Core Spark functionality.
Core Spark functionality.org.apache.spark.SparkContext serves as the main entry point toSpark, whileorg.apache.spark.rdd.RDD is the data type representing a distributed collection,and provides most parallel operations.
In addition,org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDsof key-value pairs, such asgroupByKey andjoin;org.apache.spark.rdd.DoubleRDDFunctionscontains operations available only on RDDs of Doubles; andorg.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that canbe saved as SequenceFiles. These operations are automatically available on any RDD of the righttype (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference theorg.apache.spark.api.java packagefor Spark programming APIs in Java.
Classes and methods marked withExperimental are user-facing features which have not been officially adopted by theSpark project. These are subject to change or removal in minor releases.
Classes and methods marked withDeveloper API are intended for advanced users want to extend Spark through lowerlevel interfaces. These are subject to changes or removal in minor releases.
Spark Java programming APIs.
Spark Java programming APIs.
Set of interfaces to represent functions in Spark's Java API.
Set of interfaces to represent functions in Spark's Java API. Users create implementations ofthese interfaces to pass functions to various Java API methods for Spark. Please visit Spark'sJava programming guide for more details.
Aggregate the elements of each partition, and then the results for all the partitions, usinggiven combine functions and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, usinggiven combine functions and a neutral "zero value". This function can return a different resulttype, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an Uand one operation for merging two U's, as in scala.IterableOnce. Both of these functions areallowed to modify and return their first argument instead of creating a new U to avoid memoryallocation.
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".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 formerging values between partitions. To avoid memory allocation, both of these functions areallowed to modify and return their first argument instead of creating a new U.
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".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 apartition, and the latter is used for merging values between partitions. To avoid memoryallocation, both of these functions are allowed to modify and return their first argumentinstead of creating a new U.
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".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 apartition, and the latter is used for merging values between partitions. To avoid memoryallocation, both of these functions are allowed to modify and return their first argumentinstead of creating a new U.
Persist this RDD with the default storage level (MEMORY_ONLY).
Persist this RDD with the default storage level (MEMORY_ONLY).
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs ofelements (a, b) where a is inthis and b is inother.
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs ofelements (a, b) where a is inthis and b is inother.
Mark this RDD for checkpointing.
Mark this RDD for checkpointing. It will be saved to a file inside the checkpointdirectory set with SparkContext.setCheckpointDir() and all references to its parentRDDs will be removed. This function must be called before any job has beenexecuted on this RDD. It is strongly recommended that this RDD is persisted inmemory, otherwise saving it on a file will require recomputation.
Return a new RDD that is reduced intonumPartitions partitions.
Return a new RDD that is reduced intonumPartitions partitions.
Return a new RDD that is reduced intonumPartitions partitions.
Return a new RDD that is reduced intonumPartitions partitions.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2 orother3,return a resulting RDD that contains a tuple with the list of valuesfor that key inthis,other1,other2 andother3.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother1 orother2, return a resulting RDD that contains atuple with the list of values for that key inthis,other1 andother2.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
For each key k inthis orother, return a resulting RDD that contains a tuple with thelist of values for that key inthis as well asother.
Return an array that contains all of the elements in this RDD.
Return an array that contains all of the elements in this RDD.
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
Return the key-value pairs in this RDD to the master as a Map.
Return the key-value pairs in this RDD to the master as a Map.
this method should only be used if the resulting data is expected to be small, asall the data is loaded into the driver's memory.
The asynchronous version ofcollect, which returns a future forretrieving an array containing all of the elements in this RDD.
The asynchronous version ofcollect, which returns a future forretrieving an array containing all of the elements in this RDD.
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
Return an array that contains all of the elements in a specific partition of this RDD.
Return an array that contains all of the elements in a specific partition of this RDD.
Simplified version of combineByKey that hash-partitions the resulting RDD using the existingpartitioner/parallelism level and using map-side aggregation.
Simplified version of combineByKey that hash-partitions the resulting RDD using the existingpartitioner/parallelism level and using map-side aggregation.
Simplified version of combineByKey that hash-partitions the output RDD and uses map-sideaggregation.
Simplified version of combineByKey that hash-partitions the output RDD and uses map-sideaggregation.
