title	description
Batch Processing	Utility

???+ warning This utility is currently released as beta developer preview and is intended strictly for feedback and testing purposes and not for production workloads. The version and all future versions tagged with the -beta suffix should be treated as not stable. Up until before the General Availability release we might introduce significant breaking changes and improvements in response to customers feedback.

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

stateDiagram-v2
    direction LR
    BatchSource: Amazon SQS <br/><br/> Amazon Kinesis Data Streams <br/><br/> Amazon DynamoDB Streams <br/><br/>
    LambdaInit: Lambda invocation
    BatchProcessor: Batch Processor
    RecordHandler: Record Handler function
    YourLogic: Your logic to process each batch item
    LambdaResponse: Lambda response

    BatchSource --> LambdaInit

    LambdaInit --> BatchProcessor
    BatchProcessor --> RecordHandler

    state BatchProcessor {
        [*] --> RecordHandler: Your function
        RecordHandler --> YourLogic
    }

    RecordHandler --> BatchProcessor: Collect results
    BatchProcessor --> LambdaResponse: Report items that failed processing

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Key features

Reports batch item failures to reduce number of retries for a record upon errors
Simple interface to process each batch record
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.

journey
  section Conditions
    Successful response: 5: Success
    Maximum retries: 3: Failure
    Records expired: 1: Failure

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This behavior changes when you enable ReportBatchItemFailures feature in your Lambda function event source configuration:

SQS queues. Only messages reported as failure will return to the queue for a retry, while successful ones will be deleted.
Kinesis data streams and DynamoDB streams. Single reported failure will use its sequence number as the stream checkpoint. Multiple reported failures will use the lowest sequence number as checkpoint.

???+ warning "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{target="_blank"} wherever possible.

You can find more details on how Lambda works with either [SQS](https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html){target="_blank"}, [Kinesis](https://docs.aws.amazon.com/lambda/latest/dg/with-kinesis.html){target="_blank"}, or [DynamoDB](https://docs.aws.amazon.com/lambda/latest/dg/with-ddb.html){target="_blank"} in the AWS Documentation.

Getting started

For this feature to work, you need to (1) configure your Lambda function event source to use ReportBatchItemFailures, and (2) return a specific response{target="_blank" rel="nofollow"} to report which records failed to be processed.

Use your preferred deployment framework to set the correct configuration while this utility handles the correct response to be returned.

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.

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

=== "SQS"

```yaml title="template.yaml" hl_lines="30-31"
--8<-- "docs/snippets/batch/templates/sam/sqs.yaml"
```

=== "Kinesis Data Streams"

```yaml title="template.yaml" hl_lines="44-45"
--8<-- "docs/snippets/batch/templates/sam/kinesis.yaml"
```

=== "DynamoDB Streams"

```yaml title="template.yaml" hl_lines="43-44"
--8<-- "docs/snippets/batch/templates/sam/dynamodb.yaml"
```

Processing messages from SQS

Processing batches from SQS works in three stages:

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

???+ info This code example optionally uses Logger for completion.

=== "index.ts"

```typescript hl_lines="1-5 14 17 29-31"
--8<--
 docs/snippets/batch/gettingStartedSQS.ts::16
 docs/snippets/batch/gettingStartedSQS.ts:18:29
 docs/snippets/batch/gettingStartedSQS.ts:31:34
--8<--
```

1.  **Step 1**. Creates a partial failure batch processor for SQS queues. See [partial failure mechanics for details](#partial-failure-mechanics)
2.  **Step 2**. Defines a function to receive one record at a time from the batch
3.  **Step 3**. Kicks off processing

=== "Sample response"

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

```json
--8<-- "docs/snippets/batch/samples/sampleSQSResponse.json"
```

=== "Sample event"

```json
--8<-- "docs/snippets/batch/samples/sampleSQSEvent.json"
```

FIFO queues

When using SQS FIFO queues{target="_blank"}, 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.

--8<-- "docs/snippets/batch/gettingStartedSQSFifo.ts"

Step 1. Creates a partial failure batch processor for SQS FIFO queues. See partial failure mechanics for details

!!! Note Note that SqsFifoPartialProcessor is synchronous using processPartialResponseSync. This is because we need to preserve the order of messages in the queue. See Async or sync processing section for more details.

Processing messages from Kinesis

Processing batches from Kinesis works in three stages:

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

???+ info This code example optionally uses Logger for completion.

=== "index.ts"

```typescript hl_lines="1-5 14 17 27-29"
--8<-- "docs/snippets/batch/gettingStartedKinesis.ts"
```

1.  **Step 1**. Creates a partial failure batch processor for Kinesis Data Streams. See [partial failure mechanics for details](#partial-failure-mechanics)

=== "Sample response"

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

```json
--8<-- "docs/snippets/batch/samples/sampleKinesisEvent.json"
```

=== "Sample event"

```json
--8<-- "docs/snippets/batch/samples/sampleKinesisResponse.json"
```

Processing messages from DynamoDB

Processing batches from DynamoDB Streams works in three stages:

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

???+ info This code example optionally uses Logger for completion.

