Class

org.apache.spark.graphx.impl

VertexRDDImpl

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class VertexRDDImpl[VD] extends VertexRDD[VD]

Linear Supertypes
VertexRDD[VD], RDD[(VertexId, VD)], Logging, Serializable, Serializable, AnyRef, Any
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  1. VertexRDDImpl
  2. VertexRDD
  3. RDD
  4. Logging
  5. Serializable
  6. Serializable
  7. AnyRef
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  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. def ++(other: RDD[(VertexId, VD)]): RDD[(VertexId, VD)]

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    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).

    Definition Classes
    RDD
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. def aggregate[U](zeroValue: U)(seqOp: (U, (VertexId, VD)) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): U

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    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.

    zeroValue

    the initial value for the accumulated result of each partition for the seqOp operator, and also the initial value for the combine results from different partitions for the combOp operator - this will typically be the neutral element (e.g. Nil for list concatenation or 0 for summation)

    seqOp

    an operator used to accumulate results within a partition

    combOp

    an associative operator used to combine results from different partitions

    Definition Classes
    RDD
  6. def aggregateUsingIndex[VD2](messages: RDD[(VertexId, VD2)], reduceFunc: (VD2, VD2) ⇒ VD2)(implicit arg0: ClassTag[VD2]): VertexRDD[VD2]

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    Aggregates vertices in messages that have the same ids using reduceFunc, returning a VertexRDD co-indexed with this.

    Aggregates vertices in messages that have the same ids using reduceFunc, returning a VertexRDD co-indexed with this.

    messages

    an RDD containing messages to aggregate, where each message is a pair of its target vertex ID and the message data

    reduceFunc

    the associative aggregation function for merging messages to the same vertex

    returns

    a VertexRDD co-indexed with this, containing only vertices that received messages. For those vertices, their values are the result of applying reduceFunc to all received messages.

    Definition Classes
    VertexRDDImplVertexRDD
  7. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  8. def cache(): VertexRDDImpl.this.type

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    Persists the vertex partitions at targetStorageLevel, which defaults to MEMORY_ONLY.

    Persists the vertex partitions at targetStorageLevel, which defaults to MEMORY_ONLY.

    Definition Classes
    VertexRDDImplRDD
  9. def cartesian[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((VertexId, VD), U)]

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    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.

    Definition Classes
    RDD
  10. def checkpoint(): Unit

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    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.

    Definition Classes
    VertexRDDImplRDD
  11. def clearDependencies(): Unit

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    Clears the dependencies of this RDD.

    Clears the dependencies of this RDD. This method must ensure that all references to the original parent RDDs are removed to enable the parent RDDs to be garbage collected. Subclasses of RDD may override this method for implementing their own cleaning logic. See org.apache.spark.rdd.UnionRDD for an example.

    Attributes
    protected
    Definition Classes
    RDD
  12. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  13. def coalesce(numPartitions: Int, shuffle: Boolean = false, partitionCoalescer: Option[PartitionCoalescer] = Option.empty)(implicit ord: Ordering[(VertexId, VD)] = null): RDD[(VertexId, VD)]

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    Return a new RDD that is reduced into numPartitions partitions.

    Return a new RDD that is reduced into numPartitions partitions.

    This results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions. If a larger number of 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 than you 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 the current upstream partitions will be executed in parallel (per whatever the current partitioning is).

    Definition Classes
    RDD
    Note

    With shuffle = true, you can actually coalesce to a larger number of partitions. This is useful if you have a small number of partitions, say 100, potentially with a few partitions being abnormally large. Calling coalesce(1000, shuffle = true) will result in 1000 partitions with the data distributed using a hash partitioner. The optional partition coalescer passed in must be serializable.

  14. def collect[U](f: PartialFunction[(VertexId, VD), U])(implicit arg0: ClassTag[U]): RDD[U]

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    Return an RDD that contains all matching values by applying f.

    Return an RDD that contains all matching values by applying f.

    Definition Classes
    RDD
  15. def collect(): Array[(VertexId, VD)]

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    Return an array that contains all of the elements in this RDD.

    Return an array that contains all of the elements in this RDD.

    Definition Classes
    RDD
    Note

    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.

  16. def compute(part: Partition, context: TaskContext): Iterator[(VertexId, VD)]

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    Provides the RDD[(VertexId, VD)] equivalent output.

