Error Handling is the set of mechanisms, patterns, and strategies in software and systems design for detecting, reporting, and recovering from anomalous conditions that deviate from expected operation. It encompasses exception mechanisms, error codes, retry logic, circuit breakers, fallback strategies, and graceful degradation. Robust error handling is essential to system reliability, security, and maintainability, and is a prerequisite for fault-tolerant distributed systems.
Overview
- Errors fall into several categories: recoverable (transient network timeouts), unrecoverable (data corruption), and programming errors (null pointer dereferences).
- Modern systems distinguish between errors (expected anomalies) and panics (unexpected programmer mistakes), each handled differently.
- In distributed systems, error handling must account for partial failures — conditions where some nodes succeed while others fail.
Key Aspects
- Exception handling: Language-level try/catch or Result/Either type mechanisms.
- Error propagation: Passing errors up the call stack vs handling locally.
- Retries: Exponential backoff and jitter for transient failure recovery.
- Circuit breakers: Preventing cascading failures by short-circuiting failing dependencies.
- Observability: All errors must be logged, metered, and traced.
Mechanisms
- Exception Handling in object-oriented languages uses try/catch/finally blocks; functional languages prefer typed Result/Either monads.
- Circuit Breaker patterns track failure rates and open the circuit to prevent overloading failing services.
- Retry Logic with exponential backoff and jitter is standard for network calls.
Applications
- API gateway error normalisation across microservices.
- Database transaction rollback on constraint violations.
- Smart contract revert patterns on invalid state transitions.
- AI inference pipeline fallback to cached or degraded responses.