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Batch Processing

The batch processing utility handles partial failures when processing batches from Amazon SQS, Amazon Kinesis Data Streams, and Amazon DynamoDB Streams.

Key Features

  • Reports batch item failures to reduce number of retries for a record upon errors
  • Simple interface to process each batch record
  • Integrates with Event Source Data Classes and Parser (Pydantic) for self-documenting record schema
  • Build your own batch processor by extending primitives

Background

When using SQS, Kinesis Data Streams, or DynamoDB Streams as a Lambda event source, your Lambda functions are triggered with a batch of messages.

If your function fails to process any message from the batch, the entire batch returns to your queue or stream. This same batch is then retried until either condition happens first: a) your Lambda function returns a successful response, b) record reaches maximum retry attempts, or c) when records expire.

With this utility, batch records are processed individually – only messages that failed to be processed return to the queue or stream for a further retry. This works when two mechanisms are in place:

  1. ReportBatchItemFailures is set in your SQS, Kinesis, or DynamoDB event source properties
  2. A specific response is returned so Lambda knows which records should not be deleted during partial responses
Warning: This utility lowers the chance of processing records more than once; it does not guarantee it

We recommend implementing processing logic in an idempotent manner wherever possible.

You can find more details on how Lambda works with either SQS, Kinesis, or DynamoDB in the AWS Documentation.

Getting started

Regardless whether you're using SQS, Kinesis Data Streams or DynamoDB Streams, you must configure your Lambda function event source to use ReportBatchItemFailures.

You do not need any additional IAM permissions to use this utility, except for what each event source requires.

Required resources

The remaining sections of the documentation will rely on these samples. For completeness, this demonstrates IAM permissions and Dead Letter Queue where batch records will be sent after 2 retries were attempted.

template.yaml
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AWSTemplateFormatVersion: "2010-09-09"
Transform: AWS::Serverless-2016-10-31
Description: partial batch response sample

Globals:
  Function:
    Timeout: 5
    MemorySize: 256
    Runtime: python3.10
    Tracing: Active
    Environment:
      Variables:
        LOG_LEVEL: INFO
        POWERTOOLS_SERVICE_NAME: hello

Resources:
  HelloWorldFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: app.lambda_handler
      CodeUri: hello_world
      Policies:
        - SQSPollerPolicy:
            QueueName: !GetAtt SampleQueue.QueueName
      Events:
        Batch:
          Type: SQS
          Properties:
            Queue: !GetAtt SampleQueue.Arn
            FunctionResponseTypes:
              - ReportBatchItemFailures

  SampleDLQ:
    Type: AWS::SQS::Queue

  SampleQueue:
    Type: AWS::SQS::Queue
    Properties:
      VisibilityTimeout: 30 # Fn timeout * 6
      SqsManagedSseEnabled: true
      RedrivePolicy:
        maxReceiveCount: 2
        deadLetterTargetArn: !GetAtt SampleDLQ.Arn
template.yaml
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AWSTemplateFormatVersion: "2010-09-09"
Transform: AWS::Serverless-2016-10-31
Description: partial batch response sample

Globals:
  Function:
    Timeout: 5
    MemorySize: 256
    Runtime: python3.10
    Tracing: Active
    Environment:
      Variables:
        LOG_LEVEL: INFO
        POWERTOOLS_SERVICE_NAME: hello

Resources:
  HelloWorldFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: app.lambda_handler
      CodeUri: hello_world
      Policies:
        # Lambda Destinations require additional permissions
        # to send failure records to DLQ from Kinesis/DynamoDB
        - Version: "2012-10-17"
          Statement:
            Effect: "Allow"
            Action:
              - sqs:GetQueueAttributes
              - sqs:GetQueueUrl
              - sqs:SendMessage
            Resource: !GetAtt SampleDLQ.Arn
      Events:
        KinesisStream:
          Type: Kinesis
          Properties:
            Stream: !GetAtt SampleStream.Arn
            BatchSize: 100
            StartingPosition: LATEST
            MaximumRetryAttempts: 2
            DestinationConfig:
              OnFailure:
                Destination: !GetAtt SampleDLQ.Arn
            FunctionResponseTypes:
              - ReportBatchItemFailures

