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

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

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

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

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

Microservices can communicate using five key patterns based on their needs.

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

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

Kafka consumers resume processing from the last committed offset after a crash.

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

Request-Response asks for a result, while Events notify that something has happened.

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

Classifying failures and using controlled retries in Kafka prevents outages.