Semi, leftsemi, left_semi, anti, leftanti, left_anti. Leftouter, left_outer, right, rightouter, right_outer, Inner, cross, outer, full, fullouter, full_outer, left, If you would explicitly like to perform a cross join use theĭifferent from other join functions, the join columns will only appear once in the output, agg(avg(l( "salary")), max(l( "age"))) Annotations () Source Dataset.scala SinceĮqui-join with another DataFrame using the given columns. join(department, l( "deptId").equalTo(l( "id"))) agg(avg(people( "salary")), max(people( "age")))Īnd in Java: // To create Dataset using SparkSessionĭataset people = spark.read().parquet( ".") ĭataset department = spark.read().parquet( ".") groupBy(department( "name"), people( "gender")) join(department, people( "deptId") = department( "id")) The following creates a new column that increases everybody's age by 10.Ī more concrete example in Scala: // To create Dataset using SparkSession val people = ( ".") Note that the Column type can also be manipulated through its various functions. val ageCol = people( "age") // in ScalaĬolumn ageCol = l( "age") // in Java To select a column from the Dataset, use apply method in Scala and col in Java. These operationsĪre very similar to the operations available in the data frame abstraction in R or Python. The following creates a new Dataset by applying a filter on the existing one: val names = people.map(_.name) // in Scala names is a Datasetĭataset names = people.map((Person p) -> p.name, Encoders.STRING)) ĭataset operations can also be untyped, through various domain-specific-language (DSL)įunctions defined in: Dataset (this class), Column, and functions. class)) // Javaĭatasets can also be created through transformations available on existing Datasets. val people = ( ".").as // Scalaĭataset people = spark.read().parquet( ".").as(Encoders.bean(Person. To some files on storage systems, using the read function available on a SparkSession. There are typically two ways to create a Dataset. To understand the internal binary representation for data, use the Often has much lower memory footprint as well as are optimized for efficiency in data processing With two fields, name (string) and age (int), an encoder is used to tell Spark to generateĬode at runtime to serialize the Person object into a binary structure. The domain specific type T to Spark's internal type system.
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To efficiently support domain-specific objects, an Encoder is required. Logical plan as well as optimized physical plan, use the explain function. Physical plan for efficient execution in a parallel and distributed manner. When an action is invoked, Spark's query optimizer optimizes the logical plan and generates a Internally,Ī Dataset represents a logical plan that describes the computation required to produce the data. computations are only triggered when an action is invoked.
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Example transformations include map, filter, select, and aggregate ( groupBy).Įxample actions count, show, or writing data out to file systems.ĭatasets are "lazy", i.e. TransformationsĪre the ones that produce new Datasets, and actions are the ones that trigger computation and Operations available on Datasets are divided into transformations and actions. Each Dataset also has an untyped viewĬalled a DataFrame, which is a Dataset of Row. In parallel using functional or relational operations. These are subject to changes or removal in minor releases.Īllows the execution of relational queries, including those expressed in SQL using Spark.Ī Dataset is a strongly typed collection of domain-specific objects that can be transformed These are subject to change or removal in minor releases.ĭeveloper API are intended for advanced users want to extend Spark through lower Java programmers should reference the .java packageĮxperimental are user-facing features which have not been officially adopted by the These operations are automatically available on any RDD of the right SequenceFileRDDFunctions contains operations available on RDDs that canīe saved as SequenceFiles. Of key-value pairs, such as groupByKey and join .DoubleRDDFunctionsĬontains operations available only on RDDs of Doubles and In addition, .PairRDDFunctions contains operations available only on RDDs Spark, while .RDD is the data type representing a distributed collection,