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Tiny Flink — Minimizing the memory footprint of Apache Flink

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Tiny Flink — Minimizing the memory footprint of Apache Flink
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Release Date2023

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Apache Flink has been designed for, and is mostly used with large-scale real-time data processing use-cases. Companies report about TBs of data being processed per second, or TBs of state in huge clusters. But what if you need to process low-throughput streams? Running a full, distributed Flink cluster might be an overkill, as there’s quite a bit of overhead for distributed coordination. In this talk, we’ll explore options to reduce your resource footprint. We’ll dive deeper into Flink’s MiniCluster, allowing you to run Flink in-JVM for integration tests, as a micro service or just a small processing your data in Kubernetes. We will also discuss lessons learned from running MiniCluster in production for a service offering Flink SQL in the cloud. Attend this talk if you want to learn about Apache Flink and its various options to deploy and configure it.