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Terskikh"},"license":"MIT","homepage":"https://github.com/AwesomeNiko/kafka-trail#readme","keywords":["Kafka","Kafkajs","Message queue","Service","Microservice","Producer","Consumer","Typescript","Nodejs"],"repository":{"type":"git","url":"git+https://github.com/AwesomeNiko/kafka-trail.git"},"description":"A Node.js library for managing message queue with Kafka","maintainers":[{"name":"awesomeniko","email":"awesomeniko@pm.me"}],"readme":"# Kafka-trail - MessageQueue Library\n\nA Node.js library for managing message queues with Kafka, designed to simplify creating, using, and managing Kafka topics with producers and consumers.\n\n### Based on [Kafkajs](https://kafka.js.org/)\n\n---\n\n## Features\n\n- Fully in typescript\n- Branded types\n- Connect to Kafka brokers easily.\n- Create or use existing Kafka topics with specified partitions.\n- Initialize the message queue with minimal setup.\n- Setup consumer handlers\n- Compressing ([see](https://kafka.js.org/docs/producing#compression))\n- Supports custom encoders/decoders.\n\n---\n\n## Installation\n\nInstall the library using npm or Yarn:\n\n```bash\nnpm install @awesomeniko/kafka-trail\n```\nOr with Yarn:\n\n```bash\nyarn add @awesomeniko/kafka-trail\n```\n\n### Native LZ4 codec\n\nThe default `LZ4` codec is now backed by an internal `Rust + napi-rs` native binding instead of the `lz4` npm package.\n\n- Library consumers should use the prebuilt native artifact shipped with the package.\n- If you are developing this repository from source, run `yarn build:native` before `yarn build` or `yarn test`.\n- The native module source lives in `native/lz4`.\n\n### Native build requirements\n\nIf you are building this repository from source, you need:\n\n- `Node.js`\n- `yarn` or `npm`\n- `Rust` toolchain via `rustup` with `cargo` and `rustc`\n- On macOS, `Xcode Command Line Tools`\n\nLocal development flow:\n\n```bash\nyarn install\nyarn build:native\nyarn build\n```\n\nThis setup does not require `python`, `node-gyp`, or a C++ Node addon toolchain.\n\n### OpenTelemetry tracing\n\n`KTMessageQueue` no longer relies on its own runtime copy of `@opentelemetry/api`.\n\n- If you do not pass `otel`, the library works as usual, but without tracing.\n- If you want tracing, pass your application's OpenTelemetry API instance through `tracingSettings.otel`.\n- This is useful when your app already uses its own observability package and you want `kafka-trail` to join the same trace context.\n\nExample:\n\n```typescript\nimport * as otel from \"@opentelemetry/api\";\nimport { KafkaClientId, KTMessageQueue } from \"@awesomeniko/kafka-trail\";\n\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\nconst messageQueue = new KTMessageQueue({\n  tracingSettings: {\n    otel,\n    addPayloadToTrace: false,\n  },\n});\n\nawait messageQueue.initProducer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString(\"hostname\"),\n    connectionTimeout: 30_000,\n  },\n  pureConfig: {},\n});\n```\n\nIf your application uses a wrapper package like `observability`, pass the OpenTelemetry API object from there instead of importing a separate copy directly.\n\nIf you prefer, you can also pass only the required OpenTelemetry fields explicitly:\n\n```typescript\nimport {\n  context,\n  trace,\n  SpanKind,\n  SpanStatusCode,\n} from \"@opentelemetry/api\";\nimport { KafkaClientId, KTMessageQueue } from \"@awesomeniko/kafka-trail\";\n\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\nconst messageQueue = new KTMessageQueue({\n  tracingSettings: {\n    otel: {\n      context,\n      trace,\n      SpanKind,\n      SpanStatusCode,\n    },\n    addPayloadToTrace: false,\n  },\n});\n\nawait messageQueue.initProducer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString(\"hostname\"),\n    connectionTimeout: 30_000,\n  },\n  pureConfig: {},\n});\n```\n\n### Publishing native binaries\n\nFor local development, building the native module from source is enough. For npm distribution, the better long-term setup is to publish prebuilt `napi-rs` binaries per platform so library consumers do not need Rust installed.