Idempotency

The idempotency utility allows you to retry operations within a time window with the same input, producing the same output.

Key features

• Produces the previous successful result when a function is called repeatedly with the same idempotency key
• Choose your idempotency key from one or more fields, or entire payload
• Safeguard concurrent requests, timeouts, missing idempotency keys, and payload tampering
• Support for Amazon DynamoDB, Valkey, Redis OSS, or any Redis-compatible cache as the persistence layer

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.

Idempotency key By default, this is a combination of (a) Lambda function name, (b) fully qualified name of your function, and (c) a hash of the entire payload or part(s) of the payload you specify. However, you can customize the key generation by using (a) a custom prefix name, while still incorporating (c) a hash of the entire payload or part(s) of the payload you specify.

Idempotent request is an operation with the same input previously processed that is not expired in your persistent storage or in-memory cache.

Persistence layer is a storage we use to create, read, expire, and delete idempotency records.

Idempotency record is the data representation of an idempotent request saved in the persistent layer and in its various status. We use it to coordinate whether (a) a request is idempotent, (b) it's not expired, (c) JSON response to return, and more.

classDiagram direction LR class IdempotencyRecord { idempotency_key str status Status expiry_timestamp int in_progress_expiry_timestamp int response_data str~JSON~ payload_hash str } class Status { <<Enumeration>> INPROGRESS COMPLETE EXPIRED internal_only } IdempotencyRecord -- Status

Idempotency record representation

Getting started

We use Amazon DynamoDB as the default persistence layer in the documentation. If you prefer Redis, you can learn more from this section.

IAM Permissions

When using Amazon DynamoDB as the persistence layer, you will need the following IAM permissions:

IAM Permission	Operation
dynamodb:GetItem	Retrieve idempotent record (strong consistency)
dynamodb:PutItem	New idempotent records, replace expired idempotent records
dynamodb:UpdateItem	Complete idempotency transaction, and/or update idempotent records state
dynamodb:DeleteItem	Delete idempotent records for unsuccessful idempotency transactions

First time setting it up?

We provide Infrastrucure as Code examples with AWS Serverless Application Model (SAM), AWS Cloud Development Kit (CDK), and Terraform with the required permissions.

Required resources

To start, you'll need:

• Persistent storage

  ---

  Amazon DynamoDB or Valkey/Redis OSS/Redis compatible

• :simple-awslambda:{ .lg .middle } AWS Lambda function

  ---

  With permissions to use your persistent storage

Primary key for any persistence storage

We combine the Lambda function name and the fully qualified name for classes/functions to prevent accidental reuse for similar code sharing input/output.

Primary key sample: {lambda_fn_name}.{module_name}.{fn_qualified_name}#{idempotency_key_hash}

DynamoDB table

Unless you're looking to use an existing table or customize each attribute, you only need the following:

Configuration	Value	Notes
Partition key	id
TTL attribute name	expiration	Using AWS Console? This is configurable after table creation

You can use a single DynamoDB table for all functions annotated with Idempotency.

DynamoDB IaC examples

AWS Serverless Application Model (SAM) exampleAWS Cloud Development Kit (CDK)Terraform

Transform: AWS::Serverless-2016-10-31 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: python3.12  Handler: app.py  Policies:  - Statement:  - Sid: AllowDynamodbReadWrite  Effect: Allow  Action:  - dynamodb:PutItem  - dynamodb:GetItem  - dynamodb:UpdateItem  - dynamodb:DeleteItem  Resource: !GetAtt IdempotencyTable.Arn  Environment:  Variables:  IDEMPOTENCY_TABLE: !Ref IdempotencyTable 

from aws_cdk import RemovalPolicy from aws_cdk import aws_dynamodb as dynamodb from aws_cdk import aws_iam as iam from constructs import Construct class IdempotencyConstruct(Construct): def __init__(self, scope: Construct, name: str, lambda_role: iam.Role) -> None: super().__init__(scope, name)  self.idempotency_table = dynamodb.Table(  self, "IdempotencyTable",  partition_key=dynamodb.Attribute(name="id", type=dynamodb.AttributeType.STRING),  billing_mode=dynamodb.BillingMode.PAY_PER_REQUEST, removal_policy=RemovalPolicy.DESTROY,  time_to_live_attribute="expiration",  point_in_time_recovery=True, )  self.idempotency_table.grant(  lambda_role,  "dynamodb:PutItem",  "dynamodb:GetItem", "dynamodb:UpdateItem", "dynamodb:DeleteItem", ) 

terraform {  required_providers {  aws = {  source = "hashicorp/aws"  version = "~> 4.0"  }  } } provider "aws" {  region = "us-east-1" # Replace with your desired AWS region } resource "aws_dynamodb_table" "IdempotencyTable" {  name = "IdempotencyTable"  billing_mode = "PAY_PER_REQUEST"  hash_key = "id"  attribute {  name = "id"  type = "S"  }  ttl {  attribute_name = "expiration"  enabled = true  } }  resource "aws_lambda_function" "IdempotencyFunction" {  function_name = "IdempotencyFunction"  role = aws_iam_role.IdempotencyFunctionRole.arn  runtime = "python3.12"  handler = "app.lambda_handler"  filename = "lambda.zip" } resource "aws_iam_role" "IdempotencyFunctionRole" {  name = "IdempotencyFunctionRole"  assume_role_policy = jsonencode({  Version = "2012-10-17"  Statement = [  {  Sid = ""  Effect = "Allow"  Principal = {  Service = "lambda.amazonaws.com"  }  Action = "sts:AssumeRole"  },  ]  }) } resource "aws_iam_policy" "LambdaDynamoDBPolicy" {  name = "LambdaDynamoDBPolicy"  description = "IAM policy for Lambda function to access DynamoDB"  policy = jsonencode({  Version = "2012-10-17"  Statement = [  {  Sid = "AllowDynamodbReadWrite"  Effect = "Allow"  Action = [  "dynamodb:PutItem",  "dynamodb:GetItem",  "dynamodb:UpdateItem",  "dynamodb:DeleteItem",  ]  Resource = aws_dynamodb_table.IdempotencyTable.arn  },  ]  }) } resource "aws_iam_role_policy_attachment" "IdempotencyFunctionRoleAttachment" {  role = aws_iam_role.IdempotencyFunctionRole.name  policy_arn = aws_iam_policy.LambdaDynamoDBPolicy.arn } 

`

Limitations

• DynamoDB restricts item sizes to 400KB. This means that if your annotated function's response must be smaller than 400KB, otherwise your function will fail. Consider Redis as an alternative.