  SampleDLQ:
    Type: AWS::SQS::Queue

  SampleStream:
    Type: AWS::Kinesis::Stream
    Properties:
      ShardCount: 1
      StreamEncryption:
        EncryptionType: KMS
        KeyId: alias/aws/kinesis
template.yaml
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AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: partial batch response sample

Globals:
  Function:
    Timeout: 5
    MemorySize: 256
    Runtime: python3.10
    Tracing: Active
    Environment:
      Variables:
        LOG_LEVEL: INFO
        POWERTOOLS_SERVICE_NAME: hello

Resources:
  HelloWorldFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: app.lambda_handler
      CodeUri: hello_world
      Policies:
        # Lambda Destinations require additional permissions
        # to send failure records from Kinesis/DynamoDB
        - Version: "2012-10-17"
          Statement:
            Effect: "Allow"
            Action:
              - sqs:GetQueueAttributes
              - sqs:GetQueueUrl
              - sqs:SendMessage
            Resource: !GetAtt SampleDLQ.Arn
      Events:
        DynamoDBStream:
          Type: DynamoDB
          Properties:
            Stream: !GetAtt SampleTable.StreamArn
            StartingPosition: LATEST
            MaximumRetryAttempts: 2
            DestinationConfig:
              OnFailure:
                Destination: !GetAtt SampleDLQ.Arn
            FunctionResponseTypes:
              - ReportBatchItemFailures

  SampleDLQ:
    Type: AWS::SQS::Queue

  SampleTable:
    Type: AWS::DynamoDB::Table
    Properties:
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: pk
          AttributeType: S
        - AttributeName: sk
          AttributeType: S
      KeySchema:
        - AttributeName: pk
          KeyType: HASH
        - AttributeName: sk
          KeyType: RANGE
      SSESpecification:
        SSEEnabled: true
      StreamSpecification:
        StreamViewType: NEW_AND_OLD_IMAGES

Processing messages from SQS

Processing batches from SQS works in three stages:

  1. Instantiate BatchProcessor and choose EventType.SQS for the event type
  2. Define your function to handle each batch record, and use SQSRecord type annotation for autocompletion
  3. Use process_partial_response to kick off processing
Info

This code example optionally uses Tracer and Logger for completion.

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        processed_messages = processor.process()  # kick off processing, return list[tuple]
        logger.info(f"Processed ${len(processed_messages)} messages")

    return processor.response()

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    batch_processor,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
@batch_processor(record_handler=record_handler, processor=processor)
def lambda_handler(event, context: LambdaContext):
    return processor.response()

The second record failed to be processed, therefore the processor added its message ID in the response.

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{
  "batchItemFailures": [
    {
      "itemIdentifier": "244fc6b4-87a3-44ab-83d2-361172410c3a"
    }
  ]
}

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{
  "Records": [
    {
      "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
      "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
      "body": "{\"Message\": \"success\"}",
      "attributes": {
        "ApproximateReceiveCount": "1",
        "SentTimestamp": "1545082649183",
        "SenderId": "AIDAIENQZJOLO23YVJ4VO",
        "ApproximateFirstReceiveTimestamp": "1545082649185"
      },
      "messageAttributes": {},
      "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
      "eventSource": "aws:sqs",
      "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
      "awsRegion": "us-east-1"
    },
    {
      "messageId": "244fc6b4-87a3-44ab-83d2-361172410c3a",
      "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
      "body": "SGVsbG8sIHRoaXMgaXMgYSB0ZXN0Lg==",
      "attributes": {
        "ApproximateReceiveCount": "1",
        "SentTimestamp": "1545082649183",
        "SenderId": "AIDAIENQZJOLO23YVJ4VO",
        "ApproximateFirstReceiveTimestamp": "1545082649185"
      },
      "messageAttributes": {},
      "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
      "eventSource": "aws:sqs",
      "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
      "awsRegion": "us-east-1"
    }
  ]
}

FIFO queues

When using SQS FIFO queues, we will stop processing messages after the first failure, and return all failed and unprocessed messages in batchItemFailures. This helps preserve the ordering of messages in your queue.

