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Showing content from https://docs.powertools.aws.dev/lambda/typescript/latest/features/kafka/ below:

Kafka Consumer - Powertools for AWS Lambda (TypeScript)

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.

flowchart LR
  KafkaTopic["Kafka Topic"] --> MSK["Amazon MSK"]
  KafkaTopic --> MSKServerless["Amazon MSK Serverless"]
  KafkaTopic --> SelfHosted["Self-hosted Kafka"]
  MSK --> EventSourceMapping["Event Source Mapping"]
  MSKServerless --> EventSourceMapping
  SelfHosted --> EventSourceMapping
  EventSourceMapping --> Lambda["Lambda Function"]
  Lambda --> KafkaConsumer["Kafka Consumer Utility"]
  KafkaConsumer --> Deserialization["Deserialization"]
  Deserialization --> YourLogic["Your Business Logic"]

• Automatic deserialization of Kafka messages (JSON, Avro, and Protocol Buffers)
• Simplified event record handling with intuitive interface
• Support for key and value deserialization
• Support for Standard Schema output parsing (e.g., Zod, Valibot, ArkType)
• Support for Event Source Mapping (ESM) with and without Schema Registry integration
• Out of the box error handling for deserialization issues

Event Source Mapping (ESM) A Lambda feature that reads from streaming sources (like Kafka) and invokes your Lambda function. It manages polling, batching, and error handling automatically, eliminating the need for consumer management code.

Record Key and Value A Kafka messages contain two important parts: an optional key that determines the partition and a value containing the actual message data. Both are base64-encoded in Lambda events and can be independently deserialized.

Deserialization The process of converting binary data (base64-encoded in Lambda events) into usable Python objects according to a specific format like JSON, Avro, or Protocol Buffers. Powertools handles this conversion automatically.

SchemaConfig class Contains parameters that tell Powertools how to interpret message data, including the format type (JSON, Avro, Protocol Buffers) and optional schema definitions needed for binary formats.

Output parsing A Standard Schema used to parse your data at runtime, allowing you to define how the deserialized data should be structured and validated.

Schema Registry A centralized service that stores and validates schemas, ensuring producers and consumers maintain compatibility when message formats evolve over time.

Lambda processes Kafka messages as discrete events rather than continuous streams, requiring a different approach to consumer development that Powertools for AWS helps standardize.

Depending on the schema types you want to use, install the library and the corresponding libraries:

JSONAvroProtobuf

npm install @aws-lambda-powertools/kafka

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

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.

To use the Kafka consumer utility, you need an AWS Lambda function configured with a Kafka event source. This can be Amazon MSK, MSK Serverless, or a self-hosted Kafka cluster.

gettingStartedWithMsk.yaml

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AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  KafkaConsumerFunction:
    Type: AWS::Serverless::Function
    Properties:
      Runtime: nodejs22.x
      Handler: index.js
      Timeout: 30
      Events:
        MSKEvent:
          Type: MSK
          Properties:
            StartingPosition: LATEST
            Stream: !GetAtt MyMSKCluster.Arn
            Topics:
              - my-topic-1
              - my-topic-2
      Policies:
        - AWSLambdaMSKExecutionRole

The Event Source Mapping configuration determines which mode is used. With JSON, Lambda converts all messages to JSON before invoking your function. With SOURCE mode, Lambda preserves the original format, requiring you function to handle the appropriate deserialization.

Powertools for AWS supports both Schema Registry integration modes in your Event Source Mapping configuration.

For simplicity, we will use a simple schema containing name and age in most of our examples. You can also copy the payload example with the expected Kafka event to test your code.

JSONPayload JSONAvro SchemaPayload AvroProtobuf SchemaPayload Protobuf

{
  "name": "...",
  "age": "..."
}

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{
  "eventSource": "aws:kafka",
  "eventSourceArn": "arn:aws:kafka:eu-west-3:123456789012:cluster/powertools-kafka-esm/f138df86-9253-4d2a-b682-19e132396d4f-s3",
  "bootstrapServers": "boot-z3majaui.c3.kafka-serverless.eu-west-3.amazonaws.com:9098",
  "records": {
    "python-with-avro-doc-5": [
      {
        "topic": "python-with-avro-doc",
        "partition": 5,
        "offset": 0,
        "timestamp": 1750547462087,
        "timestampType": "CREATE_TIME",
        "key": "MTIz",
        "value": "eyJuYW1lIjogIlBvd2VydG9vbHMiLCAiYWdlIjogNX0=",
        "headers": []
      }
    ]
  }
}

