diff options
13 files changed, 1037 insertions, 162 deletions
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 39f408b8c8..2142fd7327 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -622,4 +622,15 @@ object JavaPairRDD { new JavaPairRDD[K, V](rdd) implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd + + + /** Convert a JavaRDD of key-value pairs to JavaPairRDD. */ + def fromJavaRDD[K, V](rdd: JavaRDD[(K, V)]): JavaPairRDD[K, V] = { + implicit val cmk: ClassManifest[K] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] + implicit val cmv: ClassManifest[V] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] + new JavaPairRDD[K, V](rdd.rdd) + } + } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/Function3.java b/core/src/main/scala/org/apache/spark/api/java/function/Function3.java new file mode 100644 index 0000000000..ac6178924a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/java/function/Function3.java @@ -0,0 +1,36 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.java.function; + +import scala.reflect.ClassManifest; +import scala.reflect.ClassManifest$; +import scala.runtime.AbstractFunction2; + +import java.io.Serializable; + +/** + * A three-argument function that takes arguments of type T1, T2 and T3 and returns an R. + */ +public abstract class Function3<T1, T2, T3, R> extends WrappedFunction3<T1, T2, T3, R> + implements Serializable { + + public ClassManifest<R> returnType() { + return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class); + } +} + diff --git a/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala b/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala new file mode 100644 index 0000000000..d314dbdf1d --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala @@ -0,0 +1,34 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.java.function + +import scala.runtime.AbstractFunction3 + +/** + * Subclass of Function3 for ease of calling from Java. The main thing it does is re-expose the + * apply() method as call() and declare that it can throw Exception (since AbstractFunction3.apply + * isn't marked to allow that). + */ +private[spark] abstract class WrappedFunction3[T1, T2, T3, R] + extends AbstractFunction3[T1, T2, T3, R] { + @throws(classOf[Exception]) + def call(t1: T1, t2: T2, t3: T3): R + + final def apply(t1: T1, t2: T2, t3: T3): R = call(t1, t2, t3) +} + diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala index 6da2261f06..9ceff754c4 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala @@ -486,7 +486,7 @@ abstract class DStream[T: ClassManifest] ( /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: RDD[T] => Unit) { this.foreach((r: RDD[T], t: Time) => foreachFunc(r)) @@ -494,7 +494,7 @@ abstract class DStream[T: ClassManifest] ( /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: (RDD[T], Time) => Unit) { val newStream = new ForEachDStream(this, context.sparkContext.clean(foreachFunc)) @@ -504,18 +504,52 @@ abstract class DStream[T: ClassManifest] ( /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U: ClassManifest](transformFunc: RDD[T] => RDD[U]): DStream[U] = { - transform((r: RDD[T], t: Time) => transformFunc(r)) + transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r))) } /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U: ClassManifest](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = { - new TransformedDStream(this, context.sparkContext.clean(transformFunc)) + //new TransformedDStream(this, context.sparkContext.clean(transformFunc)) + val cleanedF = context.sparkContext.clean(transformFunc) + val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + assert(rdds.length == 1) + cleanedF(rdds.head.asInstanceOf[RDD[T]], time) + } + new TransformedDStream[U](Seq(this), realTransformFunc) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U: ClassManifest, V: ClassManifest]( + other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V] + ): DStream[V] = { + val cleanedF = ssc.sparkContext.clean(transformFunc) + transformWith(other, (rdd1: RDD[T], rdd2: RDD[U], time: Time) => cleanedF(rdd1, rdd2)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U: ClassManifest, V: ClassManifest]( + other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V] + ): DStream[V] = { + val cleanedF = ssc.sparkContext.clean(transformFunc) + val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + assert(rdds.length == 2) + val rdd1 = rdds(0).asInstanceOf[RDD[T]] + val rdd2 = rdds(1).asInstanceOf[RDD[U]] + cleanedF(rdd1, rdd2, time) + } + new TransformedDStream[V](Seq(this, other), realTransformFunc) } /** diff --git a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala index 757bc98981..8c12fd11ef 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala @@ -19,7 +19,7 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.streaming.dstream.{ReducedWindowedDStream, StateDStream} -import org.apache.spark.streaming.dstream.{CoGroupedDStream, ShuffledDStream} +import org.apache.spark.streaming.dstream.{ShuffledDStream} import org.apache.spark.streaming.dstream.{MapValuedDStream, FlatMapValuedDStream} import org.apache.spark.