Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

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)

Caching and uncaching of graphs


If you remember in the earlier chapters, we discussed the caching of RDDs, which basically referred to the fact that if you intend to use a particular RDD multiple times, you will need to cache the RDD, otherwise the Spark framework will recompute the RDD from scratch every time it is called.

Graphs (like RDDs) are not persisted in memory by default, and caching is the ideal option when using the graph multiple times. The implementation is quite similar where you simply call the cache on the Graph object:

myGraph.cache()

Uncaching unused objects from memory may improve performance. Cached RDDs and graphs remain memory resident until they are evicted due to memory pressures in an LRU order. For iterative computations, intermediate results from previous iterations will fill up the cache, resulting in slow garbage collection due to memory being filled up with unnecessary information.

Suggested approach: Uncache intermediate results as soon as possible, which might...