{
    "type": "record",
    "name": "User",
    "namespace": "com.example",
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "age", "type": "int"}
    ]
}

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{
  "eventSource": "aws:kafka",
  "eventSourceArn": "arn:aws:kafka:eu-west-3:123456789012:cluster/powertools-kafka-esm/f138df86-9253-4d2a-b682-19e132396d4f-s3",
  "bootstrapServers": "boot-z3majaui.c3.kafka-serverless.eu-west-3.amazonaws.com:9098",
  "records": {
    "python-with-avro-doc-3": [
      {
        "topic": "python-with-avro-doc",
        "partition": 3,
        "offset": 0,
        "timestamp": 1750547105187,
        "timestampType": "CREATE_TIME",
        "key": "MTIz",
        "value": "AwBXT2qalUhN6oaj2CwEeaEWFFBvd2VydG9vbHMK",
        "headers": []
      }
    ]
  }
}

syntax = "proto3";

package com.example;

message User {
  string name = 1;
  int32 age = 2;
}

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{
  "eventSource": "aws:kafka",
  "eventSourceArn": "arn:aws:kafka:eu-west-3:992382490249:cluster/powertools-kafka-esm/f138df86-9253-4d2a-b682-19e132396d4f-s3",
  "bootstrapServers": "boot-z3majaui.c3.kafka-serverless.eu-west-3.amazonaws.com:9098",
  "records": {
    "python-with-avro-doc-5": [
      {
        "topic": "python-with-avro-doc",
        "partition": 5,
        "offset": 1,
        "timestamp": 1750624373324,
        "timestampType": "CREATE_TIME",
        "key": "MTIz",
        "value": "Cgpwb3dlcnRvb2xzEAU=",
        "headers": []
      }
    ]
  }
}

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.

We recommend Avro for production Kafka implementations due to its schema evolution capabilities, compact binary format, and integration with Schema Registry. This offers better type safety and forward/backward compatibility compared to JSON.

Avro MessagesProtocol BuffersJSON Messages

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import { readFileSync } from 'node:fs';
import { kafkaConsumer, SchemaType } 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);

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

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

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

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

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import { kafkaConsumer, SchemaType } 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);

The kafkaConsumer function can deserialize both key and value independently based on your schema configuration. This flexibility allows you to work with different data formats in the same message.

index.tstypes.tsProductKey.avscProductInfo.avsc

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import { readFileSync } from 'node:fs';
import { kafkaConsumer, SchemaType } 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 = {
  key: {
    type: SchemaType.AVRO,
    schema: readFileSync(new URL('./ProductKey.avsc', import.meta.url), 'utf8'),
  },
  value: {
    type: SchemaType.AVRO,
    schema: readFileSync(
      new URL('./productInfo.avsc', import.meta.url),
      'utf8'
    ),
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer<ProductKey, ProductInfo>(
  async (event, _context) => {
    for (const { key, value } of event.records) {
      logger.info('processing product ID', { productId: key.productId });
      logger.info('product', { name: value.name, price: value.price });
    }
  },
  schemaConfig
);

type ProductKey = {
  productId: string;
};

type ProductInfo = {
  name: string;
  price: number;
  inStock: boolean;
};

{
  "type": "record",
  "name": "ProductKey",
  "fields": [
    {"name": "product_id", "type": "string"}
  ]
}

{
  "type": "record",
  "name": "ProductInfo",
  "fields": [
    {"name": "name", "type": "string"},
    {"name": "price", "type": "double"},
    {"name": "in_stock", "type": "boolean"}
  ]
}

You can configure the kafkaConsumer to handle only the value. This allows you to optimize your Lambda function for the specific data structure of your Kafka messages.

When working with primitive data types (strings, integers, etc.) rather than structured objects, you can simplify your configuration by omitting the schema specification for that component. Powertools for AWS will deserialize the value always as a string.

Using primitive types (strings, integers) as Kafka message keys is a common pattern for partitioning and identifying messages. The Kafka consumer automatically handles these primitive keys without requiring special configuration, making it easy to implement this popular design pattern.

