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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
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Sumit Kumar
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Understanding micro batching


Micro batching is defined as the procedure in which the incoming stream of messages is processed by dividing them into group of small batches. This helps to achieve the performance benefits of batch processing; however, at the same time, it helps to keep the latency of processing of each message minimal.

Here, an incoming stream of messages is treated as a flow of small batches of messages and each batch is passed to a processing engine, which outputs a processed stream of batches.

In Spark Streaming, a micro batch is created based on time instead of size, that is, events received in a certain time interval, usually in milliseconds, are grouped together into a batch. This assures that any message does not have to wait much before processing and helps to keep latency of processing under control.

Another advantage of micro batching is that it helps to keep the volume of control message lower. For example, if a system requires an acknowledgement is to be sent by the...

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