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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.

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

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

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.

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

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.

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.

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.

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

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

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

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.

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.

Classifying failures and using controlled retries in Kafka prevents outages.

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.

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