Primitive key

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import { kafkaConsumer } from '@aws-lambda-powertools/kafka';
import { Logger } from '@aws-lambda-powertools/logger';

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

export const handler = kafkaConsumer<string, { name: string; age: number }>(
  async (event, _context) => {
    for (const record of event.records) {
      // Key is automatically decoded as UTF-8 string
      const { key } = record;
      // Value is parsed as JSON object
      const { value } = record;

      logger.info('received value', {
        key,
        product: {
          name: value.name,
          age: value.age,
        },
      });
    }
  }
);

The Kafka consumer utility supports multiple serialization formats to match your existing Kafka implementation. Choose the format that best suits your needs based on performance, schema evolution requirements, and ecosystem compatibility.

For new applications, consider Avro or Protocol Buffers over JSON. Both provide schema validation, evolution support, and significantly better performance with smaller message sizes. Avro is particularly well-suited for Kafka due to its built-in schema evolution capabilities.

Supported FormatsFormat Comparison

Choose the serialization format that best fits your needs:

• JSON: Best for simplicity and when schema flexibility is important
• Avro: Best for systems with evolving schemas and when compatibility is critical
• Protocol Buffers: Best for performance-critical systems with structured data

Each Kafka record contains important metadata that you can access alongside the deserialized message content. This metadata helps with message processing, troubleshooting, and implementing advanced patterns like exactly-once processing.

Working with Record Metadata

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

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

export const handler = kafkaConsumer<unknown, com.example.IUser>(
  async (event, _context) => {
    for (const record of event.records) {
      const { value, topic, partition, offset, timestamp, headers } = record;
      logger.info(`processing message from topic ${topic}`, {
        partition,
        offset,
        timestamp,
      });

      if (headers) {
        for (const header of headers) {
          logger.debug(`Header: ${header.key}`, {
            value: header.value,
          });
        }
      }

      // Process the deserialized value
      logger.info('User data', {
        userName: value.name,
        userAge: value.age,
      });
    }
  },
  {
    value: {
      type: SchemaType.PROTOBUF,
      schema: com.example.User,
    },
  }
);

For debugging purposes, you can also access the original key, value, and headers in their base64-encoded form, these are available in the originalValue, originalKey, and originalHeaders properties of the record.

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.

ZodValibotArkType

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import { kafkaConsumer, SchemaType } 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
);

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import { kafkaConsumer, SchemaType } 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
);

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import { kafkaConsumer, SchemaType } 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
);

Handle errors gracefully when processing Kafka messages to ensure your application maintains resilience and provides clear diagnostic information. The Kafka consumer utility provides specific exception types to help you identify and handle deserialization issues effectively.

Tip

Fields like value, key, and headers are decoded lazily, meaning they are only deserialized when accessed. This allows you to handle deserialization errors at the point of access rather than when the record is first processed.

Basic Error HandlingParser Error Handling

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import { readFileSync } from 'node:fs';
import { kafkaConsumer, SchemaType } from '@aws-lambda-powertools/kafka';
import { KafkaConsumerDeserializationError } from '@aws-lambda-powertools/kafka/errors';
import type {
  ConsumerRecord,
  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) => {
  const results: {
    successful: number;
    failed: Array<ConsumerRecord<unknown, unknown>>;
  } = {
    successful: 0,
    failed: [],
  };
  for (const record of event.records) {
    try {
      const { value, partition, offset, topic } = record; // (1)!
      logger.setCorrelationId(`${topic}-${partition}-${offset}`);

      await processRecord(value);

      results.successful += 1;
    } catch (error) {
      if (error instanceof KafkaConsumerDeserializationError) {
        results.failed.push(record);
        logger.error('Error deserializing message', { error });
      } else {
        logger.error('Error processing message', { error });
      }
    }

    if (results.failed.length > 0) {
      // Handle failed records, e.g., send to a dead-letter queue
    }

    logger.info('Successfully processed records', {
      successful: results.successful,
    });
  }
}, schemaConfig);

1. If you want to handle deserialization and parsing errors, you should destructure or access the value, key, or headers properties of the record within the for...of loop.

