All Implemented Interfaces:

Serializable, JavaRDDLike<scala.Tuple2<K,V>,JavaPairRDD<K,V>>

Direct Known Subclasses:

JavaHadoopRDD, JavaNewHadoopRDD

public class JavaPairRDD<K,V> extends Object

See Also:

• Serialized Form

• Constructor Summary

  Constructors

  JavaPairRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> kClassTag, scala.reflect.ClassTag<V> vClassTag)

• Method Summary

  aggregateByKey(U zeroValue, int numPartitions, Function2<U,V,U> seqFunc, Function2<U,U,U> combFunc)

  Aggregate the values of each key, using given combine functions and a neutral "zero value".

  Aggregate the values of each key, using given combine functions and a neutral "zero value".

  Aggregate the values of each key, using given combine functions and a neutral "zero value".

  cache()

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

  scala.reflect.ClassTag<scala.Tuple2<K,V>>

  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.

  For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

  For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

  For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

  For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

  For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

  For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

  For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

  For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

  For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

  Return the key-value pairs in this RDD to the master as a Map.

  Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.

  Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.

  Generic function to combine the elements for each key using a custom set of aggregation functions.

  combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer)

  Generic function to combine the elements for each key using a custom set of aggregation functions.

  countApproxDistinctByKey(double relativeSD)

  Return approximate number of distinct values for each key in this RDD.

  countApproxDistinctByKey(double relativeSD, int numPartitions)

  Return approximate number of distinct values for each key in this RDD.

  Return approximate number of distinct values for each key in this RDD.

  Count the number of elements for each key, and return the result to the master as a Map.

  countByKeyApprox(long timeout)

  Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

  countByKeyApprox(long timeout, double confidence)

  Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

  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.

  Return a RDD containing only the elements in the inclusive range lower to upper.

  filterByRange(K lower, K upper)

  Return a RDD containing only the elements in the inclusive range lower to upper.

  scala.Tuple2<K,V>

  first()

  Return the first element in this RDD.

  Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.

  foldByKey(V zeroValue, int numPartitions, Function2<V,V,V> func)

  Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

  Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

  Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

  fromJavaRDD(JavaRDD<scala.Tuple2<K,V>> rdd)

  Convert a JavaRDD of key-value pairs to JavaPairRDD.

  fromRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> evidence$5, scala.reflect.ClassTag<V> evidence$6)

  Perform a full outer join of this and other.

  Perform a full outer join of this and other.

  Perform a full outer join of this and other.

  Group the values for each key in the RDD into a single sequence.

  groupByKey(int numPartitions)

  Group the values for each key in the RDD into a single sequence.

  Group the values for each key in the RDD into a single sequence.

  Return the intersection of this RDD and another one.

  Return an RDD containing all pairs of elements with matching keys in this and other.

  Return an RDD containing all pairs of elements with matching keys in this and other.

  Return an RDD containing all pairs of elements with matching keys in this and other.

  scala.reflect.ClassTag<K>

  kClassTag()

  keys()

  Return an RDD with the keys of each tuple.

  Perform a left outer join of this and other.

  Perform a left outer join of this and other.

  Perform a left outer join of this and other.

  lookup(K key)

  Return the list of values in the RDD for key key.

  Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.

  Return a copy of the RDD partitioned using the specified partitioner.

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

  RDD<scala.Tuple2<K,V>>

  rdd()

  Merge the values for each key using an associative and commutative reduce function.

  Merge the values for each key using an associative and commutative reduce function.

  Merge the values for each key using an associative and commutative reduce function.

  Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map.

  repartition(int numPartitions)

  Return a new RDD that has exactly numPartitions partitions.

  Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

  Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

  Perform a right outer join of this and other.

  Perform a right outer join of this and other.

  Perform a right outer join of this and other.

