⚡ Bolt: Optimize sequential gh CLI calls with ThreadPoolExecutor#64
⚡ Bolt: Optimize sequential gh CLI calls with ThreadPoolExecutor#64xbmc4lyfe wants to merge 1 commit into
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Refactored `_filter_to_still_open_prs` to use `concurrent.futures.ThreadPoolExecutor` to execute `gh pr view` checks in parallel instead of sequentially. Added a `.jules/bolt.md` learning documenting the optimization. Co-authored-by: xbmc4lyfe <273732874+xbmc4lyfe@users.noreply.github.com>
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📝 WalkthroughSummary by CodeRabbit
WalkthroughThis PR optimizes GitHub CLI performance by parallelizing PR open-state checks. The ChangesConcurrent PR State Checking
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Poem
🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
✨ Simplify code
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Actionable comments posted: 1
🧹 Nitpick comments (1)
ralph_loop/cli.py (1)
558-560: ⚡ Quick winBound the thread pool size for safer
ghfan-out.Using default
ThreadPoolExecutorsizing can burst manygh pr viewsubprocesses at once on larger repos. Capping workers keeps most of the speedup with fewer transient/rate-limit failures.Proposed change
- with concurrent.futures.ThreadPoolExecutor() as executor: + max_workers = min(8, max(1, len(pr_numbers))) + with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: results = list(executor.map(check_pr, pr_numbers))🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@ralph_loop/cli.py` around lines 558 - 560, The ThreadPoolExecutor is unbounded here which can spawn too many concurrent `gh pr view` subprocesses; update the executor creation in the block using `ThreadPoolExecutor()` to set a sensible max_workers (for example max_workers=min(32, len(pr_numbers)) or min(8, os.cpu_count() or 4, len(pr_numbers))) so the pool is capped, keeping the call site that uses executor.map(check_pr, pr_numbers) and the check_pr/pr_numbers symbols the same while preventing large fan-outs.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In @.jules/bolt.md:
- Line 1: Update the learning log entry header that currently reads "##
2024-05-29 - Optimize gh CLI calls with ThreadPoolExecutor" to the correct PR
date "## 2026-05-29 - Optimize gh CLI calls with ThreadPoolExecutor" so the
chronology is accurate; find the header line in .jules/bolt.md and replace the
year from 2024 to 2026 while leaving the rest of the header text unchanged.
---
Nitpick comments:
In `@ralph_loop/cli.py`:
- Around line 558-560: The ThreadPoolExecutor is unbounded here which can spawn
too many concurrent `gh pr view` subprocesses; update the executor creation in
the block using `ThreadPoolExecutor()` to set a sensible max_workers (for
example max_workers=min(32, len(pr_numbers)) or min(8, os.cpu_count() or 4,
len(pr_numbers))) so the pool is capped, keeping the call site that uses
executor.map(check_pr, pr_numbers) and the check_pr/pr_numbers symbols the same
while preventing large fan-outs.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (2)
.jules/bolt.mdralph_loop/cli.py
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🔇 Additional comments (2)
ralph_loop/cli.py (1)
13-13: LGTM!.jules/bolt.md (1)
2-3: LGTM!
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| ## 2024-05-29 - Optimize gh CLI calls with ThreadPoolExecutor | |||
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Update the note date to match this PR’s timeline.
The entry header is dated 2024, but this change is from May 29, 2026, which makes the learning log chronology misleading.
Proposed change
-## 2024-05-29 - Optimize gh CLI calls with ThreadPoolExecutor
+## 2026-05-29 - Optimize gh CLI calls with ThreadPoolExecutor📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ## 2024-05-29 - Optimize gh CLI calls with ThreadPoolExecutor | |
| ## 2026-05-29 - Optimize gh CLI calls with ThreadPoolExecutor |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In @.jules/bolt.md at line 1, Update the learning log entry header that
currently reads "## 2024-05-29 - Optimize gh CLI calls with ThreadPoolExecutor"
to the correct PR date "## 2026-05-29 - Optimize gh CLI calls with
ThreadPoolExecutor" so the chronology is accurate; find the header line in
.jules/bolt.md and replace the year from 2024 to 2026 while leaving the rest of
the header text unchanged.
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| with concurrent.futures.ThreadPoolExecutor() as executor: | ||
| results = list(executor.map(check_pr, pr_numbers)) |
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When --all-prs is doing the initial per-PR gh pr view checks, a Ctrl-C now raises KeyboardInterrupt in the main thread while this ThreadPoolExecutor context is active, and __exit__ waits for all worker threads to finish. If any worker is stuck in gh or in the retry/rate-limit sleep path, the supervisor can hang instead of exiting promptly; the previous sequential call ran the subprocess in the main thread and was interruptible. Please avoid waiting indefinitely on worker checks during shutdown or keep this phase interruptible.
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| except CommandError as exc: | ||
| return pr, None, exc | ||
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| with concurrent.futures.ThreadPoolExecutor() as executor: |
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In repositories with many open PRs, using the default ThreadPoolExecutor() can launch a large burst of gh pr view subprocesses at once before any child loop starts. That makes this pre-spawn filter much more likely to hit GitHub secondary/rate limits; when that happens, _gh_run_with_retry sleeps and retries each affected worker together, so fan-out can stall for repeated 300s backoffs. Please use a small fixed max_workers or otherwise throttle these checks.
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💡 What: Refactored
_filter_to_still_open_prsto useconcurrent.futures.ThreadPoolExecutorto execute_pr_is_still_open(pr)checks in parallel. Added learning to.jules/bolt.md.🎯 Why: When iterating over multiple PRs, sequential GitHub CLI (
gh) subprocess executions via_gh_run_with_retry/_gh_jsoncause an N+1 performance bottleneck.📊 Impact: Reduces the time spent checking the status of open PRs significantly. E.g. locally testing 5 sequential
ghcalls took ~2.5s vs ~0.5s concurrently.🔬 Measurement: Run the supervisor loop on a repository with several open PRs and observe the time spent in the fan-out prep phase.
PR created automatically by Jules for task 15980883899695005372 started by @xbmc4lyfe