[SPARK-23406][SS] Enable stream-stream self-joins

## What changes were proposed in this pull request?

Solved two bugs to enable stream-stream self joins.

### Incorrect analysis due to missing MultiInstanceRelation trait
Streaming leaf nodes did not extend MultiInstanceRelation, which is necessary for the catalyst analyzer to convert the self-join logical plan DAG into a tree (by creating new instances of the leaf relations). This was causing the error `Failure when resolving conflicting references in Join:` (see JIRA for details).

### Incorrect attribute rewrite when splicing batch plans in MicroBatchExecution
When splicing the source's batch plan into the streaming plan (by replacing the StreamingExecutionPlan), we were rewriting the attribute reference in the streaming plan with the new attribute references from the batch plan. This was incorrectly handling the scenario when multiple StreamingExecutionRelation point to the same source, and therefore eventually point to the same batch plan returned by the source. Here is an example query, and its corresponding plan transformations.
```
val df = input.toDF
val join =
      df.select('value % 5 as "key", 'value).join(
        df.select('value % 5 as "key", 'value), "key")
```
Streaming logical plan before splicing the batch plan
```
Project [key#6, value#1, value#12]
+- Join Inner, (key#6 = key#9)
   :- Project [(value#1 % 5) AS key#6, value#1]
   :  +- StreamingExecutionRelation Memory[#1], value#1
   +- Project [(value#12 % 5) AS key#9, value#12]
      +- StreamingExecutionRelation Memory[#1], value#12  // two different leaves pointing to same source
```
Batch logical plan after splicing the batch plan and before rewriting
```
Project [key#6, value#1, value#12]
+- Join Inner, (key#6 = key#9)
   :- Project [(value#1 % 5) AS key#6, value#1]
   :  +- LocalRelation [value#66]           // replaces StreamingExecutionRelation Memory[#1], value#1
   +- Project [(value#12 % 5) AS key#9, value#12]
      +- LocalRelation [value#66]           // replaces StreamingExecutionRelation Memory[#1], value#12
```
Batch logical plan after rewriting the attributes. Specifically, for spliced, the new output attributes (value#66) replace the earlier output attributes (value#12, and value#1, one for each StreamingExecutionRelation).
```
Project [key#6, value#66, value#66]       // both value#1 and value#12 replaces by value#66
+- Join Inner, (key#6 = key#9)
   :- Project [(value#66 % 5) AS key#6, value#66]
   :  +- LocalRelation [value#66]
   +- Project [(value#66 % 5) AS key#9, value#66]
      +- LocalRelation [value#66]
```
This causes the optimizer to eliminate value#66 from one side of the join.
```
Project [key#6, value#66, value#66]
+- Join Inner, (key#6 = key#9)
   :- Project [(value#66 % 5) AS key#6, value#66]
   :  +- LocalRelation [value#66]
   +- Project [(value#66 % 5) AS key#9]   // this does not generate value, incorrect join results
      +- LocalRelation [value#66]
```

**Solution**: Instead of rewriting attributes, use a Project to introduce aliases between the output attribute references and the new reference generated by the spliced plans. The analyzer and optimizer will take care of the rest.
```
Project [key#6, value#1, value#12]
+- Join Inner, (key#6 = key#9)
   :- Project [(value#1 % 5) AS key#6, value#1]
   :  +- Project [value#66 AS value#1]   // solution: project with aliases
   :     +- LocalRelation [value#66]
   +- Project [(value#12 % 5) AS key#9, value#12]
      +- Project [value#66 AS value#12]    // solution: project with aliases
         +- LocalRelation [value#66]
```

## How was this patch tested?
New unit test

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #20598 from tdas/SPARK-23406.

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

@@ -24,8 +24,8 @@ import scala.collection.mutable.{ArrayBuffer, Map => MutableMap}
 
 import org.apache.spark.sql.{Dataset, SparkSession}
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, CurrentBatchTimestamp, CurrentDate, CurrentTimestamp}
-import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
+import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeMap, CurrentBatchTimestamp, CurrentDate, CurrentTimestamp}
+import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, Project}
 import org.apache.spark.sql.execution.SQLExecution
 import org.apache.spark.sql.execution.datasources.v2.{StreamingDataSourceV2Relation, WriteToDataSourceV2}
 import org.apache.spark.sql.execution.streaming.sources.{InternalRowMicroBatchWriter, MicroBatchWriter}
@@ -415,8 +415,6 @@ class MicroBatchExecution(
       }
     }
 
-    // A list of attributes that will need to be updated.
-    val replacements = new ArrayBuffer[(Attribute, Attribute)]
     // Replace sources in the logical plan with data that has arrived since the last batch.
     val newBatchesPlan = logicalPlan transform {
       case StreamingExecutionRelation(source, output) =>
@@ -424,18 +422,18 @@ class MicroBatchExecution(
           assert(output.size == dataPlan.output.size,
             s"Invalid batch: ${Utils.truncatedString(output, ",")} != " +
               s"${Utils.truncatedString(dataPlan.output, ",")}")
-          replacements ++= output.zip(dataPlan.output)
-          dataPlan
+
+          val aliases = output.zip(dataPlan.output).map { case (to, from) =>
+            Alias(from, to.name)(exprId = to.exprId, explicitMetadata = Some(from.metadata))
+          }
+          Project(aliases, dataPlan)
         }.getOrElse {
           LocalRelation(output, isStreaming = true)
         }
     }
 
