Any concept.
clear in 60 seconds.

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

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

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

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

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

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

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

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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

Kafka rebalances consumer groups to manage partition assignments effectively.

Choosing between a shared database and a database per service affects system design significantly.

Microservices and modular monoliths differ mainly in deployment and communication methods.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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

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

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

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

Cache eviction policies determine which data to remove when caches are full.

Kafka's parallel processing depends on partitions, not just consumers or threads.

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

Kafka maintains message order within partitions using keys for routing.