editor-perf

name: editor-perf description: Guides performance investigation, benchmarking, and optimization of the Grida Canvas web editor (TypeScript reducer, Immer, React hooks). Use when profiling reducer dispatch cost, diagnosing slow interactions (drag, resize, color change), writing or running editor benchmarks, instrumenting with PerfObserver, or optimizing the JS-side state management pipeline.

Grida Canvas Editor — Performance Development

Workflow and reasoning framework for performance work on the TypeScript editor pipeline — the reducer, Immer state management, React subscription layer, and headless benchmarks.

Scope boundary: This skill covers the JS/TS editor pipeline (editor/grida-canvas/). For the Rust rendering engine (the grida crate), use the render-perf skill instead. The two pipelines are connected — a dispatch in JS may trigger a WASM re-render — but they are profiled with different tools.

Maintaining this document: If you notice a section that has gone stale (e.g. a workflow step no longer matches the code, a discovery query returns nothing, or a pitfall has been resolved), update this SKILL.md as part of your current task. Keep it high-level — reference patterns and categories rather than specific function names or measured numbers, which change frequently.

When to Use This Skill

• Benchmarking or profiling editor reducer operations
• Diagnosing slow interactions (drag, resize, color picker, opacity slider)
• Investigating Immer overhead or state cloning costs
• Instrumenting code with PerfObserver spans
• Writing or running the editor bench
• Optimizing queries, snap targets, hover resolution
• Reducing React re-render cost from editor state subscriptions

Pick your measurement tool

Performance work starts with the right signal. The three tools below are complementary — do not skip the browser trace if the user offers one, do not open a browser for a reducer-only regression.

Decision rules

1. If the user provides a browser trace, start there. It already has React, DevTools, and WASM on the same timeline — quote actual numbers from the trace rather than guessing. Parse it as JSON (see "Reading a browser trace" below).
2. Otherwise run the bench first. No user input needed — it covers every scenario at 1K and 10K and emits a PerfObserver span table that's directly comparable across runs.
3. Once bench points at a span, drill with a Node CPU profile. Use the withCpuProfile() helper in _utils.ts, or run node with --cpu-prof. Open the resulting .cpuprofile in Chrome DevTools (Performance → Load profile) for a function-level flame graph inside the span.

How to Orient Yourself

Before touching any code, build context by reading these sources in order:

1. Read editor/grida-canvas/__tests__/bench/README.md — benchmark catalog, run instructions, and the authoritative list of PerfObserver spans with a "when to trust the numbers" guide.
2. Read editor/grida-canvas/perf.ts — the PerfObserver API. Understand start(), measure(), report(), dump().
3. Skim editor/grida-canvas/editor.ts — the EditorDocumentStore class, specifically the dispatch() method. This is the single entry point for all state mutations.
4. Skim editor/grida-canvas/reducers/index.ts — the root reducer that wraps everything in Immer produceWithPatches.
5. Browse the bench files (perf-editor.test.ts, perf-per-node-sync.test.ts) to see what operations are already measured and at what scale.

Key discovery queries

The Architecture (Performance-Relevant)

Dispatch Pipeline

Every user interaction flows through this pipeline:

User action (click, drag, keystroke)
  → dispatch(action, recording)
    → Immer produceWithPatches(state, draft => { ... })
      → sub-reducers (document, event-target, surface)
      → tracked-Graph wrapper emits sync_links / delete_node ops
    → appendPatchOps(patches, buffer) lifts document.nodes patches
      into replace_node / delete_node ops
    → history.record(patches)
    → postDispatchHooks
    → emit(action, patches, opLog)
      → __wasm_on_document_change applies opLog 1:1 to WASM
      → React selectors + equality checks

The op-log produced during the recipe is the single WASM-sync channel. Structural edges come from the tracked-Graph wrapper; node-property changes are lifted from Immer patches via appendPatchOps. The subscriber applies ops directly (Scene.replaceNode / Scene.deleteNode / Scene.syncLinks) or falls back to a full re-encode only when the batch contains a full_resync op.

Performance-Sensitive Operation Categories

Gesture-bound (hot loop) — fires on every frame while the user drags a handle, slider, or object. These must complete within ~16ms (60fps budget) per frame:

• Property sliders (color picker, opacity, font size)
• Drag translate (moving nodes)
• Resize / scale (corner handles)
• Rotate

Discrete (single shot) — fires once per user click or toggle:

• Select, rename, visibility toggle
• Delete, insert
• Gesture start / end (snapshot cost)
• Pointer hover / raycast

Cost Scaling

Costs scale linearly with total node count due to Immer proxy finalization walking the entire state tree on every dispatch — even when only a single property on one node changes. This is the fundamental scaling wall for the current architecture.