    Provides the RDD[(VertexId, VD)] equivalent output.

    Definition Classes
    VertexRDDRDD
  17. def context: SparkContext

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    The org.apache.spark.SparkContext that this RDD was created on.

    The org.apache.spark.SparkContext that this RDD was created on.

    Definition Classes
    RDD
  18. def count(): Long

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    The number of vertices in the RDD.

    The number of vertices in the RDD.

    Definition Classes
    VertexRDDImplRDD
  19. def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

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    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.

    timeout

    maximum time to wait for the job, in milliseconds

    confidence

    the desired statistical confidence in the result

    returns

    a potentially incomplete result, with error bounds

    Definition Classes
    RDD
  20. def countApproxDistinct(relativeSD: Double = 0.05): Long

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    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.

    relativeSD

    Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    Definition Classes
    RDD
  21. def countApproxDistinct(p: Int, sp: Int): Long

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    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.

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

    p

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

    sp

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

    Definition Classes
    RDD
  22. def countByValue()(implicit ord: Ordering[(VertexId, VD)] = null): Map[(VertexId, VD), Long]

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    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.

    Definition Classes
    RDD
    Note

    This method should only be used if the resulting map is expected to be small, as the 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.

  23. def countByValueApprox(timeout: Long, confidence: Double = 0.95)(implicit ord: Ordering[(VertexId, VD)] = null): PartialResult[Map[(VertexId, VD), BoundedDouble]]

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    Approximate version of countByValue().

    Approximate version of countByValue().

    timeout

    maximum time to wait for the job, in milliseconds

    confidence

    the desired statistical confidence in the result

    returns

    a potentially incomplete result, with error bounds

    Definition Classes
    RDD
  24. final def dependencies: Seq[Dependency[_]]

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    Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.

    Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.

    Definition Classes
    RDD
  25. def diff(other: VertexRDD[VD]): VertexRDD[VD]

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    For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other.

    For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other. This is only guaranteed to work if the VertexRDDs share a common ancestor.

    other

    the other VertexRDD with which to diff against.

    Definition Classes
    VertexRDDImplVertexRDD
  26. def diff(other: RDD[(VertexId, VD)]): VertexRDD[VD]

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    For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other.

    For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other. This is only guaranteed to work if the VertexRDDs share a common ancestor.

    other

    the other RDD[(VertexId, VD)] with which to diff against.

    Definition Classes
    VertexRDDImplVertexRDD
  27. def distinct(): RDD[(VertexId, VD)]

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    Return a new RDD containing the distinct elements in this RDD.

    Return a new RDD containing the distinct elements in this RDD.

    Definition Classes
    RDD
  28. def distinct(numPartitions: Int)(implicit ord: Ordering[(VertexId, VD)] = null): RDD[(VertexId, VD)]

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    Return a new RDD containing the distinct elements in this RDD.

    Return a new RDD containing the distinct elements in this RDD.

    Definition Classes
    RDD
  29. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  30. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  31. def filter(pred: ((VertexId, VD)) ⇒ Boolean): VertexRDD[VD]

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    Restricts the vertex set to the set of vertices satisfying the given predicate.

    Restricts the vertex set to the set of vertices satisfying the given predicate. This operation preserves the index for efficient joins with the original RDD, and it sets bits in the bitmask rather than allocating new memory.

    It is declared and defined here to allow refining the return type from RDD[(VertexId, VD)] to VertexRDD[VD].

    pred

    the user defined predicate, which takes a tuple to conform to the RDD[(VertexId, VD)] interface

    Definition Classes
    VertexRDDRDD
  32. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  33. def first(): (VertexId, VD)

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    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes
    RDD
  34. def firstParent[U](implicit arg0: ClassTag[U]): RDD[U]

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    Returns the first parent RDD

    Returns the first parent RDD

    Attributes
    protected[org.apache.spark]
    Definition Classes
    RDD
  35. def flatMap[U](f: ((VertexId, VD)) ⇒ TraversableOnce[U])(implicit arg0: ClassTag[U]): RDD[U]

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    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.