  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.

  sampleByKey(boolean withReplacement, Map<K,Double> fractions)

  Return a subset of this RDD sampled by key (via stratified sampling).

  sampleByKey(boolean withReplacement, Map<K,Double> fractions, long seed)

  Return a subset of this RDD sampled by key (via stratified sampling).

  sampleByKeyExact(boolean withReplacement, Map<K,Double> fractions)

  Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).

  sampleByKeyExact(boolean withReplacement, Map<K,Double> fractions, long seed)

  Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).

  void

  saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)

  Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.

  <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>>
void

  Output the RDD to any Hadoop-supported file system.

  <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>>
void

  saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)

  Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.

  <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>>
void

  saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.mapred.JobConf conf)

  Output the RDD to any Hadoop-supported file system.

  void

  saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)

  Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.

  <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>>
void

  Output the RDD to any Hadoop-supported file system.

  <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>>
void

  saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.conf.Configuration conf)

  Output the RDD to any Hadoop-supported file system.

  Assign a name to this RDD

  sortByKey()

  Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.

  sortByKey(boolean ascending)

  Sort the RDD by key, so that each partition contains a sorted range of the elements.

  sortByKey(boolean ascending, int numPartitions)

  Sort the RDD by key, so that each partition contains a sorted range of the elements.

  Sort the RDD by key, so that each partition contains a sorted range of the elements.

  Sort the RDD by key, so that each partition contains a sorted range of the elements.

  sortByKey(Comparator<K> comp, boolean ascending, int numPartitions)

  Sort the RDD by key, so that each partition contains a sorted range of the 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.

  Return an RDD with the pairs from this whose keys are not in other.

  Return an RDD with the pairs from this whose keys are not in other.

  Return an RDD with the pairs from this whose keys are not in other.

  static <K, V> RDD<scala.Tuple2<K,V>>

  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.

  values()

  Return an RDD with the values of each tuple.

  scala.reflect.ClassTag<V>

  vClassTag()

  wrapRDD(RDD<scala.Tuple2<K,V>> rdd)

  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

  • JavaPairRDD

    public JavaPairRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> kClassTag, scala.reflect.ClassTag<V> vClassTag)

• Method Details

  • fromRDD

    public static <K, V> JavaPairRDD<K,V> fromRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> evidence$5, scala.reflect.ClassTag<V> evidence$6)

  • toRDD

    public static <K, V> RDD<scala.Tuple2<K,V>> toRDD(JavaPairRDD<K,V> rdd)

  • fromJavaRDD

    public static <K, V> JavaPairRDD<K,V> fromJavaRDD(JavaRDD<scala.Tuple2<K,V>> rdd)

    Convert a JavaRDD of key-value pairs to JavaPairRDD.

  • rdd

    public RDD<scala.Tuple2<K,V>> rdd()

  • kClassTag

    public scala.reflect.ClassTag<K> kClassTag()

  • vClassTag

    public scala.reflect.ClassTag<V> vClassTag()

  • wrapRDD

  • classTag

    public scala.reflect.ClassTag<scala.Tuple2<K,V>> classTag()

  • 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)

  • 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 JavaPairRDD<K,V> coalesce(int numPartitions, boolean shuffle)

    Return a new RDD that is reduced into numPartitions partitions.

    Parameters:

    numPartitions - (undocumented)

    shuffle - (undocumented)

    Returns:

    (undocumented)

  • repartition

    public JavaPairRDD<K,V> 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)

  • sample

    public JavaPairRDD<K,V> sample(boolean withReplacement, double fraction)

    Return a sampled subset of this RDD.

    Parameters:

    withReplacement - (undocumented)

    fraction - (undocumented)

    Returns:

    (undocumented)

  • sample

    public JavaPairRDD<K,V> sample(boolean withReplacement, double fraction, long seed)

    Return a sampled subset of this RDD.

    Parameters:

    withReplacement - (undocumented)

    fraction - (undocumented)

    seed - (undocumented)

    Returns:

    (undocumented)

  • sampleByKey

    public JavaPairRDD<K,V> sampleByKey(boolean withReplacement, Map<K,Double> fractions, long seed)

    Return a subset of this RDD sampled by key (via stratified sampling).