{Partitioner, HashPartitioner} @@ -359,7 +359,7 @@ extends Serializable { } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of the key. * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. * @param updateFunc State update function. If `this` function returns None, then @@ -398,11 +398,18 @@ extends Serializable { new StateDStream(self, ssc.sc.clean(updateFunc), partitioner, rememberPartitioner) } - + /** + * Return a new DStream by applying a map function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def mapValues[U: ClassManifest](mapValuesFunc: V => U): DStream[(K, U)] = { new MapValuedDStream[K, V, U](self, mapValuesFunc) } + /** + * Return a new DStream by applying a flatmap function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def flatMapValues[U: ClassManifest]( flatMapValuesFunc: V => TraversableOnce[U] ): DStream[(K, U)] = { @@ -410,9 +417,8 @@ extends Serializable { } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. HashPartitioner is used to partition each generated RDD into default number + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ def cogroup[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (Seq[V], Seq[W]))] = { @@ -420,56 +426,132 @@ extends Serializable { } /** - * Cogroup `this` DStream with `other` DStream using a partitioner. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. Partitioner is used to partition each generated RDD. + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def cogroup[W: ClassManifest](other: DStream[(K, W)], numPartitions: Int): DStream[(K, (Seq[V], Seq[W]))] = { + cogroup(other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to partition the generated RDDs. */ def cogroup[W: ClassManifest]( other: DStream[(K, W)], partitioner: Partitioner ): DStream[(K, (Seq[V], Seq[W]))] = { - - val cgd = new CoGroupedDStream[K]( - Seq(self.asInstanceOf[DStream[(K, _)]], other.asInstanceOf[DStream[(K, _)]]), - partitioner - ) - val pdfs = new PairDStreamFunctions[K, Seq[Seq[_]]](cgd)( - classManifest[K], - Manifests.seqSeqManifest + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.cogroup(rdd2, partitioner) ) - pdfs.mapValues { - case Seq(vs, ws) => - (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]]) - } } /** - * Join `this` DStream with `other` DStream. HashPartitioner is used - * to partition each generated RDD into default number of partitions. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. */ def join[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (V, W))] = { join[W](other, defaultPartitioner()) } /** - * Join `this` DStream with `other` DStream, that is, each RDD of the new DStream will - * be generated by joining RDDs from `this` and other DStream. Uses the given - * Partitioner to partition each generated RDD. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def join[W: ClassManifest](other: DStream[(K, W)], numPartitions: Int): DStream[(K, (V, W))] = { + join[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. */ def join[W: ClassManifest]( other: DStream[(K, W)], partitioner: Partitioner ): DStream[(K, (V, W))] = { - this.cogroup(other, partitioner) - .flatMapValues{ - case (vs, ws) => - for (v <- vs.iterator; w <- ws.iterator) yield (v, w) - } + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.join(rdd2, partitioner) + ) + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def leftOuterJoin[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (V, Option[W]))] = { + leftOuterJoin[W](other, defaultPartitioner()) + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def leftOuterJoin[W: ClassManifest]( + other: DStream[(K, W)], + numPartitions: Int + ): DStream[(K, (V, Option[W]))] = { + leftOuterJoin[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def leftOuterJoin[W: ClassManifest]( + other: DStream[(K, W)], + partitioner: Partitioner + ): DStream[(K, (V, Option[W]))] = { + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.leftOuterJoin(rdd2, partitioner) + ) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def rightOuterJoin[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (Option[V], W))] = { + rightOuterJoin[W](other, defaultPartitioner()) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def rightOuterJoin[W: ClassManifest]( + other: DStream[(K, W)], + numPartitions: Int + ): DStream[(K, (Option[V], W))] = { + rightOuterJoin[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def rightOuterJoin[W: ClassManifest]( + other: DStream[(K, W)], + partitioner: Partitioner + ): DStream[(K, (Option[V], W))] = { + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.rightOuterJoin(rdd2, partitioner) + ) } /** - * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is generated - * based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" + * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval + * is generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" */ def saveAsHadoopFiles[F <: OutputFormat[K, V]]( prefix: String, @@ -479,8 +561,8 @@ extends Serializable { } /** - * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is generated - * based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" + * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval + * is generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" */ def saveAsHadoopFiles( prefix: String, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index 09c2f7fd8e..70bf902143 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -478,13 +478,24 @@ class StreamingContext private ( inputStream } /** - * Create a unified DStream from multiple DStreams of the same type and same interval + * Create a unified DStream from multiple DStreams of the same type and same slide duration. */ def union[T: ClassManifest](streams: Seq[DStream[T]]): DStream[T] = { new UnionDStream[T](streams.toArray) } /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. + */ + def transform[T: ClassManifest]( + dstreams: Seq[DStream[_]], + transformFunc: (Seq[RDD[_]], Time) => RDD[T] + ): DStream[T] = { + new TransformedDStream[T](dstreams, sparkContext.clean(transformFunc)) + } + + /** * Register an input stream that will be started (InputDStream.start() called) to get the * input data. */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala index 459695b7ca..09189eadd8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala @@ -24,7 +24,8 @@ import scala.collection.JavaConversions._ import org.apache.spark.streaming._ import org.apache.spark.api.java.