JavaPairRDD
Related Docs:
object JavaPairRDD
| package java
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.
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 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.
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.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.
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.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.
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.
Mark this RDD for checkpointing.
Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation.
Return a new RDD that is reduced into numPartitions partitions.
Return a new RDD that is reduced into numPartitions partitions.
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
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 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
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 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
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.
this method should only be used if the resulting array is expected to be small, as all 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, as all the data is loaded into the driver's memory.
The asynchronous version of collect, which returns a future for
retrieving an array containing all of the elements in this RDD.
The asynchronous version of collect, which returns a future for
retrieving 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, as all 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 existing partitioner/parallelism level and using map-side aggregation.
Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
Generic function to combine the elements for each key using a custom set of aggregation functions.
Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.
Users provide three functions:
createCombiner, which turns a V into a C (e.g., creates a one-element list)mergeValue, to merge a V into a C (e.g., adds it to the end of a list)mergeCombiners, to combine two C's into a single one.In addition, users can control the partitioning of the output RDD. This method automatically uses map-side aggregation in shuffling the RDD.
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]).
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.
Users provide three functions:
createCombiner, which turns a V into a C (e.g., creates a one-element list)mergeValue, to merge a V into a C (e.g., adds it to the end of a list)mergeCombiners, to combine two C's into a single one.In addition, users can control the partitioning of the output RDD, the serializer that is use for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).
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]).
The org.apache.spark.SparkContext that this RDD was created on.
The org.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 result within a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete result within 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 result within a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be 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", available here.
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", available here.
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", available here.
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", available here.
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 of count, which returns a
future for counting the number of elements in this RDD.
The asynchronous version of count, which returns a
future 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.
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
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.
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 will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be 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 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.
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.
This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-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" 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.).
Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.
The asynchronous version of the foreach action, which
applies a function f to all the elements of this RDD.
The asynchronous version of the foreach action, which
applies 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 the foreachPartition action, which
applies a function f to each partition of this RDD.
The asynchronous version of the foreachPartition action, which
applies a function f to each partition of this RDD.
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Hash-partitions the resulting RDD into the given number of partitions.
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Hash-partitions the resulting RDD using the existing partitioner/
parallelism level.
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Uses the given Partitioner to partition the output RDD.
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 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 existing partitioner/parallelism level.
If you are grouping in order to perform an aggregation (such as a sum or average) over
each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey
will provide much better performance.
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 numPartitions partitions.
If you are grouping in order to perform an aggregation (such as a sum or average) over
each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey
will provide much better performance.
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.
If you are grouping in order to perform an aggregation (such as a sum or average) over
each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey
will provide much better performance.
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 duplicate elements, 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 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.
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. Uses the given Partitioner to partition the output RDD.
Creates tuples of the elements in this RDD by applying f.
Creates tuples of the elements in this RDD by applying f.
Return an RDD with the keys of each tuple.
Perform a left outer join of this and other.
Perform a left outer join of this and other. 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.
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 existing partitioner/parallelism level.
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 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 index of the original partition.
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original 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.
Returns the maximum element from this RDD as defined by the specified Comparator[T].
Returns the maximum element from this RDD as defined by the specified Comparator[T].
the comparator that defines ordering
the maximum of the RDD
Returns the minimum element from this RDD as defined by the specified Comparator[T].
Returns the minimum element from this RDD as defined by the specified Comparator[T].
the comparator that defines ordering
the minimum of the RDD
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 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.
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 binary operator.
Reduces the elements of this RDD using the specified commutative and associative binary operator.
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 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.
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 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.
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 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 and commutative reduce function, but return the result immediately to the master as a Map.
Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
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 uses a shuffle to redistribute data.
If you are decreasing the number of partitions in this RDD, consider using coalesce,
which can avoid performing a shuffle.
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 calling repartition and then sorting within each partition
because 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 calling repartition and then sorting within each partition
because it can push the sorting down into the shuffle machinery.
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 existing partitioner/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.
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 by
fractions, a key to sampling rate map, via simple random sampling with one pass over the
RDD, to produce a sample of size that's approximately equal to the sum of
math.ceil(numItems * samplingRate) over all key values.
Use Utils.random.nextLong as the default seed for the random number generator.
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 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.
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).
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
This method differs from sampleByKey in that we make additional passes over the RDD to
create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
over all key values with a 99.99% confidence. When sampling without replacement, we need one
additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
two additional passes.
Use Utils.random.nextLong as the default seed for the random number generator.
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).
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.
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, 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 storage system, using a Configuration object for that storage 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
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.
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).
Return an RDD with the elements from this that are not in other.
Return an RDD with the elements from this that are not in other.
Return an RDD with the elements from this that are not in other.
Return an RDD with the elements from this that are not in other.
Uses this partitioner/partition size, because even if other is huge, the resulting
RDD will be <= us.
Return an RDD with the pairs from this whose keys are not in other.
Return an RDD with the pairs from this whose keys are not in other.
Return an RDD with the pairs from this whose keys are not in other.
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.
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.
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
The asynchronous version of the take action, which returns a
future for retrieving the first num elements of this RDD.
The asynchronous version of the take action, which returns a
future for retrieving the first num elements of this RDD.
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
Returns the first k (smallest) elements from this RDD using the natural 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, as all the data is loaded into the driver's memory.
Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the first k (smallest) elements from this RDD as defined by the 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, as all 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 the natural ordering for T and maintains the order.
Returns the top k (largest) elements from this RDD using the natural 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, as all the data is loaded into the driver's memory.
Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the top k (largest) elements from this RDD as defined by the 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, as all the data is loaded into the driver's memory.
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 multiple
times (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.
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 of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying 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 number of 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 index and then the ordering of items within each partition. So the first item in the first partition 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 method won't trigger a spark job, which is different from org.apache.spark.rdd.RDD#zipWithIndex.