Jul 15 2020 54 mins 45
Apache Kafka® Committer and PMC member Matthias J. Sax explains fault tolerance, high-availability stream processing, and how it’s done in Kafka Streams. He discusses the differences between changelogging vs. checkpointing and the complexities checkpointing introduces. From there, Matthias explains what hot standbys are and how they are used in Kafka Streams, why Kafka Streams doesn’t do watermarking, and finally, why Kafka Streams is a library and not infrastructure. EPISODE LINKS Ask Confluent #7: Kafka Consumers and Streams Failover Explained ft. Matthias Sax Ask Confluent #8: Guozhang Wang on Kafka Streams Standby Tasks How to Run Kafka Streams on Kubernetes ft. Viktor Gamov Kafka Streams Interactive Queries Go Prime Time Highly Available, Fault-Tolerant Pull Queries in ksqlDB KIP-535: Allow state stores to serve stale reads during rebalance KIP-562: Allow fetching a key from a single partition rather than iterating over all the stores on an instance KIP-441: Smooth Scaling Out for Kafka Streams Skip to end of metadata Join the Confluent Community Slack Learn more with Kafka tutorials, resources, and guides at Confluent Developer Use 60PDCAST to get an additional $60 of free Confluent Cloud usage*
