Testing Asynchronous Code

Updated: Jun 17, 2018



Asynchronous code is hard. Everyone knows that. Writing asynchronous tests is even harder. Recently I fixed a flaky test and I want to share some thoughts about writing asynchronous tests.

In this post we explore a common problem with asynchronous tests—how to force a test to take a specific ordering between threads, and forcing some operations by some threads to complete before other operations by other threads. Normally we do not want to enforce ordering between the execution of different threads because it defeats the reason to use threads, which is to allow concurrency and to allow the CPU to select the best order of execution given the current resources and state of the application. But in the case of testing, deterministic ordering is sometimes required to ensure the test stability.

Testing a Throttler A throttler is a pattern in software that is responsible for limiting the number of concurrent operations to preserve some resource quota, like a connection pool, a networking buffer, or a CPU-intensive operation. Unlike other synchronization tools, the role of a throttler is to enable “fail-fast”, allowing the over-quota requests to fail immediately without waiting. Failing fast is important because the alternative, waiting, consumes resources—ports, threads, and memory.

Here is a simple implementation of a throttler (basically it is a wrapper around a Semaphore; in the real world there could be waiting, retries, etc.):



specs2



In the previous test we did not saturate the throttler simply because it is not possible with a single thread. So the next step is to test that the throttler works well in a multithreaded environment. The setup:



A simplistic way to test multithreaded behavior of the throttler is the following:



Here we’re creating maxCount threads (the calls to Future {}) that call the waitForever function, which is waiting until the end of the test. Then we try to perform another operation to bypass the throttler—maxCount + 1. By design, at this point we should get a ThrottledException. However, while we wait for an exception, one may not happen. The last call for a throttler (with expectation) may occur before one of the futures has started (causing an exception to be thrown in this future but not at the expectation).

The problem with the above test is that we do not ensure all the threads have started and are waiting in the waitForever function before we try to violate the throttler with the expected result of the throttler throwing an exception. To fix it, we need some way to wait until all futures start. Here is an approach that is familiar to many of us: just add a sleep method call with some reasonable duration.



search Google

A better approach is to synchronize the start of our threads (futures) and our expectation. Let’s use CountDownLatch class from java.util.concurrent:



barrier synchronization

By that point, we are ensured that the throttler is saturated, with maxCount threads inside it. An attempt by another thread to enter the throttler will result in an exception. We have a deterministic way to set up our test, which is to try and have the main thread enter the throttler. The main thread can and does resume at this point (the latch count reaches zero and the CountDownLatch releases the waiting thread).

We use a slightly higher timeout as a safeguard to avoid blocking infinitely if something unexpected happens. If something does happen, we fail the test. This timeout won’t affect the test duration because, unless something unexpected happens, we should not wait for it.

Conclusion When testing asynchronous code it is quite common to require a specific ordering of operations between threads for a specific test. Not using any synchronization results in flaky tests that sometimes work and sometimes fail. Using Thread.sleep slows down and reduces the flakiness of tests, but it does not solve the problem.

In most cases when we need to enforce ordering between threads in a test, we can use a CountDownLatch instead of Thread.sleep. The advantage of CountDownLatch is that we can tell it when to release the waiting (holding) thread, gaining two important benefits: deterministic ordering, and therefore more reliable tests, and faster running tests. Even for trivial waiting—for example, the waitForever function—we could have used something like Thread.sleep(Long.MAX_VALUE), but it’s always better not to use fragile approaches.

You can find the full code on GitHub.

#Code #asynchronous #CountDownLatch #latch #Scalability #semaphore #throttler

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