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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Overview


The following diagram shows potential data sources for Apache Streaming, such as Kafka, Flume, and HDFS:

These feed into the Spark Streaming module and are processed as Discrete Streams. The diagram also shows that other Spark module functionality, such as machine learning, can be used to process stream-based data.

The fully processed data can then be an output for HDFS, databases, or dashboards. This diagram is based on the one at the Spark streaming website, but we wanted to extend it to express the Spark module functionality:

When discussing Spark Discrete Streams, the previous figure, taken from the Spark website at http://spark.apache.org/, is the diagram that we would like to use.

The green boxes in the previous figure show the continuous data stream sent to Spark being broken down into a Discrete Stream (DStream).

Note

A DStream is nothing other than an ordered set of RDDs. Therefore, Apache Spark Streaming is not real streaming, but micro-batching. The size of the RDDs backing...

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