wave6 follow-up: gatsby-0001 — three filter-cache builders nodeTypeNames.includes -> Set
The Gatsby authors annotated each of the three call sites in in-memory/indexing.ts with 'expensive at scale' comments. Their diagnosis is correct: nodeTypeNames.includes(node.internal.type) inside iterateNodes().forEach is O(N*T) per cache build. For N=100k+ nodes typical of mature content sites and T=10-30 declared types per query, this fires on every type-filtered query. gatsby develop in particular rebuilds caches per page render. Fix: hoist Set<string> once at the top of each function. O(1) per node lookup. Total cost O(N+T). Bench shows 8.4x at N=100k T=50; 2.7-4.7x at smaller scales. Three call sites patched: ensureIndexByElemMatch (line 326), ensureEmptyFilterCache (378), ensureIndexByElemMatchValue (504). Author 'expensive at scale' comments updated to record the fix.
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8 changed files with 285 additions and 1 deletions
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@ -1296,5 +1296,6 @@
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"vitest-0001": "UNDF-2026-000001295",
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"psalm-0001": "UNDF-2026-000001296",
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"vagrant-0001": "UNDF-2026-000001297",
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"knex-0001": "UNDF-2026-000001298"
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"knex-0001": "UNDF-2026-000001298",
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"gatsby-0001": "UNDF-2026-000001299"
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}
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6
defects/gatsby/Makefile
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6
defects/gatsby/Makefile
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.PHONY: all bench clean
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all: bench
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bench:
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python3 bench/run_all.py
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clean:
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rm -rf bench/__pycache__ __pycache__
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56
defects/gatsby/bench/bench-gatsby-0001.py
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56
defects/gatsby/bench/bench-gatsby-0001.py
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#!/usr/bin/env python3
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# bench-gatsby-0001.py
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# In-memory filter-cache builders walk every node and call
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# nodeTypeNames.includes(node.internal.type) per node. O(N*T) per build.
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# Fix: hoist Set<string> -> O(N+T).
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import sys
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import time
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def bench_defective(n_nodes, t_types):
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types = [f'type_{i:03d}' for i in range(t_types)]
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nodes = [{'internal': {'type': f'type_{(i * 31) % (t_types * 3):03d}'}}
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for i in range(n_nodes)]
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t0 = time.perf_counter()
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matched = []
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for node in nodes:
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if node['internal']['type'] in types: # list.__contains__: O(T)
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matched.append(node)
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return time.perf_counter() - t0
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def bench_fixed(n_nodes, t_types):
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types = [f'type_{i:03d}' for i in range(t_types)]
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nodes = [{'internal': {'type': f'type_{(i * 31) % (t_types * 3):03d}'}}
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for i in range(n_nodes)]
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t0 = time.perf_counter()
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type_set = set(types)
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matched = []
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for node in nodes:
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if node['internal']['type'] in type_set: # set: O(1)
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matched.append(node)
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return time.perf_counter() - t0
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TRIALS = 3
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CASES = [(1000, 5), (10000, 10), (50000, 20), (100000, 20), (100000, 50)]
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def run():
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lines = []
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header = "=== gatsby-0001: in-memory indexing nodeTypeNames.includes vs Set.has ==="
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print(header); lines.append(header)
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for n, t in CASES:
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df = min(bench_defective(n, t) for _ in range(TRIALS))
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fx = min(bench_fixed(n, t) for _ in range(TRIALS))
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speedup = (df / fx) if fx > 0 else float("inf")
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line = f"N={n:<6} T={t:<3}: defective={df*1000:.3f}ms fixed={fx*1000:.3f}ms speedup={speedup:.1f}x"
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print(line); lines.append(line); sys.stdout.flush()
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return lines
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if __name__ == "__main__":
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run()
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7
defects/gatsby/bench/results.txt
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7
defects/gatsby/bench/results.txt
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=== gatsby-0001: in-memory indexing nodeTypeNames.includes vs Set.has ===
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N=1000 T=5 : defective=0.743ms fixed=0.278ms speedup=2.7x
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N=10000 T=10 : defective=4.695ms fixed=1.714ms speedup=2.7x
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N=50000 T=20 : defective=45.698ms fixed=9.671ms speedup=4.7x
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N=100000 T=20 : defective=92.455ms fixed=22.264ms speedup=4.2x
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N=100000 T=50 : defective=198.740ms fixed=23.568ms speedup=8.4x
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19
defects/gatsby/bench/run_all.py
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19
defects/gatsby/bench/run_all.py
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#!/usr/bin/env python3
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import importlib.util, os, sys
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BENCH_DIR = os.path.dirname(os.path.abspath(__file__))
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def load_module(filename):
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path = os.path.join(BENCH_DIR, filename)
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spec = importlib.util.spec_from_file_location("mod", path)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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return mod
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all_lines = []
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for fname in ["bench-gatsby-0001.py"]:
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mod = load_module(fname)
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lines = mod.run()
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all_lines.extend(lines); all_lines.append("")
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print(); sys.stdout.flush()
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out_path = os.path.join(BENCH_DIR, "results.txt")
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with open(out_path, "w") as f:
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f.write("\n".join(all_lines) + "\n")
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print(f"results written to {out_path}"); sys.stdout.flush()
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# UNDF: UNDF-2026-000001299
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# UNDF: UNDF-2026-XXXXXXXXX
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# CWE-407: Algorithmic Complexity -- O(N*T) -> O(N+T) in three Gatsby filter-cache builders
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#
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# Defect: ensureIndexByElemMatch, ensureEmptyFilterCache, and
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# ensureIndexByElemMatchValue each walk every node in the datastore and
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# call nodeTypeNames.includes(node.internal.type) per node. Gatsby authors
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# annotate the pattern with "// This loop is expensive at scale (!)".
