[SPARK-16818] Exchange reuse incorrectly reuses scans over different sets of partitions

## What changes were proposed in this pull request?

This fixes a bug wherethe file scan operator does not take into account partition pruning in its implementation of `sameResult()`. As a result, executions may be incorrect on self-joins over the same base file relation.

The patch here is minimal, but we should reconsider relying on `metadata` for implementing sameResult() in the future, as string representations may not be uniquely identifying.

cc rxin

## How was this patch tested?

Unit tests.

Author: Eric Liang <ekl@databricks.com>

Closes #14425 from ericl/spark-16818.

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategy.scala

@@ -130,7 +130,9 @@ private[sql] object FileSourceStrategy extends Strategy with Logging {
           createNonBucketedReadRDD(readFile, selectedPartitions, fsRelation)
       }
 
+      // These metadata values make scan plans uniquely identifiable for equality checking.
       val meta = Map(
+        "PartitionFilters" -> partitionKeyFilters.mkString("[", ", ", "]"),
         "Format" -> fsRelation.fileFormat.toString,
         "ReadSchema" -> prunedDataSchema.simpleString,
         PUSHED_FILTERS -> pushedDownFilters.mkString("[", ", ", "]"),

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategySuite.scala

@@ -30,7 +30,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.catalog.BucketSpec
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionSet, PredicateHelper}
 import org.apache.spark.sql.catalyst.util
-import org.apache.spark.sql.execution.DataSourceScanExec
+import org.apache.spark.sql.execution.{DataSourceScanExec, SparkPlan}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.sources._
@@ -408,6 +408,39 @@ class FileSourceStrategySuite extends QueryTest with SharedSQLContext with Predi
     }
   }
 
+  test("[SPARK-16818] partition pruned file scans implement sameResult correctly") {
+    withTempPath { path =>
+      val tempDir = path.getCanonicalPath
+      spark.range(100)
+        .selectExpr("id", "id as b")
+        .write
+        .partitionBy("id")
+        .parquet(tempDir)
+      val df = spark.read.parquet(tempDir)
+      def getPlan(df: DataFrame): SparkPlan = {
+        df.queryExecution.executedPlan
+      }
+      assert(getPlan(df.where("id = 2")).sameResult(getPlan(df.where("id = 2"))))
+      assert(!getPlan(df.where("id = 2")).sameResult(getPlan(df.where("id = 3"))))
+    }
+  }
+
+  test("[SPARK-16818] exchange reuse respects differences in partition pruning") {
+    spark.conf.set("spark.sql.exchange.reuse", true)
+    withTempPath { path =>
+      val tempDir = path.getCanonicalPath
+      spark.range(10)
+        .selectExpr("id % 2 as a", "id % 3 as b", "id as c")
+        .write
+        .partitionBy("a")
+        .parquet(tempDir)
+      val df = spark.read.parquet(tempDir)
+      val df1 = df.where("a = 0").groupBy("b").agg("c" -> "sum")
+      val df2 = df.where("a = 1").groupBy("b").agg("c" -> "sum")
+      checkAnswer(df1.join(df2, "b"), Row(0, 6, 12) :: Row(1, 4, 8) :: Row(2, 10, 5) :: Nil)
+    }
+  }
+
   // Helpers for checking the arguments passed to the FileFormat.
 
   protected val checkPartitionSchema =