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)

What is Structured Streaming?


We've covered discretized streams in quite a lot of detail. However, if you have been following the Spark news recently, you may have heard of the new DataFrame/DataSet-based streaming framework named Structured Streaming. Why is there a need for a new streaming framework? We've talked about how revolutionary the concept of Spark Streaming using DStreams was, and how you can actually combine multiple engines such as SQL, Streaming, Graph, and ML to build a data pipeline, so why the need for a new engine altogether?

Based on the experience with Spark Streaming, the team at Apache Spark released that there were a few issues with DStreams. The top three issues were as follows:

  • As we have seen in the preceding examples, DStreams can work with the batch time, but not the event time inside the data.
  • While every effort was made to keep the API similar, the Streaming API was still different to RDD API in the sense that you cannot take a Batch job and start running it as...