\n\nRecommended direction:\n\n- keep the Rust source in `native/lz4`\n- build platform-specific `.node` artifacts in CI\n- publish them as optional platform packages\n- let the main package depend on those optional native packages\n\nThat is the standard `napi-rs` distribution model and avoids local compilation for end users.\n\n## Usage\nHere’s an example of how to use the `@awesomeniko/kafka-trail` library in your project.\n### If you want only producer:\n\n```typescript\n// Define your Kafka broker URLs\nimport {\n  CreateKTTopic,\n  KafkaClientId,\n  KafkaMessageKey,\n  KafkaTopicName,\n  KTMessageQueue\n} from \"@awesomeniko/kafka-trail\";\n\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\n// Create a MessageQueue instance\nconst messageQueue = new KTMessageQueue();\n\n// Start producer\nawait messageQueue.initProducer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString('hostname'),\n    connectionTimeout: 30_000,\n  },\n  pureConfig: {},\n})\n\n// Create topic fn\nconst { BaseTopic: TestExampleTopic } = CreateKTTopic<{\n  fieldForPayload: number\n}>({\n  topic: KafkaTopicName.fromString('test.example'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10, // Works if batchConsuming = true\n  createDLQ: false,\n})\n\n// Create or use topic\nawait messageQueue.initTopics([\n  TestExampleTopic,\n])\n\n// Use publishSingleMessage method to publish message\nconst payload = TestExampleTopic({\n  fieldForPayload: 1,\n}, {\n  messageKey: KafkaMessageKey.NULL, // If you don't want to specify message key\n  meta: {},\n})\n\nawait messageQueue.publishSingleMessage(payload)\n```\n\n### If you want consumer only:\n```typescript\nimport type pino from \"pino\";\n\nimport {\n  KTHandler,\n  CreateKTTopic,\n  KafkaClientId,\n  KafkaTopicName,\n  KTMessageQueue\n} from \"@awesomeniko/kafka-trail\";\n\n// Another dependency example\nclass DatabaseClass {\n  #client: string\n  constructor () {\n    this.#client = 'test-client'\n  }\n\n  getClient() {\n    return this.#client\n  }\n}\n\nconst dbClass = new DatabaseClass()\n\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\n// Create a MessageQueue instance\nconst messageQueue = new KTMessageQueue({\n  // If you want pass context available in handler\n  ctx: () => {\n    return {\n      dbClass,\n    }\n  },\n});\n\nexport const { BaseTopic: TestExampleTopic } = CreateKTTopic<{\n  fieldForPayload: number\n}>({\n  topic: KafkaTopicName.fromString('test.example'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10, // Works if batchConsuming = true\n  createDLQ: false,\n})\n\n// Create topic handler\nconst testExampleTopicHandler = KTHandler({\n  topic: TestExampleTopic,\n  run: async (payload, ctx: {logger: pino.Logger, dbClass: typeof dbClass}) => {\n    // Ts will show you right type for `payload` variable from `TestExampleTopic`\n    // Ctx passed from KTMessageQueue({ctx: () => {...