Apache Spark SQL articles

What's new in Apache Spark 3.0 - local shuffle reader

So far you learned about skew optimization and coalesce shuffle partition optimizations made by the Adaptive Query Execution engine. But they're not the single ones and the next one you will discover is also related to the shuffle.

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What's new in Apache Spark 3.0 - reuse adaptive subquery

Apart from big and complex changes in the Adaptive Query Execution like skews or partitions coalescing, there are also some others, less complex. Although their smaller complexity, it doesn't mean they are not important. Especially when one of these changes offers a reuse of the subqueries.

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What's new in Apache Spark 3.0 - join skew optimization

Shuffle partitions coalesce is not the single optimization introduced with the Adaptive Query Execution. Another one, addressing maybe one of the most disliked issues in data processing, is joins skew optimization that you will discover in this blog post.

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What's new in Apache Spark 3.0 - shuffle partitions coalesce

In my previous blog post you could learn about the Adaptive Query Execution improvement added to Apache Spark 3.0. At that moment, you learned only about the general execution flow for the adaptive queries. Today it's time to see one of possible optimizations that can happen at this moment, the shuffle partition coalesce.

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What's new in Apache Spark 3.0 - Adaptive Query Execution

A query adapting to the data characteristics discovered one-by-one at runtime? Yes, in Apache Spark 3.0 it's possible thanks to the Adaptive Query Execution!

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What's new in Apache Spark 3.0 - PostgreSQL feature parity

Apart from the date and time management, another big feature of Apache Spark 3.0 is the work on the PostgreSQL feature parity, that will be the topic of my new article from the series.

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What's new in Apache Spark 3.0 - binary data source

I remember my first days with Apache Spark and the analysis of available RDD data sources. Since then, I have used a lot of them, except the binary data which is a new implemented part in Apache Spark SQL in the release 3.0.

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What's new in Apache Spark 3.0 - new SQL functions

After date time management, it's time to see another important feature of Apache Spark 3.0, he new SQL functions.

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Ignoring files issues in Apache Spark SQL

I have to consider myself as a lucky guy since I've never had to deal with incorrectly formatted files. However, that's not the case of everyone. Hopefully, Apache Spark comes with few configuration options to manage that.

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What's new in Apache Spark 3.0 - Proleptic Calendar and date time management

When I was writing my blog post about datetime conversion in Apache Spark 2.4, I wanted to check something on Apache Spark's Github. To my surprise, the code had nothing in common with the code I was analyzing locally. And that's how I discovered the first change in Apache Spark 3.0. The first among few others that I will cover in a new series "What's new in Apache Spark 3.0".

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Unions in Apache Spark SQL

You have 2 different datasets and want to process them as a single unit? Maybe you have some legacy data that you need to process alongside the brand new dataset? JOIN is not an option because the goal is to build a single processing unit and not combine the rows. UNION operation can be a good fit for that.

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Apache Spark SQL partitionBy - shuffle or not to shuffle?

I remember my first time with partitionBy method. I was reading data from an Apache Kafka topic and writing it into hourly-based partitioned directories. To my surprise, Apache Spark was generating always 1 file and my first thought... oh, it's shuffling the data. But I was wrong and in this post will explain why.

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Idempotent file generation in Apache Spark SQL

Some time ago I was thinking how to partition the data and ensure that we can reprocess it easily. Overwrite mode was not an option since the data of one partition could be generated by 2 different batch executions. That's why I started to think about implementing an idempotent file output generator and, therefore, discover file sink internals in practice.

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Apache Spark's _SUCESS anatomy

_SUCCESS file generated by Apache Spark SQL when you successfully generate a dataset, is often a big question for newcomers. Why does the framework need this file? How is it generated? I will cover these aspects in this article.

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sortWithinPartitions in Apache Spark SQL

Few weeks ago when I was preparing a talk for one local meetup, I wanted to list the most common operations we can do with Spark for the newcomers. And I found one I haven't used before, namely sortWithinPartitions.

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Reorder JOIN optimizer - star schema

I didn't know that join reordering is quite interesting, though complex, topic in Apache Spark SQL. The queries not only can be transformed into the ones using JOIN ... ON clauses. They can also be reordered accordingly to the star schema which we'll try to see in this post.

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Reorder JOIN optimizer - cost-based optimization

In my previous post I explained how Apache Spark can reorder JOINs based on the logical plan. Today I'll focus on another aspect of reordering which uses cost estimation for the proposed plans.

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Reorder JOIN optimizer

One of the reasons why I like my blogging activity is that from time to time the exchange is bidirectional. It happens mostly on Github but also on the comments under the post and I appreciate the situation when I don't know the answer and must dig a little to explain it in a blog post :) I wrote this one thanks to bithw1 issue created on my Spark playground repository (thank you for another interesting question btw :)).

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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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