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author | Reynold Xin <reynoldx@gmail.com> | 2013-08-18 20:39:29 -0700 |
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committer | Reynold Xin <reynoldx@gmail.com> | 2013-08-18 20:39:29 -0700 |
commit | 2a7b99c08b29d3002183a8d7ed3acd14fbf5dc41 (patch) | |
tree | 81719e59174193fb00ffbf0411842574bf25394b /core | |
parent | 82bf4c0339808f51c9cdffa6a0a829cb5981d92d (diff) | |
download | spark-2a7b99c08b29d3002183a8d7ed3acd14fbf5dc41.tar.gz spark-2a7b99c08b29d3002183a8d7ed3acd14fbf5dc41.tar.bz2 spark-2a7b99c08b29d3002183a8d7ed3acd14fbf5dc41.zip |
Added the missing RDD files and cleaned up SparkContext.
Diffstat (limited to 'core')
4 files changed, 126 insertions, 12 deletions
diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index c049bd3fa9..5db1767146 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -20,19 +20,14 @@ package spark import java.io._ import java.net.URI import java.util.Properties -import java.util.concurrent.ConcurrentHashMap import java.util.concurrent.atomic.AtomicInteger -import scala.collection.JavaConversions._ import scala.collection.Map import scala.collection.generic.Growable -import scala.collection.mutable.HashMap -import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap import scala.util.DynamicVariable -import scala.collection.mutable.{ConcurrentMap, HashMap} - -import akka.actor.Actor._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path @@ -54,7 +49,6 @@ import org.apache.hadoop.mapred.TextInputFormat import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.{Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} -import org.apache.hadoop.security.UserGroupInformation import org.apache.mesos.MesosNativeLibrary @@ -63,15 +57,14 @@ import spark.partial.{ApproximateEvaluator, PartialResult} import spark.rdd.{CheckpointRDD, HadoopRDD, NewHadoopRDD, UnionRDD, ParallelCollectionRDD, OrderedRDDFunctions} import spark.scheduler.{DAGScheduler, DAGSchedulerSource, ResultTask, ShuffleMapTask, SparkListener, - SplitInfo, Stage, StageInfo, TaskScheduler, ActiveJob} + SplitInfo, Stage, StageInfo, TaskScheduler} import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, ClusterScheduler, Schedulable, SchedulingMode} import spark.scheduler.local.LocalScheduler import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} import spark.storage.{StorageStatus, StorageUtils, RDDInfo, BlockManagerSource} +import spark.ui.SparkUI import spark.util.{MetadataCleaner, TimeStampedHashMap} -import ui.{SparkUI} -import spark.metrics._ /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -887,7 +880,7 @@ object SparkContext { implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest]( rdd: RDD[(K, V)]) = - new OrderedRDDFunctions(rdd.asInstanceOf[RDD[Product2[K, V]]]) + new OrderedRDDFunctions(rdd) implicit def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]) = new DoubleRDDFunctions(rdd) diff --git a/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala b/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala new file mode 100644 index 0000000000..05fdfd82c1 --- /dev/null +++ b/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala @@ -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 spark.rdd + +import spark.{TaskContext, Partition, RDD} + + +private[spark] +class FlatMappedValuesRDD[K, V, U](prev: RDD[_ <: Product2[K, V]], f: V => TraversableOnce[U]) + extends RDD[(K, U)](prev) { + + override def getPartitions = firstParent[Product2[K, V]].partitions + + override val partitioner = firstParent[Product2[K, V]].partitioner + + override def compute(split: Partition, context: TaskContext) = { + firstParent[Product2[K, V]].iterator(split, context).flatMap { case (k, v) => + f(v).map(x => (k, x)) + } + } +} diff --git a/core/src/main/scala/spark/rdd/MappedValuesRDD.scala b/core/src/main/scala/spark/rdd/MappedValuesRDD.scala new file mode 100644 index 0000000000..21ae97daa9 --- /dev/null +++ b/core/src/main/scala/spark/rdd/MappedValuesRDD.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 spark.rdd + + +import spark.{TaskContext, Partition, RDD} + +private[spark] +class MappedValuesRDD[K, V, U](prev: RDD[_ <: Product2[K, V]], f: V => U) + extends RDD[(K, U)](prev) { + + override def getPartitions = firstParent[Product2[K, U]].partitions + + override val partitioner = firstParent[Product2[K, U]].partitioner + + override def compute(split: Partition, context: TaskContext): Iterator[(K, U)] = { + firstParent[Product2[K, V]].iterator(split, context).map { case(k ,v) => (k, f(v)) } + } +} diff --git a/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala new file mode 100644 index 0000000000..6328c6a4ac --- /dev/null +++ b/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala @@ -0,0 +1,51 @@ +/* + * 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 spark.rdd + +import spark.{RangePartitioner, Logging, RDD} + +/** + * Extra functions available on RDDs of (key, value) pairs where the key is sortable through + * an implicit conversion. Import `spark.SparkContext._` at the top of your program to use these + * functions. They will work with any key type that has a `scala.math.Ordered` implementation. + */ +class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest]( + self: RDD[_ <: Product2[K, V]]) + extends Logging with Serializable { + + /** + * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling + * `collect` or `save` on the resulting RDD will return or output an ordered list of records + * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in + * order of the keys). + */ + def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size) + : RDD[(K, V)] = + { + val part = new RangePartitioner(numPartitions, self.asInstanceOf[RDD[Product2[K,V]]], ascending) + val shuffled = new ShuffledRDD[K, V](self, part) + shuffled.mapPartitions(iter => { + val buf = iter.toArray + if (ascending) { + buf.sortWith((x, y) => x._1 < y._1).iterator + } else { + buf.sortWith((x, y) => x._1 > y._1).iterator + } + }, preservesPartitioning = true) + } +} |