Troubleshooting Increased Out Of Memory Errors After K2 And Dagger Upgrade

Hey guys!

We've seen a recent spike in Out Of Memory (OOM) and Java Heap issues in our CI environment, particularly within the CIDiscussion category. This increase seems to correlate with a recent upgrade to K2 2.1.20 and Dagger 2.55. This article dives deep into the problem, exploring the changes, the symptoms, and potential solutions. Let's break it down and figure out how to get things running smoothly again.

The Problem: Out Of Memory Errors Galore

Since upgrading to K2 2.1.20 and Dagger 2.55, we've been battling a surge of Out Of Memory errors and Java Heap issues in our CI environment. These errors are impacting our builds and, more specifically, our unit tests and release app bundle generation. The increase in these memory-related issues is definitely causing headaches, and we need to address them quickly to maintain a stable and efficient development workflow. It's like our builds are suddenly hitting a memory wall, and we need to figure out what's causing the bottleneck.

These Out Of Memory errors (OOMs) are critical exceptions that occur when a Java Virtual Machine (JVM) cannot allocate memory for new objects. This typically happens when the application tries to use more memory than the JVM is configured to provide, or when there are memory leaks preventing the garbage collector from reclaiming unused memory. In the context of Android development, OOMs can lead to application crashes, unstable builds, and a frustrating experience for developers and users alike. When these issues pop up in our Continuous Integration (CI) environment, they can disrupt the entire development pipeline, delaying releases and hindering productivity. That's why pinpointing the root cause and implementing effective solutions is so crucial.

Our investigation has revealed that these issues are particularly prevalent during two key processes: running unit tests in a shard with 100+ modules and building release app bundles. The fact that these operations are memory-intensive is not surprising. Unit tests, especially when run across a large number of modules, can generate a significant amount of temporary objects and data. Similarly, building release app bundles involves complex processes like code minification, optimization, and resource packaging, all of which require substantial memory resources. The fact that these specific processes are triggering the OOM errors strongly suggests that the upgrade to K2 2.1.20 and Dagger 2.55 may have introduced changes or optimizations that exacerbate memory usage in these scenarios. This could be due to increased memory consumption by the compiler, changes in object allocation patterns, or even subtle memory leaks within the upgraded libraries. The variation in stack traces also hints at the complexity of the issue, suggesting that multiple underlying causes could be contributing to the problem. So, it's not just a simple matter of increasing the heap size; we need to dig deeper and understand the specific memory bottlenecks created by the upgrades.

The Changes: K2 and Dagger Get an Upgrade

To give you some context, the primary change we made was upgrading to K2 2.1.20 and Dagger 2.55. These are significant updates to core components of our Android development stack.

K2, the new Kotlin compiler, promises significant performance improvements and new language features. However, with any major compiler update, there's always a chance of introducing new behaviors or even bugs that could impact memory usage. It’s possible that the new compiler is generating different bytecode or using memory in a different way, which might be contributing to the OOM issues. We need to investigate if the K2 compiler is allocating more memory during the compilation process, or if it's creating objects that are not being garbage collected as efficiently as before. This could involve profiling the compilation process and analyzing the memory footprint of the generated code. It's also important to check the K2 release notes and issue tracker for any known memory-related issues or recommendations for optimization. The potential benefits of K2 are huge, but we need to ensure that its adoption doesn't come at the cost of increased memory instability in our builds.

Dagger 2.55, a popular dependency injection framework, helps us manage dependencies in our codebase. While Dagger is generally known for its efficiency, any upgrade to a framework like this could potentially introduce changes in object creation and management that impact memory usage. Dependency injection frameworks can sometimes lead to memory leaks if dependencies are not properly scoped or released. We need to examine the Dagger configuration in our project and ensure that we're not inadvertently creating circular dependencies or holding onto objects longer than necessary. We might also need to review our Dagger modules and components to see if any changes are needed to optimize memory usage. Dagger is a powerful tool, but it's crucial to understand how it interacts with the rest of our codebase to avoid memory-related pitfalls.

It's worth noting that these upgrades might not be the sole cause of the problem. It’s possible that the upgrades have simply exposed existing memory issues in our code or in our CI environment configuration. For example, we might have memory leaks in our codebase that were previously masked by the older versions of K2 and Dagger. Or, our CI environment might have memory limits that are too restrictive for the upgraded tools. Therefore, we need to approach the investigation holistically, considering both the changes introduced by the upgrades and the overall memory landscape of our project and CI environment.

The Symptoms: Stack Traces and Affected Processes

The stack traces we're seeing are varied, indicating that the Out Of Memory errors might have different underlying causes. This makes the debugging process more challenging, as we can’t simply focus on one specific area of the code. Instead, we need to analyze the different stack traces to identify common patterns or potential root causes. Some stack traces might point to issues within our own code, such as memory leaks or inefficient data structures. Others might point to issues within the K2 compiler or the Dagger framework itself. Understanding the common threads across these different stack traces will be key to developing effective solutions.