    Definition Classes
    RDD
  36. def fold(zeroValue: (VertexId, VD))(op: ((VertexId, VD), (VertexId, VD)) ⇒ (VertexId, VD)): (VertexId, VD)

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    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.

    zeroValue

    the initial value for the accumulated result of each partition for the op operator, and also the initial value for the combine results from different partitions for the op operator - this will typically be the neutral element (e.g. Nil for list concatenation or 0 for summation)

    op

    an operator used to both accumulate results within a partition and combine results from different partitions

    Definition Classes
    RDD
  37. def foreach(f: ((VertexId, VD)) ⇒ Unit): Unit

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    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes
    RDD
  38. def foreachPartition(f: (Iterator[(VertexId, VD)]) ⇒ Unit): Unit

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    Applies a function f to each partition of this RDD.

    Applies a function f to each partition of this RDD.

    Definition Classes
    RDD
  39. def getCheckpointFile: Option[String]

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    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.

    Definition Classes
    VertexRDDImplRDD
  40. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  41. def getDependencies: Seq[Dependency[_]]

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    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 only be called once, so it is safe to implement a time-consuming computation in it.

    Attributes
    protected
    Definition Classes
    RDD
  42. final def getNumPartitions: Int

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    Returns the number of partitions of this RDD.

    Returns the number of partitions of this RDD.

    Definition Classes
    RDD
    Annotations
    @Since( "1.6.0" )
  43. def getPartitions: Array[Partition]

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    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 only be 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 }

    Attributes
    protected
    Definition Classes
    VertexRDDRDD
  44. def getPreferredLocations(s: Partition): Seq[String]

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    Optionally overridden by subclasses to specify placement preferences.

    Optionally overridden by subclasses to specify placement preferences.

    Attributes
    protected
    Definition Classes
    VertexRDDImplRDD
  45. def getStorageLevel: StorageLevel

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    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.

    Definition Classes
    VertexRDDImplRDD
  46. def glom(): RDD[Array[(VertexId, VD)]]

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    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.

    Definition Classes
    RDD
  47. def groupBy[K](f: ((VertexId, VD)) ⇒ K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null): RDD[(K, Iterable[(VertexId, VD)])]

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    Return an RDD of grouped items.

    Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

    Definition Classes
    RDD
    Note

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

  48. def groupBy[K](f: ((VertexId, VD)) ⇒ K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(VertexId, VD)])]

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    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. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

    Definition Classes
    RDD
    Note

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

  49. def groupBy[K](f: ((VertexId, VD)) ⇒ K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(VertexId, VD)])]

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    Return an RDD of grouped items.

    Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

    Definition Classes
    RDD
    Note

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

  50. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  51. val id: Int

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    A unique ID for this RDD (within its SparkContext).

    A unique ID for this RDD (within its SparkContext).

    Definition Classes
    RDD
  52. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  53. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  54. def innerJoin[U, VD2](other: RDD[(VertexId, U)])(f: (VertexId, VD, U) ⇒ VD2)(implicit arg0: ClassTag[U], arg1: ClassTag[VD2]): VertexRDD[VD2]

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    Inner joins this VertexRDD with an RDD containing vertex attribute pairs.

    Inner joins this VertexRDD with an RDD containing vertex attribute pairs. If the other RDD is backed by a VertexRDD with the same index then the efficient innerZipJoin implementation is used.

    other

    an RDD containing vertices to join. If there are multiple entries for the same vertex, one is picked arbitrarily. Use aggregateUsingIndex to merge multiple entries.

    f

    the join function applied to corresponding values of this and other

    returns

    a VertexRDD co-indexed with this, containing only vertices that appear in both this and other, with values supplied by f

    Definition Classes
    VertexRDDImplVertexRDD
  55. def innerZipJoin[U, VD2](other: VertexRDD[U])(f: (VertexId, VD, U) ⇒ VD2)(implicit arg0: ClassTag[U], arg1: ClassTag[VD2]): VertexRDD[VD2]

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    Efficiently inner joins this VertexRDD with another VertexRDD sharing the same index.

    Efficiently inner joins this VertexRDD with another VertexRDD sharing the same index. See innerJoin for the behavior of the join.

    Definition Classes
    VertexRDDImplVertexRDD
  56. def intersection(other: RDD[(VertexId, VD)], numPartitions: Int): RDD[(VertexId, VD)]

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    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. Performs a hash partition across the cluster

    numPartitions

    How many partitions to use in the resulting RDD

    Definition Classes
    RDD
    Note

    This method performs a shuffle internally.

