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A JavaScript API.**","maintainers":[{"name":"inaiat","email":"inaiat@gmail.com"}],"readme":"# kafka-crab-js\n\n**Native Kafka performance. A JavaScript API.**\n\nBuild Kafka services in JavaScript and TypeScript with Rust, NAPI-RS, and librdkafka\nhandling the native transport. Publish with delivery confirmations, consume through\nWeb Streams or Node.js streams, and process batches without writing native code.\n\n[![npm](https://img.shields.io/npm/v/kafka-crab-js)](https://www.npmjs.com/package/kafka-crab-js)\n[![Node.js 24](https://img.shields.io/badge/Node.js-24-339933)](https://nodejs.org/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n\n[Quick start](#quick-start) · [Benchmarks](#benchmarks) · [Choose your API](#choose-your-api) ·\n[API reference](docs/api.md) · [Examples](../../examples/kafka/README.md) ·\n[Production recommendations](#production-recommendations)\n\n## Why kafka-crab-js\n\nKafka should move your events without consuming your entire JavaScript heap.\n`kafka-crab-js` puts Kafka transport and batch collection in native code while\nkeeping application logic in familiar async JavaScript.\n\n- **Batch throughput that makes a difference.** The current development snapshot\n  delivered **1.66 million messages/s**, **1.98× KafkaJS `eachBatch`**, in the\n  [reproducible consumer comparison](../../BENCHMARKS.md).\n- **More room in the JavaScript heap.** That batch scenario peaked at **29.0 MiB\n  of JS heap versus KafkaJS's 71.7 MiB**. Native memory still contributes to RSS;\n  the comparison reports both.\n- **An API for each processing style.** Use `Message[]` Web Stream batches for\n  bulk writes, individual messages for sequential handlers, Node streams for\n  pipelines, or `recvBatch()` for your own receive loop.\n- **Explicit delivery and offset control.** Await producer results, commit after\n  successful processing, and preserve keys, headers, and tombstones across batching.\n- **librdkafka configuration when you need it.** Configure TLS/SASL, compression,\n  idempotent production, fetch behavior, and prefetch through the native client.\n- **Observability without bundling an OTEL SDK into the core.** Diagnostic channels\n  integrate with the optional [`kafka-crab-js-otel`](../kafka-crab-js-otel/README.md) package.\n\nThe measurements above describe this source tree's development build, not the\ncurrently installed npm release. The\n[artifact fingerprints and methodology](../../BENCHMARKS.md#methodology) identify the tested build.\n\n## Install\n\n```sh\nnpm install kafka-crab-js\n# or\npnpm add kafka-crab-js\n```\n\nUse **Node.js 24**. Prebuilt binaries cover macOS x64/arm64 and Linux x64/arm64\nwith glibc or musl. Published binaries include librdkafka; supported installations\ndo not need a separate Rust toolchain or librdkafka installation. ESM, CommonJS,\nand TypeScript declarations are included.\n\n```js\nimport { KafkaClient } from \"kafka-crab-js\";\n// CommonJS: const { KafkaClient } = require('kafka-crab-js')\n```\n\nThis is a native Node.js client. Web Streams support refers to Node's Web Stream\nAPI; browser, WASM, and Windows builds are not provided. Documentation on the main\nbranch describes the current source; use a matching release tag for older packages.\n\n### Upgrading to 5.0\n\nVersion 5.0 requires Node.js 24. The previous release supported Node.js 22 and\nnewer; update service runtimes and container images before upgrading.\n\nProducer and consumer entrypoints remain available. Review the stricter\n[commit and shutdown contracts](docs/api.md#commits-and-processing-order): manual\nAsync commits require a live event listener, mixed group subscriptions and manual\nassignments are rejected, and Node.js consumer streams require object mode.\nFailed sends expose per-call delivery details through `SendFailureError`; use\nthose details when deciding retries after partial delivery.\n\n## Quick start\n\nRun a Kafka broker and create an `orders` topic before starting these examples.\nUse `KAFKA_BROKERS` for a comma-separated broker list; the examples default to\n`localhost:9092`. Save the following files as `.mjs` and run with Node.js 24.\n\n### Publish with confirmation\n\n`producer.mjs`:\n\n```js\nimport { KafkaClient } from \"kafka-crab-js\";\n\nconst client = new KafkaClient({\n  brokers: process.env.KAFKA_BROKERS ?? \"localhost:9092\",\n  clientId: \"orders-producer\",\n});\n\nconst producer = client.createProducer({\n  configuration: {\n    \"enable.idempotence\": true,\n    acks: \"all\",\n    \"compression.type\": \"lz4\",\n  },\n});\n\nconst deliveries = await producer.send({\n  topic: \"orders\",\n  messages: [\n    {\n      key: Buffer.from(\"order-42\"),\n      payload: Buffer.from(\n        JSON.stringify({ orderId: \"order-42\", total: 129.9 }),\n      ),\n      headers: { \"content-type\": Buffer.from(\"application/json\") },\n    },\n  ],\n});\n\nconsole.log(\"Delivered:\", deliveries);\n```\n\nBy default, `send()` waits for delivery results and rejects on failure. For\nhigh-volume publishing, send multiple records together and bound concurrent sends.