Improve Performance: Caching Worktree Mood Classification

by Alex Johnson 58 views

In this article, we'll explore a performance optimization technique for the categorizeWorktree function, specifically focusing on caching strategies to reduce unnecessary Git operations. This optimization is crucial for users working with multiple worktrees or those employing fast polling intervals. Let's dive into the details!

Understanding the Problem: Unnecessary Git Operations

The core issue lies in the categorizeWorktree function's frequent calls to getLastCommitAgeInDays(). This function, in turn, executes git log -1 for each mood classification. Given that mood is recalculated during polling cycles—occurring every 2-10 seconds—this leads to a high volume of redundant Git operations. This is particularly impactful for users managing numerous worktrees or those with shorter polling intervals. Imagine the strain on system resources when this operation is multiplied across dozens of worktrees every few seconds!

Identifying Affected Users and Frequency

The primary users affected by this issue are those with:

  • Many worktrees: Each worktree necessitates its own set of Git operations.
  • Fast polling intervals: More frequent polling translates to more frequent mood recalculations and, consequently, more Git calls.

This issue manifests itself during every polling cycle, which occurs every 2 seconds in active mode and every 10 seconds in background mode. This constant cycling underscores the need for an efficient caching mechanism.

The Current Behavior and Its Impact

Currently, each call to categorizeWorktree triggers a fresh git log -1 command. This behavior results in unnecessary subprocess spawns and Git operations, consuming valuable system resources. The impact is significant, especially when scaled across multiple worktrees and frequent polling intervals. This can lead to performance bottlenecks and a less responsive user experience.

Deep Dive into the Mood Classification Flow

To fully grasp the problem, let's trace the mood classification flow step-by-step:

  1. WorktreeMonitor.updateGitStatus() is called: This is the entry point, triggered on each polling cycle.
  2. categorizeWorktree(worktree, changes, mainBranch) is invoked: This function is responsible for categorizing the mood of a specific worktree.
  3. categorizeWorktree calls getLastCommitAgeInDays(worktree.path): Here, the age of the last commit is determined, which is crucial for mood classification.
  4. getLastCommitAgeInDays runs git log -1 --format=%ci via simple-git: This is the bottleneck, as it spawns a new Git subprocess for each call.

The Key Insight: Redundancy

The crucial insight here is that the last commit date only changes under specific conditions:

  • A new commit is made, leading to a change in the HEAD.
  • Infrequently, for staleness calculations (at most, once per day). This highlights the redundancy in repeatedly fetching the same information within short intervals.

Examining the Current Implementation

Let's take a closer look at the relevant code snippets to understand the current state and identify the areas for optimization.

categorizeWorktree Function (src/utils/worktreeMood.ts)

export async function categorizeWorktree(
 worktree: Worktree,
 changes: WorktreeChanges | undefined,
 mainBranch: string,
 staleThresholdDays: number = 7
): Promise<WorktreeMood> {
 // ... logic ...

 // This runs git log every time
 const ageDays = await getLastCommitAgeInDays(worktree.path);
 if (ageDays !== null && ageDays > staleThresholdDays) {
 return 'stale';
 }
 // ...
}

This snippet clearly shows that getLastCommitAgeInDays is called every time categorizeWorktree is executed, leading to the aforementioned performance issue.

getLastCommitAgeInDays Function (src/utils/git.ts)

This function is responsible for executing the git log command and retrieving the last commit age. It's the prime candidate for caching to avoid redundant Git operations. Optimizing this function is key to improving overall performance.

Proposed Solutions: Caching Strategies

To mitigate the performance bottleneck, we propose implementing caching mechanisms within the getLastCommitAgeInDays function. Here are two potential options:

Option A: Cache by (path, HEAD SHA)

This approach involves caching the commit age based on the worktree path and the HEAD SHA. This strategy ensures that the cache is invalidated whenever a new commit is made, providing accurate results while minimizing Git calls.

Implementation Snippet:

// Cache: Map<worktreePath, { headSha: string, ageDays: number, timestamp: number }>
const commitAgeCache = new Map();

export async function getLastCommitAgeInDays(worktreePath: string): Promise<number | null> {
 const git = simpleGit(worktreePath);
 const headSha = await git.revparse(['HEAD']);

 const cached = commitAgeCache.get(worktreePath);
 if (cached && cached.headSha === headSha) {
 return cached.ageDays;
 }

 // Compute and cache
 const ageDays = await computeCommitAge(git);
 commitAgeCache.set(worktreePath, { headSha, ageDays, timestamp: Date.now() });
 return ageDays;
}

This implementation uses a Map to store cached commit ages, keyed by the worktree path. The cache entry includes the HEAD SHA, age in days, and a timestamp. Before executing git log, the cache is checked for a matching entry. If found and the HEAD SHA matches, the cached age is returned. Otherwise, the commit age is computed, cached, and returned.

