JdbcRDD (Spark 2.4.0 JavaDoc)

Object
- org.apache.spark.rdd.RDD<T>
- - org.apache.spark.rdd.JdbcRDD<T>

All Implemented Interfaces:

java.io.Serializable, Logging
```
public class JdbcRDD<T>
extends RDD<T>
implements Logging
```
An RDD that executes a SQL query on a JDBC connection and reads results. For usage example, see test case JdbcRDDSuite.
param: getConnection a function that returns an open Connection. The RDD takes care of closing the connection. param: sql the text of the query. The query must contain two ? placeholders for parameters used to partition the results. For example,
```
   select title, author from books where ? <= id and id <= ?
   
```
param: lowerBound the minimum value of the first placeholder param: upperBound the maximum value of the second placeholder The lower and upper bounds are inclusive. param: numPartitions the number of partitions. Given a lowerBound of 1, an upperBound of 20, and a numPartitions of 2, the query would be executed twice, once with (1, 10) and once with (11, 20) param: mapRow a function from a ResultSet to a single row of the desired result type(s). This should only call getInt, getString, etc; the RDD takes care of calling next. The default maps a ResultSet to an array of Object.
See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static interface JdbcRDD.ConnectionFactory

Nested Classes
Modifier and Type	Class and Description
`static interface`	`JdbcRDD.ConnectionFactory`

Constructor Summary

Constructors
Constructor and Description
`JdbcRDD(SparkContext sc, scala.Function0<java.sql.Connection> getConnection, String sql, long lowerBound, long upperBound, int numPartitions, scala.Function1<java.sql.ResultSet,T> mapRow, scala.reflect.ClassTag<T> evidence$1)`

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 <U> U`	`aggregate(U zeroValue, scala.Function2<U,T,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$30)`
`static RDDBarrier<T>`	`barrier()`
`static RDD<T>`	`cache()`
`static <U> RDD<scala.Tuple2<T,U>>`	`cartesian(RDD<U> other, scala.reflect.ClassTag<U> evidence$5)`
`static void`	`checkpoint()`
`static RDD<T>`	`coalesce(int numPartitions, boolean shuffle, scala.Option<PartitionCoalescer> partitionCoalescer, scala.math.Ordering<T> ord)`
`static boolean`	`coalesce$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$29)`
`scala.collection.Iterator<T>`	`compute(Partition thePart, TaskContext context)` :: DeveloperApi :: Implemented by subclasses to compute a given partition.
`static SparkContext`	`context()`
`static long`	`count()`
`static PartialResult<BoundedDouble>`	`countApprox(long timeout, double confidence)`
`static double`	`countApprox$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$3(long timeout, double confidence)`
`static JavaRDD<Object[]>`	`create(JavaSparkContext sc, JdbcRDD.ConnectionFactory connectionFactory, String sql, long lowerBound, long upperBound, int numPartitions)` Create an RDD that executes a SQL query on a JDBC connection and reads results.
`static <T> JavaRDD<T>`	`create(JavaSparkContext sc, JdbcRDD.ConnectionFactory connectionFactory, String sql, long lowerBound, long upperBound, int numPartitions, Function<java.sql.ResultSet,T> mapRow)` Create an RDD that executes a SQL query on a JDBC connection and reads results.
`static scala.collection.Seq<Dependency<?>>`	`dependencies()`
`static RDD<T>`	`distinct()`
`static RDD<T>`	`distinct(int numPartitions, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`distinct$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$4)`
`static T`	`fold(T zeroValue, scala.Function2<T,T,T> op)`
`static void`	`foreach(scala.Function1<T,scala.runtime.BoxedUnit> f)`
`static void`	`foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> f)`
`static scala.Option<String>`	`getCheckpointFile()`
`static int`	`getNumPartitions()`
`Partition[]`	`getPartitions()` Implemented by subclasses to return the set of partitions in this RDD.
`static StorageLevel`	`getStorageLevel()`
`static RDD<Object>`	`glom()`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, scala.reflect.ClassTag<K> kt)`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, int numPartitions, scala.reflect.ClassTag<K> kt)`
`static <K> RDD<scala.Tuple2<K,scala.collection.Iterable<T>>>`	`groupBy(scala.Function1<T,K> f, Partitioner p, scala.reflect.ClassTag<K> kt, scala.math.Ordering<K> ord)`
`static <K> scala.runtime.Null$`	`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$2()`
`static <U> boolean`	`mapPartitionsWithIndexInternal$default$3()`
`static T`	`max(scala.math.Ordering<T> ord)`
`static T`	`min(scala.math.Ordering<T> ord)`
`static void`	`name_$eq(String x$1)`
`static String`	`name()`
`static scala.Option<Partitioner>`	`partitioner()`
`static Partition[]`	`partitions()`
`static RDD<T>`	`persist()`
`static RDD<T>`	`persist(StorageLevel newLevel)`
`static RDD<String>`	`pipe(scala.collection.Seq<String> command, scala.collection.Map<String,String> env, scala.Function1<scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printPipeContext, scala.Function2<T,scala.Function1<String,scala.runtime.BoxedUnit>,scala.runtime.BoxedUnit> printRDDElement, boolean separateWorkingDir, int bufferSize, String encoding)`
`static RDD<String>`	`pipe(String command)`
`static RDD<String>`	`pipe(String command, scala.collection.Map<String,String> env)`
`static scala.collection.Map<String,String>`	`pipe$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$2()`
`static T`	`reduce(scala.Function2<T,T,T> f)`
`static RDD<T>`	`repartition(int numPartitions, scala.math.Ordering<T> ord)`
`static scala.math.Ordering<T>`	`repartition$default$2(int numPartitions)`
`static Object[]`	`resultSetToObjectArray(java.sql.ResultSet rs)`
`static RDD<T>`	`sample(boolean withReplacement, double fraction, long seed)`
`static long`	`sample$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, barrier, cache, cartesian, checkpoint, 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, 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, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