Run the benchmarks to get current numbers. The perf.report() output shows exactly which spans dominate at any given scale.

Bottleneck Categories

Use GRIDA_PERF=1 to identify which category applies:

The Benchmark System

The single source of truth is perf-editor.test.ts in editor/grida-canvas/__tests__/bench/. It uses Editor.mountHeadless() with the real WASM raster backend — every dispatch runs through the same __wasm_on_document_change subscriber the browser installs. Spans under dispatch.wasm.* are end-to-end identical to the browser; spans under reducer.* are pure JS and track within ~10% of browser V8.

# Default — runs every scenario at 1K synthetic + bench.grida (10K) scales
GRIDA_PERF=1 pnpm vitest run editor/grida-canvas/__tests__/bench/perf-editor.test.ts

# With CPU profile capture (delete scenarios)
GRIDA_PERF=1 GRIDA_PERF_CPUPROFILE=1 pnpm vitest run \
  editor/grida-canvas/__tests__/bench/perf-editor.test.ts

# Large fixtures need more heap
NODE_OPTIONS="--max-old-space-size=8192" GRIDA_PERF=1 \
  pnpm vitest run editor/grida-canvas/__tests__/bench/perf-editor.test.ts

Which spans to read

After a bench run, perf.report() prints a table per scene. The bench README has the authoritative catalog — read it there. Read the table in category order rather than by specific name:

1. Total dispatch — your ceiling per action.
2. Reducer + Immer — pure JS cost. Watch p95 on gesture scenarios (drag / resize per-frame) — median can be microseconds while p95 spikes into hundreds of ms as the tree grows.
3. Document snapshot — deep-clone at gesture boundaries; pays twice per gesture (start + end).
4. WASM sync (full reload vs. op-apply) — compare dispatch.wasm.sync_document (full reload) against dispatch.wasm.op_apply (per-op replay). If the full reload fires for every dispatch and op_apply rarely fires, too many actions are routing through the slow path — usually a missing observation channel that forces full_resync (e.g. a mutation under document.* outside document.nodes[id] that no tracked channel covers).
5. Gesture / query compute — snap targets, hover ray, tree traversal. Usually small but can dominate at high selection count.

Prefer bench ratios over absolute numbers in commits and memory — absolutes shift with machine and node version; ratios stay meaningful.

Adding a new benchmark

Extend BENCH_SCENARIOS in perf-editor.test.ts. Use the bench() helper from _utils.ts for per-frame gestures and runAndTime() (or similar) for single-shot discrete actions:

const result = await bench(() => {
  h.ed.doc.dispatch({ type: "...", ... } as Action, { recording: "silent" });
}, { iterations: 10 });
logBench("my operation", result);

If you need a one-off CPU profile of a specific operation, use withCpuProfile() from _utils.ts — it wraps the call in node:inspector and writes a .cpuprofile to fixtures/local/perf/cpuprofile/ (gated by GRIDA_PERF_CPUPROFILE=1).

PerfObserver (perf.ts)

Opt-in instrumentation layer. Zero cost when disabled (returns a shared NOOP function).

Enable

GRIDA_PERF=1              # Node.js / headless tests
NEXT_PUBLIC_GRIDA_PERF=1  # Browser / Next.js (.env.local)

Or programmatically:

import { perf } from "@/grida-canvas/perf";
perf.enable();

Instrument new code

Use the __perf_ prefix for all perf variables:

import { perf } from "@/grida-canvas/perf";

function myHotFunction() {
  const __perf_end = perf.start("myHotFunction");
  // ... work ...
  __perf_end();
}

// For functions with multiple return paths, use try/finally:
function myComplexFunction() {
  const __perf_end = perf.start("myComplexFunction");
  try {
    if (earlyExit) return null;
    return result;
  } finally {
    __perf_end();
  }
}

// For wrapping a synchronous call:
const result = perf.measure("expensiveClone", () => doExpensiveWork());

Read results

perf.report(); // prints formatted table to console
perf.summarize(); // returns PerfSummaryEntry[] (for programmatic use)
perf.dump(); // returns raw PerfSample[]
perf.reset(); // clears all samples

Finding existing spans

Instrumented spans are discoverable via grep:

grep "__perf_" --include="*.ts" -r editor/grida-canvas/

The span labels use dot-notation hierarchy (dispatch.reducer, dispatch.emit, etc.) so the perf.report() table reads naturally.

Reading a browser trace

Chrome DevTools exports traces as .json.gz. They are plain JSON after decompression, so analyze them with a short Python script rather than opening DevTools by hand.

Key signals to extract:

The trace includes React DevTools overhead when the extension is installed. measureInstance @ installHook.js is the React DevTools profiler — it can easily account for ~10% of CPU and inflates render counts. Ask the user to disable the extension for "clean" traces, or subtract it out when reading.

