Скачать или смотреть Retry Mechanism for APIs in DIstributed systems | HLD: 45 | System Design Interviews

In large-scale distributed systems and cloud-native architectures, implementing a retry mechanism is critical for achieving fault tolerance, resilience, and high availability. Retries help recover from transient failures like temporary network congestion, database deadlocks, or throttling errors, ensuring that services maintain stability and reliability under load. However, poorly designed retries can amplify system failures and create cascading outages, leading to service degradation and SLA violations.

To avoid such pitfalls, modern system design uses exponential backoff with jittering. Instead of retrying at fixed intervals, exponential backoff increases the wait time after each attempt, while jittering adds randomness to prevent thundering herd problems. This combination ensures better throughput, scalability, and latency control, making systems more robust in microservices communication, API gateways, and message queues like Kafka, RabbitMQ, and SQS.

Another critical concept is idempotency. Using idempotent tokens ensures that retried operations (such as payments, order creation, or account updates) do not create duplicate side effects. Idempotent APIs guarantee data consistency, correctness, and transactional integrity across distributed databases and event-driven architectures.

Retries also need to account for system failures like service crashes, hardware faults, or region-wide outages in cloud environments (AWS, Azure, GCP). In such scenarios, retries should integrate with circuit breaker patterns, failover strategies, load balancers, and observability tools (Prometheus, Grafana, ELK) to provide resilient fault isolation and graceful degradation.

This video explains:

How retry policies handle transient vs. permanent failures

Best practices for exponential backoff and jittering

Role of idempotent tokens in API reliability

Avoiding retry storms, cascading failures, and system overload

Designing retries for distributed microservices, databases, and event-driven systems

Whether preparing for a system design interview (Google, Amazon, Microsoft) or building scalable, reliable services, mastering retry mechanisms is essential for modern software architecture.