Generic function to combine the elements for each key using a custom set of aggregationfunctions.
Generic function to combine the elements for each key using a custom set of aggregationfunctions. 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 automaticallyuses map-side aggregation in shuffling the RDD.
V and C can be different -- for example, one might group an RDD of type (Int, Int) intoan RDD of type (Int, List[Int]).
Generic function to combine the elements for each key using a custom set of aggregationfunctions.
Generic function to combine the elements for each key using a custom set of aggregationfunctions. 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 usefor the shuffle, and whether to perform map-side aggregation (if a mapper can produce multipleitems with the same key).
V and C can be different -- for example, one might group an RDD of type (Int, Int) intoan RDD of type (Int, List[Int]).
Theorg.apache.spark.SparkContext that this RDD was created on.
Theorg.apache.spark.SparkContext that this RDD was created on.
Return the number of elements in the RDD.
Return the number of elements in the RDD.
Approximate version of count() that returns a potentially incomplete resultwithin a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete resultwithin a timeout, even if not all tasks have finished.
maximum time to wait for the job, in milliseconds
Approximate version of count() that returns a potentially incomplete resultwithin a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete resultwithin a timeout, even if not all tasks have finished.
The confidence is the probability that the error bounds of the result willcontain the true value. That is, if countApprox were called repeatedlywith confidence 0.9, we would expect 90% of the results to contain thetrue count. The confidence must be in the range [0,1] or an exception willbe thrown.
maximum time to wait for the job, in milliseconds
the desired statistical confidence in the result
a potentially incomplete result, with error bounds
Return approximate number of distinct elements in the RDD.
Return approximate number of distinct elements in the 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", availablehere.
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
Return approximate number of distinct values for each key in this RDD.
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", availablehere.
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
Return approximate number of distinct values for each key in this RDD.
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", availablehere.
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
number of partitions of the resulting RDD.
Return approximate number of distinct values for each key in this RDD.
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", availablehere.
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
partitioner of the resulting RDD.
The asynchronous version ofcount, which returns afuture for counting the number of elements in this RDD.
The asynchronous version ofcount, which returns afuture for counting the number of elements in this RDD.
Count the number of elements for each key, and return the result to the master as a Map.
Count the number of elements for each key, and return the result to the master as a Map.
Approximate version of countByKey that can return a partial result if it doesnot finish within a timeout.
Approximate version of countByKey that can return a partial result if it doesnot finish within a timeout.
Approximate version of countByKey that can return a partial result if it doesnot finish within a timeout.
Approximate version of countByKey that can return a partial result if it doesnot finish within a timeout.
Return the count of each unique value in this RDD as a map of (value, count) pairs.
Return the count of each unique value in this RDD as a map of (value, count) pairs. The finalcombine step happens locally on the master, equivalent to running a single reduce task.
Approximate version of countByValue().
Approximate version of countByValue().
maximum time to wait for the job, in milliseconds
a potentially incomplete result, with error bounds
Approximate version of countByValue().
Approximate version of countByValue().
The confidence is the probability that the error bounds of the result willcontain the true value. That is, if countApprox were called repeatedlywith confidence 0.9, we would expect 90% of the results to contain thetrue count. The confidence must be in the range [0,1] or an exception willbe thrown.
maximum time to wait for the job, in milliseconds
the desired statistical confidence in the result
a potentially incomplete result, with error bounds
Return a new RDD containing the distinct elements in this RDD.
Return a new RDD containing the distinct elements in this RDD.
Return a new RDD containing the distinct elements in this RDD.
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 new RDD containing only the elements that satisfy a predicate.
Return a RDD containing only the elements in the inclusive rangelower toupper.
Return a RDD containing only the elements in the inclusive rangelower toupper.If the RDD has been partitioned using aRangePartitioner, then this operation can beperformed efficiently by only scanning the partitions that might contain matching elements.Otherwise, a standardfilter is applied to all partitions.
3.1.0
Return a RDD containing only the elements in the inclusive rangelower toupper.
Return a RDD containing only the elements in the inclusive rangelower toupper.If the RDD has been partitioned using aRangePartitioner, then this operation can beperformed efficiently by only scanning the partitions that might contain matching elements.Otherwise, a standardfilter is applied to all partitions.
