[SPARK-23243][CORE] Fix RDD.repartition() data correctness issue

## What changes were proposed in this pull request?

An alternative fix for apache#21698

When Spark rerun tasks for an RDD, there are 3 different behaviors:
1. determinate. Always return the same result with same order when rerun.
2. unordered. Returns same data set in random order when rerun.
3. indeterminate. Returns different result when rerun.

Normally Spark doesn't need to care about it. Spark runs stages one by one, when a task is failed, just rerun it. Although the rerun task may return a different result, users will not be surprised.

However, Spark may rerun a finished stage when seeing fetch failures. When this happens, Spark needs to rerun all the tasks of all the succeeding stages if the RDD output is indeterminate, because the input of the succeeding stages has been changed.

If the RDD output is determinate, we only need to rerun the failed tasks of the succeeding stages, because the input doesn't change.

If the RDD output is unordered, it's same as determinate, because shuffle partitioner is always deterministic(round-robin partitioner is not a shuffle partitioner that extends `org.apache.spark.Partitioner`), so the reducers will still get the same input data set.

This PR fixed the failure handling for `repartition`, to avoid correctness issues.

For `repartition`, it applies a stateful map function to generate a round-robin id, which is order sensitive and makes the RDD's output indeterminate. When the stage contains `repartition` reruns, we must also rerun all the tasks of all the succeeding stages.

**future improvement:**
1. Currently we can't rollback and rerun a shuffle map stage, and just fail. We should fix it later. https://issues.apache.org/jira/browse/SPARK-25341
2. Currently we can't rollback and rerun a result stage, and just fail. We should fix it later. https://issues.apache.org/jira/browse/SPARK-25342
3. We should provide public API to allow users to tag the random level of the RDD's computing function.

## How is this pull request tested?
a new test case

Closes apache#22112 from cloud-fan/repartition.

Lead-authored-by: Wenchen Fan <[email protected]>
Co-authored-by: Xingbo Jiang <[email protected]>
Signed-off-by: Xiao Li <[email protected]>

Author: 2 people

core/src/main/scala/org/apache/spark/Partitioner.scala

@@ -33,6 +33,9 @@ import org.apache.spark.util.random.SamplingUtils
 /**
  * An object that defines how the elements in a key-value pair RDD are partitioned by key.
  * Maps each key to a partition ID, from 0 to `numPartitions - 1`.
+ *
+ * Note that, partitioner must be deterministic, i.e. it must return the same partition id given
+ * the same partition key.
  */
 abstract class Partitioner extends Serializable {
   def numPartitions: Int

core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala

@@ -32,12 +32,16 @@ import org.apache.spark.{Partition, TaskContext}
  *                              doesn't modify the keys.
  * @param isFromBarrier Indicates whether this RDD is transformed from an RDDBarrier, a stage
  *                      containing at least one RDDBarrier shall be turned into a barrier stage.
+ * @param isOrderSensitive whether or not the function is order-sensitive. If it's order
+ *                         sensitive, it may return totally different result when the input order
+ *                         is changed. Mostly stateful functions are order-sensitive.
  */
 private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
     var prev: RDD[T],
     f: (TaskContext, Int, Iterator[T]) => Iterator[U],  // (TaskContext, partition index, iterator)
     preservesPartitioning: Boolean = false,
-    isFromBarrier: Boolean = false)
+    isFromBarrier: Boolean = false,
+    isOrderSensitive: Boolean = false)
   extends RDD[U](prev) {
 
   override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None
@@ -54,4 +58,12 @@ private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
 
   @transient protected lazy override val isBarrier_ : Boolean =
     isFromBarrier || dependencies.exists(_.rdd.isBarrier())
+
+  override protected def getOutputDeterministicLevel = {
+    if (isOrderSensitive && prev.outputDeterministicLevel == DeterministicLevel.UNORDERED) {
+      DeterministicLevel.INDETERMINATE
+    } else {
+      super.getOutputDeterministicLevel
+    }
+  }
 }

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -462,8 +462,9 @@ abstract class RDD[T: ClassTag](
 