{JavaPairRDD, JavaRDDLike, JavaRDD} -import org.apache.spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _} +import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function3 => JFunction3, _} import java.util import org.apache.spark.rdd.RDD import JavaDStream._ @@ -120,10 +121,12 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T * this DStream. Applying glom() to an RDD coalesces all elements within each partition into * an array. */ - def glom(): JavaDStream[JList[T]] = + def glom(): JavaDStream[JList[T]] = { new JavaDStream(dstream.glom().map(x => new java.util.ArrayList[T](x.toSeq))) + } + - /** Return the StreamingContext associated with this DStream */ + /** Return the [[org.apache.spark.streaming.StreamingContext]] associated with this DStream */ def context(): StreamingContext = dstream.context() /** Return a new DStream by applying a function to all elements of this DStream. */ @@ -238,7 +241,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: JFunction[R, Void]) { dstream.foreach(rdd => foreachFunc.call(wrapRDD(rdd))) @@ -246,7 +249,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: JFunction2[R, Time, Void]) { dstream.foreach((rdd, time) => foreachFunc.call(wrapRDD(rdd), time)) @@ -254,7 +257,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U](transformFunc: JFunction[R, JavaRDD[U]]): JavaDStream[U] = { implicit val cm: ClassManifest[U] = @@ -266,7 +269,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U](transformFunc: JFunction2[R, Time, JavaRDD[U]]): JavaDStream[U] = { implicit val cm: ClassManifest[U] = @@ -278,7 +281,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[K2, V2](transformFunc: JFunction[R, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2] = { @@ -293,7 +296,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[K2, V2](transformFunc: JFunction2[R, Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2] = { @@ -307,6 +310,82 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T } /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U, W]( + other: JavaDStream[U], + transformFunc: JFunction3[R, JavaRDD[U], Time, JavaRDD[W]] + ): JavaDStream[W] = { + implicit val cmu: ClassManifest[U] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cmv: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[W] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[U, W](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U, K2, V2]( + other: JavaDStream[U], + transformFunc: JFunction3[R, JavaRDD[U], Time, JavaPairRDD[K2, V2]] + ): JavaPairDStream[K2, V2] = { + implicit val cmu: ClassManifest[U] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cmk2: ClassManifest[K2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K2]] + implicit val cmv2: ClassManifest[V2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V2]] + def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[(K2, V2)] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[U, (K2, V2)](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[K2, V2, W]( + other: JavaPairDStream[K2, V2], + transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaRDD[W]] + ): JavaDStream[W] = { + implicit val cmk2: ClassManifest[K2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K2]] + implicit val cmv2: ClassManifest[V2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V2]] + implicit val cmw: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[(K2, V2), W](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[K2, V2, K3, V3]( + other: JavaPairDStream[K2, V2], + transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]] + ): JavaPairDStream[K3, V3] = { + implicit val cmk2: ClassManifest[K2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K2]] + implicit val cmv2: ClassManifest[V2] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V2]] + implicit val cmk3: ClassManifest[K3] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K3]] + implicit val cmv3: ClassManifest[V3] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V3]] + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[(K3, V3)] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[(K2, V2), (K3, V3)](other.dstream, scalaTransform(_, _, _)) + } + + /** * Enable periodic checkpointing of RDDs of this DStream * @param interval Time interval after which generated RDD will be checkpointed */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index faf8f36182..c6cd635afa 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -24,7 +24,7 @@ import scala.collection.JavaConversions._ import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2, Function3 => JFunction3} import org.apache.spark.Partitioner import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} @@ -36,7 +36,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.rdd.PairRDDFunctions class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( - implicit val kManifiest: ClassManifest[K], + implicit val kManifest: ClassManifest[K], implicit val vManifest: ClassManifest[V]) extends JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]] { @@ -154,7 +154,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( /** * Combine elements of each key in DStream's RDDs using custom function. This is similar to the - * combineByKey for RDDs. Please refer to combineByKey in [[PairRDDFunctions]] for more + * combineByKey for RDDs. Please refer to combineByKey in [[org.apache.spark.PairRDDFunctions]] for more * information. */ def combineByKey[C](createCombiner: JFunction[V, C], @@ -419,7 +419,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of each key. * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. * @param updateFunc State update function. If `this` function returns None, then @@ -434,7 +434,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of each key. * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. * @param updateFunc State update function. If `this` function returns None, then @@ -442,15 +442,17 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param numPartitions Number of partitions of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S]( updateFunc: JFunction2[JList[V], Optional[S], Optional[S]], numPartitions: Int) : JavaPairDStream[K, S] = { + implicit val cm: ClassManifest[S] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[S]] dstream.updateStateByKey(convertUpdateStateFunction(updateFunc), numPartitions) } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of the key. * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. * @param updateFunc State update function. If `this` function returns None, then @@ -458,19 +460,30 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param partitioner Partitioner for controlling the partitioning of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S]( updateFunc: JFunction2[JList[V], Optional[S], Optional[S]], partitioner: Partitioner ): JavaPairDStream[K, S] = { + implicit val cm: ClassManifest[S] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[S]] dstream.updateStateByKey(convertUpdateStateFunction(updateFunc), partitioner) } + + /** + * Return a new DStream by applying a map function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def mapValues[U](f: JFunction[V, U]): JavaPairDStream[K, U] = { implicit val cm: ClassManifest[U] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] dstream.mapValues(f) } + /** + * Return a new DStream by applying a flatmap function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairDStream[K, U] = { import scala.collection.JavaConverters._ def fn = (x: V) => f.apply(x).asScala @@ -480,9 +493,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. HashPartitioner is used to partition each generated RDD into default number + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JList[V], JList[W])] = { @@ -492,21 +504,36 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. Partitioner is used to partition each generated RDD. + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def cogroup[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (JList[V], JList[W])] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + dstream.cogroup(other.dstream, numPartitions) + .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + } + + /** + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def cogroup[W](other: JavaPairDStream[K, W], partitioner: Partitioner) - : JavaPairDStream[K, (JList[V], JList[W])] = { + def cogroup[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (JList[V], JList[W])] = { implicit val cm: ClassManifest[W] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] dstream.cogroup(other.dstream, partitioner) - .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) } /** - * Join `this` DStream with `other` DStream. HashPartitioner is used - * to partition each generated RDD into default number of partitions. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. */ def join[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, W)] = { implicit val cm: ClassManifest[W] = @@ -515,18 +542,112 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Join `this` DStream with `other` DStream, that is, each RDD of the new DStream will - * be generated by joining RDDs from `this` and other DStream. Uses the given - * Partitioner to partition each generated RDD. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def join[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, W)] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + dstream.join(other.dstream, numPartitions) + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. */ - def join[W](other: JavaPairDStream[K, W], partitioner: Partitioner) - : JavaPairDStream[K, (V, W)] = { + def join[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (V, W)] = { implicit val cm: ClassManifest[W] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] dstream.join(other.dstream, partitioner) } /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def leftOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.leftOuterJoin(other.dstream) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def leftOuterJoin[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.leftOuterJoin(other.dstream, numPartitions) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. + */ + def leftOuterJoin[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.leftOuterJoin(other.dstream, partitioner) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def rightOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.rightOuterJoin(other.dstream) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def rightOuterJoin[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.rightOuterJoin(other.dstream, numPartitions) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def rightOuterJoin[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassManifest[W] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + val joinResult = dstream.rightOuterJoin(other.dstream, partitioner) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ @@ -596,14 +717,19 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( dstream.saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass, conf) } + /** Convert to a JavaDStream */ + def toJavaDStream(): JavaDStream[(K, V)] = { + new JavaDStream[(K, V)](dstream) + } + override val classManifest: ClassManifest[(K, V)] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]] } object JavaPairDStream { - implicit def fromPairDStream[K: ClassManifest, V: ClassManifest](dstream: DStream[(K, V)]) - :JavaPairDStream[K, V] = + implicit def fromPairDStream[K: ClassManifest, V: ClassManifest](dstream: DStream[(K, V)]) = { new JavaPairDStream[K, V](dstream) + } def fromJavaDStream[K, V](dstream: JavaDStream[(K, V)]): JavaPairDStream[K, V] = { implicit val cmk: ClassManifest[K] = diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 6423b916b0..cf30b541e1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -19,7 +19,7 @@ package org.apache.spark.streaming.api.java import java.lang.