\nIf you opt into `autoFlush: false`, call `flush()` regularly and before shutdown.\nRecords can be transmitted before a manual flush; it collects confirmations.\nSee [producer delivery semantics](docs/api.md#delivery-and-partial-failures).\n\n### Consume batches and commit after processing\n\n`consumer.mjs`:\n\n```js\nimport { KafkaClient } from \"kafka-crab-js\";\n\nconst client = new KafkaClient({\n  brokers: process.env.KAFKA_BROKERS ?? \"localhost:9092\",\n  clientId: \"orders-consumer\",\n});\n\nconst { consumer, stream } = client.createWebStreamConsumer({\n  groupId: \"orders-workers\",\n  enableAutoCommit: false,\n  batchSize: 64,\n  batchTimeout: 5,\n  configuration: {\n    \"enable.auto.offset.store\": false,\n    \"auto.offset.reset\": \"earliest\",\n  },\n});\n\nconst reader = stream.getReader();\nconst stop = () => {\n  void reader.cancel(\"shutdown\").catch(console.error);\n};\nprocess.once(\"SIGINT\", stop);\nprocess.once(\"SIGTERM\", stop);\n\ntry {\n  await consumer.subscribe(\"orders\");\n\n  while (true) {\n    const { value: messages, done } = await reader.read();\n    if (done) break;\n\n    const processed = new Map();\n    for (const message of messages) {\n      // Replace this with an awaited, idempotent database write or handler.\n      if (message.isTombstone) {\n        console.log(\"Delete:\", message.key?.toString());\n      } else {\n        console.log(\"Order:\", JSON.parse(message.payload.toString()));\n      }\n      processed.set(\n        JSON.stringify([message.topic, message.partition]),\n        message,\n      );\n    }\n\n    // Commit once per topic/partition, only after the entire batch succeeds.\n    for (const message of processed.values()) {\n      await consumer.commitMessage(message, \"Sync\");\n    }\n  }\n} finally {\n  process.off(\"SIGINT\", stop);\n  process.off(\"SIGTERM\", stop);\n  try {\n    await reader.cancel();\n  } finally {\n    reader.releaseLock();\n    await consumer.disconnect();\n  }\n}\n```\n\nRun `node consumer.mjs` and then `node producer.mjs` in another terminal. Consumers\nsharing a group split assigned partitions; use another group for an independent\nsubscription. `earliest` applies when there is no valid committed offset.\n\nA failed handler prevents that batch's commits, so records can be replayed. Make\nside effects idempotent and never commit past unfinished work in the same partition.\nThe signal handler cancels future reads while the already handed-out batch finishes;\ncancellation can abandon prefetched work. See [shutdown semantics](docs/api.md#position-flow-control-and-shutdown)\nwhen the application needs to drain collected work instead.\n\n## Choose your API\n\n| Your workload                                     | Start with                                                         | Delivered to your code                         |\n| ------------------------------------------------- | ------------------------------------------------------------------ | ---------------------------------------------- |\n| Bulk database writes, ingestion, analytics        | `createWebStreamConsumer({ batchSize: 64, batchTimeout: 5, ... })` | `Message[]` per read                           |\n| Sequential business logic, message-level handling | `createWebStreamConsumer({ groupId, ... })`                        | One `Message` per read, with internal prefetch |\n| Existing Node.js pipelines                        | `createStreamConsumer({ groupId, batchSize: 64, ... })`            | Individual `Message` objects in object mode    |\n| A custom polling or scheduling loop               | `createConsumer()` + `recvBatch(size, timeoutMs)`                  | Up to `size` messages per call                 |\n| One direct blocking receive                       | `createConsumer()` + `recv()`                                      | One message, or `null` after disconnect        |\n\nStart with a batch size of **64 and a 5 ms timeout** for evaluation, then measure\nwith your payload sizes and handler. Larger batches amortize per-delivery work;\nsmaller batches reduce collection delay and the amount of work held by the handler.\nThe throughput comparison uses 4,096/2 ms for its small-message scenario, which is\nnot a universal production preset.\n\nFor serial Web Streams, `serialPrefetchSize` and `serialPrefetchTimeout` control\ninternal collection without changing the one-message public interface. A Node\nbatch stream still emits individual messages; choose the Web batch API when your\napplication needs arrays. Await handlers with a reader loop, async iteration, or\na pipeline sink so downstream processing applies backpressure.\n\n## Benchmarks\n\n**September 12, 2026 · Apple M4 · Node 24.20.0 · local Kafka · 120 runs per scenario.