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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

Streaming using Kafka


Kafka is a distributed, partitioned, and replicated commit log service. In simple words, it is a distributed messaging server. Kafka maintains the message feed in categories called topics. An example of a topic can be the ticker symbol of a company you would like to get news about, for example, CSCO for Cisco.

Processes that produce messages are called producers and those that consume messages are called consumers. In traditional messaging, the messaging service has one central messaging server, also called the broker. Since Kafka is a distributed messaging service, it has a cluster of brokers, which functionally acts as one Kafka broker, as shown here:

For each topic, Kafka maintains the partitioned log. This partitioned log consists of one or more partitions spread across the cluster, as shown in the following figure:

Kafka borrows a lot of concepts from Hadoop and other big data frameworks. The concept of partition is very similar to the concept of InputSplit in Hadoop...

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