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
-        new ShuffledRDD[Int, T, T](mapPartitionsWithIndex(distributePartition),
-        new HashPartitioner(numPartitions)),
+        new ShuffledRDD[Int, T, T](
+          mapPartitionsWithIndexInternal(distributePartition, isOrderSensitive = true),
+          new HashPartitioner(numPartitions)),
         numPartitions,
         partitionCoalescer).values
     } else {
@@ -807,16 +808,21 @@ abstract class RDD[T: ClassTag](
    * serializable and don't require closure cleaning.
    *
    * @param preservesPartitioning indicates whether the input function preserves the partitioner,
-   * which should be `false` unless this is a pair RDD and the input function doesn't modify
-   * the keys.
+   *                              which should be `false` unless this is a pair RDD and the input
+   *                              function doesn't modify the keys.
+   * @param isOrderSensitive whether or not the function is order-sensitive. If it's order
+   *                         sensitive, it may return totally different result when the input order
+   *                         is changed. Mostly stateful functions are order-sensitive.
    */
   private[spark] def mapPartitionsWithIndexInternal[U: ClassTag](
       f: (Int, Iterator[T]) => Iterator[U],
-      preservesPartitioning: Boolean = false): RDD[U] = withScope {
+      preservesPartitioning: Boolean = false,
+      isOrderSensitive: Boolean = false): RDD[U] = withScope {
     new MapPartitionsRDD(
       this,
       (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter),
-      preservesPartitioning)
+      preservesPartitioning = preservesPartitioning,
+      isOrderSensitive = isOrderSensitive)
   }
 
   /**
@@ -1636,6 +1642,16 @@ abstract class RDD[T: ClassTag](
     }
   }
 
+  /**
+   * Return whether this RDD is reliably checkpointed and materialized.
+   */
+  private[rdd] def isReliablyCheckpointed: Boolean = {
+    checkpointData match {
+      case Some(reliable: ReliableRDDCheckpointData[_]) if reliable.isCheckpointed => true
+      case _ => false
+    }
+  }
+
   /**
    * Gets the name of the directory to which this RDD was checkpointed.
    * This is not defined if the RDD is checkpointed locally.
@@ -1873,6 +1889,63 @@ abstract class RDD[T: ClassTag](
   // RDD chain.
   @transient protected lazy val isBarrier_ : Boolean =
     dependencies.filter(!_.isInstanceOf[ShuffleDependency[_, _, _]]).exists(_.rdd.isBarrier())
+
+  /**
+   * Returns the deterministic level of this RDD's output. Please refer to [[DeterministicLevel]]
+   * for the definition.
+   *
+   * By default, an reliably checkpointed RDD, or RDD without parents(root RDD) is DETERMINATE. For
+   * RDDs with parents, we will generate a deterministic level candidate per parent according to
+   * the dependency. The deterministic level of the current RDD is the deterministic level
+   * candidate that is deterministic least. Please override [[getOutputDeterministicLevel]] to
+   * provide custom logic of calculating output deterministic level.
+   */
+  // TODO: make it public so users can set deterministic level to their custom RDDs.
+  // TODO: this can be per-partition. e.g. UnionRDD can have different deterministic level for
+  // different partitions.
+  private[spark] final lazy val outputDeterministicLevel: DeterministicLevel.Value = {
+    if (isReliablyCheckpointed) {
+      DeterministicLevel.DETERMINATE
+    } else {
+      getOutputDeterministicLevel
+    }
+  }
+
+  @DeveloperApi
+  protected def getOutputDeterministicLevel: DeterministicLevel.Value = {
+    val deterministicLevelCandidates = dependencies.map {
+      // The shuffle is not really happening, treat it like narrow dependency and assume the output
+      // deterministic level of current RDD is same as parent.
+      case dep: ShuffleDependency[_, _, _] if dep.rdd.partitioner.exists(_ == dep.partitioner) =>
+        dep.rdd.outputDeterministicLevel
+
+      case dep: ShuffleDependency[_, _, _] =>
+        if (dep.rdd.outputDeterministicLevel == DeterministicLevel.INDETERMINATE) {
+          // If map output was indeterminate, shuffle output will be indeterminate as well
+          DeterministicLevel.INDETERMINATE
+        } else if (dep.keyOrdering.isDefined && dep.aggregator.isDefined) {
+          // if aggregator specified (and so unique keys) and key ordering specified - then
+          // consistent ordering.
+          DeterministicLevel.DETERMINATE
+        } else {
+          // In Spark, the reducer fetches multiple remote shuffle blocks at the same time, and
+          // the arrival order of these shuffle blocks are totally random. Even if the parent map
+          // RDD is DETERMINATE, the reduce RDD is always UNORDERED.
+          DeterministicLevel.UNORDERED
+        }
+
+      // For narrow dependency, assume the output deterministic level of current RDD is same as
+      // parent.
+      case dep => dep.rdd.outputDeterministicLevel
+    }
+
+    if (deterministicLevelCandidates.isEmpty) {
+      // By default we assume the root RDD is determinate.
+      DeterministicLevel.DETERMINATE
+    } else {
+      deterministicLevelCandidates.maxBy(_.id)
+    }
+  }
 }
 
