[SPARK-27111][SS] Fix a race that a continuous query may fail with In…

…terruptedException

Before a Kafka consumer gets assigned with partitions, its offset will contain 0 partitions. However, runContinuous will still run and launch a Spark job having 0 partitions. In this case, there is a race that epoch may interrupt the query execution thread after `lastExecution.toRdd`, and either `epochEndpoint.askSync[Unit](StopContinuousExecutionWrites)` or the next `runContinuous` will get interrupted unintentionally.

To handle this case, this PR has the following changes:

- Clean up the resources in `queryExecutionThread.runUninterruptibly`. This may increase the waiting time of `stop` but should be minor because the operations here are very fast (just sending an RPC message in the same process and stopping a very simple thread).
- Clear the interrupted status at the end so that it won't impact the `runContinuous` call. We may clear the interrupted status set by `stop`, but it doesn't affect the query termination because `runActivatedStream` will check `state` and exit accordingly.

I also updated the clean up codes to make sure exceptions thrown from `epochEndpoint.askSync[Unit](StopContinuousExecutionWrites)` won't stop the clean up.

Jenkins

Closes apache#24034 from zsxwing/SPARK-27111.

Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
(cherry picked from commit 6e1c082)
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala

@@ -272,14 +272,30 @@ class ContinuousExecution(
         logInfo(s"Query $id ignoring exception from reconfiguring: $t")
         // interrupted by reconfiguration - swallow exception so we can restart the query
     } finally {
-      epochEndpoint.askSync[Unit](StopContinuousExecutionWrites)
-      SparkEnv.get.rpcEnv.stop(epochEndpoint)
-
-      epochUpdateThread.interrupt()
-      epochUpdateThread.join()
-
-      stopSources()
-      sparkSession.sparkContext.cancelJobGroup(runId.toString)
+      // The above execution may finish before getting interrupted, for example, a Spark job having
+      // 0 partitions will complete immediately. Then the interrupted status will sneak here.
+      //
+      // To handle this case, we do the two things here:
+      //
+      // 1. Clean up the resources in `queryExecutionThread.runUninterruptibly`. This may increase
+      //    the waiting time of `stop` but should be minor because the operations here are very fast
+      //    (just sending an RPC message in the same process and stopping a very simple thread).
+      // 2. Clear the interrupted status at the end so that it won't impact the `runContinuous`
+      //    call. We may clear the interrupted status set by `stop`, but it doesn't affect the query
+      //    termination because `runActivatedStream` will check `state` and exit accordingly.
+      queryExecutionThread.runUninterruptibly {
+        try {
+          epochEndpoint.askSync[Unit](StopContinuousExecutionWrites)
+        } finally {
+          SparkEnv.get.rpcEnv.stop(epochEndpoint)
+          epochUpdateThread.interrupt()
+          epochUpdateThread.join()
+          stopSources()
+          // The following line must be the last line because it may fail if SparkContext is stopped
+          sparkSession.sparkContext.cancelJobGroup(runId.toString)
+        }
+      }
+      Thread.interrupted()
     }
   }