[SPARK-17698] [SQL] Join predicates should not contain filter clauses

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala

@@ -84,8 +84,9 @@ trait PredicateHelper {
    *
    * For example consider a join between two relations R(a, b) and S(c, d).
    *
-   * `canEvaluate(EqualTo(a,b), R)` returns `true` where as `canEvaluate(EqualTo(a,c), R)` returns
-   * `false`.
+   * - `canEvaluate(EqualTo(a,b), R)` returns `true`
+   * - `canEvaluate(EqualTo(a,c), R)` returns `false`
+   * - `canEvaluate(Literal(1), R)` returns `true` as literals CAN be evaluated on any plan
    */
   protected def canEvaluate(expr: Expression, plan: LogicalPlan): Boolean =
     expr.references.subsetOf(plan.outputSet)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala

@@ -59,7 +59,9 @@ object ReorderJoin extends Rule[LogicalPlan] with PredicateHelper {
       // find out the first join that have at least one join condition
       val conditionalJoin = rest.find { plan =>
         val refs = left.outputSet ++ plan.outputSet
-        conditions.filterNot(canEvaluate(_, left)).filterNot(canEvaluate(_, plan))
+        conditions
+          .filterNot(l => l.references.nonEmpty && canEvaluate(l, left))
+          .filterNot(r => r.references.nonEmpty && canEvaluate(r, plan))
           .exists(_.references.subsetOf(refs))
       }
       // pick the next one if no condition left

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala

@@ -112,6 +112,7 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
       // as join keys.
       val predicates = condition.map(splitConjunctivePredicates).getOrElse(Nil)
       val joinKeys = predicates.flatMap {
+        case EqualTo(l, r) if l.references.isEmpty || r.references.isEmpty => None
         case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => Some((l, r))
         case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => Some((r, l))
         // Replace null with default value for joining key, then those rows with null in it could
@@ -125,6 +126,7 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
         case other => None
       }
       val otherPredicates = predicates.filterNot {
+        case EqualTo(l, r) if l.references.isEmpty || r.references.isEmpty => false
         case EqualTo(l, r) =>
           canEvaluate(l, left) && canEvaluate(r, right) ||
             canEvaluate(l, right) && canEvaluate(r, left)

sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala

@@ -234,7 +234,8 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet
   private def testBucketing(
       bucketSpecLeft: Option[BucketSpec],
       bucketSpecRight: Option[BucketSpec],
-      joinColumns: Seq[String],
+      joinType: String = "inner",
+      joinCondition: (DataFrame, DataFrame) => Column,
       shuffleLeft: Boolean,
       shuffleRight: Boolean): Unit = {
     withTable("bucketed_table1", "bucketed_table2") {
@@ -256,12 +257,12 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet
         SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false") {
         val t1 = spark.table("bucketed_table1")
         val t2 = spark.table("bucketed_table2")
-        val joined = t1.join(t2, joinCondition(t1, t2, joinColumns))
+        val joined = t1.join(t2, joinCondition(t1, t2), joinType)
 
         // First check the result is corrected.
         checkAnswer(
           joined.sort("bucketed_table1.k", "bucketed_table2.k"),
-          df1.join(df2, joinCondition(df1, df2, joinColumns)).sort("df1.k", "df2.k"))
+          df1.join(df2, joinCondition(df1, df2), joinType).sort("df1.k", "df2.k"))
 
         assert(joined.queryExecution.executedPlan.isInstanceOf[SortMergeJoinExec])
         val joinOperator = joined.queryExecution.executedPlan.asInstanceOf[SortMergeJoinExec]
@@ -276,47 +277,89 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet
     }
   }
 
-  private def joinCondition(left: DataFrame, right: DataFrame, joinCols: Seq[String]): Column = {
+  private def joinCondition(joinCols: Seq[String]) (left: DataFrame, right: DataFrame): Column = {
     joinCols.map(col => left(col) === right(col)).reduce(_ && _)
   }
 
   test("avoid shuffle when join 2 bucketed tables") {
     val bucketSpec = Some(BucketSpec(8, Seq("i", "j"), Nil))
-    testBucketing(bucketSpec, bucketSpec, Seq("i", "j"), shuffleLeft = false, shuffleRight = false)
+    testBucketing(
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = bucketSpec,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = false
+    )
   }
 