    Create a sample of this RDD using variable sampling rates for different keys as specified by fractions, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.

    Parameters:

    withReplacement - (undocumented)

    fractions - (undocumented)

    seed - (undocumented)

    Returns:

    (undocumented)

  • sampleByKey

    Return a subset of this RDD sampled by key (via stratified sampling).

    Create a sample of this RDD using variable sampling rates for different keys as specified by fractions, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.

    Use Utils.random.nextLong as the default seed for the random number generator.

    Parameters:

    withReplacement - (undocumented)

    fractions - (undocumented)

    Returns:

    (undocumented)

  • sampleByKeyExact

    public JavaPairRDD<K,V> sampleByKeyExact(boolean withReplacement, Map<K,Double> fractions, long seed)

    Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).

    This method differs from sampleByKey in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.

    Parameters:

    withReplacement - (undocumented)

    fractions - (undocumented)

    seed - (undocumented)

    Returns:

    (undocumented)

  • sampleByKeyExact

    Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).

    This method differs from sampleByKey in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.

    Use Utils.random.nextLong as the default seed for the random number generator.

    Parameters:

    withReplacement - (undocumented)

    fractions - (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.

  • first

    public scala.Tuple2<K,V> first()

    Description copied from interface: JavaRDDLike

    Return the first element in this RDD.

    Returns:

    (undocumented)

  • combineByKey

    public <C> JavaPairRDD<K,C> combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer)

    Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.

    Users provide three functions:

    - createCombiner, which turns a V into a C (e.g., creates a one-element list) - mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - mergeCombiners, to combine two C's into a single one.

    In addition, users can control the partitioning of the output RDD, the serializer that is use for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).

    Parameters:

    createCombiner - (undocumented)

    mergeValue - (undocumented)

    mergeCombiners - (undocumented)

    partitioner - (undocumented)

    mapSideCombine - (undocumented)

    serializer - (undocumented)

    Returns:

    (undocumented)

    Note:

    V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).

  • combineByKey

    Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.

    Users provide three functions:

    - createCombiner, which turns a V into a C (e.g., creates a one-element list) - mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - mergeCombiners, to combine two C's into a single one.

    In addition, users can control the partitioning of the output RDD. This method automatically uses map-side aggregation in shuffling the RDD.

    Parameters:

    createCombiner - (undocumented)

    mergeValue - (undocumented)

    mergeCombiners - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

    Note:

    V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).

  • combineByKey

    Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.

    Parameters:

    createCombiner - (undocumented)

    mergeValue - (undocumented)

    mergeCombiners - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • reduceByKey

    Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.

    Parameters:

    partitioner - (undocumented)

    func - (undocumented)

    Returns:

    (undocumented)

  • reduceByKeyLocally

    Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.

    Parameters:

    func - (undocumented)

    Returns:

    (undocumented)

  • countByKey

    Count the number of elements for each key, and return the result to the master as a Map.

  • countByKeyApprox

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Parameters:

    timeout - (undocumented)

    Returns:

    (undocumented)

  • countByKeyApprox

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Parameters:

    timeout - (undocumented)

    confidence - (undocumented)

    Returns:

    (undocumented)

  • aggregateByKey

    Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.IterableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

    Parameters:

    zeroValue - (undocumented)

    partitioner - (undocumented)

    seqFunc - (undocumented)

    combFunc - (undocumented)

    Returns:

    (undocumented)

  • aggregateByKey

    public <U> JavaPairRDD<K,U> aggregateByKey(U zeroValue, int numPartitions, Function2<U,V,U> seqFunc, Function2<U,U,U> combFunc)

    Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.IterableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

    Parameters:

    zeroValue - (undocumented)

    numPartitions - (undocumented)

    seqFunc - (undocumented)

    combFunc - (undocumented)

    Returns:

    (undocumented)

  • aggregateByKey

    Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

    Parameters:

    zeroValue - (undocumented)

    seqFunc - (undocumented)

    combFunc - (undocumented)

    Returns:

    (undocumented)

  • foldByKey

    Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

    Parameters:

    zeroValue - (undocumented)

    partitioner - (undocumented)

    func - (undocumented)

    Returns:

    (undocumented)

  • foldByKey

    Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

    Parameters:

    zeroValue - (undocumented)

    numPartitions - (undocumented)

    func - (undocumented)

    Returns:

    (undocumented)

  • foldByKey

    Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).

