How to Design Safe Retries in Microservices (No Duplicates, No Overload)

Key Takeaways

Let's break down the retry pattern in distributed systems using this diagram. Whenever service A talks to service B over a network, a lot can go wrong at different points in the request response path. The request might never leave service A. It might get lost in the network or service B might crash right after receiving it. On the way back, the response might not be sent, might be lost or might reach the client in a malformed way and the client itself can also crash after the call. Now the key question is when should you actually retry? For connectivity issues like TCP timeouts or DNS lookup failures, the network is likely to recover quickly. So aggressive retries usually make sense. For throttling errors such as HTTP429, you must respect retry after headers or fall back to exponential backoff. Otherwise, you just overload the system more. When you see server faults like HTTP500 or 5003, use cautious retries with exponential backoff and stop if the failure persists because something is actually broken. But for client faults like HTTP 400 or 422, retrying is pointless since the business logic rejected the request and that won't change without fixing the input. A common antiattern is fixed back off. Here the client retries in constant intervals, say every second, which can cause synchronized waves of traffic that hammer an already unhealthy service. Instead, exponential backoff increases the delay between attempts, giving the downstream service more time to recover and smoothing out the retry load. You often combine exponential backoff with jitter, adding randomness to avoid thundering herds of retries hitting at the same time. Retries also raise a big question. What if the same request is processed twice? To keep things safe, you can use an item potency key approach. The client generates a unique key and sends it along with the request while the server and database store and check that key. If the same key comes in again, the server either returns the existing result or safely ignores the duplicate instead of inserting duplicate data. Finally, retries pair nicely with the circuit breaker pattern. A circuit breaker has three states: closed, open, and halfopen. In the closed state, calls flow as normal. If failures cross a threshold, it flips to open and immediately fails new calls without even hitting the broken dependency. After a cool down period, it moves to half open, allows a small number of test requests, and if those succeed, it closes again. If they fail, it goes back to open. By combining smart retry policies, exponential backoff, it impotency keys and circuit breakers, you can make distributed systems far more resilient to transient failures while avoiding overload and duplicate side effects.