3.1.0
Return the first element in this RDD.
Return the first element in this RDD.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Pass each value in the key-value pair RDD through a flatMap function without changing thekeys; this also retains the original RDD's partitioning.
Pass each value in the key-value pair RDD through a flatMap function without changing thekeys; this also retains the original RDD's partitioning.
Aggregate the elements of each partition, and then the results for all the partitions, using agiven associative function and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using agiven associative function and a neutral "zero value". The functionop(t1, t2) is allowed to modify t1 and return it as its result value to avoid objectallocation; however, it should not modify t2.
This behaves somewhat differently from fold operations implemented for non-distributedcollections in functional languages like Scala. This fold operation may be applied topartitions individually, and then fold those results into the final result, rather thanapply the fold to each element sequentially in some defined ordering. For functionsthat are not commutative, the result may differ from that of a fold applied to anon-distributed collection.
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" whichmay 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" whichmay 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" whichmay 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" whichmay 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.).
Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.
The asynchronous version of theforeach action, whichapplies a function f to all the elements of this RDD.
The asynchronous version of theforeach action, whichapplies a function f to all the elements of this RDD.
Applies a function f to each partition of this RDD.
Applies a function f to each partition of this RDD.
The asynchronous version of theforeachPartition action, whichapplies a function f to each partition of this RDD.
The asynchronous version of theforeachPartition action, whichapplies a function f to each partition of this RDD.
Perform a full outer join ofthis andother.
Perform a full outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother, orthe pair (k, (Some(v), None)) if no elements inother have key k. Similarly, for eachelement (k, w) inother, the resulting RDD will either contain all pairs(k, (Some(v), Some(w))) for v inthis, or the pair (k, (None, Some(w))) if no elementsinthis have key k. Hash-partitions the resulting RDD into the given number of partitions.
Perform a full outer join ofthis andother.
Perform a full outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother, orthe pair (k, (Some(v), None)) if no elements inother have key k. Similarly, for eachelement (k, w) inother, the resulting RDD will either contain all pairs(k, (Some(v), Some(w))) for v inthis, or the pair (k, (None, Some(w))) if no elementsinthis have key k. Hash-partitions the resulting RDD using the existing partitioner/parallelism level.
Perform a full outer join ofthis andother.
Perform a full outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother, orthe pair (k, (Some(v), None)) if no elements inother have key k. Similarly, for eachelement (k, w) inother, the resulting RDD will either contain all pairs(k, (Some(v), Some(w))) for v inthis, or the pair (k, (None, Some(w))) if no elementsinthis have key k. Uses the given Partitioner to partition the output RDD.
Gets the name of the file to which this RDD was checkpointed
Gets the name of the file to which this RDD was checkpointed
Return the number of partitions in this RDD.
Return the number of partitions in this RDD.
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
Return an RDD created by coalescing all elements within each partition into an array.
Return an RDD created by coalescing all elements within each partition into an array.
Return an RDD of grouped elements.
Return an RDD of grouped elements. Each group consists of a key and a sequence of elementsmapping to that key.
Return an RDD of grouped elements.
Return an RDD of grouped elements. Each group consists of a key and a sequence of elementsmapping to that key.
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. Hash-partitions theresulting RDD with the existing partitioner/parallelism level.
If you are grouping in order to perform an aggregation (such as a sum or average) overeach key, usingJavaPairRDD.reduceByKey orJavaPairRDD.combineByKeywill provide much better performance.
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. Hash-partitions theresulting RDD with intonumPartitions partitions.
If you are grouping in order to perform an aggregation (such as a sum or average) overeach key, usingJavaPairRDD.reduceByKey orJavaPairRDD.combineByKeywill provide much better performance.
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. Allows controlling thepartitioning of the resulting key-value pair RDD by passing a Partitioner.
If you are grouping in order to perform an aggregation (such as a sum or average) overeach key, usingJavaPairRDD.reduceByKey orJavaPairRDD.combineByKeywill provide much better performance.
Alias for cogroup.
Alias for cogroup.
Alias for cogroup.
Alias for cogroup.
Alias for cogroup.
Alias for cogroup.
A unique ID for this RDD (within its SparkContext).
A unique ID for this RDD (within its SparkContext).
Return the intersection of this RDD and another one.