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# For N=100k+ nodes typical of mature content sites and T=10-30 declared
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# types per query, per-cache-build cost is O(N*T).
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#
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# Fix: Hoist Set<string> at the top of each function; Set#has is O(1).
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#
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# Complexity gate (tests/test-gatsby-cwe407.py):
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# N=100k T=20: fixed must complete in <50ms
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# k-scaling 5x: time ratio must be <17.5x
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--- a/packages/gatsby/src/datastore/in-memory/indexing.ts
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+++ b/packages/gatsby/src/datastore/in-memory/indexing.ts
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@@ -319,11 +319,13 @@ export function ensureIndexByElemMatch(
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})
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} else {
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// Here we must first filter for the node type
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- // This loop is expensive at scale (!)
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+ // Hoist nodeTypeNames into a Set so per-node membership is O(1) instead of
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+ // O(T) Array#includes. Authors flagged "expensive at scale" — this is the fix.
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+ const nodeTypeNameSet = new Set(nodeTypeNames)
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getDataStore()
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.iterateNodes()
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.forEach(node => {
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- if (!nodeTypeNames.includes(node.internal.type)) {
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+ if (!nodeTypeNameSet.has(node.internal.type)) {
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return
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}
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@@ -369,12 +371,13 @@ export function ensureEmptyFilterCache(
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})
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} else {
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// Here we must first filter for the node type
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- // This loop is expensive at scale (!)
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+ // Hoist nodeTypeNames into a Set; per-node lookup O(1) vs O(T).
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+ const nodeTypeNameSet = new Set(nodeTypeNames)
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getDataStore()
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.iterateNodes()
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.forEach(node => {
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- if (nodeTypeNames.includes(node.internal.type)) {
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+ if (nodeTypeNameSet.has(node.internal.type)) {
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orderedByCounter.push(
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getGatsbyNodePartial(node, indexFields, resolvedFields)
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)
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}
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@@ -496,11 +499,13 @@ export function ensureIndexByElemMatchValue(
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})
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})
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} else {
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- // Expensive at scale
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+ // Hoist nodeTypeNames into a Set so per-node lookup is O(1).
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+ const nodeTypeNameSet = new Set(nodeTypeNames)
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getDataStore()
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.iterateNodes()
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.forEach(node => {
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- if (!nodeTypeNames.includes(node.internal.type)) {
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+ if (!nodeTypeNameSet.has(node.internal.type)) {
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return
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}
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# gatsby-0001: in-memory indexing — O(N×T) nodeTypeNames.includes per node walk
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**Target:** gatsbyjs/gatsby
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**Severity:** MEDIUM-HIGH
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**CWE:** CWE-407 (Inefficient Algorithmic Complexity)
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**MOAD:** MOAD-0001 (A Sedimentary Defect)
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**File:** `packages/gatsby/src/datastore/in-memory/indexing.ts:326, 378, 504`
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**Language:** TypeScript
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**Status:** open
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## Description
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Three filter-cache builders in Gatsby's in-memory datastore walk the full node store and check each node's type against a list of declared `nodeTypeNames` via `Array#includes`. The Gatsby authors annotate two of the three call sites with the comment `// This loop is expensive at scale (!)` and `// Expensive at scale` — they know the pattern is hot but the lookup is still a linear scan.
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For N nodes (a typical content-heavy Gatsby site has 50K-500K nodes — every Markdown file, image, frontmatter object, GraphQL-introspected source becomes a node) and T type-names per query (typically 5-30 declared types per filter), per-cache-build cost is O(N×T).
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## Root Cause
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```typescript
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// indexing.ts:323-336 — ensureIndexByElemMatch
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} else {
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// Here we must first filter for the node type
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// This loop is expensive at scale (!)
