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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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Caching and persistence


Caching and persistence are two key areas that developers can use to improve performance of Spark applications. We've looked at caching in RDDs, and while DStreams also provide the persist() method, the persist() method on a DStream will persist all RDDs within the DStream in memory. This is especially useful if the computation happens multiple times on a DStream, which is especially true in window-based operations.

It is for this reason that developers do not explicitly need to call a persist() on window-based operations and they are automatically persisted. The data persistence mechanism depends on the source of the data, for example, for data coming from network sources such as sockets or Kafka, data is replicated across a minimum of two nodes by default.

The difference between cache() and persist() are:

  • cache(): Persists the RDDs of the DStream with the default storage level (MEMORY_ONLY_SER). Cache() under the hood and calls the persist() method with the default...
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Learning Apache Spark 2
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