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    SqsFifoPartialProcessor,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = SqsFifoPartialProcessor()
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import SqsFifoPartialProcessor
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = SqsFifoPartialProcessor()
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        processor.process()  # kick off processing, return List[Tuple]

    return processor.response()

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    SqsFifoPartialProcessor,
    batch_processor,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = SqsFifoPartialProcessor()
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
@batch_processor(record_handler=record_handler, processor=processor)
def lambda_handler(event, context: LambdaContext):
    return processor.response()

Processing messages from Kinesis

Processing batches from Kinesis works in three stages:

  1. Instantiate BatchProcessor and choose EventType.KinesisDataStreams for the event type
  2. Define your function to handle each batch record, and use KinesisStreamRecord type annotation for autocompletion
  3. Use process_partial_response to kick off processing
Info

This code example optionally uses Tracer and Logger for completion.

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from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.kinesis_stream_event import (
    KinesisStreamRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.KinesisDataStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: KinesisStreamRecord):
    logger.info(record.kinesis.data_as_text)
    payload: dict = record.kinesis.data_as_json()
    logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
from aws_lambda_powertools.utilities.data_classes.kinesis_stream_event import (
    KinesisStreamRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.KinesisDataStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: KinesisStreamRecord):
    logger.info(record.kinesis.data_as_text)
    payload: dict = record.kinesis.data_as_json()
    logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        processed_messages = processor.process()  # kick off processing, return list[tuple]
        logger.info(f"Processed ${len(processed_messages)} messages")

    return processor.response()

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from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    batch_processor,
)
from aws_lambda_powertools.utilities.data_classes.kinesis_stream_event import (
    KinesisStreamRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.KinesisDataStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: KinesisStreamRecord):
    logger.info(record.kinesis.data_as_text)
    payload: dict = record.kinesis.data_as_json()
    logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
@batch_processor(record_handler=record_handler, processor=processor)
def lambda_handler(event, context: LambdaContext):
    return processor.response()

The second record failed to be processed, therefore the processor added its sequence number in the response.

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{
  "batchItemFailures": [
    {
      "itemIdentifier": "6006958808509702859251049540584488075644979031228738"
    }
  ]
}

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{
  "Records": [
    {
      "kinesis": {
        "kinesisSchemaVersion": "1.0",
        "partitionKey": "1",
        "sequenceNumber": "4107859083838847772757075850904226111829882106684065",
        "data": "eyJNZXNzYWdlIjogInN1Y2Nlc3MifQ==",
        "approximateArrivalTimestamp": 1545084650.987
      },
      "eventSource": "aws:kinesis",
      "eventVersion": "1.0",
      "eventID": "shardId-000000000006:4107859083838847772757075850904226111829882106684065",
      "eventName": "aws:kinesis:record",
      "invokeIdentityArn": "arn:aws:iam::123456789012:role/lambda-role",
      "awsRegion": "us-east-2",
      "eventSourceARN": "arn:aws:kinesis:us-east-2:123456789012:stream/lambda-stream"
    },
    {
      "kinesis": {
        "kinesisSchemaVersion": "1.0",
        "partitionKey": "1",
        "sequenceNumber": "6006958808509702859251049540584488075644979031228738",
        "data": "c3VjY2Vzcw==",
        "approximateArrivalTimestamp": 1545084650.987
      },
      "eventSource": "aws:kinesis",
      "eventVersion": "1.0",
      "eventID": "shardId-000000000006:6006958808509702859251049540584488075644979031228738",
      "eventName": "aws:kinesis:record",
      "invokeIdentityArn": "arn:aws:iam::123456789012:role/lambda-role",
      "awsRegion": "us-east-2",
      "eventSourceARN": "arn:aws:kinesis:us-east-2:123456789012:stream/lambda-stream"
    }
  ]
}

Processing messages from DynamoDB

Processing batches from DynamoDB Streams works in three stages:

  1. Instantiate BatchProcessor and choose EventType.DynamoDBStreams for the event type
  2. Define your function to handle each batch record, and use DynamoDBRecord type annotation for autocompletion
  3. Use process_partial_response to kick off processing
Info

This code example optionally uses Tracer and Logger for completion.

