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Generalized PeriodicGraphCheckpointer to PeriodicCheckpointer, with s…
…ubclasses for RDDs and Graphs.
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mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicCheckpointer.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.mllib.impl | ||
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| import scala.collection.mutable | ||
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| import org.apache.hadoop.fs.{Path, FileSystem} | ||
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| import org.apache.spark.{SparkContext, Logging} | ||
| import org.apache.spark.storage.StorageLevel | ||
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| /** | ||
| * This abstraction helps with persisting and checkpointing RDDs and types derived from RDDs | ||
| * (such as Graphs and DataFrames). In documentation, we use the phrase "Dataset" to refer to | ||
| * the distributed data type (RDD, Graph, etc.). | ||
| * | ||
| * Specifically, this abstraction automatically handles persisting and (optionally) checkpointing, | ||
| * as well as unpersisting and removing checkpoint files. | ||
| * | ||
| * Users should call update() when a new Dataset has been created, | ||
| * before the Dataset has been materialized. After updating [[PeriodicCheckpointer]], users are | ||
| * responsible for materializing the Dataset to ensure that persisting and checkpointing actually | ||
| * occur. | ||
| * | ||
| * When update() is called, this does the following: | ||
| * - Persist new Dataset (if not yet persisted), and put in queue of persisted Datasets. | ||
| * - Unpersist Datasets from queue until there are at most 3 persisted Datasets. | ||
| * - If using checkpointing and the checkpoint interval has been reached, | ||
| * - Checkpoint the new Dataset, and put in a queue of checkpointed Datasets. | ||
| * - Remove older checkpoints. | ||
| * | ||
| * WARNINGS: | ||
| * - This class should NOT be copied (since copies may conflict on which Datasets should be | ||
| * checkpointed). | ||
| * - This class removes checkpoint files once later Datasets have been checkpointed. | ||
| * However, references to the older Datasets will still return isCheckpointed = true. | ||
| * | ||
| * Example usage: | ||
| * {{{ | ||
| * val (data1, data2, data3, ...) = ... | ||
| * val cp = new PeriodicCheckpointer(data1, dir, 2) | ||
| * data1.count(); | ||
| * // persisted: data1 | ||
| * cp.update(data2) | ||
| * data2.count(); | ||
| * // persisted: data1, data2 | ||
| * // checkpointed: data2 | ||
| * cp.update(data3) | ||
| * data3.count(); | ||
| * // persisted: data1, data2, data3 | ||
| * // checkpointed: data2 | ||
| * cp.update(data4) | ||
| * data4.count(); | ||
| * // persisted: data2, data3, data4 | ||
| * // checkpointed: data4 | ||
| * cp.update(data5) | ||
| * data5.count(); | ||
| * // persisted: data3, data4, data5 | ||
| * // checkpointed: data4 | ||
| * }}} | ||
| * | ||
| * @param currentData Initial Dataset | ||
| * @param checkpointInterval Datasets will be checkpointed at this interval | ||
| * @param sc SparkContext for the Datasets given to this checkpointer | ||
| * @tparam T Dataset type, such as RDD[Double] | ||
| */ | ||
| private[mllib] abstract class PeriodicCheckpointer[T]( | ||
| var currentData: T, | ||
| val checkpointInterval: Int, | ||
| val sc: SparkContext) extends Logging { | ||
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| /** FIFO queue of past checkpointed Datasets */ | ||
| private val checkpointQueue = mutable.Queue[T]() | ||
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| /** FIFO queue of past persisted Datasets */ | ||
| private val persistedQueue = mutable.Queue[T]() | ||
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| /** Number of times [[update()]] has been called */ | ||
| private var updateCount = 0 | ||
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| update(currentData) | ||
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| /** | ||
| * Update [[currentData]] with a new Dataset. Handle persistence and checkpointing as needed. | ||
| * Since this handles persistence and checkpointing, this should be called before the Dataset | ||
| * has been materialized. | ||
| * | ||
| * @param newData New Dataset created from previous Datasets in the lineage. | ||
| */ | ||
| def update(newData: T): Unit = { | ||
| persist(newData) | ||
| persistedQueue.enqueue(newData) | ||
| // We try to maintain 2 Datasets in persistedQueue to support the semantics of this class: | ||
| // Users should call [[update()]] when a new Dataset has been created, | ||
| // before the Dataset has been materialized. | ||
| while (persistedQueue.size > 3) { | ||
| val dataToUnpersist = persistedQueue.dequeue() | ||
| unpersist(dataToUnpersist) | ||
| } | ||
| updateCount += 1 | ||
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| // Handle checkpointing (after persisting) | ||
| if ((updateCount % checkpointInterval) == 0 && sc.getCheckpointDir.nonEmpty) { | ||
| // Add new checkpoint before removing old checkpoints. | ||
| checkpoint(newData) | ||
| checkpointQueue.enqueue(newData) | ||
| // Remove checkpoints before the latest one. | ||
| var canDelete = true | ||
| while (checkpointQueue.size > 1 && canDelete) { | ||
| // Delete the oldest checkpoint only if the next checkpoint exists. | ||
| if (isCheckpointed(checkpointQueue.get(1).get)) { | ||
| removeCheckpointFile() | ||
| } else { | ||
| canDelete = false | ||
| } | ||
| } | ||
| } | ||
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| currentData = newData | ||
| } | ||
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| /** Checkpoint the Dataset */ | ||
| def checkpoint(data: T): Unit | ||
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| /** Return true iff the Dataset is checkpointed */ | ||
| def isCheckpointed(data: T): Boolean | ||
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| /** | ||
| * Persist the Dataset. | ||
| * Note: This should handle checking the current [[StorageLevel]] of the Dataset. | ||
| */ | ||
| def persist(data: T): Unit | ||
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| /** Unpersist the Dataset */ | ||
| def unpersist(data: T): Unit | ||
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| /** Get list of checkpoint files for this given Dataset */ | ||
| def getCheckpointFiles(data: T): Iterable[String] | ||
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| /** | ||
| * Call this at the end to delete any remaining checkpoint files. | ||
| */ | ||
| def deleteAllCheckpoints(): Unit = { | ||
| while (checkpointQueue.nonEmpty) { | ||
| removeCheckpointFile() | ||
| } | ||
| } | ||
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| /** | ||
| * Dequeue the oldest checkpointed Dataset, and remove its checkpoint files. | ||
| * This prints a warning but does not fail if the files cannot be removed. | ||
| */ | ||
| private def removeCheckpointFile(): Unit = { | ||
| val old = checkpointQueue.dequeue() | ||
| // Since the old checkpoint is not deleted by Spark, we manually delete it. | ||
| val fs = FileSystem.get(sc.hadoopConfiguration) | ||
| getCheckpointFiles(old).foreach { checkpointFile => | ||
| try { | ||
| fs.delete(new Path(checkpointFile), true) | ||
| } catch { | ||
| case e: Exception => | ||
| logWarning("PeriodicCheckpointer could not remove old checkpoint file: " + | ||
| checkpointFile) | ||
| } | ||
| } | ||
| } | ||
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| } |
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