playwright-test-results
Query Playwright CI test results from the aggregated DuckDB database. Answers questions about flaky tests, failure rates, slow tests, and per-run/SHA/PR…
npx skills add https://github.com/microsoft/playwright --skill playwright-test-resultsPlaywright Test Results (DuckDB)
A single DuckDB file holds recent Playwright CI test results, so you can answer questions about failures, flakiness, and slow tests with plain SQL. It is refreshed every few hours.
Get the database
Download the latest snapshot:
npm ci # first time only, from the repo root
GITHUB_TOKEN=$(gh auth token) node utils/test-results-db/cli.ts download
The snapshot may be missing the newest runs. To top it up locally, run update:
GITHUB_TOKEN=$(gh auth token) node utils/test-results-db/cli.ts update --lookback-days 3
Query it through the bundled @duckdb/node-api binding — no separate DuckDB
install needed, it ships in node_modules after npm ci:
node --input-type=module -e '
import { DuckDBInstance } from "@duckdb/node-api";
const conn = await (await DuckDBInstance.create("utils/test-results-db/test-results.duckdb")).connect();
console.table((await conn.runAndReadAll(process.argv[1])).getRowObjectsJson());
' "SELECT count(*) FROM test_results"
Integer columns come back as strings (JSON-safe), so do ranking and filtering in SQL, not in JS.
Schema
Single table test_results, one row per test result (one row per retry).
The columns are inferred from the parquet the reporter emits
(tests/config/parquetReporter.ts), plus two trailing columns this CLI adds:
| Column | Meaning |
|---|---|
run_id, run_attempt | GitHub Actions run identity |
run_started_at | when the run started |
workflow_name | e.g. tests 1 / tests 2 / tests others / MCP |
event | push / pull_request |
head_sha, head_branch, pr_number | what was tested |
bot_name | e.g. chromium-ubuntu-22.04-node20, webkit-macos-15-large — the CI bot. OS and arch are encoded here; there is no separate os column. |
project_name | CI project = browser + suite, e.g. chromium-page, webkit-library, playwright-test |
test_title | title path within the file, joined by › (describe › test) |
file, line, column_number | source location (file is relative to repo root) |
expected_status | passed / skipped / ... |
status | actual result: passed / failed / timedOut / skipped / interrupted |
retry | 0 = first attempt |
result_started_at | when this attempt started |
duration_ms | result duration |
error_message | all errors joined, ANSI-stripped (NULL when none) |
tags | list of strings, e.g. ['@slow', '@flaky'] (use list functions / list_contains) |
annotations | list of {type, description} structs, e.g. [{'type': 'skip', 'description': 'flaky on CI'}] (empty list when none) |
artifact_id | the GitHub artifact this row came from (dedupe key) |
ingested_at | debug only — when this row was imported |
Notes:
- A test is identified by
(project_name, file, test_title)— group on that tuple. (Playwright'stest_idhash is deliberately not stored; those three columns are its pre-image.) - Flakiness is derived, not stored. The signal that matters most is
cross-run: a test whose final verdict (after retries) flips between
runs — green in some, red in others. A separate within-run flake is a
test a retry rescued inside a single run (
failed→passed). - Real failures vs intentional ones: filter
expected_status = 'passed'. Tests markedtest.fail()recordstatus='failed'withexpected_status='failed'and would otherwise dominate any "most failing" list. - The db is size-capped by run count: the oldest whole runs are evicted over time, so it holds a recent window, not full history.
Example queries
Group tests by (project_name, file, test_title) and (for failure/flakiness)
scope to expected_status = 'passed' so intentional test.fail() tests don't
skew the results.