=== "index.ts"

```typescript hl_lines="1-5 14 17 32-34"
--8<-- "docs/snippets/batch/gettingStartedDynamoDBStreams.ts"
```

1.  **Step 1**. Creates a partial failure batch processor for DynamoDB Streams. See [partial failure mechanics for details](#partial-failure-mechanics)

=== "Sample response"

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

```json
--8<-- "docs/snippets/batch/samples/sampleDynamoDBStreamsResponse.json"
```

=== "Sample event"

```json
--8<-- "docs/snippets/batch/samples/sampleDynamoDBStreamsEvent.json"
```

Error handling

By default, we catch any exception raised by your record handler function. This allows us to (1) continue processing the batch, (2) collect each batch item that failed processing, and (3) return the appropriate response correctly without failing your Lambda function execution.

=== "Sample error handling with custom exception"

```typescript hl_lines="30"
--8<--
 docs/snippets/batch/gettingStartedErrorHandling.ts::29
 docs/snippets/batch/gettingStartedErrorHandling.ts:31:38
 docs/snippets/batch/gettingStartedErrorHandling.ts:40:43
--8<--
```

1. Any exception works here. See [extending BatchProcessorSync section, if you want to override this behavior.](#extending-batchprocessor)

2. Exceptions raised in `record_handler` will propagate to `process_partial_response`. <br/><br/> We catch them and include each failed batch item identifier in the response dictionary (see `Sample response` tab).

=== "Sample response"

```json
--8<-- "docs/snippets/batch/samples/sampleSQSResponse.json"
```

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

The following sequence diagrams explain how each Batch processor behaves under different scenarios.

SQS Standard

Read more about Batch Failure Reporting feature in AWS Lambda{target="_blank"}.

Sequence diagram to explain how BatchProcessor works with SQS Standard queues.

```mermaid sequenceDiagram autonumber participant SQS queue participant Lambda service participant Lambda function Lambda service->>SQS queue: Poll Lambda service->>Lambda function: Invoke (batch event) Lambda function->>Lambda service: Report some failed messages activate SQS queue Lambda service->>SQS queue: Delete successful messages SQS queue-->>SQS queue: Failed messages return Note over SQS queue,Lambda service: Process repeat deactivate SQS queue ``` SQS mechanism with Batch Item Failures

SQS FIFO

Read more about Batch Failure Reporting feature in AWS Lambda{target="_blank"}.

Sequence diagram to explain how SqsFifoPartialProcessor works with SQS FIFO queues.

```mermaid sequenceDiagram autonumber participant SQS queue participant Lambda service participant Lambda function Lambda service->>SQS queue: Poll Lambda service->>Lambda function: Invoke (batch event) activate Lambda function Lambda function-->Lambda function: Process 2 out of 10 batch items Lambda function--xLambda function: Fail on 3rd batch item Lambda function->>Lambda service: Report 3rd batch item and unprocessed messages as failure deactivate Lambda function activate SQS queue Lambda service->>SQS queue: Delete successful messages (1-2) SQS queue-->>SQS queue: Failed messages return (3-10) deactivate SQS queue ``` SQS FIFO mechanism with Batch Item Failures

Kinesis and DynamoDB Streams

Read more about Batch Failure Reporting feature{target="_blank"}.

Sequence diagram to explain how BatchProcessor works with both Kinesis Data Streams and DynamoDB Streams.

For brevity, we will use Streams to refer to either services. For theory on stream checkpoints, see this blog post{target="_blank"}

```mermaid sequenceDiagram autonumber participant Streams participant Lambda service participant Lambda function Lambda service->>Streams: Poll latest records Lambda service->>Lambda function: Invoke (batch event) activate Lambda function Lambda function-->Lambda function: Process 2 out of 10 batch items Lambda function--xLambda function: Fail on 3rd batch item Lambda function-->Lambda function: Continue processing batch items (4-10) Lambda function->>Lambda service: Report batch item as failure (3) deactivate Lambda function activate Streams Lambda service->>Streams: Checkpoints to sequence number from 3rd batch item Lambda service->>Streams: Poll records starting from updated checkpoint deactivate Streams ``` Kinesis and DynamoDB streams mechanism with single batch item failure

The behavior changes slightly when there are multiple item failures. Stream checkpoint is updated to the lowest sequence number reported.

!!! important "Note that the batch item sequence number could be different from batch item number in the illustration."

```mermaid sequenceDiagram autonumber participant Streams participant Lambda service participant Lambda function Lambda service->>Streams: Poll latest records Lambda service->>Lambda function: Invoke (batch event) activate Lambda function Lambda function-->Lambda function: Process 2 out of 10 batch items Lambda function--xLambda function: Fail on 3-5 batch items Lambda function-->Lambda function: Continue processing batch items (6-10) Lambda function->>Lambda service: Report batch items as failure (3-5) deactivate Lambda function activate Streams Lambda service->>Streams: Checkpoints to lowest sequence number Lambda service->>Streams: Poll records starting from updated checkpoint deactivate Streams ``` Kinesis and DynamoDB streams mechanism with multiple batch item failures

Async or sync processing

There are two processors you can use with this utility:

BatchProcessor and processPartialResponse – Processes messages asynchronously
BatchProcessorSync and processPartialResponseSync – Processes messages synchronously

In most cases your function will be async returning a Promise. Therefore, the BatchProcessor is the default processor handling your batch records asynchronously. There are use cases where you need to process the batch records synchronously. For example, when you need to process multiple records at the same time without conflicting with one another. For such cases we recommend to use the BatchProcessorSync and processPartialResponseSync functions.

