public class Accumulator<T> extends Accumulable<T,T>
Accumulablewhere the result type being accumulated is the same as the types of elements being merged, i.e. variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric value types, and programmers can add support for new types.
An accumulator is created from an initial value
v by calling
Tasks running on the cluster can then add to it using the
However, they cannot read its value. Only the driver program can read the accumulator's value,
using its value method.
The interpreter session below shows an accumulator being used to add up the elements of an array:
scala> val accum = sc.accumulator(0) accum: spark.Accumulator[Int] = 0 scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x) ... 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s scala> accum.value res2: Int = 10
param: initialValue initial value of accumulator
param: param helper object defining how to add elements of type
|Constructor and Description|
|Modifier and Type||Method and Description|
add, id, localValue, merge, name, setValue, toString, value, zero