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.

Shuffle writers: BypassMergeSortShuffleWriter

In the previous blog post we discovered the SortShuffleWriter. However, the SortShuffleManager's first choice is BypassMergeSortShuffleWriter, presented in this article.

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Shuffle writers: SortShuffleWriter

In the beginning I thought that the mappers sent shuffle files to the reducers. After understanding that it was the opposite, I was thinking that a part of the shuffle data is kept in memory for the performance purposes... Once I corrected all these misbeliefs about shuffle, I noted a few points to explore. One of these points are shuffle writers that I will present in the next 3 blog posts.

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Under-the-hood: repartition

Previously we discovered what happens when you coalesce a dataset. To recall, it doesn't involve shuffle operation. It's then the opposite of a repartition operation which is a first class shuffle citizen.

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State store metrics

State store is a critical part of any stateful Structured Streaming application. It's important to know what happens when your business logic and input data interact with it. State store metrics will provide you some key insight into this interaction. If you don't know them now, no worries, it's the topic of this blog post!

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Checkpoint file manager - FileSystem and FileContext

If you read my blog post, you certainly noticed that very often I get lost on the internet. Fortunately, very often it helps me write blog posts. But the internet is not the only place where I can get lost. It also happens to me to do that with Apache Spark code and one of my most recent confusions was about FileSystem and FileContext classes.

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What's new in Apache Spark 3.1.1 - new built-in functions

Every Apache Spark release brings not only completely new components but also new native functions. The 3.1.1 is not an exception and it also comes with some new built-in functions!

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What's new in Apache Spark 3.1 - JDBC (WIP) and DataSource V2 API

Even though the change I will describe in this blog post is still in progress, it's worth attention, especially that I missed the DataSource V2 evolution in my previous blog posts.

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What's new in Apache Spark 3.1 - Kubernetes Generally Available!

After several months spent as an "experimental" feature in Apache Spark, Kubernetes was officially promoted to a Generally Available scheduler in the 3.1 release! In this blog post, we'll discover the last changes made before this promotion.

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What's new in Apache Spark 3.1 - nodes decommissioning

I have a feeling that a lot of things related to the scalability happened in the 3.1 release. General Availability of Kubernetes that I will cover next week is only one of them. The second one is the nodes decommissioning!

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What's new in Apache Spark 3.1 - predicate pushdown for JSON, CSV and Apache Avro

Predicate pushdown is a data processing technique taking user-defined filters and executing them while reading the data. Apache Spark already supported it for Apache Parquet and RDBMS. Starting from Apache Spark 3.1.1, you can also use them for Apache Avro, JSON and CSV formats!

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What's new in Apache Spark 3.1 - Project Zen

I mentioned it very shortly in the first blog post ever about PySpark. Thanks to the Project Zen initiative, the Python part of Apache Spark will become more Pythonic and user friendly. How? Let's check that in this blog post!

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What's new in Apache Spark 3.1 - Structured Streaming

Aside from the joins presented in the previous blog post, Structured Streaming also got a few other interesting new features that I will present here.

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What's new in Apache Spark 3.1 - streaming joins

In the previous blog post, you discovered what changed for joins in Apache Spark 3.1. If you remember the summary sentence, it was not the single join changes in this new release. Apart from them, you can also do a bit more with Structured Streaming joins!

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What's new in Apache Spark 3.1 - join evolutions

I have waited for writing this blog post since the Data+AI Summit 2020, where Cheng Su presented the ongoing effort to improve shuffle and stream-to-stream joins in Apache Spark 3.1. And in this blog post, I will start by sharing what changed for the joins in the new release of the framework!

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Under-the-hood: coalesce

That's probably one of the most common questions you may have heard in preliminary job interviews. What's the difference between coalesce and repartition? Many answers exist, but instead of repeating them, I will try to dig a bit deeper in this blog post and see how the coalesce works.

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Stack operation in Apache Spark SQL

Pivot operation presented 2 weeks ago transforms some cells into columns. The reverse one is called stack and it's time to see how it works!

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Performance optimization lessons from Spark+AI and Data+AI Summits

I wish I could say once day: "I optimized Apache Spark pipelines in all possible ways". But I'm aware of the realty and that can be very hard to achieve. That's why I decided to rely on the experience shared by experienced Spark users in Spark+AI and, recently, Data+AI Summit, and write a summary list of interesting optimization tips from the past talks.

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Spark SQL pivot table

If you came to data engineering after having a BI career, you certainly know what the pivot is. It was not my case and was quite amazed by this operation that transforms values from rows into columns. If you want to understand how it's possible, this article will present some internals of pivoting data in Apache Spark.

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Apache Spark performance tips - look at your code

Very often you will find Apache Spark performance tips related to the hardware (memory, GC) or the configuration parameters (shuffle partitions number, broadcast join threshold). But they're not the single ones you can implement. Moreover, IMO, you should start by the ones presented in this article and optimize your pipeline code before going into more complicated hardware and configuration tuning.

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Data+AI Summit: custom state store integration feedback

After the introductory part, it's time to share what I learned from the custom state store implementation.

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