HadoopRDD (Spark 2.2.0 JavaDoc)

Object
- org.apache.spark.rdd.RDD<scala.Tuple2<K,V>>
- - org.apache.spark.rdd.HadoopRDD<K,V>

All Implemented Interfaces:

java.io.Serializable, Logging
```
public class HadoopRDD<K,V>
extends RDD<scala.Tuple2<K,V>>
implements 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).
param: sc The SparkContext to associate the RDD with. param: 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. param: initLocalJobConfFuncOpt Optional closure used to initialize any JobConf that HadoopRDD creates. param: inputFormatClass Storage format of the data to be read. param: keyClass Class of the key associated with the inputFormatClass. param: valueClass Class of the value associated with the inputFormatClass. param: minPartitions Minimum number of HadoopRDD partitions (Hadoop Splits) to generate.

See Also:

Serialized Form

Note:

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

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class HadoopRDD.HadoopMapPartitionsWithSplitRDD$

Nested Classes
Modifier and Type	Class and Description
`static class`	`HadoopRDD.HadoopMapPartitionsWithSplitRDD$`

Constructor Summary

Constructors
Constructor and Description
`HadoopRDD(SparkContext sc, Broadcast<org.apache.spark.util.SerializableConfiguration> broadcastedConf, scala.Option<scala.Function1<org.apache.hadoop.mapred.JobConf,scala.runtime.BoxedUnit>> initLocalJobConfFuncOpt, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)`
`HadoopRDD(SparkContext sc, org.apache.hadoop.mapred.JobConf conf, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static RDD<T>`	`$plus$plus(RDD<T> other)`
`static void`	`addLocalConfiguration(String jobTrackerId, int jobId, int splitId, int attemptId, org.apache.hadoop.mapred.JobConf conf)` Add Hadoop configuration specific to a single partition and attempt.
`static <U> U`	`aggregate(U zeroValue, scala.Function2<U,T,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$30)`
`static RDD<T>`	`cache()`
`static <U> RDD<scala.Tuple2<T,U>>`	`cartesian(RDD<U> other, scala.reflect.ClassTag<U> evidence$5)`
`void`	`checkpoint()` Mark this RDD for checkpointing.
`static RDD<T>`	`coalesce(int numPartitions, boolean shuffle, scala.Option<PartitionCoalescer> partitionCoalescer, scala.math.Ordering<T> ord)`
`static boolean`	`coalesce$default$2()`
`static scala.Option<PartitionCoalescer>`	`coalesce$default$3()`
`static scala.math.Ordering<T>`	`coalesce$default$4(int numPartitions, boolean shuffle, scala.Option<PartitionCoalescer> partitionCoalescer)`
`static Object`	`collect()`
`static <U> RDD<U>`	`collect(scala.PartialFunction<T,U> f, scala.reflect.ClassTag<U> evidence$29)`
`InterruptibleIterator<scala.Tuple2<K,V>>`	`compute(Partition theSplit, TaskContext context)` :: DeveloperApi :: Implemented by subclasses to compute a given partition.
`static Object`	`CONFIGURATION_INSTANTIATION_LOCK()` Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
`static boolean`	`containsCachedMetadata(String key)`
`static SparkContext`	`context()`
`static long`	`count()`
`static PartialResult<BoundedDouble>`	`countApprox(long timeout, double confidence)`
`static double`	`countApprox$default$2()`
`static long`	`countApproxDistinct(double relativeSD)`
`static long`	`countApproxDistinct(int p, int sp)`
`static double`	`countApproxDistinct$default$1()`
`static scala.collection.Map<T,Object>`	`countByValue(scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`countByValue$default$1()`
`static PartialResult<scala.collection.Map<T,BoundedDouble>>`	`countByValueApprox(long timeout, double confidence, scala.math.Ordering<T> ord)`
`static double`	`countByValueApprox$default$2()`
`static scala.math.Ordering<T>`	`countByValueApprox$default$3(long timeout, double confidence)`
`static scala.collection.Seq<Dependency<?>>`	`dependencies()`
`static RDD<T>`	`distinct()`
`static RDD<T>`	`distinct(int numPartitions, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`distinct$default$2(int numPartitions)`
`static RDD<T>`	`filter(scala.Function1<T,Object> f)`
`static T`	`first()`
`static <U> RDD<U>`	`flatMap(scala.Function1<T,scala.collection.TraversableOnce<U>> f, scala.reflect.ClassTag<U> evidence$4)`
`static T`	`fold(T zeroValue, scala.Function2<T,T,T> op)`
`static void`	`foreach(scala.Function1<T,scala.runtime.BoxedUnit> f)`
`static void`	`foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> f)`
`static Object`	`getCachedMetadata(String key)` The three methods below are helpers for accessing the local map, a property of the SparkEnv of the local process.