As mentioned earlier, the issue primarily surfaces during unit tests in a shard with 100+ modules and while building release app bundles. The fact that these two processes are affected is significant. Running unit tests across a large number of modules means that a lot of code is being compiled and executed in memory. This can put a significant strain on the JVM heap, especially if the tests are not designed to be memory-efficient. Building release app bundles, on the other hand, involves a lot of complex transformations and optimizations, such as code shrinking, obfuscation, and resource compression. These processes can also be very memory-intensive, especially for large and complex applications. The fact that both unit tests and release builds are triggering the OOM errors suggests that we need to look at the overall memory footprint of our application and identify areas where we can reduce memory usage.

To effectively diagnose the issue, we need to gather more data. This includes collecting detailed memory usage statistics during the affected processes, such as heap dumps and memory profiles. We should also try to reproduce the issue locally to facilitate debugging. This will allow us to step through the code, examine memory allocations, and identify potential memory leaks or inefficient data structures. Additionally, it’s important to monitor the memory usage of our CI environment to ensure that it has sufficient resources to run our builds and tests. We might need to increase the JVM heap size or add more memory to the CI machines to prevent OOM errors. By combining detailed diagnostics with targeted code analysis, we can hopefully pinpoint the root causes of these issues and implement effective solutions.

Potential Solutions and Next Steps

Okay, so what can we do about all this? Here are a few potential avenues we're exploring:

1. Increase Heap Size: This is the most straightforward approach, but it's more of a band-aid than a long-term solution. We could try increasing the JVM heap size in our CI environment to give the builds more breathing room. However, simply throwing more memory at the problem might not address the underlying cause, and it could even mask memory leaks or other inefficiencies. It's like trying to fix a leaky faucet by putting a bigger bucket underneath – it might catch the drips for a while, but it doesn't solve the real problem. Therefore, we should consider increasing the heap size as a temporary measure while we investigate the root cause, but we shouldn't rely on it as a permanent solution. We need to understand why the heap is filling up in the first place and address the underlying memory issues. This might involve optimizing our code, fixing memory leaks, or using more efficient data structures. By tackling the root cause, we can prevent OOM errors from recurring in the future and ensure that our application is running efficiently.

2. Analyze Heap Dumps: We can take heap dumps during the OOM errors and analyze them using tools like Memory Analyzer Tool (MAT) to identify memory leaks and large object allocations. Heap dumps are snapshots of the JVM heap memory, capturing the state of all objects in memory at a particular point in time. By analyzing these snapshots, we can identify which objects are consuming the most memory, which objects are being leaked, and the relationships between objects. This information can be invaluable in pinpointing the root cause of OOM errors. Tools like MAT provide powerful features for analyzing heap dumps, such as identifying memory leaks, finding the largest objects in the heap, and visualizing object relationships. We can use these tools to drill down into the memory usage patterns of our application and identify areas where we can optimize memory usage. Analyzing heap dumps can be a complex and time-consuming process, but it's often the most effective way to diagnose memory-related issues.

3. Review Code for Memory Leaks: We need to meticulously review our code, especially areas that have changed recently, for potential memory leaks. Memory leaks occur when objects are no longer needed by the application but are still being held in memory, preventing the garbage collector from reclaiming them. Over time, these leaks can accumulate and exhaust the available memory, leading to OOM errors. We should pay close attention to object lifecycles, ensuring that objects are released when they are no longer needed. We should also be careful about using static variables to hold references to objects, as these objects will persist for the lifetime of the application and can easily lead to memory leaks. Tools like static code analysis can help us identify potential memory leaks in our code, but manual code review is also essential. We should also consider using techniques like object pooling to reduce the overhead of object creation and destruction.

4. Profile Unit Tests: We should profile our unit tests to identify tests that are consuming excessive memory. Some unit tests might be creating a large number of objects or holding onto objects longer than necessary. By profiling our tests, we can identify these memory-hungry tests and optimize them to reduce their memory footprint. This might involve using more efficient data structures, reducing the scope of test data, or using mocks and stubs to isolate components. We can use profiling tools to monitor the memory usage of our tests and identify the tests that are contributing the most to the overall memory consumption. We should also consider running our unit tests in parallel to improve performance, but we need to be mindful of the potential impact on memory usage. Running tests in parallel can increase the overall memory consumption, so we might need to adjust the JVM heap size or the number of parallel test threads to prevent OOM errors.

5. Investigate K2 and Dagger Integration: We need to investigate how K2 and Dagger are interacting and if there are any known issues or best practices related to memory usage. As mentioned earlier, it's possible that the upgraded versions of K2 and Dagger are contributing to the OOM errors. We should check the release notes and issue trackers for these libraries to see if there are any known memory-related issues. We should also review the documentation and best practices for using K2 and Dagger together, paying particular attention to memory management. It's possible that there are specific configuration settings or coding patterns that can help us reduce memory usage. We might also need to experiment with different versions of K2 and Dagger to see if the issue is specific to a particular version combination. By thoroughly investigating the K2 and Dagger integration, we can hopefully identify any compatibility issues or misconfigurations that are contributing to the OOM errors.

We're actively working on these solutions and will keep you updated on our progress. This is a team effort, and your insights and contributions are greatly appreciated! We're committed to squashing these OOM bugs and getting our builds running smoothly again.