HGVS Unit Test Cache: Solving Growth Issues

by Omar Yusuf 44 views

Hey guys! 👋 Let's dive into a crucial discussion about the HGVS unit test cache. As the hgvs project grows, so does our cache-py3.hdp file. Recently, it ballooned to 180MB in one PR, which is a clear sign that our current strategy of caching all test data locally isn't sustainable. We need a long-term solution, and this article will explore the options and chart a path forward.

The Importance of Efficient Unit Testing

Unit testing is the cornerstone of robust software development. It ensures that individual components of our code function as expected. In the context of HGVS, this means verifying that our parsing, formatting, and validation logic works correctly for a wide range of variants. Efficient unit tests are crucial for maintaining code quality, preventing regressions, and enabling rapid development.

The Problem: Cache File Growth

Our current approach involves caching test data locally in the cache-py3.hdp file. This has served us well initially, but as the number of test cases increases, the cache file grows exponentially. A large cache file has several drawbacks:

  1. Increased Repository Size: A massive cache file bloats the repository, making it slower to clone, checkout, and update. This impacts developers' productivity and increases storage costs.
  2. Longer Test Execution Times: Loading and processing a large cache file can significantly increase test execution times. This slows down the development cycle and makes it harder to run tests frequently.
  3. Maintenance Overhead: Managing a huge cache file becomes cumbersome. Adding new test cases requires updating the cache, which can be a time-consuming process.

The Goal: A Sustainable Solution

Our goal is to find a sustainable solution that addresses these issues while maintaining the effectiveness of our unit tests. We need an approach that:

  • Reduces the size of the cache or eliminates it altogether.
  • Maintains or improves test execution times.
  • Is easy to maintain and scale.

Options for a Long-Lasting Unit Testing Approach

Let's explore the options we have for a more sustainable unit testing approach. We'll break down the pros and cons of each to help us make an informed decision. Remember, our aim is to keep our testing efficient and our repository manageable. So, let's get into it!

1. Splitting Tests into Different Repositories

One option to consider is splitting some of the tests into a different repository. This would prevent the main hgvs repository from getting clogged up with data. Here's a deeper look:

Pros:

  • Reduced Main Repository Size: By offloading some tests, we can significantly reduce the size of the main hgvs repository. This makes it easier to clone, checkout, and update the repository, improving developer productivity.
  • Focused Test Suites: Separating tests into different repositories allows us to create focused test suites. For example, we could have a separate repository for large-scale integration tests or tests that require specific external dependencies.
  • Improved Test Execution Times (Potentially): With smaller test suites in the main repository, test execution times could improve. This allows for faster feedback during development.

Cons:

  • Increased Complexity: Splitting tests across multiple repositories adds complexity to the project structure. Developers need to be aware of which tests reside in which repository and how to run them.
  • Maintenance Overhead: Maintaining multiple repositories requires more effort. We need to set up continuous integration (CI) pipelines for each repository and ensure that dependencies are managed consistently.
  • Potential for Duplication: There's a risk of duplicating code or test data across repositories. This can lead to inconsistencies and make it harder to maintain the codebase.

Use Cases:

This approach might be suitable if we have a clear separation of concerns between different types of tests. For example, we could move large-scale integration tests or tests that require specific external dependencies to a separate repository.

2. Setting Up a seqrepo-rest-api for Unit Tests

Another option is to set up a seqrepo-rest-api that can be used for unit tests. This approach would eliminate the need for storing test data locally in the repository.

Pros:

  • No Test Data in Repository: This is a major advantage. We no longer need to store large test data files in the repository, reducing its size and complexity.
  • Centralized Data Source: A seqrepo-rest-api provides a centralized data source for unit tests. This ensures consistency and reduces the risk of data duplication.
  • Scalability: A REST API can be scaled to handle a large number of requests. This makes it a suitable solution for projects with growing test suites.

Cons:

  • Dependency on a Service: This approach introduces a dependency on a seqrepo-rest-api service. The service needs to be available and reliable for tests to run successfully. This adds complexity to the test environment setup.
  • Potential Performance Bottlenecks: If the seqrepo-rest-api is not properly optimized, it could become a performance bottleneck. Test execution times might increase if the API is slow to respond.
  • Network Latency: Network latency can impact test execution times. Tests that rely on the API will be slower than tests that use local data.

Use Cases:

This approach is well-suited for projects that already use a seqrepo-rest-api for other purposes. It can also be a good option if we want to centralize our test data and reduce the size of the repository.

3. Exploring Alternative Caching Strategies

Perhaps there are alternative caching strategies we haven't yet considered. Let's brainstorm some ideas:

  • Partial Caching: Instead of caching all test data, we could cache only the most frequently used data. This would reduce the size of the cache file while still providing performance benefits for common test cases.
  • Dynamic Data Generation: We could generate test data dynamically instead of storing it in a cache file. This would eliminate the need for a cache file altogether.
  • Database Caching: We could use a database to store test data. This would allow us to query the data efficiently and scale the cache as needed.

Pros:

  • Flexibility: Alternative caching strategies can be tailored to our specific needs. We can choose the approach that best balances performance, storage, and maintenance overhead.
  • Potential Performance Improvements: Some caching strategies, such as partial caching, can improve test execution times by focusing on the most critical data.
  • Reduced Storage Requirements: Dynamic data generation eliminates the need for a cache file, reducing storage requirements.

Cons:

  • Implementation Complexity: Implementing alternative caching strategies can be complex. We need to carefully design the caching mechanism and ensure that it is efficient and reliable.
  • Maintenance Overhead: Maintaining a custom caching solution can be time-consuming. We need to monitor its performance and address any issues that arise.
  • Potential for Inconsistencies: If not implemented correctly, alternative caching strategies can introduce inconsistencies in test results.

Use Cases:

These approaches are suitable for projects that require a high degree of flexibility and control over their caching mechanism. They can also be a good option if we want to optimize test execution times or reduce storage requirements.

Making a Decision: Factors to Consider

Choosing the right approach requires careful consideration of several factors. Here are some key questions to ask:

  • What are our performance goals? Do we need to significantly reduce test execution times, or are we primarily concerned about repository size?
  • What are our maintenance capabilities? Do we have the resources to maintain multiple repositories or a custom caching solution?
  • What are our dependencies? Do we already use a seqrepo-rest-api, or would we need to set one up?
  • What is the long-term scalability of the solution? Will the chosen approach scale as our project grows?

Conclusion

Alright, guys, we've covered a lot of ground here! We've identified the problem of the growing HGVS unit test cache and explored several potential solutions. From splitting tests into different repositories to setting up a seqrepo-rest-api and considering alternative caching strategies, we have a range of options to consider.

Remember, the best approach will depend on our specific needs and priorities. By carefully evaluating the pros and cons of each option and considering the factors outlined above, we can make an informed decision that will ensure the long-term sustainability and efficiency of our unit tests. Let's keep the conversation going and work together to implement the best solution for the hgvs project!

  1. Evaluate the current test suite: Identify tests that contribute the most to the cache size.
  2. Prototype different solutions: Experiment with different approaches, such as partial caching or dynamic data generation.
  3. Measure performance: Compare the performance of different solutions in terms of test execution time and resource usage.
  4. Gather feedback: Discuss the options with the team and gather feedback on their preferences and concerns.
  5. Make a decision: Choose the approach that best meets our needs and resources.
  6. Implement the solution: Develop and deploy the chosen solution.
  7. Monitor performance: Continuously monitor the performance of the solution and make adjustments as needed.

By following these steps, we can ensure that our unit tests remain efficient and effective as our project grows. Let's make it happen!