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.
Provides several RDD implementations.
Provides several RDD implementations. Seeorg.apache.spark.rdd.RDD.
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable,partitioned collection of elements that can be operated on in parallel. This class contains thebasic operations available on all RDDs, such asmap,filter, andpersist. In addition,org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-valuepairs, such asgroupByKey andjoin;org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs ofDoubles; andorg.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs thatcan be saved as SequenceFiles.All operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)])through implicit.
Internally, each RDD is characterized by five main properties:
All of the scheduling and execution in Spark is done based on these methods, allowing each RDDto implement its own way of computing itself. Indeed, users can implement custom RDDs (e.g. forreading data from a new storage system) by overriding these functions. Please refer to theSpark paperfor more details on RDD internals.
Construct an RDD with just a one-to-one dependency on one parent
:: DeveloperApi ::Implemented by subclasses to compute a given partition.
:: DeveloperApi ::Implemented by subclasses to compute a given partition.
Implemented by subclasses to return the set of partitions in this RDD.
Implemented by subclasses to return the set of partitions in this RDD. This method will onlybe called once, so it is safe to implement a time-consuming computation in it.
The partitions in this array must satisfy the following property:rdd.partitions.zipWithIndex.forall { case (partition, index) => partition.index == index }
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).
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.
the initial value for the accumulated result of each partition for theseqOp operator, and also the initial value for the combine results from different partitions for thecombOp operator - this will typically be the neutral element (e.g.Nil for list concatenation or0 for summation)
an operator used to accumulate results within a partition
an associative operator used to combine results from different partitions
:: Experimental ::Marks the current stage as a barrier stage, where Spark must launch all tasks together.
:: Experimental ::Marks the current stage as a barrier stage, where Spark must launch all tasks together.In case of a task failure, instead of only restarting the failed task, Spark will abort theentire stage and re-launch all tasks for this stage.The barrier execution mode feature is experimental and it only handles limited scenarios.Please read the linked SPIP and design docs to understand the limitations and future plans.
anRDDBarrier instance that provides actions within a barrier stage
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.
Mark this RDD for checkpointing.
Mark this RDD for checkpointing. It will be saved to a file inside the checkpointdirectory set withSparkContext#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.
The data is only checkpointed whendoCheckpoint() is called, and this only happens at theend of the first action execution on this RDD. The final data that is checkpointed after thefirst action may be different from the data that was used during the action, due tonon-determinism of the underlying operation and retries. If the purpose of the checkpoint isto achieve saving a deterministic snapshot of the data, an eager action may need to be calledfirst on the RDD to trigger the checkpoint.
Removes an RDD's shuffles and it's non-persisted ancestors.
Removes an RDD's shuffles and it's non-persisted ancestors.When running without a shuffle service, cleaning up shuffle files enables downscaling.If you use the RDD after this call, you should checkpoint and materialize it first.If you are uncertain of what you are doing, please do not use this feature.Additional techniques for mitigating orphaned shuffle files: * Tuning the driver GC to be more aggressive, so the regular context cleaner is triggered * Setting an appropriate TTL for shuffle files to be auto cleaned
Clears the dependencies of this RDD.
Clears the dependencies of this RDD. This method must ensure that all referencesto the original parent RDDs are removed to enable the parent RDDs to be garbagecollected. Subclasses of RDD may override this method for implementing their own cleaninglogic. Seeorg.apache.spark.rdd.UnionRDD for an example.
Return a new RDD that is reduced intonumPartitions partitions.
Return a new RDD that is reduced intonumPartitions partitions.
This results in a narrow dependency, e.g. if you go from 1000 partitionsto 100 partitions, there will not be a shuffle, instead each of the 100new partitions will claim 10 of the current partitions. If a larger numberof partitions is requested, it will stay at the current number of partitions.
However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,this may result in your computation taking place on fewer nodes thanyou like (e.g. one node in the case of numPartitions = 1). To avoid this,you can pass shuffle = true. This will add a shuffle step, but means thecurrent upstream partitions will be executed in parallel (per whateverthe current partitioning is).
