All Implemented Interfaces:

Serializable, JavaRDDLike<Double,JavaDoubleRDD>

public class JavaDoubleRDD extends Object

See Also:

• Serialized Form

• Constructor Summary

  Constructors

• Method Summary

  cache()

  Persist this RDD with the default storage level (MEMORY_ONLY).

  scala.reflect.ClassTag<Double>

  classTag()

  coalesce(int numPartitions)

  Return a new RDD that is reduced into numPartitions partitions.

  coalesce(int numPartitions, boolean shuffle)

  Return a new RDD that is reduced into numPartitions partitions.

  distinct()

  Return a new RDD containing the distinct elements in this RDD.

  distinct(int numPartitions)

  Return a new RDD containing the distinct elements in this RDD.

  Return a new RDD containing only the elements that satisfy a predicate.

  first()

  Return the first element in this RDD.

  long[]

  histogram(double[] buckets)

  Compute a histogram using the provided buckets.

  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.

  long[]

  histogram(Double[] buckets, boolean evenBuckets)

  Return the intersection of this RDD and another one.

  max()

  Returns the maximum element from this RDD as defined by the default comparator natural order.

  mean()

  Compute the mean of this RDD's elements.

  meanApprox(long timeout)

  Approximate operation to return the mean within a timeout.

  meanApprox(long timeout, Double confidence)

  Return the approximate mean of the elements in this RDD.

  min()

  Returns the minimum element from this RDD as defined by the default comparator natural order.

  Set this RDD's storage level to persist its values across operations after the first time it is computed.

  popStdev()

  Compute the population standard deviation of this RDD's elements.

  Compute the population variance of this RDD's elements.

  rdd()

  repartition(int numPartitions)

  Return a new RDD that has exactly numPartitions partitions.

  sample(boolean withReplacement, Double fraction)

  Return a sampled subset of this RDD.

  sample(boolean withReplacement, Double fraction, long seed)

  Return a sampled subset of this RDD.

  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).

  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).

  Assign a name to this RDD

  srdd()

  stats()

  Return a StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.

  stdev()

  Compute the population standard deviation of this RDD's elements.

  Return an RDD with the elements from this that are not in other.

  Return an RDD with the elements from this that are not in other.

  Return an RDD with the elements from this that are not in other.

  sum()

  Add up the elements in this RDD.

  sumApprox(long timeout)

  Approximate operation to return the sum within a timeout.

  sumApprox(long timeout, Double confidence)

  Approximate operation to return the sum within a timeout.

  Return the union of this RDD and another one.

  unpersist()

  Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

  unpersist(boolean blocking)

  Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

  variance()

  Compute the population variance of this RDD's elements.

  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 Details

  • JavaDoubleRDD

    public JavaDoubleRDD(RDD<Object> srdd)

• Method Details

  • fromRDD

  • toRDD

  • srdd

  • classTag

    public scala.reflect.ClassTag<Double> classTag()

  • rdd

  • wrapRDD

  • cache

    Persist this RDD with the default storage level (MEMORY_ONLY).

    Returns:

    (undocumented)

  • persist

    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

    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

    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

    Description copied from interface: JavaRDDLike

    Return the first element in this RDD.

    Returns:

    (undocumented)

  • distinct

    Return a new RDD containing the distinct elements in this RDD.

    Returns:

    (undocumented)

  • distinct

    Return a new RDD containing the distinct elements in this RDD.

    Parameters:

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • filter

    Return a new RDD containing only the elements that satisfy a predicate.

    Parameters:

    f - (undocumented)

    Returns:

    (undocumented)

  • coalesce

    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

    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

    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 &lt;= us.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • subtract

    Return an RDD with the elements from this that are not in other.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • subtract

    Return an RDD with the elements from this that are not in other.

    Parameters:

    other - (undocumented)

    p - (undocumented)

    Returns:

    (undocumented)

  • sample

    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

    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

    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

    Add up the elements in this RDD.

  • min

    Returns the minimum element from this RDD as defined by the default comparator natural order.

    Returns:

    the minimum of the RDD

  • max

    Returns the maximum element from this RDD as defined by the default comparator natural order.

    Returns:

    the maximum of the RDD

  • stats

    Return a StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.

    Returns:

    (undocumented)

  • mean

    Compute the mean of this RDD's elements.

  • variance

    Compute the population variance of this RDD's elements.

  • 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

    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

    Return the approximate mean of the elements in this RDD.

  • meanApprox

    Approximate operation to return the mean within a timeout.

    Parameters:

    timeout - (undocumented)

    Returns:

    (undocumented)

  • sumApprox

    Approximate operation to return the sum within a timeout.

    Parameters:

    timeout - (undocumented)

    confidence - (undocumented)

    Returns:

    (undocumented)

  • sumApprox

    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&lt;=x&lt;10 , 10&lt;=x&lt;20, 20&lt;=x&lt;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

    Assign a name to this RDD