• Expect 2 WCU per non-idempotent call. During the first invocation, we use PutItem for locking and UpdateItem for completion. Consider reviewing DynamoDB pricing documentation to estimate cost.

• Old boto3 versions can increase costs. For cost optimization, we use a conditional PutItem to always lock a new idempotency record. If locking fails, it means we already have an idempotency record saving us an additional GetItem call. However, this is only supported in boto3 1.26.194 and higher (June 30th 2023).

Cache database

We recommend starting with a managed cache service, such as Amazon ElastiCache for Valkey and for Redis OSS or Amazon MemoryDB.

In both services, you'll need to configure VPC access to your AWS Lambda.

Cache configuration

AWS CloudFormation example

Prefer AWS Console/CLI?

Follow the official tutorials for Amazon ElastiCache for Redis or Amazon MemoryDB for Redis

AWSTemplateFormatVersion: "2010-09-09" Transform: AWS::Serverless-2016-10-31 Resources:  CacheServerlessIdempotency:  Type: AWS::ElastiCache::ServerlessCache  Properties:  Engine: redis  ServerlessCacheName: redis-cache  SecurityGroupIds: # (1)!  - sg-07d998809154f9d88  SubnetIds:  - subnet-{your_subnet_id_1}  - subnet-{your_subnet_id_2}  IdempotencyFunction:  Type: AWS::Serverless::Function  Properties:  Runtime: python3.13  Handler: app.py  VpcConfig: # (1)!  SecurityGroupIds:  - sg-07d998809154f9d88  SubnetIds:  - subnet-{your_subnet_id_1}  - subnet-{your_subnet_id_2}  Environment:  Variables:  POWERTOOLS_SERVICE_NAME: sample  CACHE_HOST: !GetAtt CacheServerlessIdempotency.Endpoint.Address  CACHE_PORT: !GetAtt CacheServerlessIdempotency.Endpoint.Port 

1. Replace the Security Group ID and Subnet ID to match your VPC settings.
2. Replace the Security Group ID and Subnet ID to match your VPC settings.

Once setup, you can find a quick start and advanced examples for Cache in the persistent layers section.

Idempotent decorator

For simple use cases, you can use the idempotent decorator on your Lambda handler function.

It will treat the entire event as an idempotency key. That is, the same event will return the previously stored result within a configurable time window (1 hour, by default).

Idempotent decoratorSample event

You can also choose one or more fields as an idempotency key.

import os from dataclasses import dataclass, field from uuid import uuid4 from aws_lambda_powertools.utilities.idempotency import (  DynamoDBPersistenceLayer,  idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table)  @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

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

Idempotent_function decorator

For full flexibility, you can use the idempotent_function decorator for any synchronous Python function.

When using this decorator, you must call your decorated function using keyword arguments.

You can use data_keyword_argument to tell us the argument to extract an idempotency key. We support JSON serializable data, Dataclasses, Pydantic Models, and Event Source Data Classes

Using DataclassesUsing Pydantic

import os from dataclasses import dataclass from aws_lambda_powertools.utilities.idempotency import (  DynamoDBPersistenceLayer,  IdempotencyConfig,  idempotent_function, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section @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): # (1)! return f"processed order {order.order_id}" def lambda_handler(event: dict, context: LambdaContext): # see Lambda timeouts section config.register_lambda_context(context) # (2)! order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

1. Notice how data_keyword_argument matches the name of the parameter.
   
   This allows us to extract one or all fields as idempotency key.
2. Different from idempotent decorator, we must explicitly register the Lambda context to protect against timeouts.

import os from aws_lambda_powertools.utilities.idempotency import (  DynamoDBPersistenceLayer,  IdempotencyConfig,  idempotent_function, ) from aws_lambda_powertools.utilities.parser import BaseModel from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section 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: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

Output serialization

By default, idempotent_function serializes, stores, and returns your annotated function's result as a JSON object. You can change this behavior using output_serializer parameter.

The output serializer supports any JSON serializable data, Python Dataclasses and Pydantic Models.

Info

When using the output_serializer parameter, the data will continue to be stored in your persistent storage as a JSON string.

Function returns must be annotated with a single type, optionally wrapped in Optional or Union with None.

PydanticDataclassesAny type

Use PydanticSerializer to automatically serialize what's retrieved from the persistent storage based on the return type annotated.

Inferring via the return typeExplicit model type

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.serialization.pydantic import PydanticSerializer from aws_lambda_powertools.utilities.parser import BaseModel from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section class OrderItem(BaseModel): sku: str description: str class Order(BaseModel): item: OrderItem order_id: int class OrderOutput(BaseModel):  order_id: int @idempotent_function( data_keyword_argument="order", config=config, persistence_store=dynamodb,  output_serializer=PydanticSerializer, ) # order output is inferred from return type def process_order(order: Order) -> OrderOutput: # (1)!  return OrderOutput(order_id=order.order_id) def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

1. We'll use OrderOutput to instantiate a new object using the data retrieved from persistent storage as input.
   
   This ensures the return of the function is not impacted when @idempotent_function is used.

Alternatively, you can provide an explicit model as an input to PydanticSerializer.

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.serialization.pydantic import PydanticSerializer from aws_lambda_powertools.utilities.parser import BaseModel from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section class OrderItem(BaseModel): sku: str description: str class Order(BaseModel): item: OrderItem order_id: int class OrderOutput(BaseModel):  order_id: int @idempotent_function( data_keyword_argument="order", config=config, persistence_store=dynamodb,  output_serializer=PydanticSerializer(model=OrderOutput), ) def process_order(order: Order): return OrderOutput(order_id=order.order_id) def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

Use DataclassSerializer to automatically serialize what's retrieved from the persistent storage based on the return type annotated.