Return the intersection of this RDD and another one. The output will not contain any duplicateelements, even if the input RDDs did.
This method performs a shuffle internally.
Return whether this RDD has been checkpointed or not
Return whether this RDD has been checkpointed or not
true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition.
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.This shouldnot be called by users directly, but is available for implementers of customsubclasses of RDD.
Return an RDD containing all pairs of elements with matching keys inthis andother.
Return an RDD containing all pairs of elements with matching keys inthis andother. Eachpair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis and(k, v2) is inother. Performs a hash join across the cluster.
Return an RDD containing all pairs of elements with matching keys inthis andother.
Return an RDD containing all pairs of elements with matching keys inthis andother. Eachpair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis and(k, v2) is inother. Performs a hash join across the cluster.
Return an RDD containing all pairs of elements with matching keys inthis andother.
Return an RDD containing all pairs of elements with matching keys inthis andother. Eachpair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis and(k, v2) is inother. Uses the given Partitioner to partition the output RDD.
Creates tuples of the elements in this RDD by applyingf.
Creates tuples of the elements in this RDD by applyingf.
Return an RDD with the keys of each tuple.
Return an RDD with the keys of each tuple.
Perform a left outer join ofthis andother.
Perform a left outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (v, Some(w))) for w inother, or thepair (k, (v, None)) if no elements inother have key k. Hash-partitions the outputintonumPartitions partitions.
Perform a left outer join ofthis andother.
Perform a left outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (v, Some(w))) for w inother, or thepair (k, (v, None)) if no elements inother have key k. Hash-partitions the outputusing the existing partitioner/parallelism level.
Perform a left outer join ofthis andother.
Perform a left outer join ofthis andother. For each element (k, v) inthis, theresulting RDD will either contain all pairs (k, (v, Some(w))) for w inother, or thepair (k, (v, None)) if no elements inother have key k. Uses the given Partitioner topartition the output RDD.
Return the list of values in the RDD for keykey.
Return the list of values in the RDD for keykey. This operation is done efficiently if theRDD has a known partitioner by only searching the partition that the key maps to.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD, while tracking the indexof the original partition.
Return a new RDD by applying a function to each partition of this RDD, while tracking the indexof the original partition.
Maps over a partition, providing the InputSplit that was used as the base of the partition.
Maps over a partition, providing the InputSplit that was used as the base of the partition.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
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.
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.
Returns the maximum element from this RDD as defined by the specifiedComparator[T].
Returns the maximum element from this RDD as defined by the specifiedComparator[T].
the comparator that defines ordering
the maximum of the RDD
Returns the minimum element from this RDD as defined by the specifiedComparator[T].
Returns the minimum element from this RDD as defined by the specifiedComparator[T].
the comparator that defines ordering
the minimum of the RDD
Return a copy of the RDD partitioned using the specified partitioner.
Return a copy of the RDD partitioned using the specified partitioner.
The partitioner of this RDD.
The partitioner of this RDD.
Set of partitions in this RDD.
Set of partitions in this RDD.
Set this RDD's storage level to persist its values across operations after the first timeit is computed.
Set this RDD's storage level to persist its values across operations after the first timeit is computed. Can only be called once on each RDD.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Reduces the elements of this RDD using the specified commutative and associative binaryoperator.
Reduces the elements of this RDD using the specified commutative and associative binaryoperator.
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. This willalso perform the merging locally on each mapper before sending results to a reducer, similarlyto a "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/parallelism level.
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. This willalso perform the merging locally on each mapper before sending results to a reducer, similarlyto a "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
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. This willalso perform the merging locally on each mapper before sending results to a reducer, similarlyto a "combiner" in MapReduce.
Merge the values for each key using an associative and commutative reduce function, but returnthe result immediately to the master as a Map.
Merge the values for each key using an associative and commutative reduce function, but returnthe result immediately to the master as a Map. This will also perform the merging locally oneach mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
Return a new RDD that has exactly numPartitions partitions.
Return a new RDD that has exactly numPartitions partitions.
Can increase or decrease the level of parallelism in this RDD. Internally, this usesa shuffle to redistribute data.
If you are decreasing the number of partitions in this RDD, consider usingcoalesce,which can avoid performing a shuffle.
