In distributed systems, failures happen — network glitches, retries, or even duplicate requests. Without proper handling, this can lead to unintended side effects like multiple charges, duplicated tasks, or inconsistent states.
This is where idempotency comes in.
Idempotency means that performing the same operation multiple times produces the same result, no matter how many retries occur. Think of pressing an elevator button — pressing it once or ten times doesn't make the elevator arrive faster; the action has the same effect.
Recently, we refactored parts of our system to ensure idempotency across critical operations. Using Go and Google Cloud Platform (GCP) services like Pub/Sub, we introduced queues and deduplication strategies that guarantee:
No duplicate processing: We track request IDs to ensure each request is processed only once.
Safe retries: If a worker crashes or a request times out, retries don't break consistency.
Deterministic outputs: The same inputs always produce the same result.
A few lessons learned:
Design for retries: Assume every call may happen more than once.
Use queues: use queues with at-least-once delivery (e.g., Pub/Sub) but build deduplication logic into your service.
Keep business logic deterministic: avoid side effects like random values unless controlled.
Implementing idempotency might feel like extra work, but it’s essential for building resilient, fault-tolerant systems, especially at scale.
Have you implemented idempotency in your APIs or distributed workflows? How did you handle retries and deduplication?
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