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[SPARK-6222][Streaming] Dont delete checkpoint data when doing pre-ba…
…tch-start checkpoint This is another alternative approach to apache#4964 I think this is a simpler fix that can be backported easily to other branches (1.2 and 1.3). All it does it introduce a flag so that the pre-batch-start checkpoint does not call clear checkpoint. There is not unit test yet. I will add it when this approach is commented upon. Not sure if this is testable easily. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes apache#5008 from tdas/SPARK-6222 and squashes the following commits: 7315bc2 [Tathagata Das] Removed empty line. c438de4 [Tathagata Das] Revert unnecessary change. 5e98374 [Tathagata Das] Added unit test 50cb60b [Tathagata Das] Fixed style issue 295ca5c [Tathagata Das] Fixing SPARK-6222
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streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.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.streaming.scheduler | ||
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| import java.util.concurrent.CountDownLatch | ||
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| import scala.concurrent.duration._ | ||
| import scala.language.postfixOps | ||
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| import org.scalatest.concurrent.Eventually._ | ||
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| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.streaming._ | ||
| import org.apache.spark.util.{ManualClock, Utils} | ||
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| class JobGeneratorSuite extends TestSuiteBase { | ||
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| // SPARK-6222 is a tricky regression bug which causes received block metadata | ||
| // to be deleted before the corresponding batch has completed. This occurs when | ||
| // the following conditions are met. | ||
| // 1. streaming checkpointing is enabled by setting streamingContext.checkpoint(dir) | ||
| // 2. input data is received through a receiver as blocks | ||
| // 3. a batch processing a set of blocks takes a long time, such that a few subsequent | ||
| // batches have been generated and submitted for processing. | ||
| // | ||
| // The JobGenerator (as of Mar 16, 2015) checkpoints twice per batch, once after generation | ||
| // of a batch, and another time after the completion of a batch. The cleanup of | ||
| // checkpoint data (including block metadata, etc.) from DStream must be done only after the | ||
| // 2nd checkpoint has completed, that is, after the batch has been completely processed. | ||
| // However, the issue is that the checkpoint data and along with it received block data is | ||
| // cleaned even in the case of the 1st checkpoint, causing pre-mature deletion of received block | ||
| // data. For example, if the 3rd batch is still being process, the 7th batch may get generated, | ||
| // and the corresponding "1st checkpoint" will delete received block metadata of batch older | ||
| // than 6th batch. That, is 3rd batch's block metadata gets deleted even before 3rd batch has | ||
| // been completely processed. | ||
| // | ||
| // This test tries to create that scenario by the following. | ||
| // 1. enable checkpointing | ||
| // 2. generate batches with received blocks | ||
| // 3. make the 3rd batch never complete | ||
| // 4. allow subsequent batches to be generated (to allow premature deletion of 3rd batch metadata) | ||
| // 5. verify whether 3rd batch's block metadata still exists | ||
| // | ||
| test("SPARK-6222: Do not clear received block data too soon") { | ||
| import JobGeneratorSuite._ | ||
| val checkpointDir = Utils.createTempDir() | ||
| val testConf = conf | ||
| testConf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") | ||
| testConf.set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") | ||
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| withStreamingContext(new StreamingContext(testConf, batchDuration)) { ssc => | ||
| val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] | ||
| val numBatches = 10 | ||
| val longBatchNumber = 3 // 3rd batch will take a long time | ||
| val longBatchTime = longBatchNumber * batchDuration.milliseconds | ||
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| val testTimeout = timeout(10 seconds) | ||
| val inputStream = ssc.receiverStream(new TestReceiver) | ||
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| inputStream.foreachRDD((rdd: RDD[Int], time: Time) => { | ||
| if (time.milliseconds == longBatchTime) { | ||
| while (waitLatch.getCount() > 0) { | ||
| waitLatch.await() | ||
| println("Await over") | ||
| } | ||
| } | ||
| }) | ||
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| val batchCounter = new BatchCounter(ssc) | ||
| ssc.checkpoint(checkpointDir.getAbsolutePath) | ||
| ssc.start() | ||
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| // Make sure the only 1 batch of information is to be remembered | ||
| assert(inputStream.rememberDuration === batchDuration) | ||
| val receiverTracker = ssc.scheduler.receiverTracker | ||
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| // Get the blocks belonging to a batch | ||
| def getBlocksOfBatch(batchTime: Long) = { | ||
| receiverTracker.getBlocksOfBatchAndStream(Time(batchTime), inputStream.id) | ||
| } | ||
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| // Wait for new blocks to be received | ||
| def waitForNewReceivedBlocks() { | ||
| eventually(testTimeout) { | ||
| assert(receiverTracker.hasUnallocatedBlocks) | ||
| } | ||
| } | ||
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| // Wait for received blocks to be allocated to a batch | ||
| def waitForBlocksToBeAllocatedToBatch(batchTime: Long) { | ||
| eventually(testTimeout) { | ||
| assert(getBlocksOfBatch(batchTime).nonEmpty) | ||
| } | ||
| } | ||
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| // Generate a large number of batches with blocks in them | ||
| for (batchNum <- 1 to numBatches) { | ||
| waitForNewReceivedBlocks() | ||
| clock.advance(batchDuration.milliseconds) | ||
| waitForBlocksToBeAllocatedToBatch(clock.getTimeMillis()) | ||
| } | ||
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| // Wait for 3rd batch to start | ||
| eventually(testTimeout) { | ||
| ssc.scheduler.getPendingTimes().contains(Time(numBatches * batchDuration.milliseconds)) | ||
| } | ||
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| // Verify that the 3rd batch's block data is still present while the 3rd batch is incomplete | ||
| assert(getBlocksOfBatch(longBatchTime).nonEmpty, "blocks of incomplete batch already deleted") | ||
| assert(batchCounter.getNumCompletedBatches < longBatchNumber) | ||
| waitLatch.countDown() | ||
| } | ||
| } | ||
| } | ||
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| object JobGeneratorSuite { | ||
| val waitLatch = new CountDownLatch(1) | ||
| } |