Data processing articles

Looking for something else? Check the categories of Data processing:

Apache Beam Apache Spark Apache Spark GraphFrames Apache Spark GraphX Apache Spark SQL Apache Spark Streaming Apache Spark Structured Streaming PySpark

If not, below you can find all articles belonging to Data processing.

Apache Kafka source in Structured Streaming - "beyond the offsets"

Even though I've already written a few posts about Apache Kafka as a data source in Apache Spark Structured Streaming, I still had some questions in my head. In this post I will try to answer them and let this Kafka integration in Spark topic for investigation later.

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Docker images and Apache Spark applications

Containers are with us, data engineers, for several years. The concept was already introduced on YARN but the technology that really made them popular was Docker. In this post I will focus on its recommended practices to make our Apache Spark images better.

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Setting up Apache Spark on Kubernetes with microk8s

When I discovered microk8s I was delighted! An easy installation in very few steps and you can start to play with Kubernetes locally (tried on Ubuntu 16). However, running Apache Spark 2.4.4 on top of microk8s is not an easy piece of cake. In this post I will show you 4 different problems you may encounter, and propose possible solutions.

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Apache Kafka sink in Structured Streaming

I've written a lot about data sources, including Apache Kafka. However, Apache Spark is not only about sources but also about targets called sinks. In this post I will focus on Apache Kafka sink integration and try to answer some question in FAQ mode.

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Schema case sensitivity for JSON source in Apache Spark SQL

On the one hand, I appreciate JSON for its flexibility but also from the other one, I hate it for exactly the same thing. It's particularly painful when you work on a project without good data governance. The most popular pain is an inconsistent field type - Spark can manage that by getting the most common type. Unfortunately, it's a little bit trickier for less common problems, for instance when a same field has different case sensitivity.

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Apache Spark and line-based data sources

Under one of my posts I got an interesting question about ignoring maxPartitionBytes configuration entry by Apache Spark for text-based data sources. In this post I will try to answer it.

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Troubleshooting 'System memory must be at least' error

When the unit tests work on "your machine" but fail on your colleague's, you know you did something wrong. When the failures are not about test assertions but technical reasons, the "something wrong" transforms into "something strange". And it may happen with Apache Spark as well.

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Implicit datetime conversion in Apache Spark SQL

If you've ever wondered why when you write "2019-05-10T20:00", Apache Spark considers it as a timestamp field? The fact of defining it as a TimestampType is one of the reasons, but another question here is, how Apache Spark does the conversion from a string into the timestamp type? I will give you some hints in this blog post.

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Extending state store in Structured Streaming - reprocessing and limits

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.

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Extending state store in Structured Streaming - reading and writing state

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 :)

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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.

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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.

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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.

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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.

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Local deduplication or dropDuplicates?

One of the points I wanted to cover during my talk but for which I haven't enough time, was the dilemma about using a local deduplication or Apache Spark's dropDuplicates method to not integrate duplicated logs. That will be the topic of this post.

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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.

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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.

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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.

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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.

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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!

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