{Long => JLong, Integer => JInt} import java.io.InputStream -import java.util.{Map => JMap} +import java.util.{Map => JMap, List => JList} import scala.collection.JavaConversions._ @@ -33,7 +33,7 @@ import twitter4j.auth.Authorization import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} -import org.apache.spark.api.java.{JavaSparkContext, JavaRDD} +import org.apache.spark.api.java.{JavaPairRDD, JavaRDDLike, JavaSparkContext, JavaRDD} import org.apache.spark.streaming._ import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.receivers.{ActorReceiver, ReceiverSupervisorStrategy} @@ -594,6 +594,77 @@ class JavaStreamingContext(val ssc: StreamingContext) { } /** + * Create a unified DStream from multiple DStreams of the same type and same slide duration. + */ + def union[T](first: JavaDStream[T], rest: JList[JavaDStream[T]]): JavaDStream[T] = { + val dstreams: Seq[DStream[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream) + implicit val cm: ClassManifest[T] = first.classManifest + ssc.union(dstreams)(cm) + } + + /** + * Create a unified DStream from multiple DStreams of the same type and same slide duration. + */ + def union[K, V]( + first: JavaPairDStream[K, V], + rest: JList[JavaPairDStream[K, V]] + ): JavaPairDStream[K, V] = { + val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream) + implicit val cm: ClassManifest[(K, V)] = first.classManifest + implicit val kcm: ClassManifest[K] = first.kManifest + implicit val vcm: ClassManifest[V] = first.vManifest + new JavaPairDStream[K, V](ssc.union(dstreams)(cm))(kcm, vcm) + } + + /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. The order of the JavaRDDs in the transform function parameter will be the + * same as the order of corresponding DStreams in the list. Note that for adding a + * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using + * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream(). + * In the transform function, convert the JavaRDD corresponding to that JavaDStream to + * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD(). + */ + def transform[T]( + dstreams: JList[JavaDStream[_]], + transformFunc: JFunction2[JList[JavaRDD[_]], Time, JavaRDD[T]] + ): JavaDStream[T] = { + implicit val cmt: ClassManifest[T] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + val scalaDStreams = dstreams.map(_.dstream).toSeq + val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList + transformFunc.call(jrdds, time).rdd + } + ssc.transform(scalaDStreams, scalaTransformFunc) + } + + /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. The order of the JavaRDDs in the transform function parameter will be the + * same as the order of corresponding DStreams in the list. Note that for adding a + * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using + * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream(). + * In the transform function, convert the JavaRDD corresponding to that JavaDStream to + * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD(). + */ + def transform[K, V]( + dstreams: JList[JavaDStream[_]], + transformFunc: JFunction2[JList[JavaRDD[_]], Time, JavaPairRDD[K, V]] + ): JavaPairDStream[K, V] = { + implicit val cmk: ClassManifest[K] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] + implicit val cmv: ClassManifest[V] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] + val scalaDStreams = dstreams.map(_.dstream).toSeq + val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList + transformFunc.call(jrdds, time).rdd + } + ssc.transform(scalaDStreams, scalaTransformFunc) + } + + /** * Sets the context to periodically checkpoint the DStream operations for master * fault-tolerance. The graph will be checkpointed every batch interval. * @param directory HDFS-compatible directory where the checkpoint data will be reliably stored diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala deleted file mode 100644 index 4eddc755b9..0000000000 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming.dstream - -import org.apache.spark.Partitioner -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.CoGroupedRDD -import org.apache.spark.streaming.{Time, DStream, Duration} - -private[streaming] -class CoGroupedDStream[K : ClassManifest]( - parents: Seq[DStream[(K, _)]], - partitioner: Partitioner - ) extends DStream[(K, Seq[Seq[_]])](parents.head.ssc) { - - if (parents.length == 0) { - throw new IllegalArgumentException("Empty array of parents") - } - - if (parents.map(_.ssc).distinct.size > 1) { - throw new IllegalArgumentException("Array of parents have different StreamingContexts") - } - - if (parents.map(_.slideDuration).distinct.size > 1) { - throw new IllegalArgumentException("Array of parents have different slide times") - } - - override def dependencies = parents.toList - - override def slideDuration: Duration = parents.head.slideDuration - - override def compute(validTime: Time): Option[RDD[(K, Seq[Seq[_]])]] = { - val part = partitioner - val rdds = parents.flatMap(_.getOrCompute(validTime)) - if (rdds.size > 0) { - val q = new CoGroupedRDD[K](rdds, part) - Some(q) - } else { - None - } - } - -} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala index 60485adef9..71bcb2b390 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala @@ -21,16 +21,22 @@ import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Duration, DStream, Time} private[streaming] -class TransformedDStream[T: ClassManifest, U: ClassManifest] ( - parent: DStream[T], - transformFunc: (RDD[T], Time) => RDD[U] - ) extends DStream[U](parent.ssc) { +class TransformedDStream[U: ClassManifest] ( + parents: Seq[DStream[_]], + transformFunc: (Seq[RDD[_]], Time) => RDD[U] + ) extends DStream[U](parents.head.ssc) { - override def dependencies = List(parent) + require(parents.length > 0, "List of DStreams to transform is empty") + require(parents.map(_.ssc).distinct.size == 1, "Some of the DStreams have different contexts") + require(parents.map(_.slideDuration).distinct.size == 1, + "Some of the DStreams have different slide durations") - override def slideDuration: Duration = parent.slideDuration + override def dependencies = parents.toList + + override def slideDuration: Duration = parents.head.slideDuration override def compute(validTime: Time): Option[RDD[U]] = { - parent.getOrCompute(validTime).map(transformFunc(_, validTime)) + val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq + Some(transformFunc(parentRDDs, validTime)) } } diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index 9da8adda83..ad4a8b9535 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -23,6 +23,7 @@ import com.google.common.collect.Maps; import com.google.common.io.Files; import kafka.serializer.StringDecoder; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; +import org.apache.spark.streaming.api.java.JavaDStreamLike; import org.junit.After; import org.junit.Assert; import org.junit.Before; @@ -256,7 +257,7 @@ public class JavaAPISuite implements Serializable { } }); JavaTestUtils.attachTestOutputStream(mapped); - List<List<List<String>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<String>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } @@ -325,8 +326,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(7,8,9)); JavaSparkContext jsc = new JavaSparkContext(ssc.ssc().sc()); - JavaRDD<Integer> rdd1 = ssc.sc().parallelize(Arrays.asList(1,2,3)); - JavaRDD<Integer> rdd2 = ssc.sc().parallelize(Arrays.asList(4,5,6)); + JavaRDD<Integer> rdd1 = ssc.sc().parallelize(Arrays.asList(1, 2, 3)); + JavaRDD<Integer> rdd2 = ssc.sc().parallelize(Arrays.asList(4, 5, 6)); JavaRDD<Integer> rdd3 = ssc.sc().parallelize(Arrays.asList(7,8,9)); LinkedList<JavaRDD<Integer>> rdds = Lists.newLinkedList(); @@ -353,17 +354,19 @@ public class JavaAPISuite implements Serializable { Arrays.asList(9,10,11)); JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream<Integer> transformed = - stream.transform(new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { - @Override - public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { - return in.map(new Function<Integer, Integer>() { - @Override - public Integer call(Integer i) throws Exception { - return i + 2; - } - }); - }}); + JavaDStream<Integer> transformed = stream.transform( + new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { + @Override + public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { + return in.map(new Function<Integer, Integer>() { + @Override + public Integer call(Integer i) throws Exception { + return i + 2; + } + }); + } + }); + JavaTestUtils.attachTestOutputStream(transformed); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -371,6 +374,316 @@ public class JavaAPISuite implements Serializable { } @Test + public void testVariousTransform() { + // tests whether all variations of transform can be called from Java + + List<List<Integer>> inputData = Arrays.asList(Arrays.asList(1)); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + + List<List<Tuple2<String, Integer>>> pairInputData = + Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1))); + JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData, 1)); + + JavaDStream<Integer> transformed1 = stream.transform( + new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { + @Override + public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> transformed2 = stream.transform( + new Function2<JavaRDD<Integer>, Time, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, Integer> transformed3 = stream.transform( + new Function<JavaRDD<Integer>, JavaPairRDD<String, Integer>>() { + @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, Integer> transformed4 = stream.transform( + new Function2<JavaRDD<Integer>, Time, JavaPairRDD<String, Integer>>() { + @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> pairTransformed1 = pairStream.transform( + new Function<JavaPairRDD<String, Integer>, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> pairTransformed2 = pairStream.transform( + new Function2<JavaPairRDD<String, Integer>, Time, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, String> pairTransformed3 = pairStream.transform( + new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, String>>() { + @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, String> pairTransformed4 = pairStream.transform( + new Function2<JavaPairRDD<String, Integer>, Time, JavaPairRDD<String, String>>() { + @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) throws Exception { + return null; + } + } + ); + + } + + @Test + public void testTransformWith() { + List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList( + Arrays.asList( + new Tuple2<String, String>("california", "dodgers"), + new Tuple2<String, String>("new york", "yankees")), + Arrays.asList( + new Tuple2<String, String>("california", "sharks"), + new Tuple2<String, String>("new york", "rangers"))); + + List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList( + Arrays.asList( + new Tuple2<String, String>("california", "giants"), + new Tuple2<String, String>("new