With shuffle = true, you can actually coalesce to a larger numberof partitions. This is useful if you have a small number of partitions,say 100, potentially with a few partitions being abnormally large. Callingcoalesce(1000, shuffle = true) will result in 1000 partitions with thedata distributed using a hash partitioner. The optional partition coalescerpassed in must be serializable.
Return an RDD that contains all matching values by applyingf.
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.
Theorg.apache.spark.SparkContext that this RDD was created on.
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.
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 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.
The relative accuracy is approximately1.054 / sqrt(2^p). Setting a nonzero (sp is greaterthanp) would trigger sparse representation of registers, which may reduce the memoryconsumption and increase accuracy when the cardinality is small.
The precision value for the normal set.p must be a value between 4 andsp ifsp is not zero (32 max).
The precision value for the sparse set, between 0 and 32. Ifsp equals 0, the sparse representation is skipped.
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
This method should only be used if the resulting map is expected to be small, asthe whole thing is loaded into the driver's memory.To handle very large results, consider using
rdd.map(x=> (x,1L)).reduceByKey(_ + _)
, which returns an RDD[T, Long] instead of a map.
Approximate version of countByValue().
Approximate version of countByValue().
maximum time to wait for the job, in milliseconds
the desired statistical confidence in the result
a potentially incomplete result, with error bounds
Get the list of dependencies of this RDD, taking into account whether theRDD is checkpointed or not.
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.
Returns the first parent RDD
Returns the first parent RDD
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
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.
the initial value for the accumulated result of each partition for theop operator, and also the initial value for the combine results from different partitions for theop operator - this will typically be the neutral element (e.g.Nil for list concatenation or0 for summation)
an operator used to both accumulate results within a partition and combine results from different partitions
Applies a function f to all elements of this RDD.
Applies a function f to each partition of this RDD.
Gets the name of the directory to which this RDD was checkpointed.
Gets the name of the directory to which this RDD was checkpointed.This is not defined if the RDD is checkpointed locally.
Implemented by subclasses to return how this RDD depends on parent RDDs.
Implemented by subclasses to return how this RDD depends on parent RDDs. This method will onlybe called once, so it is safe to implement a time-consuming computation in it.
Returns the number of partitions of this RDD.
Returns the number of partitions of this RDD.
Optionally overridden by subclasses to specify placement preferences.
Optionally overridden by subclasses to specify placement preferences.
Get the ResourceProfile specified with this RDD or null if it wasn't specified.
Get the ResourceProfile specified with this RDD or null if it wasn't specified.
the user specified ResourceProfile or null (for Java compatibility) if none was specified
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 of grouped items.
Return an RDD of grouped items. Each group consists of a key and a sequence of elementsmapping to that key. The ordering of elements within each group is not guaranteed, andmay even differ each time the resulting RDD is evaluated.
This operation may be very expensive. If you are grouping in order to perform anaggregation (such as a sum or average) over each key, usingPairRDDFunctions.aggregateByKeyorPairRDDFunctions.reduceByKey will provide much better performance.
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. The ordering of elements within each group is not guaranteed, andmay even differ each time the resulting RDD is evaluated.
This operation may be very expensive. If you are grouping in order to perform anaggregation (such as a sum or average) over each key, usingPairRDDFunctions.aggregateByKeyorPairRDDFunctions.reduceByKey will provide much better performance.
Return an RDD of grouped items.
Return an RDD of grouped items. Each group consists of a key and a sequence of elementsmapping to that key. The ordering of elements within each group is not guaranteed, andmay even differ each time the resulting RDD is evaluated.
This operation may be very expensive. If you are grouping in order to perform anaggregation (such as a sum or average) over each key, usingPairRDDFunctions.aggregateByKeyorPairRDDFunctions.reduceByKey will provide much better performance.
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. Performs a hash partition across the cluster
How many partitions to use in the resulting RDD
This method performs a shuffle internally.
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.
Partitioner to use for the resulting RDD
This method performs a shuffle internally.
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 is checkpointed and materialized, either reliably or locally.
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.
