org.apache.spark.rdd

Class PairRDDFunctions<K,V>

Object

org.apache.spark.rdd.PairRDDFunctions<K,V>

All Implemented Interfaces:

java.io.Serializable, Logging

public class PairRDDFunctions<K,V>
extends Object
implements Logging, scala.Serializable

Extra functions available on RDDs of (key, value) pairs through an implicit conversion. Import org.apache.spark.SparkContext._ at the top of your program to use these functions.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PairRDDFunctions(RDD<scala.Tuple2<K,V>> self, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord)

Method Summary

Methods

Modifier and Type	Method and Description
<U> RDD<scala.Tuple2<K,U>>	aggregateByKey(U zeroValue, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$3) Aggregate the values of each key, using given combine functions and a neutral "zero value".
<U> RDD<scala.Tuple2<K,U>>	aggregateByKey(U zeroValue, int numPartitions, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$2) Aggregate the values of each key, using given combine functions and a neutral "zero value".
<U> RDD<scala.Tuple2<K,U>>	aggregateByKey(U zeroValue, Partitioner partitioner, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$1) Aggregate the values of each key, using given combine functions and a neutral "zero value".
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(RDD<scala.Tuple2<K,W>> 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.
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(RDD<scala.Tuple2<K,W>> other, int numPartitions) 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.
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> 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.
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, int numPartitions) 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.
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, Partitioner partitioner)
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3)
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<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.
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>	cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<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.
scala.collection.Map<K,V>	collectAsMap() Return the key-value pairs in this RDD to the master as a Map.
<C> RDD<scala.Tuple2<K,C>>	combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners) Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level.
<C> RDD<scala.Tuple2<K,C>>	combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, int numPartitions) Simplified version of combineByKey that hash-partitions the output RDD.
<C> RDD<scala.Tuple2<K,C>>	combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.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.
RDD<scala.Tuple2<K,Object>>	countApproxDistinctByKey(double relativeSD) Return approximate number of distinct values for each key in this RDD.
RDD<scala.Tuple2<K,Object>>	countApproxDistinctByKey(double relativeSD, int numPartitions) Return approximate number of distinct values for each key in this RDD.
RDD<scala.Tuple2<K,Object>>	countApproxDistinctByKey(double relativeSD, Partitioner partitioner) Return approximate number of distinct values for each key in this RDD.
RDD<scala.Tuple2<K,Object>>	countApproxDistinctByKey(int p, int sp, Partitioner partitioner) :: Experimental ::
scala.collection.Map<K,Object>	countByKey() Count the number of elements for each key, and return the result to the master as a Map.
PartialResult<scala.collection.Map<K,BoundedDouble>>	countByKeyApprox(long timeout, double confidence) :: Experimental :: Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
<U> RDD<scala.Tuple2<K,U>>	flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> f) 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.
RDD<scala.Tuple2<K,V>>	foldByKey(V zeroValue, scala.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.).
RDD<scala.Tuple2<K,V>>	foldByKey(V zeroValue, int numPartitions, scala.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.).
RDD<scala.Tuple2<K,V>>	foldByKey(V zeroValue, Partitioner partitioner, scala.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.).
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey() Group the values for each key in the RDD into a single sequence.
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey(int numPartitions) Group the values for each key in the RDD into a single sequence.
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey(Partitioner partitioner) Group the values for each key in the RDD into a single sequence.
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	groupWith(RDD<scala.Tuple2<K,W>> other) Alias for cogroup.
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>	groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2) Alias for cogroup.
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>	groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3) Alias for cogroup.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(RDD<scala.Tuple2<K,W>> other) Return an RDD containing all pairs of elements with matching keys in this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(RDD<scala.Tuple2<K,W>> other, int numPartitions) Return an RDD containing all pairs of elements with matching keys in this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner) Return an RDD containing all pairs of elements with matching keys in this and other.
RDD<K>	keys() Return an RDD with the keys of each tuple.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(RDD<scala.Tuple2<K,W>> other) Perform a left outer join of this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions) Perform a left outer join of this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner) Perform a left outer join of this and other.
scala.collection.Seq<V>	lookup(K key) Return the list of values in the RDD for key key.
<U> RDD<scala.Tuple2<K,U>>	mapValues(scala.Function1<V,U> f) 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.
RDD<scala.Tuple2<K,V>>	partitionBy(Partitioner partitioner) Return a copy of the RDD partitioned using the specified partitioner.
RDD<scala.Tuple2<K,V>>	reduceByKey(scala.Function2<V,V,V> func) Merge the values for each key using an associative reduce function.
RDD<scala.Tuple2<K,V>>	reduceByKey(scala.Function2<V,V,V> func, int numPartitions) Merge the values for each key using an associative reduce function.
RDD<scala.Tuple2<K,V>>	reduceByKey(Partitioner partitioner, scala.Function2<V,V,V> func) Merge the values for each key using an associative reduce function.
scala.collection.Map<K,V>	reduceByKeyLocally(scala.Function2<V,V,V> func) Merge the values for each key using an associative reduce function, but return the results immediately to the master as a Map.
scala.collection.Map<K,V>	reduceByKeyToDriver(scala.Function2<V,V,V> func) Alias for reduceByKeyLocally
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(RDD<scala.Tuple2<K,W>> other) Perform a right outer join of this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions) Perform a right outer join of this and other.
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner) Perform a right outer join of this and other.
RDD<scala.Tuple2<K,V>>	sampleByKey(boolean withReplacement, scala.collection.Map<K,Object> fractions, long seed) Return a subset of this RDD sampled by key (via stratified sampling).
RDD<scala.Tuple2<K,V>>	sampleByKeyExact(boolean withReplacement, scala.collection.Map<K,Object> fractions, long seed) ::Experimental:: 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.
void	saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec) Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
void	saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.mapred.JobConf conf, scala.Option<Class<? extends org.apache.hadoop.io.compress.CompressionCodec>> codec) Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void	saveAsHadoopFile(String path, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec, scala.reflect.ClassTag<F> fm) Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void	saveAsHadoopFile(String path, scala.reflect.ClassTag<F> fm) Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
void	saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf) Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop Configuration object for that storage system.
void	saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.conf.Configuration conf) Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
<F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void	saveAsNewAPIHadoopFile(String path, scala.reflect.ClassTag<F> fm) Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
<W> RDD<scala.Tuple2<K,V>>	subtractByKey(RDD<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$4) Return an RDD with the pairs from this whose keys are not in other.
<W> RDD<scala.Tuple2<K,V>>	subtractByKey(RDD<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$5) Return an RDD with the pairs from `this` whose keys are not in `other`.
<W> RDD<scala.Tuple2<K,V>>	subtractByKey(RDD<scala.Tuple2<K,W>> other, Partitioner p, scala.reflect.ClassTag<W> evidence$6) Return an RDD with the pairs from `this` whose keys are not in `other`.
RDD<V>	values() Return an RDD with the values of each tuple.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initialized, initializeIfNecessary, initializeLogging, initLock, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

