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An example of applying Consumer-Driven Contract Testing (CDC) for testing microservice compatibility in isolation.

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Consumer-Driven Contract Testing with Pact

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An example of applying Consumer-Driven Contract Testing (CDC) for testing microservice compatibility in isolation.

What is Contract Testing

One of the most useful properties of microservices is the ability to develop, test, and deploy services independently from other development teams in the organization. It gives us the possibility to independently scale teams and reduce dependencies between them, ultimately increasing the agility of the whole organization. To facilitate loose coupling between microservices and team autonomy, contracts are established between microservice and team boundaries.

A contract establishes a clear shared understanding of how different microservices will communicate with each other. A contract includes both the syntactic and semantic expectations of the communication. Syntax is the expected message schema and data types. Semantics is the meaning of the message, e.g. valid combinations of provided values, and the resulting behavior.

Microservices create value only when they interact with other microservices in the system, so they must communicate with each other. That's the puzzle: we want to reap the benefits of independent deployability, but we also must make sure that the services are compatible with each other, i.e. there are no breaking changes in the contracts.

The most widely used approach for testing compatibility is integrated end-to-end testing. With this approach, before releasing a single microservice to the production environment, all microservices are deployed to a shared testing environment, and a suite of end-to-end tests is run. The promise of integrated end-to-end tests is to discover all incompatible changes between the system components and verify that the system works as a whole.

To summarize, to ensure that a single microservice is compatible with the whole system, it must be deployed and tested together with all other microservices in a testing environment. This contradicts the microservice goal of independent deployability because you can't deploy a service until it's tested with all other services together at the same time.

The end-to-end testing approach is useful, but in the microservice-based system it has some significant drawbacks at a certain scale point:

  • Integrated end-to-end tests by their nature are slow compared to isolated microservice-level tests. As the system grows and more end-to-end tests are added, the test execution time will grow super-linearly. An alternative to running all the tests at once is to run only a subset of tests that concern a particular microservice's business domain, but that will increase the risk of missing a breaking change in other parts of the system.

  • End-to-end tests are brittle and flaky. Since they depend on the whole system and the state of a testing environment, many factors can cause the tests to fail, even if the system is working correctly. Flaky tests erode the trust in the test suite and the whole deployment pipeline.

  • Since all the teams will depend on the same testing environment, there's a possibility of a queue forming for running the tests. While one team is working on fixing a failing test that fails the whole deployment pipeline, other teams will have to wait. This will increase the deployment lead time, ultimately reducing the number of deployments and increasing batch size, since teams will have to wait for their turn to test their changes. You can scale the number of testing environments or have a dedicated testing environment for each team, but only up to a certain point due to the infrastructure and maintenance costs.

  • End-to-end test ownership without introducing dependencies between teams challenging. A single end-to-end test might span across many microservices and team boundaries, testing bigger features as a whole. Therefore, an organization might have a dedicated test automation team that writes the end-to-end tests. This is a problem in it's own regard, because the development teams will be disconnected and uninterested in end-to-end test cases. When a test gets broken and needs changes, a development team will have a dependency on the test automation team. Another approach is to have collective ownership of the tests, but that could result in an explosion of test cases and decrease in the test codebase coherence, as different teams won't have a clear visibility into what's already been tested.

End-to-end tests in a microservice-based system are still useful to verify the system as a whole, however, they should be used sparingly, for example, for testing the most critical business flows.

You could also experiment with when the end-to-end tests are run. For example, instead of running end-to-end tests in a deployment pipeline, they could be periodically run in a production environment. To make it safe, the tests must use fake data, e.g. place orders to known fake customers that are configured to not ship real products. This approach is called Synthetic Monitoring (or Semantic Monitoring). It will serve for detecting system-wide issues, but won't be able to prevent the release of incompatible changes.

The drawbacks of end-to-end tests can be mitigated by adjusting the scope of the tests, experimenting with test ownership and test environment availability, but the fundamental problem of end-to-end tests are that they don't scale well as the system grows.

Take a look at Emily Bache's talk "End-to-End Automated Testing in a Microservices Architecture" to learn more techniques for keeping end-to-end test suite manageable. For more overall guidance on testing microservices, see "Testing Strategies in a Microservice Architecture". More on challenges with scaling end-to-end testing at "Nubank: Why We Killed Our End-to-End Test Suite"

For testing microservice compatibility (but not functionality), an alternative approach to integrated end-to-end testing is contract testing.