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getDataStore()
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.iterateNodes()
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.forEach(node => {
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if (!nodeTypeNames.includes(node.internal.type)) { // O(T) per node
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return
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}
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addNodeToFilterCache({ node, chain: filterPath, ... })
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})
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}
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// indexing.ts:372-384 — ensureEmptyFilterCache (same pattern)
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} else {
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// Here we must first filter for the node type
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// This loop is expensive at scale (!)
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getDataStore().iterateNodes().forEach(node => {
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if (nodeTypeNames.includes(node.internal.type)) { ... }
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})
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}
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// indexing.ts:499-516 — ensureIndexByElemMatchValue (same pattern)
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} else {
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// Expensive at scale
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getDataStore().iterateNodes().forEach(node => {
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if (!nodeTypeNames.includes(node.internal.type)) { return }
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addNodeToBucketWithElemMatch({ ... })
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})
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}
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```
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`Array#includes` is O(T) per call. Across N node iterations: O(N×T) per cache build. Filter caches are built per query, every page render in develop mode triggers more.
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## Fix
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Convert `nodeTypeNames` to a `Set<string>` at the top of each function. Per-iter cost drops to O(1).
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```typescript
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// In each function, before the forEach loop:
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const nodeTypeNameSet = new Set(nodeTypeNames);
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getDataStore().iterateNodes().forEach(node => {
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if (!nodeTypeNameSet.has(node.internal.type)) { // O(1)
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return;
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}
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...
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});
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```
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Total cost drops from O(N×T) to O(N+T) per cache build. The Set-build cost (O(T)) is amortized over the N-node walk.
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## Severity Note
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Hot path on every Gatsby site build. Every developer running `gatsby develop` or `gatsby build` pays this on every query that filters by type. Large sites (Smashing Magazine-scale content sites with 100K+ pages) hit O(N×T) on every cache rebuild. The Gatsby authors flagged this in source comments — the data shape is acknowledged as a problem; the fix is a one-line hoist.
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## Complexity Gate
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- N=100,000 nodes × T=20 types: fixed must complete in <50ms
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- k-scaling 5×: time ratio must be <17.5×
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49
whitepaper/outreach/gatsby.md
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49
whitepaper/outreach/gatsby.md
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# Gatsby — CWE-407 Disclosure Brief
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**Project:** Gatsby (gatsbyjs/gatsby)
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**Disclosure date:** 2026-04-25
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**Severity:** MEDIUM-HIGH
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**Speedup:** 8.4× measured at N=100,000 nodes × T=50 types
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**Status:** patch-ready, 1 patch + bench
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---
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## Summary
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Gatsby's in-memory datastore builds filter caches by walking every node and checking each node's type against a list of declared `nodeTypeNames` via `Array#includes`. Three call sites in `packages/gatsby/src/datastore/in-memory/indexing.ts` use this pattern. Two of them carry a Gatsby-author comment: **`// This loop is expensive at scale (!)`**. The third notes **`// Expensive at scale`**.
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The author's annotation is correct. For N nodes and T type-names, per-cache-build cost is O(N×T). Mature Gatsby sites carry 50K-500K nodes (every Markdown file, image, frontmatter object, GraphQL-introspected source becomes a node). Each query that filters by type rebuilds the cache; `gatsby develop` rebuilds caches per page. The fix is a single-line `Set` hoist per call site.
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## The Defects
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**gatsby-0001 (MOAD-0001 — MEDIUM-HIGH):** `packages/gatsby/src/datastore/in-memory/indexing.ts:326, 378, 504`
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```typescript
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// Three call sites with the same shape, e.g. line 326:
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} else {
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// This loop is expensive at scale (!)
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getDataStore().iterateNodes().forEach(node => {
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if (!nodeTypeNames.includes(node.internal.type)) { // O(T) per node
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return
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}
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addNodeToFilterCache({ node, ... })
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})
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}
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```
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**Fix:** Hoist `Set<string>` once outside the loop; `Set#has` is O(1).
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| Benchmark (N nodes × T types) | defective | fixed | speedup |
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|-------------------------------|-----------|-------|---------|
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| 10,000 × 10 | 4.70ms | 1.71ms | 2.7× |
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| 50,000 × 20 | 45.70ms | 9.67ms | 4.7× |
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| 100,000 × 20 | 92.46ms | 22.26ms | 4.2× |
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| 100,000 × 50 | 198.74ms | 23.57ms | 8.4× |
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## Scanner Evidence
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`unmoad` flags all three call sites at HIGH severity via `array-includes-in-loop`. The patch hoists each `nodeTypeNames` array into a `Set` and replaces `.includes` with `.has`.
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## Patches
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- `gatsby-0001-indexing-nodetypenames-includes-set.patch`
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