JdbcRDD

public JdbcRDD(SparkContext sc,
               scala.Function0<java.sql.Connection> getConnection,
               String sql,
               long lowerBound,
               long upperBound,
               int numPartitions,
               scala.Function1<java.sql.ResultSet,T> mapRow,
               scala.reflect.ClassTag<T> evidence$1)

Method Detail

resultSetToObjectArray

public static Object[] resultSetToObjectArray(java.sql.ResultSet rs)

create
```
public static <T> JavaRDD<T> create(JavaSparkContext sc,
                                    JdbcRDD.ConnectionFactory connectionFactory,
                                    String sql,
                                    long lowerBound,
                                    long upperBound,
                                    int numPartitions,
                                    Function<java.sql.ResultSet,T> mapRow)
```
Create an RDD that executes a SQL query on a JDBC connection and reads results. For usage example, see test case JavaAPISuite.testJavaJdbcRDD.
Parameters:

connectionFactory - a factory that returns an open Connection. The RDD takes care of closing the connection.
sql - the text of the query. The query must contain two ? placeholders for parameters used to partition the results. For example,
```
   select title, author from books where ? <= id and id <= ?
   
```
lowerBound - the minimum value of the first placeholder

upperBound - the maximum value of the second placeholder The lower and upper bounds are inclusive.

numPartitions - the number of partitions. Given a lowerBound of 1, an upperBound of 20, and a numPartitions of 2, the query would be executed twice, once with (1, 10) and once with (11, 20)

mapRow - a function from a ResultSet to a single row of the desired result type(s). This should only call getInt, getString, etc; the RDD takes care of calling next. The default maps a ResultSet to an array of Object.

sc - (undocumented)

Returns:

(undocumented)

create
```
public static JavaRDD<Object[]> create(JavaSparkContext sc,
                                       JdbcRDD.ConnectionFactory connectionFactory,
                                       String sql,
                                       long lowerBound,
                                       long upperBound,
                                       int numPartitions)
```
Create an RDD that executes a SQL query on a JDBC connection and reads results. Each row is converted into a Object array. For usage example, see test case JavaAPISuite.testJavaJdbcRDD.
Parameters:

connectionFactory - a factory that returns an open Connection. The RDD takes care of closing the connection.
sql - the text of the query. The query must contain two ? placeholders for parameters used to partition the results. For example,
```
   select title, author from books where ? <= id and id <= ?
   
```
lowerBound - the minimum value of the first placeholder

upperBound - the maximum value of the second placeholder The lower and upper bounds are inclusive.

numPartitions - the number of partitions. Given a lowerBound of 1, an upperBound of 20, and a numPartitions of 2, the query would be executed twice, once with (1, 10) and once with (11, 20)

sc - (undocumented)

Returns:

(undocumented)

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)

persist

public static RDD<T> persist(StorageLevel newLevel)

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

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)

checkpoint
```
public static void checkpoint()
```

localCheckpoint

public static RDD<T> localCheckpoint()

isCheckpointed

public static boolean isCheckpointed()

getCheckpointFile

public static scala.Option<String> getCheckpointFile()

barrier
```
public static RDDBarrier<T> barrier()
```

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()

mapPartitionsWithIndexInternal$default$3

public static <U> boolean mapPartitionsWithIndexInternal$default$3()

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 scala.collection.Iterator<T> compute(Partition thePart,
                                            TaskContext context)
```
Description copied from class: RDD

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

Specified by:

compute in class RDD<T>

Parameters:

thePart - (undocumented)

context - (undocumented)

Returns:

(undocumented)

Class JdbcRDD<T>

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

JdbcRDD

Method Detail

resultSetToObjectArray

create

create

partitioner

sparkContext

id

name

name_$eq

setName

persist

persist

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

countApproxDistinct