     // Rewire the plan to use the new attributes that were returned by the source.
-    val replacementMap = AttributeMap(replacements)
     val newAttributePlan = newBatchesPlan transformAllExpressions {
-      case a: Attribute if replacementMap.contains(a) =>
-        replacementMap(a).withMetadata(a.metadata)
       case ct: CurrentTimestamp =>
         CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs,
           ct.dataType)

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

@@ -20,9 +20,9 @@ package org.apache.spark.sql.execution.streaming
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.sql.catalyst.plans.logical.LeafNode
-import org.apache.spark.sql.catalyst.plans.logical.Statistics
+import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
 import org.apache.spark.sql.execution.LeafExecNode
 import org.apache.spark.sql.execution.datasources.DataSource
 import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceV2}
@@ -42,7 +42,7 @@ object StreamingRelation {
  * passing to [[StreamExecution]] to run a query.
  */
 case class StreamingRelation(dataSource: DataSource, sourceName: String, output: Seq[Attribute])
-  extends LeafNode {
+  extends LeafNode with MultiInstanceRelation {
   override def isStreaming: Boolean = true
   override def toString: String = sourceName
 
@@ -53,6 +53,8 @@ case class StreamingRelation(dataSource: DataSource, sourceName: String, output:
   override def computeStats(): Statistics = Statistics(
     sizeInBytes = BigInt(dataSource.sparkSession.sessionState.conf.defaultSizeInBytes)
   )
+
+  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))
 }
 
 /**
@@ -62,7 +64,7 @@ case class StreamingRelation(dataSource: DataSource, sourceName: String, output:
 case class StreamingExecutionRelation(
     source: BaseStreamingSource,
     output: Seq[Attribute])(session: SparkSession)
-  extends LeafNode {
+  extends LeafNode with MultiInstanceRelation {
 
   override def isStreaming: Boolean = true
   override def toString: String = source.toString
@@ -74,6 +76,8 @@ case class StreamingExecutionRelation(
   override def computeStats(): Statistics = Statistics(
     sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
   )
+
+  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session)
 }
 
 // We have to pack in the V1 data source as a shim, for the case when a source implements
@@ -92,13 +96,15 @@ case class StreamingRelationV2(
     extraOptions: Map[String, String],
     output: Seq[Attribute],
     v1Relation: Option[StreamingRelation])(session: SparkSession)
-  extends LeafNode {
+  extends LeafNode with MultiInstanceRelation {
   override def isStreaming: Boolean = true
   override def toString: String = sourceName
 
   override def computeStats(): Statistics = Statistics(
     sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
   )
+
+  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session)
 }
 
 /**
@@ -108,7 +114,7 @@ case class ContinuousExecutionRelation(
     source: ContinuousReadSupport,
     extraOptions: Map[String, String],
     output: Seq[Attribute])(session: SparkSession)
-  extends LeafNode {
+  extends LeafNode with MultiInstanceRelation {
 
   override def isStreaming: Boolean = true
   override def toString: String = source.toString
@@ -120,6 +126,8 @@ case class ContinuousExecutionRelation(
   override def computeStats(): Statistics = Statistics(
     sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
   )
+
+  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session)
 }
 
 /**

sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala

@@ -28,7 +28,9 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SparkSession}
 import org.apache.spark.sql.catalyst.analysis.StreamingJoinHelper
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, AttributeSet, Literal}
 import org.apache.spark.sql.catalyst.plans.logical.{EventTimeWatermark, Filter}
-import org.apache.spark.sql.execution.LogicalRDD
+import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.execution.{FileSourceScanExec, LogicalRDD}
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.execution.streaming.{MemoryStream, StatefulOperatorStateInfo, StreamingSymmetricHashJoinHelper}
 import org.apache.spark.sql.execution.streaming.state.{StateStore, StateStoreProviderId}
 import org.apache.spark.sql.functions._
@@ -323,6 +325,27 @@ class StreamingInnerJoinSuite extends StreamTest with StateStoreMetricsTest with
     assert(e.toString.contains("Stream stream joins without equality predicate is not supported"))
   }
 
+  test("stream stream self join") {
+    val input = MemoryStream[Int]
+    val df = input.toDF
+    val join =
+      df.select('value % 5 as "key", 'value).join(
+        df.select('value % 5 as "key", 'value), "key")
+
+    testStream(join)(
+      AddData(input, 1, 2),
+      CheckAnswer((1, 1, 1), (2, 2, 2)),
+      StopStream,
+      StartStream(),
+      AddData(input, 3, 6),
+      /*
+      (1, 1)     (1, 1)
+      (2, 2)  x  (2, 2)  =  (1, 1, 1), (1, 1, 6), (2, 2, 2), (1, 6, 1), (1, 6, 6)
+      (1, 6)     (1, 6)
+      */
+      CheckAnswer((3, 3, 3), (1, 1, 1), (1, 1, 6), (2, 2, 2), (1, 6, 1), (1, 6, 6)))
+  }
+
   test("locality preferences of StateStoreAwareZippedRDD") {
     import StreamingSymmetricHashJoinHelper._