Inferring via the return typeExplicit model type

import os from dataclasses import dataclass from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.serialization.dataclass import DataclassSerializer from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section @dataclass class OrderItem: sku: str description: str @dataclass class Order: item: OrderItem order_id: int @dataclass class OrderOutput:  order_id: int @idempotent_function( data_keyword_argument="order", config=config, persistence_store=dynamodb,  output_serializer=DataclassSerializer, ) # order output is inferred from return type def process_order(order: Order) -> OrderOutput: # (1)!  return OrderOutput(order_id=order.order_id) def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

1. We'll use OrderOutput to instantiate a new object using the data retrieved from persistent storage as input.
   
   This ensures the return of the function is not impacted when @idempotent_function is used.

Alternatively, you can provide an explicit model as an input to DataclassSerializer.

import os from dataclasses import dataclass from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.serialization.dataclass import DataclassSerializer from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section @dataclass class OrderItem: sku: str description: str @dataclass class Order: item: OrderItem order_id: int @dataclass class OrderOutput:  order_id: int @idempotent_function( data_keyword_argument="order", config=config, persistence_store=dynamodb,  output_serializer=DataclassSerializer(model=OrderOutput), ) def process_order(order: Order):  return OrderOutput(order_id=order.order_id) def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

Use CustomDictSerializer to have full control over the serialization process for any type. It expects two functions:

• to_dict. Function to convert any type to a JSON serializable dictionary before it saves into the persistent storage.
• from_dict. Function to convert from a dictionary retrieved from persistent storage and serialize in its original form.

import os from typing import Dict, Type from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.serialization.custom_dict import CustomDictSerializer from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section class OrderItem: def __init__(self, sku: str, description: str): self.sku = sku self.description = description class Order: def __init__(self, item: OrderItem, order_id: int): self.item = item self.order_id = order_id class OrderOutput: def __init__(self, order_id: int): self.order_id = order_id def order_to_dict(x: Type[OrderOutput]) -> Dict: # (1)!  return dict(x.__dict__) def dict_to_order(x: Dict) -> OrderOutput: # (2)!  return OrderOutput(**x) order_output_serializer = CustomDictSerializer( # (3)!  to_dict=order_to_dict, from_dict=dict_to_order, ) @idempotent_function( data_keyword_argument="order", config=config, persistence_store=dynamodb,  output_serializer=order_output_serializer, ) def process_order(order: Order) -> OrderOutput:  return OrderOutput(order_id=order.order_id) def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work  process_order(order=order) 

1. This function does the following
   
   1. Receives the return from process_order
   2. Converts to dictionary before it can be saved into the persistent storage.
2. This function does the following
   
   1. Receives the dictionary saved into the persistent storage
   1 Serializes to OrderOutput before @idempotent returns back to the caller.
3. This serializer receives both functions so it knows who to call when to serialize to and from dictionary.

Using in-memory cache

In-memory cache is local to each Lambda execution environment.

You can enable caching with the use_local_cache parameter in IdempotencyConfig. When enabled, you can adjust cache capacity (256) with local_cache_max_items.

By default, caching is disabled since we don't know how big your response could be in relation to your configured memory size.

Enabling cacheSample event

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig( event_key_jmespath="powertools_json(body)", # by default, it holds 256 items in a Least-Recently-Used (LRU) manner  use_local_cache=True, # (1)! ) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event, context: LambdaContext): return event 

1. You can adjust cache capacity with local_cache_max_items parameter.

{  "body": "{\"user_id\":\"xyz\",\"product_id\":\"123456789\"}" } 

Choosing a payload subset

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 event_key_jmespath parameter in IdempotencyConfig to select one or more payload parts as your idempotency key.

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

We want to use user_id and product_id fields as our idempotency key. If we were to treat the entire request as our idempotency key, a simple HTTP header change would cause our function to run again.

Deserializing JSON strings in payloads for increased accuracy.

The payload extracted by the event_key_jmespath is treated as a string by default. This means there could be 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.

Payment functionSample event

import json import os from dataclasses import dataclass, field from uuid import uuid4 from aws_lambda_powertools.utilities.idempotency import (  DynamoDBPersistenceLayer,  IdempotencyConfig,  idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) # Deserialize JSON string under the "body" key # then extract "user" and "product_id" data config = IdempotencyConfig(event_key_jmespath='powertools_json(body).["user_id", "product_id"]')  @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): try: payment_info: str = event.get("body", "") payment: Payment = create_subscription_payment(json.loads(payment_info)) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

{  "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_id\":\"xyz\",\"product_id\":\"123456789\"}",  "isBase64Encoded": false } 

Adjusting expiration window

By default, we expire idempotency records after an hour (3600 seconds). After that, a transaction with the same payload will not be considered idempotent.

You can change this expiration window with the expires_after_seconds parameter. There is no limit on how long this expiration window can be set to.

Adjusting expiration windowSample event

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig( event_key_jmespath="body",  expires_after_seconds=24 * 60 * 60, # 24 hours ) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event, context: LambdaContext): return event 

{  "body": "{\"user_id\":\"xyz\",\"product_id\":\"123456789\"}" } 

Idempotency record expiration vs DynamoDB time-to-live (TTL)

DynamoDB TTL is a feature to remove items after a certain period of time, it may occur within 48 hours of expiration.

We don't rely on DynamoDB or any persistence storage layer to determine whether a record is expired to avoid eventual inconsistency states.

Instead, Idempotency records saved in the storage layer contain timestamps that can be verified upon retrieval and double checked within Idempotency feature.

Why?

A record might still be valid (COMPLETE) when we retrieved, but in some rare cases it might expire a second later. A record could also be cached in memory. You might also want to have idempotent transactions that should expire in seconds.

