Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

What is DataSet API?


Spark announced Dataset API in Spark 1.6, an extension of DataFrame API representing a strongly-typed immutable collection of objects mapped to a relational schema. Dataset API was developed to take advantage of the Catalyst optimiser by exposing expressions and data fields to the query planner. Dataset brings the compile-type safety, which means you can check your production applications for errors before they are run, an issue that constantly comes up with DataFrame API.

One of the major benefits of DataSet API was a reduction in the amount of memory being used, as Spark framework understood the structure of the data in the dataset and hence created an optimal layout in the memory space when caching datasets. Tests have shown that DataSet API can utilize 4.5x lesser memory space compared to the same data representation with an RDD.

Figure 4.1 shows analysis errors shown by Spark with various APIs for a distributed job with SQL at one end of the spectrum and Datasets...