   // Enable it after fix https://issues.apache.org/jira/browse/SPARK-12704
   ignore("avoid shuffle when join keys are a super-set of bucket keys") {
     val bucketSpec = Some(BucketSpec(8, Seq("i"), Nil))
-    testBucketing(bucketSpec, bucketSpec, Seq("i", "j"), shuffleLeft = false, shuffleRight = false)
+    testBucketing(
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = bucketSpec,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = false
+    )
   }
 
   test("only shuffle one side when join bucketed table and non-bucketed table") {
     val bucketSpec = Some(BucketSpec(8, Seq("i", "j"), Nil))
-    testBucketing(bucketSpec, None, Seq("i", "j"), shuffleLeft = false, shuffleRight = true)
+    testBucketing(
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = None,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = true
+    )
   }
 
   test("only shuffle one side when 2 bucketed tables have different bucket number") {
     val bucketSpec1 = Some(BucketSpec(8, Seq("i", "j"), Nil))
     val bucketSpec2 = Some(BucketSpec(5, Seq("i", "j"), Nil))
-    testBucketing(bucketSpec1, bucketSpec2, Seq("i", "j"), shuffleLeft = false, shuffleRight = true)
+    testBucketing(
+      bucketSpecLeft = bucketSpec1,
+      bucketSpecRight = bucketSpec2,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = true
+    )
   }
 
   test("only shuffle one side when 2 bucketed tables have different bucket keys") {
     val bucketSpec1 = Some(BucketSpec(8, Seq("i"), Nil))
     val bucketSpec2 = Some(BucketSpec(8, Seq("j"), Nil))
-    testBucketing(bucketSpec1, bucketSpec2, Seq("i"), shuffleLeft = false, shuffleRight = true)
+    testBucketing(
+      bucketSpecLeft = bucketSpec1,
+      bucketSpecRight = bucketSpec2,
+      joinCondition = joinCondition(Seq("i")),
+      shuffleLeft = false,
+      shuffleRight = true
+    )
   }
 
   test("shuffle when join keys are not equal to bucket keys") {
     val bucketSpec = Some(BucketSpec(8, Seq("i"), Nil))
-    testBucketing(bucketSpec, bucketSpec, Seq("j"), shuffleLeft = true, shuffleRight = true)
+    testBucketing(
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = bucketSpec,
+      joinCondition = joinCondition(Seq("j")),
+      shuffleLeft = true,
+      shuffleRight = true
+    )
   }
 
   test("shuffle when join 2 bucketed tables with bucketing disabled") {
     val bucketSpec = Some(BucketSpec(8, Seq("i", "j"), Nil))
     withSQLConf(SQLConf.BUCKETING_ENABLED.key -> "false") {
-      testBucketing(bucketSpec, bucketSpec, Seq("i", "j"), shuffleLeft = true, shuffleRight = true)
+      testBucketing(
+        bucketSpecLeft = bucketSpec,
+        bucketSpecRight = bucketSpec,
+        joinCondition = joinCondition(Seq("i", "j")),
+        shuffleLeft = true,
+        shuffleRight = true
+      )
     }
   }
 
@@ -348,6 +391,23 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet
     }
   }
 
+  test("SPARK-17698 Join predicates should not contain filter clauses") {
+    val bucketSpec = Some(BucketSpec(8, Seq("i"), Seq("i")))
+    testBucketing(
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = bucketSpec,
+      joinType = "fullouter",
+      joinCondition = (left: DataFrame, right: DataFrame) => {
+        val joinPredicates = left("i") === right("i")
+        val filterLeft = left("i") === Literal("1")
+        val filterRight = right("i") === Literal("1")
+        joinPredicates && filterLeft && filterRight
+      },
+      shuffleLeft = false,
+      shuffleRight = false
+    )
+  }
+
   test("error if there exists any malformed bucket files") {
     withTable("bucketed_table") {
       df1.write.format("parquet").bucketBy(8, "i").saveAsTable("bucketed_table")