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

## What changes were proposed in this pull request?

Jira : https://issues.apache.org/jira/browse/SPARK-17698

`ExtractEquiJoinKeys` is incorrectly using filter predicates as the join condition for joins. `canEvaluate` [0] tries to see if the an `Expression` can be evaluated using output of a given `Plan`. In case of filter predicates (eg. `a.id='1'`), the `Expression` passed for the right hand side (ie. '1' ) is a `Literal` which does not have any attribute references. Thus `expr.references` is an empty set which theoretically is a subset of any set. This leads to `canEvaluate` returning `true` and `a.id='1'` is treated as a join predicate. While this does not lead to incorrect results but in case of bucketed + sorted tables, we might miss out on avoiding un-necessary shuffle + sort. See example below:

[0] : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala#L91

eg.

```
val df = (1 until 10).toDF("id").coalesce(1)
hc.sql("DROP TABLE IF EXISTS table1").collect
df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table1")
hc.sql("DROP TABLE IF EXISTS table2").collect
df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table2")

sqlContext.sql("""
  SELECT a.id, b.id
  FROM table1 a
  FULL OUTER JOIN table2 b
  ON a.id = b.id AND a.id='1' AND b.id='1'
""").explain(true)
```

BEFORE: This is doing shuffle + sort over table scan outputs which is not needed as both tables are bucketed and sorted on the same columns and have same number of buckets. This should be a single stage job.

```
SortMergeJoin [id#38, cast(id#38 as double), 1.0], [id#39, 1.0, cast(id#39 as double)], FullOuter
:- *Sort [id#38 ASC NULLS FIRST, cast(id#38 as double) ASC NULLS FIRST, 1.0 ASC NULLS FIRST], false, 0
:  +- Exchange hashpartitioning(id#38, cast(id#38 as double), 1.0, 200)
:     +- *FileScan parquet default.table1[id#38] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
+- *Sort [id#39 ASC NULLS FIRST, 1.0 ASC NULLS FIRST, cast(id#39 as double) ASC NULLS FIRST], false, 0
   +- Exchange hashpartitioning(id#39, 1.0, cast(id#39 as double), 200)
      +- *FileScan parquet default.table2[id#39] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
```

AFTER :

```
SortMergeJoin [id#32], [id#33], FullOuter, ((cast(id#32 as double) = 1.0) && (cast(id#33 as double) = 1.0))
:- *FileScan parquet default.table1[id#32] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
+- *FileScan parquet default.table2[id#33] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
```

## How was this patch tested?

- Added a new test case for this scenario : `SPARK-17698 Join predicates should not contain filter clauses`
- Ran all the tests in `BucketedReadSuite`

Author: Tejas Patil <tejasp@fb.com>

Closes #15272 from tejasapatil/SPARK-17698_join_predicate_filter_clause.

Author: tejasapatil

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

@@ -65,7 +65,9 @@ object ReorderJoin extends Rule[LogicalPlan] with PredicateHelper {
       val conditionalJoin = rest.find { planJoinPair =>
         val plan = planJoinPair._1
         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

@@ -235,7 +235,8 @@ 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,
       sortLeft: Boolean = true,
@@ -268,12 +269,12 @@ 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]
@@ -297,86 +298,144 @@ 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
+      )
     }
   }
 
   test("avoid shuffle and sort when bucket and sort columns are join keys") {
     val bucketSpec = Some(BucketSpec(8, Seq("i", "j"), Seq("i", "j")))
     testBucketing(
-      bucketSpec, bucketSpec, Seq("i", "j"),
-      shuffleLeft = false, shuffleRight = false,
-      sortLeft = false, sortRight = false
+      bucketSpecLeft = bucketSpec,
+      bucketSpecRight = bucketSpec,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = false,
+      sortLeft = false,
+      sortRight = false
     )
   }
 
   test("avoid shuffle and sort when sort columns are a super set of join keys") {
     val bucketSpec1 = Some(BucketSpec(8, Seq("i"), Seq("i", "j")))
     val bucketSpec2 = Some(BucketSpec(8, Seq("i"), Seq("i", "k")))
     testBucketing(
-      bucketSpec1, bucketSpec2, Seq("i"),
-      shuffleLeft = false, shuffleRight = false,
-      sortLeft = false, sortRight = false
+      bucketSpecLeft = bucketSpec1,
+      bucketSpecRight = bucketSpec2,
+      joinCondition = joinCondition(Seq("i")),
+      shuffleLeft = false,
+      shuffleRight = false,
+      sortLeft = false,
+      sortRight = false
     )
   }
 
   test("only sort one side when sort columns are different") {
     val bucketSpec1 = Some(BucketSpec(8, Seq("i", "j"), Seq("i", "j")))
     val bucketSpec2 = Some(BucketSpec(8, Seq("i", "j"), Seq("k")))
     testBucketing(
-      bucketSpec1, bucketSpec2, Seq("i", "j"),
-      shuffleLeft = false, shuffleRight = false,
-      sortLeft = false, sortRight = true
+      bucketSpecLeft = bucketSpec1,
+      bucketSpecRight = bucketSpec2,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = false,
+      sortLeft = false,
+      sortRight = true
     )
   }
 
   test("only sort one side when sort columns are same but their ordering is different") {
     val bucketSpec1 = Some(BucketSpec(8, Seq("i", "j"), Seq("i", "j")))
     val bucketSpec2 = Some(BucketSpec(8, Seq("i", "j"), Seq("j", "i")))
     testBucketing(
-      bucketSpec1, bucketSpec2, Seq("i", "j"),
-      shuffleLeft = false, shuffleRight = false,
-      sortLeft = false, sortRight = true
+      bucketSpecLeft = bucketSpec1,
+      bucketSpecRight = bucketSpec2,
+      joinCondition = joinCondition(Seq("i", "j")),
+      shuffleLeft = false,
+      shuffleRight = false,
+      sortLeft = false,
+      sortRight = true
     )
   }
 
@@ -408,6 +467,25 @@ 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 = Seq("i").map(col => left(col) === right(col)).reduce(_ && _)
+        val filterLeft = left("i") === Literal("1")
+        val filterRight = right("i") === Literal("1")
+        joinPredicates && filterLeft && filterRight
+      },
+      shuffleLeft = false,
+      shuffleRight = false,
+      sortLeft = false,
+      sortRight = false
+    )
+  }
+
   test("error if there exists any malformed bucket files") {
     withTable("bucketed_table") {
       df1.write.format("parquet").bucketBy(8, "i").saveAsTable("bucketed_table")