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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Concepts
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Taking a closer look at Structured Streaming


Structured Streaming has been introduced in various places in this chapter, but let's use this recipe to discuss some more details. Structured Streaming is essentially a stream-processing engine built on top of the Spark SQL engine. 

An alternative way to look at streaming data is to think of it as an infinite/unbounded table that gets continuously appended as new data arrives.

The four fundamental concepts in Structured Streaming are:

  • Input table: To input the table
  • Trigger: How often the table gets updated
  • Result table: The final table after every trigger update
  • Output table: What part of the result to write to storage after every trigger

A query may be interested in only newly appended data (since the last query), all of the data that has been updated (including appended obviously), or the whole table; this leads to the three output modes in Structured Streaming, as follows:

  • Append
  • Update
  • Complete

The DataFrame/Dataset API that is used for bounded tables...

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