Understanding the CAP Theorem with Apache Kafka
summary
Explains how Apache Kafka maps CAP tradeoffs to concrete settings: shows a three-broker cluster under network split losing unreplicated messages or triggering leader elections, describes replication (replicas consume from leader and write to disk), and details metadata in ZooKeeper using ephemeral znodes. Covers durability vs latency choices via producer acks=0/1/all and ISR/replication factor (e.g., 2 vs 5), shows performance impacts, and points to Jepsen-style tests for real-world failures.
AI-written summary · about
chapters (9)
- 0:00Distributed systems and CAP
- 10:10High availability in Kafka
- 14:25CAP theorem with Kafka
- 18:27Consistency vs availability
- 22:39PACELC implications
- 26:59Advanced tuning for balance
- 30:56Consistency and availability levels
- 37:15Linear and sequential consistency
- 40:02Resources and conclusion
related talks
53mDemystifying Kafka
52mCAP Theorem - two decades and few clouds later
20mCAP Theorem Reloaded
24mMake Your Kafka Cluster Production-Ready
02h 05mA Deep Dive into Apache Kafka This is Event Streaming
57mScaling the CAP Theorem for Modern Distributed Systems
01h 00mKafka as a Platform: the Ecosystem from the Ground Up
13mScaling Kafka on Kubernetes