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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

Consistency models determine what data reads in distributed systems can see.

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

Graceful degradation allows apps to function partially during service failures.

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

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

You can update Elasticsearch indices without downtime using alias switching.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Elasticsearch aliases allow zero-downtime switching between indices for applications.

Replicas keep your system available, while snapshots allow for data recovery.