york", "mets")), + Arrays.asList( + new Tuple2<String, String>("california", "ducks"), + new Tuple2<String, String>("new york", "islanders"))); + + + List<List<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList( + Arrays.asList( + new Tuple2<String, Tuple2<String, String>>("california", + new Tuple2<String, String>("dodgers", "giants")), + new Tuple2<String, Tuple2<String, String>>("new york", + new Tuple2<String, String>("yankees", "mets"))), + Arrays.asList( + new Tuple2<String, Tuple2<String, String>>("california", + new Tuple2<String, String>("sharks", "ducks")), + new Tuple2<String, Tuple2<String, String>>("new york", + new Tuple2<String, String>("rangers", "islanders")))); + + JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream1, 1); + JavaPairDStream<String, String> pairStream1 = JavaPairDStream.fromJavaDStream(stream1); + + JavaDStream<Tuple2<String, String>> stream2 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream2, 1); + JavaPairDStream<String, String> pairStream2 = JavaPairDStream.fromJavaDStream(stream2); + + JavaPairDStream<String, Tuple2<String, String>> joined = pairStream1.transformWith( + pairStream2, + new Function3< + JavaPairRDD<String, String>, + JavaPairRDD<String, String>, + Time, + JavaPairRDD<String, Tuple2<String, String>> + >() { + @Override + public JavaPairRDD<String, Tuple2<String, String>> call( + JavaPairRDD<String, String> rdd1, + JavaPairRDD<String, String> rdd2, + Time time + ) throws Exception { + return rdd1.join(rdd2); + } + } + ); + + JavaTestUtils.attachTestOutputStream(joined); + List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected, result); + } + + + @Test + public void testVariousTransformWith() { + // tests whether all variations of transformWith can be called from Java + + List<List<Integer>> inputData1 = Arrays.asList(Arrays.asList(1)); + List<List<String>> inputData2 = Arrays.asList(Arrays.asList("x")); + JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, inputData1, 1); + JavaDStream<String> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 1); + + List<List<Tuple2<String, Integer>>> pairInputData1 = + Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1))); + List<List<Tuple2<Double, Character>>> pairInputData2 = + Arrays.asList(Arrays.asList(new Tuple2<Double, Character>(1.0, 'x'))); + JavaPairDStream<String, Integer> pairStream1 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData1, 1)); + JavaPairDStream<Double, Character> pairStream2 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData2, 1)); + + JavaDStream<Double> transformed1 = stream1.transformWith( + stream2, + new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> transformed2 = stream1.transformWith( + pairStream1, + new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> transformed3 = stream1.transformWith( + stream2, + new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> transformed4 = stream1.transformWith( + pairStream1, + new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> pairTransformed1 = pairStream1.transformWith( + stream2, + new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> pairTransformed2_ = pairStream1.transformWith( + pairStream1, + new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> pairTransformed3 = pairStream1.transformWith( + stream2, + new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> pairTransformed4 = pairStream1.transformWith( + pairStream2, + new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<Double, Character>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) throws Exception { + return null; + } + } + ); + } + + @Test + public void testStreamingContextTransform(){ + List<List<Integer>> stream1input = Arrays.asList( + Arrays.asList(1), + Arrays.asList(2) + ); + + List<List<Integer>> stream2input = Arrays.asList( + Arrays.asList(3), + Arrays.asList(4) + ); + + List<List<Tuple2<Integer, String>>> pairStream1input = Arrays.asList( + Arrays.asList(new Tuple2<Integer, String>(1, "x")), + Arrays.asList(new Tuple2<Integer, String>(2, "y")) + ); + + List<List<Tuple2<Integer, Tuple2<Integer, String>>>> expected = Arrays.asList( + Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(1, new Tuple2<Integer, String>(1, "x"))), + Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(2, new Tuple2<Integer, String>(2, "y"))) + ); + + JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, stream1input, 1); + JavaDStream<Integer> stream2 = JavaTestUtils.attachTestInputStream(ssc, stream2input, 1); + JavaPairDStream<Integer, String> pairStream1 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairStream1input, 1)); + + List<JavaDStream<?>> listOfDStreams1 = Arrays.<JavaDStream<?>>asList(stream1, stream2); + + // This is just to test whether this transform to JavaStream compiles + JavaDStream<Long> transformed1 = ssc.transform( + listOfDStreams1, + new Function2<List<JavaRDD<?>>, Time, JavaRDD<Long>>() { + public JavaRDD<Long> call(List<JavaRDD<?>> listOfRDDs, Time time) { + assert(listOfRDDs.size() == 2); + return null; + } + } + ); + + List<JavaDStream<?>> listOfDStreams2 = + Arrays.<JavaDStream<?>>asList(stream1, stream2, pairStream1.toJavaDStream()); + + JavaPairDStream<Integer, Tuple2<Integer, String>> transformed2 = ssc.transform( + listOfDStreams2, + new Function2<List<JavaRDD<?>>, Time, JavaPairRDD<Integer, Tuple2<Integer, String>>>() { + public JavaPairRDD<Integer, Tuple2<Integer, String>> call(List<JavaRDD<?