Commit 47c8db8
Memory profiling harness and baseline investigation
Summary:
Scripts and findings for profiling Metro's memory and CPU during bundling, and an
end-to-end benchmark of the compact VLQ source-map work stacked on top.
**Methodology:**
- Start Metro with `NODE_ARGS="--expose-gc --inspect=9230" DEV=1 js1 run --prefetch=false`
- WildeBundle URL: `GET http://localhost:8081/xplat/js/RKJSModules/EntryPoints/WildeBundle.bundle?platform=ios&dev=true&app=com.facebook.Wilde`
- RSS profiling via /proc, heap snapshots via Chrome DevTools Protocol
- Graph freed via DELETE to the bundle URL (same as fill-http-cache)
**Scripts added:**
- `fb-metro-cli/memory-investigation/heap-profile.js` — Automated CDP-based profiler: captures 3 heap snapshots (baseline, post-build, post-delete) and compares them
- `fb-metro-cli/memory-investigation/heap-compare.js` — Standalone snapshot comparator with streaming parser for multi-GB .heapsnapshot files
- `fb-metro-cli/memory-investigation/heap-injector.js` — Optional in-process module exposing /memory, /gc, /snapshot HTTP endpoints
- `metro/scripts/profile-memory.sh` — Quick RSS-only profiling via /proc
- `fb-metro-cli/memory-investigation/compact-bench-measure.js` — One measurement cycle: builds WildeBundle, then requests WildeBundle.map, recording memory (RSS/heap) + build CPU + .map serialize CPU via CDP
- `fb-metro-cli/memory-investigation/run-compact-bench.sh` — Orchestrator: fresh Metro per repeat across three configs (base / compact_flat / compact_indexed), cold or warm cache
- `fb-metro-cli/memory-investigation/compact-bench-stats.js` — Welch t-test analysis between any two configs
- `fb-metro-cli/memory-investigation/README.md`, `compact-sourcemaps-benchmark-results.md` — Full writeup of methodology and results
**Baseline results (WildeBundle, June 2025):**
- Startup: 819 MB RSS / 426 MB heap used
- Post-build: 2,338 MB RSS / 1,549 MB heap used (+1,122 MB heap)
- Post-delete: 507 MB heap used (DELETE frees 93% of build growth)
- Arrays dominate: 10M Array objects + backing stores = 858 MB (77% of growth)
- Source maps stored as decoded number-tuple arrays are the primary consumer:
~678 MB, 60% of build growth (9,866,476 tuples across 16,562 modules)
**Compact source maps — end-to-end benchmark (n=3, WildeBundle):**
Three configs: `base` (decoded tuples), `compact_flat` (VLQ storage, flat .map),
`compact_indexed` (VLQ storage, indexed passthrough .map).
- Memory (both compact configs): heap −51% cold / −53% warm; RSS −48%
(1654→810 MB heap cold; all Welch p < 1e-5).
- Build CPU: unchanged cold; ~20% faster warm with compact storage.
- Serialize CPU (`.map` request): `compact_flat` +18% vs base (decode + re-encode),
`compact_indexed` −49% vs base (passthrough). Flat .map is byte-identical to base;
indexed .map is +3.4% larger. Bundle output byte-identical across all configs.
Full tables in `compact-sourcemaps-benchmark-results.md`.
Differential Revision: D1078793921 parent 16fa9ce commit 47c8db8
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