`static scala.Option<String>`	`getCheckpointFile()`
`org.apache.hadoop.conf.Configuration`	`getConf()`
`static int`	`getNumPartitions()`
`Partition[]`	`getPartitions()` Implemented by subclasses to return the set of partitions in this RDD.
`scala.collection.Seq<String>`	`getPreferredLocations(Partition split)` Optionally overridden by subclasses to specify placement preferences.
`static StorageLevel`	`getStorageLevel()`
`static RDD<Object>`	`glom()`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, scala.reflect.ClassTag<K> kt)`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, int numPartitions, scala.reflect.ClassTag<K> kt)`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, Partitioner p, scala.reflect.ClassTag<K> kt, scala.math.Ordering<K> ord)`
`static <K> scala.runtime.Null$`	`groupBy$default$4(scala.Function1<T,K> f, Partitioner p)`
`static int`	`id()`
`static RDD<T>`	`intersection(RDD<T> other)`
`static RDD<T>`	`intersection(RDD<T> other, int numPartitions)`
`static RDD<T>`	`intersection(RDD<T> other, Partitioner partitioner, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`intersection$default$3(RDD<T> other, Partitioner partitioner)`
`static boolean`	`isCheckpointed()`
`static boolean`	`isEmpty()`
`static scala.collection.Iterator<T>`	`iterator(Partition split, TaskContext context)`
`static <K> RDD<scala.Tuple2<K,T>>`	`keyBy(scala.Function1<T,K> f)`
`static RDD<T>`	`localCheckpoint()`
`static <U> RDD<U>`	`map(scala.Function1<T,U> f, scala.reflect.ClassTag<U> evidence$3)`
`static <U> RDD<U>`	`mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> f, boolean preservesPartitioning, scala.reflect.ClassTag<U> evidence$6)`
`static <U> boolean`	`mapPartitions$default$2()`
`static <U> boolean`	`mapPartitionsInternal$default$2()`
`static <U> RDD<U>`	`mapPartitionsWithIndex(scala.Function2<Object,scala.collection.Iterator<T>,scala.collection.Iterator<U>> f, boolean preservesPartitioning, scala.reflect.ClassTag<U> evidence$9)`
`static <U> boolean`	`mapPartitionsWithIndex$default$2()`
`static <U> boolean`	`mapPartitionsWithIndexInternal$default$2()`
`<U> RDD<U>`	`mapPartitionsWithInputSplit(scala.Function2<org.apache.hadoop.mapred.InputSplit,scala.collection.Iterator<scala.Tuple2<K,V>>,scala.collection.Iterator<U>> f, boolean preservesPartitioning, scala.reflect.ClassTag<U> evidence$1)` Maps over a partition, providing the InputSplit that was used as the base of the partition.
`static T`	`max(scala.math.Ordering<T> ord)`
`static T`	`min(scala.math.Ordering<T> ord)`
`static void`	`name_$eq(String x$1)`
`static String`	`name()`
`static scala.Option<Partitioner>`	`partitioner()`
`static Partition[]`	`partitions()`
`HadoopRDD<K,V>`	`persist(StorageLevel storageLevel)` Set this RDD's storage level to persist its values across operations after the first time it is computed.