  57. def intersection(other: RDD[(VertexId, VD)], partitioner: Partitioner)(implicit ord: Ordering[(VertexId, VD)] = null): RDD[(VertexId, VD)]

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    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.

    partitioner

    Partitioner to use for the resulting RDD

    Definition Classes
    RDD
    Note

    This method performs a shuffle internally.

  58. def intersection(other: RDD[(VertexId, VD)]): RDD[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
    Note

    This method performs a shuffle internally.

  59. def isCheckpointed: Boolean

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    Return whether this RDD is checkpointed and materialized, either reliably or locally.

    Return whether this RDD is checkpointed and materialized, either reliably or locally.

    Definition Classes
    VertexRDDImplRDD
  60. def isEmpty(): Boolean

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    returns

    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.

    Definition Classes
    RDD
    Note

    Due to complications in the internal implementation, this method will raise an exception if called on an RDD of Nothing or Null. This may be come up in practice because, for example, the type of parallelize(Seq()) is RDD[Nothing]. (parallelize(Seq()) should be avoided anyway in favor of parallelize(Seq[T]()).)

  61. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  62. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  63. final def iterator(split: Partition, context: TaskContext): Iterator[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
  64. def keyBy[K](f: ((VertexId, VD)) ⇒ K): RDD[(K, (VertexId, VD))]

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    Creates tuples of the elements in this RDD by applying f.

    Creates tuples of the elements in this RDD by applying f.

    Definition Classes
    RDD
  65. def leftJoin[VD2, VD3](other: RDD[(VertexId, VD2)])(f: (VertexId, VD, Option[VD2]) ⇒ VD3)(implicit arg0: ClassTag[VD2], arg1: ClassTag[VD3]): VertexRDD[VD3]

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    Left joins this VertexRDD with an RDD containing vertex attribute pairs.

    Left joins this VertexRDD with an RDD containing vertex attribute pairs. If the other RDD is backed by a VertexRDD with the same index then the efficient leftZipJoin implementation is used. The resulting VertexRDD contains an entry for each vertex in this. If other is missing any vertex in this VertexRDD, f is passed None. If there are duplicates, the vertex is picked arbitrarily.

    VD2

    the attribute type of the other VertexRDD

    VD3

    the attribute type of the resulting VertexRDD

    other

    the other VertexRDD with which to join

    f

    the function mapping a vertex id and its attributes in this and the other vertex set to a new vertex attribute.

    returns

    a VertexRDD containing all the vertices in this VertexRDD with the attributes emitted by f.

    Definition Classes
    VertexRDDImplVertexRDD
  66. def leftZipJoin[VD2, VD3](other: VertexRDD[VD2])(f: (VertexId, VD, Option[VD2]) ⇒ VD3)(implicit arg0: ClassTag[VD2], arg1: ClassTag[VD3]): VertexRDD[VD3]

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    Left joins this RDD with another VertexRDD with the same index.

    Left joins this RDD with another VertexRDD with the same index. This function will fail if both VertexRDDs do not share the same index. The resulting vertex set contains an entry for each vertex in this. If other is missing any vertex in this VertexRDD, f is passed None.

    VD2

    the attribute type of the other VertexRDD

    VD3

    the attribute type of the resulting VertexRDD

    other

    the other VertexRDD with which to join.

    f

    the function mapping a vertex id and its attributes in this and the other vertex set to a new vertex attribute.

    returns

    a VertexRDD containing the results of f

    Definition Classes
    VertexRDDImplVertexRDD
  67. def localCheckpoint(): VertexRDDImpl.this.type

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    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 expensive step of replicating the materialized data in a reliable distributed file system. This is useful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).

    Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointed data 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 along with their cached blocks. If you must use both features, you are advised to set spark.dynamicAllocation.cachedExecutorIdleTimeout to a high value.

    The checkpoint directory set through SparkContext#setCheckpointDir is not used.

    Definition Classes
    RDD
  68. def log: Logger

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  69. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  73. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  75. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  78. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  80. def map[U](f: ((VertexId, VD)) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

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    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.

    Definition Classes
    RDD
  81. def mapPartitions[U](f: (Iterator[(VertexId, VD)]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

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    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, which should be false unless this is a pair RDD and the input function doesn't modify the keys.

    Definition Classes
    RDD
  82. def mapPartitionsWithIndex[U](f: (Int, Iterator[(VertexId, VD)]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

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    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.

    preservesPartitioning indicates whether the input function preserves the partitioner, which should be false unless this is a pair RDD and the input function doesn't modify the keys.