>> listOfRDDs, Time time) { + assert(listOfRDDs.size() == 3); + JavaRDD<Integer> rdd1 = (JavaRDD<Integer>)listOfRDDs.get(0); + JavaRDD<Integer> rdd2 = (JavaRDD<Integer>)listOfRDDs.get(1); + JavaRDD<Tuple2<Integer, String>> rdd3 = (JavaRDD<Tuple2<Integer, String>>)listOfRDDs.get(2); + JavaPairRDD<Integer, String> prdd3 = JavaPairRDD.fromJavaRDD(rdd3); + PairFunction<Integer, Integer, Integer> mapToTuple = new PairFunction<Integer, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Integer i) throws Exception { + return new Tuple2<Integer, Integer>(i, i); + } + }; + return rdd1.union(rdd2).map(mapToTuple).join(prdd3); + } + } + ); + JavaTestUtils.attachTestOutputStream(transformed2); + List<List<Tuple2<Integer, Tuple2<Integer, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + Assert.assertEquals(expected, result); + } + + @Test public void testFlatMap() { List<List<String>> inputData = Arrays.asList( Arrays.asList("go", "giants"), @@ -1132,7 +1445,7 @@ public class JavaAPISuite implements Serializable { JavaPairDStream<String, Tuple2<List<String>, List<String>>> grouped = pairStream1.cogroup(pairStream2); JavaTestUtils.attachTestOutputStream(grouped); - List<List<Tuple2<String, String>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<Tuple2<String, Tuple2<List<String>, List<String>>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } @@ -1175,7 +1488,38 @@ public class JavaAPISuite implements Serializable { JavaPairDStream<String, Tuple2<String, String>> joined = pairStream1.join(pairStream2); JavaTestUtils.attachTestOutputStream(joined); - List<List<Tuple2<String, Long>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected, result); + } + + @Test + public void testLeftOuterJoin() { + List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList( + Arrays.asList(new Tuple2<String, String>("california", "dodgers"), + new Tuple2<String, String>("new york", "yankees")), + Arrays.asList(new Tuple2<String, String>("california", "sharks") )); + + List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList( + Arrays.asList(new Tuple2<String, String>("california", "giants") ), + Arrays.asList(new Tuple2<String, String>("new york", "islanders") ) + + ); + + List<List<Long>> expected = Arrays.asList(Arrays.asList(2L), Arrays.asList(1L)); + + JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream1, 1); + JavaPairDStream<String, String> pairStream1 = JavaPairDStream.fromJavaDStream(stream1); + + JavaDStream<Tuple2<String, String>> stream2 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream2, 1); + JavaPairDStream<String, String> pairStream2 = JavaPairDStream.fromJavaDStream(stream2); + + JavaPairDStream<String, Tuple2<String, Optional<String>>> joined = pairStream1.leftOuterJoin(pairStream2); + JavaDStream<Long> counted = joined.count(); + JavaTestUtils.attachTestOutputStream(counted); + List<List<Long>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index 55cfcb371a..259ef1608c 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -18,7 +18,10 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ -import scala.runtime.RichInt + +import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext._ + import util.ManualClock class BasicOperationsSuite extends TestSuiteBase { @@ -181,6 +184,72 @@ class BasicOperationsSuite extends TestSuiteBase { ) } + test("union") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 8, 101 to 108, 201 to 208) + testOperation( + input, + (s: DStream[Int]) => s.union(s.map(_ + 4)) , + output + ) + } + + test("StreamingContext.union") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 12, 101 to 112, 201 to 212) + // union over 3 DStreams + testOperation( + input, + (s: DStream[Int]) => s.context.union(Seq(s, s.map(_ + 4), s.map(_ + 8))), + output + ) + } + + test("transform") { + val input = Seq(1 to 4, 5 to 8, 9 to 12) + testOperation( + input, + (r: DStream[Int]) => r.transform(rdd => rdd.map(_.toString)), // RDD.map in transform + input.map(_.map(_.toString)) + ) + } + + test("transformWith") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (1, "x")), ("b", (1, "x")) ), + Seq( ("", (1, "x")) ), + Seq( ), + Seq( ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + val t1 = s1.map(x => (x, 1)) + val t2 = s2.map(x => (x, "x")) + t1.transformWith( // RDD.join in transform + t2, + (rdd1: RDD[(String, Int)], rdd2: RDD[(String, String)]) => rdd1.join(rdd2) + ) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("StreamingContext.transform") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 12, 101 to 112, 201 to 212) + + // transform over 3 DStreams by doing union of the 3 RDDs + val operation = (s: DStream[Int]) => { + s.context.transform( + Seq(s, s.map(_ + 4), s.map(_ + 8)), // 3 DStreams + (rdds: Seq[RDD[_]], time: Time) => + rdds.head.context.union(rdds.map(_.asInstanceOf[RDD[Int]])) // union of RDDs + ) + } + + testOperation(input, operation, output) + } + test("cogroup") { val inputData1 = Seq( Seq("a", "a", "b"), Seq("a", ""), Seq(""), Seq() ) val inputData2 = Seq( Seq("a", "a", "b"), Seq("b", ""), Seq(), Seq() ) @@ -206,7 +275,37 @@ class BasicOperationsSuite extends TestSuiteBase { Seq( ) ) val operation = (s1: DStream[String], s2: DStream[String]) => { - s1.map(x => (x,1)).join(s2.map(x => (x,"x"))) + s1.map(x => (x, 1)).join(s2.map(x => (x, "x"))) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("leftOuterJoin") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (1, Some("x"))), ("b", (1, Some("x"))) ), + Seq( ("", (1, Some("x"))), ("a", (1, None)) ), + Seq( ("", (1, None)) ), + Seq( ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + s1.map(x => (x, 1)).leftOuterJoin(s2.map(x => (x, "x"))) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("rightOuterJoin") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (Some(1), "x")), ("b", (Some(1), "x")) ), + Seq( ("", (Some(1), "x")), ("b", (None, "x")) ), + Seq( ), + Seq( ("", (None, "x")) ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + s1.map(x => (x, 1)).rightOuterJoin(s2.map(x => (x, "x"))) } testOperation(inputData1, inputData2, operation, outputData, true) } |