JavaDoubleRDD (Spark 3.4.1 JavaDoc)

Object
- org.apache.spark.api.java.JavaDoubleRDD

All Implemented Interfaces:

java.io.Serializable, JavaRDDLike<Double,JavaDoubleRDD>
```
public class JavaDoubleRDD
extends Object
```
See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

JavaDoubleRDD(RDD<Object> srdd)

Constructors
Constructor and Description
`JavaDoubleRDD(RDD<Object> srdd)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`JavaDoubleRDD`	`cache()` Persist this RDD with the default storage level (`MEMORY_ONLY`).
`scala.reflect.ClassTag<Double>`	`classTag()`
`JavaDoubleRDD`	`coalesce(int numPartitions)` Return a new RDD that is reduced into `numPartitions` partitions.
`JavaDoubleRDD`	`coalesce(int numPartitions, boolean shuffle)` Return a new RDD that is reduced into `numPartitions` partitions.
`JavaDoubleRDD`	`distinct()` Return a new RDD containing the distinct elements in this RDD.
`JavaDoubleRDD`	`distinct(int numPartitions)` Return a new RDD containing the distinct elements in this RDD.
`JavaDoubleRDD`	`filter(Function<Double,Boolean> f)` Return a new RDD containing only the elements that satisfy a predicate.
`Double`	`first()` Return the first element in this RDD.
`static JavaDoubleRDD`	`fromRDD(RDD<Object> rdd)`
`long[]`	`histogram(double[] buckets)` Compute a histogram using the provided buckets.
`long[]`	`histogram(Double[] buckets, boolean evenBuckets)`
`scala.Tuple2<double[],long[]>`	`histogram(int bucketCount)` Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD.
`JavaDoubleRDD`	`intersection(JavaDoubleRDD other)` Return the intersection of this RDD and another one.
`Double`	`max()` Returns the maximum element from this RDD as defined by the default comparator natural order.
`Double`	`mean()` Compute the mean of this RDD's elements.
`PartialResult<BoundedDouble>`	`meanApprox(long timeout)` Approximate operation to return the mean within a timeout.
`PartialResult<BoundedDouble>`	`meanApprox(long timeout, Double confidence)` Return the approximate mean of the elements in this RDD.
`Double`	`min()` Returns the minimum element from this RDD as defined by the default comparator natural order.
`JavaDoubleRDD`	`persist(StorageLevel newLevel)` Set this RDD's storage level to persist its values across operations after the first time it is computed.
`Double`	`popStdev()` Compute the population standard deviation of this RDD's elements.
`Double`	`popVariance()` Compute the population variance of this RDD's elements.
`RDD<Double>`	`rdd()`
`JavaDoubleRDD`	`repartition(int numPartitions)` Return a new RDD that has exactly numPartitions partitions.
`JavaDoubleRDD`	`sample(boolean withReplacement, Double fraction)` Return a sampled subset of this RDD.
`JavaDoubleRDD`	`sample(boolean withReplacement, Double fraction, long seed)` Return a sampled subset of this RDD.
`Double`	`sampleStdev()` Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).
`Double`	`sampleVariance()` Compute the sample variance of this RDD's elements (which corrects for bias in estimating the standard variance by dividing by N-1 instead of N).
`JavaDoubleRDD`	`setName(String name)` Assign a name to this RDD
`RDD<Object>`	`srdd()`
`StatCounter`	`stats()` Return a `StatCounter` object that captures the mean, variance and count of the RDD's elements in one operation.
`Double`	`stdev()` Compute the population standard deviation of this RDD's elements.
`JavaDoubleRDD`	`subtract(JavaDoubleRDD other)` Return an RDD with the elements from `this` that are not in `other`.
`JavaDoubleRDD`	`subtract(JavaDoubleRDD other, int numPartitions)` Return an RDD with the elements from `this` that are not in `other`.
`JavaDoubleRDD`	`subtract(JavaDoubleRDD other, Partitioner p)` Return an RDD with the elements from `this` that are not in `other`.
`Double`	`sum()` Add up the elements in this RDD.
`PartialResult<BoundedDouble>`	`sumApprox(long timeout)` Approximate operation to return the sum within a timeout.
`PartialResult<BoundedDouble>`	`sumApprox(long timeout, Double confidence)` Approximate operation to return the sum within a timeout.
`static RDD<Object>`	`toRDD(JavaDoubleRDD rdd)`
`JavaDoubleRDD`	`union(JavaDoubleRDD other)` Return the union of this RDD and another one.
`JavaDoubleRDD`	`unpersist()` Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
`JavaDoubleRDD`	`unpersist(boolean blocking)` Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
`Double`	`variance()` Compute the population variance of this RDD's elements.
`JavaDoubleRDD`	`wrapRDD(RDD<Double> rdd)`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.api.java.JavaRDDLike
aggregate, cartesian, checkpoint, collect, collectAsync, collectPartitions, context, count, countApprox, countApprox, countApproxDistinct, countAsync, countByValue, countByValueApprox, countByValueApprox, flatMap, flatMapToDouble, flatMapToPair, fold, foreach, foreachAsync, foreachPartition, foreachPartitionAsync, getCheckpointFile, getNumPartitions, getStorageLevel, glom, groupBy, groupBy, id, isCheckpointed, isEmpty, iterator, keyBy, map, mapPartitions, mapPartitions, mapPartitionsToDouble, mapPartitionsToDouble, mapPartitionsToPair, mapPartitionsToPair, mapPartitionsWithIndex, mapToDouble, mapToPair, max, min, name, partitioner, partitions, pipe, pipe, pipe, pipe, pipe, reduce, saveAsObjectFile, saveAsTextFile, saveAsTextFile, take, takeAsync, takeOrdered, takeOrdered, takeSample, takeSample, toDebugString, toLocalIterator, top, top, treeAggregate, treeAggregate, treeAggregate, treeReduce, treeReduce, zip, zipPartitions, zipWithIndex, zipWithUniqueId

