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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.
*/
package org.apache.spark.streaming.scheduler
import java.util.concurrent.CountDownLatch
import scala.concurrent.duration._
import scala.language.postfixOps
import org.scalatest.concurrent.Eventually._
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming._
import org.apache.spark.util.{ManualClock, Utils}
class JobGeneratorSuite extends TestSuiteBase {
// 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")
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
val testTimeout = timeout(10 seconds)
val inputStream = ssc.receiverStream(new TestReceiver)
inputStream.foreachRDD((rdd: RDD[Int], time: Time) => {
if (time.milliseconds == longBatchTime) {
while (waitLatch.getCount() > 0) {
waitLatch.await()
}
}
})
val batchCounter = new BatchCounter(ssc)
ssc.checkpoint(checkpointDir.getAbsolutePath)
ssc.start()
// Make sure the only 1 batch of information is to be remembered
assert(inputStream.rememberDuration === batchDuration)
val receiverTracker = ssc.scheduler.receiverTracker
// Get the blocks belonging to a batch
def getBlocksOfBatch(batchTime: Long): Seq[ReceivedBlockInfo] = {
receiverTracker.getBlocksOfBatchAndStream(Time(batchTime), inputStream.id)
}
// Wait for new blocks to be received
def waitForNewReceivedBlocks() {
eventually(testTimeout) {
assert(receiverTracker.hasUnallocatedBlocks)
}
}
// Wait for received blocks to be allocated to a batch
def waitForBlocksToBeAllocatedToBatch(batchTime: Long) {
eventually(testTimeout) {
assert(getBlocksOfBatch(batchTime).nonEmpty)
}
}
// Generate a large number of batches with blocks in them
for (batchNum <- 1 to numBatches) {
waitForNewReceivedBlocks()
clock.advance(batchDuration.milliseconds)
waitForBlocksToBeAllocatedToBatch(clock.getTimeMillis())
}
// Wait for 3rd batch to start
eventually(testTimeout) {
ssc.scheduler.getPendingTimes().contains(Time(numBatches * batchDuration.milliseconds))
}
// 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()
ssc.stop()
}
}
}
object JobGeneratorSuite {
val waitLatch = new CountDownLatch(1)
}
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