Customizing the Idempotency key generation

Warning: Changing the idempotency key generation will invalidate existing idempotency records

Use key_prefix parameter in the @idempotent or @idempotent_function decorators to define a custom prefix for your Idempotency Key. This allows you to decouple idempotency key name from function names. It can be useful during application refactoring, for example.

Using a custom prefix in Lambda HandlerUsing a custom prefix in standalone functions

import os from dataclasses import dataclass, field from uuid import uuid4 from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(persistence_store=persistence_layer, key_prefix="my_custom_prefix") # (1)! def lambda_handler(event: dict, context: LambdaContext): try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

1. The Idempotency record will be something like my_custom_prefix#c4ca4238a0b923820dcc509a6f75849b

import os from dataclasses import dataclass from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="order_id") # see Choosing a payload subset section @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,  key_prefix="my_custom_prefix", # (1)! ) def process_order(order: Order): return f"processed order {order.order_id}" def lambda_handler(event: dict, context: LambdaContext): # see Lambda timeouts section config.register_lambda_context(context) order_item = OrderItem(sku="fake", description="sample") order = Order(item=order_item, order_id=1) # `order` parameter must be called as a keyword argument to work process_order(order=order) 

1. The Idempotency record will be something like my_custom_prefix#c4ca4238a0b923820dcc509a6f75849b

Lambda timeouts

You can skip this section if you are using the @idempotent decorator

By default, we protect against concurrent executions with the same payload using a locking mechanism. However, if your Lambda function times out before completing the first invocation it will only accept the same request when the idempotency record expire.

To prevent extended failures, use register_lambda_context function from your idempotency config to calculate and include the remaining invocation time in your idempotency record.

import os from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) 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: dict, context: LambdaContext):  config.register_lambda_context(context)  return record_handler(event) 

Mechanics

If a second invocation happens after this timestamp, and the record is marked as INPROGRESS, we will run the invocation again as if it was in the EXPIRED state.

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

Handling exceptions

There are two failure modes that can cause new invocations to execute your code again despite having the same payload:

• Unhandled exception. We catch them to delete the idempotency record to prevent inconsistencies, then propagate them.
• Persistent layer errors. We raise IdempotencyPersistenceLayerError for any persistence layer errors e.g., remove idempotency record.

If an exception is handled or raised outside your decorated function, then idempotency will be maintained.

import os import requests from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.exceptions import IdempotencyPersistenceLayerError from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig() @idempotent_function(data_keyword_argument="data", config=config, persistence_store=persistence_layer) def call_external_service(data: dict):  # Any exception raised will lead to idempotency record to be deleted  result: requests.Response = requests.post( "https://jsonplaceholder.typicode.com/comments/", json=data, ) return result.json() def lambda_handler(event: dict, context: LambdaContext): try: call_external_service(data=event)  except IdempotencyPersistenceLayerError as e:  # No idempotency, but you can decide to error differently. raise RuntimeError(f"Oops, can't talk to persistence layer. Permissions? error: {e}") # This exception will not impact the idempotency of 'call_external_service' # because it happens in isolation, or outside their scope.  raise SyntaxError("Oops, this shouldn't be here.") 

Persistence layers

DynamoDBPersistenceLayer

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

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(  table_name=table,  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", )  @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext) -> dict: return event 

Using a composite primary key

Use sort_key_attr parameter when your table is configured with a composite primary key (hash+range key).

When enabled, we will save the idempotency key in the sort key instead. 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 keySample Event

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table, sort_key_attr="sort_key")  @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext) -> dict: user_id: str = event.get("body", "")["user_id"] return {"message": "success", "user_id": user_id} 

{  "body": "{\"user_id\":\"xyz\",\"product_id\":\"123456789\"}" } 

Click to expand and learn how table items would look like

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

DynamoDB attributes

You can customize the attribute names during initialization:

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.

CachePersistenceLayer

The CachePersistenceLayer enables you to use Valkey, Redis OSS, or any Redis-compatible cache as the persistence layer for idempotency state.

We recommend using valkey-glide for Valkey or redis for Redis. However, any Redis OSS-compatible client should work.

For simple setups, initialize CachePersistenceLayer with your Cache endpoint and port to connect. Note that for security, we enforce SSL connections by default; to disable it, set ssl=False.

Cache quick startUsing an existing Valkey Glide clientUsing an existing Redis clientSample event

import os from dataclasses import dataclass, field from uuid import uuid4 from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.cache import (  CachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext redis_endpoint = os.getenv("CACHE_CLUSTER_ENDPOINT", "localhost") persistence_layer = CachePersistenceLayer(host=redis_endpoint, port=6379)  @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") from exc def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

import os from dataclasses import dataclass, field from uuid import uuid4 from glide import GlideClient, GlideClientConfiguration, NodeAddress  from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.cache import (  CachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext cache_endpoint = os.getenv("CACHE_CLUSTER_ENDPOINT", "localhost") client_config = GlideClientConfiguration(  addresses=[  NodeAddress(  host="localhost",  port=6379,  ),  ], ) client = GlideClient.create(config=client_config)  persistence_layer = CachePersistenceLayer(client=client) # type: ignore[arg-type] @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ...   @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

import os from dataclasses import dataclass, field from uuid import uuid4 from redis import Redis  from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.cache import (  CachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext cache_endpoint = os.getenv("CACHE_CLUSTER_ENDPOINT", "localhost") client = Redis(  host=cache_endpoint, port=6379, socket_connect_timeout=5, socket_timeout=5, max_connections=1000, ) persistence_layer = CachePersistenceLayer(client=client)  @dataclass class Payment: user_id: str product_id: str payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext):  try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

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

Cache SSL connections

We recommend using AWS Secrets Manager to store and rotate certificates safely, and the Parameters feature to fetch and cache optimally.

For advanced configurations, we recommend using an existing Valkey client for optimal compatibility like SSL certificates and timeout.