`static RDD<String>`	`pipe(scala.collection.Seq<String> command, scala.collection.Map<String,String> env, scala.Function1<scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printPipeContext, scala.Function2<T,scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printRDDElement, boolean separateWorkingDir, int bufferSize, String encoding)`
`static RDD<String>`	`pipe(String command)`
`static RDD<String>`	`pipe(String command, scala.collection.Map<String,String> env)`
`static scala.collection.Map<String,String>`	`pipe$default$2()`
`static scala.Function1<scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit>`	`pipe$default$3()`
`static scala.Function2<T,scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit>`	`pipe$default$4()`
`static boolean`	`pipe$default$5()`
`static int`	`pipe$default$6()`
`static String`	`pipe$default$7()`
`static scala.collection.Seq<String>`	`preferredLocations(Partition split)`
`static RDD<T>[]`	`randomSplit(double[] weights, long seed)`
`static long`	`randomSplit$default$2()`
`static int`	`RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES()` Update the input bytes read metric each time this number of records has been read
`static T`	`reduce(scala.Function2<T,T,T> f)`
`static RDD<T>`	`repartition(int numPartitions, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`repartition$default$2(int numPartitions)`
`static RDD<T>`	`sample(boolean withReplacement, double fraction, long seed)`
`static long`	`sample$default$3()`
`static void`	`saveAsObjectFile(String path)`
`static void`	`saveAsTextFile(String path)`
`static void`	`saveAsTextFile(String path, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)`
`static RDD<T>`	`setName(String _name)`
`static <K> RDD<T>`	`sortBy(scala.Function1<T,K> f, boolean ascending, int numPartitions, scala.math.Ordering<K> ord, scala.reflect.ClassTag<K> ctag)`
`static <K> boolean`	`sortBy$default$2()`
`static <K> int`	`sortBy$default$3()`
`static SparkContext`	`sparkContext()`
`static RDD<T>`	`subtract(RDD<T> other)`
`static RDD<T>`	`subtract(RDD<T> other, int numPartitions)`
`static RDD<T>`	`subtract(RDD<T> other, Partitioner p, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`subtract$default$3(RDD<T> other, Partitioner p)`
`static Object`	`take(int num)`
`static Object`	`takeOrdered(int num, scala.math.Ordering<T> ord)`
`static Object`	`takeSample(boolean withReplacement, int num, long seed)`
`static long`	`takeSample$default$3()`
`static String`	`toDebugString()`
`static JavaRDD<T>`	`toJavaRDD()`
`static scala.collection.Iterator<T>`	`toLocalIterator()`
`static Object`	`top(int num, scala.math.Ordering<T> ord)`
`static String`	`toString()`
`static <U> U`	`treeAggregate(U zeroValue, scala.Function2<U,T,U> seqOp, scala.Function2<U,U,U> combOp, int depth, scala.reflect.ClassTag<U> evidence$31)`
`static <U> int`	`treeAggregate$default$4(U zeroValue)`
`static T`	`treeReduce(scala.Function2<T,T,T> f, int depth)`
`static int`	`treeReduce$default$2()`
`static RDD<T>`	`union(RDD<T> other)`
`static RDD<T>`	`unpersist(boolean blocking)`
`static boolean`	`unpersist$default$1()`
`static <U> RDD<scala.Tuple2<T,U>>`	`zip(RDD<U> other, scala.reflect.ClassTag<U> evidence$10)`
`static <B,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, boolean preservesPartitioning, scala.Function2<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$11, scala.reflect.ClassTag<V> evidence$12)`
`static <B,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, scala.Function2<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$13, scala.reflect.ClassTag<V> evidence$14)`
`static <B,C,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, RDD<C> rdd3, boolean preservesPartitioning, scala.Function3<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$15, scala.reflect.ClassTag<C> evidence$16, scala.reflect.ClassTag<V> evidence$17)`
`static <B,C,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, RDD<C> rdd3, scala.Function3<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$18, scala.reflect.ClassTag<C> evidence$19, scala.reflect.ClassTag<V> evidence$20)`
