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Boost Usage Tracker

Overview

Collects Boost library usage signals (e.g. repository metadata tied to Boost) and runs periodic database update commands. Commands split between the main tracker run and smaller run_update_* jobs.

Data workflow

Commands here focus on GitHub-derived usage signals (repository content, stars) and periodic DB maintenance helpers. Service details: docs/service_api/boost_usage_tracker.md. See docs/Architecture_data_flow.md for how this fits the wider platform.

Where we fetch data

GitHub search and repository APIs (via core.operations.github_ops patterns) for run_boost_usage_tracker tasks such as monitor_content and monitor_stars, within the date and star thresholds you pass on the CLI.

How data is saved to the database

Usage signals and discovered repositories are upserted into this app’s models (run_boost_usage_tracker, run_update_created_repos_by_language). run_update_db performs targeted refreshes or housekeeping defined in that command’s implementation. Optional CSV or staging paths may use WORKSPACE_DIR for exports. References: docs/Schema.md, section 4 — Boost Usage Tracker · models.py · docs/service_api/boost_usage_tracker.md.

How content is published to GitHub

Not applicable. This app records analytics in PostgreSQL; it does not push Markdown repos or open PRs as part of its collectors.

How vectors sync to Pinecone

Not applicable. There is no Pinecone sync phase in this app. Other collectors follow docs/Pinecone_preprocess_guideline.md when they upsert usage-adjacent text.

Common tasks

  • Full tracker: python manage.py run_boost_usage_tracker --help
  • DB refresh helpers: run_update_db, run_update_created_repos_by_language (see the Management commands section below).

Main command: run_boost_usage_tracker

Runs monitor_content (repo/content signals) and/or monitor_stars (monthly star counts) inside one collector invocation unless --task narrows it.

Option Description
--task monitor_content | monitor_stars — run only that task. Default: both, in order (monitor_content then monitor_stars).
--since YYYY-MM-DD lower bound for monitor_content (default: yesterday).
--until YYYY-MM-DD upper bound for monitor_content (default: today).
--min-stars Minimum stars filter for monitor_stars (default 10).
--dry-run Log actions only; no DB changes.

Package

  • Django app label: boost_usage_tracker
  • Path (from repo root): boost_usage_tracker/
  • Registration: Listed under INSTALLED_APPS in config/settings.py as boost_usage_tracker.

Management commands

Command Description
run_boost_usage_tracker Primary collector: monitor_content searches GitHub for C++ repos pushed in a date range and records Boost #include usage; monitor_stars scans highly starred C++ repos for new candidates. Updates BoostExternalRepository, BoostUsage, and related rows (see command help).
run_update_created_repos_by_language Calls the GitHub API to count new repositories per language per year (star threshold configurable) and upserts github_activity_tracker.CreatedReposByLanguage.
run_update_db Bulk import / repair from JSON or CSV under WORKSPACE_DIR (or --source): --target chooses the pipeline (github_account, repository, githubfile, boostusage) to refresh GitHub accounts, repos, files, or usage rows.

Run python manage.py <command> --help for options.

Tests

Typical invocation (from repo root, after README prerequisites):

python -m pytest boost_usage_tracker/tests/ -v