[SPARK-23053][CORE] taskBinarySerialization and task partitions calcu…

…late in DagScheduler.submitMissingTasks should keep the same RDD checkpoint status

## What changes were proposed in this pull request?

When we run concurrent jobs using the same rdd which is marked to do checkpoint. If one job has finished running the job, and start the process of RDD.doCheckpoint, while another job is submitted, then submitStage and submitMissingTasks will be called. In [submitMissingTasks](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L961), will serialize taskBinaryBytes and calculate task partitions which are both affected by the status of checkpoint, if the former is calculated before doCheckpoint finished, while the latter is calculated after doCheckpoint finished, when run task, rdd.compute will be called, for some rdds with particular partition type such as [UnionRDD](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala) who will do partition type cast, will get a ClassCastException because the part params is actually a CheckpointRDDPartition.
This error occurs  because rdd.doCheckpoint occurs in the same thread that called sc.runJob, while the task serialization occurs in the DAGSchedulers event loop.

## How was this patch tested?

the exist uts and also add a test case in DAGScheduerSuite to show the exception case.

Author: huangtengfei <[email protected]>

Closes #20244 from ivoson/branch-taskpart-mistype.

core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala

@@ -39,7 +39,7 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.internal.config
 import org.apache.spark.network.util.JavaUtils
 import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{RDD, RDDCheckpointData}
 import org.apache.spark.rpc.RpcTimeout
 import org.apache.spark.storage._
 import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
@@ -1016,15 +1016,24 @@ class DAGScheduler(
     // might modify state of objects referenced in their closures. This is necessary in Hadoop
     // where the JobConf/Configuration object is not thread-safe.
     var taskBinary: Broadcast[Array[Byte]] = null
+    var partitions: Array[Partition] = null
     try {
       // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
       // For ResultTask, serialize and broadcast (rdd, func).
-      val taskBinaryBytes: Array[Byte] = stage match {
-        case stage: ShuffleMapStage =>
-          JavaUtils.bufferToArray(
-            closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
-        case stage: ResultStage =>
-          JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
+      var taskBinaryBytes: Array[Byte] = null
+      // taskBinaryBytes and partitions are both effected by the checkpoint status. We need
+      // this synchronization in case another concurrent job is checkpointing this RDD, so we get a
+      // consistent view of both variables.
+      RDDCheckpointData.synchronized {
+        taskBinaryBytes = stage match {
+          case stage: ShuffleMapStage =>
+            JavaUtils.bufferToArray(
+              closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
+          case stage: ResultStage =>
+            JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
+        }
+
+        partitions = stage.rdd.partitions
       }
 
       taskBinary = sc.broadcast(taskBinaryBytes)
@@ -1049,7 +1058,7 @@ class DAGScheduler(
           stage.pendingPartitions.clear()
           partitionsToCompute.map { id =>
             val locs = taskIdToLocations(id)
-            val part = stage.rdd.partitions(id)
+            val part = partitions(id)
             stage.pendingPartitions += id
             new ShuffleMapTask(stage.id, stage.latestInfo.attemptNumber,
               taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId),
@@ -1059,7 +1068,7 @@ class DAGScheduler(
         case stage: ResultStage =>
           partitionsToCompute.map { id =>
             val p: Int = stage.partitions(id)
-            val part = stage.rdd.partitions(p)
+            val part = partitions(p)
             val locs = taskIdToLocations(id)
             new ResultTask(stage.id, stage.latestInfo.attemptNumber,
               taskBinary, part, locs, id, properties, serializedTaskMetrics,