Advanced configuration using AWS SecretsAdvanced configuration with local certificates

from __future__ import annotations from typing import Any from glide import BackoffStrategy, GlideClient, GlideClientConfiguration, NodeAddress, ServerCredentials  from aws_lambda_powertools.utilities import parameters from aws_lambda_powertools.utilities.idempotency import IdempotencyConfig, idempotent from aws_lambda_powertools.utilities.idempotency.persistence.cache import (  CachePersistenceLayer, )  cache_values: dict[str, Any] = parameters.get_secret("cache_info", transform="json") # (1)!  client_config = GlideClientConfiguration(  addresses=[ NodeAddress(  host=cache_values.get("CACHE_HOST", "localhost"),  port=cache_values.get("CACHE_PORT", 6379),  ), ], credentials=ServerCredentials(  password=cache_values.get("CACHE_PASSWORD", ""),  ), request_timeout=10, use_tls=True, reconnect_strategy=BackoffStrategy(num_of_retries=10, factor=2, exponent_base=1), ) valkey_client = GlideClient.create(config=client_config) persistence_layer = CachePersistenceLayer(client=valkey_client) # type: ignore[arg-type] config = IdempotencyConfig( expires_after_seconds=2 * 60, # 2 minutes ) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event, context): return {"message": "Hello"} 

1. JSON stored:

   { "CACHE_HOST": "127.0.0.1", "CACHE_PORT": "6379", "CACHE_PASSWORD": "cache-secret" } 

from __future__ import annotations from typing import Any from redis import Redis from aws_lambda_powertools.shared.functions import abs_lambda_path from aws_lambda_powertools.utilities import parameters from aws_lambda_powertools.utilities.idempotency import IdempotencyConfig, idempotent from aws_lambda_powertools.utilities.idempotency.persistence.cache import ( CachePersistenceLayer, ) cache_values: dict[str, Any] = parameters.get_secret("cache_info", transform="json") # (1)!  redis_client = Redis( host=cache_values.get("REDIS_HOST", "localhost"), port=cache_values.get("REDIS_PORT", 6379), password=cache_values.get("REDIS_PASSWORD"), decode_responses=True, socket_timeout=10.0, ssl=True, retry_on_timeout=True,  ssl_certfile=f"{abs_lambda_path()}/certs/cache_user.crt", # (2)!  ssl_keyfile=f"{abs_lambda_path()}/certs/cache_user_private.key", # (3)!  ssl_ca_certs=f"{abs_lambda_path()}/certs/cache_ca.pem", # (4)! ) persistence_layer = CachePersistenceLayer(client=redis_client) config = IdempotencyConfig( expires_after_seconds=2 * 60, # 2 minutes ) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event, context): return {"message": "Hello"} 

1. JSON stored:

   { "CACHE_HOST": "127.0.0.1", "CACHE_PORT": "6379", "CACHE_PASSWORD": "cache-secret" } 

2. cache_user.crt file stored in the "certs" directory of your Lambda function
3. cache_user_private.key file stored in the "certs" directory of your Lambda function
4. cache_ca.pem file stored in the "certs" directory of your Lambda function

Cache attributes

You can customize the attribute names during initialization:

Parameter	Required	Default	Description
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

import os from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.redis import ( RedisCachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext redis_endpoint = os.getenv("REDIS_CLUSTER_ENDPOINT", "localhost") persistence_layer = RedisCachePersistenceLayer( host=redis_endpoint, port=6379,  in_progress_expiry_attr="in_progress_expiration",  status_attr="status",  data_attr="data",  validation_key_attr="validation", ) @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext) -> dict: return event 

Common use cases

Batch processing

You can can easily integrate with Batch using the idempotent_function decorator to handle idempotency per message/record in a given batch.

Choosing an unique batch record attribute

In this example, we choose messageId as our idempotency key 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.

Integration with Batch ProcessorSample event

import os from typing import Any, Dict  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.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.typing import LambdaContext processor = BatchProcessor(event_type=EventType.SQS) table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(event_key_jmespath="messageId")  @idempotent_function(data_keyword_argument="record", config=config, persistence_store=dynamodb) def record_handler(record: SQSRecord): return {"message": record.body} def lambda_handler(event: Dict[str, Any], context: LambdaContext):  config.register_lambda_context(context) # see Lambda timeouts section   return process_partial_response( event=event, context=context, processor=processor, record_handler=record_handler, ) 

{  "Records": [  {  "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",  "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a...",  "body": "Test message.",  "attributes": {  "ApproximateReceiveCount": "1",  "SentTimestamp": "1545082649183",  "SenderId": "replace-to-pass-gitleak",  "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"  }  ] } 

Idempotency request flow

The following sequence diagrams explain how the Idempotency feature behaves under different scenarios.

Successful request

sequenceDiagram participant Client participant Lambda participant Persistence Layer alt initial request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Client: Response sent to client else retried request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Persistence Layer-->>Lambda: Already exists in persistence layer. deactivate Persistence Layer Note over Lambda,Persistence Layer: Record status is COMPLETE and not expired Lambda-->>Client: Same response sent to client end

Idempotent successful request

Successful request with cache enabled

In-memory cache is disabled by default.

sequenceDiagram participant Client participant Lambda participant Persistence Layer alt initial request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Lambda: Save record and result in memory Lambda-->>Client: Response sent to client else retried request Client->>Lambda: Invoke (event) Lambda-->>Lambda: Get idempotency_key=hash(payload) Note over Lambda,Persistence Layer: Record status is COMPLETE and not expired Lambda-->>Client: Same response sent to client end

Idempotent successful request cached

Successful request with response_hook configured

sequenceDiagram participant Client participant Lambda participant Response hook participant Persistence Layer alt initial request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Client: Response sent to client else retried request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Persistence Layer-->>Response hook: Already exists in persistence layer. deactivate Persistence Layer Note over Response hook,Persistence Layer: Record status is COMPLETE and not expired Response hook->>Lambda: Response hook invoked Lambda-->>Client: Manipulated idempotent response sent to client end