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.dynamo_db_stream_event import (
    DynamoDBRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.DynamoDBStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: DynamoDBRecord):
    if record.dynamodb and record.dynamodb.new_image:
        logger.info(record.dynamodb.new_image)
        message = record.dynamodb.new_image.get("Message")
        if message:
            payload: dict = json.loads(message)
            logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
from aws_lambda_powertools.utilities.data_classes.dynamo_db_stream_event import (
    DynamoDBRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.DynamoDBStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: DynamoDBRecord):
    if record.dynamodb and record.dynamodb.new_image:
        logger.info(record.dynamodb.new_image)
        message = record.dynamodb.new_image.get("Message")
        if message:
            payload: dict = json.loads(message)
            logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        processed_messages = processor.process()  # kick off processing, return list[tuple]
        logger.info(f"Processed ${len(processed_messages)} messages")

    return processor.response()

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    batch_processor,
)
from aws_lambda_powertools.utilities.data_classes.dynamo_db_stream_event import (
    DynamoDBRecord,
)
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.DynamoDBStreams)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: DynamoDBRecord):
    if record.dynamodb and record.dynamodb.new_image:
        logger.info(record.dynamodb.new_image)
        message = record.dynamodb.new_image.get("Message")
        if message:
            payload: dict = json.loads(message)
            logger.info(payload)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
@batch_processor(record_handler=record_handler, processor=processor)
def lambda_handler(event, context: LambdaContext):
    return processor.response()

The second record failed to be processed, therefore the processor added its sequence number in the response.

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{
  "batchItemFailures": [
    {
      "itemIdentifier": "8640712661"
    }
  ]
}

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{
  "Records": [
    {
      "eventID": "1",
      "eventVersion": "1.0",
      "dynamodb": {
        "Keys": {
          "Id": {
            "N": "101"
          }
        },
        "NewImage": {
          "Message": {
            "S": "failure"
          }
        },
        "StreamViewType": "NEW_AND_OLD_IMAGES",
        "SequenceNumber": "3275880929",
        "SizeBytes": 26
      },
      "awsRegion": "us-west-2",
      "eventName": "INSERT",
      "eventSourceARN": "eventsource_arn",
      "eventSource": "aws:dynamodb"
    },
    {
      "eventID": "1",
      "eventVersion": "1.0",
      "dynamodb": {
        "Keys": {
          "Id": {
            "N": "101"
          }
        },
        "NewImage": {
          "SomethingElse": {
            "S": "success"
          }
        },
        "StreamViewType": "NEW_AND_OLD_IMAGES",
        "SequenceNumber": "8640712661",
        "SizeBytes": 26
      },
      "awsRegion": "us-west-2",
      "eventName": "INSERT",
      "eventSourceARN": "eventsource_arn",
      "eventSource": "aws:dynamodb"
    }
  ]
}

Partial failure mechanics

All records in the batch will be passed to this handler for processing, even if exceptions are thrown - Here's the behaviour after completing the batch:

  • All records successfully processed. We will return an empty list of item failures {'batchItemFailures': []}
  • Partial success with some exceptions. We will return a list of all item IDs/sequence numbers that failed processing
  • All records failed to be processed. We will raise BatchProcessingError exception with a list of all exceptions raised when processing

Processing messages asynchronously

New to AsyncIO? Read this comprehensive guide first.

You can use AsyncBatchProcessor class and async_process_partial_response function to process messages concurrently.

When is this useful?

Your use case might be able to process multiple records at the same time without conflicting with one another.

For example, imagine you need to process multiple loyalty points and incrementally save in a database. While you await the database to confirm your records are saved, you could start processing another request concurrently.

The reason this is not the default behaviour is that not all use cases can handle concurrency safely (e.g., loyalty points must be updated in order).

High-concurrency with AsyncBatchProcessor
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import httpx  # external dependency

from aws_lambda_powertools.utilities.batch import (
    AsyncBatchProcessor,
    EventType,
    async_process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = AsyncBatchProcessor(event_type=EventType.SQS)


async def async_record_handler(record: SQSRecord):
    # Yield control back to the event loop to schedule other tasks
    # while you await from a response from httpbin.org
    async with httpx.AsyncClient() as client:
        ret = await client.get("https://httpbin.org/get")

    return ret.status_code


def lambda_handler(event, context: LambdaContext):
    return async_process_partial_response(
        event=event, record_handler=async_record_handler, processor=processor, context=context
    )
Using tracer?

AsyncBatchProcessor uses asyncio.gather which can cause side effects and reach trace limits at high concurrency.

