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refactor(test): extract prod-DB cross-check to scripts/verify-prod-db.mjs
continuous.live-db-write.test.js Part 4 read the on-disk audit.db and asserted content thresholds (distinct_nodes > 100, finished batches, error codes). That asserts facts about whatever DB happens to be on disk, so it only passed on a populated production DB and FAILED on dev boxes / CI / fresh clones with a sparse fixture DB — a permanent red that masks real regressions. It is an operational health check, not a regression test. - Moved Part 4 verbatim into scripts/verify-prod-db.mjs (read-only, closes in finally, SKIPs when audit.db absent). Run on demand: npm run verify:prod-db. - continuous.live-db-write.test.js is now hermetic (in-memory DB only): Parts 1-3 still verify the real insertBatch → insertBatchResult → updateBatchOnFinish write path. 25/0, deterministic on any machine. - npm run test:integration now goes fully green regardless of local DB state. The two in-gate smoke tests already deliberately never assert against prod-DB contents; this brings the lone violator in line with that pattern.
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‎package.json‎

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"test": "node test/smoke.test.js && node test/db.smoke.test.js && node test/continuous.smoke.test.js && node test/security.test.js && node test/failure-log-ux.test.js && node test/public-no-action-buttons.test.js && node test/onchain-decode.test.js",
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"test:ui": "node test/ui.smoke.test.js",
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"test:integration": "node test/continuous.audit-integration.test.js && node test/continuous.live-db-write.test.js && node test/node-detail.smoke.test.js",
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"verify:prod-db": "node scripts/verify-prod-db.mjs",
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"cleanup": "node scripts/cleanup.mjs"
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},
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"keywords": [

‎scripts/verify-prod-db.mjs‎

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#!/usr/bin/env node
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/**
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* verify-prod-db.mjs — Operational health check for the live audit.db
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*
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* This is NOT a regression test. It is an on-demand operational audit the
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* operator runs against their real instrument to confirm the live
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* continuous-loop write path has populated production data sanely:
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*
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* - finished batches exist with finished_at > started_at
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* - the most recent batch has passed+failed > 0 and batch_results rows
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* - the corpus covers a meaningful number of distinct nodes (>100)
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* - failure rows carry error codes (proves the failure-log path works)
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* - batch mode distribution looks reasonable
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*
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* It was extracted from test/continuous.live-db-write.test.js (Part 4). That
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* cross-check asserts facts about whatever audit.db is on disk, so it can only
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* pass on a genuinely-populated production DB — it FAILED on dev boxes / CI /
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* fresh clones with a sparse fixture DB, masking real regressions. Automated
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* suites must be deterministic and environment-independent; an "is my live DB
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* healthy" question is an ops check, so it lives here instead.
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*
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* The hermetic write-path test (Parts 1-3) stays in test/continuous.live-db-write.test.js
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* and runs in `npm run test:integration`.
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*
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* Run: node scripts/verify-prod-db.mjs
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* Exit: 0 = all health checks passed (or prod DB absent → SKIP)
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* 1 = prod DB present but one or more health checks failed
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*
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* DB-lock note (per CLAUDE.md): opens audit.db read-only and closes it in a
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* finally block. Don't run this in parallel with a starting server.
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*/
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import path from 'path';
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import { fileURLToPath } from 'url';
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import { existsSync } from 'fs';
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import Database from 'better-sqlite3';
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const __dirname = path.dirname(fileURLToPath(import.meta.url));
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const PROD_DB = path.join(__dirname, '..', 'data', 'audit.db');
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const out = { pass: 0, fail: 0, errors: [] };
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function ok(cond, name) {
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if (cond) { out.pass++; console.log(` PASS ${name}`); }
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else { out.fail++; out.errors.push(name); console.log(` FAIL ${name}`); }
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}
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function main() {
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console.log(`Production DB health check — ${PROD_DB}`);
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if (!existsSync(PROD_DB)) {
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console.log(' SKIP prod audit.db not present — nothing to verify.');
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console.log(`\n${'='.repeat(60)}\nRESULTS: SKIPPED (no prod DB)\n${'='.repeat(60)}`);
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process.exit(0);
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}
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const prod = new Database(PROD_DB, { readonly: true });
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try {
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// Most recent finished batch
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const last = prod.prepare(`
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SELECT id, started_at, finished_at, snapshot_size, passed, failed, mode,
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(SELECT COUNT(*) FROM batch_results WHERE batch_id = batches.id) AS rows_count
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FROM batches WHERE finished_at IS NOT NULL ORDER BY id DESC LIMIT 1
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`).get();
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ok(last && last.id > 0, `prod has finished batches (most recent id=${last?.id})`);
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ok(last && last.finished_at > last.started_at, 'prod batch finished_at > started_at');
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ok(last && (last.passed + last.failed) > 0, `prod batch has passed+failed > 0 (${last?.passed}+${last?.failed})`);
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ok(last && last.rows_count > 0, `prod batch has batch_results rows (${last?.rows_count})`);
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// Aggregate
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const agg = prod.prepare(`
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SELECT COUNT(DISTINCT b.id) AS batches,
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COUNT(br.id) AS results,
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COUNT(DISTINCT br.node_address) AS distinct_nodes
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FROM batches b LEFT JOIN batch_results br ON br.batch_id = b.id
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`).get();
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ok(agg.batches > 0, `prod has ${agg.batches} batches recorded`);
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ok(agg.results > 0, `prod has ${agg.results} batch_results rows`);
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ok(agg.distinct_nodes > 100,
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`prod has ${agg.distinct_nodes} distinct nodes tested across all batches`);
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// Schema sanity — error_code distinct values prove failure-log path works
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const codes = prod.prepare(`
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SELECT error_code, COUNT(*) c FROM batch_results
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WHERE error_code IS NOT NULL GROUP BY error_code ORDER BY c DESC LIMIT 5
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`).all();
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console.log(' prod error_code distribution (top 5):');
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for (const r of codes) console.log(` ${r.error_code.padEnd(30)} ${r.c}`);
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ok(codes.length > 0, 'prod batch_results contains failure rows with error codes');
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// Mode distribution
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const modes = prod.prepare(`SELECT mode, COUNT(*) c FROM batches GROUP BY mode`).all();
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console.log(' prod batch mode distribution:');
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for (const m of modes) console.log(` ${m.mode.padEnd(15)} ${m.c}`);
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} finally {
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prod.close();
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}
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console.log(`\n${'='.repeat(60)}\nRESULTS: ${out.pass} passed, ${out.fail} failed`);
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if (out.errors.length) for (const e of out.errors) console.log(` FAIL: ${e}`);
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console.log('='.repeat(60));
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process.exit(out.fail ? 1 : 0);
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}
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main();

