File size: 4,232 Bytes
48be8c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | # 22 — Cornerstone optimization · results
**Run:** May 8, 2026 · MacBook (local), Python 3.12, GeoPandas 1.1.3,
Shapely 2.1.2, Rasterio 1.5.0.
## Headline
The "33s DEP join" reported on HF Space is **not the join** — it's the
GDB **cold-load**. On Mac with a warm `lru_cache`:
| Layer | Cold-load | Warm join (1 pt) |
|---|---:|---:|
| `dep_extreme_2080` | **30.9s** | ~4 ms |
| `dep_moderate_2050` | 1.5s | ~3 ms |
| `dep_moderate_current` | 0.9s | ~3 ms |
| `sandy_inundation` | 1.8s | ~1 ms |
On HF Space's shared CPU, with worker memory pressure evicting the
cache, every query pays cold-load again. That's the source of the
ReadTimeouts.
## Bench summary (per-query ms, all 3 DEP scenarios + Sandy)
| Address | baseline | strtree | bbox-prefilter | raster |
|---|---:|---:|---:|---:|
| 80 Pioneer St, Brooklyn | 13.0 | 1.6 | 17.7 | 3.2 |
| 2508 Beach Channel Dr, Queens | 11.2 | 1.9 | 12.5 | 2.4 |
| Coney Island I Houses, BK | 10.8 | 1.9 | 7.1 | 2.1 |
| Carleton Manor, Queens | 10.8 | 1.6 | 25.5 | 1.9 |
**All four paths achieve full parity with baseline** on `depth_class`
per scenario and `sandy.inside` for every canonical address.
## Cold-load comparison (paid once at HF Space boot)
| Path | Cold init |
|---|---:|
| baseline (`gpd.read_file` GDB ×3 + GeoJSON) | **~35 s** |
| strtree (same load + tree build) | ~35 s |
| **raster (`rasterio.open` ×4, mmap)** | **73 ms** |
Raster reduces boot-to-first-query from 35s to under 100ms while
also cutting per-query latency in half vs strtree.
## Disk footprint
DEFLATE-compressed uint8 GeoTIFF, NYC-wide grid at 10 ft/px:
| File | Size |
|---|---:|
| `dep_extreme_2080.tif` | 3.6 MB |
| `dep_moderate_2050.tif` | 1.3 MB |
| `dep_moderate_current.tif` | 0.9 MB |
| `sandy.tif` | 1.2 MB |
| **Total baked** | **7.0 MB** |
Compare: source GDBs total ~46 MB and the Sandy GeoJSON is 87 MB —
raster bake is 7% the size of the originals.
## Verdict
**Ship the raster bake.** It wins on every axis:
- Per-query: ~5× faster than baseline (2 ms vs 11 ms locally; on HF
CPU the multiplier will be larger).
- Cold-load: ~500× faster (73 ms vs ~35 s). This is the actual fix
for the 33s ReadTimeouts.
- Disk: 7 MB shipped vs 46 MB GDB + 87 MB GeoJSON. Faster
HF Space pulls.
- Parity: identical depth class on all 4 canonical addresses, all 3
DEP scenarios, plus Sandy.
STRtree is a useful fallback if for any reason we cannot ship the
baked rasters (e.g. demo-time edits to source layers), but the
default integration plan is raster.
## Live vs bakeable — recap of triage
These layers are **all** baked (Cornerstone = "what the ground
remembers"; static by definition):
- DEP stormwater scenarios — modeled, NYC DEP republishes ~every 5y
- Sandy 2012 inundation — historical, will not change
- Ida 2021 HWMs — already a small point set; haversine is fast
- Microtopo (DEM/HAND/TWI) — already raster
- Prithvi-EO Ida polygons — already baked artifact
These layers stay **live** for demo recency (and also because they're
fast):
- Geocoding (Geosearch + Nominatim fallback)
- FloodNet sensor pull (Touchstone)
- TTM battery surge / pluvial forecast (Lodestone)
- NYCHA / DOE / MTA registers (semi-static, prebuilt at boot)
## Integration plan
1. Move `bake_rasters.py` → `scripts/bake_cornerstone_rasters.py`.
2. Add `data/baked/` to repo (7 MB; well under HF Space limits).
3. Refactor `app/flood_layers/dep_stormwater.py` and
`app/flood_layers/sandy_inundation.py` to expose:
- the existing GDB-backed `join()` (kept as fallback if raster
missing)
- a new `join_raster()` that opens the baked GeoTIFF on first use
and `sample()`s each asset point
4. `step_dep` and `step_sandy` in `app/fsm.py` call `join_raster()`.
5. Re-run `scripts/probe_addresses.py` (5/5 must pass) and the 20-query
batch from FRIDAY-REPORT to verify ReadTimeouts are gone.
`coverage_for_polygon` (neighborhood mode) stays on the GDB path for
now since polygon × polygon overlap fraction is harder to do well in
raster — but neighborhood mode is not on the demo critical path.
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