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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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I/O tuning

I/O is critical when you are reading data to/from disk. The major things that you need to consider are:

Data locality

The closer your computer is to your data, the better the performance for your jobs. Moving compute to your data is more optimal compared to moving data to your compute, and therein lies the concept of data locality. Process and data can be quite close to each other, or in some cases on entirely different nodes. The locality levels are defined as follows:

  • PROCESS_LOCAL: This is the best possible option where data resides in the same JVM as the process, and hence is called local to the process.
  • NODE_LOCAL: This indicates that the data is not in the same JVM, but is on the same node. This provides a fast way to access the data, despite it being slower than PROCESS_LOCAL, since the data has to be transferred from either the disk or another process.
  • RACK_LOCAL: There can be multiple servers in the RACK. This option indicates that the data is on the same rack as the current...
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