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* feat(engine): add addLinksBatch + addTimelineEntriesBatch via unnest()
Multi-row INSERT...SELECT FROM unnest() JOIN pages ON CONFLICT DO NOTHING
RETURNING 1. 4 array-typed bound parameters (links) or 5 (timeline)
regardless of batch size, sidesteps Postgres's 65535-parameter cap.
Returns count of rows actually inserted (excluding ON CONFLICT no-ops
and JOIN-dropped rows whose slugs don't exist).
Per-row addLink / addTimelineEntry signatures and SQL behavior unchanged.
All 10 existing call sites compile and behave identically.
Tests: 11 PGLite cases (empty batch, missing optionals, within-batch dedup,
JOIN drops missing slug, half-existing batch, batch of 100) + 9 E2E
postgres-engine cases against real Postgres+pgvector.
* fix(migrate): pre-create btree helper in v8 + v9 dedup; bump phaseASchema timeout
Production bug: v0.12.0 schema migration timed out at Supabase Management API's
60s ceiling on brains with 80K+ duplicate timeline rows. The DELETE...USING
self-join was O(n²) without an index on the dedup columns.
Fix: pre-create idx_links_dedup_helper / idx_timeline_dedup_helper on the
dedup columns BEFORE the DELETE, drop after. Turns O(n²) into O(n log n).
On 80K+ rows the migration completes in <1s instead of timing out.
Also bumps the v0.12.0 orchestrator's phaseASchema timeout 60s -> 600s as
belt-and-suspenders for unforeseen slowness.
Exports MIGRATIONS for structural test assertions.
Tests: 2 structural assertions (helper-index DDL must appear in v8/v9 SQL
in the right order — catches regression even at 0-row scale) + 2 behavioral
regression tests (1000-row dedup completes <5s).
* perf(extract): kill N+1 dedup pre-load; switch to batched writes
Production bug: gbrain extract hung 10+ minutes producing zero output on
47K-page brains. The pre-load loop called engine.getLinks(slug) (or
getTimeline) once per page across engine.listPages({limit: 100000}) — 47K
serial round-trips over the Supabase pooler before the first file was read.
Both engines already enforced uniqueness at the SQL layer
(UNIQUE(from, to, link_type) on links, idx_timeline_dedup on timeline_entries).
The in-memory dedup Set was redundant insurance that became the bottleneck.
Fix: delete the pre-load entirely. Buffer 100 candidates per file walk,
flush via engine.addLinksBatch / engine.addTimelineEntriesBatch. ~99% fewer
DB round-trips per re-extract.
Also fixes counter accuracy: 'created' now counts rows actually inserted
(via batch RETURNING 1 row count). Re-run on a fully-extracted brain
prints 'Done: 0 links' instead of lying.
Dry-run mode keeps a per-run dedup Set so duplicate candidates from N
markdown files print exactly once, not N times.
Batch errors are visible in BOTH json and human modes — silent loss of
100 rows is worse than per-row error visibility.
Tests: extract-fs.test.ts (idempotency + truthful counter + dry-run dedup
+ perf regression guard <2s).
* chore: bump version + changelog (v0.12.1)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: update CLAUDE.md for v0.12.1 (batch engine API, test counts)
Reflect what shipped in v0.12.1:
- New engine methods addLinksBatch + addTimelineEntriesBatch (PGLite via
unnest() + manual $N, postgres-engine via INSERT...SELECT FROM
unnest($1::text[], ...) JOIN pages ON CONFLICT DO NOTHING).
- extract.ts no longer pre-loads dedup set; candidates are buffered 100
at a time and flushed via the new batch methods.
- v0.12.0 orchestrator phaseASchema timeout bumped 60s to 600s.
- Test counts 1297 unit / 105 E2E to 1412 unit / 119 E2E.
- New test/extract-fs.test.ts covers the N+1 regression guard.
- BrainEngine method count 37/38 to 40.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
162 lines
6.1 KiB
TypeScript
162 lines
6.1 KiB
TypeScript
/**
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* Tests for `gbrain extract --source fs` (the default, FS-walking path).
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*
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* Companion to test/extract-db.test.ts. Specifically guards against the
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* v0.12.0 N+1 hang: extractLinksFromDir / extractTimelineFromDir used to
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* pre-load the entire dedup set with one engine.getLinks() per page across
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* engine.listPages(), which on a 47K-page brain meant 47K sequential
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* round-trips before any work happened.
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*
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* Verifies:
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* 1. Single run extracts the expected links + timeline entries.
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* 2. Second run reports `created: 0` (proves DO NOTHING in batch + accurate
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* counter via RETURNING).
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* 3. --dry-run prints the same link found across multiple files exactly
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* once (proves the dry-run-only dedup Set works).
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* 4. Second run wall-clock < 2s (regression guard against any future change
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* that re-introduces the N+1 read pre-load).