`static <B,C,D,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, RDD<C> rdd3, RDD<D> rdd4, boolean preservesPartitioning, scala.Function4<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<D>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$21, scala.reflect.ClassTag<C> evidence$22, scala.reflect.ClassTag<D> evidence$23, scala.reflect.ClassTag<V> evidence$24)`
`static <B,C,D,V> RDD<V>`	`zipPartitions(RDD<B> rdd2, RDD<C> rdd3, RDD<D> rdd4, scala.Function4<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<D>,scala.collection.Iterator<V>> f, scala.reflect.ClassTag<B> evidence$25, scala.reflect.ClassTag<C> evidence$26, scala.reflect.ClassTag<D> evidence$27, scala.reflect.ClassTag<V> evidence$28)`
`static RDD<scala.Tuple2<T,Object>>`	`zipWithIndex()`
`static RDD<scala.Tuple2<T,Object>>`	`zipWithUniqueId()`

Methods inherited from class org.apache.spark.rdd.RDD
aggregate, cache, cartesian, coalesce, collect, collect, context, count, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, dependencies, distinct, distinct, doubleRDDToDoubleRDDFunctions, filter, first, flatMap, fold, foreach, foreachPartition, getCheckpointFile, getNumPartitions, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, isEmpty, iterator, keyBy, localCheckpoint, map, mapPartitions, mapPartitionsWithIndex, max, min, name, numericRDDToDoubleRDDFunctions, partitioner, partitions, persist, pipe, pipe, pipe, preferredLocations, randomSplit, rddToAsyncRDDActions, rddToOrderedRDDFunctions, rddToPairRDDFunctions, rddToSequenceFileRDDFunctions, reduce, repartition, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sortBy, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toDebugString, toJavaRDD, toLocalIterator, top, toString, treeAggregate, treeReduce, union, unpersist, zip, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipWithIndex, zipWithUniqueId

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

HadoopRDD

public HadoopRDD(SparkContext sc,
                 Broadcast<org.apache.spark.util.SerializableConfiguration> broadcastedConf,
                 scala.Option<scala.Function1<org.apache.hadoop.mapred.JobConf,scala.runtime.BoxedUnit>> initLocalJobConfFuncOpt,
                 Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
                 Class<K> keyClass,
                 Class<V> valueClass,
                 int minPartitions)

HadoopRDD

public HadoopRDD(SparkContext sc,
                 org.apache.hadoop.mapred.JobConf conf,
                 Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
                 Class<K> keyClass,
                 Class<V> valueClass,
                 int minPartitions)

Method Detail

CONFIGURATION_INSTANTIATION_LOCK
```
public static Object CONFIGURATION_INSTANTIATION_LOCK()
```
Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456). Therefore, we synchronize on this lock before calling new JobConf() or new Configuration().

Returns:

(undocumented)

RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES
```
public static int RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES()
```
Update the input bytes read metric each time this number of records has been read

getCachedMetadata
```
public static Object getCachedMetadata(String key)
```
The three methods below are helpers for accessing the local map, a property of the SparkEnv of the local process.

Parameters:

key - (undocumented)

Returns:

(undocumented)

containsCachedMetadata

public static boolean containsCachedMetadata(String key)

addLocalConfiguration

public static void addLocalConfiguration(String jobTrackerId,
                                         int jobId,
                                         int splitId,
                                         int attemptId,
                                         org.apache.hadoop.mapred.JobConf conf)

Add Hadoop configuration specific to a single partition and attempt.