Flaky across runs — the test's final verdict flips between runs (this is
what makes a red CI run ambiguous). least(failed_runs, passed_runs) ranks
genuinely bimodal tests above both always-broken and one-off failures:
WITH per_run AS (
SELECT project_name, file, test_title, run_id, run_attempt,
arg_max(status, retry) AS final_status,
any_value(expected_status) AS expected
FROM test_results
GROUP BY project_name, file, test_title, run_id, run_attempt)
SELECT project_name, test_title,
count(*) AS runs,
count(*) FILTER (WHERE final_status IN ('failed','timedOut')) AS failed_runs,
count(*) FILTER (WHERE final_status = 'passed') AS passed_runs,
round(100.0 * count(*) FILTER (WHERE final_status IN ('failed','timedOut'))
/ count(*), 1) AS fail_pct
FROM per_run
WHERE expected = 'passed'
GROUP BY project_name, test_title
HAVING failed_runs > 0 AND passed_runs > 0 AND runs >= 10
ORDER BY least(failed_runs, passed_runs) DESC, failed_runs DESC
LIMIT 20;
Filter by tag (tags is a list, not a string):
SELECT project_name, test_title, count(*) AS runs
FROM test_results
WHERE list_contains(tags, '@slow')
GROUP BY project_name, test_title
ORDER BY runs DESC
LIMIT 20;
Generate a linked emoji run history
For a compact result that drops straight into a GitHub comment, render each final run verdict as a linked square. Edit the four test identity fields, then run:
node --input-type=module <<'EOF'
import { DuckDBInstance } from "@duckdb/node-api";
const repository = "microsoft/playwright";
const test = {
projectName: "firefox-library",
file: "library/proxy.spec.ts",
testTitle: "should exclude patterns",
botName: "firefox-macos-15-large",
};
const conn = await (await DuckDBInstance.create(
"utils/test-results-db/test-results.duckdb"
)).connect();
const result = await conn.runAndReadAll(`
WITH per_run AS (
SELECT run_id, run_attempt,
any_value(run_started_at) AS run_started_at,
arg_max(status, retry) AS final_status,
arg_max(expected_status, retry) AS expected_status,
list(status ORDER BY retry) AS attempt_statuses
FROM test_results
WHERE project_name = $projectName
AND file = $file
AND test_title = $testTitle
AND bot_name = $botName
GROUP BY run_id, run_attempt
)
SELECT run_id, run_attempt, final_status, attempt_statuses
FROM per_run
WHERE expected_status = 'passed'
AND final_status IN ('passed', 'failed', 'timedOut')
ORDER BY run_started_at, run_id, run_attempt
`, test);
const markdown = result.getRowObjectsJson().map(row => {
const rescued = row.final_status === "passed" &&
row.attempt_statuses.some(status => status === "failed" || status === "timedOut");
const emoji = rescued ? "🟧" : row.final_status === "passed" ? "🟩" : "🟥";
const url = `https://github.com/${repository}/actions/runs/${row.run_id}/attempts/${row.run_attempt}`;
return `[${emoji}](${url})`;
}).join("");
console.log(markdown);
EOF
The output is Markdown:
[🟩](https://github.com/microsoft/playwright/actions/runs/123/attempts/1)[🟧](https://github.com/microsoft/playwright/actions/runs/456/attempts/1)[🟥](https://github.com/microsoft/playwright/actions/runs/789/attempts/1)
Each square is one workflow run attempt, oldest first. Green means passed,
orange means a retry rescued an earlier failure, and red means failed or timed
out. arg_max(status, retry) picks the final verdict after retries, while
grouping by (run_id, run_attempt) keeps retries from turning into extra
squares. The /attempts/<n> URL links to the exact rerun that produced the
result.
Fetching the full detail
The db stores per-result summaries. For the full step tree / attachments / stdio,
fetch the original blob report for that run, if the run uploaded one. A row
identifies it by run_id + bot_name: the run's blob artifact is named
blob-report-<bot_name>.
# List the run's blob artifacts and find the one for this bot_name:
gh api /repos/microsoft/playwright/actions/runs/<run_id>/artifacts \
--jq '.artifacts[] | select(.name | startswith("blob-report")) | {id, name}'
# Download it (name == "blob-report-<bot_name>"):
gh api /repos/microsoft/playwright/actions/artifacts/<artifact_id>/zip > blob.zip
Blob and parquet artifacts have a 7-day retention, so this works only for recent runs; the db itself retains summaries longer (until run-count eviction).