CCAAK · Question #37
You are responsible for the operation and performance tuning of a Kafka cluster. This Kafka cluster handles a variety of workloads including real-time transaction processing, large-scale batch data…
The correct answer is A. Enable compression (compression.type) on the producer side to reduce the size of messages C. Increase 'min.insync.replicas' and ensure acks is set to all for producers so messages are durably E. Increase 'buffer.memory' and 'max.block.ms' for producers to handle higher volumes and rates of. Compression reduces message size, improving broker throughput and decreasing consumer lag under high load. Increasing the minimum in-sync replicas together with producer acknowledgments ensures durability and consistency, which is critical as throughput grows. Expanding…
Question
You are responsible for the operation and performance tuning of a Kafka cluster. This Kafka cluster handles a variety of workloads including real-time transaction processing, large-scale batch data processing for end-of-da y reports, and high-throughput data ingestion from various external data sources. Considerations:
- There is an increase in the volume and velocity of data being
processed
- Some critical consumer applications are experiencing delays in
message consumption, impacting real-time analytics
- A new large-volume data source is expected to double the throughput
demand on the system You need to adjust the Kafka configuration to reduce consumer lag for real-time transaction processing, ensure the system can handle the upcoming increase in throughput, and maintain or improve data durability and consistency. Which three actions should you take to meet these needs? (Choose three.)
Options
- AEnable compression (compression.type) on the producer side to reduce the size of messages
- BAdjust 'log.flush.interval.messages' and 'log.flush.interval.ms' settings to optimize disk I/O on
- CIncrease 'min.insync.replicas' and ensure acks is set to all for producers so messages are durably
- DSet 'unclean.leader.election.enable' to true to maintain availability even at the risk of data
- EIncrease 'buffer.memory' and 'max.block.ms' for producers to handle higher volumes and rates of
How the community answered
(34 responses)- A79% (27)
- B15% (5)
- D6% (2)
Explanation
Compression reduces message size, improving broker throughput and decreasing consumer lag under high load. Increasing the minimum in-sync replicas together with producer acknowledgments ensures durability and consistency, which is critical as throughput grows. Expanding producer buffer capacity allows producers to handle higher ingest rates smoothly, preventing backpressure that can otherwise increase consumer lag.
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