Retries feel comforting.
Something failed. The system tried again. Eventually it worked.
That story sounds reassuring, but it hides a lot of risk.
Retries are not a safety net. They are a design decision.
Why Retries Feel Harmless
Most platforms make retries easy.
Azure Functions retry automatically. Azure Storage Queues redeliver messages. HTTP clients retry transient failures.
Early on, this feels like free resilience.
You do not have to think deeply about failure. The platform will “handle it.”
Until it does not.
Retries Change System Behavior
Every retry is more load.
More database connections. More downstream calls. More contention.
When one service slows down, retries amplify the problem instead of absorbing it.
What started as a small failure becomes a system wide event.
That is not bad luck. That is design.
We had a service that called an external API. When that API started responding slowly, our retry logic kept hammering it. Each retry took longer. Timeouts stacked up. Within minutes, we had thousands of pending requests. The external team called us to ask why we were DDoSing them.
We weren’t malicious. We were just retrying.
The Retry Storm Problem
Retry storms are rarely caused by a single bad component.
They happen when:
- timeouts are too aggressive
- retry intervals are too short
- retry limits are too high
- backoff is missing or misunderstood
- failures are not distinguishable
In these scenarios, retries do exactly what you asked them to do. They just do it at scale.
Not All Failures Deserve a Retry
One of the most important questions is simple.
“Is retrying this likely to succeed?”
Transient network blip? Maybe. Temporary service throttling? Possibly. Invalid input? Never. Downstream system under load? Probably not immediately.
Blind retries ignore context. Context is everything.
Azure Service Bus handles this well with dead letter queues. Messages that fail processing repeatedly get moved aside instead of blocking the queue forever. That’s intentional failure handling, not blind retrying.
Idempotency Is the Hidden Requirement
Retries without idempotency are dangerous.
If an operation cannot be safely repeated, retries introduce corruption instead of resilience.
Before enabling retries, you need to know:
- what happens if this runs twice
- what happens if it runs three times
- what happens if it partially succeeds
If those answers are unclear, retries are not a safety net. They are a liability.
Platform Defaults Are Not Neutral
Azure retries for you in many places. That does not mean the defaults are correct for your workload.
Defaults assume:
- short lived failures
- healthy downstream systems
- low contention
- safe repetition
Those assumptions are often wrong in real systems.
Relying on them without thought is still a choice.
Making Retries Intentional
The systems that behaved best were the ones where retries were explicit.
We made decisions about:
- which failures were retryable
- how long to wait
- how many attempts were allowed
- when to fail fast
- when to escalate
Once retries were intentional, behavior became predictable.
Predictability beats optimism every time.
We implemented exponential backoff with jitter for external API calls. First retry after 1 second, second after 2-4 seconds, third after 4-8 seconds. After that, we fail and alert. This gave downstream services time to recover without us hammering them.
When Not Retrying Is the Right Answer
Sometimes the best retry strategy is none at all.
Failing fast can:
- protect downstream systems
- surface issues earlier
- reduce cascading failures
- force clearer ownership
That feels uncomfortable at first. It is often the right call.
Final Thought
Retries feel like resilience because they delay failure.
But delayed failure is still failure.
Real resilience comes from understanding what happens when things go wrong and designing for it deliberately.
Retries are a design choice. Treat them like one.
Building reliable systems? Question every automatic retry. Know what happens when they succeed, when they fail, and when they make things worse.