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7 Retry Patterns You Should Know
- Retry mechanisms are crucial for system self-healing capabilities in handling failures caused by network issues, service overload, or unstable interfaces.
- Designing retries poorly can lead to request storms, cascading failures, and incidents, while well-designed retries improve success rates and user experience.
- Common retry strategies discussed include Brute-force Looping, Spring Retry, Resilience4j, MQ Queue, Scheduled Task, Two-Phase Commit, and Distributed Lock mechanisms.
- Brute-force Looping demonstrated issues with fixed delay and retrying non-transient errors, recommending random delays and filtering out non-retriable exceptions.
- Spring Retry offers declarative annotations, exponential backoff, and circuit breaker integration, providing clean code and automatic retry interval adjustments.
- Resilience4j is suitable for complex systems, offering custom backoff algorithms, circuit breaker strategies, and multi-layer protection, resulting in significant improvements in API timeout rates.
- MQ Queue is used for high-concurrency asynchronous scenarios, where messages are retried with preset delays and moved to a dead letter queue after reaching max retries.
- Scheduled Tasks are beneficial for batch processing tasks that allow retries, such as file imports, providing a scheduled job approach for handling failures.
- Two-Phase Commit is recommended for strict data consistency requirements like fund transfers, involving recording transactions, remote API calls, and compensation tasks for retrying transactions.
- Distributed Locks are useful in environments requiring idempotency, like flash sales, utilizing Redis + Lua for acquiring locks and handling retries safely in multi-threaded scenarios.
- Choosing the appropriate retry mechanism is essential for system stability, emphasizing the importance of balancing offense and defense strategies based on business requirements.
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