The saying goes that there are only two hard things in Computer Science: cache invalidation, and naming things. Well, turns out the first one is solved actually ;)
Join us for this session to learn how to keep read views of your data in distributed caches close to your users, always kept in sync with your primary data stores change data capture. You will learn how to
- Implement a low-latency data pipeline for cache updates based on Debezium, Apache Kafka, and Infinispan
- Create denormalized views of your data using Kafka Streams and make them accessible via plain key look-ups from a cache cluster close by
- Propagate updates between cache clusters using cross-site replication
We'll also touch on some advanced concepts, such as detecting and rejecting writes to the system of record which are derived from outdated cached state, and show in a demo how all the pieces come together, of course connected via Apache Kafka.
발표자
Gunnar Morling
DecodableGunnar Morling is a software engineer and open-source enthusiast by heart, currently working at Decodable on stream processing based on Apache Flink. In his prior role as a software engineer at Red Hat, he led the Debezium project, a distributed platform for change data capture. He is a Java Champion and has founded multiple open source projects such as JfrUnit, kcctl, and MapStruct. Gunnar is an avid blogger and has spoken at a wide range of conferences like QCon, Java One, and Devoxx.