# Cloudflare Pipelines Streaming ingest: receive events over HTTP/Workers/Logpush, transform with SQL, write to R2 as Iceberg tables or Parquet/JSON files. ## Documentation This reference is a fast-start with verified code and gotchas. For limits, settings, full SQL syntax, and pricing, **retrieve the live docs** — use the `cloudflare-docs` MCP/search tool if available, otherwise `webfetch` the URL. Docs are source of truth over this file. | Topic | URL | |-------|-----| | Overview / getting started | `https://developers.cloudflare.com/pipelines/getting-started/` | | Streams (write, manage, Logpush) | `https://developers.cloudflare.com/pipelines/streams/` | | Sinks | `https://developers.cloudflare.com/pipelines/sinks/` | | Pipelines & SQL transforms | `https://developers.cloudflare.com/pipelines/pipelines/` | | SQL reference (statements, types) | `https://developers.cloudflare.com/pipelines/sql-reference/` | | Wrangler commands | `https://developers.cloudflare.com/pipelines/reference/wrangler-commands/` | | Terraform | `https://developers.cloudflare.com/pipelines/reference/terraform/` | | Limits | `https://developers.cloudflare.com/pipelines/platform/limits/` | | Pricing | `https://developers.cloudflare.com/pipelines/platform/pricing/` | | Metrics (GraphQL) | `https://developers.cloudflare.com/pipelines/observability/metrics/` | ## Three Components ``` Sources → Stream → Pipeline (SQL) → Sink → R2 ↑ ↓ ↓ HTTP / Workers / Transform Iceberg (Data Catalog) Logpush (row-level) or Parquet/JSON files ``` | Component | Purpose | |-----------|---------| | **Stream** | Receives events (HTTP endpoint, Worker binding, or Logpush). Structured (schema-validated) or unstructured. | | **Pipeline** | SQL connecting a stream to a sink. Row-level transforms only — no GROUP BY/aggregation. | | **Sink** | Writes to R2 — Iceberg via Data Catalog, or raw Parquet/JSON. | **Status:** Open beta (Workers Paid for production). Pricing announced; verify billing status in docs. ## Quick Start ```bash # Interactive — creates stream + sink + pipeline, optionally bucket + catalog npx wrangler pipelines setup ``` Minimal Worker producer: ```typescript interface Env { MY_STREAM: Pipeline; } export default { async fetch(req: Request, env: Env, ctx: ExecutionContext): Promise { ctx.waitUntil(env.MY_STREAM.send([{ event_id: crypto.randomUUID(), amount: 29.99 }])); return new Response("OK"); } } satisfies ExportedHandler; ``` ## Which Sink Type? ``` Need SQL queries / ACID / time-travel on the data? → R2 Data Catalog (Iceberg) ✅ R2 SQL, schema evolution ❌ more setup Just archival / external tools (Spark, Athena)? → R2 raw files (Parquet/JSON) ✅ simple, partitioned files ❌ no built-in SQL ``` ## Critical Behaviors (read before building) These are non-obvious and prevent most failures — see [gotchas.md](gotchas.md) for detail. - **Everything is immutable after creation** — stream schema, pipeline SQL, sink config. To change, delete and recreate. - **Sinks create their own table** — they cannot target an existing Iceberg table. - **`__ingest_ts` is added automatically** (TIMESTAMP, partitioned by day). Don't define it in your schema. - **Data isn't queryable immediately** — first flush takes **3–7 minutes** (warm-up + table creation) even with a short roll interval. - **Schema validation is deferred** — invalid events are accepted then silently dropped. Monitor via GraphQL error metrics. - **Binding field renamed `pipeline` → `stream`** (June 2026); old field still accepted. ## Reading Order 1. [configuration.md](configuration.md) — schema, streams, sinks, pipelines (CLI + REST + Terraform), bindings 2. [api.md](api.md) — `send()`, HTTP ingest, REST API, pipeline SQL, lifecycle states 3. [patterns.md](patterns.md) — fire-and-forget, validation, Logpush, observability, end-to-end 4. [gotchas.md](gotchas.md) — silent drops, immutability, REST≠CLI field names ## See Also - [r2-data-catalog](../r2-data-catalog/) — Iceberg sink destination - [r2-sql](../r2-sql/) — query the ingested data - [r2](../r2/) · [queues](../queues/) · [workers](../workers/)