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⚡ Bolt: optimize nearby issues search with bounding box pre-filter #910

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Merged
RohanExploit merged 2 commits into from
Jul 15, 2026
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⚡ Bolt: optimize nearby issues search with bounding box pre-filter #910

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0221e7e
perf: optimize nearby issues search with bounding box pre-filter
RohanExploit Jul 15, 2026
dbd4616
Update backend/spatial_utils.py
RohanExploit Jul 15, 2026
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4 changes: 4 additions & 0 deletions .jules/bolt.md
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Original file line number Diff line number Diff line change
Expand Up @@ -21,3 +21,7 @@
## 2025-05-30 - O(1) Blockchain Hash Lookup
**Learning:** Calculating a blockchain-style hash chain requires finding the previous record's hash. A naive DB scan is O(log N) with indexes, but high-concurrency follow operations can be optimized.
**Action:** Implement an in-memory thread-safe cache for the last hash of each grievance to achieve O(1) lookup during follower creation, falling back to DB on cache miss.

## 2025-07-15 - Fast Bounding Box Pre-filter
**Learning:** Calculating great circle distance (Haversine) for every issue against a target location is computationally expensive (O(N) with heavy math ops like sin, cos, atan2). In high-traffic aggregations, this can become a bottleneck.
**Action:** Use a fast bounding box pre-filter (`get_bounding_box` with a 5% epsilon) to quickly discard issues that are definitely outside the search radius before running the expensive exact haversine distance calculation.

@cubic-dev-ai cubic-dev-ai Bot Jul 15, 2026

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P2: Nearby issues across the ±180° meridian are dropped even though they are within the radius, so this action is not safe as written. The bounding-box implementation should normalize longitudes or split a dateline-crossing box before applying the pre-filter.

Prompt for AI agents 
Check if this issue is valid — if so, understand the root cause and fix it. At .jules/bolt.md, line 27:

<comment>Nearby issues across the ±180° meridian are dropped even though they are within the radius, so this action is not safe as written. The bounding-box implementation should normalize longitudes or split a dateline-crossing box before applying the pre-filter.</comment>

<file context>
@@ -21,3 +21,7 @@
+
+## 2025-07-15 - Fast Bounding Box Pre-filter
+**Learning:** Calculating great circle distance (Haversine) for every issue against a target location is computationally expensive (O(N) with heavy math ops like sin, cos, atan2). In high-traffic aggregations, this can become a bottleneck.
+**Action:** Use a fast bounding box pre-filter (`get_bounding_box` with a 5% epsilon) to quickly discard issues that are definitely outside the search radius before running the expensive exact haversine distance calculation.
</file context>

14 changes: 14 additions & 0 deletions backend/spatial_utils.py
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Expand Up @@ -76,10 +76,24 @@ def find_nearby_issues(
"""
nearby_issues = []

# Fast bounding box pre-filter with a 5% epsilon to optimize expensive haversine calculations
epsilon_radius = radius_meters * 1.05
min_lat, max_lat, min_lon, max_lon = get_bounding_box(
target_lat, target_lon, epsilon_radius
)

for issue in issues:
if issue.latitude is None or issue.longitude is None:
continue

# Skip issues outside the bounding box
lon_delta = (issue.longitude - target_lon + 180) % 360 - 180
lon_half_width = max(target_lon - min_lon, max_lon - target_lon)
if not (min_lat <= issue.latitude <= max_lat and (
abs(target_lat) >= 89.9 or abs(lon_delta) <= lon_half_width
)):
continue

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distance = haversine_distance(
target_lat, target_lon,
issue.latitude, issue.longitude
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