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Idempotency

The idempotency utility provides a simple solution to convert your Lambda functions into idempotent operations which are safe to retry.

Terminology

The property of idempotency means that an operation does not cause additional side effects if it is called more than once with the same input parameters.

Idempotent operations will return the same result when they are called multiple times with the same parameters. This makes idempotent operations safe to retry.

Idempotency key is a hash representation of either the entire event or a specific configured subset of the event, and invocation results are JSON serialized and stored in your persistence storage layer.

Key features

  • Prevent Lambda handler from executing more than once on the same event payload during a time window
  • Ensure Lambda handler returns the same result when called with the same payload
  • Select a subset of the event as the idempotency key using JMESPath expressions
  • Set a time window in which records with the same payload should be considered duplicates
  • Expires in-progress executions if the Lambda function times out halfway through

Getting started

IAM Permissions

Your Lambda function IAM Role must have dynamodb:GetItem, dynamodb:PutItem, dynamodb:UpdateItem and dynamodb:DeleteItem IAM permissions before using this feature.

Note

If you're using our example AWS Serverless Application Model (SAM), it already adds the required permissions.

Required resources

Before getting started, you need to create a persistent storage layer where the idempotency utility can store its state - your lambda functions will need read and write access to it.

As of now, Amazon DynamoDB is the only supported persistent storage layer, so you'll need to create a table first.

Default table configuration

If you're not changing the default configuration for the DynamoDB persistence layer, this is the expected default configuration:

Configuration Value Notes
Partition key id
TTL attribute name expiration This can only be configured after your table is created if you're using AWS Console
Tip: You can share a single state table for all functions

You can reuse the same DynamoDB table to store idempotency state. We add module_name and qualified name for classes and functions in addition to the idempotency key as a hash key.

AWS Serverless Application Model (SAM) example
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Resources:
  IdempotencyTable:
    Type: AWS::DynamoDB::Table
    Properties:
      AttributeDefinitions:
        -   AttributeName: id
            AttributeType: S
      KeySchema:
        -   AttributeName: id
            KeyType: HASH
      TimeToLiveSpecification:
        AttributeName: expiration
        Enabled: true
      BillingMode: PAY_PER_REQUEST

  HelloWorldFunction:
  Type: AWS::Serverless::Function
  Properties:
    Runtime: python3.8
    ...
    Policies:
      - DynamoDBCrudPolicy:
          TableName: !Ref IdempotencyTable
Warning: Large responses with DynamoDB persistence layer

When using this utility with DynamoDB, your function's responses must be smaller than 400KB.

Larger items cannot be written to DynamoDB and will cause exceptions.

Info: DynamoDB

Each function invocation will generally make 2 requests to DynamoDB. If the result returned by your Lambda is less than 1kb, you can expect 2 WCUs per invocation. For retried invocations, you will see 1WCU and 1RCU. Review the DynamoDB pricing documentation to estimate the cost.

Idempotent decorator

You can quickly start by initializing the DynamoDBPersistenceLayer class and using it with the idempotent decorator on your lambda handler.

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from aws_lambda_powertools.utilities.idempotency import (
    DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

@idempotent(persistence_store=persistence_layer)
def handler(event, context):
    payment = create_subscription_payment(
        user=event['user'],
        product=event['product_id']
    )
    ...
    return {
        "payment_id": payment.id,
        "message": "success",
        "statusCode": 200,
    }

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{
  "username": "xyz",
  "product_id": "123456789"
}

Idempotent_function decorator

Similar to idempotent decorator, you can use idempotent_function decorator for any synchronous Python function.

When using idempotent_function, you must tell us which keyword parameter in your function signature has the data we should use via data_keyword_argument.

We support JSON serializable data, Python Dataclasses, Parser/Pydantic Models, and our Event Source Data Classes.

Limitation

Make sure to call your decorated function using keyword arguments.

