Verwenden von Schemaregistern mit Kafka-Ereignisquellen in Lambda

aws lambda create-event-source-mapping \
  --function-name my-schema-validator-function \
  --event-source-arn arn:aws:kafka:us-east-1:123456789012:cluster/my-cluster/a1b2c3d4-5678-90ab-cdef-11111EXAMPLE \
  --topics my-kafka-topic \
  --provisioned-poller-config MinimumPollers=1,MaximumPollers=1 \
  --amazon-managed-kafka-event-source-mapping '{
      "SchemaRegistryConfig" : {
          "SchemaRegistryURI": "https://abcd-ef123.us-west-2.aws.confluent.cloud",
          "AccessConfigs": [{
              "Type": "BASIC_AUTH", 
              "URI": "arn:aws:secretsmanager:us-east-1:123456789012:secret:secretName"
          }],
          "EventRecordFormat": "JSON",
          "SchemaValidationConfigs": [
          { 
              "Attribute": "KEY" 
          },
          { 
              "Attribute": "VALUE" 
          }]
      }
  }'

aws lambda update-event-source-mapping \
    --uuid a1b2c3d4-5678-90ab-cdef-EXAMPLE11111 \
    --amazon-managed-kafka-event-source-mapping '{
        "SchemaRegistryConfig" : {
            "SchemaRegistryURI": "arn:aws:glue:us-east-1:123456789012:registry/registryName",
            "EventRecordFormat": "JSON",
            "SchemaValidationConfigs": [
            { 
                "Attribute": "KEY" 
            },
            { 
                "Attribute": "VALUE" 
            }]
        }
    }'

aws lambda update-event-source-mapping \
    --uuid a1b2c3d4-5678-90ab-cdef-EXAMPLE11111 \
    --amazon-managed-kafka-event-source-mapping '{
        "SchemaRegistryConfig" : {
            "SchemaRegistryURI": "https://abcd-ef123.us-west-2.aws.confluent.cloud",
            "AccessConfigs": [{
                "Type": "BASIC_AUTH", 
                "URI": "arn:aws:secretsmanager:us-east-1:123456789012:secret:secretName"
            }],
            "EventRecordFormat": "JSON",
            "SchemaValidationConfigs": [
            { 
                "Attribute": "KEY" 
            },
            { 
                "Attribute": "VALUE" 
            }]
        }
    }'

aws lambda update-event-source-mapping \
    --uuid a1b2c3d4-5678-90ab-cdef-EXAMPLE11111 \
    --amazon-managed-kafka-event-source-mapping '{
        "SchemaRegistryConfig" : {
            "SchemaRegistryURI": "https://abcd-ef123.us-west-2.aws.confluent.cloud",
            "EventRecordFormat": "JSON",
            "SchemaValidationConfigs": [
            { 
                "Attribute": "KEY" 
            },
            { 
                "Attribute": "VALUE" 
            }]
        }
    }'

Um eine Schemaregistrierungskonfiguration aus Ihrer Ereignisquellenzuordnung zu entfernen, können Sie den CLI-Befehl UpdateEventSourceMappingin der AWS Lambda API-Referenz verwenden.

aws lambda update-event-source-mapping \
    --uuid a1b2c3d4-5678-90ab-cdef-EXAMPLE11111 \
    --amazon-managed-kafka-event-source-mapping '{
        "SchemaRegistryConfig" : {}
    }'

Java-Beispiel:

package org.demo.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.demo.kafka.avro.AvroProduct;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.amazonaws.services.lambda.runtime.Context;
import com.amazonaws.services.lambda.runtime.RequestHandler;

import software.amazon.lambda.powertools.kafka.Deserialization;
import software.amazon.lambda.powertools.kafka.DeserializationType;
import software.amazon.lambda.powertools.logging.Logging;

public class AvroDeserializationFunction implements RequestHandler<ConsumerRecords<String, AvroProduct>, String> {

    private static final Logger LOGGER = LoggerFactory.getLogger(AvroDeserializationFunction.class);

    @Override
    @Logging
    @Deserialization(type = DeserializationType.KAFKA_AVRO)
    public String handleRequest(ConsumerRecords<String, AvroProduct> records, Context context) {
        for (ConsumerRecord<String, AvroProduct> consumerRecord : records) {
            LOGGER.info("ConsumerRecord: {}", consumerRecord);

            AvroProduct product = consumerRecord.value();
            LOGGER.info("AvroProduct: {}", product);

            String key = consumerRecord.key();
            LOGGER.info("Key: {}", key);
        }

        return "OK";
    }

}

Python-Beispiel:

from aws_lambda_powertools.utilities.kafka_consumer.kafka_consumer import kafka_consumer
from aws_lambda_powertools.utilities.kafka_consumer.schema_config import SchemaConfig
from aws_lambda_powertools.utilities.kafka_consumer.consumer_records import ConsumerRecords

from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger

logger = Logger(service="kafkaConsumerPowertools")

value_schema_str = open("customer_profile.avsc", "r").read()

schema_config = SchemaConfig(
value_schema_type="AVRO",
value_schema=value_schema_str)