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import { readFileSync } from 'node:fs';
import { kafkaConsumer, SchemaType } from '@aws-lambda-powertools/kafka';
import { KafkaConsumerParserError } from '@aws-lambda-powertools/kafka/errors';
import type {
  ConsumerRecord,
  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 schemaConfig = {
  value: {
    type: SchemaType.AVRO,
    schema: readFileSync(new URL('./user.avsc', import.meta.url), 'utf8'),
    parserSchema: z.object({
      id: z.number(),
      name: z.string(),
      email: z.email(),
    }),
  },
} satisfies SchemaConfig;

export const handler = kafkaConsumer(async (event, _context) => {
  const results: {
    successful: number;
    failed: Array<ConsumerRecord<unknown, unknown>>;
  } = {
    successful: 0,
    failed: [],
  };
  for (const record of event.records) {
    try {
      const { value, partition, offset, topic } = record;
      logger.setCorrelationId(`${topic}-${partition}-${offset}`);

      await processRecord(value);
      results.successful += 1;
    } catch (error) {
      if (error instanceof KafkaConsumerParserError) {
        results.failed.push(record);
        logger.error(
          `Error deserializing message - ${z.prettifyError({ issues: error.cause } as z.ZodError)}`,
          { error } // (1)!
        );
      } else {
        logger.error('Error processing message', { error });
      }
    }

    if (results.failed.length > 0) {
      // Handle failed records, e.g., send to a dead-letter queue
    }

    logger.info('Successfully processed records', {
      successful: results.successful,
    });
  }
}, schemaConfig);

1. The cause property of the error is populated with the original Standard Schema parsing error, allowing you to access detailed information about the parsing failure.

When processing Kafka messages in Lambda, failed batches can result in message reprocessing. The Idempotency utility prevents duplicate processing by tracking which messages have already been handled, ensuring each message is processed exactly once.

The Idempotency utility automatically stores the result of each successful operation, returning the cached result if the same message is processed again, which prevents potentially harmful duplicate operations like double-charging customers or double-counting metrics.

Tip

By using the Kafka record's unique coordinates (topic, partition, offset) as the idempotency key, you ensure that even if a batch fails and Lambda retries the messages, each message will be processed exactly once.

Idempotent Kafka Processing

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import {
  IdempotencyConfig,
  makeIdempotent,
} from '@aws-lambda-powertools/idempotency';
import { DynamoDBPersistenceLayer } from '@aws-lambda-powertools/idempotency/dynamodb';
import { kafkaConsumer, SchemaType } from '@aws-lambda-powertools/kafka';
import type { SchemaConfig } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import { com } from './samples/user.generated.js'; // protobuf generated class

const logger = new Logger({ serviceName: 'kafka-consumer' });
const persistenceStore = new DynamoDBPersistenceLayer({
  tableName: 'IdempotencyTable',
});

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

const processRecord = makeIdempotent(
  async (user, topic, partition, offset) => {
    logger.info('processing user', {
      user,
      meta: {
        topic,
        partition,
        offset,
      },
    });

    // ...your business logic here

    return {
      success: true,
      user,
    };
  },
  {
    persistenceStore,
    config: new IdempotencyConfig({
      eventKeyJmesPath: `topic & '-' & partition & '-' & offset`,
    }),
  }
);

export const handler = kafkaConsumer(async (event, _context) => {
  for (const { value, topic, partition, offset } of event.records) {
    await processRecord(value, topic, partition, offset);
  }
}, schemaConfig);

When processing large Kafka messages in Lambda, be mindful of memory limitations. Although the Kafka consumer utility optimizes memory usage, large deserialized messages can still exhaust the function resources.

Handling Large Messages

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import { kafkaConsumer } from '@aws-lambda-powertools/kafka';
import { Logger } from '@aws-lambda-powertools/logger';
import { object, safeParse, string } from 'valibot';

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

const LargeMessage = object({
  key: string(),
  bucket: string(),
});

export const handler = kafkaConsumer(async (event, _context) => {
  for (const record of event.records) {
    const { topic, value, originalValue } = record;
    const valueSize = Buffer.byteLength(originalValue, 'utf8');
    const parsedValue = safeParse(LargeMessage, value);
    if (
      topic === 'product-catalog' &&
      valueSize > 3_000_000 &&
      parsedValue.success
    ) {
      logger.info('Large message detected, processing from S3', {
        size: valueSize,
      });

      const { key, bucket } = parsedValue.output;
      await processRecordFromS3({ key, bucket });

      logger.info('Processed large message from S3', {
        key,
        bucket,
      });
    }

