August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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Design of Sparkling Water
Sparkling Water is designed to be executed as a regular Spark application. Consequently, it is launched inside a Spark executor created after submitting the application. At this point, H2O starts services, including a distributed key-value (K/V) store and memory manager, and orchestrates them into a cloud. The topology of the created cloud follows the topology of the underlying Spark cluster.
As stated previously, Sparkling Water enables transformation between different types of RDDs/DataFrames and H2O's frame, and vice versa. When converting from a hex frame to an RDD, a wrapper is created around the hex frame to provide an RDD-like API. In this case, data is not duplicated but served directly from the underlying ...
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