PairRDDFunctions

public PairRDDFunctions(RDD<scala.Tuple2<K,V>> self,
                scala.reflect.ClassTag<K> kt,
                scala.reflect.ClassTag<V> vt,
                scala.math.Ordering<K> ord)

Method Detail

combineByKey

public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
                                      scala.Function2<C,V,C> mergeValue,
                                      scala.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 an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C Note that V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]). 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, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).

combineByKey

public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
                                      scala.Function2<C,V,C> mergeValue,
                                      scala.Function2<C,C,C> mergeCombiners,
                                      int numPartitions)

Simplified version of combineByKey that hash-partitions the output RDD.

aggregateByKey

public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
                                        Partitioner partitioner,
                                        scala.Function2<U,V,U> seqOp,
                                        scala.Function2<U,U,U> combOp,
                                        scala.reflect.ClassTag<U> evidence$1)

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.TraversableOnce. 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.

aggregateByKey

public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
                                        int numPartitions,
                                        scala.Function2<U,V,U> seqOp,
                                        scala.Function2<U,U,U> combOp,
                                        scala.reflect.ClassTag<U> evidence$2)

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.TraversableOnce. 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.

aggregateByKey

public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
                                        scala.Function2<U,V,U> seqOp,
                                        scala.Function2<U,U,U> combOp,
                                        scala.reflect.ClassTag<U> evidence$3)

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.TraversableOnce. 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.

foldByKey

public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue,
                               Partitioner partitioner,
                               scala.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.).

foldByKey

public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue,
                               int numPartitions,
                               scala.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.).

foldByKey

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

sampleByKey

public RDD<scala.Tuple2<K,V>> sampleByKey(boolean withReplacement,
                                 scala.collection.Map<K,Object> 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 - whether to sample with or without replacement

fractions - map of specific keys to sampling rates

seed - seed for the random number generator

Returns:

RDD containing the sampled subset

sampleByKeyExact

public RDD<scala.Tuple2<K,V>> sampleByKeyExact(boolean withReplacement,
                                      scala.collection.Map<K,Object> fractions,
                                      long seed)

::Experimental:: 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 - whether to sample with or without replacement

fractions - map of specific keys to sampling rates

seed - seed for the random number generator

Returns:

RDD containing the sampled subset

reduceByKey

public RDD<scala.Tuple2<K,V>> reduceByKey(Partitioner partitioner,
                                 scala.Function2<V,V,V> func)

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

reduceByKey

public RDD<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> func,
                                 int numPartitions)

Merge the values for each key using an associative 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.

reduceByKey

public RDD<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> func)

Merge the values for each key using an associative 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.

reduceByKeyLocally

public scala.collection.Map<K,V> reduceByKeyLocally(scala.Function2<V,V,V> func)

Merge the values for each key using an associative reduce function, but return the results 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.

reduceByKeyToDriver

public scala.collection.Map<K,V> reduceByKeyToDriver(scala.Function2<V,V,V> func)

Alias for reduceByKeyLocally

countByKey

public scala.collection.Map<K,Object> countByKey()

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

countByKeyApprox

public PartialResult<scala.collection.Map<K,BoundedDouble>> countByKeyApprox(long timeout,
                                                                    double confidence)

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

countApproxDistinctByKey

public RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(int p,
                                                   int sp,
                                                   Partitioner partitioner)

:: Experimental ::

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.