Contract testing is a technique for testing an integration point by checking each application in isolation to ensure the messages it sends or receives conform to a shared understanding that is documented in a "contract". — https://docs.pact.io/#what-is-contract-testing

There're multiple types of contract tests:

  • Consumer-driven contract testing
  • Provider-driven contract testing
  • Bi-directional contract testing

This example project will focus on the consumer-driven contract testing.

Consumer-Driven Contract Testing

Consumer-driven contract testing is a type of contract testing where a Consumer of a Provider service expresses its expectations about the Provider's behavior in a contract and shares the contract with the Provider. The Provider uses the given contract to verify that it meets the expectations.

Consumer-driven contract is also a collaboration technique between the Consumer and Provider teams, or colleagues on the same team with both the Consumer and Provider services are managed by the same team. Contract testing makes the dependencies between teams explicit and visible.

It fosters clear lines of communication and collaboration, between microservices and teams that consume them. A contract is a great enabler for creating clear service and domain boundaries, driven by examples and living documentations. As Agile stories are often referred to as a placeholder for a conversation, consumer-driven contracts are just like that.

Despite having the Consumers make the first move in creating a contract, it doesn't mean that the Provider team must unquestionably follow the Consumer's expectations. If the Provider sees that the Consumer's expectations would expose private data, too many implementation details, or would be too expensive to implement, the Provider team will have a conversation with the Consumer team for aligning on the best solution. As in Agile, it's the conversations that matter the most, not a contract or a particular tooling and process.

As a Consumer, don't come to your Provider with a fully specified contract and all the functionality and features already implemented. Instead, before starting the implementation come to your Provider with a draft contract or no contract at all, and start a conversation. Test your contracts early and often.

If you're familiar with the Test Pyramid (Mike Cohn), consumer-driven contract tests fit into the service layer. Consumer-driven contract tests are scoped to a single service, and use stubbed/mocked components for testing the service in isolation.

Integrating consumer-driven tests into the test pyramid Source: Building Microservices by Sam Newman

Pact as a Consumer-Driven Contract Testing Tool

This example project uses Pact for consumer-driven contract testing.

Pact is a code-first tool for testing HTTP and message integrations using contract tests. Contract tests assert that inter-application messages conform to a shared understanding that is documented in a contract. Without contract testing, the only way to ensure that applications will work correctly together is by using expensive and brittle integration tests. — https://docs.pact.io/

The example project is written in Python, so it uses Pact Python implementation. Pact is available in many other languages as well, see the full list on the Pact website.

How Pact Works

Check out the "How Pact contract testing works" animated slides on the PactFlow website - https://pactflow.io/how-pact-works/

Putting it all together:

How Pact works summary Source: https://docs.pact.io/#what-is-contract-testing

When Consumer-Driven Contract Testing and Pact are Not for Your Use Case

  • When a Provider team is not using the same contract-testing tool as the Consumer, e.g. Pact. It will create a lot of friction and misalignments.

  • When you don't have control over a Consumer or Provider, e.g. one of them is a third-party service.

  • When Consumer and Provider teams don't have a good communication channel. Contract testing will require good collaboration, otherwise teams will be blocking each other by breaking each other's contract tests and not fixing the problems timely.

  • Testing public APIs when number of Consumers is unknown.

  • Performance and load testing, functional testing, UI testing. Check out "Testing Strategies in a Microservice Architecture" presentation to find a correct testing strategy for each of those use cases.

More cases at https://docs.pact.io/getting_started/what_is_pact_good_for#what-is-it-not-good-for

Example Application Architecture (C4)

The example application is a made-up e-commerce system built as microservices.

In the real world, the microservices would be separated into different repositories, but for the sake of the example simplicity, all the services are in the same repository.

System Context Diagram

System context diagram

Container Diagram

Container diagram

Using Pact for Testing the Example Application

The example application demonstrates how to use Pact for contract testing microservices that communicate over synchronous HTTP (REST and GraphQL), and asynchronous messaging (AWS SNS SQS).

In this example application, the Pacticipant names follow a convention <application-name>--<communication-protocol>. For example, service-customers--rest is a service-customers application communicating over REST (synchronous HTTP), and service-customers--sns is a service-customers application communicating over SNS (asynchronous messaging).

