Packages

Definition Classes: root

Definition Classes: root

Definition Classes: org

Core Spark functionality.

Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes: apache

package sql

Allows the execution of relational queries, including those expressed in SQL using Spark.

Definition Classes: spark

package connector

Definition Classes: sql

package read

Definition Classes: connector

package colstats

Definition Classes: read

package partitioning

Definition Classes: read

KeyGroupedPartitioning

Partitioning

UnknownPartitioning

package streaming

Definition Classes: read

org.apache.spark.sql.connector.read

partitioning

package partitioning

Type Members

class KeyGroupedPartitioning extends Partitioning
Represents a partitioning where rows are split across partitions based on the partition transform expressions returned by KeyGroupedPartitioning#keys.
Represents a partitioning where rows are split across partitions based on the partition transform expressions returned by KeyGroupedPartitioning#keys.
Note: Data source implementations should make sure for a single partition, all of its rows must be evaluated to the same partition value after being applied by KeyGroupedPartitioning#keys expressions. Different partitions can share the same partition value: Spark will group these into a single logical partition during planning phase.
Annotations
@Evolving()
Since
3.3.0
trait Partitioning extends AnyRef
An interface to represent the output data partitioning for a data source, which is returned by SupportsReportPartitioning#outputPartitioning().
An interface to represent the output data partitioning for a data source, which is returned by SupportsReportPartitioning#outputPartitioning().
Note: implementors should NOT directly implement this interface. Instead, they should use one of the following subclasses:
- KeyGroupedPartitioning
- UnknownPartitioning
Annotations
@Evolving()
Since
3.0.0
class UnknownPartitioning extends Partitioning
Represents a partitioning where rows are split across partitions in an unknown pattern.
Represents a partitioning where rows are split across partitions in an unknown pattern.
Annotations
@Evolving()
Since
3.3.0

Packages

partitioning

package partitioning

Type Members

Ungrouped

partitioning