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from dataclasses import dataclass

from aws_lambda_powertools.utilities.idempotency import (
    DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function)

dynamodb = DynamoDBPersistenceLayer(table_name="idem")
config =  IdempotencyConfig(
    event_key_jmespath="order_id",  # see Choosing a payload subset section
    use_local_cache=True,
)

@dataclass
class OrderItem:
    sku: str
    description: str

@dataclass
class Order:
    item: OrderItem
    order_id: int


@idempotent_function(data_keyword_argument="order", config=config, persistence_store=dynamodb)
def process_order(order: Order):
    return f"processed order {order.order_id}"

def lambda_handler(event, context):
    config.register_lambda_context(context) # see Lambda timeouts section
    order_item = OrderItem(sku="fake", description="sample")
    order = Order(item=order_item, order_id="fake-id")

    # `order` parameter must be called as a keyword argument to work
    process_order(order=order)

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from aws_lambda_powertools.utilities.idempotency import (
    DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function)
from aws_lambda_powertools.utilities.parser import BaseModel

dynamodb = DynamoDBPersistenceLayer(table_name="idem")
config =  IdempotencyConfig(
    event_key_jmespath="order_id",  # see Choosing a payload subset section
    use_local_cache=True,
)


class OrderItem(BaseModel):
    sku: str
    description: str


class Order(BaseModel):
    item: OrderItem
    order_id: int


@idempotent_function(data_keyword_argument="order", config=config, persistence_store=dynamodb)
def process_order(order: Order):
    return f"processed order {order.order_id}"

def lambda_handler(event, context):
    config.register_lambda_context(context) # see Lambda timeouts section
    order_item = OrderItem(sku="fake", description="sample")
    order = Order(item=order_item, order_id="fake-id")

    # `order` parameter must be called as a keyword argument to work
    process_order(order=order)

Batch integration

You can can easily integrate with Batch utility via context manager. This ensures that you process each record in an idempotent manner, and guard against a Lambda timeout idempotent situation.

Choosing an unique batch record attribute

In this example, we choose messageId as our idempotency token since we know it'll be unique.

Depending on your use case, it might be more accurate to choose another field your producer intentionally set to define uniqueness.

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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.idempotency import (
    DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function)


processor = BatchProcessor(event_type=EventType.SQS)
dynamodb = DynamoDBPersistenceLayer(table_name="idem")
config =  IdempotencyConfig(
    event_key_jmespath="messageId",  # see Choosing a payload subset section
    use_local_cache=True,
)


@idempotent_function(data_keyword_argument="record", config=config, persistence_store=dynamodb)
def record_handler(record: SQSRecord):
    return {"message": record.body}


def lambda_handler(event, context):
    config.register_lambda_context(context) # see Lambda timeouts section

    # with Lambda context registered for Idempotency
    # we can now kick in the Bach processing logic
    batch = event["Records"]
    with processor(records=batch, handler=record_handler):
        # in case you want to access each record processed by your record_handler
        # otherwise ignore the result variable assignment
        processed_messages = processor.process()

    return processor.response()

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{
    "Records": [
        {
            "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
            "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a...",
            "body": "Test message.",
            "attributes": {
                "ApproximateReceiveCount": "1",
                "SentTimestamp": "1545082649183",
                "SenderId": "AIDAIENQZJOLO23YVJ4VO",
                "ApproximateFirstReceiveTimestamp": "1545082649185"
            },
            "messageAttributes": {
                "testAttr": {
                "stringValue": "100",
                "binaryValue": "base64Str",
                "dataType": "Number"
                }
            },
            "md5OfBody": "e4e68fb7bd0e697a0ae8f1bb342846b3",
            "eventSource": "aws:sqs",
            "eventSourceARN": "arn:aws:sqs:us-east-2:123456789012:my-queue",
            "awsRegion": "us-east-2"
        }
    ]
}

Choosing a payload subset for idempotency

Tip: Dealing with always changing payloads

When dealing with a more elaborate payload, where parts of the payload always change, you should use event_key_jmespath parameter.

Use IdempotencyConfig to instruct the idempotent decorator to only use a portion of your payload to verify whether a request is idempotent, and therefore it should not be retried.

Payment scenario

In this example, we have a Lambda handler that creates a payment for a user subscribing to a product. We want to ensure that we don't accidentally charge our customer by subscribing them more than once.

Imagine the function executes successfully, but the client never receives the response due to a connection issue. It is safe to retry in this instance, as the idempotent decorator will return a previously saved response.

Warning: Idempotency for JSON payloads

The payload extracted by the event_key_jmespath is treated as a string by default, so will be sensitive to differences in whitespace even when the JSON payload itself is identical.

