August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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GraphFrames
Note that so far, to compute any interesting indicators on a given graph, we had to use the compute model of the graph, an extension of what we know from RDDs. With Spark's DataFrame or Dataset concept in mind, the reader may wonder if there is any possibility to use an SQL-like language to do run queries against a graph for analytics. Query languages often provide a convenient way to get results quickly.
This is indeed possible with GraphFrames. The library was developed by Databricks and serves as natural extension of GraphX graphs to Spark DataFrames. Unfortunately, GraphFrames are not part of Spark GraphX, but instead available as Spark package. To load GraphFrames upon starting spark-submit, simply run
spark-shell --packages ...
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