Class

org.apache.spark.rdd

HadoopRDD

Related Doc: package rdd

Permalink

class HadoopRDD[K, V] extends RDD[(K, V)] with Logging

:: DeveloperApi :: An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, sources in HBase, or S3), using the older MapReduce API (org.apache.hadoop.mapred).

Annotations
@DeveloperApi()
Note

Instantiating this class directly is not recommended, please use org.apache.spark.SparkContext.hadoopRDD()

Linear Supertypes
RDD[(K, V)], Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. HadoopRDD
  2. RDD
  3. Logging
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new HadoopRDD(sc: SparkContext, conf: JobConf, inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], minPartitions: Int)

    Permalink
  2. new HadoopRDD(sc: SparkContext, broadcastedConf: Broadcast[SerializableConfiguration], initLocalJobConfFuncOpt: Option[(JobConf) ⇒ Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], minPartitions: Int)

    Permalink

    sc

    The SparkContext to associate the RDD with.

    broadcastedConf

    A general Hadoop Configuration, or a subclass of it. If the enclosed variable references an instance of JobConf, then that JobConf will be used for the Hadoop job. Otherwise, a new JobConf will be created on each slave using the enclosed Configuration.

    initLocalJobConfFuncOpt

    Optional closure used to initialize any JobConf that HadoopRDD creates.

    inputFormatClass

    Storage format of the data to be read.

    keyClass

    Class of the key associated with the inputFormatClass.

    valueClass

    Class of the value associated with the inputFormatClass.

    minPartitions

    Minimum number of HadoopRDD partitions (Hadoop Splits) to generate.

Value Members

  1. final def !=(arg0: Any): Boolean

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

    Permalink
    Definition Classes
    AnyRef → Any
  3. def ++(other: RDD[(K, V)]): RDD[(K, V)]

    Permalink

    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

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

    Permalink

    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. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  7. def cache(): HadoopRDD.this.type

    Permalink

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

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

    Definition Classes
    RDD
  8. def cartesian[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, V), U)]

    Permalink

    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
  9. def checkpoint(): Unit

    Permalink

    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
    HadoopRDDRDD
  10. def clearDependencies(): Unit

    Permalink

    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
  11. def clone(): AnyRef

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

    Permalink

    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.

  13. def collect[U](f: PartialFunction[(K, V), U])(implicit arg0: ClassTag[U]): RDD[U]

    Permalink

    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
  14. def collect(): Array[(K, V)]

    Permalink

    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.

  15. def compute(theSplit: Partition, context: TaskContext): InterruptibleIterator[(K, V)]

    Permalink

    :: DeveloperApi :: Implemented by subclasses to compute a given partition.

    :: DeveloperApi :: Implemented by subclasses to compute a given partition.

    Definition Classes
    HadoopRDDRDD
  16. def context: SparkContext

    Permalink

    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
  17. def count(): Long

    Permalink

    Return the number of elements in the RDD.

    Return the number of elements in the RDD.

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

    Permalink

    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
  19. def countApproxDistinct(relativeSD: Double = 0.05): Long

    Permalink

    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
  20. def countApproxDistinct(p: Int, sp: Int): Long

    Permalink

    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
  21. def countByValue()(implicit ord: Ordering[(K, V)] = null): Map[(K, V), Long]

    Permalink

    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.

  22. def countByValueApprox(timeout: Long, confidence: Double = 0.95)(implicit ord: Ordering[(K, V)] = null): PartialResult[Map[(K, V), BoundedDouble]]

    Permalink

    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
  23. final def dependencies: Seq[Dependency[_]]

    Permalink

    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
  24. def distinct(): RDD[(K, V)]

    Permalink

    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
  25. def distinct(numPartitions: Int)(implicit ord: Ordering[(K, V)] = null): RDD[(K, V)]

    Permalink

    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
  26. final def eq(arg0: AnyRef): Boolean

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

    Permalink
    Definition Classes
    AnyRef → Any
  28. def filter(f: ((K, V)) ⇒ Boolean): RDD[(K, V)]

    Permalink

    Return a new RDD containing only the elements that satisfy a predicate.

    Return a new RDD containing only the elements that satisfy a predicate.