partitioner

public static scala.Option<Partitioner> partitioner()

sparkContext

public static SparkContext sparkContext()

id
```
public static int id()
```

name
```
public static String name()
```

name_$eq

public static void name_$eq(String x$1)

setName

public static RDD<T> setName(String _name)

cache
```
public static RDD<T> cache()
```

unpersist

public static RDD<T> unpersist(boolean blocking)

getStorageLevel

public static StorageLevel getStorageLevel()

dependencies

public static final scala.collection.Seq<Dependency<?>> dependencies()

partitions

public static final Partition[] partitions()

getNumPartitions

public static final int getNumPartitions()

preferredLocations

public static final scala.collection.Seq<String> preferredLocations(Partition split)

iterator

public static final scala.collection.Iterator<T> iterator(Partition split,
                                                          TaskContext context)

map

public static <U> RDD<U> map(scala.Function1<T,U> f,
                             scala.reflect.ClassTag<U> evidence$3)

flatMap

public static <U> RDD<U> flatMap(scala.Function1<T,scala.collection.TraversableOnce<U>> f,
                                 scala.reflect.ClassTag<U> evidence$4)

filter

public static RDD<T> filter(scala.Function1<T,Object> f)

distinct

public static RDD<T> distinct(int numPartitions,
                              scala.math.Ordering<T> ord)

distinct
```
public static RDD<T> distinct()
```

repartition

public static RDD<T> repartition(int numPartitions,
                                 scala.math.Ordering<T> ord)

coalesce

public static RDD<T> coalesce(int numPartitions,
                              boolean shuffle,
                              scala.Option<PartitionCoalescer> partitionCoalescer,
                              scala.math.Ordering<T> ord)

sample

public static RDD<T> sample(boolean withReplacement,
                            double fraction,
                            long seed)

randomSplit

public static RDD<T>[] randomSplit(double[] weights,
                                   long seed)

takeSample

public static Object takeSample(boolean withReplacement,
                                int num,
                                long seed)

union

public static RDD<T> union(RDD<T> other)

$plus$plus

public static RDD<T> $plus$plus(RDD<T> other)

sortBy

public static <K> RDD<T> sortBy(scala.Function1<T,K> f,
                                boolean ascending,
                                int numPartitions,
                                scala.math.Ordering<K> ord,
                                scala.reflect.ClassTag<K> ctag)

intersection

public static RDD<T> intersection(RDD<T> other)

intersection

public static RDD<T> intersection(RDD<T> other,
                                  Partitioner partitioner,
                                  scala.math.Ordering<T> ord)

intersection

public static RDD<T> intersection(RDD<T> other,
                                  int numPartitions)

glom
```
public static RDD<Object> glom()
```

cartesian

public static <U> RDD<scala.Tuple2<T,U>> cartesian(RDD<U> other,
                                                   scala.reflect.ClassTag<U> evidence$5)

groupBy

public static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>> groupBy(scala.Function1<T,K> f,
                                                                            scala.reflect.ClassTag<K> kt)

groupBy

public static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>> groupBy(scala.Function1<T,K> f,
                                                                            int numPartitions,
                                                                            scala.reflect.ClassTag<K> kt)

groupBy

public static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>> groupBy(scala.Function1<T,K> f,
                                                                            Partitioner p,
                                                                            scala.reflect.ClassTag<K> kt,
                                                                            scala.math.Ordering<K> ord)

pipe

public static RDD<String> pipe(String command)

pipe

public static RDD<String> pipe(String command,
                               scala.collection.Map<String,String> env)

pipe

public static RDD<String> pipe(scala.collection.Seq<String> command,
                               scala.collection.Map<String,String> env,
                               scala.Function1<scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printPipeContext,
