Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.
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 of
elements (a, b) where a is in this and b is in other.
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
elements (a, b) where a is in this and b is in other.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
Return an array that contains all of the elements in this RDD.
Return an array that contains all of the elements in this RDD.
Return the key-value pairs in this RDD to the master as a Map.
Simplified version of combineByKey that hash-partitions the resulting RDD using the default parallelism level.
Simplified version of combineByKey that hash-partitions the output RDD.
Generic function to combine the elements for each key using a custom set of aggregation functions.
Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C * 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, List[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).
The SparkContext that this RDD was created on.
The SparkContext that this RDD was created on.
Return the number of elements in the RDD.
Return the number of elements in the RDD.
(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
Count the number of elements for each key, and return the result to the master as a Map.
(Experimental) Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
(Experimental) Approximate version of countByKey that can return a partial result if it does not 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 final combine step happens locally on the master, equivalent to running a single reduce task.
(Experimental) Approximate version of countByValue().
(Experimental) Approximate version of countByValue().
(Experimental) Approximate version of countByValue().
(Experimental) Approximate version of countByValue().
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 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.
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.
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.
Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.
Get the RDD's current storage level, or StorageLevel.
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 elements mapping 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 elements mapping 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 the resulting RDD with the default parallelism level.
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 the
resulting RDD with into numSplits partitions.
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 the partitioning of the resulting key-value pair RDD by passing a Partitioner.
Alias for cogroup.
Alias for cogroup.
A unique ID for this RDD (within its SparkContext).
A unique ID for this RDD (within its SparkContext).
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 should not be called by users directly, but is available for implementors of custom subclasses of RDD.
Return an RDD containing all pairs of elements with matching keys in this and other.
Return an RDD containing all pairs of elements with matching keys in this and other. 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.
Return an RDD containing all pairs of elements with matching keys in this and other.
Return an RDD containing all pairs of elements with matching keys in this and other. 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.
Merge the values for each key using an associative reduce function.
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.
Perform a left outer join of this and 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
into numSplits partitions.
Perform a left outer join of this and 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 default level of parallelism.
Perform a left outer join of this and 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. Uses the given Partitioner to
partition the output RDD.
Return the list of values in the RDD for key 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.
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.
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.
Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
Return a copy of the RDD partitioned using the specified partitioner.
Return a copy of the RDD partitioned using the specified partitioner. If mapSideCombine
is true, Spark will group values of the same key together on the map side before the
repartitioning, to only send each key over the network once. If a large number of
duplicated keys are expected, and the size of the keys are large, mapSideCombine should
be set to true.
Set this RDD's storage level to persist its values across operations after the first time it is computed.
Set this RDD's storage level to persist its values across operations after the first time it is computed. Can only be called once on each RDD.
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 associative binary operator.
Reduces the elements of this RDD using the specified associative binary operator.
Merge the values for each key using an associative reduce function.
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 default parallelism level.
Merge the values for each key using an associative reduce function.
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 numSplits splits.
Merge the values for each key using an associative reduce function.
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.
Merge the values for each key using an associative reduce function, but return the results immediately to the master as a Map.
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.
Perform a right outer join of this and 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 into the given number of partitions.
Perform a right outer join of this and 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 default parallelism level.
Perform a right outer join of this and 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. Uses the given Partitioner to
partition the output RDD.
Return a sampled subset of this RDD.
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.
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.
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 text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect or save on the resulting RDD will return or output an ordered list of records
(in the save case, they will be written to multiple part-X files in the filesystem, in
order of the keys).
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. Calling
collect or save on the resulting RDD will return or output an ordered list of records
(in the save case, they will be written to multiple part-X files in the filesystem, in
order of the keys).
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. Calling
collect or save on the resulting RDD will return or output an ordered list of records
(in the save case, they will be written to multiple part-X files in the filesystem, in
order of the keys).
Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.
Sort the RDD by key, so that each partition contains a sorted range of the elements in
ascending order. Calling collect or save on the resulting RDD will return or output an
ordered list of records (in the save case, they will be written to multiple part-X files
in the filesystem, in order of the keys).
Set of partitions in this RDD.
Set of partitions in this RDD.
Take the first num elements of the RDD.
Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.
Return the union of this RDD and another one.
Return the union of this RDD and another one. Any identical elements will appear multiple
times (use .distinct() to eliminate them).