Retry Pattern

Overview

Overview The Retry pattern handles transient failures by automatically re-attempting failed operations. In distributed systems, transient failures are routine: network blips, temporary service overloads, connection resets, and DNS resolution delays. Most of these resolve within seconds, making retries an effective strategy.

However, naive retries are dangerous. If a service is struggling under load and 1000 clients simultaneously retry their failed requests, the service receives a thundering herd of retry traffic on top of normal load, making the problem worse. This is a retry storm, and it has caused major outages at companies like AWS, Slack, and GitHub.

The key to safe retries is combining exponential backoff (wait longer between each attempt), jitter (randomize wait times), retry budgets (cap total retry traffic), and idempotency (ensure repeated requests produce the same result). Interviewers expect you to understand all four concepts and explain why each is necessary.

Key concepts

• Exponential backoff algorithm
• Jitter strategies (full, equal, decorrelated)
• Retry budgets and rate limiting
• Idempotency keys and requirements
• Retry storms and thundering herd
• Transient vs permanent failure classification
• Circuit breaker integration

Interview questions

• Explain exponential backoff with jitter and why both are necessary.
• What is a retry budget and how does it prevent retry storms?
• Why is idempotency critical for retries, and how do you implement it?
• How do you classify errors to decide whether to retry?

More Microservices topics

• Backend for Frontend (BFF)
• Bulkhead Pattern
• Circuit Breaker Pattern
• Configuration Externalization
• CQRS
• Event-Driven Architecture
• Saga Pattern
• Service Discovery
• Sidecar Pattern
• Strangler Fig Pattern