Don’t you just hate telling your customers “No”? “No, I can’t get you the data that quickly”, or “No that logic isn’t possible to implement” really aren’t fun to say. But what if you had a tool that would allow you to implement those use cases? What if it was in a technology you were already familiar with — say, Spark Structured Streaming? There is a brand new arbitrary stateful operations API called TransformWithState, and after attending this deep dive you won’t have to say “No” anymore. During this presentation we’ll go through some real-world use cases and build them step-by-step. Everything from state variables, process vs. event time, watermarks, timers, state TTL, and even how you can initialize state with the checkpoint of another stream. Unlock your use cases with the power of Structured Streaming’s TransformWithState!

Let’s face it -- data is messy. And your company’s business requirements? Even messier. You’re staring at your screen, knowing there is a tool that will let you give your business partners the information they need as quickly as they need it. There’s even a Python version of it now. But…it looks kind of scary. You’ve never used it before, and you don’t know where to start. Yes, we’re talking about the dreaded flatMapGroupsWithState. But fear not - we’ve got you covered.

In this session, we’ll take a real-word use case and use it to show you how to break down flatMapGroupsWithState into its basic building blocks. We’ll explain each piece in both Scala and the newly-released Python, and at the end we’ll illustrate how it all comes together to enable the implementation of arbitrary stateful operations with Spark Structured Streaming.

Talk by: Angela Chu

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