To alter this behaviour, we can use the JMESPath built-in function powertools_json() to treat the payload as a JSON object (dict) rather than a string.

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import json
from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

# Treat everything under the "body" key
# in the event json object as our payload
config = IdempotencyConfig(event_key_jmespath="powertools_json(body)")

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
    body = json.loads(event['body'])
    payment = create_subscription_payment(
        user=body['user'],
        product=body['product_id']
    )
    ...
    return {
        "payment_id": payment.id,
        "message": "success",
        "statusCode": 200
    }

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{
  "version":"2.0",
  "routeKey":"ANY /createpayment",
  "rawPath":"/createpayment",
  "rawQueryString":"",
  "headers": {
    "Header1": "value1",
    "Header2": "value2"
  },
  "requestContext":{
    "accountId":"123456789012",
    "apiId":"api-id",
    "domainName":"id.execute-api.us-east-1.amazonaws.com",
    "domainPrefix":"id",
    "http":{
      "method":"POST",
      "path":"/createpayment",
      "protocol":"HTTP/1.1",
      "sourceIp":"ip",
      "userAgent":"agent"
    },
    "requestId":"id",
    "routeKey":"ANY /createpayment",
    "stage":"$default",
    "time":"10/Feb/2021:13:40:43 +0000",
    "timeEpoch":1612964443723
  },
  "body":"{\"user\":\"xyz\",\"product_id\":\"123456789\"}",
  "isBase64Encoded":false
}

Idempotency request flow

This sequence diagram shows an example flow of what happens in the payment scenario:

sequenceDiagram
    participant Client
    participant Lambda
    participant Persistence Layer
    alt initial request
        Client->>Lambda: Invoke (event)
        Lambda->>Persistence Layer: Get or set (id=event.search(payload))
        activate Persistence Layer
        Note right of Persistence Layer: Locked to prevent concurrent<br/>invocations with <br/> the same payload.
        Lambda-->>Lambda: Call handler (event)
        Lambda->>Persistence Layer: Update record with result
        deactivate Persistence Layer
        Persistence Layer-->>Persistence Layer: Update record with result
        Lambda-->>Client: Response sent to client
    else retried request
        Client->>Lambda: Invoke (event)
        Lambda->>Persistence Layer: Get or set (id=event.search(payload))
        Persistence Layer-->>Lambda: Already exists in persistence layer. Return result
        Lambda-->>Client: Response sent to client
    end
Idempotent sequence

The client was successful in receiving the result after the retry. Since the Lambda handler was only executed once, our customer hasn't been charged twice.

Note

Bear in mind that the entire Lambda handler is treated as a single idempotent operation. If your Lambda handler can cause multiple side effects, consider splitting it into separate functions.

Lambda timeouts

Note

This is automatically done when you decorate your Lambda handler with @idempotent decorator.

To prevent against extended failed retries when a Lambda function times out, Powertools calculates and includes the remaining invocation available time as part of the idempotency record.

Example

If a second invocation happens after this timestamp, and the record is marked as INPROGRESS, we will execute the invocation again as if it was in the EXPIRED state (e.g, expire_seconds field elapsed).

This means that if an invocation expired during execution, it will be quickly executed again on the next retry.

Important

If you are only using the @idempotent_function decorator to guard isolated parts of your code, you must use register_lambda_context available in the idempotency config object to benefit from this protection.

Here is an example on how you register the Lambda context in your handler:

Registering the Lambda context
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from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, idempotent_function
)

persistence_layer = DynamoDBPersistenceLayer(table_name="...")

config = IdempotencyConfig()

@idempotent_function(data_keyword_argument="record", persistence_store=persistence_layer, config=config)
def record_handler(record: SQSRecord):
    return {"message": record["body"]}


def lambda_handler(event, context):
    config.register_lambda_context(context)

    return record_handler(event)