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.
This is more efficient than callingrepartition and then sorting within each partitionbecause it can push the sorting down into the shuffle machinery.
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.
This is more efficient than callingrepartition and then sorting within each partitionbecause it can push the sorting down into the shuffle machinery.
Perform a right outer join ofthis andother.
Perform a right outer join ofthis andother. For each element (k, w) inother, theresulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis, or thepair (k, (None, w)) if no elements inthis have key k. Hash-partitions the resultingRDD into the given number of partitions.
Perform a right outer join ofthis andother.
Perform a right outer join ofthis andother. For each element (k, w) inother, theresulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis, or thepair (k, (None, w)) if no elements inthis have key k. Hash-partitions the resultingRDD using the existing partitioner/parallelism level.
Perform a right outer join ofthis andother.
Perform a right outer join ofthis andother. For each element (k, w) inother, theresulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis, or thepair (k, (None, w)) if no elements inthis have key k. Uses the given Partitioner topartition the output RDD.
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
Return a subset of this RDD sampled by key (via stratified sampling).
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 byfractions, a key to sampling rate map, via simple random sampling with one pass over theRDD, to produce a sample of size that's approximately equal to the sum ofmath.ceil(numItems * samplingRate) over all key values.
Use Utils.random.nextLong as the default seed for the random number generator.
Return a subset of this RDD sampled by key (via stratified sampling).
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 byfractions, a key to sampling rate map, via simple random sampling with one pass over theRDD, to produce a sample of size that's approximately equal to the sum ofmath.ceil(numItems * samplingRate) over all key values.
Return a subset of this RDD sampled by key (via stratified sampling) containing exactlymath.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
Return a subset of this RDD sampled by key (via stratified sampling) containing exactlymath.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
This method differs fromsampleByKey in that we make additional passes over the RDD tocreate 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 oneadditional pass over the RDD to guarantee sample size; when sampling with replacement, we needtwo additional passes.
Use Utils.random.nextLong as the default seed for the random number generator.
Return a subset of this RDD sampled by key (via stratified sampling) containing exactlymath.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
Return a subset of this RDD sampled by key (via stratified sampling) containing exactlymath.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
This method differs fromsampleByKey in that we make additional passes over the RDD tocreate 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 oneadditional pass over the RDD to guarantee sample size; when sampling with replacement, we needtwo additional passes.
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object forthat storage system.
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object forthat 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 HadoopMapReduce job.
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported storage system, usinga Configuration object for that storage system.
Output the RDD to any Hadoop-supported storage system, usinga Configuration object for that storage system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
Save this RDD as a SequenceFile of serialized objects.
Save this RDD as a SequenceFile of serialized objects.
Save this RDD as a compressed text file, using string representations of elements.
Save this RDD as a compressed text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
Assign a name to this RDD
Assign a name to this RDD
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. Callingcollect orsave on the resulting RDD will return or output an ordered list of records(in thesave case, they will be written to multiplepart-X files in the filesystem, inorder of the keys).
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. Callingcollect orsave on the resulting RDD will return or output an ordered list of records(in thesave case, they will be written to multiplepart-X files in the filesystem, inorder of the keys).
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. Callingcollect orsave on the resulting RDD will return or output an ordered list of records(in thesave case, they will be written to multiplepart-X files in the filesystem, inorder of the keys).
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. Callingcollect orsave on the resulting RDD will return or output an ordered list of records(in thesave case, they will be written to multiplepart-X files in the filesystem, inorder of the keys).
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. Callingcollect orsave on the resulting RDD will return or output an ordered list of records(in thesave case, they will be written to multiplepart-X files in the filesystem, inorder of the keys).
Sort the RDD by key, so that each partition contains a sorted range of the elements inascending order.
Sort the RDD by key, so that each partition contains a sorted range of the elements inascending order. Callingcollect orsave on the resulting RDD will return or output anordered list of records (in thesave case, they will be written to multiplepart-X filesin the filesystem, in order of the keys).
Return an RDD with the elements fromthis that are not inother.
Return an RDD with the elements fromthis that are not inother.
Return an RDD with the elements fromthis that are not inother.
Return an RDD with the elements fromthis that are not inother.
Return an RDD with the elements fromthis that are not inother.