                               scala.Function2<T,scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printRDDElement,
                               boolean separateWorkingDir,
                               int bufferSize,
                               String encoding)

mapPartitions

public static <U> RDD<U> mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> f,
                                       boolean preservesPartitioning,
                                       scala.reflect.ClassTag<U> evidence$6)

mapPartitionsWithIndex

public static <U> RDD<U> mapPartitionsWithIndex(scala.Function2<Object,scala.collection.Iterator<T>,scala.collection.Iterator<U>> f,
                                                boolean preservesPartitioning,
                                                scala.reflect.ClassTag<U> evidence$9)

zip

public static <U> RDD<scala.Tuple2<T,U>> zip(RDD<U> other,
                                             scala.reflect.ClassTag<U> evidence$10)

zipPartitions

public static <B,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                         boolean preservesPartitioning,
                                         scala.Function2<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<V>> f,
                                         scala.reflect.ClassTag<B> evidence$11,
                                         scala.reflect.ClassTag<V> evidence$12)

zipPartitions

public static <B,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                         scala.Function2<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<V>> f,
                                         scala.reflect.ClassTag<B> evidence$13,
                                         scala.reflect.ClassTag<V> evidence$14)

zipPartitions

public static <B,C,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                           RDD<C> rdd3,
                                           boolean preservesPartitioning,
                                           scala.Function3<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<V>> f,
                                           scala.reflect.ClassTag<B> evidence$15,
                                           scala.reflect.ClassTag<C> evidence$16,
                                           scala.reflect.ClassTag<V> evidence$17)

zipPartitions

public static <B,C,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                           RDD<C> rdd3,
                                           scala.Function3<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<V>> f,
                                           scala.reflect.ClassTag<B> evidence$18,
                                           scala.reflect.ClassTag<C> evidence$19,
                                           scala.reflect.ClassTag<V> evidence$20)

zipPartitions

public static <B,C,D,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                             RDD<C> rdd3,
                                             RDD<D> rdd4,
                                             boolean preservesPartitioning,
                                             scala.Function4<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<D>,scala.collection.Iterator<V>> f,
                                             scala.reflect.ClassTag<B> evidence$21,
                                             scala.reflect.ClassTag<C> evidence$22,
                                             scala.reflect.ClassTag<D> evidence$23,
                                             scala.reflect.ClassTag<V> evidence$24)

zipPartitions

public static <B,C,D,V> RDD<V> zipPartitions(RDD<B> rdd2,
                                             RDD<C> rdd3,
                                             RDD<D> rdd4,
                                             scala.Function4<scala.collection.Iterator<T>,scala.collection.Iterator<B>,scala.collection.Iterator<C>,scala.collection.Iterator<D>,scala.collection.Iterator<V>> f,
                                             scala.reflect.ClassTag<B> evidence$25,
                                             scala.reflect.ClassTag<C> evidence$26,
                                             scala.reflect.ClassTag<D> evidence$27,
                                             scala.reflect.ClassTag<V> evidence$28)

foreach

public static void foreach(scala.Function1<T,scala.runtime.BoxedUnit> f)

foreachPartition

public static void foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> f)

collect
```
public static Object collect()
```

toLocalIterator

public static scala.collection.Iterator<T> toLocalIterator()

collect

public static <U> RDD<U> collect(scala.PartialFunction<T,U> f,
                                 scala.reflect.ClassTag<U> evidence$29)

