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

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

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

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

Consistent hashing helps distribute data across servers with minimal movement.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

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

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

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

Vector clocks help track the order and concurrency of events in distributed systems.

Consensus ensures that distributed systems can agree on decisions even during failures.

Jitter helps spread out client retries to avoid traffic spikes.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

In distributed systems, timestamps don't always reflect the true order of events.

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

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

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

Elasticsearch has different node roles that help manage data and scale clusters effectively.

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

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