Successful idempotent request with a response hook

Expired idempotency records

sequenceDiagram participant Client participant Lambda participant Persistence Layer alt initial request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Client: Response sent to client else retried request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Persistence Layer-->>Lambda: Already exists in persistence layer. deactivate Persistence Layer Note over Lambda,Persistence Layer: Record status is COMPLETE but expired hours ago loop Repeat initial request process Note over Lambda,Persistence Layer: 1. Set record to INPROGRESS, <br> 2. Call your function, <br> 3. Set record to COMPLETE end Lambda-->>Client: Same response sent to client end

Previous Idempotent request expired

Concurrent identical in-flight requests

sequenceDiagram participant Client participant Lambda participant Persistence Layer Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload par Second request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) Lambda--xLambda: IdempotencyAlreadyInProgressError Lambda->>Client: Error sent to client if unhandled end Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Client: Response sent to client

Concurrent identical in-flight requests

Unhandled exception

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

Lambda request timeout

sequenceDiagram participant Client participant Lambda participant Persistence Layer alt initial request Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Note right of Lambda: Time out Lambda--xLambda: Time out error Lambda-->>Client: Return error response deactivate Persistence Layer else retry after Lambda timeout elapses Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Reset in_progress_expiry attribute Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Lambda-->>Client: Response sent to client end

Idempotent request during and after Lambda timeouts

Optional idempotency key

sequenceDiagram participant Client participant Lambda participant Persistence Layer alt request with idempotency key Client->>Lambda: Invoke (event) Lambda->>Persistence Layer: Get or set idempotency_key=hash(payload) activate Persistence Layer Note over Lambda,Persistence Layer: Set record status to INPROGRESS. <br> Prevents concurrent invocations <br> with the same payload Lambda-->>Lambda: Call your function Lambda->>Persistence Layer: Update record with result deactivate Persistence Layer Persistence Layer-->>Persistence Layer: Update record Note over Lambda,Persistence Layer: Set record status to COMPLETE. <br> New invocations with the same payload <br> now return the same result Lambda-->>Client: Response sent to client else request(s) without idempotency key Client->>Lambda: Invoke (event) Note over Lambda: Idempotency key is missing Note over Persistence Layer: Skips any operation to fetch, update, and delete Lambda-->>Lambda: Call your function Lambda-->>Client: Response sent to client end

Optional idempotency key

Race condition with Cache

graph TD; A(Existing orphan record in cache)-->A1; A1[Two Lambda invoke at same time]-->B1[Lambda handler1]; B1-->B2[Fetch from Cache]; B2-->B3[Handler1 got orphan record]; B3-->B4[Handler1 acquired lock]; B4-->B5[Handler1 overwrite orphan record] B5-->B6[Handler1 continue to execution]; A1-->C1[Lambda handler2]; C1-->C2[Fetch from Cache]; C2-->C3[Handler2 got orphan record]; C3-->C4[Handler2 failed to acquire lock]; C4-->C5[Handler2 wait and fetch from Cache]; C5-->C6[Handler2 return without executing]; B6-->D(Lambda handler executed only once); C6-->D;

Race condition with Cache

Advanced

Customizing the default behavior

You can override and further extend idempotency behavior via IdempotencyConfig with the following options:

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 that the specified fields haven't changed across requests for the same idempotency key e.g., payload tampering.
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, allowing a new transaction with the same idempotency key
use_local_cache	False	Whether to cache idempotency results in-memory to save on persistence storage latency and costs
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.
response_hook	None	Function to use for processing the stored Idempotent response. This function hook is called when an existing idempotent response is found. See Manipulating The Idempotent Response

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.

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

Payload validationSample event 1Sample event 2

import os from dataclasses import dataclass, field from uuid import uuid4 from aws_lambda_powertools import Logger from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.idempotency.exceptions import IdempotencyValidationError from aws_lambda_powertools.utilities.typing import LambdaContext logger = Logger() table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig( event_key_jmespath='["user_id", "product_id"]',  payload_validation_jmespath="amount", ) @dataclass class Payment: user_id: str product_id: str charge_type: str  amount: int  payment_id: str = field(default_factory=lambda: f"{uuid4()}") class PaymentError(Exception): ... @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): try: payment: Payment = create_subscription_payment(event) return { "payment_id": payment.payment_id, "message": "success", "statusCode": 200, } except IdempotencyValidationError: logger.exception("Payload tampering detected", payment=payment, failure_type="validation") return { "message": "Unable to process payment at this time. Try again later.", "statusCode": 500, } except Exception as exc: raise PaymentError(f"Error creating payment {str(exc)}") def create_subscription_payment(event: dict) -> Payment: return Payment(**event) 

{  "user_id": 1,  "product_id": 1500,  "charge_type": "subscription",  "amount": 500 } 

{  "user_id": 1,  "product_id": 1500,  "charge_type": "subscription",  "amount": 10 } 

In this example, the user_id and product_id 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.

Warning

To prevent errors, transactions will not be treated as idempotent if raise_on_no_idempotency_key is set to False and the evaluation of event_key_jmespath is None. Therefore, no data will be fetched, stored, or deleted in the idempotency storage layer.

Idempotency key requiredSuccess EventFailure Event

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig( event_key_jmespath='["user.uid", "order_id"]',  raise_on_no_idempotency_key=True, ) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext) -> dict: return event 

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

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

Custom sessionCustom configSample Event

import os import boto3  from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext # See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#module-boto3.session boto3_session = boto3.session.Session()  table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table, boto3_session=boto3_session)  config = IdempotencyConfig(event_key_jmespath="body") @idempotent(persistence_store=persistence_layer, config=config) def lambda_handler(event: dict, context: LambdaContext) -> dict: return event 

import os from botocore.config import Config  from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext # See: https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html#botocore-config boto_config = Config()  table = os.getenv("IDEMPOTENCY_TABLE", "") persistence_layer = DynamoDBPersistenceLayer(table_name=table, boto_config=boto_config)  config = IdempotencyConfig(event_key_jmespath="body") @idempotent(persistence_store=persistence_layer, config=config) def lambda_handler(event: dict, context: LambdaContext) -> dict: return event 

{  "body": "{\"user_id\":\"xyz\",\"product_id\":\"123456789\"}" } 

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 create your own persistent store from scratch by inheriting the BasePersistenceLayer class, and implementing _get_record(), _put_record(), _update_record() and _delete_record().

