Ensures that repeating an operation (e.g., retrying a request) does not cause unintended side effects.

Communicates that an operation is safe to repeat.

When designing APIs, distributed systems, or any process where retries may occur due to network failures or timeouts.

Distributed systems, RESTful APIs, database transactions, message queues, configuration management.

design API endpoints as idempotent → allow safe retries → ensure consistent state

• GET
• PUT
• DELETE
• POST
• retry
• safe
• repeat
• request ID
• conditional update

• Use unique request IDs for deduplication → adds storage overhead
• Use conditional updates (e.g.
• version check) → more complex logic

Assuming all GET requests are idempotent → may expose mutable data; Implementing idempotency by simply ignoring errors → can hide real failures; Forgetting to make side-effectful operations idempotent → leads to duplicate processing on retries

Idempotent vs safe HTTP methods – safe means no state change, idempotent may change state but repeatably; Idempotent vs at-least-once delivery – idempotency ensures correctness despite multiple deliveries

Coming from Python: treating functions without side effects as automatically idempotent → overlooks external resources like files or databases

• Idempotency
• Retry safety
• Fault tolerance

Do not use idempotency when operations must be strictly once (e.g., generating unique IDs); Idempotent checks can add latency due to extra lookups; Edge case: non-deterministic functions (e.g., random) cannot be made idempotent without caching results

Ensures that retries or duplicate messages do not corrupt system state, improving reliability of distributed services

Idempotent operation is like a light switch that stays on no matter how many times you flip it up.

Implementing idempotency often requires storing a request fingerprint or using conditional writes provided by the storage layer

Idempotency patterns in distributed systems