[SPARK-23748][SS] Fix SS continuous process doesn't support SubqueryAlias issue

## What changes were proposed in this pull request?

Current SS continuous doesn't support processing on temp table or `df.as("xxx")`, SS will throw an exception as LogicalPlan not supported, details described in [here](https://issues.apache.org/jira/browse/SPARK-23748).

So here propose to add this support.

## How was this patch tested?

new UT.

Author: jerryshao <[email protected]>

Closes apache#21017 from jerryshao/SPARK-23748.

Author: jerryshao

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala

@@ -345,7 +345,7 @@ object UnsupportedOperationChecker {
     plan.foreachUp { implicit subPlan =>
       subPlan match {
         case (_: Project | _: Filter | _: MapElements | _: MapPartitions |
-              _: DeserializeToObject | _: SerializeFromObject) =>
+              _: DeserializeToObject | _: SerializeFromObject | _: SubqueryAlias) =>
         case node if node.nodeName == "StreamingRelationV2" =>
         case node =>
           throwError(s"Continuous processing does not support ${node.nodeName} operations.")

sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala

@@ -171,6 +171,25 @@ class ContinuousSuite extends ContinuousSuiteBase {
       "Continuous processing does not support current time operations."))
   }
 
+  test("subquery alias") {
+    val df = spark.readStream
+      .format("rate")
+      .option("numPartitions", "5")
+      .option("rowsPerSecond", "5")
+      .load()
+      .createOrReplaceTempView("rate")
+    val test = spark.sql("select value from rate where value > 5")
+
+    testStream(test, useV2Sink = true)(
+      StartStream(longContinuousTrigger),
+      AwaitEpoch(0),
+      Execute(waitForRateSourceTriggers(_, 2)),
+      IncrementEpoch(),
+      Execute(waitForRateSourceTriggers(_, 4)),
+      IncrementEpoch(),
+      CheckAnswerRowsContains(scala.Range(6, 20).map(Row(_))))
+  }
+
   test("repeatedly restart") {
     val df = spark.readStream
       .format("rate")