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 connector
    Definition Classes
    sql
  • package metric
    Definition Classes
    connector
  • CustomAvgMetric
  • CustomMetric
  • CustomSumMetric
  • CustomTaskMetric

trait CustomTaskMetric extends AnyRef

A custom task metric. This is a logical representation of a metric reported by data sources at the executor side. During query execution, Spark will collect the task metrics per partition by PartitionReader and update internal metrics based on collected metric values. For streaming query, Spark will collect and combine metrics for a final result per micro batch.

The metrics will be gathered during query execution back to the driver and then combined. How the task metrics are combined is defined by corresponding CustomMetric with same metric name. The final result will be shown up in the data source scan operator in Spark UI.

There are a few special metric names: "bytesWritten" and "recordsWritten". If the data source defines custom metrics with the same names, the metric values will also be updated to corresponding task metrics.

Annotations
@Evolving()
Source
CustomTaskMetric.java
Since

3.2.0

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Abstract Value Members

  1. abstract def name(): String

    Returns the name of custom task metric.

  2. abstract def value(): Long

    Returns the long value of custom task metric.

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  8. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  9. def hashCode(): Int
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  10. final def isInstanceOf[T0]: Boolean
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  11. final def ne(arg0: AnyRef): Boolean
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  12. final def notify(): Unit
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    @IntrinsicCandidate() @native()
  13. final def notifyAll(): Unit
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    @IntrinsicCandidate() @native()
  14. final def synchronized[T0](arg0: => T0): T0
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  15. def toString(): String
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  16. final def wait(arg0: Long, arg1: Int): Unit
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  17. final def wait(arg0: Long): Unit
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  18. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
    Attributes
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

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