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Learning Apache Spark 2

Learning Apache Spark 2

By : Abbasi
3.8 (6)
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Learning Apache Spark 2

Learning Apache Spark 2

3.8 (6)
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)
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Performance tuning

Most of you would have heard of the old adage "Good, Fast, Cheap - Pick any two". That adage is still true, though the scales have shifted slightly with the open source model where the software is free but does need a relevant skillset to make the best use of it. That skillset comes at a cost, and performance tuning is one area where that specialized skillset is a must-have. When you talk about performance tuning, the underlying assumption is that your system is already working, fully functional.

Performance tuning

Figure 11.1: Good - Fast and Cheap

You are not happy with the response rates. However, that does not have to be so all the time. You can take certain key decisions that can help you build a relatively optimized system early on.

So what are the key areas for consideration? Each distributed application has to work with five major computing resources:

  • Network
  • Disk
  • I/O
  • CPU
  • Memory

For an application to perform at its optimum level, it has to make sure it makes the best of all...

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