Why Exponential Backoff Works Perfectly | Generated by AI
This is exponential backoff, and it’s a really good design for several reasons:
It gives temporary problems time to resolve. Network issues, server overloads, or rate limits are often transient. The increasing delays give systems time to recover - a problem that persists after 1 second might be gone after 4 or 16 seconds.
It prevents making things worse. If a server is struggling under load, immediately retrying just adds more load. The exponential spacing means you’re not hammering an already-stressed system, which would slow recovery or cause cascading failures.
It’s fair to other users. When many clients encounter errors simultaneously (like during a brief outage), exponential backoff naturally staggers their retries instead of creating a “thundering herd” where everyone retries at exactly the same moment.
It balances responsiveness with resource usage. Early retries happen quickly (1, 2 seconds) to catch brief hiccups, but the growing intervals prevent your application from wasting resources on likely-futile attempts while still eventually checking if things have recovered.
It plays well with rate limits. Many APIs enforce rate limits (like “100 requests per minute”). Exponential backoff naturally spaces out retries so you’re less likely to hit the limit again immediately after being throttled.
Most production implementations also add jitter (randomizing the delay slightly, like 8±2 seconds instead of exactly 8) to prevent synchronized retries when multiple clients experience the same outage, and a maximum delay cap (like 60 seconds) so you don’t wait hours between attempts.