ingestion-pipeline-doctor-nodejs
Tổng quan kiến trúc pipeline tiếp nhận và tham chiếu quy ước. Sử dụng khi bạn cần định hướng nhanh về khung pipeline hoặc muốn biết bác sĩ nào…
npx skills add https://github.com/posthog/posthog-foss --skill ingestion-pipeline-doctor-nodejsPipeline Doctor
Quick reference for PostHog's ingestion pipeline framework and its convention-checking agents.
Architecture overview
The ingestion pipeline processes events through a typed, composable step chain:
Kafka message
→ messageAware()
→ parse headers/body
→ sequentially() for preprocessing
→ filterMap() to enrich context (e.g., team lookup)
→ teamAware()
→ concurrentlyPerGroup(token:distinctId) for per-entity processing
→ gather()
→ pipeChunk() for chunk operations
→ handleIngestionWarnings()
→ handleResults()
→ handleSideEffects()
→ build()
See nodejs/src/ingestion/pipelines/analytics/joined-ingestion-pipeline.ts for the real implementation.
Key file locations
| What | Where |
|---|---|
| Step type | nodejs/src/ingestion/framework/steps.ts |
| Result types | nodejs/src/ingestion/framework/results.ts |
| Doc-test chapters | nodejs/src/ingestion/framework/docs/*.test.ts |
| Joined pipeline | nodejs/src/ingestion/pipelines/analytics/joined-ingestion-pipeline.ts |
| Doctor agents | .claude/agents/ingestion/ |
| Test helpers | nodejs/src/ingestion/framework/docs/helpers.ts |
Which agent to use
| Concern | Agent | When to use |
|---|---|---|
| Step structure | pipeline-step-doctor | Factory pattern, type extension, config injection, naming |
| Result handling | pipeline-result-doctor | ok/dlq/drop/redirect, side effects, ingestion warnings |
| Composition | pipeline-composition-doctor | Builder chain, concurrency, grouping, branching, retries |
| Testing | pipeline-testing-doctor | Test helpers, assertions, fake timers, doc-test style |
Quick convention reference
Steps: Factory function returning a named inner function. Generic <T extends Input> for type extension. No any. Config via closure.
Results: Use ok(), dlq(), drop(), redirect() constructors. Side effects as promises in ok(value, [effects]). Warnings as third parameter.
Composition: messageAware wraps the pipeline. handleResults inside messageAware. handleSideEffects after. concurrentlyPerGroup for per-entity work. gather before chunk steps.
Batching lifecycle hooks (BatchingPipeline beforeBatch/afterBatch): enrich-only. Hooks may enrich elements and batch context but must return exactly the elements they received — a count change is a broken invariant and feed() throws. Filtering belongs in sub-pipeline steps that return drop(). An empty feed() is a no-op (no hooks, no capacity). Details: nodejs/src/ingestion/framework/docs/14-batching.test.ts.
Fan-out/fan-in (fanOut(fn).via((sub) => …).fanIn(fn)): per-element sub-work with cardinality restored — one element fans out to N sub-elements (e.g. per-blob uploads), a regular sub-pipeline processes them (maxConcurrency on the sub concurrently block, retry on the per-sub step), and fan-in folds the OK results back into the parent. Reach for it over concurrently/concurrentlyPerGroup when the unit of concurrency is smaller than the element; hand-rolled p-limit/Promise.all inside a step is the tell. Sequencing is compile-time enforced (an unclosed stage cannot build). Sub-result contract: OK collected; DROP excludes the sub silently; DLQ fails the parent with aggregated reasons; REDIRECT is excluded with a warning — sub redirects never escape the stage. Sub-pipelines are context-agnostic: team/message data goes in the sub-element value, and context-gated surface (teamAware, handleIngestionWarnings, …) is uncallable. Fan-out/fan-in functions are cheap, synchronous, and named. Parents emit unordered as they complete. Details: nodejs/src/ingestion/framework/docs/17-fan-out-fan-in.test.ts.
Testing: Step tests call factory directly. Use consumeAll()/collectChunks() helpers. Fake timers for async. Type guards for result assertions. No any.
Running all doctors
Ask Claude to "run all pipeline doctors on my recent changes" to get a comprehensive review across all 4 concern areas.