@aws-lambda-powertools/kafka

Powertools for AWS Lambda (TypeScript) - Kafka Utility

Powertools for AWS Lambda (TypeScript) is a developer toolkit to implement Serverless best practices and increase developer velocity.

You can use the package in both TypeScript and JavaScript code bases.

Intro

The Kafka Consumer utility transparently handles message deserialization, provides an intuitive developer experience, and integrates seamlessly with the rest of the Powertools for AWS Lambda ecosystem.

Usage

To get started, depending on the schema types you want to use, install the library and the corresponding libraries:

For JSON schemas:

npm install @aws-lambda-powertools/kafka

For Avro schemas:

npm install @aws-lambda-powertools/kafka avro-js

For Protobuf schemas:

npm install @aws-lambda-powertools/kafka protobufjs

Additionally, if you want to use output parsing with Standard Schema, you can install any of the supported libraries, for example: Zod, Valibot, or ArkType.

Deserialization

The Kafka consumer utility transforms raw Kafka events into an intuitive format for processing. To handle messages effectively, you'll need to configure a schema that matches your data format.

JSON Schema

import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';

const logger = new Logger({ serviceName: 'kafka-consumer' });

const schemaConfig = {
  value: {
    type: SchemaType.JSON,
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer(async (event, _context) => {
  for (const { value } of event.records) {
    logger.info('received value', { value });
  }
}, schemaConfig);

Avro Schema

import { readFileSync } from 'node:fs';
import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';

const logger = new Logger({ serviceName: 'kafka-consumer' });

const schemaConfig = {
  value: {
    type: SchemaType.AVRO,
    schema: readFileSync(new URL('./user.avsc', import.meta.url), 'utf8'),
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer(async (event, _context) => {
  for (const { value } of event.records) {
    logger.info('received value', { value });
  }
}, schemaConfig);

Protobuf Schema

import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import { User } from './samples/user.es6.generated.js'; // protobuf generated class

const logger = new Logger({ serviceName: 'kafka-consumer' });

const schemaConfig = {
  value: {
    type: SchemaType.PROTOBUF,
    schema: User,
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer(async (event, _context) => {
  for (const { value } of event.records) {
    logger.info('received value', { value });
  }
}, schemaConfig);

Additional Parsing

You can parse deserialized data using your preferred parsing library. This can help you integrate Kafka data with your domain schemas and application architecture, providing type hints, runtime parsing and validation, and advanced data transformations.

Zod

import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import { z } from 'zod/v4';

const logger = new Logger({ serviceName: 'kafka-consumer' });

const OrderItemSchema = z.object({
  productId: z.string(),
  quantity: z.number().int().positive(),
  price: z.number().positive(),
});

const OrderSchema = z.object({
  id: z.string(),
  customerId: z.string(),
  items: z.array(OrderItemSchema).min(1, 'Order must have at least one item'),
  createdAt: z.iso.datetime(),
  totalAmount: z.number().positive(),
});

const schemaConfig = {
  value: {
    type: SchemaType.JSON,
    parserSchema: OrderSchema,
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer<unknown, z.infer<typeof OrderSchema>>(
  async (event, _context) => {
    for (const record of event.records) {
      const {
        value: { id, items },
      } = record;
      logger.setCorrelationId(id);
      logger.debug(`order includes ${items.length} items`);
    }
  },
  schemaConfig
);

Valibot

import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import * as v from 'valibot';

const logger = new Logger({ serviceName: 'kafka-consumer' });

const OrderItemSchema = v.object({
  productId: v.string(),
  quantity: v.pipe(v.number(), v.integer(), v.toMinValue(1)),
  price: v.pipe(v.number(), v.integer()),
});

const OrderSchema = v.object({
  id: v.string(),
  customerId: v.string(),
  items: v.pipe(
    v.array(OrderItemSchema),
    v.minLength(1, 'Order must have at least one item')
  ),
  createdAt: v.pipe(v.string(), v.isoDateTime()),
  totalAmount: v.pipe(v.number(), v.toMinValue(0)),
});

const schemaConfig = {
  value: {
    type: SchemaType.JSON,
    parserSchema: OrderSchema,
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer<unknown, v.InferInput<typeof OrderSchema>>(
  async (event, _context) => {
    for (const record of event.records) {
      const {
        value: { id, items },
      } = record;
      logger.setCorrelationId(id);
      logger.debug(`order includes ${items.length} items`);
    }
  },
  schemaConfig
);

ArkType

import { SchemaType, kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import { type } from 'arktype';

const logger = new Logger({ serviceName: 'kafka-consumer' });

const OrderItemSchema = type({
  productId: 'string',
  quantity: 'number.integer >= 1',
  price: 'number.integer',
});

const OrderSchema = type({
  id: 'string',
  customerId: 'string',
  items: OrderItemSchema.array().moreThanLength(0),
  createdAt: 'string.date',
  totalAmount: 'number.integer >= 0',
});

const schemaConfig = {
  value: {
    type: SchemaType.JSON,
    parserSchema: OrderSchema,
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer<unknown, typeof OrderSchema.infer>(
  async (event, _context) => {
    for (const record of event.records) {
      const {
        value: { id, items },
      } = record;
      logger.setCorrelationId(id);
      logger.debug(`order includes ${items.length} items`);
    }
  },
  schemaConfig
);

See the documentation for more details on how to use the Kafka utility.

Contribute

If you are interested in contributing to this project, please refer to our Contributing Guidelines.

Roadmap

The roadmap of Powertools for AWS Lambda (TypeScript) is driven by customers’ demand.
Help us prioritize upcoming functionalities or utilities by upvoting existing RFCs and feature requests, or creating new ones, in this GitHub repository.

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License

This library is licensed under the MIT-0 License. See the LICENSE file.

Changelog

2.24.0 (2025-07-15)

Improvements

• metrics optimize addDimensions method to avoid O(n²) complexity (#4156) (3982b4a)
• tracer replace class-based env access with functional helpers (#4146) (51d9b98)

Bug Fixes

• metrics addDimensions() documentation and tests (#3964) (a801636)
• tracer skip tracing CONNECT requests (#4148) (a147c3b)
• parser remove nullable from md5OfMessageAttributes in SqsRecordSchema (#4165) (d6cbde0)

Maintenance

• batch exclude deprecated code from coverage (#4152) (30bbf5a)
• parser remove deprecated parser type (#4154) (a59db36)

Features

• parser support Standard Schema and upgrade to Zod v4 (#4164) (67549f6)