    Definition Classes
    RDD
  29. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  30. def first(): (K, V)

    Permalink

    Return the first element in this RDD.

    Return the first element in this RDD.

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

    Permalink

    Returns the first parent RDD

    Returns the first parent RDD

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

    Permalink

    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
  33. def fold(zeroValue: (K, V))(op: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

    Permalink

    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
  34. def foreach(f: ((K, V)) ⇒ Unit): Unit

    Permalink

    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes
    RDD
  35. def foreachPartition(f: (Iterator[(K, V)]) ⇒ Unit): Unit

    Permalink

    Applies a function f to each partition of this RDD.

    Applies a function f to each partition of this RDD.

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

    Permalink

    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
    RDD
  37. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  38. def getConf: Configuration

    Permalink
  39. def getDependencies: Seq[Dependency[_]]

    Permalink

    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
  40. def getInputFormat(conf: JobConf): InputFormat[K, V]

    Permalink
    Attributes
    protected
  41. def getJobConf(): JobConf

    Permalink
    Attributes
    protected
  42. final def getNumPartitions: Int

    Permalink

    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]

    Permalink

    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 }

    Definition Classes
    HadoopRDDRDD
  44. def getPreferredLocations(split: Partition): Seq[String]

    Permalink

    Optionally overridden by subclasses to specify placement preferences.

    Optionally overridden by subclasses to specify placement preferences.

    Definition Classes
    HadoopRDDRDD
  45. def getStorageLevel: StorageLevel

    Permalink

    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
    RDD
  46. def glom(): RDD[Array[(K, V)]]

    Permalink

    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: ((K, V)) ⇒ K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null): RDD[(K, Iterable[(K, V)])]

    Permalink

    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: ((K, V)) ⇒ K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, V)])]

    Permalink

    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: ((K, V)) ⇒ K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, V)])]

    Permalink

    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

    Permalink
    Definition Classes
    AnyRef → Any
  51. val id: Int

    Permalink

    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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  54. val inputFormatCacheKey: String

    Permalink
    Attributes
    protected
  55. def intersection(other: RDD[(K, V)], numPartitions: Int): RDD[(K, V)]

    Permalink

    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.

  56. def intersection(other: RDD[(K, V)], partitioner: Partitioner)(implicit ord: Ordering[(K, V)] = null): RDD[(K, V)]

    Permalink

    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.

  57. def intersection(other: RDD[(K, V)]): RDD[(K, V)]

    Permalink

    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.

  58. def isCheckpointed: Boolean

    Permalink

    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
    RDD
  59. def isEmpty(): Boolean

    Permalink

    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]()).)

  60. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  61. def isTraceEnabled(): Boolean

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  62. final def iterator(split: Partition, context: TaskContext): Iterator[(K, V)]

    Permalink

    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
  63. val jobConfCacheKey: String

    Permalink
    Attributes
    protected
  64. def keyBy[K](f: ((K, V)) ⇒ K): RDD[(K, (K, V))]

    Permalink

    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 localCheckpoint(): HadoopRDD.this.type

    Permalink

    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
  66. def log: Logger

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

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

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

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

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  73. def logName: String

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

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

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  78. def map[U](f: ((K, V)) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

    Permalink

    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
  79. def mapPartitions[U](f: (Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

    Permalink

    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
  80. def mapPartitionsWithIndex[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

    Permalink

    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
  81. def mapPartitionsWithInputSplit[U](f: (InputSplit, Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

    Permalink

    Maps over a partition, providing the InputSplit that was used as the base of the partition.

    Maps over a partition, providing the InputSplit that was used as the base of the partition.

    Annotations
    @DeveloperApi()
  82. def max()(implicit ord: Ordering[(K, V)]): (K, V)

    Permalink

    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
  83. def min()(implicit ord: Ordering[(K, V)]): (K, V)

    Permalink

    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
  84. var name: String

    Permalink

    A friendly name for this RDD

    A friendly name for this RDD

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

    Permalink
    Definition Classes
    AnyRef
  86. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  87. final def notifyAll(): Unit

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

    Permalink

    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
  89. val partitioner: Option[Partitioner]

    Permalink

    Optionally overridden by subclasses to specify how they are partitioned.