- Constructor Detail
  - JavaDoubleRDD
```
public JavaDoubleRDD(RDD<Object> srdd)
```
- Method Detail
  - fromRDD
```
public static JavaDoubleRDD fromRDD(RDD<Object> rdd)
```
  - toRDD
```
public static RDD<Object> toRDD(JavaDoubleRDD rdd)
```
  - srdd
```
public RDD<Object> srdd()
```
  - classTag
```
public scala.reflect.ClassTag<Double> classTag()
```
  - rdd
```
public RDD<Double> rdd()
```
  - wrapRDD
```
public JavaDoubleRDD wrapRDD(RDD<Double> rdd)
```
  - cache
```
public JavaDoubleRDD cache()
```
    Persist this RDD with the default storage level (MEMORY_ONLY).
    
    Returns:
    
    (undocumented)
  - persist
```
public JavaDoubleRDD persist(StorageLevel newLevel)
```
    Set this RDD's storage level to persist its values across operations after the first time it is computed. Can only be called once on each RDD.
    
    Parameters:
    
    newLevel - (undocumented)
    
    Returns:
    
    (undocumented)
  - unpersist
```
public JavaDoubleRDD unpersist()
```
    Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. This method blocks until all blocks are deleted.
    
    Returns:
    
    (undocumented)
  - unpersist
```
public JavaDoubleRDD unpersist(boolean blocking)
```
    Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
    
    Parameters:
    
    blocking - Whether to block until all blocks are deleted.
    
    Returns:
    
    (undocumented)
  - first
```
public Double first()
```
    Description copied from interface: JavaRDDLike
    
    Return the first element in this RDD.
    
    Returns:
    
    (undocumented)
  - distinct
```
public JavaDoubleRDD distinct()
```
    Return a new RDD containing the distinct elements in this RDD.
    
    Returns:
    
    (undocumented)
  - distinct
```
public JavaDoubleRDD distinct(int numPartitions)
```
    Return a new RDD containing the distinct elements in this RDD.
    
    Parameters:
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
  - filter
```
public JavaDoubleRDD filter(Function<Double,Boolean> f)
```
    Return a new RDD containing only the elements that satisfy a predicate.
    
    Parameters:
    
    f - (undocumented)
    
    Returns:
    
    (undocumented)
  - coalesce
```
public JavaDoubleRDD coalesce(int numPartitions)
```
    Return a new RDD that is reduced into numPartitions partitions.
    
    Parameters:
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
  - coalesce
```
public JavaDoubleRDD coalesce(int numPartitions,
                              boolean shuffle)
```
    Return a new RDD that is reduced into numPartitions partitions.
    
    Parameters:
    
    numPartitions - (undocumented)
    
    shuffle - (undocumented)
    
    Returns:
    
    (undocumented)
  - repartition
```
public JavaDoubleRDD repartition(int numPartitions)
```
    Return a new RDD that has exactly numPartitions partitions.
    Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data.
    If you are decreasing the number of partitions in this RDD, consider using coalesce, which can avoid performing a shuffle.
    
    Parameters:
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
  - subtract
```
public JavaDoubleRDD subtract(JavaDoubleRDD other)
```
    Return an RDD with the elements from this that are not in other.
    Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be <= us.
    
    Parameters:
    
    other - (undocumented)
    
    Returns:
    
    (undocumented)
  - subtract
```
public JavaDoubleRDD subtract(JavaDoubleRDD other,
                              int numPartitions)
```
    Return an RDD with the elements from this that are not in other.
    
    Parameters:
    
    other - (undocumented)
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
  - subtract
```
public JavaDoubleRDD subtract(JavaDoubleRDD other,
                              Partitioner p)
```
    Return an RDD with the elements from this that are not in other.
    
    Parameters:
    
    other - (undocumented)
    
    p - (undocumented)
    
    Returns:
    
    (undocumented)
  - sample
```
public JavaDoubleRDD sample(boolean withReplacement,
                            Double fraction)
```
    Return a sampled subset of this RDD.
    
    Parameters:
    
    withReplacement - (undocumented)
    
    fraction - (undocumented)
    
    Returns:
    
    (undocumented)
  - sample
```
public JavaDoubleRDD sample(boolean withReplacement,
                            Double fraction,
                            long seed)
```
    Return a sampled subset of this RDD.
    