    Definition Classes
    RDD
  83. def mapValues[VD2](f: (VertexId, VD) ⇒ VD2)(implicit arg0: ClassTag[VD2]): VertexRDD[VD2]

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    Maps each vertex attribute, additionally supplying the vertex ID.

    Maps each vertex attribute, additionally supplying the vertex ID.

    VD2

    the type returned by the map function

    f

    the function applied to each ID-value pair in the RDD

    returns

    a new VertexRDD with values obtained by applying f to each of the entries in the original VertexRDD. The resulting VertexRDD retains the same index.

    Definition Classes
    VertexRDDImplVertexRDD
  84. def mapValues[VD2](f: (VD) ⇒ VD2)(implicit arg0: ClassTag[VD2]): VertexRDD[VD2]

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    Maps each vertex attribute, preserving the index.

    Maps each vertex attribute, preserving the index.

    VD2

    the type returned by the map function

    f

    the function applied to each value in the RDD

    returns

    a new VertexRDD with values obtained by applying f to each of the entries in the original VertexRDD

    Definition Classes
    VertexRDDImplVertexRDD
  85. def max()(implicit ord: Ordering[(VertexId, VD)]): (VertexId, VD)

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    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].

    returns

    the maximum element of the RDD

    Definition Classes
    RDD
  86. def min()(implicit ord: Ordering[(VertexId, VD)]): (VertexId, VD)

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    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].

    returns

    the minimum element of the RDD

    Definition Classes
    RDD
  87. def minus(other: VertexRDD[VD]): VertexRDD[VD]

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    For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.

    For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.

    other

    a VertexRDD to run the set operation against

    Definition Classes
    VertexRDDImplVertexRDD
  88. def minus(other: RDD[(VertexId, VD)]): VertexRDD[VD]

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    For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.

    For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.

    other

    an RDD to run the set operation against

    Definition Classes
    VertexRDDImplVertexRDD
  89. var name: String

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    A friendly name for this RDD

    A friendly name for this RDD

    Definition Classes
    RDD
  90. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  91. final def notify(): Unit

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    Definition Classes
    AnyRef
  92. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  93. def parent[U](j: Int)(implicit arg0: ClassTag[U]): RDD[U]

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    Returns the jth parent RDD: e.g.

    Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T]

    Attributes
    protected[org.apache.spark]
    Definition Classes
    RDD
  94. val partitioner: Option[Partitioner]

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    Optionally overridden by subclasses to specify how they are partitioned.

    Optionally overridden by subclasses to specify how they are partitioned.

    Definition Classes
    VertexRDDImplRDD
  95. final def partitions: Array[Partition]

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    Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.

    Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.

    Definition Classes
    RDD
  96. val partitionsRDD: RDD[ShippableVertexPartition[VD]]

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    Definition Classes
    VertexRDDImplVertexRDD
  97. def persist(newLevel: StorageLevel): VertexRDDImpl.this.type

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    Persists the vertex partitions at the specified storage level, ignoring any existing target storage level.

    Persists the vertex partitions at the specified storage level, ignoring any existing target storage level.

    Definition Classes
    VertexRDDImplRDD
  98. def persist(): VertexRDDImpl.this.type

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    Persist this RDD with the default storage level (MEMORY_ONLY).

    Persist this RDD with the default storage level (MEMORY_ONLY).

    Definition Classes
    RDD
  99. def pipe(command: Seq[String], env: Map[String, String] = Map(), printPipeContext: ((String) ⇒ Unit) ⇒ Unit = null, printRDDElement: ((VertexId, VD), (String) ⇒ Unit) ⇒ Unit = null, separateWorkingDir: Boolean = false, bufferSize: Int = 8192, encoding: String = Codec.defaultCharsetCodec.name): RDD[String]

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    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 RDD is computed by executing the given process once per partition. All elements of each input partition are written to a process's stdin as lines of input separated by a newline. The resulting partition consists of the process's stdout output, with each line of stdout resulting in one element of the output partition. A process is invoked even for empty partitions.

