Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

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...