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*/
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import { describe, test, expect, beforeAll, afterAll, beforeEach } from 'bun:test';
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import { mkdtempSync, writeFileSync, mkdirSync, rmSync } from 'fs';
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import { join } from 'path';
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import { tmpdir } from 'os';
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import { PGLiteEngine } from '../src/core/pglite-engine.ts';
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import { runExtract } from '../src/commands/extract.ts';
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import type { PageInput } from '../src/core/types.ts';
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let engine: PGLiteEngine;
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let brainDir: string;
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beforeAll(async () => {
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engine = new PGLiteEngine();
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await engine.connect({});
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await engine.initSchema();
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});
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afterAll(async () => {
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await engine.disconnect();
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});
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async function truncateAll() {
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for (const t of ['content_chunks', 'links', 'tags', 'raw_data', 'timeline_entries', 'page_versions', 'ingest_log', 'pages']) {
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await (engine as any).db.exec(`DELETE FROM ${t}`);
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}
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}
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const personPage = (title: string, body = ''): PageInput => ({
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type: 'person', title, compiled_truth: body, timeline: '',
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});
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const companyPage = (title: string, body = ''): PageInput => ({
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type: 'company', title, compiled_truth: body, timeline: '',
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});
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beforeEach(async () => {
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await truncateAll();
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brainDir = mkdtempSync(join(tmpdir(), 'gbrain-extract-fs-'));
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});
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function writeFile(rel: string, content: string) {
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const full = join(brainDir, rel);
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mkdirSync(join(full, '..'), { recursive: true });
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writeFileSync(full, content);
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}
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describe('gbrain extract links --source fs', () => {
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test('first run inserts links, second run reports 0 (idempotent + truthful counter)', async () => {
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// Set up brain in DB matching the file structure
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await engine.putPage('people/alice', personPage('Alice'));
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await engine.putPage('people/bob', personPage('Bob'));
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await engine.putPage('companies/acme', companyPage('Acme'));
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// Set up matching markdown files on disk
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writeFile('people/alice.md', '---\ntitle: Alice\n---\n\n[Bob](../people/bob.md) is a friend.\n');
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writeFile('people/bob.md', '---\ntitle: Bob\n---\n\nWorks at [Acme](../companies/acme.md).\n');
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writeFile('companies/acme.md', '---\ntitle: Acme\n---\n\nFounded by [Alice](../people/alice.md).\n');
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// First run — write batch path
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await runExtract(engine, ['links', '--dir', brainDir]);
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const linksAfter1 = (await engine.getLinks('people/alice'))
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.concat(await engine.getLinks('people/bob'))
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.concat(await engine.getLinks('companies/acme'));
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expect(linksAfter1.length).toBeGreaterThanOrEqual(3);
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// Second run — must dedup via ON CONFLICT and report 0 new (truthful counter)
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const start = Date.now();
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await runExtract(engine, ['links', '--dir', brainDir]);
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const elapsedMs = Date.now() - start;
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const linksAfter2 = (await engine.getLinks('people/alice'))
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.concat(await engine.getLinks('people/bob'))
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.concat(await engine.getLinks('companies/acme'));
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expect(linksAfter2.length).toBe(linksAfter1.length);
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// Perf regression guard: re-run on tiny fixture must not loop through
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// listPages + per-page getLinks. ~10 files should complete in well under
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// 2s even on a slow CI box.
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expect(elapsedMs).toBeLessThan(2000);
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});
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test('--dry-run dedups duplicate candidates across files (printed once, not N times)', async () => {
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await engine.putPage('people/alice', personPage('Alice'));
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await engine.putPage('companies/acme', companyPage('Acme'));
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// Same link target appears in 3 different files. The target file must
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// exist on disk so the FS extractor's allSlugs Set includes it.
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writeFile('companies/acme.md', '---\ntitle: Acme\n---\n');
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writeFile('a.md', '[Acme](companies/acme.md)\n');
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writeFile('b.md', '[Acme](companies/acme.md)\n');
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writeFile('c.md', '[Acme](companies/acme.md)\n');
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// Capture stdout to check print frequency
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const lines: string[] = [];
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const origLog = console.log;
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console.log = (...args: unknown[]) => { lines.push(args.join(' ')); };
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try {
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await runExtract(engine, ['links', '--dry-run', '--dir', brainDir]);
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} finally {
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console.log = origLog;
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}
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// Each (from, to, link_type) tuple should print at most once.
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// Three distinct from_slugs (a, b, c) all link to companies/acme, so
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// we expect 3 link lines (one per source file), not 9.
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const linkLines = lines.filter(l => l.includes('→') && l.includes('companies/acme'));
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expect(linkLines.length).toBe(3);
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// No actual writes happened
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const links = await engine.getLinks('companies/acme');
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expect(links.length).toBe(0);
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});
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});
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describe('gbrain extract timeline --source fs', () => {
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test('first run inserts entries, second run reports 0 (idempotent + truthful counter)', async () => {
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await engine.putPage('people/alice', personPage('Alice'));
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writeFile('people/alice.md', `---
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title: Alice
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---
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## Timeline
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- **2024-01-15** | source — Founded NovaMind
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- **2024-06-01** | source — Raised seed round
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`);
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await runExtract(engine, ['timeline', '--dir', brainDir]);
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const after1 = await engine.getTimeline('people/alice');
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expect(after1.length).toBe(2);
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const start = Date.now();
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await runExtract(engine, ['timeline', '--dir', brainDir]);
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const elapsedMs = Date.now() - start;
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const after2 = await engine.getTimeline('people/alice');
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expect(after2.length).toBe(2);
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expect(elapsedMs).toBeLessThan(2000);
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});
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});
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