Lambda timeout sequence diagram

This sequence diagram shows an example flow of what happens if a Lambda function times out:

sequenceDiagram
    participant Client
    participant Lambda
    participant Persistence Layer
    alt initial request
        Client->>Lambda: Invoke (event)
        Lambda->>Persistence Layer: Get or set (id=event.search(payload))
        activate Persistence Layer
        Note right of Persistence Layer: Locked to prevent concurrent<br/>invocations with <br/> the same payload.
        Note over Lambda: Time out
        Lambda--xLambda: Call handler (event)
        Lambda-->>Client: Return error response
        deactivate Persistence Layer
    else concurrent request before timeout
        Client->>Lambda: Invoke (event)
        Lambda->>Persistence Layer: Get or set (id=event.search(payload))
        Persistence Layer-->>Lambda: Request already INPROGRESS
        Lambda--xClient: Return IdempotencyAlreadyInProgressError
    else retry after Lambda timeout
        Client->>Lambda: Invoke (event)
        Lambda->>Persistence Layer: Get or set (id=event.search(payload))
        activate Persistence Layer
        Note right of Persistence Layer: Locked to prevent concurrent<br/>invocations with <br/> the same payload.
        Lambda-->>Lambda: Call handler (event)
        Lambda->>Persistence Layer: Update record with result
        deactivate Persistence Layer
        Persistence Layer-->>Persistence Layer: Update record with result
        Lambda-->>Client: Response sent to client
    end
Idempotent sequence for Lambda timeouts

Handling exceptions

If you are using the idempotent decorator on your Lambda handler, any unhandled exceptions that are raised during the code execution will cause the record in the persistence layer to be deleted. This means that new invocations will execute your code again despite having the same payload. If you don't want the record to be deleted, you need to catch exceptions within the idempotent function and return a successful response.

sequenceDiagram
    participant Client
    participant Lambda
    participant Persistence Layer
    Client->>Lambda: Invoke (event)
    Lambda->>Persistence Layer: Get or set (id=event.search(payload))
    activate Persistence Layer
    Note right of Persistence Layer: Locked during this time. Prevents multiple<br/>Lambda invocations with the same<br/>payload running concurrently.
    Lambda--xLambda: Call handler (event).<br/>Raises exception
    Lambda->>Persistence Layer: Delete record (id=event.search(payload))
    deactivate Persistence Layer
    Lambda-->>Client: Return error response
Idempotent sequence exception

If you are using idempotent_function, any unhandled exceptions that are raised inside the decorated function will cause the record in the persistence layer to be deleted, and allow the function to be executed again if retried.

If an Exception is raised outside the scope of the decorated function and after your function has been called, the persistent record will not be affected. In this case, idempotency will be maintained for your decorated function. Example:

Exception not affecting idempotency record sample
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def lambda_handler(event, context):
    # If an exception is raised here, no idempotent record will ever get created as the
    # idempotent function does not get called
    do_some_stuff()

    result = call_external_service(data={"user": "user1", "id": 5})

    # This exception will not cause the idempotent record to be deleted, since it
    # happens after the decorated function has been successfully called
    raise Exception


@idempotent_function(data_keyword_argument="data", config=config, persistence_store=dynamodb)
def call_external_service(data: dict, **kwargs):
    result = requests.post('http://example.com', json={"user": data['user'], "transaction_id": data['id']}
    return result.json()
Warning

We will raise IdempotencyPersistenceLayerError if any of the calls to the persistence layer fail unexpectedly.

As this happens outside the scope of your decorated function, you are not able to catch it if you're using the idempotent decorator on your Lambda handler.

Persistence layers

DynamoDBPersistenceLayer

This persistence layer is built-in, and you can either use an existing DynamoDB table or create a new one dedicated for idempotency state (recommended).

Customizing DynamoDBPersistenceLayer to suit your table structure
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from aws_lambda_powertools.utilities.idempotency import DynamoDBPersistenceLayer

persistence_layer = DynamoDBPersistenceLayer(
    table_name="IdempotencyTable",
    key_attr="idempotency_key",
    expiry_attr="expires_at",
    in_progress_expiry_attr="in_progress_expires_at",
    status_attr="current_status",
    data_attr="result_data",
    validation_key_attr="validation_key",
)

When using DynamoDB as a persistence layer, you can alter the attribute names by passing these parameters when initializing the persistence layer:

Parameter Required Default Description
table_name ✔ Table name to store state
key_attr id Partition key of the table. Hashed representation of the payload (unless sort_key_attr is specified)
expiry_attr expiration Unix timestamp of when record expires
in_progress_expiry_attr in_progress_expiration Unix timestamp of when record expires while in progress (in case of the invocation times out)
status_attr status Stores status of the lambda execution during and after invocation
data_attr data Stores results of successfully executed Lambda handlers
validation_key_attr validation Hashed representation of the parts of the event used for validation
sort_key_attr Sort key of the table (if table is configured with a sort key).
static_pk_value idempotency#{LAMBDA_FUNCTION_NAME} Static value to use as the partition key. Only used when sort_key_attr is set.