The relative accuracy is approximately 1.054 / sqrt(2^p). Setting a nonzero sp > p would trigger sparse representation of registers, which may reduce the memory consumption and increase accuracy when the cardinality is small.

Parameters:

p - The precision value for the normal set. p must be a value between 4 and sp if sp is not zero (32 max).

sp - The precision value for the sparse set, between 0 and 32. If sp equals 0, the sparse representation is skipped.

partitioner - Partitioner to use for the resulting RDD.

countApproxDistinctByKey

public RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(double relativeSD,
                                                   Partitioner partitioner)

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

countApproxDistinctByKey

public RDD<scala.Tuple2<K,Object>> 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

countApproxDistinctByKey

public RDD<scala.Tuple2<K,Object>> 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.

groupByKey

public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(Partitioner partitioner)

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.

Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

groupByKey

public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(int numPartitions)

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

Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

partitionBy

public RDD<scala.Tuple2<K,V>> partitionBy(Partitioner partitioner)

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

join

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other,
                                              Partitioner partitioner)

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.

leftOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other,
                                                                     Partitioner partitioner)

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.

rightOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other,
                                                                      Partitioner partitioner)

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.

combineByKey

public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
                                      scala.Function2<C,V,C> mergeValue,
                                      scala.Function2<C,C,C> mergeCombiners)

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

groupByKey

public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> 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.

Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

join

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other)

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.

join

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other,
                                              int numPartitions)

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.

leftOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other)

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.

leftOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other,
                                                                     int numPartitions)

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.

rightOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other)

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.

rightOuterJoin

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other,
                                                                      int numPartitions)

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.

collectAsMap

public scala.collection.Map<K,V> collectAsMap()

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

Warning: this doesn't return a multimap (so if you have multiple values to the same key, only one value per key is preserved in the map returned)

mapValues

public <U> RDD<scala.Tuple2<K,U>> mapValues(scala.Function1<V,U> f)

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.

flatMapValues

public <U> RDD<scala.Tuple2<K,U>> flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> f)

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.

cogroup

public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                                                           RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                                                           RDD<scala.Tuple2<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.

cogroup

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> other,
                                                                                                       Partitioner partitioner)

cogroup

public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                          RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                          Partitioner partitioner)

cogroup

public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                                                           RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                                                           RDD<scala.Tuple2<K,W3>> other3)

cogroup

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> 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.

cogroup

public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                          RDD<scala.Tuple2<K,W2>> 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.

cogroup

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> other,
                                                                                                       int numPartitions)

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.

cogroup

public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                          RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                          int numPartitions)

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.

cogroup

public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                                                           RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                                                           RDD<scala.Tuple2<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.

groupWith

public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> groupWith(RDD<scala.Tuple2<K,W>> other)

Alias for cogroup.

groupWith

public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                            RDD<scala.Tuple2<K,W2>> other2)

Alias for cogroup.

groupWith

public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1,
                                                                                                                                                                             RDD<scala.Tuple2<K,W2>> other2,
                                                                                                                                                                             RDD<scala.Tuple2<K,W3>> other3)

Alias for cogroup.

subtractByKey

public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
                                       scala.reflect.ClassTag<W> evidence$4)

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 <= us.

subtractByKey

public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
                                       int numPartitions,
                                       scala.reflect.ClassTag<W> evidence$5)

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

subtractByKey

public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
                                       Partitioner p,
                                       scala.reflect.ClassTag<W> evidence$6)

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

lookup

public scala.collection.Seq<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.

saveAsHadoopFile

public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFile(String path,
                                                                           scala.reflect.ClassTag<F> fm)

Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.

saveAsHadoopFile

public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFile(String path,
                                                                           Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec,
                                                                           scala.reflect.ClassTag<F> fm)

Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD. Compress the result with the supplied codec.

saveAsNewAPIHadoopFile

public <F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void saveAsNewAPIHadoopFile(String path,
                                                                                    scala.reflect.ClassTag<F> fm)

Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.

saveAsNewAPIHadoopFile

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

Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.

saveAsHadoopFile

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

Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD. Compress with the supplied codec.

saveAsHadoopFile

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

Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.

saveAsNewAPIHadoopDataset

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

Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop Configuration object for that storage system. The Conf 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.

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.

keys

public RDD<K> keys()

Return an RDD with the keys of each tuple.

values

public RDD<V> values()

Return an RDD with the values of each tuple.