**\nTwo full runs used opposite crab pair orders, fresh processes per block, explicit\nwarmup, and all samples. Throughput is total measured messages / total measured\ntime. Heap and RSS are maximum sampled lifecycle values across four processes.\n\n| Consumer                      | Interface                    |    Messages/s | Peak JS heap |      Peak RSS |\n| ----------------------------- | ---------------------------- | ------------: | -----------: | ------------: |\n| **kafka-crab-js development** | **Web Stream batch**         | **1,663,382** | **29.0 MiB** | **266.3 MiB** |\n| @platformatic/kafka 2.11.0    | Message stream               |       998,122 |     87.0 MiB |     256.7 MiB |\n| **kafka-crab-js development** | **Web Stream serial**        |   **915,664** | **21.9 MiB** | **178.9 MiB** |\n| KafkaJS 2.2.4                 | `eachBatch`                  |       841,194 |     71.7 MiB |     238.9 MiB |\n| KafkaJS 2.2.4                 | `eachMessage`                |       655,365 |     77.8 MiB |     245.6 MiB |\n| KafkaJS 2.2.4                 | `eachMessage`, concurrency 3 |       646,613 |     83.2 MiB |     246.7 MiB |\n\nFor this workload, current batch throughput was **1.98× KafkaJS `eachBatch`**;\ncurrent serial was **1.40× KafkaJS `eachMessage`**. Platformatic's message stream\nwas faster than crab serial. Crab batch used less JS heap than the measured JS clients\nbut more total RSS; native allocations are outside the JS heap.\n\nThese are consumer delivery rates for small, already published messages, without\napplication processing, manual commit costs, or end-to-end producer latency.\nThey are not a universal ranking. The topic has three partitions, two nonempty;\nKafkaJS concurrency 3 therefore does not imply three active message partitions.\nCrab uses its default fetch queue backoff of 20 ms. Cluster observations and other\nmeasurement limits are recorded in the full report.\n\n[Full results, percentiles, configuration, and reproduction](../../BENCHMARKS.md) ·\n[Comparison with the previous release](../../BENCHMARKS.md#comparison-with-the-previous-release) ·\n[Portable evidence: all 960 measurements](docs/rfc/review/evidence/consumer-comparison-2026-09-12.json)\n\n## How it compares\n\nChoose the client that matches your processing model and deployment constraints.\n\n| Decision                   | kafka-crab-js                                                                | KafkaJS                                                     | @platformatic/kafka                                       |\n| -------------------------- | ---------------------------------------------------------------------------- | ----------------------------------------------------------- | --------------------------------------------------------- |\n| Implementation             | Rust + NAPI-RS + librdkafka                                                  | JavaScript                                                  | TypeScript/JavaScript                                     |\n| Consumer style             | Direct receive, Node streams, serial/batch Web Streams                       | `eachMessage` and `eachBatch` callbacks                     | Node streams and events                                   |\n| Best reason to evaluate it | Native batch throughput, explicit delivery/offset control, librdkafka tuning | JavaScript-only deployment and callback-oriented processing | JavaScript-only deployment and stream-oriented processing |\n| Configuration model        | Typed wrapper plus librdkafka properties                                     | KafkaJS options                                             | Platformatic options                                      |\n| Installation consideration | Prebuilt native targets, Node 24                                             | No native addon                                             | No native addon                                           |\n\nCompetitor API descriptions come from the official\n[KafkaJS documentation](https://kafka.js.org/docs/consuming) and\n[Platformatic repository](https://github.com/platformatic/kafka). The comparison\ncovers these interfaces, not every feature or every client in the Kafka ecosystem.\n\n`kafka-crab-js` is a strong fit for ingestion workers, event-driven services, and\nbulk processing where you can benefit from native batch delivery. If you need Kafka transactions, a built-in schema registry client, or other\nadministrative operations beyond record deletion, account for the current public\nAPI's scope before migrating.