The need to name Pacticipants depending on the communication protocol is because Pact uses different mechanisms for verifying contracts depending on whether it's a synchronous HTTP-based protocol or asynchronous messaging. Pact HTTP contract tests use pact.Verifier, and Pact asynchronous messaging contract tests use pact.MessageProvider, and they can't be mixed.

  • List of the example application's Pacticipants:
    • frontend--graphql
    • frontend--rest
    • service-customers--rest
    • service-customers--sns
    • service-order-history--graphql
    • service-order-history--sns
    • service-orders--rest
    • service-orders--sns

Example Application's Pact Network Diagram

Generated from Pact Broker's http://localhost:9292/integrations endpoint with generate_pact_network_diagram.py script.

Pact network diagram

Run Pact Contract Tests Locally with a Self-Hosted Pact Broker

The Pact Broker is an application for sharing consumer driven contracts and verification results. The Pact Broker is an open source tool that requires you to deploy, administer and host it yourself. — https://docs.pact.io/pact_broker

curl -fsSL https://raw.githubusercontent.com/pact-foundation/pact-ruby-standalone/master/install.sh | PACT_CLI_VERSION=v2.0.10 bash
  • Install Python dependencies with Poetry.
poetry install --with dev
poetry shell
  • Set PYTHONPATH to include the src directory.
export PYTHONPATH=src:$PYTHONPATH
  • Run example application and Pact Broker locally with Docker Compose.
docker compose up

  • Open local Pact Broker UI - http://localhost:9292. Login with pactbroker/pactbroker credentials. The list of contracts should be empty because we haven't run any tests yet.

  • Run consumer tests first (remember about consumer-driven). Pact will generate contract files and save them in the pacts directory.

poetry run pytest -m "consumer"

  • Pact Broker UI (http://localhost:9292) should still be empty because we haven't published contracts from pacts directory to the Pact Broker yet.

  • Publish contracts from the pacts directory to the local Pact Broker. Note: don't store your Pact Broker credentials in plain text; this project is just an example.

pact-broker publish --auto-detect-version-properties \
    --broker-base-url http://localhost:9292 \
    --broker-username pactbroker \
    --broker-password pactbroker \
    pacts
  • Refresh Pact Broker UI (http://localhost:9292) and you should see the list of contracts. Notice that Last Verified column is empty because the contracts haven't been verified against the providers yet.

Pact Broker - contracts are not verified Pact Broker - contracts are not verified

  • Verify provider contracts from the local Pact Broker. The Pact provider tests will fetch the latest contracts from the Pact Broker and run the tests against them. Verification results will be published back to the Pact Broker because PACT_PUBLISH_VERIFICATION_RESULTS environment variable is set to true. Usually, you would publish test results to the Pact Broker only in the CI/CD pipeline, so when working on a production project, omit the environment variable when running tests locally.
PACT_PUBLISH_VERIFICATION_RESULTS=true poetry run pytest -m "provider"

Pact Broker - contracts are verified Pact Broker - contracts are verified

Run Pact Contract Tests with PactFlow.io

PactFlow.io is a SaaS version of Pact Broker. It has a free tier for up to 5 integrations, which is suitable for a proof-of-concept. Read more about PactFlow in the PactFlow documentation.

export PACT_BROKER_BASE_URL=https://<your-account-name>.pactflow.io
 export PACT_BROKER_TOKEN=<your-read-write-api-token>
  • Remove previously generated contracts from the pacts directory.
rm -r pacts/*.json
  • Run consumer tests. This time, include pactflow marker to run only a subset of tests, since the PactFlow free tier has a limit of 5 integrations.
poetry run pytest -m "consumer and pactflow"
  • Publish contracts from the pacts directory to the PactFlow. Notice that we don't need to specify Pact Broker URL and credentials because they are already set in the environment variables.
pact-broker publish --auto-detect-version-properties pacts

PactFlow - contracts are not verified PactFlow - contracts are not verified

  • Verify Pacts and publish verification results. Usually, you would publish test results to the Pact Broker only in CI/CD pipeline, so when working on a production project, omit the environment variable when running tests locally.
PACT_PUBLISH_VERIFICATION_RESULTS=true pytest -m "provider and pactflow"

PactFlow - contracts are verified PactFlow - contracts are verified

Running Pact in a Deployment Pipeline (CI/CD)

For a complete guide to integrating Pact into your CI/CI workflow, take a look at Pact documentation - CI/CD setup guide.