    Parameters:
    
    withReplacement - (undocumented)
    
    fraction - (undocumented)
    
    seed - (undocumented)
    
    Returns:
    
    (undocumented)
  - union
```
public JavaDoubleRDD union(JavaDoubleRDD other)
```
    Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).
    
    Parameters:
    
    other - (undocumented)
    
    Returns:
    
    (undocumented)
  - intersection
```
public JavaDoubleRDD intersection(JavaDoubleRDD other)
```
    Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.
    
    Parameters:
    
    other - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This method performs a shuffle internally.
  - sum
```
public Double sum()
```
    Add up the elements in this RDD.
  - min
```
public Double min()
```
    Returns the minimum element from this RDD as defined by the default comparator natural order.
    
    Returns:
    
    the minimum of the RDD
  - max
```
public Double max()
```
    Returns the maximum element from this RDD as defined by the default comparator natural order.
    
    Returns:
    
    the maximum of the RDD
  - stats
```
public StatCounter stats()
```
    Return a StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.
    
    Returns:
    
    (undocumented)
  - mean
```
public Double mean()
```
    Compute the mean of this RDD's elements.
  - variance
```
public Double variance()
```
    Compute the population variance of this RDD's elements.
  - stdev
```
public Double stdev()
```
    Compute the population standard deviation of this RDD's elements.
  - sampleStdev
```
public Double sampleStdev()
```
    Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).
    
    Returns:
    
    (undocumented)
  - sampleVariance
```
public Double sampleVariance()
```
    Compute the sample variance of this RDD's elements (which corrects for bias in estimating the standard variance by dividing by N-1 instead of N).
    
    Returns:
    
    (undocumented)
  - popStdev
```
public Double popStdev()
```
    Compute the population standard deviation of this RDD's elements.
    
    Returns:
    
    (undocumented)
  - popVariance
```
public Double popVariance()
```
    Compute the population variance of this RDD's elements.
    
    Returns:
    
    (undocumented)
  - meanApprox
```
public PartialResult<BoundedDouble> meanApprox(long timeout,
                                               Double confidence)
```
    Return the approximate mean of the elements in this RDD.
  - meanApprox
```
public PartialResult<BoundedDouble> meanApprox(long timeout)
```
    Approximate operation to return the mean within a timeout.
    
    Parameters:
    
    timeout - (undocumented)
    
    Returns:
    
    (undocumented)
  - sumApprox
```
public PartialResult<BoundedDouble> sumApprox(long timeout,
                                              Double confidence)
```
    Approximate operation to return the sum within a timeout.
    
    Parameters:
    
    timeout - (undocumented)
    
    confidence - (undocumented)
    
    Returns:
    
    (undocumented)
  - sumApprox
```
public PartialResult<BoundedDouble> sumApprox(long timeout)
```
    Approximate operation to return the sum within a timeout.
    
    Parameters:
    
    timeout - (undocumented)
    
    Returns:
    
    (undocumented)
  - histogram
```
public scala.Tuple2<double[],long[]> histogram(int bucketCount)
```
    Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD. For example if the min value is 0 and the max is 100 and there are two buckets the resulting buckets will be [0,50) [50,100]. bucketCount must be at least 1 If the RDD contains infinity, NaN throws an exception If the elements in RDD do not vary (max == min) always returns a single bucket.
    
    Parameters:
    
    bucketCount - (undocumented)
    
    Returns:
    
    (undocumented)
  - histogram
```
public long[] histogram(double[] buckets)
```
    Compute a histogram using the provided buckets. The buckets are all open to the left except for the last which is closed e.g. for the array [1,10,20,50] the buckets are [1,10) [10,20) [20,50] e.g 1<=x<10 , 10<=x<20, 20<=x<50 And on the input of 1 and 50 we would have a histogram of 1,0,0
    
    Parameters:
    
    buckets - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Note:
    
    If your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched from an O(log n) insertion to O(1) per element. (where n = # buckets) if you set evenBuckets to true. buckets must be sorted and not contain any duplicates. buckets array must be at least two elements All NaN entries are treated the same. If you have a NaN bucket it must be the maximum value of the last position and all NaN entries will be counted in that bucket.
  - histogram
```
public long[] histogram(Double[] buckets,
                        boolean evenBuckets)
```
  - setName
```
public JavaDoubleRDD setName(String name)
```
    Assign a name to this RDD

Class JavaDoubleRDD

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.api.java.JavaRDDLike

Constructor Detail

JavaDoubleRDD

Method Detail

fromRDD

toRDD

srdd

classTag

rdd

wrapRDD

cache

persist

unpersist

unpersist

first

distinct

distinct

filter

coalesce

coalesce

repartition

subtract

subtract

subtract

sample

sample

union

intersection

sum

min

max

stats

mean

variance

stdev

sampleStdev

sampleVariance

popStdev

popVariance

meanApprox

meanApprox

sumApprox

sumApprox

histogram

histogram

histogram

setName