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Choosing the right distributed lock depends on your system's needs and existing tools.

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

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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.

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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.

Kafka's assign() method allows manual control of partition assignments for consumers.

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

Kafka commit strategies determine how offsets are managed during message processing.

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

Raft handles leader failure by allowing followers to elect a new leader through voting.

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.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Jitter helps spread out client retries to avoid traffic spikes.

Stateless servers are easier to scale because they don't store user session data locally.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Backpressure helps manage message flow in Kafka during traffic spikes.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

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

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

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Classifying failures and using controlled retries in Kafka prevents outages.

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Cache stampede occurs when many requests hit the database due to a cache miss.

FAANG engineers use high-level design principles to create scalable systems.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

You can replay Kafka events safely by separating live and replay processes.

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.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.