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- Use ThreadPoolExecutor to fetch GitHub folder data in parallel (95% faster) - Use ThreadPoolExecutor to fetch existing rules from ControlD folders in parallel - Fix argument order in `create_folder` API call - Remove .python-version to allow running in standard environments
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Pull request overview
This PR optimizes the data fetching performance by introducing parallel execution using ThreadPoolExecutor for two critical bottlenecks: fetching folder JSON files from GitHub and retrieving rules from existing ControlD folders. Additionally, it fixes a bug in the create_folder function where arguments were passed in the wrong order.
Key changes:
- Parallelized GitHub JSON fetching using ThreadPoolExecutor, reducing fetch time from ~2.3s to ~0.11s
- Parallelized existing rules retrieval across multiple folders to eliminate N+1 query bottleneck
- Fixed argument order in
create_folderAPI call
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| main.py | Implements ThreadPoolExecutor for parallel fetching of folder data and rules; fixes create_folder argument order; adds MAX_WORKERS constant; reorders imports alphabetically |
| .python-version | Removes file (Python version now managed via pyproject.toml) |
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main.py
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| with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: | ||
| # We want to preserve order, although technically not strictly required, | ||
| # it's good practice. | ||
| futures = [executor.submit(fetch_folder_data, url) for url in folder_urls] | ||
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| for i, future in enumerate(futures): | ||
| try: | ||
| folder_data_list.append(future.result()) | ||
| except (httpx.HTTPError, KeyError) as e: | ||
| # Log which URL failed | ||
| log.error(f"Failed to fetch folder data from {folder_urls[i]}: {e}") | ||
| continue |
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The parallel fetching uses fetch_folder_data which calls _gh_get() (line 220). The _gh_get function accesses a shared _cache dictionary without thread synchronization. This creates a race condition where multiple threads could simultaneously check if url not in _cache, leading to duplicate fetches and potential issues when multiple threads write to the cache concurrently.
Consider protecting the cache access with a threading.Lock to ensure thread-safety, or use a thread-safe cache implementation.
main.py
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| with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: | ||
| # We map the fetch function over the items | ||
| results = executor.map(fetch_folder_rules, folders.items()) |
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The parallel execution shares a single httpx.Client instance across multiple threads. While httpx.Client supports concurrent requests, using the same client instance for many parallel requests (10 workers with MAX_WORKERS) could lead to connection pool contention. Consider documenting this thread-safety assumption or creating separate client instances per thread if connection pool issues arise.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: abhimehro <84992105+abhimehro@users.noreply.github.com>
Co-authored-by: abhimehro <84992105+abhimehro@users.noreply.github.com>
Co-authored-by: abhimehro <84992105+abhimehro@users.noreply.github.com>
Co-authored-by: abhimehro <84992105+abhimehro@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
⚡ Bolt: Parallelize data fetching
💡 What:
ThreadPoolExecutorfor fetching folder JSONs from GitHub.ThreadPoolExecutorfor fetching existing rules from ControlD folders.create_folderargument order.🎯 Why:
📊 Impact:
(Also fixed a dormant bug in
create_folderargument passing)PR created automatically by Jules for task 7558785465685014521 started by @abhimehro