    The print behavior can be customized by providing two functions.

    command

    command to run in forked process.

    env

    environment variables to set.

    printPipeContext

    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.

    printRDDElement

    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)}
    separateWorkingDir

    Use separate working directories for each task.

    bufferSize

    Buffer size for the stdin writer for the piped process.

    encoding

    Char encoding used for interacting (via stdin, stdout and stderr) with the piped process

    returns

    the result RDD

    Definition Classes
    RDD
  100. def pipe(command: String, env: Map[String, String]): RDD[String]

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    Return an RDD created by piping elements to a forked external process.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes
    RDD
  101. def pipe(command: String): RDD[String]

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    Return an RDD created by piping elements to a forked external process.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes
    RDD
  102. final def preferredLocations(split: Partition): Seq[String]

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    Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.

    Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.

    Definition Classes
    RDD
  103. def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[(VertexId, VD)]]

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    Randomly splits this RDD with the provided weights.

    Randomly splits this RDD with the provided weights.

    weights

    weights for splits, will be normalized if they don't sum to 1

    seed

    random seed

    returns

    split RDDs in an array

    Definition Classes
    RDD
  104. def reduce(f: ((VertexId, VD), (VertexId, VD)) ⇒ (VertexId, VD)): (VertexId, VD)

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    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.

    Definition Classes
    RDD
  105. def reindex(): VertexRDD[VD]

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    Construct a new VertexRDD that is indexed by only the visible vertices.

    Construct a new VertexRDD that is indexed by only the visible vertices. The resulting VertexRDD will be based on a different index and can no longer be quickly joined with this RDD.

    Definition Classes
    VertexRDDImplVertexRDD
  106. def repartition(numPartitions: Int)(implicit ord: Ordering[(VertexId, VD)] = null): RDD[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
  107. def reverseRoutingTables(): VertexRDD[VD]

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    Returns a new VertexRDD reflecting a reversal of all edge directions in the corresponding EdgeRDD.

    Returns a new VertexRDD reflecting a reversal of all edge directions in the corresponding EdgeRDD.

    Definition Classes
    VertexRDDImplVertexRDD
  108. def sample(withReplacement: Boolean, fraction: Double, seed: Long = Utils.random.nextLong): RDD[(VertexId, VD)]

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    Return a sampled subset of this RDD.

    Return a sampled subset of this RDD.

    withReplacement

    can elements be sampled multiple times (replaced when sampled out)

    fraction

    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

    seed for the random number generator

    Definition Classes
    RDD
    Note

    This is NOT guaranteed to provide exactly the fraction of the count of the given RDD.

  109. def saveAsObjectFile(path: String): Unit

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    Save this RDD as a SequenceFile of serialized objects.

    Save this RDD as a SequenceFile of serialized objects.

    Definition Classes
    RDD
  110. def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit

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    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.

    Definition Classes
    RDD
  111. def saveAsTextFile(path: String): Unit

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    Save this RDD as a text file, using string representations of elements.

    Save this RDD as a text file, using string representations of elements.

    Definition Classes
    RDD
  112. def setName(_name: String): VertexRDDImpl.this.type

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    Assign a name to this RDD

    Assign a name to this RDD

    Definition Classes
    VertexRDDImplRDD
  113. def sortBy[K](f: ((VertexId, VD)) ⇒ K, ascending: Boolean = true, numPartitions: Int = this.partitions.length)(implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[(VertexId, VD)]

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    Return this RDD sorted by the given key function.

    Return this RDD sorted by the given key function.

    Definition Classes
    RDD
  114. def sparkContext: SparkContext

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    The SparkContext that created this RDD.

    The SparkContext that created this RDD.

    Definition Classes
    RDD
  115. def subtract(other: RDD[(VertexId, VD)], p: Partitioner)(implicit ord: Ordering[(VertexId, VD)] = null): RDD[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
  116. def subtract(other: RDD[(VertexId, VD)], numPartitions: Int): RDD[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
  117. def subtract(other: RDD[(VertexId, VD)]): RDD[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
  118. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  119. def take(num: Int): Array[(VertexId, VD)]

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    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 the results from that partition to estimate the number of additional partitions needed to satisfy the limit.

    Definition Classes
    RDD
    Note

    Due to complications in the internal implementation, this method will raise an exception if called on an RDD of Nothing or Null.

    ,

    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.

  120. def takeOrdered(num: Int)(implicit ord: Ordering[(VertexId, VD)]): Array[(VertexId, VD)]

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    Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.

    Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of top. 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)
    num

    k, the number of elements to return

    ord

    the implicit ordering for T

    returns

    an array of top elements

    Definition Classes
    RDD
    Note

    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.