• _get_record() – Retrieves an item from the persistence store using an idempotency key and returns it as a DataRecord instance.
• _put_record() – Adds a DataRecord to the persistence store if it doesn't already exist with that key. Raises an ItemAlreadyExists exception if a non-expired entry already exists.
• _update_record() – Updates an item in the persistence store.
• _delete_record() – Removes an item from the persistence store.

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 MyOwnPersistenceLayer(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, ): boto3_session = boto3_session or boto3.session.Session() self._ddb_resource = boto3_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().__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 self.table.update_item( Key={self.key_attr: data_record.idempotency_key}, UpdateExpression=update_expression, ExpressionAttributeValues=expression_attr_values, ExpressionAttributeNames=expression_attr_names, ) 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.

Manipulating the Idempotent Response

You can set up a response_hook in the IdempotentConfig class to manipulate the returned data when an operation is idempotent. The hook function will be called with the current deserialized response object and the Idempotency record.

Using an Idempotent Response HookSample event

import os import uuid from typing import Dict from aws_lambda_powertools import Logger from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent_function, ) from aws_lambda_powertools.utilities.idempotency.persistence.datarecord import ( DataRecord, ) from aws_lambda_powertools.utilities.typing import LambdaContext logger = Logger() def my_response_hook(response: Dict, idempotent_data: DataRecord) -> Dict:  # Return inserted Header data into the Idempotent Response  response["x-idempotent-key"] = idempotent_data.idempotency_key   # expiry_timestamp can be None so include if set expiry_timestamp = idempotent_data.get_expiration_datetime() if expiry_timestamp: response["x-idempotent-expiration"] = expiry_timestamp.isoformat()  # Must return the response here  return response table = os.getenv("IDEMPOTENCY_TABLE", "") dynamodb = DynamoDBPersistenceLayer(table_name=table) config = IdempotencyConfig(response_hook=my_response_hook)  @idempotent_function(data_keyword_argument="order", config=config, persistence_store=dynamodb) def process_order(order: dict) -> dict: # create the order_id order_id = str(uuid.uuid4()) # create your logic to save the order # append the order_id created order["order_id"] = order_id # return the order return {"order": order} def lambda_handler(event: dict, context: LambdaContext): config.register_lambda_context(context) # see Lambda timeouts section try: logger.info(f"Processing order id {event.get('order_id')}") return process_order(order=event.get("order")) except Exception as err: return {"status_code": 400, "error": f"Error processing {str(err)}"} 

{  "order" : {  "user_id": "xyz",  "product_id": "123456789",  "quantity": 2,  "value": 30  } } 

Info: Using custom de-serialization?

The response_hook is called after the custom de-serialization so the payload you process will be the de-serialized version.

Being a good citizen

When using response hooks to manipulate returned data from idempotent operations, it's important to follow best practices to avoid introducing complexity or issues. Keep these guidelines in mind:

1. Response hook works exclusively when operations are idempotent. The hook will not be called when an operation is not idempotent, or when the idempotent logic fails.

2. Catch and Handle Exceptions. Your response hook code should catch and handle any exceptions that may arise from your logic. Unhandled exceptions will cause the Lambda function to fail unexpectedly.

3. Keep Hook Logic Simple Response hooks should consist of minimal and straightforward logic for manipulating response data. Avoid complex conditional branching and aim for hooks that are easy to reason about.

Compatibility with other utilities

JSON Schema Validation

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 behavior, if you set the event_key_jmespath.

Using Idempotency with validation utilitySample Event

import os from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, IdempotencyConfig, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext from aws_lambda_powertools.utilities.validation import envelopes, validator table = os.getenv("IDEMPOTENCY_TABLE", "") config = IdempotencyConfig(event_key_jmespath='["message", "username"]') persistence_layer = DynamoDBPersistenceLayer(table_name=table) @validator(envelope=envelopes.API_GATEWAY_HTTP) @idempotent(config=config, persistence_store=persistence_layer) def lambda_handler(event, context: LambdaContext): return {"message": event["message"], "statusCode": 200} 

{  "version": "2.0",  "routeKey": "$default",  "rawPath": "/my/path",  "rawQueryString": "parameter1=value1&parameter1=value2&parameter2=value",  "cookies": [  "cookie1",  "cookie2"  ],  "headers": {  "Header1": "value1",  "Header2": "value1,value2"  },  "queryStringParameters": {  "parameter1": "value1,value2",  "parameter2": "value"  },  "requestContext": {  "accountId": "123456789012",  "apiId": "api-id",  "authentication": {  "clientCert": {  "clientCertPem": "CERT_CONTENT",  "subjectDN": "www.example.com",  "issuerDN": "Example issuer",  "serialNumber": "a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1:a1",  "validity": {  "notBefore": "May 28 12:30:02 2019 GMT",  "notAfter": "Aug 5 09:36:04 2021 GMT"  }  }  },  "authorizer": {  "jwt": {  "claims": {  "claim1": "value1",  "claim2": "value2"  },  "scopes": [  "scope1",  "scope2"  ]  }  },  "domainName": "id.execute-api.us-east-1.amazonaws.com",  "domainPrefix": "id",  "http": {  "method": "POST",  "path": "/my/path",  "protocol": "HTTP/1.1",  "sourceIp": "192.168.0.1/32",  "userAgent": "agent"  },  "requestId": "id",  "routeKey": "$default",  "stage": "$default",  "time": "12/Mar/2020:19:03:58 +0000",  "timeEpoch": 1583348638390  },  "body": "{\"message\": \"hello world\", \"username\": \"tom\"}",  "pathParameters": {  "parameter1": "value1"  },  "isBase64Encoded": false,  "stageVariables": {  "stageVariable1": "value1",  "stageVariable2": "value2"  } } 

Tip: JMESPath Powertools for AWS Lambda (Python) 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.

