Design a System to Handle Retries and Backoff

To handle transient errors effectively, I would design a client-side retry mechanism centered on two pillars: Exponential Backoff and Randomized Jitter. First, when a service returns a transient error like a 503 or timeout, the client should not immediately retry. Instead, it waits for an exponentially increasing interval. For example, the first retry waits one second, the second waits two, the third four, capping at a maximum threshold to avoid indefinite hanging. However, simply using exponential backoff is insufficient because if thousands of clients fail simultaneously, they will all wake up at the exact same moment, causing a thundering herd problem that crashes the recovering service. To solve this, I introduce jitter. By adding a random variance to each backoff interval—say, plus or minus 20% of the calculated time—we ensure that retry requests are spread out over time rather than hitting in a synchronized wave. Furthermore, I would implement a Circuit Breaker pattern. If the failure rate exceeds a specific percentage within a sliding window, the circuit opens, failing fast immediately without attempting retries for a cooldown period. This prevents the system from wasting resources on doomed requests. Finally, since retries inherently risk sending duplicate data, the downstream API must be idempotent. This approach ensures high availability while protecting the infrastructure from self-inflicted denial of service, which is critical for enterprise platforms like Salesforce that handle massive concurrent transaction volumes.