Option B: Simple TTL Cache (Simpler)

Alternatively, a time-to-live (TTL) based cache can be implemented. This approach caches the results for a fixed duration, such as 60 seconds, which is sufficient given that staleness is calculated in days. This simpler strategy avoids the need to track HEAD SHA changes.

Implementation Snippet:

const commitAgeCache = new Map<string, { ageDays: number, expires: number }>();

export async function getLastCommitAgeInDays(worktreePath: string): Promise<number | null> {
 const cached = commitAgeCache.get(worktreePath);
 if (cached && Date.now() < cached.expires) {
 return cached.ageDays;
 }

 const ageDays = await computeCommitAge(worktreePath);
 commitAgeCache.set(worktreePath, { ageDays, expires: Date.now() + 60000 });
 return ageDays;
}

This TTL-based cache stores the age in days and an expiration timestamp. Before computing the commit age, the cache is checked. If a valid entry exists (i.e., the current time is before the expiration time), the cached age is returned. Otherwise, the commit age is computed, cached with a new expiration time, and returned.

Testing the Caching Mechanism

To ensure the caching mechanism functions correctly, comprehensive tests are crucial. Here are some key test scenarios:

  • Test that the cache returns the same value within the TTL: This verifies that the cache is effectively storing and retrieving values.
  • Test that the cache invalidates after the TTL expires: This ensures that stale data is not served from the cache.
  • Test that the cache invalidates when HEAD changes (if using Option A): This validates that the cache is correctly updated when new commits are made.
  • Test that mood updates still work correctly: This confirms that the caching mechanism does not interfere with the overall mood classification functionality. Thorough testing is essential to guarantee the robustness of the caching implementation.

Technical Specifications and Performance Impact

Footprint

The changes will primarily impact the following files:

  • src/utils/git.ts (where the caching mechanism will be implemented).
  • src/utils/worktreeMood.ts (where getLastCommitAgeInDays is called).

Performance

The expected performance improvement is significant:

  • Before: 1 Git subprocess per worktree per polling cycle.
  • After: 1 Git subprocess per worktree per TTL period (or per commit, depending on the chosen strategy). This reduction in Git calls will lead to a substantial decrease in resource consumption and improved responsiveness.

Tasks and Acceptance Criteria

Tasks

The following tasks need to be completed to implement the caching mechanism:

  • Add a cache mechanism to getLastCommitAgeInDays in src/utils/git.ts.
  • Choose an invalidation strategy (HEAD-based or TTL-based).
  • Add cache invalidation when a worktree is removed.
  • Add tests for cache behavior.
  • Verify that mood classification still works correctly.

Acceptance Criteria

The implementation will be considered successful if the following criteria are met:

  • git log -1 is not called on every poll cycle.
  • The cache is invalidated appropriately (TTL or HEAD change).
  • Mood classification remains accurate.
  • Tests verify caching behavior.
  • There are no memory leaks from unbounded cache growth. Meeting these criteria will ensure that the caching mechanism is effective and reliable.

Edge Cases and Risks

Potential Risks

  • Risk: Stale cache showing incorrect mood. This can be mitigated with a reasonable TTL (30-60s). Careful consideration should be given to the TTL value to balance performance and accuracy.

Edge Cases

  • Edge case: A worktree is removed while cached. The solution is to clear the cache entry on a worktree removal event.
  • Edge case: Fast commits. HEAD-based invalidation handles this automatically, ensuring the cache is updated promptly. Addressing these edge cases will contribute to a more robust caching implementation.

Conclusion

Implementing a caching strategy for the categorizeWorktree function is crucial for optimizing performance, especially for users with numerous worktrees or fast polling intervals. By reducing unnecessary Git operations, we can significantly improve system resource utilization and enhance the overall user experience. This optimization is a valuable step toward building a more efficient and responsive application.

For further reading on caching strategies and performance optimization, visit Google's Web Fundamentals on Caching. This external resource provides valuable insights and best practices for effective caching.