See Tracing concurrent asynchronous functions.

Advanced

Pydantic integration

You can bring your own Pydantic models via model parameter when inheriting from SqsRecordModel, KinesisDataStreamRecord, or DynamoDBStreamRecordModel

Inheritance is importance because we need to access message IDs and sequence numbers from these records in the event of failure. Mypy is fully integrated with this utility, so it should identify whether you're passing the incorrect Model.

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from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.parser import BaseModel
from aws_lambda_powertools.utilities.parser.models import SqsRecordModel
from aws_lambda_powertools.utilities.parser.types import Json
from aws_lambda_powertools.utilities.typing import LambdaContext


class Order(BaseModel):
    item: dict


class OrderSqsRecord(SqsRecordModel):
    body: Json[Order]  # deserialize order data from JSON string


processor = BatchProcessor(event_type=EventType.SQS, model=OrderSqsRecord)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: OrderSqsRecord):
    logger.info(record.body.item)
    return record.body.item


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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{
    "Records": [
      {
        "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
        "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
        "body": "{\"item\": {\"laptop\": \"amd\"}}",
        "attributes": {
          "ApproximateReceiveCount": "1",
          "SentTimestamp": "1545082649183",
          "SenderId": "AIDAIENQZJOLO23YVJ4VO",
          "ApproximateFirstReceiveTimestamp": "1545082649185"
        },
        "messageAttributes": {},
        "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
        "eventSource": "aws:sqs",
        "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
        "awsRegion": "us-east-1"
      },
      {
        "messageId": "244fc6b4-87a3-44ab-83d2-361172410c3a",
        "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
        "body": "{\"item\": {\"keyboard\": \"classic\"}}",
        "attributes": {
          "ApproximateReceiveCount": "1",
          "SentTimestamp": "1545082649183",
          "SenderId": "AIDAIENQZJOLO23YVJ4VO",
          "ApproximateFirstReceiveTimestamp": "1545082649185"
        },
        "messageAttributes": {},
        "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
        "eventSource": "aws:sqs",
        "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
        "awsRegion": "us-east-1"
      }
    ]
  }

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from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.parser import BaseModel
from aws_lambda_powertools.utilities.parser.models import (
    KinesisDataStreamRecord,
    KinesisDataStreamRecordPayload,
)
from aws_lambda_powertools.utilities.parser.types import Json
from aws_lambda_powertools.utilities.typing import LambdaContext


class Order(BaseModel):
    item: dict


class OrderKinesisPayloadRecord(KinesisDataStreamRecordPayload):
    data: Json[Order]


class OrderKinesisRecord(KinesisDataStreamRecord):
    kinesis: OrderKinesisPayloadRecord


processor = BatchProcessor(event_type=EventType.KinesisDataStreams, model=OrderKinesisRecord)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: OrderKinesisRecord):
    logger.info(record.kinesis.data.item)
    return record.kinesis.data.item


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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{
    "Records": [
      {
        "kinesis": {
          "kinesisSchemaVersion": "1.0",
          "partitionKey": "1",
          "sequenceNumber": "4107859083838847772757075850904226111829882106684065",
          "data": "eyJpdGVtIjogeyJsYXB0b3AiOiAiYW1kIn19Cg==",
          "approximateArrivalTimestamp": 1545084650.987
        },
        "eventSource": "aws:kinesis",
        "eventVersion": "1.0",
        "eventID": "shardId-000000000006:4107859083838847772757075850904226111829882106684065",
        "eventName": "aws:kinesis:record",
        "invokeIdentityArn": "arn:aws:iam::123456789012:role/lambda-role",
        "awsRegion": "us-east-2",
        "eventSourceARN": "arn:aws:kinesis:us-east-2:123456789012:stream/lambda-stream"
      },
      {
        "kinesis": {
          "kinesisSchemaVersion": "1.0",
          "partitionKey": "1",
          "sequenceNumber": "6006958808509702859251049540584488075644979031228738",
          "data": "eyJpdGVtIjogeyJrZXlib2FyZCI6ICJjbGFzc2ljIn19Cg==",
          "approximateArrivalTimestamp": 1545084650.987
        },
        "eventSource": "aws:kinesis",
        "eventVersion": "1.0",
        "eventID": "shardId-000000000006:6006958808509702859251049540584488075644979031228738",
        "eventName": "aws:kinesis:record",
        "invokeIdentityArn": "arn:aws:iam::123456789012:role/lambda-role",
        "awsRegion": "us-east-2",
        "eventSourceARN": "arn:aws:kinesis:us-east-2:123456789012:stream/lambda-stream"
      }
    ]
  }