The Pact guide covers more than just configuring Pact in the CI/CD pipeline, but first getting the team aligned on the process of contract testing, and getting started with the simplest and non-intrusive setup that will let you evaluate if contract testing and Pact are a good fit for your project.

Since contract testing is a collaboration technique, it's important to get the team on board first, before introducing mandatory blocking steps to the deployment pipeline.

Contracts are not a replacement for good communication between or within teams. In fact, contracts require collaboration and communication.

A summary of topics covered by the Pact CI/CD setup guide:

  1. Learn about Pact, talk, and get team alignment to try it out.
  2. Get a single test working manually.
  3. Manually integrate with Pact Broker/PactFlow.
  4. Integrate Pact Broker/PactFlow into the deployment pipeline.
  5. Use Can-I-Deploy to verify if the version of your application you're about to deploy is compatible with other applications that are already deployed in that environment (not covered in this example project).
  6. Record deployments and releases to the Pact Broker/PactFlow (not covered in this example project).

Setting up the Deployment Pipeline for the Example Application

When using Pact in a CI/CD pipeline, there are two reasons for a Pact verification task to take place:

  • When the Consumer changes - to see if the Provider is compatible with the new expectations
  • When the Provider changes - to make sure it does not break any existing Consumer expectations

— https://github.com/pactflow/example-provider

The important part is to establish quick feedback loops for the changes in the contract:

  • Trigger the Provider contract tests in the Provider CI/CD pipeline when a new Consumer contract version is published.
  • Get notified that changes in your Consumer contract are incompatible with the existing Provider contract, i.e. the Provider contract tests failed in the Provider CI/CD pipeline.
  • Before deploying a new version of the Consumer, verify that it's compatible with the currently deployed version of the Provider. Using Can-I-Deploy is one way of doing it.

The example application uses GitHub Actions for running the deployment pipeline. Implemented workflow examples with PactFlow and GitHub Actions are in .github/workflows/. There are two deployment pipeline workflows:

  • build.yml - a regular deployment pipeline workflow that runs on every commit. It builds and tests the application, including running Pact contract tests.
  • pact-verify-provider-contract.yml - a workflow that's triggered by a webhook from Pact Broker/PactFlow when a new Consumer contract version is published. The workflow runs the Provider contract tests against the new Consumer contract version and publishes the verification results back to the Pact Broker/PactFlow.

The examples in this project don't go the full way of setting up the deployment pipeline using Can-I-Deploy and recording deployments and releases. The concrete implementation will differ depending on the existing CI/CD setup and adopted ways of working.

For more examples, see:

When the Consumer Changes

sequenceDiagram
    participant Consumer as Consumer CI/CD (GitHub Actions)
    participant PactBroker as Pact Broker/PactFlow
    participant Provider as Provider CI/CD (GitHub Actions)

    activate Consumer
    Consumer->>Consumer: On commit: run Pact contract tests with 'pytest'
    Consumer->>PactBroker: Pytest: publish a contract with a new git commit ref
    deactivate Consumer

    activate PactBroker
    PactBroker->>PactBroker: Contract has changed
    PactBroker->>Provider: Webhook: 'Consumer' contract requiring verification published
    deactivate PactBroker

    activate Provider
    Provider->>PactBroker: Pytest: fetch new Consumer contract version
    Provider->>Provider: Pytest: 'run Pact Provider contract tests' against Provider's 'main' branch
    Provider->>Provider: Pytest: provider contract tests passed
    deactivate Provider

    activate PactBroker
    Provider->>PactBroker: Pytest: publish successful verification results
    PactBroker->>PactBroker: Mark contract as verified
    deactivate PactBroker
Loading 
sequenceDiagram
    participant Consumer as Consumer CI/CD (GitHub Actions)
    participant PactBroker as Pact Broker/PactFlow
    participant Provider as Provider CI/CD (GitHub Actions)

    activate Consumer
    Consumer->>Consumer: On commit: run Pact contract tests with 'pytest'
    Consumer->>PactBroker: Pytest: publish a contract with a new git commit ref
    deactivate Consumer

