Book Image

Learning Apache Spark 2

By : Abbasi
Book Image

Learning Apache Spark 2

By: Abbasi

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)

Sizing up your executors

When you set up Spark, executors are run on the nodes in the cluster. To put it simply, executors are the processes where you:

  • Run your compute
  • Store your data

Each application has its own executor processes and they will stay up and running until your application is up and running. So by definition, they seem to be quite important from a performance perspective, and hence the three key metrics during a Spark deployment are:

  • --num-executors: How many executors you need?
  • --executor-cores: How many CPU cores would you want to allocate to each executor?
  • --executor-memory: How much memory will you like to assign to each executor process?

So how do you allocate physical resources to Spark? While this may generally depend on the nature of the workload, you can vary between the following extreme parameters.

Sizing up your executors

Figure 11.2: Executor granularity

Extreme approaches are generally a bad option except for very specific workload situations. For example, if you define very small sized executors...