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import json
from typing import Dict, Optional

from typing_extensions import Literal

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.parser import BaseModel, validator
from aws_lambda_powertools.utilities.parser.models import (
    DynamoDBStreamChangedRecordModel,
    DynamoDBStreamRecordModel,
)
from aws_lambda_powertools.utilities.typing import LambdaContext


class Order(BaseModel):
    item: dict


class OrderDynamoDB(BaseModel):
    Message: Order

    # auto transform json string
    # so Pydantic can auto-initialize nested Order model
    @validator("Message", pre=True)
    def transform_message_to_dict(cls, value: Dict[Literal["S"], str]):
        return json.loads(value["S"])


class OrderDynamoDBChangeRecord(DynamoDBStreamChangedRecordModel):
    NewImage: Optional[OrderDynamoDB]
    OldImage: Optional[OrderDynamoDB]


class OrderDynamoDBRecord(DynamoDBStreamRecordModel):
    dynamodb: OrderDynamoDBChangeRecord


processor = BatchProcessor(event_type=EventType.DynamoDBStreams, model=OrderDynamoDBRecord)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: OrderDynamoDBRecord):
    if record.dynamodb.NewImage and record.dynamodb.NewImage.Message:
        logger.info(record.dynamodb.NewImage.Message.item)
        return record.dynamodb.NewImage.Message.item


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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{
    "Records": [
      {
        "eventID": "1",
        "eventVersion": "1.0",
        "dynamodb": {
          "Keys": {
            "Id": {
              "N": "101"
            }
          },
          "NewImage": {
            "Message": {
              "S": "{\"item\": {\"laptop\": \"amd\"}}"
            }
          },
          "StreamViewType": "NEW_AND_OLD_IMAGES",
          "SequenceNumber": "3275880929",
          "SizeBytes": 26
        },
        "awsRegion": "us-west-2",
        "eventName": "INSERT",
        "eventSourceARN": "eventsource_arn",
        "eventSource": "aws:dynamodb"
      },
      {
        "eventID": "1",
        "eventVersion": "1.0",
        "dynamodb": {
          "Keys": {
            "Id": {
              "N": "101"
            }
          },
          "NewImage": {
            "SomethingElse": {
              "S": "success"
            }
          },
          "StreamViewType": "NEW_AND_OLD_IMAGES",
          "SequenceNumber": "8640712661",
          "SizeBytes": 26
        },
        "awsRegion": "us-west-2",
        "eventName": "INSERT",
        "eventSourceARN": "eventsource_arn",
        "eventSource": "aws:dynamodb"
      }
    ]
  }

Accessing processed messages

Use the context manager to access a list of all returned values from your record_handler function.

  • When successful. We will include a tuple with success, the result of record_handler, and the batch record
  • When failed. We will include a tuple with fail, exception as a string, and the batch record
Accessing processed messages via context manager
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from __future__ import annotations

import json
from typing import List, Tuple

from typing_extensions import Literal

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        processed_messages: List[Tuple] = processor.process()

    for message in processed_messages:
        status: Literal["success"] | Literal["fail"] = message[0]
        record: SQSRecord = message[2]

        logger.info(status, record=record)

    return processor.response()

Accessing Lambda Context

Within your record_handler function, you might need access to the Lambda context to determine how much time you have left before your function times out.

We can automatically inject the Lambda context into your record_handler if your function signature has a parameter named lambda_context. When using a context manager, you also need to pass the Lambda context object like in the example below.