    activate PactBroker
    PactBroker->>PactBroker: Contract has changed
    PactBroker->>Provider: Webhook: 'Consumer' contract requiring verification published
    deactivate PactBroker

    activate Provider
    Provider->>PactBroker: Pytest: fetch new Consumer contract version
    Provider->>Provider: Pytest: 'run Pact Provider contract tests' against Provider's 'main' branch
    Provider->>Provider: Pytest: provider contract tests failed
    deactivate Provider

    activate PactBroker
    Provider->>PactBroker: Pytest: publish failed verification results
    PactBroker->>PactBroker: Mark contract verification as failed
    deactivate PactBroker
Loading
  • On a commit in Consumer repository, but Consumer contract hasn't changed - automatically mark it as verified in Pact Broker/PactFlow without running the Provider contract tests.
sequenceDiagram
    participant Consumer as Consumer CI/CD (GitHub Actions)
    participant PactBroker as Pact Broker/PactFlow
    participant Provider as Provider CI/CD (GitHub Actions)

    activate Consumer
    Consumer->>Consumer: On commit: run Pact contract tests
    Consumer->>PactBroker: Pytest: publish a contract with a new git commit ref
    deactivate Consumer

    activate PactBroker
    PactBroker->>PactBroker: Contract hasn't changed
    PactBroker->>PactBroker: Mark contract as verified
    deactivate PactBroker
Loading

When the Provider Changes

  • On a new commit in Provider repository - verify Provider contract against Consumer contracts from Pact Broker/PactFlow (.github/workflows/build.yml).
sequenceDiagram
    participant Provider as Provider CI/CD (GitHub Actions)
    participant PactBroker as Pact Broker/PactFlow
    participant Consumer as Consumer CI/CD (GitHub Actions)

    activate Provider
    Provider->>Provider: On commit: run Pact contract tests with 'pytest'
    Provider->>PactBroker: Pytest: fetch Consumer contracts from its 'main' branch or latest deployed version
    Provider->>Provider: Pytest: 'run Pact Provider contract tests' against Provider's current branch
    Provider->>Provider: Pytest: provider contract tests passed
    Provider->>PactBroker: Pytest: publish successful verification results
    Provider->>Provider: GitHub Actions: mark build as successful
    deactivate Provider
Loading 
sequenceDiagram
    participant Provider as Provider CI/CD (GitHub Actions)
    participant PactBroker as Pact Broker/PactFlow
    participant Consumer as Consumer CI/CD (GitHub Actions)

    activate Provider
    Provider->>Provider: On commit: run Pact contract tests with 'pytest'
    Provider->>PactBroker: Pytest: fetch Consumer contracts from its 'main' branch or latest deployed version
    Provider->>Provider: Pytest: 'run Pact Provider contract tests' against Provider's current branch
    Provider->>Provider: Pytest: provider contract tests failed
    Provider->>PactBroker: Pytest: publish failed verification results
    Provider->>Provider: GitHub Actions: mark build as failed
    deactivate Provider
Loading

Configuring Pact Broker/PactFlow with Terraform

Setting up Pacticipants and their webhook configurations in Pact Broker/PactFlow manually is tedious and error-prone, especially when you're growing the number of services and teams.

You can use Pact Broker Terraform Provider to automate the configuration of Pact Broker/PactFlow.

The example project uses Terraform for automating the configuration of PactFlow:

  • Creation of user accounts and teams.
  • Creation of Pacticipants.
  • Configuration of Webhooks.

See terraform-pactflow/ directory for the example Terraform configuration.

Testing Breaking Changes in Contracts with Pact

In this section we'll test that Pact detects breaking changes in both the Consumer and Producer contracts, and observe how PactFlow displays the results. See Run Pact Contract Tests with PactFlow.io section for getting started with PactFlow.

Contract Testing Protobufs and Similar Structured Data Serialization Formats

The example application uses Protobuf for serializing structured data in the asynchronous messaging between services over AWS SNS SQS.

Protobuf (Protocol Buffers) is a language-neutral, platform-neutral extensible mechanisms for serializing structured data. Protobuf is schema-driven and has built-in versioning or backwards/forwards compatibility. With Protobuf, Consumers and Producers can work with different versions of the schema at the same time, and they will continue to work as long as the changes are backwards/forwards compatible.