}})\n\n    const [data] = payload\n\n    if (!data) {\n      return Promise.resolve()\n    }\n\n    const logger = ctx.logger.child({\n      payload: data.fieldForPayload,\n    })\n\n    logger.info(dbClass.getClient())\n\n    return Promise.resolve()\n  },\n})\n\nmessageQueue.registerHandlers([\n  testExampleTopicHandler,\n])\n\n// Start consumer\nawait messageQueue.initConsumer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString('hostname'),\n    connectionTimeout: 30_000,\n    consumerGroupId: 'consumer-group-id',\n    batchConsuming: true // default false\n  },\n  pureConfig: {},\n})\n```\n\n\n### For both consumer and producer:\n```typescript\nimport {\n  KTHandler,\n  CreateKTTopic,\n  KafkaClientId,\n  KafkaMessageKey,\n  KafkaTopicName,\n  KTMessageQueue\n} from \"@awesomeniko/kafka-trail\";\n\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\n// Create a MessageQueue instance\nconst messageQueue = new KTMessageQueue();\n\n// Create topic fn\nconst { BaseTopic: TestExampleTopic } = CreateKTTopic<{\n  fieldForPayload: number\n}>({\n  topic: KafkaTopicName.fromString('test.example'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10, // Works if batchConsuming = true\n  createDLQ: false,\n})\n\n// Required, because inside handler we are going to publish data\nawait messageQueue.initProducer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString('hostname'),\n    connectionTimeout: 30_000,\n  },\n  pureConfig: {},\n})\n\n// Create or use topic\nawait messageQueue.initTopics([\n  TestExampleTopic,\n])\n\n// Create topic handler\nconst testExampleTopicHandler = KTHandler({\n  topic: TestExampleTopic,\n  run: async (payload, _, publisher, { resolveOffset }) => { // resolveOffset available for batchConsuming = true only\n    // Ts will show you right type for `payload` variable from `TestExampleTopic`\n\n    const [data] = payload\n\n    if (!data) {\n      return Promise.resolve()\n    }\n\n    const newPayload = TestExampleTopic({\n      fieldForPayload: data.fieldForPayload + 1,\n    }, {\n      messageKey: KafkaMessageKey.NULL,\n      meta: {},\n    })\n\n    await publisher.publishSingleMessage(newPayload)\n\n    if (resolveOffset) {\n      // optional manual offset control when needed\n    }\n  },\n})\n\nmessageQueue.registerHandlers([\n  testExampleTopicHandler,\n])\n\n// Start consumer\nawait messageQueue.initConsumer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString('hostname'),\n    connectionTimeout: 30_000,\n    consumerGroupId: 'consumer-group-id',\n    batchConsuming: true // default false\n  },\n  pureConfig: {},\n})\n```\n\n### Destroying all will help you perform graceful shutdown\n```javascript\nconst messageQueue = new KTMessageQueue();\n\nprocess.on(\"SIGINT\", async () => {\n  await messageQueue.destroyAll()\n});\n\nprocess.on(\"SIGTERM\", async () => {\n  await messageQueue.destroyAll()\n});\n```\n\n## Configurations\n\n### Compression codec\nBy default, lib using LZ4 codec to compress and decompress data.\nYou can override it, by passing via `KTKafkaSettings` type. Be careful - producer and consumer should have same codec.\n[Ref docs](https://kafka.js.org/docs/producing#compression). Example:\n\n```typescript\nimport { KafkaClientId, KTMessageQueue } from \"@awesomeniko/kafka-trail\";\nimport { CompressionTypes } from \"kafkajs\";\n\nconst customLz4Codec = {\n  compress(encoder: Buffer) {\n    return encoder;\n  },\n\n  decompress<T>(buffer: Buffer) {\n    return buffer as T;\n  },\n};\n\n// Instanciate messageQueue\nconst kafkaBrokerUrls = [\"localhost:19092\"];\n\nconst messageQueue = new KTMessageQueue();\n\nawait messageQueue.initProducer({\n  kafkaSettings: {\n    brokerUrls: kafkaBrokerUrls,\n    clientId: KafkaClientId.fromString('hostname'),\n    connectionTimeout: 30_000,\n    compressionCodec: {\n      codecType: CompressionTypes.LZ4,\n      codecFn: customLz4Codec,\n    },\n  },\n  pureConfig: {},\n})\n```\n\nThe example above shows the shape of a custom codec. In a real codec implementation, `compress` and `decompress` should perform matching transformations.