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from typing import Optional

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord, lambda_context: Optional[LambdaContext] = None):
    if lambda_context is not None:
        remaining_time = lambda_context.get_remaining_time_in_millis()
        logger.info(remaining_time)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

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from typing import Optional

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    batch_processor,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord, lambda_context: Optional[LambdaContext] = None):
    if lambda_context is not None:
        remaining_time = lambda_context.get_remaining_time_in_millis()
        logger.info(remaining_time)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
@batch_processor(record_handler=record_handler, processor=processor)
def lambda_handler(event, context: LambdaContext):
    return processor.response()

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from typing import Optional

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord, lambda_context: Optional[LambdaContext] = None):
    if lambda_context is not None:
        remaining_time = lambda_context.get_remaining_time_in_millis()
        logger.info(remaining_time)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    batch = event["Records"]
    with processor(records=batch, handler=record_handler, lambda_context=context):
        result = processor.process()

    return result

Extending BatchProcessor

You might want to bring custom logic to the existing BatchProcessor to slightly override how we handle successes and failures.

For these scenarios, you can subclass BatchProcessor and quickly override success_handler and failure_handler methods:

  • success_handler() – Keeps track of successful batch records
  • failure_handler() – Keeps track of failed batch records
Example

Let's suppose you'd like to add a metric named BatchRecordFailures for each batch record that failed processing

Extending failure handling mechanism in BatchProcessor
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import json

from aws_lambda_powertools import Logger, Metrics, Tracer
from aws_lambda_powertools.metrics import MetricUnit
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    ExceptionInfo,
    FailureResponse,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext


class MyProcessor(BatchProcessor):
    def failure_handler(self, record: SQSRecord, exception: ExceptionInfo) -> FailureResponse:
        metrics.add_metric(name="BatchRecordFailures", unit=MetricUnit.Count, value=1)
        return super().failure_handler(record, exception)


processor = MyProcessor(event_type=EventType.SQS)
metrics = Metrics(namespace="test")
logger = Logger()
tracer = Tracer()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@metrics.log_metrics(capture_cold_start_metric=True)
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

Create your own partial processor

You can create your own partial batch processor from scratch by inheriting the BasePartialProcessor class, and implementing _prepare(), _clean(), _process_record() and _async_process_record().

  • _process_record() – handles all processing logic for each individual message of a batch, including calling the record_handler (self.handler)
  • _prepare() – called once as part of the processor initialization
  • _clean() – teardown logic called once after _process_record completes
  • _async_process_record() – If you need to implement asynchronous logic, use this method, otherwise define it in your class with empty logic

You can then use this class as a context manager, or pass it to batch_processor to use as a decorator on your Lambda handler function.

Creating a custom batch processor
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import os
import sys
from random import randint
from typing import Any

import boto3

from aws_lambda_powertools import Logger
from aws_lambda_powertools.utilities.batch import (
    BasePartialBatchProcessor,
    EventType,
    process_partial_response,
)

table_name = os.getenv("TABLE_NAME", "table_not_found")

logger = Logger()


class MyPartialProcessor(BasePartialBatchProcessor):
    """
    Process a record and stores successful results at a Amazon DynamoDB Table

    Parameters
    ----------
    table_name: str
        DynamoDB table name to write results to
    """

    def __init__(self, table_name: str):
        self.table_name = table_name

        super().__init__(event_type=EventType.SQS)

    def _prepare(self):
        # It's called once, *before* processing
        # Creates table resource and clean previous results
        self.ddb_table = boto3.resource("dynamodb").Table(self.table_name)
        self.success_messages.clear()

    def _clean(self):
        # It's called once, *after* closing processing all records (closing the context manager)
        # Here we're sending, at once, all successful messages to a ddb table
        with self.ddb_table.batch_writer() as batch:
            for result in self.success_messages:
                batch.put_item(Item=result)

    def _process_record(self, record):
        # It handles how your record is processed
        # Here we're keeping the status of each run
        # where self.handler is the record_handler function passed as an argument
        try:
            result = self.handler(record)  # record_handler passed to decorator/context manager
            return self.success_handler(record, result)
        except Exception as exc:
            logger.error(exc)
            return self.failure_handler(record, sys.exc_info())

    def success_handler(self, record, result: Any):
        entry = ("success", result, record)
        self.success_messages.append(record)
        return entry

    async def _async_process_record(self, record: dict):
        raise NotImplementedError()


processor = MyPartialProcessor(table_name)


def record_handler(record):
    return randint(0, 100)


def lambda_handler(event, context):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

Caveats

Tracer response auto-capture for large batch sizes

When using Tracer to capture responses for each batch record processing, you might exceed 64K of tracing data depending on what you return from your record_handler function, or how big is your batch size.