    // regular processing of the record
  }
});

For large messages, consider these proven approaches:

• Store the data: use Amazon S3 and include only the S3 reference in your Kafka message
• Split large payloads: use multiple smaller messages with sequence identifiers
• Increase memory: Increase your Lambda function's memory allocation, which also increases CPU capacity

The number of Kafka records processed per Lambda invocation is controlled by your Event Source Mapping configuration. Properly sized batches optimize cost and performance.

Handling Large Messages

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Resources:
OrderProcessingFunction:
    Type: AWS::Serverless::Function
    Properties:
    Runtime: nodejs22.x
    Handler: index.js
    Events:
        KafkaEvent:
        Type: MSK
        Properties:
            Stream: !GetAtt OrdersMSKCluster.Arn
            Topics:
            - order-events
            - payment-events
            # Configuration for optimal throughput/latency balance
            BatchSize: 100
            MaximumBatchingWindowInSeconds: 5
            StartingPosition: LATEST
            # Enable partial batch success reporting
            FunctionResponseTypes:
            - ReportBatchItemFailures

Different workloads benefit from different batch configurations:

• High-volume, simple processing: Use larger batches (100-500 records) with short timeout
• Complex processing with database operations: Use smaller batches (10-50 records)
• Mixed message sizes: Set appropriate batching window (1-5 seconds) to handle variability

When using binary serialization formats across multiple programming languages, ensure consistent schema handling to prevent deserialization failures.

Common cross-language challenges to address:

• Field naming conventions: camelCase in Java vs snake_case in Python
• Date/time: representation differences
• Numeric precision handling: especially decimals, doubles, and floats

When encountering deserialization errors with your Kafka messages, follow this systematic troubleshooting approach to identify and resolve the root cause.

First, check that your schema definition exactly matches the message format. Even minor discrepancies can cause deserialization failures, especially with binary formats like Avro and Protocol Buffers.

For binary messages that fail to deserialize, examine the raw encoded data:

// DO NOT include this code in production handlers
// For troubleshooting purposes only
import base64

const rawBytes = Buffer.from(record.originalValue, 'base64');
console.log(`Message size: ${rawBytes.length} bytes`);
console.log(`First 50 bytes (hex): ${rawBytes.slice(0, 50).toString('hex')}`);

Schema compatibility issues often manifest as successful connections but failed deserialization. Common causes include:

• Schema evolution without backward compatibility: New producer schema is incompatible with consumer schema
• Field type mismatches: For example, a field changed from string to integer across systems
• Missing required fields: Fields required by the consumer schema but absent in the message
• Default value discrepancies: Different handling of default values between languages

When using Schema Registry, verify schema compatibility rules are properly configured for your topics and that all applications use the same registry.

Lambda functions processing Kafka messages may encounter resource constraints, particularly with large batches or complex processing logic.

For memory errors:

• Increase Lambda memory allocation, which also provides more CPU resources
• Process fewer records per batch by adjusting the BatchSize parameter in your event source mapping
• Consider optimizing your message format to reduce memory footprint

For timeout issues:

• Extend your Lambda function timeout setting to accommodate processing time
• Implement chunked or asynchronous processing patterns for time-consuming operations
• Monitor and optimize database operations, external API calls, or other I/O operations in your handler

Monitoring memory usage

Use CloudWatch metrics to track your function's memory utilization. If it consistently exceeds 80% of allocated memory, consider increasing the memory allocation or optimizing your code.

