Fixing Unstable Network Tests On GitHub Actions

by Admin 48 views
Fixing Unstable Network Tests on GitHub Actions

Hey guys, let's dive into a frustrating issue that's been bugging us: the flaky network chaos tests we run on GitHub Actions runners. These tests are super important because they help us make sure our network behaves as expected under various challenging conditions. Unfortunately, they've been acting up lately, causing a lot of headaches. In this article, we'll explore the problem, the potential causes, and how we can make these tests more reliable. This is crucial because unstable tests hide real issues, making it tough to catch and fix problems in our code. Let's get started!

The Problem: Unstable Network Chaos Tests

So, what's the deal? Well, the network chaos tests, designed to simulate different network conditions, are failing frequently on GitHub Actions. You can check out a specific example here: https://github.com/xmtp/xmtp-qa-tools/actions/runs/18988341680. The main issue is that these tests are unstable. This means that they don't consistently pass, which makes it hard to trust the results and identify actual problems with our software. When running these tests locally, the success rate is almost perfect, but when running them on GitHub Actions, it drops significantly. This inconsistency is a major problem, as it can hide genuine issues within our code and infrastructure.

What are Network Chaos Tests?

Network chaos tests are designed to simulate various network conditions to ensure our software behaves correctly. These tests involve introducing artificial network issues like increased latency, packet loss, or bandwidth limitations. By running these tests, we can identify how our systems react to these stressful conditions. It's like putting a car through rigorous tests to ensure it handles different terrains and weather. These tests are essential for ensuring the resilience and reliability of our network-dependent software, so their instability is a significant concern.

The Impact of Unstable Tests

Unstable tests have several negative consequences. They increase the time and effort required to validate changes and identify issues. When tests fail randomly, developers spend time trying to understand if a failure is due to a real problem or an environmental issue. This wastes time and can lead to frustration. Moreover, unreliable tests can mask underlying problems, as developers may become desensitized to failures. The primary goal of these tests is to confirm whether the system works correctly under specified conditions. Frequent and unpredictable failures undermine this goal, impacting the team's ability to maintain a robust and reliable system. Therefore, stabilizing these tests is a high priority.

Identifying the Root Cause

Why are these tests failing on GitHub Actions while passing locally? One likely reason is the environment differences between the local setup and the GitHub Actions runners. GitHub Actions runners are virtual machines with shared resources, making them subject to variations in performance and resource availability. This might affect the complex network shaping operations we're using. Another aspect to consider is the specifics of how the tests are configured and the dependencies they rely on. The versions of libraries and tools used in the tests can lead to different results, as well as the test's configuration file. Therefore, we should delve deep into the test scripts, configuration files, and the environment they are running in. Let's pinpoint the issue.

Potential Culprits

  • GitHub Actions Environment: The GitHub Actions environment itself might be the source of instability. Runners may have resource limitations or variations in network performance that interfere with the chaos tests.
  • Traffic Control and iptables: The tools we use to shape the network traffic (traffic control and iptables) might not work consistently in the GitHub Actions environment. These tools are crucial for simulating network conditions. Any inconsistencies could cause the tests to fail.
  • Resource Contention: The runners are shared, and resource contention (CPU, memory, network bandwidth) can impact the tests. This can lead to delays or failures.
  • Test Configuration: The way the tests are set up and configured could be problematic. Improper configuration of network conditions or test parameters can lead to unpredictable results.
  • Dependencies and Versions: Inconsistencies in the versions of libraries and tools used in the tests, as well as the testing framework itself, can lead to different outcomes.

Local vs. GitHub Actions:

The disparity in test results between local and GitHub Actions environments highlights the importance of diagnosing the root cause. This discrepancy leads us to the fact that the tests pass flawlessly when run locally. This suggests a problem is in the GitHub Actions environment rather than in the core logic of the tests or our codebase. Local environments often have more stable resources and less overhead. It is thus very important that our primary objective be understanding these environmental inconsistencies.

Steps to Stabilize the Tests

So, how do we fix this? Here's a plan to get those tests back on track and make them reliable. Improving the reliability of these tests will involve several steps, from identifying the root cause to implementing solutions and continuously monitoring the tests.

1. Detailed Investigation

First things first, we need to dive deep into the logs and metrics. This involves scrutinizing the logs from the test runs on GitHub Actions to look for clues about what went wrong. Pay close attention to error messages, timeouts, and any unusual behavior. We should also collect metrics on resource usage (CPU, memory, network) during the test runs to see if there are any bottlenecks or contention issues. The aim is to gather as much data as possible to understand the failures.

2. Isolate the Problem

Next, we should try to isolate the problem. This might involve running the tests on different GitHub Actions runners or different configurations. We can also simplify the tests to identify the specific steps that cause the failures. By doing this, we can determine the exact source of the instability. We should try to pinpoint which part of the test suite is causing the problem.

3. Environment Tweaks

We might need to tweak the GitHub Actions environment. This can include increasing the resources allocated to the runners (if possible), or adjusting the network configuration to make it more stable. We can try different runner types to see if it makes a difference.

4. Code and Configuration Review

Review the test code and configuration. We need to ensure that the tests are correctly configured to simulate network conditions. We should also check for any race conditions or dependencies that might be causing problems. Make sure all the dependencies are correctly declared, and the test scripts are running in the correct order.

5. Test Framework Upgrades

Make sure the test framework and related tools are up-to-date. This can include updating the libraries and tools used for network shaping (traffic control and iptables). It might be that the older versions are not fully compatible with the GitHub Actions environment.

6. Implement Retries and Tolerances

Implement retries for failing tests. This won't fix the underlying problem, but it can make the tests more resilient to transient failures. We might also consider adding tolerances to the tests so that they can tolerate minor variations in network conditions.

7. Continuous Monitoring

Once the tests are stabilized, continuously monitor their performance. We can track the pass/fail rate and investigate any new failures immediately. This will help us prevent future instability.

Expected Outcome

The goal is to make the tests as reliable as they are locally, with a pass rate of 90% or higher. This will help us avoid false positives and gain confidence in our software's ability to handle difficult network conditions. The tests should pass barring underlying SDK/V3 issues, and the test suite should reliably confirm the behavior of our software. Achieving this will significantly reduce the time spent troubleshooting failed tests, allowing us to focus on the more important work of improving our product.

Conclusion

Fixing the unstable network chaos tests on GitHub Actions is crucial for ensuring the reliability of our network-dependent software. By carefully investigating the root causes, implementing the right solutions, and continuously monitoring the results, we can make the tests reliable. This will save us time and energy, and make sure that our tests provide reliable data about our product's performance. By stabilizing these tests, we not only ensure the quality of our current software but also build a foundation for future development, allowing us to be more confident in our ability to handle complex and dynamic network conditions. Thanks for sticking with me, and let's get those tests fixed!