Tracer

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

First execution

During the first execution with a payload, Lambda performs a PutItem followed by an UpdateItem operation to persist the record in DynamoDB.

Subsequent executions

On subsequent executions with the same payload, Lambda optimistically tries to save the record in DynamoDB. If the record already exists, DynamoDB returns the item.

Explore how to handle conditional write errors in high-concurrency scenarios with DynamoDB in this blog post.

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:

test_disabling_idempotency_utility.pyapp_test_disabling_idempotency_utility.py

from dataclasses import dataclass import app_test_disabling_idempotency_utility import pytest  @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 @pytest.fixture def lambda_context() -> LambdaContext: return LambdaContext() def test_idempotent_lambda_handler(monkeypatch, lambda_context: LambdaContext):  # Set POWERTOOLS_IDEMPOTENCY_DISABLED before calling decorated functions  monkeypatch.setenv("POWERTOOLS_IDEMPOTENCY_DISABLED", 1) result = app_test_disabling_idempotency_utility.lambda_handler({}, lambda_context) assert result 

from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable") @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): print("expensive operation") return { "payment_id": 12345, "message": "success", "statusCode": 200, } 

Testing with DynamoDB Local

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

test_with_dynamodb_local.pyapp_test_dynamodb_local.py

from dataclasses import dataclass import app_test_dynamodb_local import boto3 import pytest  @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 @pytest.fixture def lambda_context() -> LambdaContext: return LambdaContext() def test_idempotent_lambda(lambda_context):  # Configure the boto3 to use the endpoint for the DynamoDB Local instance  dynamodb_local_client = boto3.client("dynamodb", endpoint_url="http://localhost:8000")  app_test_dynamodb_local.persistence_layer.client = dynamodb_local_client # If desired, you can use a different DynamoDB Local table name than what your code already uses # app.persistence_layer.table_name = "another table name" # noqa: ERA001 result = app_test_dynamodb_local.handler({"testkey": "testvalue"}, lambda_context) assert result["payment_id"] == 12345 

from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable") @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): 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.

test_with_io_operations.pyapp_test_io_operations.py

from dataclasses import dataclass from unittest.mock import MagicMock import app_test_io_operations import pytest  @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 @pytest.fixture def lambda_context() -> LambdaContext: return LambdaContext() def test_idempotent_lambda(lambda_context):  mock_client = MagicMock()  app_test_io_operations.persistence_layer.client = mock_client  result = app_test_io_operations.handler({"testkey": "testvalue"}, lambda_context)  mock_client.put_item.assert_called() assert result 

from aws_lambda_powertools.utilities.idempotency import ( DynamoDBPersistenceLayer, idempotent, ) from aws_lambda_powertools.utilities.typing import LambdaContext persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable") @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): print("expensive operation") return { "payment_id": 12345, "message": "success", "statusCode": 200, } 

Testing with Redis

To test locally, you can either utilize fakeredis-py for a simulated Redis environment or refer to the MockRedis class used in our tests to mock Redis operations.

test_with_mock_redis.pymock_redis.py

from dataclasses import dataclass  import pytest from mock_redis import MockRedis from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.redis import ( RedisCachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext @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 1000 return LambdaContext()  def test_idempotent_lambda(lambda_context):  # Init the Mock redis client  redis_client = MockRedis(decode_responses=True) # Establish persistence layer using the mock redis client persistence_layer = RedisCachePersistenceLayer(client=redis_client) # setup idempotent with redis persistence layer @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): print("expensive operation") return { "payment_id": 12345, "message": "success", "statusCode": 200, } # Inovke the sim lambda handler result = lambda_handler({"testkey": "testvalue"}, lambda_context) assert result["payment_id"] == 12345 

import time as t from typing import Dict # Mock redis class that includes all operations we used in Idempotency class MockRedis: def __init__(self, decode_responses, cache: Dict, **kwargs): self.cache = cache or {} self.expire_dict: Dict = {} self.decode_responses = decode_responses self.acl: Dict = {} self.username = "" def hset(self, name, mapping): self.expire_dict.pop(name, {}) self.cache[name] = mapping def from_url(self, url: str): pass def expire(self, name, time): self.expire_dict[name] = t.time() + time # return {} if no match def hgetall(self, name): if self.expire_dict.get(name, t.time() + 1) < t.time(): self.cache.pop(name, {}) return self.cache.get(name, {}) def get_connection_kwargs(self): return {"decode_responses": self.decode_responses} def auth(self, username, **kwargs): self.username = username def delete(self, name): self.cache.pop(name, {}) 

If you want to set up a real Redis client for integration testing, you can reference the code provided below.

test_with_real_redis.pyMakefile

from dataclasses import dataclass import pytest import redis  from aws_lambda_powertools.utilities.idempotency import ( idempotent, ) from aws_lambda_powertools.utilities.idempotency.persistence.redis import ( RedisCachePersistenceLayer, ) from aws_lambda_powertools.utilities.typing import LambdaContext @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 1000 return LambdaContext()  @pytest.fixture def persistence_store_standalone_redis(): # init a Real Redis client and connect to the Port set in the Makefile redis_client = redis.Redis( host="localhost", port="63005", decode_responses=True, )   # return a persistence layer with real Redis return RedisCachePersistenceLayer(client=redis_client) def test_idempotent_lambda(lambda_context, persistence_store_standalone_redis): # Establish persistence layer using the real redis client persistence_layer = persistence_store_standalone_redis # setup idempotent with redis persistence layer @idempotent(persistence_store=persistence_layer) def lambda_handler(event: dict, context: LambdaContext): print("expensive operation") return { "payment_id": 12345, "message": "success", "statusCode": 200, } # Inovke the sim lambda handler result = lambda_handler({"testkey": "testvalue"}, lambda_context) assert result["payment_id"] == 12345 

test-idempotency-redis: # (1)!  docker run --name test-idempotency-redis -d -p 63005:6379 redis  pytest test_with_real_redis.py;docker stop test-idempotency-redis;docker rm test-idempotency-redis 

1. Use this script to setup a temp Redis docker and auto remove it upon completion

Extra resources

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