Idempotency Patterns Serverless Applications
If you’re building serverless applications on AWS, there’s a harsh reality you need to accept: duplicate events will happen. It’s not a matter of if, but when. Understanding how to handle these duplicates gracefully is the difference between a robust production system and one that creates billing nightmares, corrupts data, or frustrates your users.
Why Duplicate Events Are Inevitable in Serverless
Serverless architectures are distributed by nature. When you’re working with services like Lambda, SQS, SNS, EventBridge, or DynamoDB Streams, you’re dealing with distributed systems that prioritize availability and durability over strict exactly-once delivery semantics.
Most AWS services guarantee at-least-once delivery. This means that in certain failure scenarios, network hiccups, timeout issues, or service retries, the same event can be delivered multiple times to your Lambda function or other consumers.
Imagine this scenario: A user uploads a photo, triggering a Lambda function that processes the image and charges their credit card $5. If that event is processed twice, you’ve just double-charged your customer. Not great for customer satisfaction or your compliance posture.
Understanding Idempotency
The solution to handling duplicate events is idempotency, one of those computer science terms that sounds more complicated than it actually is.
Simply put, an idempotent operation produces the same result whether you execute it once or multiple times. If I tell you to “set the thermostat to 72 degrees,” it doesn’t matter if I repeat that instruction five times—the end result is identical. That’s idempotency.
In contrast, if I tell you to “increase the thermostat by 5 degrees,” repeating that instruction five times produces very different results. That’s a non-idempotent operation.
For serverless applications, making your functions idempotent means that processing the same event multiple times won’t cause unintended side effects like duplicate charges, repeated notifications, or inconsistent data states.
Common Sources of Duplicate Events
Before we dive into solutions, let’s understand where these duplicates come from:
Lambda Function Retries
When your Lambda function fails or times out, AWS automatically retries it. Depending on your event source, this retry behavior varies:
Asynchronous invocations are retried twice automatically
Stream-based sources (Kinesis, DynamoDB Streams) retry until success or data expiration
SQS queues will redeliver messages if they’re not deleted within the visibility timeout
SQS Standard Queues
Standard SQS queues explicitly support at-least-once delivery. Amazon makes no guarantees about duplicate prevention—in fact, they document that duplicates can and will occur, especially during high-throughput scenarios.
Network Issues and Timeouts
Sometimes your function successfully processes an event and completes its work, but the acknowledgment back to the event source fails due to network issues. The event source doesn’t know the processing succeeded, so it delivers the event again.
Event Source Replay
Services like EventBridge Archive or Kinesis allow you to replay events. While powerful, this intentionally creates duplicate event scenarios that your application must handle.
Strategies for Achieving Idempotency
Now for the practical part—how do you actually build idempotent serverless functions?
1. Idempotency Keys and Deduplication Tables
The most common pattern involves tracking which events you’ve already processed. Before processing an event, check if you’ve seen it before. If yes, skip processing and return the cached result. If no, process it and record that you’ve handled it.
You need a unique identifier for each event—an idempotency key. This might be:
An event ID provided by the service (like SQS message ID)
A request ID from API Gateway
A business-level unique identifier (order ID, transaction ID)
A hash of the event payload for events without built-in IDs
Store these keys in a fast, durable data store. DynamoDB is a natural choice in AWS because it’s serverless, fast, and scales automatically. Use DynamoDB’s conditional writes to ensure only one function instance can successfully claim processing of an event.
2. Set Appropriate TTLs
Your deduplication table will grow forever if you don’t manage it. Use DynamoDB’s Time-to-Live (TTL) feature to automatically expire old idempotency records.
How long should you retain them? Consider:
How far back your event sources might replay events
Your compliance requirements
Your budget (storage costs)
A common pattern is 24-72 hours for most applications, though some scenarios require longer retention.
3. Use FIFO Queues When Appropriate
SQS FIFO queues provide exactly-once processing within a 5-minute deduplication interval. They use message deduplication IDs to identify duplicates automatically.
FIFO queues are perfect when:
You need strict ordering
Your processing fits within the 300 messages per second throughput limit
Your deduplication window is short enough
However, FIFO queues cost more and have lower throughput than Standard queues, so evaluate whether you actually need these guarantees or if implementing your own deduplication logic makes more sense.
