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Distributed backpressure helps manage the flow of work between services to prevent overload.

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

The Token Bucket algorithm helps manage how many requests a server can handle.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

Graceful degradation allows apps to function partially during service failures.

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

Read-after-write consistency ensures users see their latest updates immediately.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Leader election helps distributed systems select one coordinator from many nodes.