diff options
| -rw-r--r-- | core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala | 46 | ||||
| -rw-r--r-- | core/src/main/scala/org/apache/spark/rdd/RDD.scala | 12 | ||||
| -rw-r--r-- | project/MimaExcludes.scala | 159 |
3 files changed, 115 insertions, 102 deletions
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 9d62d53fcb..29038b0359 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -125,7 +125,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) zeroBuffer.get(zeroArray) lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance() - def createZero() = cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray)) + val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray)) combineByKey[U]((v: V) => seqOp(createZero(), v), seqOp, combOp, partitioner) } @@ -171,7 +171,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) // When deserializing, use a lazy val to create just one instance of the serializer per task lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance() - def createZero() = cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray)) + val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray)) combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner) } @@ -214,22 +214,22 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) throw new SparkException("reduceByKeyLocally() does not support array keys") } - def reducePartition(iter: Iterator[(K, V)]): Iterator[JHashMap[K, V]] = { + val reducePartition = (iter: Iterator[(K, V)]) => { val map = new JHashMap[K, V] iter.foreach { case (k, v) => val old = map.get(k) map.put(k, if (old == null) v else func(old, v)) } Iterator(map) - } + } : Iterator[JHashMap[K, V]] - def mergeMaps(m1: JHashMap[K, V], m2: JHashMap[K, V]): JHashMap[K, V] = { + val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => { m2.foreach { case (k, v) => val old = m1.get(k) m1.put(k, if (old == null) v else func(old, v)) } m1 - } + } : JHashMap[K, V] self.mapPartitions(reducePartition).reduce(mergeMaps) } @@ -361,11 +361,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) // groupByKey shouldn't use map side combine because map side combine does not // reduce the amount of data shuffled and requires all map side data be inserted // into a hash table, leading to more objects in the old gen. - def createCombiner(v: V) = ArrayBuffer(v) - def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v - def mergeCombiners(c1: ArrayBuffer[V], c2: ArrayBuffer[V]) = c1 ++ c2 + val createCombiner = (v: V) => ArrayBuffer(v) + val mergeValue = (buf: ArrayBuffer[V], v: V) => buf += v + val mergeCombiners = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => c1 ++ c2 val bufs = combineByKey[ArrayBuffer[V]]( - createCombiner _, mergeValue _, mergeCombiners _, partitioner, mapSideCombine=false) + createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine=false) bufs.mapValues(_.toIterable) } @@ -710,14 +710,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) self.partitioner match { case Some(p) => val index = p.getPartition(key) - def process(it: Iterator[(K, V)]): Seq[V] = { + val process = (it: Iterator[(K, V)]) => { val buf = new ArrayBuffer[V] for ((k, v) <- it if k == key) { buf += v } buf - } - val res = self.context.runJob(self, process _, Array(index), false) + } : Seq[V] + val res = self.context.runJob(self, process, Array(index), false) res(0) case None => self.filter(_._1 == key).map(_._2).collect() @@ -840,7 +840,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) jobFormat.checkOutputSpecs(job) } - def writeShard(context: TaskContext, iter: Iterator[(K,V)]): Int = { + val writeShard = (context: TaskContext, iter: Iterator[(K,V)]) => { // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt @@ -861,19 +861,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val (k, v) = iter.next() writer.write(k, v) } - } - finally { + } finally { writer.close(hadoopContext) } committer.commitTask(hadoopContext) - return 1 - } + 1 + } : Int val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0) val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId) val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext) jobCommitter.setupJob(jobTaskContext) - self.context.runJob(self, writeShard _) + self.context.runJob(self, writeShard) jobCommitter.commitJob(jobTaskContext) } @@ -912,7 +911,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val writer = new SparkHadoopWriter(hadoopConf) writer.preSetup() - def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) { + val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => { // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt @@ -921,19 +920,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) writer.open() try { var count = 0 - while(iter.hasNext) { + while (iter.hasNext) { val record = iter.next() count += 1 writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef]) } - } - finally { + } finally { writer.close() } writer.commit() } - self.context.runJob(self, writeToFile _) + self.context.runJob(self, writeToFile) writer.commitJob() } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index a25f263bea..88a918aebf 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -328,7 +328,7 @@ abstract class RDD[T: ClassTag]( : RDD[T] = { if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ - def distributePartition(index: Int, items: Iterator[T]): Iterator[(Int, T)] = { + val distributePartition = (index: Int, items: Iterator[T]) => { var position = (new Random(index)).nextInt(numPartitions) items.map { t => // Note that the hash code of the key will just be the key itself. The HashPartitioner @@ -336,7 +336,7 @@ abstract class RDD[T: ClassTag]( position = position + 1 (position, t) } - } + } : Iterator[(Int, T)] // include a shuffle step so that our upstream tasks are still distributed new CoalescedRDD( @@ -919,19 +919,19 @@ abstract class RDD[T: ClassTag]( throw new SparkException("countByValue() does not support arrays") } // TODO: This should perhaps be distributed by default. - def countPartition(iter: Iterator[T]): Iterator[OpenHashMap[T,Long]] = { + val countPartition = (iter: Iterator[T]) => { val map = new OpenHashMap[T,Long] iter.foreach { t => map.changeValue(t, 1L, _ + 1L) } Iterator(map) - } - def mergeMaps(m1: OpenHashMap[T,Long], m2: OpenHashMap[T,Long]): OpenHashMap[T,Long] = { + }: Iterator[OpenHashMap[T,Long]] + val mergeMaps = (m1: OpenHashMap[T,Long], m2: OpenHashMap[T,Long]) => { m2.foreach { case (key, value) => m1.changeValue(key, value, _ + value) } m1 - } + }: OpenHashMap[T,Long] val myResult = mapPartitions(countPartition).reduce(mergeMaps) // Convert to a Scala mutable map val mutableResult = scala.collection.mutable.Map[T,Long]() diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index d67c6571a0..3487f7c5c1 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -31,76 +31,91 @@ import com.typesafe.tools.mima.core._ * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") */ object MimaExcludes { - def excludes(version: String) = - version match { - case v if v.startsWith("1.1") => - Seq( - MimaBuild.excludeSparkPackage("deploy"), - MimaBuild.excludeSparkPackage("graphx") - ) ++ - Seq( - // Adding new method to JavaRDLike trait - we should probably mark this as a developer API. - ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"), - // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values - // for countApproxDistinct* functions, which does not work in Java. We later removed - // them, and use the following to tell Mima to not care about them. - ProblemFilters.exclude[IncompatibleResultTypeProblem]( - "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"), - ProblemFilters.exclude[IncompatibleResultTypeProblem]( - "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.storage.MemoryStore.Entry"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$" - + "createZero$1") - ) ++ - Seq( - ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this") - ) ++ - Seq( // Ignore some private methods in ALS. - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"), - ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments. - "org.apache.spark.mllib.recommendation.ALS.this"), - ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7") - ) ++ - MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++ - MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++ - MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++ - MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++ - MimaBuild.excludeSparkClass("storage.Values") ++ - MimaBuild.excludeSparkClass("storage.Entry") ++ - MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") - case v if v.startsWith("1.0") => - Seq( - MimaBuild.excludeSparkPackage("api.java"), - MimaBuild.excludeSparkPackage("mllib"), - MimaBuild.excludeSparkPackage("streaming") - ) ++ - MimaBuild.excludeSparkClass("rdd.ClassTags") ++ - MimaBuild.excludeSparkClass("util.XORShiftRandom") ++ - MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++ - MimaBuild.excludeSparkClass("graphx.VertexRDD") ++ - MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++ - MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++ - MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++ - MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++ - MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++ - MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++ - MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++ - MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++ - MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD") - case _ => Seq() - } + + def excludes(version: String) = version match { + case v if v.startsWith("1.1") => + Seq( + MimaBuild.excludeSparkPackage("deploy"), + MimaBuild.excludeSparkPackage("graphx") + ) ++ + closures.map(method => ProblemFilters.exclude[MissingMethodProblem](method)) ++ + Seq( + // Adding new method to JavaRDLike trait - we should probably mark this as a developer API. + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"), + // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values + // for countApproxDistinct* functions, which does not work in Java. We later removed + // them, and use the following to tell Mima to not care about them. + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.storage.MemoryStore.Entry"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$" + + "createZero$1") + ) ++ + Seq( + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this") + ) ++ + Seq( // Ignore some private methods in ALS. + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"), + ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments. + "org.apache.spark.mllib.recommendation.ALS.this"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7") + ) ++ + MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++ + MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++ + MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++ + MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++ + MimaBuild.excludeSparkClass("storage.Values") ++ + MimaBuild.excludeSparkClass("storage.Entry") ++ + MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") + case v if v.startsWith("1.0") => + Seq( + MimaBuild.excludeSparkPackage("api.java"), + MimaBuild.excludeSparkPackage("mllib"), + MimaBuild.excludeSparkPackage("streaming") + ) ++ + MimaBuild.excludeSparkClass("rdd.ClassTags") ++ + MimaBuild.excludeSparkClass("util.XORShiftRandom") ++ + MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++ + MimaBuild.excludeSparkClass("graphx.VertexRDD") ++ + MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++ + MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++ + MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++ + MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++ + MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++ + MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++ + MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++ + MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++ + MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD") + case _ => Seq() + } + + private val closures = Seq( + "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$mergeMaps$1", + "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$countPartition$1", + "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$distributePartition$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeValue$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeToFile$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$reducePartition$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeShard$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeCombiners$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$process$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$createCombiner$1", + "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeMaps$1" + ) } |