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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav
Toc

Table of Contents (19) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

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 DataFrame. To make that happen, Datastax, the company behind Cassandra, has contributed spark-cassandra-connector. This package lets you load Cassandra tables as DataFrames, write back to Cassandra, and execute CQL queries.

How...

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