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)

How is Spark being used?


Matei Zaharia is the creator of Apache Spark project and co-founder of DataBricks, the company which was formed by the creators of Apache Spark. Matei in his keynote at the Spark summit in Europe during fall of 2015 mentioned some key metrics on how Spark is being used in various runtime environments. The numbers were a bit surprising to me, as I had thought Spark on YARN would have higher numbers than what was presented. Here are the key figures:

  • Spark in Standalone mode - 48%
  • Spark on YARN - 40%
  • Spark on MESOS - 11%

As we can see from the numbers, almost 90% of Apache Spark installations are in standalone mode or on YARN. When Spark is being configured on YARN, we can make an assumption that the organization has chosen Hadoop as their data operating system, and are planning to move their data onto Hadoop, which means our primary source of data ingest might be Hive, HDFS, HBase, or other No SQL systems.

When Apache Spark is installed in standalone mode, the possibility...