sequenceDiagram
  participant Kafka
  participant ESM as Event Source Mapping
  participant SchemaRegistry as Schema Registry
  participant Lambda
  participant KafkaConsumer
  participant YourCode
  Kafka->>+ESM: Send batch of records
  ESM->>+SchemaRegistry: Validate schema
  SchemaRegistry-->>-ESM: Confirm schema is valid
  ESM->>+Lambda: Invoke with validated records (still encoded)
  Lambda->>+KafkaConsumer: Pass Kafka event
  KafkaConsumer->>KafkaConsumer: Parse event structure
  loop For each record
      KafkaConsumer->>KafkaConsumer: Decode base64 data
      KafkaConsumer->>KafkaConsumer: Deserialize based on schema_type
      alt Output serializer provided
          KafkaConsumer->>KafkaConsumer: Apply output serializer
      end
  end
  KafkaConsumer->>+YourCode: Provide ConsumerRecords
  YourCode->>YourCode: Process records
  YourCode-->>-KafkaConsumer: Return result
  KafkaConsumer-->>-Lambda: Pass result back
  Lambda-->>-ESM: Return response
  ESM-->>-Kafka: Acknowledge processed batch

sequenceDiagram
  participant Kafka
  participant ESM as Event Source Mapping
  participant SchemaRegistry as Schema Registry
  participant Lambda
  participant KafkaConsumer
  participant YourCode
  Kafka->>+ESM: Send batch of records
  ESM->>+SchemaRegistry: Validate and deserialize
  SchemaRegistry->>SchemaRegistry: Deserialize records
  SchemaRegistry-->>-ESM: Return deserialized data
  ESM->>+Lambda: Invoke with pre-deserialized JSON records
  Lambda->>+KafkaConsumer: Pass Kafka event
  KafkaConsumer->>KafkaConsumer: Parse event structure
  loop For each record
      KafkaConsumer->>KafkaConsumer: Record is already deserialized
      alt Output serializer provided
          KafkaConsumer->>KafkaConsumer: Apply output serializer
      end
  end
  KafkaConsumer->>+YourCode: Provide ConsumerRecords
  YourCode->>YourCode: Process records
  YourCode-->>-KafkaConsumer: Return result
  KafkaConsumer-->>-Lambda: Pass result back
  Lambda-->>-ESM: Return response
  ESM-->>-Kafka: Acknowledge processed batch

sequenceDiagram
  participant Kafka
  participant Lambda
  participant KafkaConsumer
  participant YourCode
  Kafka->>+Lambda: Invoke with batch of records (direct integration)
  Lambda->>+KafkaConsumer: Pass raw Kafka event
  KafkaConsumer->>KafkaConsumer: Parse event structure
  loop For each record
      KafkaConsumer->>KafkaConsumer: Decode base64 data
      KafkaConsumer->>KafkaConsumer: Deserialize based on schema_type
      alt Output serializer provided
          KafkaConsumer->>KafkaConsumer: Apply output serializer
      end
  end
  KafkaConsumer->>+YourCode: Provide ConsumerRecords
  YourCode->>YourCode: Process records
  YourCode-->>-KafkaConsumer: Return result
  KafkaConsumer-->>-Lambda: Pass result back
  Lambda-->>-Kafka: Acknowledge processed batch

Testing Kafka consumer code requires simulating Lambda events with Kafka messages. You can create simple test cases using local JSON files without needing a live Kafka cluster. Below an example of how to simulate a JSON message.

Testing your code

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import type { MSKEvent } from '@aws-lambda-powertools/kafka/types';
import type { Context } from 'aws-lambda';
import { expect, it } from 'vitest';
import { handler } from './gettingStartedPrimitiveValues.js';

it('handles complex protobuf messages from Glue Schema Registry', async () => {
  // Prepare
  const event = {
    eventSource: 'aws:kafka',
    eventSourceArn:
      'arn:aws:kafka:us-east-1:123456789012:cluster/MyCluster/12345678-1234-1234-1234-123456789012-1',
    bootstrapServers:
      'b-1.mskcluster.abcde12345.us-east-1.kafka.amazonaws.com:9092',
    records: {
      'orders-topic': [
        {
          topic: 'orders-topic',
          partition: 0,
          offset: 15,
          timestamp: 1545084650987,
          timestampType: 'CREATE_TIME',
          headers: [],
          key: undefined,
          keySchemaMetadata: {
            dataFormat: 'JSON',
          },
          valueSchemaMetadata: {
            dataFormat: 'JSON',
            schemaId: undefined,
          },
          value: Buffer.from(
            JSON.stringify({ order_id: '12345', amount: 99.95 })
          ).toString('base64'),
        },
      ],
    },
  } as MSKEvent;

  // Act
  const result = await handler(event, {} as Context);

  // Assess
  expect(result).toBeDefined();
  // You can add more specific assertions based on your handler's logic
});

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