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

Kafka consumer lag shows how far behind consumers are from new messages.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka consumers resume processing from the last committed offset after a crash.

Apache Kafka is a system for managing large volumes of events efficiently.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.

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

Kafka lag recovery time is how fast consumers can process backlogged messages.

Kafka's pause temporarily stops reading messages, while seek changes the reading position.

Kafka's `seek()` lets you replay only specific messages, saving time.

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses state stores to remember previous events in stateful stream processing.

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

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