Due to complications in the internal implementation, this method will raise anexception if called on an RDD ofNothing orNull. This may be come up in practicebecause, for example, the type ofparallelize(Seq()) isRDD[Nothing].(parallelize(Seq()) should be avoided anyway in favor ofparallelize(Seq[T]()).)
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.
Creates tuples of the elements in this RDD by applyingf.
Mark this RDD for local checkpointing using Spark's existing caching layer.
Mark this RDD for local checkpointing using Spark's existing caching layer.
This method is for users who wish to truncate RDD lineages while skipping the expensivestep of replicating the materialized data in a reliable distributed file system. This isuseful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).
Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointeddata is written to ephemeral local storage in the executors instead of to a reliable,fault-tolerant storage. The effect is that if an executor fails during the computation,the checkpointed data may no longer be accessible, causing an irrecoverable job failure.
This is NOT safe to use with dynamic allocation, which removes executors alongwith their cached blocks. If you must use both features, you are advised to setspark.dynamicAllocation.cachedExecutorIdleTimeout to a high value.
The checkpoint directory set throughSparkContext#setCheckpointDir is not used.
The data is only checkpointed whendoCheckpoint() is called, and this only happens at theend of the first action execution on this RDD. The final data that is checkpointed after thefirst action may be different from the data that was used during the action, due tonon-determinism of the underlying operation and retries. If the purpose of the checkpoint isto achieve saving a deterministic snapshot of the data, an eager action may need to be calledfirst on the RDD to trigger the checkpoint.
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.
preservesPartitioning indicates whether the input function preserves the partitioner, whichshould befalse unless this is a pair RDD and the input function doesn't modify the keys.
Return a new RDD by applying an evaluator to each partition of this RDD.
Return a new RDD by applying an evaluator to each partition of this RDD. The given evaluatorfactory will be serialized and sent to executors, and each task will create an evaluator withthe factory, and use the evaluator to transform the data of the input partition.
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.
preservesPartitioning indicates whether the input function preserves the partitioner, whichshould befalse unless this is a pair RDD and the input function doesn't modify the keys.
Returns the max of this RDD as defined by the implicit Ordering[T].
Returns the max of this RDD as defined by the implicit Ordering[T].
the maximum element of the RDD
Returns the min of this RDD as defined by the implicit Ordering[T].
Returns the min of this RDD as defined by the implicit Ordering[T].
the minimum element of the RDD
A friendly name for this RDD
Returns the jth parent RDD: e.g.
Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T]
Optionally overridden by subclasses to specify how they are partitioned.
Get the array of partitions of this RDD, taking into account whether theRDD is checkpointed or not.
Persist this RDD with the default storage level (MEMORY_ONLY).
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. This can only be used to assign a new storage level if the RDD does nothave a storage level set yet. Local checkpointing is an exception.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process. The resulting RDDis computed by executing the given process once per partition. All elementsof each input partition are written to a process's stdin as lines of input separatedby a newline. The resulting partition consists of the process's stdout output, witheach line of stdout resulting in one element of the output partition. A process is invokedeven for empty partitions.
The print behavior can be customized by providing two functions.
command to run in forked process.
environment variables to set.
Before piping elements, this function is called as an opportunity to pipe context data. Print line function (like out.println) will be passed as printPipeContext's parameter.
Use this function to customize how to pipe elements. This function will be called with each RDD element as the 1st parameter, and the print line function (like out.println()) as the 2nd parameter. An example of pipe the RDD data of groupBy() in a streaming way, instead of constructing a huge String to concat all the elements:
def printRDDElement(record:(String,Seq[String]), f:String=>Unit) =for (e<- record._2) {f(e)}
Use separate working directories for each task.
Buffer size for the stdin writer for the piped process.
Char encoding used for interacting (via stdin, stdout and stderr) with the piped process
the result 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.
Get the preferred locations of a partition, taking into account whether theRDD is checkpointed.
Randomly splits this RDD with the provided weights.
Randomly splits this RDD with the provided weights.
weights for splits, will be normalized if they don't sum to 1
random seed
split RDDs in an array
Reduces the elements of this RDD using the specified commutative andassociative binary operator.
