show () // +-+ // | value| // +-+ // |Name: Justin| // +-+ // or by field name Dataset teenagerNamesByFieldDF = teenagersDF. getString ( 0 ), stringEncoder ) teenagerNamesByIndexDF. map ( ( MapFunction ) row -> "Name: " + row. STRING () Dataset teenagerNamesByIndexDF = teenagersDF. sql ( "SELECT name FROM people WHERE age BETWEEN 13 AND 19" ) // The columns of a row in the result can be accessed by field index Encoder stringEncoder = Encoders. createOrReplaceTempView ( "people" ) // SQL statements can be run by using the sql methods provided by spark Dataset teenagersDF = spark. class ) // Register the DataFrame as a temporary view peopleDF. Import import import java.io.Serializable import. import .Dataset import .Row import .Encoder import .Encoders public static class Person implements Serializable ) // Apply a schema to an RDD of JavaBeans to get a DataFrame Dataset peopleDF = spark. Getting Started Starting Point: SparkSession Throughout this document, we will often refer to Scala/Java Datasets of Rows as DataFrames. While, in Java API, users need to use Dataset to represent a DataFrame. In the Scala API, DataFrame is simply a type alias of Dataset. In Scala and Java, a DataFrame is represented by a Dataset of Rows. DataFrames can be constructed from a wide array of sources suchĪs: structured data files, tables in Hive, external databases, or existing RDDs. It is conceptuallyĮquivalent to a table in a relational database or a data frame in R/Python, but with richer The case for R is similar.Ī DataFrame is a Dataset organized into named columns. you can access the field of a row by name naturally 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. UDF Registration Moved to sqlContext.udf (Java & Scala).Removal of the type aliases in for DataType (Scala-only).Isolation of Implicit Conversions and Removal of dsl Package (Scala-only).Upgrading from Spark SQL 1.0-1.2 to 1.3.Behavior change on DataFrame.withColumn. Interacting with Different Versions of Hive Metastore.Specifying storage format for Hive tables.Hive metastore Parquet table conversion.Type-Safe User-Defined Aggregate Functions.Untyped User-Defined Aggregate Functions.Untyped Dataset Operations (aka DataFrame Operations).
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