Advanced Operations and Optimizations in Spark
In this chapter, we will delve into the advanced capabilities of Apache Spark, equipping you with the knowledge and techniques necessary to optimize your data processing workflows. From the inner workings of the Catalyst optimizer to the intricacies of different types of joins, we will explore advanced Spark operations that empower you to harness the full potential of this powerful framework.
The chapter will cover the following topics:
- Different options to group data in Spark DataFrames.
- Various types of joins in Spark, including inner join, left join, right join, outer join, cross join, broadcast join, and shuffle join, each with its unique use cases and implications
- Shuffle and broadcast joins, with a focus on broadcast hash joins and shuffle sort-merge joins, along with their applications and optimization strategies
- Reading and writing data to disk in Spark using different data formats, such as CSV, Parquet,...
