Apache Spark SQL articles

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Range partitioning in Apache Spark SQL

The most popular partitioning strategy divides the dataset by the hash computed from one or more values of the record. However other partitioning strategies exist as well and one of them is range partitioning implemented in Apache Spark SQL with repartitionByRange method, described in this post. Continue Reading →

Regression tests with Apache Spark SQL joins

Regressions are one of the risks of our profession. Fortunately, we can limit the risk thanks to different testing strategies. One of them are regression tests that we can use to check whether the modified data processing logic didn't introduce the regressions simply by comparing two datasets. Continue Reading →

Apache Spark SQL and types resolution in semi-structured data

One of data governance goals is to ensure data consistency across different producers. Unfortunately, very often it's only a theory and especially when the data format is schemaless. It's why the data exploration is an important step in the process of data pipeline definition. In this post I wanted to do a small exercise and check how Apache Spark SQL behaves with inconsistent data. Continue Reading →

Introduction to custom optimization in Apache Spark SQL

In November 2018 bithw1 pointed out to me a feature that I haven't used yet in Apache Spark - custom optimization. After some months consacred to learning Apache Spark GraphX, I finally found a moment to explore it. This post begins a new series about Apache Spark customization and it covers the basics, i.e. the 2 available methods to add the custom optimizations. Continue Reading →

Apache Spark 2.4.0 features - Avro data source

Apache Avro became one of the serialization standards, among others because of its use in Apache Kafka's schema registry. Previously to work with Avro files with Apache Spark we needed Databrick's external package. But it's no longer the case starting from 2.4.0 release where Avro became first-class citizen data source. Continue Reading →

Defining schemas in Apache Spark SQL with builder design pattern

Schemas are one of the key parts of Apache Spark SQL and its distinction point with old RDD-based API. When we deal with data coming from a structured data source as a relational database or schema-based file formats, we can let the framework to resolve the schema for us. But the things complicate when we're working with semi-structured data as JSON and we must define the schema by hand. Continue Reading →

Multiple SparkSession for one SparkContext

Some months ago bithw1 posted an interesting question on my Github about multiple SparkSessions sharing the same SparkContext. If you have similar interrogations, feel free to ask - maybe it will give a birth to more detailed post adding some more value to the community. This post, at least, tries to do so by answering the question. Continue Reading →

Apache Spark SQL, Hive and insertInto command

Some time ago on my Github bithw1 pointed out an interesting behavior of Hive integration on Apache Spark SQL. To not delve too much into details now, I can tell that the behavior was about not respected DataFrame schema. Our quick exchange ended up with an explanation but it also encouraged me to go much more into details to understand the hows and whys. Continue Reading →

Dealing with nested data in Apache Spark SQL

Nested data structure is very useful in data denormalization for Big Data needs. It avoids joins that we could use for several related and fully normalized datasets. But processing such data structures is not always simple. Fortunately Apache Spark SQL provides different utility functions helping to work with them. Continue Reading →