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Learning Apache Spark 2

You're reading from   Learning Apache Spark 2 A beginner's guide to real-time Big Data processing using the Apache Spark framework

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785885136
Length 356 pages
Edition 1st Edition
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Author (1):
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Muhammad Asif Abbasi Muhammad Asif Abbasi
Author Profile Icon Muhammad Asif Abbasi
Muhammad Asif Abbasi
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Table of Contents (12) Chapters Close

Preface 1. Architecture and Installation 2. Transformations and Actions with Spark RDDs FREE CHAPTER 3. ETL with Spark 4. Spark SQL 5. Spark Streaming 6. Machine Learning with Spark 7. GraphX 8. Operating in Clustered Mode 9. Building a Recommendation System 10. Customer Churn Prediction Theres More with Spark

Steps involved in a streaming app

Let's look at the steps involved in building a streaming application.

  1. The first thing is to create a Streaming context. This can be done as shown in the preceding code example. If you have a SparkContext already available, you can reuse the SparkContext to create a Streaming context as follows:
            val ssc = new StreamingContext(sc, Seconds(5))
            sc = Spark Context reference

    Seconds(5) is the batch duration. This can be specified in milliseconds, seconds, or minutes.

    It is important to note that in local testing, while specifying the master in the configuration object, do not use local or local[1]. This will mean that only a single thread will be used for running the tasks locally.

    Note

    If you are using an input stream based on a receiver, such as, Kafka, Sockets, or Flume, then the single thread will be utilized to run the receiver, leaving you with no threads to process the incoming data. You should always allocate enough cores for your streaming...

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