From 0221e7eb5fd7c8ff1b87b98ea32fee12e2aa7902 Mon Sep 17 00:00:00 2001
From: RohanExploit <178623867+RohanExploit@users.noreply.github.com>
Date: Wed, 15 Jul 2026 13:48:18 +0000
Subject: [PATCH 1/2] perf: optimize nearby issues search with bounding box
 pre-filter

Added a fast bounding box pre-filter with a 5% epsilon to `find_nearby_issues` in `backend/spatial_utils.py` to quickly discard coordinates outside the search radius before running the computationally expensive exact haversine distance calculations. Also appended this learning to `.jules/bolt.md`.
---
 .jules/bolt.md           |  4 ++++
 backend/spatial_utils.py | 10 ++++++++++
 2 files changed, 14 insertions(+)

diff --git a/.jules/bolt.md b/.jules/bolt.md
index ba84fca0..bf784525 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -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.
diff --git a/backend/spatial_utils.py b/backend/spatial_utils.py
index 8af329a3..281135f6 100644
--- a/backend/spatial_utils.py
+++ b/backend/spatial_utils.py
@@ -76,10 +76,20 @@ 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
+        if not (min_lat <= issue.latitude <= max_lat and min_lon <= issue.longitude <= max_lon):
+            continue
+
         distance = haversine_distance(
             target_lat, target_lon,
             issue.latitude, issue.longitude

From dbd4616016bb63c981c317f87b3cea96b8141cf4 Mon Sep 17 00:00:00 2001
From: Rohan  Gaikwad <itzrohan007@gmail.com>
Date: Wed, 15 Jul 2026 21:47:07 +0530
Subject: [PATCH 2/2] Update backend/spatial_utils.py

Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com>
---
 backend/spatial_utils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/backend/spatial_utils.py b/backend/spatial_utils.py
index 281135f6..213bfeaa 100644
--- a/backend/spatial_utils.py
+++ b/backend/spatial_utils.py
@@ -87,7 +87,11 @@ def find_nearby_issues(
             continue
 
         # Skip issues outside the bounding box
-        if not (min_lat <= issue.latitude <= max_lat and min_lon <= issue.longitude <= max_lon):
+        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
 
         distance = haversine_distance(