\n\n### Coming from KafkaJS\n\n| KafkaJS concept                            | kafka-crab-js equivalent                                       |\n| ------------------------------------------ | -------------------------------------------------------------- |\n| `new Kafka({ brokers: [...] })`            | `new KafkaClient({ brokers: 'host:9092,...' })`                |\n| `kafka.producer()`                         | `client.createProducer()`                                      |\n| `producer.send({ messages: [{ value }] })` | `producer.send({ messages: [{ payload: Buffer.from(...) }] })` |\n| `eachMessage`                              | Serial Web Stream + an awaited message handler                 |\n| `eachBatch`                                | Batch Web Stream + an awaited batch handler                    |\n| Commit after successful work               | `consumer.commitMessage(message, 'Sync')`                      |\n\nThis is an API migration, not an import-only replacement. Review message encoding,\npartition ordering, commit policy, retries, and shutdown. Start by replaying a\nrepresentative topic into an independent group and compare throughput, latency,\nRSS, and verified processing outcomes. The [API reference](docs/api.md) describes\nindividual contracts and partial-failure behavior.\n\n## Production recommendations\n\n**Commit after the work succeeds.** Disable automatic commits/offset storage when\napplication completion defines delivery. Commit the last successfully processed\nmessage per topic/partition. Keep side effects idempotent so replay is safe. For\nAsync commits, observe `CommitCallback`; resolving the scheduling promise is not\nbroker confirmation.\n\n**Budget memory across the entire process.** The current native batch queue's\n32 MiB accounted-byte budget includes headers and keys, with an oversized-batch\nexception. It does not cap RSS or librdkafka's separate prefetch queues. Bound\napplication concurrency and retained messages, and monitor RSS alongside JS heap.\n\n**Use a smaller prefetch profile for large headers and reader stalls.** Evaluate\nthese consumer properties against your own workload:\n\n```js\nconst configuration = {\n  \"queued.max.messages.kbytes\": 65536,\n  \"queued.min.messages\": 256,\n  \"fetch.max.bytes\": 8 * 1024 * 1024,\n  \"max.partition.fetch.bytes\": 1024 * 1024,\n  \"fetch.queue.backoff.ms\": 20,\n};\n```\n\nIn the [concurrent workload](docs/rfc/review/performance.md#concurrent-workload-with-natural-gc--2026-09-12),\n640,000 new records totaling 12.736 GiB passed byte/order checks and broker-verified\ncommits with zero final backlog. Using this profile reduced the worst observed\nconsumer RSS by **50.1% serial / 54.3% batch** versus the 100,000-message threshold.\nNormal p95 stayed around 60 ms; p99 and recovery time increased. Each consumer ran\nfor two minutes on macOS with natural GC. This validates a specific profile, not\nlong-term stability, a hard memory ceiling, or production failure recovery.\n\n**Tune for your application.** Larger fetches can help small-message throughput;\nlarge batches can raise memory and latency. Use the\n[benchmark harness](../../benchmarks/kafka/README.md) and your own handler to find\nthe useful tradeoff. Keep confirmed send/commit costs in end-to-end measurements.\n\n**Connect and shut down deliberately.** Use TLS/SASL appropriate to your cluster,\nkeep credentials in a secret store, and retain certificate validation. Finish\naccepted work before committing/closing. Await sends and manual flushes; cancel\nand release Web readers or destroy Node streams and await close. Details and\nexamples are in the [API reference](docs/api.md).\n\n## Observability\n\nInstall `kafka-crab-js-otel` when you need OpenTelemetry tracing and metrics.\nConfigure your SDK/exporter, enable instrumentation before creating clients, and\nkeep client diagnostics enabled. Consumer processing spans should end after the\nhandler completes.\n\n[OTEL setup](../kafka-crab-js-otel/README.md) ·\n[Runnable tracing and metrics examples](../../examples/kafka/README.md)\n\n## Documentation and development\n\n- [API reference](docs/api.md): configuration, producer, consumer, streams, commits, and shutdown.\n- [Examples](../../examples/kafka/README.md): direct APIs, streams, retries, and observability.\n- [Benchmarks](../../BENCHMARKS.md): measured comparisons and reproducible commands.\n- [Engineering docs](docs/README.md): RFCs, validation evidence, and remaining release checks.\n- [Issues and feature requests](https://github.com/flash-tecnologia/crab-js/issues): share reproductions and proposals.\n\nFrom the repository root:\n\n```sh\npnpm install\npnpm --filter kafka-crab-js build\npnpm --filter kafka-crab-js test\nRUN_KAFKA_INTEGRATION=true pnpm --filter kafka-crab-js test:integration\n```\n\nBuilding from source requires Rust and the native build prerequisites. Kafka must\nbe running for integration tests. See [test infrastructure](js-tests/integration/README.md)\nfor the Compose setup and broker requirements. The [release review](docs/rfc/review/README.md) tracks\nvalidation separately from the performance claims above.\n\n## License\n\n[MIT](https://opensource.org/licenses/MIT)\n","readmeFilename":"README.md"}