Troubleshooting Flaky Hard Delete Issues

Oct 23, 2025 by TheNnagam 41 views

Hey guys, let's dive into a common headache in software development: flaky tests. Specifically, we're going to dissect a situation where a hard delete operation is causing some trouble. We're talking about the /mob/living/carbon/human/consistent path, and how it's failing intermittently. This isn't just about a simple test failing; it's about understanding why the same test passes sometimes and fails others. This inconsistency, as we'll see, is what makes a test 'flaky'. And flaky tests are a pain because they waste time and erode confidence in your test suite. So, let's get our hands dirty and figure out what's going on.

Understanding Flaky Tests

First off, what exactly is a flaky test? Simply put, it's a test that gives different results on different runs without any changes to the code. You run it once, it passes. You run it again, and it fails. Then, you run it a third time, and it passes again. This behavior makes it incredibly difficult to debug. You might spend hours chasing ghosts, only to find that the issue was transient, caused by some external factor, or a race condition. Flaky tests are the enemy of continuous integration and continuous delivery (CI/CD) pipelines. They can block deployments, generate false positives and negatives, and lead to wasted developer time.

In our case, the specific area of concern is the create_and_destroy test in the context of hard deleting /mob/living/carbon/human/consistent. As the error message indicates, this test has failed once out of 808 total delete attempts. This low failure rate might make it seem like a minor issue, but even a small percentage of flaky tests can be a major problem. It's like having a leaky faucet; it might not seem like a big deal at first, but it can lead to bigger issues down the line. We need to identify the root cause and ensure it's addressed so that our tests are reliable and our software is stable. Analyzing the provided information, the error message, and understanding the context will help us pinpoint the potential source of the issue. We'll explore possible culprits like race conditions, resource contention, and timing issues.

The Anatomy of the Error

Let's break down the error message we've got: create_and_destroy: /mob/living/carbon/human/consistent hard deleted 1 times out of a total del count of 808 at code/modules/unit_tests/create_and_destroy.dm:99. This tells us a few key things:

Test Name: create_and_destroy - This gives us a clue about the test's purpose. It likely involves creating an object (in this case, related to a 'mob', which could be a character or entity), and then hard deleting it.
Target Path: /mob/living/carbon/human/consistent - This path likely represents the location or ID of the object that the test is interacting with. It's crucial for understanding the scope of the test and the objects involved.
Hard Delete: The error focuses on a hard delete. This implies that the object is not just being marked as deleted, but is actually being removed from the system. This process is generally more resource-intensive than a soft delete (where the object is marked but not immediately removed), and therefore, more prone to issues.
Failure Count: The error occurred 1 time out of 808 attempts. While the failure rate is low, the fact that a failure occurred at all indicates a problem. Even a single failure can lead to CI/CD pipeline issues and reduced confidence in the software's reliability.
File and Line Number: code/modules/unit_tests/create_and_destroy.dm:99 - This is gold! This points us directly to the line of code that's failing, allowing us to examine the code and understand the logic of the hard delete operation. It's important to understand the code on line 99 and the context around that line of code to figure out what's going wrong. It could involve dependencies on external resources, race conditions with other threads or processes, or timing issues that cause the hard delete to fail. The line of code is the starting point for investigation.

Potential Causes of the Flaky Behavior

Alright, so what could be causing this flaky behavior? Here are some common suspects:

Race Conditions: This is a big one. If multiple threads or processes are trying to access or modify the same resource (the object represented by /mob/living/carbon/human/consistent) at the same time, it can lead to unpredictable behavior. For example, one thread might be trying to create the object while another is trying to delete it. The timing of these operations can determine whether the delete succeeds or fails.
Resource Contention: This occurs when multiple parts of the system compete for the same resource, such as memory, disk space, or database connections. If the hard delete operation requires exclusive access to certain resources, contention can cause the operation to fail intermittently. For example, if another process has a lock on the object's data, the delete might fail until the lock is released.
Timing Issues: The timing of operations can be a critical factor. If the test relies on certain actions happening within a specific timeframe, even slight variations in execution speed can cause problems. For example, if the test deletes the object before it has been fully created and initialized, it may fail. This is especially relevant in distributed systems or environments where network latency can introduce unpredictable delays.
External Dependencies: The hard delete operation might depend on external services or resources, such as databases, file systems, or network connections. If these dependencies are unreliable or have performance issues, they can directly impact the success or failure of the test. For instance, a database connection error could prevent the object from being hard deleted.
Concurrency Issues: If the system is not designed to handle concurrent operations properly, it can lead to data corruption or unexpected behavior. Hard deletes, in particular, must be handled with care to ensure the consistency of the data and the integrity of the system. Race conditions and synchronization problems are the core of this issue.

Troubleshooting Steps and Solutions

Okay, now that we have some possible causes in mind, let's talk about how to tackle this problem:

Examine the Code: Start by carefully reviewing the code at code/modules/unit_tests/create_and_destroy.dm:99 and the surrounding code. Understand exactly what the test does, how it creates and deletes the object, and what dependencies it has. Look for any potential concurrency issues, race conditions, or timing dependencies. Consider questions like: Is there any synchronization logic in place? Are there any asynchronous operations? What happens if the delete operation fails?
Add Logging and Monitoring: Implement comprehensive logging to capture detailed information about the hard delete operation. Log the start and end of the operation, any errors encountered, and any relevant data, such as timestamps, resource usage, and thread IDs. Monitoring these logs can help you identify patterns and pinpoint the root cause. This should include timestamps to check the execution order of operations.
Reproduce the Flakiness: Try to reproduce the flaky behavior consistently. You can do this by running the test multiple times in a row, or by introducing artificial delays or variations in the environment to simulate potential race conditions or timing issues. This makes the debugging process far easier.
Isolate the Test: If possible, try to isolate the test from external dependencies. Mock or stub out any external services or resources to ensure that the test is self-contained and does not rely on external factors. Reduce the complexity to simplify the debugging process.
Use Debugging Tools: Use debugging tools to step through the code, inspect variables, and monitor the execution flow. This can help you identify any unexpected behavior or errors that might be causing the flaky behavior. Breakpoints will be essential here.
Implement Synchronization Mechanisms: If you suspect race conditions, use synchronization mechanisms, such as mutexes, semaphores, or locks, to protect shared resources. This can prevent multiple threads or processes from accessing the same resource simultaneously, which can lead to unpredictable behavior. Make sure the code is thread-safe.
Introduce Retries: In some cases, it might be beneficial to implement retries for the hard delete operation. If the operation fails due to transient issues, such as temporary network errors or resource contention, retrying the operation after a short delay might resolve the problem. Beware of infinite loops!
Optimize Resource Usage: If resource contention is a concern, optimize resource usage to reduce the likelihood of conflicts. This might involve optimizing database queries, reducing the size of data transfers, or improving memory management. Ensure that resources are released properly after use.
Review the Environment: Ensure that the test environment is stable and consistent. Any differences in the environment (e.g., different versions of dependencies, different hardware configurations) can introduce unexpected behavior. Test in a controlled environment to minimize variability.
Refactor and Simplify: If the code is complex or difficult to understand, consider refactoring it to improve readability and reduce the likelihood of errors. Simplify the hard delete operation and remove any unnecessary complexity. Less code, less problems.

Conclusion

Dealing with flaky hard delete tests can be a real pain, but by systematically investigating the issue, using the right tools, and understanding the potential causes, you can root out the problem. Remember to focus on the code, test the system, and be patient. The goal is to build a reliable and robust system, so putting in the effort now will pay off in the long run. Good luck, and happy debugging!