@kafka_consumer(schema_config=schema_config)
def lambda_handler(event: ConsumerRecords, context:LambdaContext):

  for record in event.records:
      value = record.value
      logger.info(f"Received value: {value}")

TypeScript Beispiel:

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

import type { ConsumerRecords } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import type { Context } from 'aws-lambda';

const logger = new Logger();

type Value = {
    id: number;
    name: string;
    price: number;
};

const schema = '{   
    "type": "record",   
    "name": "Product",   
    "fields": [     
        { "name": "id", "type": "int" },     
        { "name": "name", "type": "string" },     
        { "name": "price", "type": "double" }   
    ] 
}';

export const handler = kafkaConsumer<string, Value>(
    (event: ConsumerRecords<string, Value>, _context: Context) => {
        for (const record of event.records) {
            logger.info(Processing record with key: ${record.key});
            logger.info(Record value: ${JSON.stringify(record.value)});
            // You can add more processing logic here
        }
    },
    {
        value: {
            type: 'avro',
            schema: schema,
        },
    }
);

.NET-Beispiel:

using Amazon.Lambda.Core;
using AWS.Lambda.Powertools.Kafka;
using AWS.Lambda.Powertools.Kafka.Avro;
using AWS.Lambda.Powertools.Logging;
using Com.Example;

// Assembly attribute to enable the Lambda function's Kafka event to be converted into a .NET class.
[assembly: LambdaSerializer(typeof(PowertoolsKafkaAvroSerializer))]

namespace ProtoBufClassLibrary;

public class Function
{
    public string FunctionHandler(ConsumerRecords<string, CustomerProfile> records, ILambdaContext context)
    {
        foreach (var record in records)
        {
            Logger.LogInformation("Processing messagem from topic: {topic}", record.Topic);
            Logger.LogInformation("Partition: {partition}, Offset: {offset}", record.Partition, record.Offset);
            Logger.LogInformation("Produced at: {timestamp}", record.Timestamp);
            
            foreach (var header in record.Headers.DecodedValues())
            {
                Logger.LogInformation($"{header.Key}: {header.Value}");
            }
            
            Logger.LogInformation("Processing order for: {fullName}", record.Value.FullName);
        }
    
        return "Processed " + records.Count() + " records";
    }
}

Java-Beispiel:

package org.demo.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.demo.kafka.protobuf.ProtobufProduct;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.amazonaws.services.lambda.runtime.Context;
import com.amazonaws.services.lambda.runtime.RequestHandler;

import software.amazon.lambda.powertools.kafka.Deserialization;
import software.amazon.lambda.powertools.kafka.DeserializationType;
import software.amazon.lambda.powertools.logging.Logging;

public class ProtobufDeserializationFunction
        implements RequestHandler<ConsumerRecords<String, ProtobufProduct>, String> {

    private static final Logger LOGGER = LoggerFactory.getLogger(ProtobufDeserializationFunction.class);

    @Override
    @Logging
    @Deserialization(type = DeserializationType.KAFKA_PROTOBUF)
    public String handleRequest(ConsumerRecords<String, ProtobufProduct> records, Context context) {
        for (ConsumerRecord<String, ProtobufProduct> consumerRecord : records) {
            LOGGER.info("ConsumerRecord: {}", consumerRecord);

            ProtobufProduct product = consumerRecord.value();
            LOGGER.info("ProtobufProduct: {}", product);

            String key = consumerRecord.key();
            LOGGER.info("Key: {}", key);
        }

        return "OK";
    }

}

Python-Beispiel:

from aws_lambda_powertools.utilities.kafka_consumer.kafka_consumer import kafka_consumer

from aws_lambda_powertools.utilities.kafka_consumer.schema_config import SchemaConfig
from aws_lambda_powertools.utilities.kafka_consumer.consumer_records import ConsumerRecords

from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger

from user_pb2 import User # protobuf generated class

logger = Logger(service="kafkaConsumerPowertools")

schema_config = SchemaConfig(
value_schema_type="PROTOBUF",
value_schema=User)

@kafka_consumer(schema_config=schema_config)
def lambda_handler(event: ConsumerRecords, context:LambdaContext):

  for record in event.records:
      value = record.value
      logger.info(f"Received value: {value}")

TypeScript Beispiel:

import { kafkaConsumer } from '@aws-lambda-powertools/kafka';
import type { ConsumerRecords } from '@aws-lambda-powertools/kafka/types';
import { Logger } from '@aws-lambda-powertools/logger';
import type { Context } from 'aws-lambda';
import { Product } from './product.generated.js';

const logger = new Logger();

type Value = {
    id: number;
    name: string;
    price: number;
};

export const handler = kafkaConsumer<string, Value>(
    (event: ConsumerRecords<string, Value>, _context: Context) => {
        for (const record of event.records) {
            logger.info(Processing record with key: ${record.key});
            logger.info(Record value: ${JSON.stringify(record.value)});
        }
    },
    {
        value: {
            type: 'protobuf',
            schema: Product,
        },
    }
);

