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The retry pattern helps systems recover from temporary failures but can cause overload if misused.

A timeout indicates a deadline was exceeded, not necessarily a failure.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Retry storms occur when too many retries overload a failing service.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Jitter helps spread out client retries to avoid traffic spikes.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

Fallbacks allow systems to handle failures safely and maintain user experience.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Poison messages in Kafka can cause infinite retries, risking system stability.

Partial failure means some services fail while others keep running, impacting system reliability.

Cache stampede occurs when many requests hit the database due to a cache miss.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Classifying failures and using controlled retries in Kafka prevents outages.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.