December 2016
Beginner to intermediate
392 pages
8h 13m
English
Content preview from Apache Spark for Data Science CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Working with the Spark programming model
This recipe explains the fundamentals of the Spark programming model. It covers the RDD basics that is, Spark provides a Resilient Distributed Dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated in parallel. It also covers how to create and perform transformations and actions on RDDs.
How to do it…
- Let's create RDDs and apply a few transformations such as
mapandfilter, and afewactions such ascount,take,top, and so on, in Spark-shell:scala> val data = Array(1, 2, 3, 4, 5) scala> val rddData = sc.parallelize(data) scala> val mydata = data.filter(ele => ele%2==0) mydata: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[3] at filter at ...
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