An Introduction To Property-Based Testing In Rust

This article is a sample from Zero To Production In Rust, a book on backend development in Rust.
You can get a copy of the book on zero2prod.com.
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TL;DR

We need to verify a user-provided email address. We'll use this as an opportunity to explore the tools available in the Rust ecosystem to perform property-based testing.
We will start with fake, a Rust crate to generate randomised test data.
We will then combine fake with quickcheck to generate multiple samples on every cargo test invocation using its Arbitrary trait.

Chapter 6.5

  1. Previously In Zero To Production In Rust
  2. The Email Format
  3. The SubscriberEmail Type
  4. Property-based Testing
  5. Payload Validation
  6. Summary

Previously In Zero To Production In Rust

You can find the snapshot of the codebase at the beginning of this chapter on GitHub.

At the beginning of chapter six we wrote an integration test to stress how poor our user input validation was - let's look at it again:

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

#[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 then worked to introduce input validation into our newsletter project: the subscriber name in the payload of our POST /subscriptions endpoint is now thoroughly inspected before any saving or processing takes place.
That is how we got the empty name case to pass, but we are still failing on the empty email one:

--- 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

Turning this test green will be our focus for today.

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 crate1

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.

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.

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.

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

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.

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.

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.

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?

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:

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!

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>,
) -> Result<HttpResponse, HttpResponse> {
   let name = SubscriberName::parse(form.0.name)
           .map_err(|_| 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,
   };
   insert_subscriber(&pool, &new_subscriber)
           .await
           .map_err(|_| HttpResponse::InternalServerError().finish())?;
   Ok(HttpResponse::Ok().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>,
) -> Result<HttpResponse, HttpResponse> {
   let name = SubscriberName::parse(form.0.name)
           .map_err(|_| HttpResponse::BadRequest().finish())?;
   let email = SubscriberEmail::parse(form.0.email)
           .map_err(|_| 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!

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>,
) -> Result<HttpResponse, HttpResponse> {
   let name = SubscriberName::parse(form.0.name)
           .map_err(|_| HttpResponse::BadRequest().finish())?;
   let email = SubscriberEmail::parse(form.0.email)
           .map_err(|_| HttpResponse::BadRequest().finish())?;
   let new_subscriber = NewSubscriber { email, name };
   insert_subscriber(&pool, &new_subscriber)
           .await
           .map_err(|_| HttpResponse::InternalServerError().finish())?;
   Ok(HttpResponse::Ok().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>,
) -> Result<HttpResponse, HttpResponse> {
   let new_subscriber = parse_subscriber(form.0)
           .map_err(|_| HttpResponse::BadRequest().finish())?;
   insert_subscriber(&pool, &new_subscriber)
           .await
           .map_err(|_| HttpResponse::InternalServerError().finish())?;
   Ok(HttpResponse::Ok().finish())
}

The refactoring gives us a clearer separation of concerns:

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>,
) -> Result<HttpResponse, HttpResponse> {
   let new_subscriber = form.0.try_into()
           .map_err(|_| HttpResponse::BadRequest().finish())?;
   insert_subscriber(&pool, &new_subscriber)
           .await
           .map_err(|_| HttpResponse::InternalServerError().finish())?;
   Ok(HttpResponse::Ok().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.

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.

As always, all the code we wrote in this chapter can be found on GitHub.


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Footnotes

Click to expand!
1

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.

Book - Table Of Contents

Click to expand!

The Table of Contents is provisional and might change over time. The draft below is the most accurate picture at this point in time.

  1. Getting Started
    • Installing The Rust Toolchain
    • Project Setup
    • IDEs
    • Continuous Integration
  2. Our Driving Example
    • What Should Our Newsletter Do?
    • Working In Iterations
  3. Sign Up A New Subscriber
  4. Telemetry
    • Unknown Unknowns
    • Observability
    • Logging
    • Instrumenting /POST subscriptions
    • Structured Logging
  5. Go Live
    • We Must Talk About Deployments
    • Choosing Our Tools
    • A Dockerfile For Our Application
    • Deploy To DigitalOcean Apps Platform
  6. Rejecting Invalid Subscribers #1
    • Requirements
    • First Implementation
    • Validation Is A Leaky Cauldron
    • Type-Driven Development
    • Ownership Meets Invariants
    • Panics
    • Error As Values - Result
  7. Reject Invalid Subscribers #2
    • The Email Format
    • Confirmation Email
    • Ports And Adapters Architecture
    • Refactoring Our Project Structure
  8. Publish A Newsletter Issue
    • The Newsletter State Machine
    • /newsletters/draft
    • /newsletters/issue
  9. Securing Our API
    • Security Properties
    • Trasport Layer Security (TLS)
    • Authentication / Authorization
  10. Hardening Our Email Delivery Logic
  • Send Emails Asynchronously
  1. Monitoring
  • Metrics: Prometheus
  • Dashboards: Grafana
  • Alerts: AlertManager
  1. Performance
  • Micro-benchmarking
  • Load Testing