.NET-Beispiel:

using Amazon.Lambda.Core;
using AWS.Lambda.Powertools.Kafka;
using AWS.Lambda.Powertools.Kafka.Protobuf;
using AWS.Lambda.Powertools.Logging;
using Com.Example;

// Assembly attribute to enable the Lambda function's Kafka event to be converted into a .NET class.
[assembly: LambdaSerializer(typeof(PowertoolsKafkaProtobufSerializer))]

namespace ProtoBufClassLibrary;

public class Function
{
    public string FunctionHandler(ConsumerRecords<string, CustomerProfile> records, ILambdaContext context)
    {
        foreach (var record in records)
        {
            Logger.LogInformation("Processing messagem from topic: {topic}", record.Topic);
            Logger.LogInformation("Partition: {partition}, Offset: {offset}", record.Partition, record.Offset);
            Logger.LogInformation("Produced at: {timestamp}", record.Timestamp);
            
            foreach (var header in record.Headers.DecodedValues())
            {
                Logger.LogInformation($"{header.Key}: {header.Value}");
            }
            
            Logger.LogInformation("Processing order for: {fullName}", record.Value.FullName);
        }
    
        return "Processed " + records.Count() + " records";
    }
}

Java-Beispiel:

package org.demo.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.amazonaws.services.lambda.runtime.Context;
import com.amazonaws.services.lambda.runtime.RequestHandler;

import software.amazon.lambda.powertools.kafka.Deserialization;
import software.amazon.lambda.powertools.kafka.DeserializationType;
import software.amazon.lambda.powertools.logging.Logging;

public class JsonDeserializationFunction implements RequestHandler<ConsumerRecords<String, Product>, String> {

    private static final Logger LOGGER = LoggerFactory.getLogger(JsonDeserializationFunction.class);

    @Override
    @Logging
    @Deserialization(type = DeserializationType.KAFKA_JSON)
    public String handleRequest(ConsumerRecords<String, Product> consumerRecords, Context context) {
        for (ConsumerRecord<String, Product> consumerRecord : consumerRecords) {
            LOGGER.info("ConsumerRecord: {}", consumerRecord);

            Product product = consumerRecord.value();
            LOGGER.info("Product: {}", product);

            String key = consumerRecord.key();
            LOGGER.info("Key: {}", key);
        }

        return "OK";
    }
}

Python-Beispiel:

from aws_lambda_powertools.utilities.kafka_consumer.kafka_consumer import kafka_consumer

from aws_lambda_powertools.utilities.kafka_consumer.schema_config import SchemaConfig
from aws_lambda_powertools.utilities.kafka_consumer.consumer_records import ConsumerRecords

from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger

logger = Logger(service="kafkaConsumerPowertools")

schema_config = SchemaConfig(value_schema_type="JSON")

@kafka_consumer(schema_config=schema_config)
def lambda_handler(event: ConsumerRecords, context:LambdaContext):

  for record in event.records:
      value = record.value
      logger.info(f"Received value: {value}")

TypeScript Beispiel:

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

const logger = new Logger();

type Value = {
    id: number;
    name: string;
    price: number;
};

export const handler = kafkaConsumer<string, Value>(
    (event: ConsumerRecords<string, Value>, _context: Context) => {
        for (const record of event.records) {
            logger.info(Processing record with key: ${record.key});
            logger.info(Record value: ${JSON.stringify(record.value)});
            // You can add more processing logic here
        }
    },
    {
        value: {
            type: 'json',
        },
    }
);

.NET-Beispiel:

using Amazon.Lambda.Core;
using AWS.Lambda.Powertools.Kafka;
using AWS.Lambda.Powertools.Kafka.Json;
using AWS.Lambda.Powertools.Logging;
using Com.Example;

// Assembly attribute to enable the Lambda function's Kafka event to be converted into a .NET class.
[assembly: LambdaSerializer(typeof(PowertoolsKafkaJsonSerializer))]

namespace JsonClassLibrary;

public class Function
{
    public string FunctionHandler(ConsumerRecords<string, CustomerProfile> records, ILambdaContext context)
    {
        foreach (var record in records)
        {
            Logger.LogInformation("Processing messagem from topic: {topic}", record.Topic);
            Logger.LogInformation("Partition: {partition}, Offset: {offset}", record.Partition, record.Offset);
            Logger.LogInformation("Produced at: {timestamp}", record.Timestamp);
            
            foreach (var header in record.Headers.DecodedValues())
            {
                Logger.LogInformation($"{header.Key}: {header.Value}");
            }
            
            Logger.LogInformation("Processing order for: {fullName}", record.Value.FullName);
        }
    
        return "Processed " + records.Count() + " records";
    }
}