In my previous post I have shown you the writing and reading parts of my custom state store implementation. Today it's time to cover the data reprocessing and also the limits of the solution.
In my previous post I introduced the classes involved in the interactions with the state store, and also shown the big picture of the implementation. Today it's time to write some code :)
In my last Spark+AI Summit 2019 follow-up posts I'm implementing a custom state store. The extension is inspired by the default state store. At the moment of code analysis, one of the places that intrigued me was the put(key: UnsafeRow, value: UnsafeRow) method. Keep reading if you're curious why.
When I started to think about implementing my own state store, I had an idea to load the state on demand for given key from a distributed and single-digit milliseconds latency store like AWS DynamoDB. However, after analyzing StateStore API and how it's used in different places, I saw it won't be easy.
After my Summit's talk I got an interesting question on "off" for the data reprocessing of sessionization streaming pipeline. I will try to develop the answer I gave in this post.
In this post I will start the customization part of the topics covered during my talk. The first customized class will be the class responsible for the checkpoint management.
I'm slowly going closer to the end of Spark+AI Summit follow-up posts series. But before I terminated, I owe you an explanation for how to run the pipeline from my Github on Kinesis.
In the first version of my demo application I used Kafka's timestamp field as the watermark. At that moment I was exploring the internals of arbitrary stateful processing so it wasn't a big deal. But just in case if you're wondering what I didn't keep that for the official demo version, I wrote this article.
During my talk, I insisted a lot on the reprocessing part. Maybe because it's the less pleasant part to work with. After all, we all want to test new pipelines rather than reprocess the data because of some regressions in the code or any other errors. Despite that, it's important to know how Structured Streaming integrates with this data engineering task.
The series of notes I took during my Apache Spark Summit preparation continues. Today it's time to cover output modes that I also used in the presented solution for sessionization problem.
In this post about state store in Structured Streaming I will focus on the state lifecycle management. The goal is to see what happens when the state expires, why removing it from the state store is so important and some other interesting questions!
State store uses checkpoint location to persist state which is locally cached in memory for faster access during the processing. The checkpoint location is used at the recovery stage. An important thing to know here is that there are 2 file formats with checkpointed state, delta and snapshot files.
I was already taking about watermark on my blog but this time I will focus more on its use in the context of a stateful processing.
After checkpointing, it's time to start a new chapter of Spark Summit AI 2019 preparation posts. And in this new chapter I will describe the state store. It's the first of 3 articles about this important part of the stateful processing.
At the moment of writing this post I'm preparing the content for my first Spark Summit talk about solving sessionization problem in batch or streaming. Since I'm almost sure that I will be unable to say everything I prepared, I decided to take notes and transform them into blog posts. You're currently reading the first post from this series (#Spark Summit 2019 talk notes).
Some time ago I got 3 interesting questions about the implementation of Apache Kafka connector in Apache Spark Structured Streaming. I will answer them in this post.
Some time ago I was asked by Sunil whether it was possible to load the initial state in Apache Spark Structured Streaming like in DStream-based API. Since the response was not obvious, I decided to investigate and share the findings through this post.
When I first heard about the foreachBatch feature, I thought that it was the implementation of foreachPartition in the Structured Streaming module. However, after some analysis I saw how I was wrong because this new feature addresses other but also important problems. You will find more .
The series about Apache Spark 2.4.0 features continues. After last week's discovery of bucket pruning, it's time to switch to Structured Streaming module and see its major evolution.
In 3 recent posts about Apache Spark Structured Streaming we discovered streaming joins: inner joins, outer joins and state management strategies. Discovering what happens under-the-hood of all of these operations is a good point to sum up the series.