Advanced

Customizing the default behavior

Idempotent decorator can be further configured with IdempotencyConfig as seen in the previous example. These are the available options for further configuration

Parameter Default Description
event_key_jmespath "" JMESPath expression to extract the idempotency key from the event record using built-in functions
payload_validation_jmespath "" JMESPath expression to validate whether certain parameters have changed in the event while the event payload
raise_on_no_idempotency_key False Raise exception if no idempotency key was found in the request
expires_after_seconds 3600 The number of seconds to wait before a record is expired
use_local_cache False Whether to locally cache idempotency results
local_cache_max_items 256 Max number of items to store in local cache
hash_function md5 Function to use for calculating hashes, as provided by hashlib in the standard library.

Handling concurrent executions with the same payload

This utility will raise an IdempotencyAlreadyInProgressError exception if you receive multiple invocations with the same payload while the first invocation hasn't completed yet.

Info

If you receive IdempotencyAlreadyInProgressError, you can safely retry the operation.

This is a locking mechanism for correctness. Since we don't know the result from the first invocation yet, we can't safely allow another concurrent execution.

Using in-memory cache

By default, in-memory local caching is disabled, since we don't know how much memory you consume per invocation compared to the maximum configured in your Lambda function.

Note: This in-memory cache is local to each Lambda execution environment

This means it will be effective in cases where your function's concurrency is low in comparison to the number of "retry" invocations with the same payload, because cache might be empty.

You can enable in-memory caching with the use_local_cache parameter:

Caching idempotent transactions in-memory to prevent multiple calls to storage
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from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")
config =  IdempotencyConfig(
    event_key_jmespath="body",
    use_local_cache=True,
)

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
    ...

When enabled, the default is to cache a maximum of 256 records in each Lambda execution environment - You can change it with the local_cache_max_items parameter.

Expiring idempotency records

Note

By default, we expire idempotency records after an hour (3600 seconds).

In most cases, it is not desirable to store the idempotency records forever. Rather, you want to guarantee that the same payload won't be executed within a period of time.

You can change this window with the expires_after_seconds parameter:

Adjusting cache TTL
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from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")
config =  IdempotencyConfig(
    event_key_jmespath="body",
    expires_after_seconds=5*60,  # 5 minutes
)

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
    ...

This will mark any records older than 5 minutes as expired, and the lambda handler will be executed as normal if it is invoked with a matching payload.

Note: DynamoDB time-to-live field

This utility uses expiration as the TTL field in DynamoDB, as demonstrated in the SAM example earlier.

Payload validation

Question: What if your function is invoked with the same payload except some outer parameters have changed?

Example: A payment transaction for a given productID was requested twice for the same customer, however the amount to be paid has changed in the second transaction.

By default, we will return the same result as it returned before, however in this instance it may be misleading; we provide a fail fast payload validation to address this edge case.

With payload_validation_jmespath, you can provide an additional JMESPath expression to specify which part of the event body should be validated against previous idempotent invocations

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from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

config = IdempotencyConfig(
    event_key_jmespath="[userDetail, productId]",
    payload_validation_jmespath="amount"
)
persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
    # Creating a subscription payment is a side
    # effect of calling this function!
    payment = create_subscription_payment(
      user=event['userDetail']['username'],
      product=event['product_id'],
      amount=event['amount']
    )
    ...
    return {
        "message": "success",
        "statusCode": 200,
        "payment_id": payment.id,
        "amount": payment.amount
    }

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{
    "userDetail": {
        "username": "User1",
        "user_email": "user@example.com"
    },
    "productId": 1500,
    "charge_type": "subscription",
    "amount": 500
}

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{
    "userDetail": {
        "username": "User1",
        "user_email": "user@example.com"
    },
    "productId": 1500,
    "charge_type": "subscription",
    "amount": 1
}

In this example, the userDetail and productId keys are used as the payload to generate the idempotency key, as per event_key_jmespath parameter.