# Minimal trace reader — extract events and CPU profile nodes
import json, gzip
from collections import defaultdict, Counter
data = json.load(gzip.open("logs/Trace-....json.gz"))
events = data["traceEvents"] if isinstance(data, dict) else data

# Component renders by name
renders = Counter()
for e in events:
    if e.get("cat") == "blink.user_timing" and e.get("ph") == "b":
        renders[e.get("name","")] += 1

# CPU self-time by function
nodes_by_id = {}
self_time = defaultdict(int)
for e in events:
    if e.get("name") == "ProfileChunk":
        cp = e["args"]["data"].get("cpuProfile", {})
        for n in cp.get("nodes", []):
            nodes_by_id[n["id"]] = n
        for sid, dt in zip(cp.get("samples", []),
                           e["args"]["data"].get("timeDeltas", [])):
            self_time[sid] += dt

Node CPU profiles

When an editor-bench run flags a span but you need function-level detail, capture a .cpuprofile:

# Via the bench harness (preferred — uses withCpuProfile wrapper)
GRIDA_PERF=1 GRIDA_PERF_CPUPROFILE=1 pnpm vitest run \
  editor/grida-canvas/__tests__/bench/perf-editor.test.ts
# → writes fixtures/local/perf/cpuprofile/*.cpuprofile

# Or wrap a specific call in code:
import { withCpuProfile } from "./_utils";
await withCpuProfile("my-scenario", async () => { ... });

Open the resulting .cpuprofile in Chrome DevTools (Performance → Load profile) or VS Code for an interactive flame graph.

For microtask-level detail, start Node with --cpu-prof --cpu-prof-interval=100 (µs between samples). For trace events / async task timing, --trace-events-enabled captures a chrome://tracing-compatible JSON. Both are Node built-ins — no extra tooling required.

The Verification Workflow

Every performance change follows this sequence.

Step 1: Baseline

Run the bench BEFORE any changes. Save the output.

GRIDA_PERF=1 pnpm vitest run editor/grida-canvas/__tests__/bench/perf-editor.test.ts

If the user provided a browser trace, also record its pre-change numbers (render counts and hot-function self-times) — these are the only way to verify React-side wins.

Step 2: Implement

Make the change. After each logical step, verify:

pnpm vitest run grida-canvas/__tests__/headless/
pnpm turbo typecheck --filter=editor

Step 3: Measure

Run the same benchmarks AFTER the change. Compare.

Step 4: Regression check

An optimization for one operation must not regress others. Run the full bench suite at both 1K and 10K scales, not just the target operation — many improvements that help 10K scenes add constant overhead that hurts 1K.

Step 5: Accept or iterate

How to Design an Optimization

1. Measure first

Run benchmarks to quantify the problem. Use GRIDA_PERF=1 to get the internal span breakdown. Identify which span dominates.

2. Classify the bottleneck

See the "Bottleneck Categories" table above. The perf.report() output directly tells you which category you're dealing with.

3. Implement incrementally

Each step should compile, pass existing tests, produce a measurable improvement in the target benchmark, and not regress other benchmarks.

4. Add a benchmark if one doesn't exist

If optimizing an operation that has no bench coverage, extend BENCH_SCENARIOS in perf-editor.test.ts first. Measure before and after.

Pitfalls

Immer makes everything O(N)

Immer's produceWithPatches walks the entire proxy tree during finalization regardless of how many properties changed. This is the fundamental scaling wall — even a no-op dispatch has a cost proportional to total node count.

Gesture start can freeze the UI

Gesture start may deep-clone the entire document state for undo snapshots. At scale this causes multi-second freezes and high memory pressure. Check how snapshot data is captured when investigating gesture-start lag.

Bench omits React cost — that is intentional

The bench measures the reducer + emit + WASM pipeline, not the React subscription layer. If the complaint is about interaction feel (a pointer move stalls, a panel re-renders too often), bench will not see it. Use a browser trace for that; see "Pick your measurement tool" above.

WASM geometry adds memory pressure

The WASM raster backend allocates a real scene in memory. At 10K+ nodes, running full gesture benchmarks may OOM at default heap size. Use NODE_OPTIONS="--max-old-space-size=8192".

Gesture-bound operations vary wildly

Drag translate can be orders of magnitude slower than resize at the same node count because translate triggers snap-target computation (tree queries per selection item) while single-node resize skips that path. Always bench the specific gesture type, not just "gesture" generically.

recording: "silent" skips history

Benchmarks use { recording: "silent" } to avoid history stack growth during repeated dispatches. This correctly isolates reducer cost but skips the history recording path. Use { recording: "on" } when specifically benchmarking history overhead.