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.
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
can elements be sampled multiple times (replaced when sampled out)
expected size of the sample as a fraction of this RDD's size without replacement: probability that each element is chosen; fraction must be [0, 1] with replacement: expected number of times each element is chosen; fraction must be greater than or equal to 0
seed for the random number generator
This is NOT guaranteed to provide exactly the fraction of the countof the givenRDD.
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 text file, using string representations of elements.
Assign a name to this RDD
Return this RDD sorted by the given key function.
The SparkContext that created this RDD.
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.
Take the first num elements of the RDD.
Take the first num elements of the RDD. It works by first scanning one partition, and use theresults from that partition to estimate the number of additional partitions needed to satisfythe limit.
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.
,Due to complications in the internal implementation, this method will raisean exception if called on an RDD ofNothing orNull.
Returns the first k (smallest) elements from this RDD as defined by the specifiedimplicit Ordering[T] and maintains the ordering.
Returns the first k (smallest) elements from this RDD as defined by the specifiedimplicit Ordering[T] and maintains the ordering. This does the opposite oftop.For example:
sc.parallelize(Seq(10,4,2,12,3)).takeOrdered(1)// returns Array(2)sc.parallelize(Seq(2,3,4,5,6)).takeOrdered(2)// returns Array(2, 3)
k, the number of elements to return
the implicit ordering for T
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.
Return a fixed-size sampled subset of this RDD in an array
Return a fixed-size sampled subset of this RDD in an array
whether sampling is done with replacement
size of the returned sample
seed for the random number generator
sample of specified size in an array
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.
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.
This results in multiple Spark jobs, and if the input RDD is the resultof a wide transformation (e.g. join with different partitioners), to avoidrecomputing the input RDD should be cached first.
Returns the top k (largest) elements from this RDD as defined by the specifiedimplicit Ordering[T] and maintains the ordering.
Returns the top k (largest) elements from this RDD as defined by the specifiedimplicit Ordering[T] and maintains the ordering. This does the opposite oftakeOrdered. For example:
sc.parallelize(Seq(10,4,2,12,3)).top(1)// returns Array(12)sc.parallelize(Seq(2,3,4,5,6)).top(2)// returns Array(6, 5)
k, the number of top elements to return
the implicit ordering for T
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.rdd.RDD#treeAggregate with a parameter to do the finalaggregation on the executor
org.apache.spark.rdd.RDD#treeAggregate with a parameter to do the finalaggregation on the executor
do final aggregation on executor
Aggregates the elements of this RDD in a multi-level tree pattern.
Aggregates the elements of this RDD in a multi-level tree pattern.This method is semantically identical toorg.apache.spark.rdd.RDD#aggregate.
suggested depth of the tree (default: 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 (default: 2)
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 (default: false)
This RDD.
Specify a ResourceProfile to use when calculating this RDD.
Specify a ResourceProfile to use when calculating this RDD. This is only supported oncertain cluster managers and currently requires dynamic allocation to be enabled.It will result in new executors with the resources specified being acquired tocalculate the RDD.
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.
Zip this RDD's partitions with another RDD and return a new RDD by applying an evaluator tothe zipped partitions.
Zip this RDD's partitions with another RDD and return a new RDD by applying an evaluator tothe zipped partitions. Assumes that the two RDDs have the *same number of partitions*, butdoes *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 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.
Some RDDs, such as those returned by groupBy(), do not guarantee order ofelements in a partition. The index assigned to each element is therefore not guaranteed,and may even change if the RDD is reevaluated. If a fixed ordering is required to guaranteethe same index assignments, you should sort the RDD with sortByKey() or save it to a file.
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.
Some RDDs, such as those returned by groupBy(), do not guarantee order ofelements in a partition. The unique ID assigned to each element is therefore not guaranteed,and may even change if the RDD is reevaluated. If a fixed ordering is required to guaranteethe same index assignments, you should sort the RDD with sortByKey() or save it to a file.
(Since version 9)