\n\n\n### Data encoding / decoding\nYou can provide custom encoders / decoders for sending / receiving data. Example:\n\n```typescript\nimport { CreateKTTopic, KafkaTopicName } from \"@awesomeniko/kafka-trail\";\n\ntype MyModel = {\n  fieldForPayload: number\n}\n\nconst { BaseTopic: TestExampleTopic } = CreateKTTopic<MyModel>({\n  topic: KafkaTopicName.fromString('test.example'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10, // Works if batchConsuming = true\n  createDLQ: false,\n}, {\n  encode: (data) => {\n    return JSON.stringify(data)\n  },\n  decode: (data: string | Buffer) => {\n    if (Buffer.isBuffer(data)) {\n      data = data.toString()\n    }\n\n    return JSON.parse(data) as MyModel\n  },\n})\n```\n\n### AJV schema adapter\nUse `createAjvCodecFromSchema` when your payload contract is JSON Schema and you want runtime validation via AJV.\n\n```typescript\nimport { Ajv } from \"ajv\";\nimport { CreateKTTopic, KafkaTopicName, createAjvCodecFromSchema } from \"@awesomeniko/kafka-trail\";\n\ntype UserEvent = {\n  id: number\n}\n\nconst ajv = new Ajv()\n\nconst codec = createAjvCodecFromSchema<UserEvent>({\n  ajv,\n  schema: {\n    $id: \"user-event-id\",\n    title: \"user-event\",\n    type: \"object\",\n    properties: {\n      id: {\n        type: \"number\",\n      },\n    },\n    required: [\"id\"],\n    additionalProperties: false,\n  },\n})\n\nconst { BaseTopic } = CreateKTTopic<UserEvent>({\n  topic: KafkaTopicName.fromString('test.ajv.topic'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10,\n  createDLQ: false,\n}, codec)\n```\n\n### Zod schema adapter\nUse `createZodCodec` when schema is defined in application code with Zod.\n\n```typescript\nimport { z } from \"zod\";\nimport { CreateKTTopic, KafkaTopicName, createZodCodec } from \"@awesomeniko/kafka-trail\";\n\ntype UserEvent = {\n  id: number\n}\n\nconst userEventSchema = z.object({\n  id: z.number(),\n}).meta({\n  id: \"user-event\",\n  schemaVersion: \"1\",\n})\n\nconst codec = createZodCodec<UserEvent>(userEventSchema)\n\nconst { BaseTopic } = CreateKTTopic<UserEvent>({\n  topic: KafkaTopicName.fromString('test.zod.topic'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10,\n  createDLQ: false,\n}, codec)\n```\n\n### Sending batch messages\nYou can send batch messages instead of sending one by one, but it required a little different usage. Example:\n\n```javascript\n// Create topic fn\nconst { BaseTopic: TestExampleTopic } = CreateKTTopicBatch({\n  topic: KafkaTopicName.fromString('test.example'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10,\n  createDLQ: false,\n})\n\n// Create or use topic\nawait messageQueue.initTopics([\n  TestExampleTopic,\n])\n\n// Use publishBatchMessages method to publish message\nconst payload = TestExampleTopic([{\n  value: {\n    test: 1,\n    test2: 2,\n  },\n  key: '1',\n}, {\n  value: {\n    test: 3,\n    test2: 4,\n  },\n  key: '2',\n}, {\n  value: {\n    test: 5,\n    test2: 6,\n  },\n  key: '3',\n}])\n\nawait messageQueue.publishBatchMessages(payload)\n```\n\n### Dead Letter Queue (DLQ)\nAutomatically route failed messages to DLQ topics for later analysis and reprocessing.\n\n`initProducer` must be called before `initConsumer` when at least one registered handler uses `createDLQ: true`, otherwise `ProducerInitRequiredForDLQError` is thrown.