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Primary shards partition data, while replica shards duplicate it for redundancy.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

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

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

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

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

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