Apache Spark Structured Streaming articles

Why UnsafeRow.copy() for state persistence in the state store?

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.

Continue Reading β†’

Extending state store in Structured Streaming - introduction

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.

Continue Reading β†’

Extending data reprocessing period for arbitrary stateful processing applications

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.

Continue Reading β†’

Custom checkpoint file manager in Structured Streaming

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.

Continue Reading β†’

Sessionization pipeline - from Kafka to Kinesis version

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.

Continue Reading β†’

Kafka timestamp as the watermark

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.

Continue Reading β†’

Reprocessing stateful data pipelines in Structured Streaming

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.

Continue Reading β†’

Output modes in Structured Streaming

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.

Continue Reading β†’

State lifecycle management in Structured Streaming

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!

Continue Reading β†’

Delta and snapshot state store formats

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.

Continue Reading β†’

Watermark in Structured Streaming

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.

Continue Reading β†’

State store 101

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.

Continue Reading β†’

Checkpoint storage in Structured Streaming

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).

Continue Reading β†’

Apache Spark Structured Streaming and Apache Kafka offsets management

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.

Continue Reading β†’

Initializing state in Structured Streaming

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.

Continue Reading β†’

Apache Spark 2.4.0 features - foreachBatch

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 .

Continue Reading β†’

Apache Spark 2.4.0 features - watermark configuration

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.

Continue Reading β†’

Stream-to-stream joins internals

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.

Continue Reading β†’

Stream-to-stream state management

Last weeks we've discovered 2 stream-to-stream join types in Apache Spark Structured Streaming. As told in these posts, state management logic may be sometimes omitted (for inner joins) but generally it's advised to reduce the memory pressure. Apache Spark proposes 3 different state management strategies that will be detailed in the following sections.

Continue Reading β†’

Outer joins in Apache Spark Structured Streaming

Previously we discovered inner stream-to-stream joins in Apache Spark but they aren't the single supported type. Another one are outer joins that let us to combine streams without matching rows.

Continue Reading β†’