Scalable datastore for metrics, events, and real-time analytics
| Rating | Summary | |
|---|---|---|
| Quality | solid (18/24) | Actively maintained, well-adopted production database with strong engineering signals; loses points on issue volume, missing CI badge in README, and no inline usage examples. |
| PAI Relevance | integrate (0.50) | Borderline INTEGRATE: fills a real gap as a queryable time-series backend for PAI's observability events, but requires running a separate server rather than a simple bun add. |
18/24 — maintained / adequately-documented / high-discipline
Failed:
.github/ directory is referenced in Cargo.toml excludes but not in the README itself.Passed:
archived: false.Failed:
Passed:
Failed:
Passed:
influxdb3_test_helpers, core/test_helpers, core/futures_test_utils, and core/test_helpers_authz — dedicated test infrastructure crates.| Dimension | Score | Assessment |
|---|---|---|
| Harvest Value | 0 | InfluxDB's WAL+Parquet architecture and embedded Python trigger VM are interesting patterns, but PAI already has an event-hook system (46 hooks) and file-based memory; nothing here is novel enough to lift a PAI subsystem design. |
| Integration Readiness | 1 | Rust binary with a clean HTTP API on port 8181, line-protocol writes, and SQL/InfluxQL query endpoints; self-hosted and offline-capable. A PAI skill could wrap it via HTTP calls with moderate adapter code, but it cannot be bun add-ed. |
| Overlap Risk | 0 | PAI has no time-series database or queryable metrics store in its manifest — the 46 observability hooks generate events but there is no persistent, indexed time-series backend to query against. |
| Gap Fill | 1 | Addresses a functional gap: PAI's observability hooks produce telemetry (hook execution timing, agent invocations, memory operations) but have no persistent, queryable time-series store to back them; InfluxDB would serve that role. |
Composite: 0.50
Capture-to-Knowledge Pipeline: Deploy InfluxDB 3 Core locally via Docker and instrument each pipeline stage (capture, validation, Haiku clean-room pass) to write a line-protocol point on every item processed — recording stage name, duration, item count, and pass/fail tag. Query trends with InfluxDB's SQL interface. — You gain a persistent, time-series view of pipeline throughput and validation pass-rates across runs, so regressions caused by prompt or schema changes become visible as metric drift rather than silent failures.
Petites Fugues: Add a thin write call to the InfluxDB 1.x-compatible HTTP API (no new SDK required — the endpoint is /api/v2/write with line protocol) in the order-handling path to record each sale as a timestamped event tagged by item category (book / CD / vinyl). — Sales velocity, category mix, and revenue rate become queryable time-series rather than requiring a manual spreadsheet export; sub-10ms last-value queries mean a live dashboard refresh is practical on any cheap VPS.
AI tool eval tracking (Fabric Recommender, Aphoria): From existing Bun test scripts, write one line-protocol record per eval run — fields: score, latency, model, prompt version; tag: tool name — to a local InfluxDB instance using a fetch POST to the write API. — Eval scores accumulate into a queryable history so a SELECT * FROM evals WHERE tool='fab' ORDER BY time DESC LIMIT 20 immediately shows whether the latest Fabric pattern update improved or regressed recommendation quality across real runs.
Category: Data & Storage
In this category: simonw--datasette (excellent, 20/24, integrate)
Standing: InfluxDB is a purpose-built, high-scale time-series engine with 31K stars and a complete v3 rewrite; datasette is a lightweight exploration tool for arbitrary SQLite data — they occupy opposite ends of the data-storage spectrum with no direct overlap.
Density: 10/10 — Full repo metadata (stars, forks, issues, dates, license, archived status) available; README first 8KB provided; workspace Cargo.toml with full member and dependency list available; release metadata with tag and date available; topic tags and language available. Missing: full test suite stats, contributor count, actual CI workflow config content.
The landscape context entry for this repo listed poor (4.8/24) — that figure appears to be a placeholder or uncalculated stub; the careful probe-by-probe assessment here yields 18/24 solid, consistent with InfluxDB's standing as a production-grade, actively maintained database with a decade of history. The PAI INTEGRATE verdict is mechanical and borderline (exactly 0.50); practically, integrating InfluxDB means running and managing a separate server process, which is non-trivial for a CLI-first personal system. The strongest use case is routing PAI's observability hook telemetry into InfluxDB for queryable dashboards, not storing agent memory or knowledge.