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

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

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

Failover ensures systems remain operational by managing leader changes during failures.

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

Crash failures stop a service, while network failures disrupt communication.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Jitter helps spread out client retries to avoid traffic spikes.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Load shedding helps systems reject excess requests to maintain performance during high demand.

The CAP Theorem explains the trade-offs in distributed systems during network failures.