Confluent Platform 7.0 と Cluster Linking でクラウドへのリアルタイムブリッジを構築 | ブログを読む

The Flux Capacitor of Kafka Streams and ksqlDB

How does Kafka Streams and ksqlDB reason about time, how does it affect my application, and how do I take advantage of it? In this talk, we explore the "time engine" of Kafka Streams and ksqlDB and answer important questions how you can work with time. What is the difference between sliding, time, and session windows and how do they relate to time? What timestamps are computed for result records? What temporal semantics are offered in joins? And why does the suppress() operator not emit data? Besides answering those questions, we will share tips and tricks how you can "bend" time to your needs and when mixing event-time and processing-time semantics makes sense. Six month ago, the question "What's the time? …and Why?" was asked and partly answered at Kafka Summit in San Francisco, focusing on writing data, data storage and retention, as well as consuming data. In this talk, we continue our journey and delve into data stream processing with Kafka Streams and ksqlDB, that both offer rich time semantics. At the end of the talk, you will be well prepared to process past, present, and future data with Kafka Streams and ksqlDB.

プレゼンター

Matthias J. Sax

Matthias is an Apache Kafka committer and PMC member, and works as a software engineer at Confluent. His focus is data stream processing in general, and thus he contributes to ksqlDB and Kafka Streams. Before joining Confluent, Matthias conducted research on distributed data stream processing systems at Humboldt-University of Berlin, were he received his Ph.D. Matthias is also a committer at Apache Flink and Apache Storm.