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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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The skew problem

Distributed systems just like teams of people working on an activity perform at the most optimum level when the work is evenly distributed among all the members of the team or the cluster. Both suffer, if the work is unevenly distributed and the system performs only as fast as the slowest component.

In the case of Spark, data is distributed across the cluster. You might have come across cases where a map job runs fairly quickly by your joins or shuffles take an excessive time. In most real life cases you would have popular keys or null values in your data, which would result in some tasks getting more work than others, thus resulting in a system skew. In the database world, original keys would actually be used to create new keys with random values such that the resultant keys would be fairly unique and thus allow the system to distribute the data more evenly across the system. Of course, you would need to do a multiple stage aggregation, but this would in most cases be faster...

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