4. Make Your Operations Naturally Idempotent
Sometimes you can design operations to be inherently idempotent:
Use PUT instead of POST: When updating resources, use operations that set a specific state rather than incrementing or appending.
Conditional updates: DynamoDB conditional writes and expressions let you specify that updates should only occur if certain conditions are met. For example, only update if the current version number matches what you expect.
Idempotent external APIs: When calling third-party services, use idempotency keys they provide. Stripe, for example, accepts an idempotency key header for API calls.
5. Separate Side Effects from Core Logic
Structure your functions so that side effects (charging credit cards, sending emails, updating external systems) happen only after you’ve confirmed the event hasn’t been processed before.
The pattern is:
Check if event has been processed
If yes, return cached result
If no, process the core business logic
Execute side effects
Store result and mark as processed
This ensures that even if something fails partway through, your side effects don’t execute multiple times.
AWS-Specific Considerations
Lambda Powertools for Idempotency
AWS provides Lambda Powertools libraries (for Python, TypeScript, Java, and .NET) that include built-in idempotency utilities. These handle the heavy lifting of:
Generating idempotency keys
Managing DynamoDB tables for deduplication
Handling TTLs and cleanup
Providing configuration options
This is a great starting point and follows AWS best practices out of the box.
EventBridge Idempotency
EventBridge events include a unique ID field that you can use as an idempotency key. However, remember that EventBridge can invoke multiple targets for the same event, and each target needs to handle idempotency independently.
API Gateway Request IDs
API Gateway generates unique request IDs that work well as idempotency keys for HTTP APIs. Extract these from the request context and use them to deduplicate requests.
Step Functions for Complex Workflows
For complex multi-step processes, consider using Step Functions. They provide built-in state management and can help ensure that even if individual Lambda functions are retried, the overall workflow executes correctly. Step Functions has built-in deduplication for starting executions with the same name within 8 hours.
Practical Implementation Tips
Start with the Critical Paths
You don’t need to make every single Lambda function idempotent from day one. Prioritize:
Functions that handle financial transactions
Functions that send notifications or communications
Functions that modify critical business data
Functions that call rate-limited or expensive external APIs
Monitor Your Duplicate Rate
Add CloudWatch metrics to track how often you’re seeing duplicate events. This helps you:
Understand if you have a configuration problem causing excessive retries
Justify the complexity of idempotency measures
Detect anomalies that might indicate issues
Test Your Idempotency Logic
Intentionally send duplicate events in your testing:
Invoke your functions multiple times with the same event
Test timeout scenarios where processing succeeds but acknowledgment fails
Verify that your deduplication table works under concurrent access
Handle Race Conditions
When two Lambda instances process the same event simultaneously, race conditions can occur. Use DynamoDB’s conditional writes or similar atomic operations to ensure only one instance proceeds with processing.
Consider Costs vs. Complexity
Idempotency adds complexity and operational overhead. For low-stakes operations where duplicates don’t matter much, the simplest solution might be to accept occasional duplicates. Not everything needs the same level of protection.
Common Pitfalls to Avoid
Using non-unique identifiers: Make sure your idempotency keys are actually unique. A timestamp or user ID alone usually isn’t sufficient.
Storing too much in your deduplication table: You typically only need the idempotency key and a small result payload. Don’t store entire event payloads.
Forgetting about partial failures: What happens if you mark an event as processed but the actual processing fails later? Design for these scenarios.
Ignoring the cold start impact: Adding DynamoDB lookups to every function invocation adds latency. Measure and optimize if necessary.
Wrapping Up
Duplicate events are a fundamental characteristic of distributed serverless systems, not a bug to be eliminated. The sooner you embrace this reality and design for it, the more robust your applications will be.
Idempotency isn’t just a defensive measure, it’s a design principle that leads to more predictable, testable, and reliable systems. Yes, it adds complexity, but dealing with production issues from duplicate processing is far worse.
Start with your critical paths, use the tools AWS provides, and remember that perfect exactly-once processing is usually unnecessary. At-least-once delivery with idempotent processing is a pragmatic, proven pattern that serves the vast majority of serverless applications well.