Note

If we try to send the same request but with a different amount, we will raise IdempotencyValidationError.

Without payload validation, we would have returned the same result as we did for the initial request. Since we're also returning an amount in the response, this could be quite confusing for the client.

By using payload_validation_jmespath="amount", we prevent this potentially confusing behavior and instead raise an Exception.

Making idempotency key required

If you want to enforce that an idempotency key is required, you can set raise_on_no_idempotency_key to True.

This means that we will raise IdempotencyKeyError if the evaluation of event_key_jmespath is None.

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from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

# Requires "user"."uid" and "order_id" to be present
config = IdempotencyConfig(
    event_key_jmespath="[user.uid, order_id]",
    raise_on_no_idempotency_key=True,
)

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
    pass

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{
    "user": {
        "uid": "BB0D045C-8878-40C8-889E-38B3CB0A61B1",
        "name": "Foo"
    },
    "order_id": 10000
}

Notice that order_id is now accidentally within user key

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{
    "user": {
        "uid": "DE0D000E-1234-10D1-991E-EAC1DD1D52C8",
        "name": "Joe Bloggs",
        "order_id": 10000
    },
}

Customizing boto configuration

The boto_config and boto3_session parameters enable you to pass in a custom botocore config object or a custom boto3 session when constructing the persistence store.

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import boto3
from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

boto3_session = boto3.session.Session()
persistence_layer = DynamoDBPersistenceLayer(
    table_name="IdempotencyTable",
    boto3_session=boto3_session
)

config = IdempotencyConfig(event_key_jmespath="body")

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
   ...

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from botocore.config import Config
from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

config = IdempotencyConfig(event_key_jmespath="body")
boto_config = Config()
persistence_layer = DynamoDBPersistenceLayer(
    table_name="IdempotencyTable",
    boto_config=boto_config
)

@idempotent(config=config, persistence_store=persistence_layer)
def handler(event, context):
   ...

Using a DynamoDB table with a composite primary key

When using a composite primary key table (hash+range key), use sort_key_attr parameter when initializing your persistence layer.

With this setting, we will save the idempotency key in the sort key instead of the primary key. By default, the primary key will now be set to idempotency#{LAMBDA_FUNCTION_NAME}.

You can optionally set a static value for the partition key using the static_pk_value parameter.

Reusing a DynamoDB table that uses a composite primary key
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from aws_lambda_powertools.utilities.idempotency import DynamoDBPersistenceLayer, idempotent

persistence_layer = DynamoDBPersistenceLayer(
    table_name="IdempotencyTable",
    sort_key_attr='sort_key')


@idempotent(persistence_store=persistence_layer)
def handler(event, context):
    return {"message": "success": "id": event['body']['id]}

The example function above would cause data to be stored in DynamoDB like this:

id sort_key expiration status data
idempotency#MyLambdaFunction 1e956ef7da78d0cb890be999aecc0c9e 1636549553 COMPLETED {"id": 12391, "message": "success"}
idempotency#MyLambdaFunction 2b2cdb5f86361e97b4383087c1ffdf27 1636549571 COMPLETED {"id": 527212, "message": "success"}
idempotency#MyLambdaFunction f091d2527ad1c78f05d54cc3f363be80 1636549585 IN_PROGRESS

Bring your own persistent store

This utility provides an abstract base class (ABC), so that you can implement your choice of persistent storage layer.

You can inherit from the BasePersistenceLayer class and implement the abstract methods _get_record, _put_record, _update_record and _delete_record.

Excerpt DynamoDB Persistence Layer implementation for reference
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import datetime
import logging
from typing import Any, Dict, Optional

import boto3
from botocore.config import Config

from aws_lambda_powertools.utilities.idempotency import BasePersistenceLayer
from aws_lambda_powertools.utilities.idempotency.exceptions import (
    IdempotencyItemAlreadyExistsError,
    IdempotencyItemNotFoundError,
)
from aws_lambda_powertools.utilities.idempotency.persistence.base import DataRecord

logger = logging.getLogger(__name__)


