s hat at times. For similar scenarios there should be configuration knobs (e.g. --verbose or an environment variable for a CLI) to clearly inform the software of the human intent so that it can provide diagnostics at the right level of detail and abstraction.↩

3. In an ideal scenario we would actually be writing a test to verify the properties of the logs emitted by our application. This is somewhat cumbersome to do today - I am looking forward to revising this chapter when better tooling becomes available (or I get nerd-sniped into writing it).↩

4. I pinky-swear that I am going to submit a PR to actix_web to improve this section of the documentation.↩

5. Check out the relevant chapter in the Rust book for an in-depth introduction to trait objects.↩

6. The Error trait provides a downcast_ref which can be used to obtain a concrete type back from dyn Error, assuming you know what type to downcast to. There are legitimate usecases for downcasting, but if you find yourself reaching for it too often it might be a sign that something is not quite right in your design/error handling strategy.↩

7. There is a chain method on Error that fulfills the same purpose - it has not been stabilised yet.↩

8. We are wrapping dyn std::error::Error into a Box because the size of trait objects is not known at compile-time: trait objects can be used to store different types which will most likely have a different layout in memory. To use Rust’s terminology, they are unsized - they do not implement the Sized marker trait. A Box stores the trait object itself on the heap, while we store the pointer to its heap location in SubscribeError::UnexpectedError - the pointer itself has a known size at compile-time - problem solved, we are Sized again.↩

9. It turns out that we are speaking of the same person that authored serde, syn, quote and many other foundational crates in the Rust ecosystem - @dtolnay. Consider sponsoring their OSS work.↩

10. This is a common pattern in the Rust community, known as extension trait, to provide additional methods for types exposed by the standard library (or other common crates in the ecosystem).↩