September 2016
Beginner to intermediate
326 pages
6h 47m
English
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Persistence and caching
One of the unique features of Spark is persisting RDDs in memory. You can persist an RDD with persist or cache transformations as shown in the following:
>>> myRDD.cache() >>> myRDD.persist()
Both the preceding statements are the same and cache data at the MEMORY_ONLY storage level. The difference is cache refers to the MEMORY_ONLY storage level, whereas persist can choose different storage levels as needed, as shown in the following table. The first time it is computed with an action, it will be kept in memory on the nodes. The easiest way to know the percentage of the cached RDD and its size is to check the Storage tab in the UI as shown in Figure 3.11:
Figure 3.11: Cached RDD – percentage and size cached.
Storage levels ...
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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.
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.