Telemetry puts nexrad_decode_png at 1.76 s/call × 37,847 calls/h
— ~18.5 h of CPU across the cluster every real hour, the single
biggest CPU consumer in the system. The map-click path
(fetch_rain_cells) already cached decoded frames by 5-min
bucket, but the per-contact CommonVolumeRadarWorker path
(fetch_decoded_frame) went straight to network + decode on every
call. Backfill means many contacts share a 5-min window, so the
same 66 MB frame was being decoded dozens of times.
Wire fetch_decoded_frame through NexradCache keyed on the
rounded timestamp. Add a 20-entry size cap in NexradCache so
backfill processing contacts in random timestamp order can't
grow the ETS table to hundreds of GB. Each frame is ~66 MB, so
20 = ~1.3 GB worst case, well under typical pod memory.
Expected impact: cuts sustained decode load by an order of
magnitude depending on backfill temporal locality; map-click
path is unchanged.
- ScoreCache stores {band, valid_time} as %{{lat, lon} => score} map so
point lookups are O(1); adds fetch_point/4 and valid_times/1
- available_valid_times/1 reads directly from ScoreCache when warm,
falls back to DB on cold start
- point_forecast/3 iterates cached valid_times and uses fetch_point/4
instead of hitting the DB per click
- NexradCache: node-local ETS cache of decoded n0q PNG pixel buffers
keyed by 5-minute rounded timestamp; skips ~1-5s HTTP+decode on
concurrent/repeat clicks within the same window
- MapLive: start_async the rain_scatter fetch so point_detail renders
immediately with a pending marker; push rain_scatter_update when
NEXRAD resolves
- MapLive: preload all 18 remaining forecast hours for the current
viewport after mount/band change/propagation_updated; client caches
them and renders timeline scrubs instantly without a server roundtrip.
Adds set_selected_time event for fast-path state sync.
- Propagation map JS: forecastCache map + drawScatterMarkers helper,
timeline click uses preloaded cache when available
