January 2019
Beginner to intermediate
154 pages
4h 31m
English
Content preview from Apache Spark Quick Start Guide
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Spark Optimizations
In the previous chapters, we learned how to use Spark to implement a variety of use cases using features such as RDDs, DataFrames, Spark SQL, MLlib, GraphX/Graphframes, and Spark Streaming. We also discussed how to monitor your applications to better understand their behavior in production. However, sometimes, you would want your jobs to run efficiently. We measure the efficiency of any job on two parameters: runtime and storage space. In the Spark application, you might also be interested in the statistic of the data shuffles between the nodes. We discussed some of the optimizations in the earlier chapters, but, in this chapter, we'll discuss more optimizations that can help you achieve some performance benefits.
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.