subtract

public static RDD<T> subtract(RDD<T> other)

subtract

public static RDD<T> subtract(RDD<T> other,
                              int numPartitions)

subtract

public static RDD<T> subtract(RDD<T> other,
                              Partitioner p,
                              scala.math.Ordering<T> ord)

reduce

public static T reduce(scala.Function2<T,T,T> f)

treeReduce

public static T treeReduce(scala.Function2<T,T,T> f,
                           int depth)

fold

public static T fold(T zeroValue,
                     scala.Function2<T,T,T> op)

aggregate

public static <U> U aggregate(U zeroValue,
                              scala.Function2<U,T,U> seqOp,
                              scala.Function2<U,U,U> combOp,
                              scala.reflect.ClassTag<U> evidence$30)

treeAggregate

public static <U> U treeAggregate(U zeroValue,
                                  scala.Function2<U,T,U> seqOp,
                                  scala.Function2<U,U,U> combOp,
                                  int depth,
                                  scala.reflect.ClassTag<U> evidence$31)

count
```
public static long count()
```

countApprox

public static PartialResult<BoundedDouble> countApprox(long timeout,
                                                       double confidence)

countByValue

public static scala.collection.Map<T,Object> countByValue(scala.math.Ordering<T> ord)

countByValueApprox

public static PartialResult<scala.collection.Map<T,BoundedDouble>> countByValueApprox(long timeout,
                                                                                      double confidence,
                                                                                      scala.math.Ordering<T> ord)

countApproxDistinct

public static long countApproxDistinct(int p,
                                       int sp)

countApproxDistinct

public static long countApproxDistinct(double relativeSD)

zipWithIndex

public static RDD<scala.Tuple2<T,Object>> zipWithIndex()

zipWithUniqueId

public static RDD<scala.Tuple2<T,Object>> zipWithUniqueId()

take
```
public static Object take(int num)
```

first
```
public static T first()
```

top

public static Object top(int num,
                         scala.math.Ordering<T> ord)

takeOrdered

public static Object takeOrdered(int num,
                                 scala.math.Ordering<T> ord)

max

public static T max(scala.math.Ordering<T> ord)

min

public static T min(scala.math.Ordering<T> ord)

isEmpty
```
public static boolean isEmpty()
```

saveAsTextFile

public static void saveAsTextFile(String path)

saveAsTextFile

public static void saveAsTextFile(String path,
                                  Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)

saveAsObjectFile

public static void saveAsObjectFile(String path)

keyBy

public static <K> RDD<scala.Tuple2<K,T>> keyBy(scala.Function1<T,K> f)

localCheckpoint

public static RDD<T> localCheckpoint()

isCheckpointed

public static boolean isCheckpointed()

getCheckpointFile

public static scala.Option<String> getCheckpointFile()

context
```
public static SparkContext context()
```

toDebugString
```
public static String toDebugString()
```

toString
```
public static String toString()
```

toJavaRDD
```
public static JavaRDD<T> toJavaRDD()
```

sample$default$3
```
public static long sample$default$3()
```

mapPartitionsWithIndex$default$2

public static <U> boolean mapPartitionsWithIndex$default$2()

unpersist$default$1

public static boolean unpersist$default$1()

distinct$default$2

public static scala.math.Ordering<T> distinct$default$2(int numPartitions)

coalesce$default$2

public static boolean coalesce$default$2()

coalesce$default$3

public static scala.Option<PartitionCoalescer> coalesce$default$3()

coalesce$default$4

public static scala.math.Ordering<T> coalesce$default$4(int numPartitions,
                                                        boolean shuffle,
                                                        scala.Option<PartitionCoalescer> partitionCoalescer)

repartition$default$2

public static scala.math.Ordering<T> repartition$default$2(int numPartitions)

subtract$default$3

public static scala.math.Ordering<T> subtract$default$3(RDD<T> other,
                                                        Partitioner p)

intersection$default$3

public static scala.math.Ordering<T> intersection$default$3(RDD<T> other,
                                                            Partitioner partitioner)

randomSplit$default$2

public static long randomSplit$default$2()

sortBy$default$2

public static <K> boolean sortBy$default$2()

