⚡ Bolt: optimize spatial distance calculation hot-path

[RohanExploit]

💡 What: Optimized the find_nearby_issues function in backend/spatial_utils.py by hoisting coordinate conversion constants and trigonometric calculations out of the inner loop.

🎯 Why: The original implementation performed redundant math.radians and math.cos calls for every issue checked, which added significant function call and calculation overhead in the search hot-path.

📊 Impact: Reduced average search latency from ~1.64ms to ~0.90ms per 1000 issues (~45% improvement) as measured by benchmark_spatial_optimized.py.

🔬 Measurement: Verified using a custom benchmark script simulating 1000 issues and 1000 searches. Confirmed logic correctness with existing spatial unit and performance tests.

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PR created automatically by Jules for task 1216709419177204748 started by @RohanExploit

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Summary by cubic

Optimized find_nearby_issues in backend/spatial_utils.py by hoisting conversions/trig out of the inner loop and using an equirectangular distance in meters. Improves small-radius (<10 km) search latency by ~45%.

• Performance
  • Precomputed meters-per-degree at target latitude; avoided per-issue math.radians and math.cos calls.
  • Performed degree deltas with dateline wrapping in degrees; kept squared-distance check, sqrt only for matches.
  • Benchmarked at ~0.90ms vs ~1.64ms per 1000 issues (~45% faster) in benchmark_spatial_optimized.py; existing tests pass.

^{Written for commit 766c0b9. Summary will update on new commits.}

Summary by CodeRabbit

Release Notes

• Refactor
  • Optimized spatial distance calculations to improve performance when filtering nearby locations.

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[github-actions]

🙏 Thank you for your contribution, @RohanExploit!

PR Details:

• Title: ⚡ Bolt: optimize spatial distance calculation hot-path
• Number: ⚡ Bolt: optimize spatial distance calculation hot-path #761

Quality Checklist:
Please ensure your PR meets the following criteria:

• Code follows the project's style guidelines
• Self-review of code completed
• Code is commented where necessary
• Documentation updated (if applicable)
• No new warnings generated
• Tests added/updated (if applicable)
• All tests passing locally
• No breaking changes to existing functionality

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ℹ️ Recent review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: c62c819a-18b5-4541-9a90-0cafa51f592e

📥 Commits

Reviewing files that changed from the base of the PR and between f837f7b and 766c0b9.

📒 Files selected for processing (2)

• .jules/bolt.md
• backend/spatial_utils.py

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📝 Walkthrough

Walkthrough

This PR optimizes the spatial distance filtering in find_nearby_issues by precomputing degree-to-meter conversion factors at the target latitude and using planar meter-based distance approximation with squared-distance checks. The change reduces repeated trigonometric operations inside tight loops and is documented in a Jules Bolt learning entry.

Changes

Spatial Hot-Path Optimization

Layer / File(s)	Summary
Hot-path constant hoisting and planar distance approximation backend/spatial_utils.py, .jules/bolt.md	The small-radius path now precomputes meters-per-degree factors outside the loop, normalizes dlat/dlon in degrees with dateline-aware wrapping, computes planar dx/dy offsets, compares squared distances against radius_sq, and computes sqrt(dist_sq) only for matches. The optimization pattern is documented as a Jules Bolt learning entry explaining constant hoisting to reduce per-iteration overhead.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• RohanExploit/VishwaGuru#485: Both PRs modify the find_nearby_issues small-radius distance filtering path by replacing per-iteration coordinate conversions with precomputed target latitude constants and squared-distance checks.
• RohanExploit/VishwaGuru#398: Both PRs optimize find_nearby_issues small-radius distance computation using planar/approximate meters with dateline-aware longitude handling.

Suggested labels

size/m

Poem

A loop runs swift through spatial lands,
where Jules hoists constants close at hand—
no radians twist on every turn,
just meters plain that help you learn.
Squared distances filter quick and true,
sqrt blooms only for the few. 🐰

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically identifies the main change: optimizing the spatial distance calculation hot-path using the Bolt framework, which aligns with the file changes and performance improvements documented.
Description check	✅ Passed	The description covers the what, why, and impact of changes with performance metrics and testing details, though some required template sections (Type of Change checkbox, Related Issue number, Testing checklist) are not formally filled out.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

📝 Generate docstrings

• Create stacked PR
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🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch bolt-spatial-opt-1216709419177204748

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[Copilot]

Pull request overview

This PR optimizes the find_nearby_issues hot-path in backend/spatial_utils.py by hoisting constant computations out of the per-issue loop to reduce repeated trig/conversion overhead during nearby-issue searches.

Changes:

• Pre-computes degree→meter conversion factors once per search and uses them for fast squared-distance checks in the <10km path.
• Updates Bolt learnings documentation with a new entry describing the constant-hoisting optimization.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

File	Description
backend/spatial_utils.py	Hoists trig/conversion constants out of the loop to speed up nearby-issue distance checks for small radii.
.jules/bolt.md	Adds a Bolt note documenting the spatial hot-path optimization learning/action.

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[cubic-dev-ai]

No issues found across 2 files