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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Loading data from Apache Cassandra


Apache Cassandra is a NoSQL database with a masterless ring cluster structure. While HDFS is a good fit for streaming data access, it does not work well with random access. For example, HDFS will work well when your average file size is 100 MB and you want to read the whole file. If you frequently access the nth line in a file or some other part as a record, HDFS would be too slow.

Relational databases have traditionally provided a solution to that, providing low latency, random access, but they do not work well with big data. NoSQL databases such as Cassandra fill the gap by providing relational database type access but in a distributed architecture on commodity servers.

In this recipe, we will load data from Cassandra as a Spark RDD. To make that happen Datastax, the company behind Cassandra, has contributed spark-cassandra-connector. This connector lets you load Cassandra tables as Spark RDDs, write Spark RDDs back to Cassandra, and execute CQL queries...

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