Some weeks ago I was asked how to properly do integration testing in a microservices environment. At that time, I was aware that testing basic behaviour with integration tests was not a smart move. Guaranteeing compatibility among services’ interfaces, however, was something I could see the value of. The dos and don’ts, in such effort, were not familiar to me, though. Therefore, I decided to dive in and find some answers.
To begin with, higher level (e.g. end-to-end, integration) testing lacks several benefits of unit testing, many of which we have come to value as an industry. On the other hand, not all bugs are apparent at an unit level. They could also happen on the wiring between components or even in those off-the-shelf solutions that you employed to speed up your development. Yet you often heard the agile community endorsing unit tests as the backbone of a solid testing strategy. People like @mikewcohn, who established the initial model of the testing pyramid, were key in developing the notion that the ratio of a particular kind of test, in your test suite, should be inversely proportional to the degree of granularity of the tested scope. Principle that helps a solid test suite to be built in the most cost-effective way. And beware, since going in the opposite direction may result in an instance of the ice cream cone anti pattern. So, even though you certainly could find several definitions for the properties of good unit tests out there, those roughly translate to:
The first one is easy to explain, as long-running tests are the easiest way to make programmers develop the bad habit of avoid running tests frequently. And, if that happens, what is the point anyway? Automated since the intention is to facilitate adoption, not making people do repetitive work. Isolated because they should not overlap, otherwise you would have more places to look for when something fails. Informative, because the context of the failure should be explicit. Yep, you probably do not want people to analyse your test’s source so that they can understand what went wrong. Finally, idempotency implies that they should behave the same, no matter which order or how many times they were run. Believe me, tests that randomly fail are a recipe for madness. They are worse than no tests at all, as they undermine developers’ trust in their test suite.
So, how do those properties apply to higher level (e.g. end-to-end, integration) testing. First, they are not as fast as unit tests. Especially if you are testing two microservices, process that would involve exchanging some packets over the network (latency sucks :/). They are not idempotent too, as there are many ways they could go wrong. Units that rely in global state (e.g. singleton pattern) can also suffer from that unpredictability, but a proper use of dependency injection can fix the problem. As for lost packets, network partition, good luck with it. Informative, well, you know something in between a set of components/services is not working well. Isolated? Nope, even though you can be cautious enough to avoid chatty components/services, one bug in one of them and you would suddenly find yourself in a situation in which every code path along the way fail. But hey, they could be automated.
Did you find my point of view a bit extreme? Then try @jbrains amazing talk titled Integrated Tests Are a Scam. Seriously, if you want to laugh a bit with integrated tests’ infamous positive feedback loop of negative emotions, watch it. Additionally, a written equivalent of it is available on Integrated Tests are a Scam: Part 1. In the aforementioned talk, @jbrains shows how the promises of high level testing lures developers into favouring writing more tests of the same kind that, at the end, would provide very few coverage. Due to the combinatorial explosion of required tests, needed for the continuously increasing code paths. Instead, he advocates that we should spend our time with worthwhile tests. By worthwhile he means tests that help assessing the quality of our architecture, allowing us to improve its design in the long run. Position that is understandable, since he is a test-driven development (TDD) practitioner. After all, TDD is not about testing, it is about design. As for the tests, they are solely a pleasant by-product.
In Part 2: Some Hidden Costs of Integration Tests, he also discusses about an important side-effect of slow tests, they destroy developers’ productivity. Waiting for a few seconds is OK, but it is not rare to find tests suites that take ten minutes or more. Unfortunately, one cannot simply return to him/her peak performance right after such a long interruption. In Part 3: The risks associated with lengthy tests, the focus changes to the insidious consequences of frequent false alerts, given the lack of isolation when things fail. And with less trust in the test suite, a fear of change starts to evolve among developers. Individuals that tend to justify their behaviour by mentioning an old engineering saying “If it works, why change it?”. Rationale that will ultimately lead to an architectural stagnation and to high interest rates in the form of technical debt. Quite the contrary to what you expected when you got bought into the practice of self testing code, right?
But do not get him wrong, his disregard for integration testing limits itself to cases in which they are used for assessing basic correctness. Role that is better suited to unit tests, in the first place. In Using integration tests mindfully: a case study, for instance, he does see a value in employing integration tests for identifying system-level issues like broken database schema, mistaken cache integration, and more complex problems. That is, using integration tests to check the presence of a expected feature, is perfectly fine.
Still, in Integrated Tests Are a Scam, @jbrains proposes an alternative for testing the interaction between components without resorting to integration testing. He suggests combining collaboration and contract tests. Collaboration tests are a well known practice, often named as test doubles. More specifically, stubs are the the kind of doubles we are interested in. Stubs tend to mimic others’ interfaces, but instead of doing real work, they return pre-computed results. Behaviour that is really useful when non-deterministic or slow operations (e.g. IO) are at stake, as we can employ fast and predictable unit-like tests to achieve a similar end. As for contract tests, they check the format of an component/service response, not its data. So, in the case of microservices, you would be testing if the outcome of a particular call has the fields you expected and if so, whether they comply with your use cases. Similarly, @martinfowler also see the combination of stubs and contract tests as a good way to tackle the slowness and unreliability of integration tests, as stated in integration contract test.
One challenge though is that testing against a double does not guarantee that the external component/service is being accurately represented. And even if so, future changes would require the double to be updated accordingly. One alternative to streamline the updates of stubs would be what @martinfowler calls self initialising fakes. Similarly, contract testing also suffers from the same synchronisation burden, however self initialising fakes cannot help contract tests in the same manner. Additionally, there is also the possibility of contracts and stubs getting out of sync. Problem that could be mitigated by a shared metadata file or data structure that specifies available calls and what should be received in response, so that you do not have to concern yourself with it.
To reduce the odds of getting out of sync, therefore breaking your test cases, or even worse, being misled by passing tests that should have failed, it is recommended to adopt a consumer-driven contract approach. In Consumer-Driven Contracts: A Service Evolution Pattern, the concept is explained in a relatively implementation agnostic fashion. In a nutshell, consumer-driven contracts are a means of applying “just enough” validation, as proposed by John Postel’s Law, which puts the responsibility to specify what the service provider must comply on the clients. The service provider must then check the union of its consumers’ expectations, in order to verify that there were no regressions. Additionally, that approach has some notable design advantages. First, it facilitates evolving your interface, as you wouldn’t have to rely on schema extension points for adding new fields to your messages. Second, since what is consumed is explicitly stated, deprecating a field that nobody is using is way easier.
Fortunately, the usefulness of mixing stubs and consumer-driven contract tests have led to the development of frameworks such as Pact and Pacto, both written in Ruby. More importantly, they facilitate your stubs and contract tests to be in sync. Personally, I think that frameworks like that are a really promising way for guaranteeing compatibility among services’ interfaces, while maintaining many of the unit testing properties. So, next time you get yourself considering to test some microservices with integration testing, think twice. If you just want to check compatibility among services’ interfaces, invest in stubs and consumer-driven contract testing instead.