If that's the case, you can configure Tracer to disable response auto-capturing.

Disabling Tracer response auto-capturing
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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method(capture_response=False)
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)

Testing your code

As there is no external calls, you can unit test your code with BatchProcessor quite easily.

Example:

Given a SQS batch where the first batch record succeeds and the second fails processing, we should have a single item reported in the function response.

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import json
from dataclasses import dataclass
from pathlib import Path

import pytest
from getting_started_with_test_app import lambda_handler, processor


def load_event(path: Path):
    with path.open() as f:
        return json.load(f)


@pytest.fixture
def lambda_context():
    @dataclass
    class LambdaContext:
        function_name: str = "test"
        memory_limit_in_mb: int = 128
        invoked_function_arn: str = "arn:aws:lambda:eu-west-1:809313241:function:test"
        aws_request_id: str = "52fdfc07-2182-154f-163f-5f0f9a621d72"

    return LambdaContext()


@pytest.fixture()
def sqs_event():
    """Generates API GW Event"""
    return load_event(path=Path("events/sqs_event.json"))


def test_app_batch_partial_response(sqs_event, lambda_context):
    # GIVEN
    processor_result = processor  # access processor for additional assertions
    successful_record = sqs_event["Records"][0]
    failed_record = sqs_event["Records"][1]
    expected_response = {"batchItemFailures": [{"itemIdentifier": failed_record["messageId"]}]}

    # WHEN
    ret = lambda_handler(sqs_event, lambda_context)

    # THEN
    assert ret == expected_response
    assert len(processor_result.fail_messages) == 1
    assert processor_result.success_messages[0] == successful_record

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import json

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.typing import LambdaContext

processor = BatchProcessor(event_type=EventType.SQS)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: SQSRecord):
    payload: str = record.body
    if payload:
        item: dict = json.loads(payload)
        logger.info(item)


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)
events/sqs_event.json
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{
  "Records": [
    {
      "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
      "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
      "body": "{\"Message\": \"success\"}",
      "attributes": {
        "ApproximateReceiveCount": "1",
        "SentTimestamp": "1545082649183",
        "SenderId": "AIDAIENQZJOLO23YVJ4VO",
        "ApproximateFirstReceiveTimestamp": "1545082649185"
      },
      "messageAttributes": {},
      "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
      "eventSource": "aws:sqs",
      "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
      "awsRegion": "us-east-1"
    },
    {
      "messageId": "244fc6b4-87a3-44ab-83d2-361172410c3a",
      "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
      "body": "SGVsbG8sIHRoaXMgaXMgYSB0ZXN0Lg==",
      "attributes": {
        "ApproximateReceiveCount": "1",
        "SentTimestamp": "1545082649183",
        "SenderId": "AIDAIENQZJOLO23YVJ4VO",
        "ApproximateFirstReceiveTimestamp": "1545082649185"
      },
      "messageAttributes": {},
      "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
      "eventSource": "aws:sqs",
      "eventSourceARN": "arn:aws:sqs:us-east-2: 123456789012:my-queue",
      "awsRegion": "us-east-1"
    }
  ]
}

FAQ

Choosing between method and context manager

Use context manager when you want access to the processed messages or handle BatchProcessingError exception when all records within the batch fail to be processed.

What's the difference between the decorator and process_partial_response functions?

batch_processor and async_batch_processor decorators are now considered legacy. Historically, they were kept due to backwards compatibility and to minimize code changes between V1 and V2.

As 2.12.0, process_partial_response and async_process_partial_response are the recommended instead. It reduces boilerplate, smaller memory/CPU cycles, and it makes it less error prone - e.g., decorators required an additional return.

Integrating exception handling with Sentry.io

When using Sentry.io for error monitoring, you can override failure_handler to capture each processing exception with Sentry SDK:

Credits to Charles-Axel Dein

Integrating error tracking with Sentry.io
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from sentry_sdk import capture_exception

from aws_lambda_powertools.utilities.batch import BatchProcessor, FailureResponse


class MyProcessor(BatchProcessor):
    def failure_handler(self, record, exception) -> FailureResponse:
        capture_exception()  # send exception to Sentry
        return super().failure_handler(record, exception)
Last update: 2023-05-11