⚡ One HLD concept. 60 seconds. Interview ready· 1 of 51
Kafka uses partitions to allow multiple consumers to process messages in parallel.
⚡ One HLD concept. 60 seconds. Interview ready· 2 of 51
Kafka consumer lag shows how far behind consumers are from new messages.
⚡ One HLD concept. 60 seconds. Interview ready· 3 of 51
Kafka uses retention to keep historical data and compaction to keep the latest state.
⚡ One HLD concept. 60 seconds. Interview ready· 4 of 51
Kafka can help ensure messages are processed once, but requires careful design.
⚡ One HLD concept. 60 seconds. Interview ready· 5 of 51
Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.
⚡ One HLD concept. 60 seconds. Interview ready· 6 of 51
Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.
⚡ One HLD concept. 60 seconds. Interview ready· 7 of 51
Kafka maintains message order within partitions using keys for routing.
⚡ One HLD concept. 60 seconds. Interview ready· 8 of 51
Kafka consumer offsets help track message processing to avoid loss and duplication.
⚡ One HLD concept. 60 seconds. Interview ready· 9 of 51
Kafka uses retries and Dead Letter Queues to manage message processing failures.
⚡ One HLD concept. 60 seconds. Interview ready· 10 of 51
Backpressure prevents system overload by managing message processing rates in Kafka.
⚡ One HLD concept. 60 seconds. Interview ready· 11 of 51
Kafka uses brokers for data handling and KRaft controllers for metadata management.
⚡ One HLD concept. 60 seconds. Interview ready· 12 of 51
Kafka uses replication and in-sync replicas to ensure data is always available.
⚡ One HLD concept. 60 seconds. Interview ready· 13 of 51
Kafka Partition Reassignment moves replicas between brokers while keeping data safe.
⚡ One HLD concept. 60 seconds. Interview ready· 14 of 51
Kafka rebalances consumer groups to manage partition assignments effectively.
⚡ One HLD concept. 60 seconds. Interview ready· 15 of 51
Kafka uses different strategies to assign partitions to consumers in a group efficiently.
⚡ One HLD concept. 60 seconds. Interview ready· 16 of 51
Kafka uses heartbeats and timeouts to check if consumers are alive.
⚡ One HLD concept. 60 seconds. Interview ready· 17 of 51
Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.
⚡ One HLD concept. 60 seconds. Interview ready· 18 of 51
Kafka lag recovery time is how fast consumers can process backlogged messages.
⚡ One HLD concept. 60 seconds. Interview ready· 19 of 51
Kafka consumers can fetch data in batches to improve throughput and reduce latency.
⚡ One HLD concept. 60 seconds. Interview ready· 20 of 51
Fetch limits control data size from Kafka, while poll records limit processed records.
⚡ One HLD concept. 60 seconds. Interview ready· 21 of 51
Eager rebalancing revokes all assignments, while cooperative allows incremental changes.
⚡ One HLD concept. 60 seconds. Interview ready· 22 of 51
Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.
⚡ One HLD concept. 60 seconds. Interview ready· 23 of 51
Kafka Rebalance Listeners help manage state during partition changes in consumer groups.
⚡ One HLD concept. 60 seconds. Interview ready· 24 of 51
Kafka offset reset determines where a consumer starts reading messages without a valid offset.
⚡ One HLD concept. 60 seconds. Interview ready· 25 of 51
Kafka allows safe message reprocessing by resetting consumer positions without altering data.
⚡ One HLD concept. 60 seconds. Interview ready· 26 of 51
Kafka commit strategies determine how offsets are managed during message processing.
⚡ One HLD concept. 60 seconds. Interview ready· 27 of 51
Kafka's commitSync waits for confirmation, while commitAsync continues immediately.
⚡ One HLD concept. 60 seconds. Interview ready· 28 of 51
Older offsets can overwrite newer ones in Kafka if not handled properly.
⚡ One HLD concept. 60 seconds. Interview ready· 29 of 51
Kafka's parallel processing depends on partitions, not just consumers or threads.
⚡ One HLD concept. 60 seconds. Interview ready· 30 of 51
Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.
⚡ One HLD concept. 60 seconds. Interview ready· 31 of 51
Kafka uses partitions to allow parallel processing while ensuring message order with keys.
⚡ One HLD concept. 60 seconds. Interview ready· 32 of 51
Kafka consumers can pause and resume message processing to handle backpressure effectively.
⚡ One HLD concept. 60 seconds. Interview ready· 33 of 51
Backpressure helps manage message flow in Kafka during traffic spikes.
⚡ One HLD concept. 60 seconds. Interview ready· 34 of 51
Graceful shutdown in Kafka ensures no data loss during consumer termination.
⚡ One HLD concept. 60 seconds. Interview ready· 35 of 51
Classifying failures and using controlled retries in Kafka prevents outages.
⚡ One HLD concept. 60 seconds. Interview ready· 36 of 51
Kafka uses retry topics and delayed retries to manage message failures efficiently.
⚡ One HLD concept. 60 seconds. Interview ready· 37 of 51
Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.
⚡ One HLD concept. 60 seconds. Interview ready· 38 of 51
Poison messages in Kafka can cause infinite retries, risking system stability.
⚡ One HLD concept. 60 seconds. Interview ready· 39 of 51
Kafka consumer reprocessing involves safely replaying events to correct system states.
⚡ One HLD concept. 60 seconds. Interview ready· 40 of 51
You can replay Kafka events safely by separating live and replay processes.
⚡ One HLD concept. 60 seconds. Interview ready· 41 of 51
Kafka's pause temporarily stops reading messages, while seek changes the reading position.
⚡ One HLD concept. 60 seconds. Interview ready· 42 of 51
Kafka's `seek()` lets you replay only specific messages, saving time.
⚡ One HLD concept. 60 seconds. Interview ready· 43 of 51
Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.
⚡ One HLD concept. 60 seconds. Interview ready· 44 of 51
Kafka's assign() method allows manual control of partition assignments for consumers.
⚡ One HLD concept. 60 seconds. Interview ready· 45 of 51
Kafka rebalance can lead to lost or duplicated work if not handled carefully.
⚡ One HLD concept. 60 seconds. Interview ready· 46 of 51
Kafka consumers resume processing from the last committed offset after a crash.
⚡ One HLD concept. 60 seconds. Interview ready· 47 of 51
Kafka consumers have three important states: Position, Committed Offset, and Application State.
⚡ One HLD concept. 60 seconds. Interview ready· 48 of 51
Kafka uses state stores to remember previous events in stateful stream processing.
⚡ One HLD concept. 60 seconds. Interview ready· 49 of 51
Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.
⚡ One HLD concept. 60 seconds. Interview ready· 50 of 51
Kafka Streams uses local state for quick data access and changelogs for recovery.
⚡ One HLD concept. 60 seconds. Interview ready· 51 of 51
Kafka Streams uses tumbling and hopping windows for time-based data aggregation.
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