April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Broadcast variables and accumulators
Another core feature of Spark is the ability to create two special types of variables--broadcast variables and accumulators.
A broadcast variable is a read-only variable that is created from the driver program object and made available to the nodes that will execute the computation. This is very useful in applications that need to make the same data available to the worker nodes in an efficient manner, such as distributed systems. Spark makes creating broadcast variables as simple as calling a method on SparkContext, as follows:
val broadcastAList = sc.broadcast(List("a", "b", "c", "d", "e"))
A broadcast variable can be accessed from nodes other than the driver program that created it (that is, the worker ...
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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.