That is an extremely valuable feature when you’re dealing with a big production system, because it allows you to update different components of the system independently, at different times, without worrying about compatibility. — https://pactflow.io/blog/the-case-for-contract-testing-protobufs-grpc-avro/

Protobuf is a schema-driven format, and having a defined schema if very important when working with big production systems. However, just adhering to the schema is not enough to ensure compatibility between the Consumer and Provider. Therefore, it's important to distinguish schema from the contract semantics.

The Protobuf definition schema is a syntactic construct which doesn't enforce any semantic meaning to the data. The contract semantics is the meaning of the data. contract testing allows us to verify that a Consumer is not only able to parse the data with a schema, but also understand the meaning of the data.

For Protobuf schema management it's worth mentioning buf.build. It's is a Protobuf schema management tool that helps you define, lint, and generate code for your Protobuf schema. One of it's advantages is a schema breaking change detection tool, that will help you catch syntactic breaking changes. For detecting semantic breaking changes, you would still need to use contract testing.

Read more about the case case for contract testing Protobufs and similar interface definition languages (IDL) in the Pact blog:

For more benefits of contract testing beyond catching breaking changes (both syntactic and semantic), see Contract Testing Beyond Catching Breaking Changes section.

Pact supports testing Protobuf contracts with a Protobuf plugin. At the time of writing, Pact Plugin for Protobuf was not available in the Python Pact library, so the example project converts Protobuf messages to JSON and tests them as JSON contracts. See more Pact Protobuf testing examples in the Pact documentation.

Breaking Consumer Contract

Testing of the Consumer contract changes happens in the Provider repository because it's the Provider that should pull the new contract version and run the contract tests against it. The testing is integrated with the Pact Broker/Pact Flow, which triggers contract requiring verification published webhook to the CI/CD server (e.g. GitHub Actions) to run the tests in the Provider repository.

Taking service-customers--sns as an example consumer. We'll change the expectation about the order_total.units format. Currently, it expects a string value, but we'll change it to integer.

  • In the test_sns_consumer_contract__orders.py, change order_total.units from string to integer. Protobuf will still serialize the value as a string, but the contract will expect an integer, which will cause the contract test to fail.
--- a/tests/customers/test_sns_consumer_contract__orders.py
+++ b/tests/customers/test_sns_consumer_contract__orders.py
@@ -70,7 +70,7 @@ async def test_consume_order_created_event(
         "order_id": Term(Format.Regexes.uuid.value, "cc935616-e439-45a9-89ed-c6ef32bbc59e"),
         "order_total": Like(
             {
-                "units": "100",
+                "units": 100,
                 "nanos": 990000000,
             }
  • Commit the changes under a new branch, e.g. test/break-consumer-contract. This will trigger the deployment pipeline and the new contract version will be published to PactFlow.

PactFlow - contract is in the Unverified state PactFlow - contract is in Unverified state

If the PactFlow is configured to work in a CI/CD pipeline as per the example in Running Pact in a Deployment Pipeline (CI/CD) section, the new Consumer contract version will trigger contract requiring verification published event in PactFlow. The PactFlow event will send a webhook to GitHub, which will trigger the pact-verify-provider-contract.yml GitHub Actions workflow.

The workflow will run the Provider contract tests against the new Consumer contract version and publish the verification results. This process should happen automatically, and the service-customers--sns Consumer team shortly will be notified that their changes are incompatible with the existing service-orders--sns Provider contract.

Failures:

  1) Verifying a pact between service-customers--sns and service-orders--sns Given New order is created OrderCreated event has matching content
     Failure/Error: expect(actual_contents).to match_term expected_contents, diff_options, example

       Actual:
        {
          "event_id": "fddf40ed-9c73-418b-9e12-bbf3ecc59f67",
          "correlation_id": "58b587a2-860c-4c4a-a9af-70457ffae596",
          "order_id": "c7a3ced2-163c-44a7-8996-7604ed0194ad",
          "customer_id": "1e5df855-a757-4aa5-a55f-2ddf6930b250",
          "order_total": { "units": "123", "nanos": 990000000 },
          "created_at": "2023-11-14T19:25:21.425832+00:00"
        }

Diff
--------------------------------------
Key: - is expected
    + is actual
Matching keys and values are not shown

{
  "order_total": {
-    "units": Integer
+    "units": String
  }
}

Description of differences
--------------------------------------
* Expected an Integer (like 100) but got a String ("123") at $.order_total.unit

This should spark a conversation between the teams (or colleagues on the same team if their own the Provider service) about the changes in the contract and the best way to proceed with the changes.

