Many of the benefits of the Dataset API are already available (i.e. Python does not have the support for the Dataset API. The Dataset API is available in Scala and Manipulated using functional transformations ( map, flatMap, filter, etc.). A Dataset can be constructed from JVM objects and then Typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimizedĮxecution engine. Datasets and DataFramesĪ Dataset is a distributed collection of data.ĭataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong You can also interact with the SQL interface using the command-line SQL from within another programming language the results will be returned as a Dataset/DataFrame. For more on how toĬonfigure this feature, please refer to the Hive Tables section. Spark SQL can also be used to read data from an existing Hive installation. One use of Spark SQL is to execute SQL queries. The spark-shell, pyspark shell, or sparkR shell. This unification means that developers can easily switch back and forth betweenĭifferent APIs based on which provides the most natural way to express a given transformation.Īll of the examples on this page use sample data included in the Spark distribution and can be run in The same execution engine is used, independent of which API/language you are using to express theĬomputation. Interact with Spark SQL including SQL and the Dataset API. Spark SQL uses this extra information to perform extra optimizations. Unlike the basic Spark RDD API, the interfaces providedīy Spark SQL provide Spark with more information about the structure of both the data and the computation being performed.
Spark SQL is a Spark module for structured data processing.
#MYSQL CREATE VIEW DYNAMIC TABLE NAME FXML REGISTRATION#
UDF Registration Moved to sqlContext.udf (Java & Scala).
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