sortBy$default$3

public static <K> int sortBy$default$3()

mapPartitions$default$2

public static <U> boolean mapPartitions$default$2()

groupBy$default$4

public static <K> scala.runtime.Null$ groupBy$default$4(scala.Function1<T,K> f,
                                                        Partitioner p)

pipe$default$2

public static scala.collection.Map<String,String> pipe$default$2()

pipe$default$3

public static scala.Function1<scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> pipe$default$3()

pipe$default$4

public static scala.Function2<T,scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> pipe$default$4()

pipe$default$5

public static boolean pipe$default$5()

pipe$default$6
```
public static int pipe$default$6()
```

pipe$default$7
```
public static String pipe$default$7()
```

treeReduce$default$2

public static int treeReduce$default$2()

treeAggregate$default$4

public static <U> int treeAggregate$default$4(U zeroValue)

countApprox$default$2

public static double countApprox$default$2()

countByValue$default$1

public static scala.math.Ordering<T> countByValue$default$1()

countByValueApprox$default$2

public static double countByValueApprox$default$2()

countByValueApprox$default$3

public static scala.math.Ordering<T> countByValueApprox$default$3(long timeout,
                                                                  double confidence)

takeSample$default$3

public static long takeSample$default$3()

countApproxDistinct$default$1

public static double countApproxDistinct$default$1()

mapPartitionsWithIndexInternal$default$2

public static <U> boolean mapPartitionsWithIndexInternal$default$2()

mapPartitionsInternal$default$2

public static <U> boolean mapPartitionsInternal$default$2()

getPartitions
```
public Partition[] getPartitions()
```
Description copied from class: 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 }

Returns:

(undocumented)

compute
```
public InterruptibleIterator<scala.Tuple2<K,V>> compute(Partition theSplit,
                                                        TaskContext context)
```
Description copied from class: RDD

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

Specified by:

compute in class RDD<scala.Tuple2<K,V>>

Parameters:

theSplit - (undocumented)

context - (undocumented)

Returns:

(undocumented)

mapPartitionsWithInputSplit

public <U> RDD<U> mapPartitionsWithInputSplit(scala.Function2<org.apache.hadoop.mapred.InputSplit,scala.collection.Iterator<scala.Tuple2<K,V>>,scala.collection.Iterator<U>> f,
                                              boolean preservesPartitioning,
                                              scala.reflect.ClassTag<U> evidence$1)

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

getPreferredLocations
```
public scala.collection.Seq<String> getPreferredLocations(Partition split)
```
Description copied from class: RDD

Optionally overridden by subclasses to specify placement preferences.

Parameters:

split - (undocumented)

Returns:

(undocumented)

checkpoint
```
public void checkpoint()
```
Description copied from class: RDD

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.

Overrides:

checkpoint in class RDD<scala.Tuple2<K,V>>

persist
```
public HadoopRDD<K,V> persist(StorageLevel storageLevel)
```
Description copied from class: RDD

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.

Overrides:

persist in class RDD<scala.Tuple2<K,V>>

Parameters:

storageLevel - (undocumented)

Returns:

(undocumented)

getConf

public org.apache.hadoop.conf.Configuration getConf()

Class HadoopRDD<K,V>

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class org.apache.spark.rdd.RDD

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Constructor Detail

HadoopRDD

HadoopRDD

Method Detail

CONFIGURATION_INSTANTIATION_LOCK

RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES

getCachedMetadata

containsCachedMetadata

addLocalConfiguration

partitioner

sparkContext

id

name

name_$eq

setName

cache

unpersist

getStorageLevel

dependencies

partitions

getNumPartitions

preferredLocations

iterator

map

flatMap

filter

distinct

distinct

repartition

coalesce

sample

randomSplit

takeSample

union

$plus$plus

sortBy

intersection

intersection

intersection

glom

cartesian

groupBy

groupBy

groupBy

pipe

pipe

pipe

mapPartitions

mapPartitionsWithIndex

zip

zipPartitions

zipPartitions

zipPartitions

zipPartitions

zipPartitions

zipPartitions

foreach

foreachPartition

collect

toLocalIterator

collect

subtract

subtract

subtract

reduce

treeReduce

fold

aggregate

treeAggregate

count

countApprox

countByValue

countByValueApprox