!!! info "Note that you need match your processing function with the right batch processor" * If your function is async returning a Promise, use BatchProcessor and processPartialResponse * If your function is not async, use BatchProcessorSync and processPartialResponseSync

The difference between the two processors in implementation is that BatchProcessor uses Promise.all() while BatchProcessorSync loops through each record to preserve the order.

???+ question "When is this useful?"

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).

Advanced

Accessing processed messages

Use the BatchProcessor directly in your function to access a list of all returned values from your recordHandler function.

When successful. We will include a tuple with success, the result of recordHandler, and the batch record
When failed. We will include a tuple with fail, exception as a string, and the batch record

--8<-- "docs/snippets/batch/accessProcessedMessages.ts"

The processor requires the records array. This is typically handled by processPartialResponse.
You need to register the batch, the recordHandler function, and optionally the context to access the Lambda context.

Accessing Lambda Context

Within your recordHandler 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{target="_blank"} into your recordHandler as optional second argument if you register it when using BatchProcessorSync or the processPartialResponseSync function.

--8<-- "docs/snippets/batch/accessLambdaContext.ts"

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 successHandler and failureHandler methods:

successHandler() – Keeps track of successful batch records
failureHandler() – 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

```typescript hl_lines="17 21 25 31 35" title="Extending failure handling mechanism in BatchProcessor"
--8<-- "docs/snippets/batch/extendingFailure.ts"
```

Create your own partial processor

You can create your own partial batch processor from scratch by inheriting the BasePartialProcessor class, and implementing the prepare(), clean(), processRecord() and processRecordSync() abstract methods.

```mermaid classDiagram direction LR class BasePartialProcessor { <> +prepare() +clean() +processRecord(record: BaseRecord) +processRecordSync(record: BaseRecord) } class YourCustomProcessor { +prepare() +clean() +processRecord(record: BaseRecord) +processRecordSyc(record: BaseRecord) } BasePartialProcessor <|-- YourCustomProcessor : extends ``` Visual representation to bring your own processor

prepare() – called once as part of the processor initialization
clean() – teardown logic called once after processRecord completes
processRecord() – If you need to implement asynchronous logic, use this method, otherwise define it in your class with empty logic
processRecordSync() – handles all processing logic for each individual message of a batch, including calling the recordHandler (this.handler)

You can then use this class as a context manager, or pass it to processPartialResponseSync to process the records in your Lambda handler function.

--8<-- "docs/snippets/batch/customPartialProcessor.ts"

Tracing with AWS X-Ray

You can use Tracer to create subsegments for each batch record processed. To do so, you can open a new subsegment for each record, and close it when you're done processing it. When adding annotations and metadata to the subsegment, you can do so directly without calling tracer.setSegment(subsegment). This allows you to work with the subsegment directly and avoid having to either pass the parent subsegment around or have to restore the parent subsegment at the end of the record processing.

--8<-- "docs/snippets/batch/advancedTracingRecordHandler.ts"

Retrieve the current segment, then create a subsegment for the record being processed
You can add annotations and metadata to the subsegment directly without calling tracer.setSegment(subsegment)
Close the subsegment when you're done processing the record

Testing your code

As there is no external calls, you can unit test your code with BatchProcessorSync 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.

=== "index.test.ts"

```typescript
--8<-- "docs/snippets/batch/testingYourCode.ts"
```

=== "index.ts"

```typescript
--8<-- "docs/snippets/batch/gettingStartedSQS.ts"
```

=== "Sample SQS event"

```json title="events/sqs_event.json"
--8<-- "docs/snippets/batch/samples/sampleSQSEvent.json"
```

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batch.md

batch.md

Key features

Background

Getting started

Required resources

Processing messages from SQS

FIFO queues

Processing messages from Kinesis

Processing messages from DynamoDB

Error handling

Partial failure mechanics

SQS Standard

SQS FIFO

Kinesis and DynamoDB Streams

Async or sync processing

Advanced

Accessing processed messages

Accessing Lambda Context

Extending BatchProcessor

Create your own partial processor

Tracing with AWS X-Ray

Testing your code

Files

batch.md

Latest commit

History

batch.md

File metadata and controls

Key features

Background

Getting started

Required resources

Processing messages from SQS

FIFO queues

Processing messages from Kinesis

Processing messages from DynamoDB

Error handling

Partial failure mechanics

SQS Standard

SQS FIFO

Kinesis and DynamoDB Streams

Async or sync processing

Advanced

Accessing processed messages

Accessing Lambda Context

Extending BatchProcessor

Create your own partial processor

Tracing with AWS X-Ray

Testing your code