Topic archive
Kafka
Kafka architecture, consumer strategies, and event driven design.
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Kafka Security with Mutual TLS and ACLs: A KRaft Lab, the Principal Trap, and the Commands That Still Exist
A single-node Kafka 4.3.1 broker with mutual TLS on every listener and ACLs, tested with eight client scenarios: what the authorizer sees as your identity, why the ZooKeeper-era ACL commands fail, and what each failure looks like.
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Kafka Consumer Lag: What the Number Measures, Why Your Client Metric Disagrees, and What Actually Moves It
Consumer lag measured three ways on Kafka 4.3.1 (CLI, Admin API, client metric), a 480 ms latency penalty from a popular fetch.min.bytes setting, and two ‘safety’ branches in a common poll loop that never execute.
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Exactly-Once in Kafka Streams: One Crash, Two Guarantees, and Where the Transaction Stops
The same crash-and-restart run under exactly_once_v2 and at_least_once on Kafka 4.3.1: 100 correct outputs versus 159 with double counts, what a read_uncommitted consumer sees during the aborted transaction, and a side effect that ran 160 times for 100 inputs either way.
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Kafka Client Quotas on 4.3.1: What Throttling Actually Does to a Producer, Measured
Byte-rate and request quotas on a Kafka 4.3.1 broker: the Admin API as it really exists, a 30 MB run that went from 0.36 s to 12.1 s with zero retries, the burst allowance nobody mentions, a user quota that silently ignored an unauthenticated client, and the JMX names that do exist.
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Kafka Producer Metrics in Java: What the Client Reports, How Micrometer Exposes It to Prometheus, and a Per-Record Latency Timer That Compiles
All 110 metrics a kafka-clients 4.3.1 producer reports, checked against the docs and JMX; the exact Prometheus text Micrometer’s KafkaClientMetrics produces; and a send-to-ack interceptor built on the headers-aware onAcknowledgement overload, replacing sample code that never compiled.
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Implementing Kafka Producer Idempotence for Exactly-Once Delivery in Distributed Systems
Learn to configure and implement Kafka producer idempotence using Java for exactly-once message delivery, including code examples, troubleshooting, security, and performance best practices.
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Implementing Kafka Consumer Group Rebalance Listeners for Seamless Partition Transitions
Implement Kafka rebalance listeners to ensure smooth partition transitions, precise offset commits, and resilient consumer group handling.
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Implementing Kafka Consumer Offset Management for Fault-Tolerant Processing
This guide shows how to implement manual Kafka consumer offset management for reliable, fault-tolerant message processing with rebalance handling, commit control, and error recovery.
