This started simple and then got uncomfortable fast.
Start by explaining the mechanics of Kafka partitioning—how messages are assigned to partitions via key hashing or round-robin, and how partitions enable parallelism and ordering. Then discuss the trade-offs in choosing partition count, covering throughput, consumer parallelism, ordering guarantees, and operational overhead. Conclude with a practical framework for deciding partition count based on workload characteristics and future scaling.
Pro tip: Mention that partition count can be increased but never decreased, and that increasing partitions breaks key-based ordering guarantees for existing keys—so it's better to over-provision slightly than to under-provision.
Describe how Kafka assigns messages to partitions: producers use a key hash (or round-robin if no key), and each partition is an ordered, immutable log. Mention that partitions are the unit of parallelism and replication.
Explain that within a consumer group, each partition is consumed by exactly one consumer, so partition count caps consumer parallelism. Also note that ordering is only guaranteed within a partition, not across the topic.
Cover throughput (target MB/s per partition), consumer parallelism (number of consumers needed), message key distribution, and latency requirements. Also consider replication factor and broker resources.
Highlight that more partitions increase throughput and parallelism but also add overhead: more open file handles, longer leader elections, higher memory usage, and increased end-to-end latency. Fewer partitions simplify operations but may limit scalability.
Suggest starting with a number based on expected peak throughput and consumer count, then monitor and adjust. Mention that for keyed topics, partition count should be a multiple of the maximum expected consumers to allow even distribution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining at-least-once delivery as a system-wide guarantee spanning producer, broker, and consumer. Then walk through the configuration choices at each stage—acks=all, idempotent producers, replication, manual offset commits—and finish by candidly discussing where duplicates can still occur, such as producer retries, consumer reprocessing, and rebalances.
Pro tip: Emphasize that at-least-once is a trade-off: you accept duplicates to avoid data loss, and the real solution is making consumers idempotent. Mention that exactly-once semantics exist but come with complexity and performance costs, showing you understand the broader design space.
Clarify that at-least-once means every message is delivered one or more times, and it requires end-to-end configuration across producers, brokers, and consumers.
Set acks=all, enable idempotence, configure retries and max.in.flight.requests.per.connection to avoid reordering, and use a key to ensure partitioning consistency.
Ensure replication.factor >= 3, min.insync.replicas >= 2, and disable unclean leader election to prevent data loss on broker failures.
Disable auto-commit, process messages, then manually commit offsets only after successful processing. Use a consumer group and handle rebalances gracefully.
Discuss scenarios: producer retries after ack loss, consumer crashes after processing but before commit, rebalances causing reprocessing, and duplicate messages from upstream systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Batching, linger.ms, compression, partition count.
Structure your answer by grouping producer configuration levers into categories: batching, compression, acknowledgment, and parallelism. For each lever, explain how it increases throughput and the corresponding trade-off in latency, durability, or resource usage. Conclude by emphasizing that tuning depends on the specific use case and requires benchmarking.
Pro tip: Mention that increasing batch.size and linger.ms together often yields the biggest throughput gains, but you must monitor end-to-end latency and adjust based on SLA. Also, highlight that compression reduces network I/O but adds CPU overhead, so choose the algorithm wisely (e.g., lz4 for speed, zstd for ratio).
Group producer settings into batching, compression, acknowledgment, and parallelism to provide a clear structure.
For each category, describe how specific configurations (e.g., batch.size, linger.ms, compression.type, acks, max.in.flight.requests.per.connection) increase throughput.
For each lever, discuss the downsides such as increased latency, higher CPU usage, reduced durability, or potential message loss.
Emphasize that optimal settings depend on the use case (e.g., real-time vs. batch) and require iterative testing and monitoring.
Conclude that maximizing throughput involves trade-offs and that the goal is to find the right balance for the application's requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The unclean leader election part is what got me.
Start by defining each term clearly, then explain how they interact to balance durability and throughput. Use a concrete example to illustrate the trade-offs and tie it back to Illumio's need for reliable, high-performance systems.
Pro tip: Mention that min.insync.replicas is a producer-side setting that works with acks=all to enforce durability, and that ISR shrinkage can trigger producer failures—showing you understand real-world failure modes.
Briefly define replication factor (number of copies), ISR (in-sync replicas), and min.insync.replicas (minimum replicas that must acknowledge a write).
Describe how replication factor sets the maximum copies, ISR tracks which are caught up, and min.insync.replicas enforces a minimum for writes when acks=all.
Higher replication factor and min.insync.replicas increase durability by requiring more acknowledgments, reducing data loss risk.
More replicas and higher min.insync.replicas increase write latency and reduce throughput due to additional network and disk I/O.
Explain how to balance these settings based on use case, e.g., higher durability for critical data vs. higher throughput for analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.