‎test/continuous.live-db-write.test.js‎

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* SAME sequence and shape, simulating one full iteration.
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* 3. Reads back the rows and asserts schema + content match.
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*
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* Then it cross-checks the production audit.db — proving prior continuous
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* runs wrote real data through this same path.
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* Hermetic and deterministic — uses only an in-memory DB. The read-only
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* cross-check of the live production audit.db that used to follow these steps
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* has moved to scripts/verify-prod-db.mjs: it asserted facts about whatever
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* audit.db was on disk, so it could only pass on a populated production DB and
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* failed on dev/CI/fresh clones. That is an operational health check, not a
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* regression test.
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*
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* Run: NODE_ENV=test node test/continuous.live-db-write.test.js
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*/
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import path from 'path';
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import { fileURLToPath } from 'url';
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import Database from 'better-sqlite3';
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import { existsSync } from 'fs';
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const __dirname = path.dirname(fileURLToPath(import.meta.url));
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const PROD_DB = path.join(__dirname, '..', 'data', 'audit.db');
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const out = { pass: 0, fail: 0, errors: [] };
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function ok(cond, name) {
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if (cond) { out.pass++; console.log(` PASS ${name}`); }
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ok(closedRow.failed === 1, 'batches.failed=1');
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ok(closedRow.snapshot_size === 3, 'batches.snapshot_size unchanged');
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// ─── 4. Cross-check production DB — prove this same code path ran live ──
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console.log('[4] Cross-check production audit.db (read-only)');
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if (!existsSync(PROD_DB)) {
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console.log(' SKIP prod audit.db not present');
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} else {
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const prod = new Database(PROD_DB, { readonly: true });
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try {
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// Most recent finished batch
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const last = prod.prepare(`
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SELECT id, started_at, finished_at, snapshot_size, passed, failed, mode,
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(SELECT COUNT(*) FROM batch_results WHERE batch_id = batches.id) AS rows_count
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FROM batches WHERE finished_at IS NOT NULL ORDER BY id DESC LIMIT 1
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`).get();
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ok(last && last.id > 0, `prod has finished batches (most recent id=${last?.id})`);
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ok(last && last.finished_at > last.started_at, 'prod batch finished_at > started_at');
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ok(last && (last.passed + last.failed) > 0, `prod batch has passed+failed > 0 (${last?.passed}+${last?.failed})`);
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ok(last && last.rows_count > 0, `prod batch has batch_results rows (${last?.rows_count})`);
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// Aggregate
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const agg = prod.prepare(`
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SELECT COUNT(DISTINCT b.id) AS batches,
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COUNT(br.id) AS results,
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COUNT(DISTINCT br.node_address) AS distinct_nodes
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FROM batches b LEFT JOIN batch_results br ON br.batch_id = b.id
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`).get();
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ok(agg.batches > 0, `prod has ${agg.batches} batches recorded`);
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ok(agg.results > 0, `prod has ${agg.results} batch_results rows`);
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ok(agg.distinct_nodes > 100,
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`prod has ${agg.distinct_nodes} distinct nodes tested across all batches`);
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// Schema sanity — error_code distinct values prove failure-log path works
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const codes = prod.prepare(`
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SELECT error_code, COUNT(*) c FROM batch_results
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WHERE error_code IS NOT NULL GROUP BY error_code ORDER BY c DESC LIMIT 5
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`).all();
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console.log(' prod error_code distribution (top 5):');
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for (const r of codes) console.log(` ${r.error_code.padEnd(30)} ${r.c}`);
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ok(codes.length > 0, 'prod batch_results contains failure rows with error codes');
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// Mode distribution
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const modes = prod.prepare(`SELECT mode, COUNT(*) c FROM batches GROUP BY mode`).all();
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console.log(' prod batch mode distribution:');
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for (const m of modes) console.log(` ${m.mode.padEnd(15)} ${m.c}`);
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} finally {
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prod.close();
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}
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}
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// The prod-DB cross-check that used to live here (Part 4) has moved to
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// scripts/verify-prod-db.mjs — it asserted facts about whatever audit.db was
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// on disk (distinct_nodes > 100, etc.), so it could only pass on a populated
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// production DB and failed on dev/CI/fresh clones. That is an operational
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// health check, not a regression test; this file stays hermetic.
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console.log(`\n${'='.repeat(60)}\nRESULTS: ${out.pass} passed, ${out.fail} failed`);
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if (out.errors.length) for (const e of out.errors) console.log(` FAIL: ${e}`);

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