 
@@ -1926,3 +1999,18 @@ object RDD {
     new DoubleRDDFunctions(rdd.map(x => num.toDouble(x)))
   }
 }
+
+/**
+ * The deterministic level of RDD's output (i.e. what `RDD#compute` returns). This explains how
+ * the output will diff when Spark reruns the tasks for the RDD. There are 3 deterministic levels:
+ * 1. DETERMINATE: The RDD output is always the same data set in the same order after a rerun.
+ * 2. UNORDERED: The RDD output is always the same data set but the order can be different
+ *               after a rerun.
+ * 3. INDETERMINATE. The RDD output can be different after a rerun.
+ *
+ * Note that, the output of an RDD usually relies on the parent RDDs. When the parent RDD's output
+ * is INDETERMINATE, it's very likely the RDD's output is also INDETERMINATE.
+ */
+private[spark] object DeterministicLevel extends Enumeration {
+  val DETERMINATE, UNORDERED, INDETERMINATE = Value
+}

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

@@ -40,7 +40,7 @@ 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, RDDCheckpointData}
+import org.apache.spark.rdd.{DeterministicLevel, RDD, RDDCheckpointData}
 import org.apache.spark.rpc.RpcTimeout
 import org.apache.spark.storage._
 import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
@@ -1487,6 +1487,63 @@ private[spark] class DAGScheduler(
             failedStages += failedStage
             failedStages += mapStage
             if (noResubmitEnqueued) {
+              // If the map stage is INDETERMINATE, which means the map tasks may return
+              // different result when re-try, we need to re-try all the tasks of the failed
+              // stage and its succeeding stages, because the input data will be changed after the
+              // map tasks are re-tried.
+              // Note that, if map stage is UNORDERED, we are fine. The shuffle partitioner is
+              // guaranteed to be determinate, so the input data of the reducers will not change
+              // even if the map tasks are re-tried.
+              if (mapStage.rdd.outputDeterministicLevel == DeterministicLevel.INDETERMINATE) {
+                // It's a little tricky to find all the succeeding stages of `failedStage`, because
+                // each stage only know its parents not children. Here we traverse the stages from
+                // the leaf nodes (the result stages of active jobs), and rollback all the stages
+                // in the stage chains that connect to the `failedStage`. To speed up the stage
+                // traversing, we collect the stages to rollback first. If a stage needs to
+                // rollback, all its succeeding stages need to rollback to.
+                val stagesToRollback = scala.collection.mutable.HashSet(failedStage)
+
+                def collectStagesToRollback(stageChain: List[Stage]): Unit = {
+                  if (stagesToRollback.contains(stageChain.head)) {
+                    stageChain.drop(1).foreach(s => stagesToRollback += s)
+                  } else {
+                    stageChain.head.parents.foreach { s =>
+                      collectStagesToRollback(s :: stageChain)
+                    }
+                  }
+                }
+
+                def generateErrorMessage(stage: Stage): String = {
+                  "A shuffle map stage with indeterminate output was failed and retried. " +
+                    s"However, Spark cannot rollback the $stage to re-process the input data, " +
+                    "and has to fail this job. Please eliminate the indeterminacy by " +
+                    "checkpointing the RDD before repartition and try again."
+                }
+
+                activeJobs.foreach(job => collectStagesToRollback(job.finalStage :: Nil))
+
+                stagesToRollback.foreach {
+                  case mapStage: ShuffleMapStage =>
+                    val numMissingPartitions = mapStage.findMissingPartitions().length
+                    if (numMissingPartitions < mapStage.numTasks) {
+                      // TODO: support to rollback shuffle files.
+                      // Currently the shuffle writing is "first write wins", so we can't re-run a
+                      // shuffle map stage and overwrite existing shuffle files. We have to finish
+                      // SPARK-8029 first.
+                      abortStage(mapStage, generateErrorMessage(mapStage), None)
+                    }
+
+                  case resultStage: ResultStage if resultStage.activeJob.isDefined =>
+                    val numMissingPartitions = resultStage.findMissingPartitions().length
+                    if (numMissingPartitions < resultStage.numTasks) {
+                      // TODO: support to rollback result tasks.
+                      abortStage(resultStage, generateErrorMessage(resultStage), None)
+                    }
+
+                  case _ =>
+                }
+              }
+
               // We expect one executor failure to trigger many FetchFailures in rapid succession,
               // but all of those task failures can typically be handled by a single resubmission of
               // the failed stage.  We avoid flooding the scheduler's event queue with resubmit