⚡ Bolt: Optimize orbital prediction loop by deferring object allocation

.jules/bolt.md

@@ -0,0 +1,4 @@
+
+## 2024-05-15 - Defer Expensive Operations in High-Frequency Loops
+**Learning:** In high-frequency orbital prediction loops (like `backend/api/routers/orbital.py`), calling `strftime` and allocating dictionaries for every sampled orbital point—even when the satellite is not visible—creates a massive performance bottleneck due to unnecessary string formatting and memory allocation overhead.
+**Action:** Always defer expensive operations like `strftime` and object allocation until *after* filtering conditions (e.g., `el >= min_elevation`) have been met.

backend/api/routers/orbital.py

@@ -187,14 +187,16 @@ async def get_passes(
             r_ecef = teme_to_ecef(r, jd, fr)
             az, el, rng = ecef_to_topocentric(obs_ecef, r_ecef, lat, lon)
 
-            point = {
-                "t": t.strftime("%Y-%m-%dT%H:%M:%SZ"),
-                "az": round(az, 2),
-                "el": round(el, 2),
-                "slant_range_km": round(rng, 3),
-            }
-
             if el >= min_elevation:
+                # OPTIMIZATION: Defer expensive strftime and dict allocation
+                # until after visibility check passes
+                point = {
+                    "t": t.strftime("%Y-%m-%dT%H:%M:%SZ"),
+                    "az": round(az, 2),
+                    "el": round(el, 2),
+                    "slant_range_km": round(rng, 3),
+                }
+
                 if not in_pass:
                     in_pass = True
                     current_pass_points = []