February 2017
Intermediate to advanced
274 pages
5h 58m
English
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Chapter 2. Resilient Distributed Datasets
Resilient Distributed Datasets (RDDs) are a distributed collection of immutable JVM objects that allow you to perform calculations very quickly, and they are the backbone of Apache Spark.
As the name suggests, the dataset is distributed; it is split into chunks based on some key and distributed to executor nodes. Doing so allows for running calculations against such datasets very quickly. Also, as already mentioned in Chapter 1, Understanding Spark, RDDs keep track (log) of all the transformations applied to each chunk to speed up the computations and provide a fallback if things go wrong and that portion of the data is lost; in such cases, RDDs can recompute the data. This data lineage is another line ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
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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.