[SPARK-23087][SQL] CheckCartesianProduct too restrictive when condition is false/null

## What changes were proposed in this pull request?

CheckCartesianProduct raises an AnalysisException also when the join condition is always false/null. In this case, we shouldn't raise it, since the result will not be a cartesian product.

## How was this patch tested?

added UT

Author: Marco Gaido <marcogaido91@gmail.com>

Closes #20333 from mgaido91/SPARK-23087.

Author: mgaido91

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

@@ -1108,15 +1108,19 @@ object CheckCartesianProducts extends Rule[LogicalPlan] with PredicateHelper {
    */
   def isCartesianProduct(join: Join): Boolean = {
     val conditions = join.condition.map(splitConjunctivePredicates).getOrElse(Nil)
-    !conditions.map(_.references).exists(refs => refs.exists(join.left.outputSet.contains)
-        && refs.exists(join.right.outputSet.contains))
+
+    conditions match {
+      case Seq(Literal.FalseLiteral) | Seq(Literal(null, BooleanType)) => false
+      case _ => !conditions.map(_.references).exists(refs =>
+        refs.exists(join.left.outputSet.contains) && refs.exists(join.right.outputSet.contains))
+    }
   }
 
   def apply(plan: LogicalPlan): LogicalPlan =
     if (SQLConf.get.crossJoinEnabled) {
       plan
     } else plan transform {
-      case j @ Join(left, right, Inner | LeftOuter | RightOuter | FullOuter, condition)
+      case j @ Join(left, right, Inner | LeftOuter | RightOuter | FullOuter, _)
         if isCartesianProduct(j) =>
           throw new AnalysisException(
             s"""Detected cartesian product for ${j.joinType.sql} join between logical plans

sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala

@@ -274,4 +274,18 @@ class DataFrameJoinSuite extends QueryTest with SharedSQLContext {
     checkAnswer(innerJoin, Row(1) :: Nil)
   }
 
+  test("SPARK-23087: don't throw Analysis Exception in CheckCartesianProduct when join condition " +
+    "is false or null") {
+    val df = spark.range(10)
+    val dfNull = spark.range(10).select(lit(null).as("b"))
+    val planNull = df.join(dfNull, $"id" === $"b", "left").queryExecution.analyzed
+
+    spark.sessionState.executePlan(planNull).optimizedPlan
+
+    val dfOne = df.select(lit(1).as("a"))
+    val dfTwo = spark.range(10).select(lit(2).as("b"))
+    val planFalse = dfOne.join(dfTwo, $"a" === $"b", "left").queryExecution.analyzed
+
+    spark.sessionState.executePlan(planFalse).optimizedPlan
+  }
 }