For more details, please read the API doc.
You can get values from Dataset directly, by calling some actions, or transform the Dataset to get a new one. textFile ( "README.md" ) textFile : .Dataset = Let’s make a new Dataset from the text of the README file in the Spark source directory: scala > val textFile = spark. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. Spark’s primary abstraction is a distributed collection of items called a Dataset. Start it by running the following in the Spark directory: It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. Interactive Analysis with the Spark Shell Basics See the SQL programming guide to get more information about Dataset.
However, we highly recommend you to switch to use Dataset, which has better performance than RDD. The RDD interface is still supported, and you can get a more detailed reference at the RDD programming guide. After Spark 2.0, RDDs are replaced by Dataset, which is strongly-typed like an RDD, but with richer optimizations under the hood. Note that, before Spark 2.0, the main programming interface of Spark was the Resilient Distributed Dataset (RDD). You can download a package for any version of Hadoop. To follow along with this guide, first, download a packaged release of Spark from the Then show how to write applications in Java, Scala, and Python. We will first introduce the API through Spark’s This tutorial provides a quick introduction to using Spark. Interactive Analysis with the Spark Shell.
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