Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    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.

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

    Definition Classes
    spark
  • package avro
    Definition Classes
    sql
  • SchemaConverters
  • functions

object functions

Source
functions.scala
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Value Members

  1. def from_avro(data: Column, jsonFormatSchema: String, options: Map[String, String]): Column

    Converts a binary column of Avro format into its corresponding catalyst value.

    Converts a binary column of Avro format into its corresponding catalyst value. The specified schema must match actual schema of the read data, otherwise the behavior is undefined: it may fail or return arbitrary result. To deserialize the data with a compatible and evolved schema, the expected Avro schema can be set via the option avroSchema.

    data

    the binary column.

    jsonFormatSchema

    the avro schema in JSON string format.

    options

    options to control how the Avro record is parsed.

    Annotations
    @Experimental()
    Since

    3.0.0

  2. def from_avro(data: Column, jsonFormatSchema: String): Column

    Converts a binary column of avro format into its corresponding catalyst value.

    Converts a binary column of avro format into its corresponding catalyst value. The specified schema must match the read data, otherwise the behavior is undefined: it may fail or return arbitrary result.

    data

    the binary column.

    jsonFormatSchema

    the avro schema in JSON string format.

    Annotations
    @Experimental()
    Since

    3.0.0

  3. def to_avro(data: Column, jsonFormatSchema: String): Column

    Converts a column into binary of avro format.

    Converts a column into binary of avro format.

    data

    the data column.

    jsonFormatSchema

    user-specified output avro schema in JSON string format.

    Annotations
    @Experimental()
    Since

    3.0.0

  4. def to_avro(data: Column): Column

    Converts a column into binary of avro format.

    Converts a column into binary of avro format.

    data

    the data column.

    Annotations
    @Experimental()
    Since

    3.0.0