class DynamoDBPersistenceLayer(BasePersistenceLayer):
    def __init__(
        self,
        table_name: str,
        key_attr: str = "id",
        expiry_attr: str = "expiration",
        status_attr: str = "status",
        data_attr: str = "data",
        validation_key_attr: str = "validation",
        boto_config: Optional[Config] = None,
        boto3_session: Optional[boto3.session.Session] = None,
    ):
        boto_config = boto_config or Config()
        session = boto3_session or boto3.session.Session()
        self._ddb_resource = session.resource("dynamodb", config=boto_config)
        self.table_name = table_name
        self.table = self._ddb_resource.Table(self.table_name)
        self.key_attr = key_attr
        self.expiry_attr = expiry_attr
        self.status_attr = status_attr
        self.data_attr = data_attr
        self.validation_key_attr = validation_key_attr
        super(DynamoDBPersistenceLayer, self).__init__()

    def _item_to_data_record(self, item: Dict[str, Any]) -> DataRecord:
        """
        Translate raw item records from DynamoDB to DataRecord

        Parameters
        ----------
        item: Dict[str, Union[str, int]]
            Item format from dynamodb response

        Returns
        -------
        DataRecord
            representation of item

        """
        return DataRecord(
            idempotency_key=item[self.key_attr],
            status=item[self.status_attr],
            expiry_timestamp=item[self.expiry_attr],
            response_data=item.get(self.data_attr),
            payload_hash=item.get(self.validation_key_attr),
        )

    def _get_record(self, idempotency_key) -> DataRecord:
        response = self.table.get_item(Key={self.key_attr: idempotency_key}, ConsistentRead=True)

        try:
            item = response["Item"]
        except KeyError:
            raise IdempotencyItemNotFoundError
        return self._item_to_data_record(item)

    def _put_record(self, data_record: DataRecord) -> None:
        item = {
            self.key_attr: data_record.idempotency_key,
            self.expiry_attr: data_record.expiry_timestamp,
            self.status_attr: data_record.status,
        }

        if self.payload_validation_enabled:
            item[self.validation_key_attr] = data_record.payload_hash

        now = datetime.datetime.now()
        try:
            logger.debug(f"Putting record for idempotency key: {data_record.idempotency_key}")
            self.table.put_item(
                Item=item,
                ConditionExpression=f"attribute_not_exists({self.key_attr}) OR {self.expiry_attr} < :now",
                ExpressionAttributeValues={":now": int(now.timestamp())},
            )
        except self._ddb_resource.meta.client.exceptions.ConditionalCheckFailedException:
            logger.debug(f"Failed to put record for already existing idempotency key: {data_record.idempotency_key}")
            raise IdempotencyItemAlreadyExistsError

    def _update_record(self, data_record: DataRecord):
        logger.debug(f"Updating record for idempotency key: {data_record.idempotency_key}")
        update_expression = "SET #response_data = :response_data, #expiry = :expiry, #status = :status"
        expression_attr_values = {
            ":expiry": data_record.expiry_timestamp,
            ":response_data": data_record.response_data,
            ":status": data_record.status,
        }
        expression_attr_names = {
            "#response_data": self.data_attr,
            "#expiry": self.expiry_attr,
            "#status": self.status_attr,
        }

        if self.payload_validation_enabled:
            update_expression += ", #validation_key = :validation_key"
            expression_attr_values[":validation_key"] = data_record.payload_hash
            expression_attr_names["#validation_key"] = self.validation_key_attr

        kwargs = {
            "Key": {self.key_attr: data_record.idempotency_key},
            "UpdateExpression": update_expression,
            "ExpressionAttributeValues": expression_attr_values,
            "ExpressionAttributeNames": expression_attr_names,
        }

        self.table.update_item(**kwargs)

    def _delete_record(self, data_record: DataRecord) -> None:
        logger.debug(f"Deleting record for idempotency key: {data_record.idempotency_key}")
        self.table.delete_item(Key={self.key_attr: data_record.idempotency_key},)
Danger

Pay attention to the documentation for each - you may need to perform additional checks inside these methods to ensure the idempotency guarantees remain intact.

For example, the _put_record method needs to raise an exception if a non-expired record already exists in the data store with a matching key.

Compatibility with other utilities

Batch

See Batch integration above.

Validation utility

The idempotency utility can be used with the validator decorator. Ensure that idempotency is the innermost decorator.

