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

Availability is about access; reliability is about correct performance.

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

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

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

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

Kafka uses replication and in-sync replicas to ensure data is always available.

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

Graceful degradation allows apps to function partially during service failures.

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

Choosing the right boundaries for microservices is crucial for effective architecture.

Observability helps engineers understand system behavior to debug production issues effectively.

Database replication helps keep data available and allows systems to handle more read requests.

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

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