PactFlow - contract is in the Failed state PactFlow - contract is in the Failed state

  • To fix the contract, change order_total.units back to string, commit and push the changes to GitHub. This time, the contract requiring verification published event won't be triggered, because the Consumer contract returned to its previous state and has been already verified, so triggering the Provider contract verification is not necessary.

Breaking Provider Contract

Unlike testing breaking changes from the Consumer side, the Provider contract side happens exclusively inside the Provider repository. It's because the Provider tests will fetch the latest contract version from Pact Broker/PactFlow, and it doesn't have to trigger the deployment pipeline in the Consumer repository. This way, the Provider development team gets very quick feedback from their tests - by running the tests locally or in their own deployment pipeline.

  • Taking the service-customers--sns as an example provider, go to events.py and break the contract by setting the field name to an empty string. Since the Consumer server-order-history--sns depends on the name field, the contract will be broken.
--- a/src/customers/events.py
+++ b/src/customers/events.py
@@ -31,7 +31,7 @@ class CustomerCreatedEvent:
             event_id=str(self.event_id),
             correlation_id=str(self.correlation_id),
             customer_id=str(self.customer_id),
-            name=self.name,
+            name="",
             created_at=self.created_at.isoformat(),
         )
  • Run the Provider contract tests locally with pytest -m "provider and customers__sns" command and observe the failure. The Provider team got immediate feedback that their changes are incompatible with the existing Consumer contract.
Failures:

  1) Verifying a pact between service-order-history--sns and service-customers--sns Given New customer is created CustomerCreated event has matching content
     Failure/Error: expect(actual_contents).to match_term expected_contents, diff_options, example

       Actual:
        {
          "event_id": "1e5df855-a757-4aa5-a55f-2ddf6930b250",
          "correlation_id": "293178a5-4838-4e6a-8d63-18062093027e",
          "customer_id": "1e5df855-a757-4aa5-a55f-2ddf6930b250",
          "created_at": "2023-11-15T12:49:25.208438+00:00"
        }

Diff
--------------------------------------
Key: - is expected
    + is actual
Matching keys and values are not shown

{
-  "name": String
}

Description of differences
--------------------------------------
* Could not find key "name" (keys present are: event_id, correlation_id, customer_id, created_at) at $

Best Practices for Writing Consumer Contract Tests with Pact

Based on "Best practices for writing consumer tests" by Beth Skurrie. Watch the full video!

  • The key is knowing what not to test.

  • Keep the scope of contract tests limited. Ideally, test only the components of your application that interact with a Provider in isolation. Since the Contract Tests are not a replacement for functional tests, you don't need to test the whole application end-to-end. Read more at "Testing scope".

  • Don't use Pact for the functional testing of Consumers or Providers.

  • Don't use Pact for testing business logic or UI.

  • Don't write too restrictive Pact matchers. Make your response expectations as loose as possible.

  • Try to use BDD style notation to describe the business actions, rather than describing HTTP or messaging mechanisms. The contract tests should read like a proper, coherent sentence.

    • Use Pact's given, upon_receiving, with_request and will_respond_with constructs to describe a business action.

  • Use deterministic data. Avoid random data in your Pacts because it will mark a contract as changed, when in fact it hasn't.

  • Don't be afraid of duplication between functional and contract tests.

    • You can use fakes and mocks in contract tests to simplify and limit their scope.
    • Functional tests that use real components will catch all functional bugs. It's not in the scope of contract tests.

  • It's not the job of the Consumer to be a test harness for the Provider.

  • Responses can have extra keys without failing the verification, but requests cannot - Postel's law.

  • Contract tests aren't designed to operate alone. Contract tests supplement unit, functional, and all other types of tests.

  • Contract tests focus only on the messages (requests and responses). Functional tests also check for side effects.

  • Don't check for side effects in contract tests, e.g. if an object is stored in a database. It's the responsibility of the functional tests.

Contract Testing Beyond Catching Breaking Changes

The benefits of contract testing go beyond catching breaking changes in the contract, both syntactic and semantic, before release to production.