Warning

If you use an envelope with the validator, the event received by the idempotency utility will be the unwrapped event - not the "raw" event Lambda was invoked with.

Make sure to account for this behaviour, if you set the event_key_jmespath.

Using Idempotency with JSONSchema Validation utility
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from aws_lambda_powertools.utilities.validation import validator, envelopes
from aws_lambda_powertools.utilities.idempotency import (
    IdempotencyConfig, DynamoDBPersistenceLayer, idempotent
)

config = IdempotencyConfig(event_key_jmespath="[message, username]")
persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

@validator(envelope=envelopes.API_GATEWAY_HTTP)
@idempotent(config=config, persistence_store=persistence_layer)
def lambda_handler(event, context):
    cause_some_side_effects(event['username')
    return {"message": event['message'], "statusCode": 200}
Tip: JMESPath Powertools functions are also available

Built-in functions known in the validation utility like powertools_json, powertools_base64, powertools_base64_gzip are also available to use in this utility.

Testing your code

The idempotency utility provides several routes to test your code.

Disabling the idempotency utility

When testing your code, you may wish to disable the idempotency logic altogether and focus on testing your business logic. To do this, you can set the environment variable POWERTOOLS_IDEMPOTENCY_DISABLED with a truthy value. If you prefer setting this for specific tests, and are using Pytest, you can use monkeypatch fixture:

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from dataclasses import dataclass

import pytest

import app


@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"

        def get_remaining_time_in_millis(self) -> int:
          return 5

    return LambdaContext()


def test_idempotent_lambda_handler(monkeypatch, lambda_context):
    # Set POWERTOOLS_IDEMPOTENCY_DISABLED before calling decorated functions
    monkeypatch.setenv("POWERTOOLS_IDEMPOTENCY_DISABLED", 1)

    result = handler({}, lambda_context)
    ...

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from aws_lambda_powertools.utilities.idempotency import (
DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="idempotency")

@idempotent(persistence_store=persistence_layer)
def handler(event, context):
    print('expensive operation')
    return {
        "payment_id": 12345,
        "message": "success",
        "statusCode": 200,
    }

Testing with DynamoDB Local

To test with DynamoDB Local, you can replace the Table resource used by the persistence layer with one you create inside your tests. This allows you to set the endpoint_url.

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from dataclasses import dataclass

import boto3
import pytest

import app


@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"

        def get_remaining_time_in_millis(self) -> int:
          return 5

    return LambdaContext()

def test_idempotent_lambda(lambda_context):
    # Create our own Table resource using the endpoint for our DynamoDB Local instance
    resource = boto3.resource("dynamodb", endpoint_url='http://localhost:8000')
    table = resource.Table(app.persistence_layer.table_name)
    app.persistence_layer.table = table

    result = app.handler({'testkey': 'testvalue'}, lambda_context)
    assert result['payment_id'] == 12345

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from aws_lambda_powertools.utilities.idempotency import (
DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="idempotency")

@idempotent(persistence_store=persistence_layer)
def handler(event, context):
    print('expensive operation')
    return {
        "payment_id": 12345,
        "message": "success",
        "statusCode": 200,
    }

How do I mock all DynamoDB I/O operations

The idempotency utility lazily creates the dynamodb Table which it uses to access DynamoDB. This means it is possible to pass a mocked Table resource, or stub various methods.

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from dataclasses import dataclass
from unittest.mock import MagicMock

import boto3
import pytest

import app


@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"

        def get_remaining_time_in_millis(self) -> int:
          return 5

    return LambdaContext()


def test_idempotent_lambda(lambda_context):
    table = MagicMock()
    app.persistence_layer.table = table
    result = app.handler({'testkey': 'testvalue'}, lambda_context)
    table.put_item.assert_called()
    ...

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from aws_lambda_powertools.utilities.idempotency import (
DynamoDBPersistenceLayer, idempotent
)

persistence_layer = DynamoDBPersistenceLayer(table_name="idempotency")

@idempotent(persistence_store=persistence_layer)
def handler(event, context):
    print('expensive operation')
    return {
        "payment_id": 12345,
        "message": "success",
        "statusCode": 200,
    }

Extra resources

If you're interested in a deep dive on how Amazon uses idempotency when building our APIs, check out this article.

Last update: 2023-02-09