spark.rdd.HadoopRDD

Instance Constructors

new HadoopRDD(sc: SparkContext, conf: JobConf, inputFormatClass: Class[_ <: org.apache.hadoop.mapred.InputFormat[K,V]], keyClass: Class[K], valueClass: Class[V], minSplits: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

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

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
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
def aggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassManifest[U]): U

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.

Definition Classes
RDD
final def asInstanceOf[T0]: T0

Definition Classes
Any
def cache(): RDD[(K, V)]

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

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

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
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws()
def collect(): Array[(K, V)]

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
def compute(theSplit: Split): Iterator[(K, V)]

Function for computing a given partition.
Function for computing a given partition.

Definition Classes
HadoopRDD → RDD
val confBroadcast: Broadcast[SerializableWritable[JobConf]]
def context: SparkContext

The SparkContext that this RDD was created on.
The SparkContext that this RDD was created on.

Definition Classes
RDD
def count(): Long

Return the number of elements in the RDD.
Return the number of elements in the RDD.

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

(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
(Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

Definition Classes
RDD
def countByValue(): Map[(K, V), Long]

Return the count of each unique value in this RDD as a map of (value, count) pairs.
Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.

Definition Classes
RDD
def countByValueApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[(K, V), BoundedDouble]]

(Experimental) Approximate version of countByValue().
(Experimental) Approximate version of countByValue().

Definition Classes
RDD
def createInputFormat(conf: JobConf): InputFormat[K, V]
val dependencies: List[spark.Dependency[_]]

How this RDD depends on any parent RDDs.
How this RDD depends on any parent RDDs.

Definition Classes
HadoopRDD → RDD
def distinct(numSplits: Int = splits.size): RDD[(K, V)]

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
final def eq(arg0: AnyRef): Boolean

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

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

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
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws()
def first(): (K, V)

Return the first element in this RDD.
Return the first element in this RDD.

Definition Classes
RDD
def flatMap[U](f: ((K, V)) ⇒ TraversableOnce[U])(implicit arg0: ClassManifest[U]): RDD[U]

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

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.

Definition Classes
RDD
def foreach(f: ((K, V)) ⇒ Unit): Unit

Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.

Definition Classes
RDD
final def getClass(): java.lang.Class[_]

Definition Classes
AnyRef → Any
def getStorageLevel: StorageLevel

Get the RDD's current storage level, or StorageLevel.
Get the RDD's current storage level, or StorageLevel.NONE if none is set.

Definition Classes
RDD
def glom(): RDD[Array[(K, V)]]

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
def groupBy[K](f: ((K, V)) ⇒ K)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

Return an RDD of grouped items.
Return an RDD of grouped items.

Definition Classes
RDD
def groupBy[K](f: ((K, V)) ⇒ K, numSplits: Int)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

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.

Definition Classes
RDD
def hashCode(): Int

Definition Classes
AnyRef → Any
val id: Int

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

Definition Classes
RDD
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def iterator(split: Split): Iterator[(K, V)]

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
def map[U](f: ((K, V)) ⇒ U)(implicit arg0: ClassManifest[U]): RDD[U]

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
def mapPartitions[U](f: (Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

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.

Definition Classes
RDD
def mapPartitionsWithSplit[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

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.

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val partitioner: Option[Partitioner]

Optionally overridden by subclasses to specify how they are partitioned.
Optionally overridden by subclasses to specify how they are partitioned.

Definition Classes
RDD
def persist(): RDD[(K, V)]

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

Definition Classes
RDD
def persist(newLevel: StorageLevel): RDD[(K, V)]

Set this RDD's storage level to persist its values across operations after the first time it is computed.
Set this RDD's storage level to persist its values across operations after the first time it is computed. Can only be called once on each RDD.

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

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
def pipe(command: Seq[String]): RDD[String]

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

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
def preferredLocations(split: Split): WrappedArray[String]

Optionally overridden by subclasses to specify placement preferences.
Optionally overridden by subclasses to specify placement preferences.

Definition Classes
HadoopRDD → RDD
def reduce(f: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

Reduces the elements of this RDD using the specified associative binary operator.
Reduces the elements of this RDD using the specified associative binary operator.

Definition Classes
RDD
def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[(K, V)]

Return a sampled subset of this RDD.
Return a sampled subset of this RDD.

Definition Classes
RDD
def saveAsObjectFile(path: String): Unit

Save this RDD as a SequenceFile of serialized objects.
Save this RDD as a SequenceFile of serialized objects.

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

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
def splits: Array[Split]

Set of partitions in this RDD.
Set of partitions in this RDD.

Definition Classes
HadoopRDD → RDD
val splits_: Array[Split]
final def synchronized[T0](arg0: ⇒ T0): T0

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

Take the first num elements of the RDD.
Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.

Definition Classes
RDD
def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[(K, V)]

Definition Classes
RDD
def toArray(): Array[(K, V)]

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
def toString(): String

Definition Classes
AnyRef → Any
def union(other: RDD[(K, V)]): RDD[(K, V)]

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
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws()

HadoopRDD

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

Instance Constructors

new HadoopRDD(sc: SparkContext, conf: JobConf, inputFormatClass: Class[_ <: org.apache.hadoop.mapred.InputFormat[K,V]], keyClass: Class[K], valueClass: Class[V], minSplits: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

def ++(other: RDD[(K, V)]): RDD[(K, V)]

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def aggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassManifest[U]): U

final def asInstanceOf[T0]: T0

def cache(): RDD[(K, V)]

def cartesian[U](other: RDD[U])(implicit arg0: ClassManifest[U]): RDD[((K, V), U)]

def clone(): AnyRef

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

def compute(theSplit: Split): Iterator[(K, V)]

val confBroadcast: Broadcast[SerializableWritable[JobConf]]

def context: SparkContext

def count(): Long

def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

def countByValue(): Map[(K, V), Long]

def countByValueApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[(K, V), BoundedDouble]]

def createInputFormat(conf: JobConf): InputFormat[K, V]

val dependencies: List[spark.Dependency[_]]

def distinct(numSplits: Int = splits.size): RDD[(K, V)]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(f: ((K, V)) ⇒ Boolean): RDD[(K, V)]

def finalize(): Unit

def first(): (K, V)

def flatMap[U](f: ((K, V)) ⇒ TraversableOnce[U])(implicit arg0: ClassManifest[U]): RDD[U]

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

def foreach(f: ((K, V)) ⇒ Unit): Unit

final def getClass(): java.lang.Class[_]

def getStorageLevel: StorageLevel

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

def groupBy[K](f: ((K, V)) ⇒ K)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

def groupBy[K](f: ((K, V)) ⇒ K, numSplits: Int)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

def hashCode(): Int

val id: Int

final def isInstanceOf[T0]: Boolean

final def iterator(split: Split): Iterator[(K, V)]

def map[U](f: ((K, V)) ⇒ U)(implicit arg0: ClassManifest[U]): RDD[U]

def mapPartitions[U](f: (Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

def mapPartitionsWithSplit[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val partitioner: Option[Partitioner]

def persist(): RDD[(K, V)]

def persist(newLevel: StorageLevel): RDD[(K, V)]

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

def pipe(command: Seq[String]): RDD[String]

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

def preferredLocations(split: Split): WrappedArray[String]

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

def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[(K, V)]

def saveAsObjectFile(path: String): Unit

def saveAsTextFile(path: String): Unit

def splits: Array[Split]

val splits_: Array[Split]

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

def take(num: Int): Array[(K, V)]

def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[(K, V)]

def toArray(): Array[(K, V)]

def toString(): String

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

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from RDD[(K, V)]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any