Ingestion Pipeline Doctor Nodejs

Pipeline 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.