  • Consumer-Driven contract testing is more than a testing approach. Used a collaboration technique, it helps to establish clear lines of communication and collaboration between microservices and teams that consume them.

  • Ensures that compatible versions of the Consumer and Provider are deployed to production, e.g. by using Pact's Can-I-Deploy tool.

  • Provides full visibility into a Provider usage - consumer-driven contract testing gives the Provider team visibility into how their service is being used by Consumers. All the contracts are stored in a centralized place like Pact Broker/PactFlow, and the Provider team can see all the Consumers that depend on their service. It helps discover unused fields and messages that are not used by any Consumers, and misunderstandings about the Provider usage.

  • Specification by example - Consumer-Driven contract testing facilitates the collaboration between the Consumer and Provider teams of defining the contract, driven by real-world examples of how the Consumer uses the Provider, rather than using abstract requirements. As a result, the Consumer and Provider teams will have a better shared understanding of the contract and the business domain.

  • Living documentation - since consumer-driven contracts were defined by real-world examples, they can be used as a living documentation of how the Consumer uses the Provider. The contracts are always up to date and reflect the current state of Consumer/Provider relationships. The contract examples can be used as a reference when onboarding new team members, as a base for discussing new feature implementations, and in many other useful ways.

  • Consumer/Provider network map - the contracts stored in Pact Broker/PactFlow can be used to generate a network map of the Consumer/Provider relationships. It's a useful tool for visualizing the dependencies between services and teams. See example Application's Pact Network Diagram section.

  • Transport concerns like HTTP verbs, paths, headers and messaging topic names may be encapsulated in a contract.

Limitations and Corner Cases

  • At the time of writing, Pact Python doesn't support Pact Plugins, which makes testing GraphQL and Protobufs contracts more difficult, but not impossible.

  • Pact Pacticipant name must include a communication protocol, .e.g --rest or --sns. Due to Pact handling synchronous HTTP and asynchronous messaging contract tests differently, they can't be mixed in the same Pacticipant.

  • Due to the above, a single service can have multiple Pacticipants, one for each communication protocol the service supports. To run tests against the specific protocol, e.g. in the case when a Consumer contract has changed and you need to verify it against the specific Provider's protocol, you would need to use pytest markers and test selectors (pytest -m "orders__sns"). This is implemented in this example project.

  • GraphQL contract testing with python-pact - queries and mutations must be formatted in the same way as in the contract, including spaces and new lines, otherwise Pact Mock Server would not be able to match the request body (when the request is not JSON, Pact Mock Server does the full string match; GraphQL request is a string). One way out would be to always "minify" (remove all whitespace and newlines) the GraphQL queries and mutations before sending the requests. At the time of writing, GraphQL plugin is not available in the Python Pact library, but is implemented for other languages, e.g. Pact JS.

References

Development

Example Application's Sample Requests

  • Create customer.
curl -X POST --header "Content-Type: application/json" -d '{
  "name": "John Doe"
}' http://localhost:9701/customer
  • Get customer.
curl http://localhost:9701/customer/d5c6999b-9ee3-4ba1-aec0-6fbe8d9d8636
  • Create order.
curl -X POST --header "Content-Type: application/json" -d '{
  "customer_id": "d5c6999b-9ee3-4ba1-aec0-6fbe8d9d8636",
  "order_total": 12399
}' http://localhost:9702/order
  • Get order.
curl http://localhost:9702/order/8fccc85c-bbdd-47fb-b6c9-c5ed9a8d88df
  • Get order history for all customers (GraphQL query).
curl -X POST -H "Content-Type: application/json" -d '{"query": "{getAllCustomers {id name orders {id orderTotal state}}}"}' http://localhost:9703/graphql

Links

Commands

  • Format and lint code.
poetry run format
poetry run lint
  • Run tests. Test execution order is configured with pytest-order to run consumer tests first, then provider tests.
poetry run test
poetry run test-ci  # With test coverage
  • Generate Protobuf code with buf.
brew install bufbuild/buf/buf

cd src/adapters/proto
buf generate .
java -jar plantuml.jar -DRELATIVE_INCLUDE="." docs/**/*.puml

About

An example of applying Consumer-Driven Contract Testing (CDC) for testing microservice compatibility in isolation.

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python testing microservices integration-testing test-automation end-to-end-testing pact cdc contract-testing consumer-driven-contract-testing python-pact

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