    Parameters:

    zeroValue - (undocumented)

    func - (undocumented)

    Returns:

    (undocumented)

  • reduceByKey

    Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.

    Parameters:

    func - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • groupByKey

    Group the values for each key in the RDD into a single sequence. Allows controlling the partitioning of the resulting key-value pair RDD by passing a Partitioner.

    Parameters:

    partitioner - (undocumented)

    Returns:

    (undocumented)

    Note:

    If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.

  • groupByKey

    Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with into numPartitions partitions.

    Parameters:

    numPartitions - (undocumented)

    Returns:

    (undocumented)

    Note:

    If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.

  • 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)

  • subtractByKey

    Return an RDD with the pairs from this whose keys 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)

  • subtractByKey

    Return an RDD with the pairs from this whose keys are not in other.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • subtractByKey

    Return an RDD with the pairs from this whose keys are not in other.

    Parameters:

    other - (undocumented)

    p - (undocumented)

    Returns:

    (undocumented)

  • partitionBy

    Return a copy of the RDD partitioned using the specified partitioner.

    Parameters:

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • join

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Uses the given Partitioner to partition the output RDD.

    Parameters:

    other - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • leftOuterJoin

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Uses the given Partitioner to partition the output RDD.

    Parameters:

    other - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • rightOuterJoin

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.

    Parameters:

    other - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • fullOuterJoin

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.

    Parameters:

    other - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • combineByKey

    Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.

    Parameters:

    createCombiner - (undocumented)

    mergeValue - (undocumented)

    mergeCombiners - (undocumented)

    Returns:

    (undocumented)

  • reduceByKey

    Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ parallelism level.

    Parameters:

    func - (undocumented)

    Returns:

    (undocumented)

  • groupByKey

    Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with the existing partitioner/parallelism level.

    Returns:

    (undocumented)

    Note:

    If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.

  • join

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Performs a hash join across the cluster.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • join

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Performs a hash join across the cluster.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • leftOuterJoin

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Hash-partitions the output using the existing partitioner/parallelism level.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • leftOuterJoin

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Hash-partitions the output into numPartitions partitions.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • rightOuterJoin

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Hash-partitions the resulting RDD using the existing partitioner/parallelism level.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • rightOuterJoin

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Hash-partitions the resulting RDD into the given number of partitions.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • fullOuterJoin

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD using the existing partitioner/ parallelism level.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • fullOuterJoin

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD into the given number of partitions.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • collectAsMap

    public Map<K,V> collectAsMap()

    Return the key-value pairs in this RDD to the master as a Map.

    Returns:

    (undocumented)

    Note:

    this method should only be used if the resulting data is expected to be small, as all the data is loaded into the driver's memory.

  • mapValues

    Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.

    Parameters:

    f - (undocumented)

    Returns:

    (undocumented)

  • flatMapValues

    Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.

    Parameters:

    f - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

    Parameters:

    other - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    public <W1, W2, W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3, Partitioner partitioner)

    For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    other3 - (undocumented)

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

    Parameters:

    other - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    public <W1, W2, W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3)

    For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    other3 - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.

    Parameters:

    other - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • cogroup

    public <W1, W2, W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3, int numPartitions)

    For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.

    Parameters:

    other1 - (undocumented)

    other2 - (undocumented)

    other3 - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • groupWith

    Alias for cogroup.

  • groupWith

    Alias for cogroup.

  • groupWith

    public <W1, W2, W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> groupWith(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3)

    Alias for cogroup.