\n\n```typescript\n// DLQ topics are automatically created with 'dlq.' prefix\nconst { BaseTopic: TestExampleTopic, DLQTopic: DLQTestExampleTopic } = CreateKTTopic<MyPayload>({\n  topic: KafkaTopicName.fromString('my.topic'),\n  numPartitions: 1,\n  batchMessageSizeToConsume: 10,\n  createDLQ: true, // Enables DLQ\n})\n\n// Create or use topic\nawait messageQueue.initTopics([\n  TestExampleTopic,\n  DLQTestExampleTopic\n])\n\n// Failed messages automatically sent to: dlq.my.topic with next model:\n{\n  originalOffset: \"123\",\n  originalTopic: \"user.events\",\n  originalPartition: 0,\n  key: '[\"user123\",\"user456\"]',\n  value: [\n    { userId: \"user123\", action: \"login\" },\n    { userId: \"user456\", action: \"logout\" }\n  ],\n  errorMessage: \"Database connection failed\",\n  failedAt: 1703123456789\n}\n```\n\n### AWS Glue Schema Registry (with in-memory cache)\nYou can create a codec from AWS Glue Schema Registry and reuse it in `CreateKTTopic` / `CreateKTTopicBatch`.\nThe codec is initialized asynchronously (schema is fetched before codec creation), then works synchronously at runtime.\n\n1) Create native AWS Glue adapter (IAM/default credentials):\n\n```typescript\nimport { Ajv } from \"ajv\";\nimport {\n  createAwsGlueCodec,\n  createAwsGlueSchemaRegistryAdapter,\n  clearAwsGlueSchemaCache,\n} from \"@awesomeniko/kafka-trail\";\n\ntype UserEvent = {\n  id: number\n}\n\nconst ajv = new Ajv()\nconst glueAdapter = await createAwsGlueSchemaRegistryAdapter({\n  region: \"eu-central-1\",\n  preload: {\n    schemas: [{\n      registryName: \"my-registry\",\n      schemaName: \"user-events\",\n      schemaVersionId: \"schema-version-id\",\n    }],\n  },\n})\n\nconst codec = await createAwsGlueCodec<UserEvent>({\n  ajv,\n  glue: glueAdapter,\n  schema: {\n    registryName: \"my-registry\",\n    schemaName: \"user-events\",\n    schemaVersionId: \"schema-version-id\",\n  },\n})\n\n// clearAwsGlueSchemaCache() // optional manual cache reset\n```\n\n2) Static AWS keys (instead of IAM/default chain):\n\n```typescript\nconst glueAdapter = await createAwsGlueSchemaRegistryAdapter({\n  region: \"eu-central-1\",\n  credentials: {\n    accessKeyId: process.env.AWS_ACCESS_KEY_ID!,\n    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY!,\n    sessionToken: process.env.AWS_SESSION_TOKEN,\n  },\n})\n```\n\n3) Zod mode (same Glue adapter, no manual `getSchema`):\n\n```typescript\nimport { z } from \"zod\";\nimport { createAwsGlueCodec, createAwsGlueSchemaRegistryAdapter } from \"@awesomeniko/kafka-trail\";\n\ntype UserEvent = {\n  id: number\n}\n\nconst glueAdapter = await createAwsGlueSchemaRegistryAdapter({\n  region: \"eu-central-1\",\n})\n\nconst codec = await createAwsGlueCodec<UserEvent>({\n  validator: \"zod\",\n  glue: glueAdapter,\n  schema: {\n    registryName: \"my-registry\",\n    schemaName: \"user-events\",\n  },\n  zodSchemaFactory: ({ schema }) => {\n    // Build your zod schema using Glue JSON schema payload\n    return z.object({\n      id: z.number(),\n    })\n  },\n})\n```\n\nNotes:\n- cache is in-memory and enabled by default per process;\n- cache key is based on registry + schema identifiers and is shared for AJV/Zod modes;\n- unsupported Glue data formats (for example AVRO/PROTOBUF) are rejected in this version;\n- call `glueAdapter.destroy()` on shutdown if you want to close the AWS SDK client explicitly;\n- call `clearAwsGlueSchemaCache()` if you need to invalidate cached schemas manually.\n\n### Deprecated topic creators\n`KTTopic(...)` and `KTTopicBatch(...)` were deprecated in previous version.\nCurrent versions intentionally throw runtime errors if these APIs are invoked (for teams that have not migrated yet).\nIt's planned to be removed in the next version:\n- `Deprecated. use CreateKTTopic(...)`\n- `Deprecated. use CreateKTTopicBatch(...)`\n\n## Contributing\nContributions are welcome! If you’d like to improve this library:\n\n1. Fork the repository.\n2. Create a new branch.\n3. Make your changes and submit a pull request.\n\n## License\nThis library is open-source and licensed under the [MIT License](LICENSE).\n","readmeFilename":"README.md"}