Return an RDD with the elements fromthis that are not inother.
Usesthis partitioner/partition size, because even ifother is huge, the resultingRDD will be <= us.
Return an RDD with the pairs fromthis whose keys are not inother.
Return an RDD with the pairs fromthis whose keys are not inother.
Return an RDD with the pairs fromthis whose keys are not inother.
Return an RDD with the pairs fromthis whose keys are not inother.
Return an RDD with the pairs fromthis whose keys are not inother.
Return an RDD with the pairs fromthis whose keys are not inother.
Usesthis partitioner/partition size, because even ifother is huge, the resultingRDD will be <= us.
Take the first num elements of the RDD.
Take the first num elements of the RDD. This currently scans the partitions *one by one*, soit will be slow if a lot of partitions are required. In that case, use collect() to get thewhole RDD instead.
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
The asynchronous version of thetake action, which returns afuture for retrieving the firstnum elements of this RDD.
The asynchronous version of thetake action, which returns afuture for retrieving the firstnum elements of this RDD.
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
Returns the first k (smallest) elements from this RDD using thenatural ordering for T while maintain the order.
Returns the first k (smallest) elements from this RDD using thenatural ordering for T while maintain the order.
k, the number of top elements to return
an array of top elements
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
Returns the first k (smallest) elements from this RDD as defined bythe specified Comparator[T] and maintains the order.
Returns the first k (smallest) elements from this RDD as defined bythe specified Comparator[T] and maintains the order.
k, the number of elements to return
the comparator that defines the order
an array of top elements
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
A description of this RDD and its recursive dependencies for debugging.
A description of this RDD and its recursive dependencies for debugging.
Return an iterator that contains all of the elements in this RDD.
Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
Returns the top k (largest) elements from this RDD using thenatural ordering for T and maintains the order.
Returns the top k (largest) elements from this RDD using thenatural ordering for T and maintains the order.
k, the number of top elements to return
an array of top elements
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
Returns the top k (largest) elements from this RDD as defined bythe specified Comparator[T] and maintains the order.
Returns the top k (largest) elements from this RDD as defined bythe specified Comparator[T] and maintains the order.
k, the number of top elements to return
the comparator that defines the order
an array of top elements
this method should only be used if the resulting array is expected to be small, asall the data is loaded into the driver's memory.
org.apache.spark.api.java.JavaRDDLike.treeAggregate with a parameter to do thefinal aggregation on the executor.
org.apache.spark.api.java.JavaRDDLike.treeAggregate with a parameter to do thefinal aggregation on the executor.
org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.
org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.
Aggregates the elements of this RDD in a multi-level tree pattern.
Aggregates the elements of this RDD in a multi-level tree pattern.
suggested depth of the tree
org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.
org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.
Reduces the elements of this RDD in a multi-level tree pattern.
Reduces the elements of this RDD in a multi-level tree pattern.
suggested depth of the tree
Return the union of this RDD and another one.
Return the union of this RDD and another one. Any identical elements will appear multipletimes (use.distinct() to eliminate them).
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
Whether to block until all blocks are deleted.
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.This method blocks until all blocks are deleted.
Return an RDD with the values of each tuple.
Return an RDD with the values of each tuple.
Zips this RDD with another one, returning key-value pairs with the first element in each RDD,second element in each RDD, etc.
Zips this RDD with another one, returning key-value pairs with the first element in each RDD,second element in each RDD, etc. Assumes that the two RDDs have the *same number ofpartitions* and the *same number of elements in each partition* (e.g. one was made througha map on the other).
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD byapplying a function to the zipped partitions.
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD byapplying a function to the zipped partitions. Assumes that all the RDDs have the*same number of partitions*, but does *not* require them to have the same numberof elements in each partition.
Zips this RDD with its element indices.
Zips this RDD with its element indices. The ordering is first based on the partition indexand then the ordering of items within each partition. So the first item in the firstpartition gets index 0, and the last item in the last partition receives the largest index.This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type.This method needs to trigger a spark job when this RDD contains more than one partitions.
Zips this RDD with generated unique Long ids.
Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k,2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this methodwon't trigger a spark job, which is different fromorg.apache.spark.rdd.RDD#zipWithIndex.
(Since version 9)