  • lookup

    public List<V> lookup(K key)

    Return the list of values in the RDD for key key. This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to.

    Parameters:

    key - (undocumented)

    Returns:

    (undocumented)

  • saveAsHadoopFile

    public <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>> void saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.mapred.JobConf conf)

    Output the RDD to any Hadoop-supported file system.

  • saveAsHadoopFile

    public <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>> void saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)

    Output the RDD to any Hadoop-supported file system.

  • saveAsHadoopFile

    public <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>> void saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)

    Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.

  • saveAsNewAPIHadoopFile

    public <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>> void saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.conf.Configuration conf)

    Output the RDD to any Hadoop-supported file system.

  • saveAsNewAPIHadoopDataset

    public void saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)

    Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.

    Parameters:

    conf - (undocumented)

  • saveAsNewAPIHadoopFile

    public <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>> void saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)

    Output the RDD to any Hadoop-supported file system.

  • saveAsHadoopDataset

    public void saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)

    Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system. The JobConf should set an OutputFormat and any output paths required (e.g. a table name to write to) in the same way as it would be configured for a Hadoop MapReduce job.

    Parameters:

    conf - (undocumented)

  • repartitionAndSortWithinPartitions

    Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

    This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.

    Parameters:

    partitioner - (undocumented)

    Returns:

    (undocumented)

  • repartitionAndSortWithinPartitions

    Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

    This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.

    Parameters:

    partitioner - (undocumented)

    comp - (undocumented)

    Returns:

    (undocumented)

  • sortByKey

    Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Returns:

    (undocumented)

  • sortByKey

    Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Parameters:

    ascending - (undocumented)

    Returns:

    (undocumented)

  • sortByKey

    public JavaPairRDD<K,V> sortByKey(boolean ascending, int numPartitions)

    Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Parameters:

    ascending - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • sortByKey

    Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Parameters:

    comp - (undocumented)

    Returns:

    (undocumented)

  • sortByKey

    Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Parameters:

    comp - (undocumented)

    ascending - (undocumented)

    Returns:

    (undocumented)

  • sortByKey

    Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).

    Parameters:

    comp - (undocumented)

    ascending - (undocumented)

    numPartitions - (undocumented)

    Returns:

    (undocumented)

  • filterByRange

    Return a RDD containing only the elements in the inclusive range lower to upper. If the RDD has been partitioned using a RangePartitioner, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standard filter is applied to all partitions.

    Parameters:

    lower - (undocumented)

    upper - (undocumented)

    Returns:

    (undocumented)

    Since:

    3.1.0

  • filterByRange

    Return a RDD containing only the elements in the inclusive range lower to upper. If the RDD has been partitioned using a RangePartitioner, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standard filter is applied to all partitions.

    Parameters:

    comp - (undocumented)

    lower - (undocumented)

    upper - (undocumented)

    Returns:

    (undocumented)

    Since:

    3.1.0

  • keys

    Return an RDD with the keys of each tuple.

    Returns:

    (undocumented)

  • values

    Return an RDD with the values of each tuple.

    Returns:

    (undocumented)

  • countApproxDistinctByKey

    Return approximate number of distinct values for each key in this RDD.

    The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

    Parameters:

    relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    partitioner - partitioner of the resulting RDD.

    Returns:

    (undocumented)

  • countApproxDistinctByKey

    public JavaPairRDD<K,Long> countApproxDistinctByKey(double relativeSD, int numPartitions)

    Return approximate number of distinct values for each key in this RDD.

    The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

    Parameters:

    relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    numPartitions - number of partitions of the resulting RDD.

    Returns:

    (undocumented)

  • countApproxDistinctByKey

    public JavaPairRDD<K,Long> countApproxDistinctByKey(double relativeSD)

    Return approximate number of distinct values for each key in this RDD.

    The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

    Parameters:

    relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    Returns:

    (undocumented)

  • setName

    Assign a name to this RDD