  121. def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[(VertexId, VD)]

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    Return a fixed-size sampled subset of this RDD in an array

    Return a fixed-size sampled subset of this RDD in an array

    withReplacement

    whether sampling is done with replacement

    num

    size of the returned sample

    seed

    seed for the random number generator

    returns

    sample of specified size in an array

    Definition Classes
    RDD
    Note

    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.

  122. val targetStorageLevel: StorageLevel

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  123. def toDebugString: String

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    A description of this RDD and its recursive dependencies for debugging.

    A description of this RDD and its recursive dependencies for debugging.

    Definition Classes
    RDD
  124. def toJavaRDD(): JavaRDD[(VertexId, VD)]

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    Definition Classes
    RDD
  125. def toLocalIterator: Iterator[(VertexId, VD)]

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    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.

    Definition Classes
    RDD
    Note

    This results in multiple Spark jobs, and if the input RDD is the result of a wide transformation (e.g. join with different partitioners), to avoid recomputing the input RDD should be cached first.

  126. def toString(): String

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    Definition Classes
    RDD → AnyRef → Any
  127. def top(num: Int)(implicit ord: Ordering[(VertexId, VD)]): Array[(VertexId, VD)]

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    Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.

    Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of takeOrdered. 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)
    num

    k, the number of top elements to return

    ord

    the implicit ordering for T

    returns

    an array of top elements

    Definition Classes
    RDD
    Note

    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.

  128. def treeAggregate[U](zeroValue: U)(seqOp: (U, (VertexId, VD)) ⇒ U, combOp: (U, U) ⇒ U, depth: Int = 2)(implicit arg0: ClassTag[U]): U

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    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 to org.apache.spark.rdd.RDD#aggregate.

    depth

    suggested depth of the tree (default: 2)

    Definition Classes
    RDD
  129. def treeReduce(f: ((VertexId, VD), (VertexId, VD)) ⇒ (VertexId, VD), depth: Int = 2): (VertexId, VD)

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    Reduces the elements of this RDD in a multi-level tree pattern.

    Reduces the elements of this RDD in a multi-level tree pattern.

    depth

    suggested depth of the tree (default: 2)

    Definition Classes
    RDD
    See also

    org.apache.spark.rdd.RDD#reduce

  130. def union(other: RDD[(VertexId, VD)]): RDD[(VertexId, VD)]

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    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).

    Definition Classes
    RDD
  131. def unpersist(blocking: Boolean = true): VertexRDDImpl.this.type

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    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.

    blocking

    Whether to block until all blocks are deleted.

    returns

    This RDD.

    Definition Classes
    VertexRDDImplRDD
  132. implicit val vdTag: ClassTag[VD]

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    Attributes
    protected
    Definition Classes
    VertexRDDImplVertexRDD
  133. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  134. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  135. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  136. def withEdges(edges: EdgeRDD[_]): VertexRDD[VD]

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    Prepares this VertexRDD for efficient joins with the given EdgeRDD.

    Prepares this VertexRDD for efficient joins with the given EdgeRDD.

    Definition Classes
    VertexRDDImplVertexRDD
  137. def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((VertexId, VD), U)]

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    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).

    Definition Classes
    RDD
  138. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[(VertexId, VD)], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

    Permalink
    Definition Classes
    RDD
  139. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[(VertexId, VD)], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

    Permalink
    Definition Classes
    RDD
  140. def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[(VertexId, VD)], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

    Permalink
    Definition Classes
    RDD
  141. def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)(f: (Iterator[(VertexId, VD)], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

    Permalink
    Definition Classes
    RDD
  142. def zipPartitions[B, V](rdd2: RDD[B])(f: (Iterator[(VertexId, VD)], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

    Permalink
    Definition Classes
    RDD
  143. def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[(VertexId, VD)], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

    Permalink

    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.

    Definition Classes
    RDD
  144. def zipWithIndex(): RDD[((VertexId, VD), Long)]

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    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.

    Definition Classes
    RDD
    Note

    Some RDDs, such as those returned by groupBy(), do not guarantee order of elements 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 guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.

  145. def zipWithUniqueId(): RDD[((VertexId, VD), Long)]

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    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.

    Definition Classes
    RDD
    Note

    Some RDDs, such as those returned by groupBy(), do not guarantee order of elements 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 guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.

Inherited from VertexRDD[VD]

Inherited from RDD[(VertexId, VD)]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped