d7ae0f889a
Bumps [urllib3](https://github.com/urllib3/urllib3) from 1.26.16 to 1.26.17. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/1.26.16...1.26.17) --- updated-dependencies: - dependency-name: urllib3 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> |
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.. | ||
SQL_queries | ||
.gitignore | ||
analyze_jobs.ipynb | ||
buildbot_monitoring.py | ||
buildbot_status_emails.py | ||
buildbots.py | ||
buildkite_master_stats.py | ||
connect_db.sh | ||
download_buildkite_builds_pg.sh | ||
jenkins.py | ||
load_buildkite.py | ||
Pipfile | ||
Pipfile.lock | ||
pull_phab_build_stats.py | ||
README.md | ||
repo.py | ||
repo_hist.py | ||
repo_hist_db.py | ||
server_monitoring.py |
Metrics
To measure the impact and usefulness of the pre-merge checks, we want to collect a set of metrics. This doc will summarize the metrics and tools. All of the data shall be collected as time series, so that we can see changes over time.
-
Impact - The metrics we ultimately want to improve
- Percentage of build-bot build on main failing. (Buildbot_percentage_failing)
- Time to fix a broken main build: Time between start of failing builds until the build is fixed. (BuildBot_time_to_fix)
- Percentage of Revisions on Phabricator where a broken build was fixed afterwards. This would indicate that a bug was found and fixed during the code review phase. (Premerge_fixes)
- Number of reverts on main. This indicates that something was broken on main that slipped through the pre-merge tests or was submitted without any review. (Upstream_reverts)
-
Users and behavior - Interesting to see and useful to adapt our approach.
- Percentage of commits to main that went through Phabricator.
- Number of participants in pre-merge tests.
- Percentage of Revisions with pre-merge tests executed
- Number of 30-day active committers on main and Phabricator.
-
Builds - See how the infrastructure is doing.
- Time between upload of diff until build results available.
- Percentage of Revisions with successful/failed tests
- Number of pre-merge builds/day.
- Build queuing time.
- Individual times for
cmake
,ninja all
,ninja check-all
per OS/architecture. - Result storage size.
- Percentage of builds failing.
Requirements
- Must:
- Do not collect/store personal data.
- Should:
- Minimize the amount of additional tools/scripts we need to maintain.
- Collect all metrics in a central location for easy evaluation (e.g. database, CSV files).
- Nice to have:
- As the data is from an open source project and available anyway, give public access to the metrics (numbers and charts).
- Send out alerts/notifications.
- Show live data in charts.
Data sources
This section will explain where we can get the data from.
- build bot statistics
Solution
We need to find solutions for these parts:
- Collect the data (regularly).
- Store the time series somewhere.
- Create & display charts.
Some ideas for this:
- bunch of scripts:
- Run a bunch of scripts manually to generate the metrics every now and then. Phabricator already has a database and most entries there have timestamps. So we could also reconstruct the history from that.
- TODO: Figure out if we can collect the most important metrics this way. This requires that we can reconstruct historic values from the current logs/git/database/... entries.
- Jenkins + CSV + Sheets:
- collect data with jenkins
- store numbers as CSV in this repo
- Charts are created manually on Google Sheets
- do it yourself:
- Collect data with Jenkins jobs
- Store the data on Prometheus
- Visualize with Grafana
- host all tools ourselves
- Stackdriver on GCP:
- TODO: figure out if we can get all the required data into Stackdriver
- Jupyter notebooks:
- TODO: figure out how that works