    Optionally overridden by subclasses to specify how they are partitioned.

    Definition Classes
    RDD
  90. final def partitions: Array[Partition]

    Permalink

    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
  91. def persist(storageLevel: StorageLevel): HadoopRDD.this.type

    Permalink

    Set this RDD's storage level to persist its values across operations after the first time it is computed.

    Set this RDD's storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. Local checkpointing is an exception.

    Definition Classes
    HadoopRDDRDD
  92. def persist(): HadoopRDD.this.type

    Permalink

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

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

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

    Permalink

    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
  94. def pipe(command: String, env: Map[String, String]): RDD[String]

    Permalink

    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
  95. def pipe(command: String): RDD[String]

    Permalink

    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
  96. final def preferredLocations(split: Partition): Seq[String]

    Permalink

    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
  97. def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[(K, V)]]

    Permalink

    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
  98. def reduce(f: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

    Permalink

    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
  99. def repartition(numPartitions: Int)(implicit ord: Ordering[(K, V)] = null): RDD[(K, V)]

    Permalink

    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
  100. def sample(withReplacement: Boolean, fraction: Double, seed: Long = Utils.random.nextLong): RDD[(K, V)]

    Permalink

    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.

  101. def saveAsObjectFile(path: String): Unit

    Permalink

    Save this RDD as a SequenceFile of serialized objects.

    Save this RDD as a SequenceFile of serialized objects.

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

    Permalink

    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
  103. def saveAsTextFile(path: String): Unit

    Permalink

    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
  104. def setName(_name: String): HadoopRDD.this.type

    Permalink

    Assign a name to this RDD

    Assign a name to this RDD

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

    Permalink

    Return this RDD sorted by the given key function.

    Return this RDD sorted by the given key function.

    Definition Classes
    RDD
  106. def sparkContext: SparkContext

    Permalink

    The SparkContext that created this RDD.

    The SparkContext that created this RDD.

    Definition Classes
    RDD
  107. def subtract(other: RDD[(K, V)], p: Partitioner)(implicit ord: Ordering[(K, V)] = null): RDD[(K, V)]

    Permalink

    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
  108. def subtract(other: RDD[(K, V)], numPartitions: Int): RDD[(K, V)]

    Permalink

    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
  109. def subtract(other: RDD[(K, V)]): RDD[(K, V)]

    Permalink

    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
  110. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  111. def take(num: Int): Array[(K, V)]

    Permalink

    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.

  112. def takeOrdered(num: Int)(implicit ord: Ordering[(K, V)]): Array[(K, V)]

    Permalink

    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.

  113. def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[(K, V)]

    Permalink

    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.

  114. def toDebugString: String

    Permalink

    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
  115. def toJavaRDD(): JavaRDD[(K, V)]

    Permalink
    Definition Classes
    RDD
  116. def toLocalIterator: Iterator[(K, V)]

    Permalink

    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.

  117. def toString(): String

    Permalink
    Definition Classes
    RDD → AnyRef → Any
  118. def top(num: Int)(implicit ord: Ordering[(K, V)]): Array[(K, V)]

    Permalink

    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.

  119. def treeAggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U, depth: Int = 2)(implicit arg0: ClassTag[U]): U

    Permalink

    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
  120. def treeReduce(f: ((K, V), (K, V)) ⇒ (K, V), depth: Int = 2): (K, V)

    Permalink

    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

  121. def union(other: RDD[(K, V)]): RDD[(K, V)]

    Permalink

    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
  122. def unpersist(blocking: Boolean = true): HadoopRDD.this.type

    Permalink

    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
    RDD
  123. final def wait(): Unit

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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  126. def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, V), U)]

    Permalink

    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
  127. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[(K, V)], 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
  128. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[(K, V)], 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
  129. def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[(K, V)], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

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

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

    Permalink
    Definition Classes
    RDD
  132. def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[(K, V)], 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
  133. def zipWithIndex(): RDD[((K, V), Long)]

    Permalink

    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.

  134. def zipWithUniqueId(): RDD[((K, V), Long)]

    Permalink

    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 RDD[(K, V)]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped