[SPARK-25708][SQL] HAVING without GROUP BY means global aggregate

## What changes were proposed in this pull request?

According to the SQL standard, when a query contains `HAVING`, it indicates an aggregate operator. For more details please refer to https://blog.jooq.org/2014/12/04/do-you-really-understand-sqls-group-by-and-having-clauses/

However, in Spark SQL parser, we treat HAVING as a normal filter when there is no GROUP BY, which breaks SQL semantic and lead to wrong result. This PR fixes the parser.

## How was this patch tested?

new test

Closes #22696 from cloud-fan/having.

Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>

docs/sql-programming-guide.md

@@ -1977,6 +1977,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
   - Since Spark 2.4, Metadata files (e.g. Parquet summary files) and temporary files are not counted as data files when calculating table size during Statistics computation.
   - Since Spark 2.4, empty strings are saved as quoted empty strings `""`. In version 2.3 and earlier, empty strings are equal to `null` values and do not reflect to any characters in saved CSV files. For example, the row of `"a", null, "", 1` was writted as `a,,,1`. Since Spark 2.4, the same row is saved as `a,,"",1`. To restore the previous behavior, set the CSV option `emptyValue` to empty (not quoted) string.  
   - Since Spark 2.4, The LOAD DATA command supports wildcard `?` and `*`, which match any one character, and zero or more characters, respectively. Example: `LOAD DATA INPATH '/tmp/folder*/'` or `LOAD DATA INPATH '/tmp/part-?'`. Special Characters like `space` also now work in paths. Example: `LOAD DATA INPATH '/tmp/folder name/'`.
+  - In Spark version 2.3 and earlier, HAVING without GROUP BY is treated as WHERE. This means, `SELECT 1 FROM range(10) HAVING true` is executed as `SELECT 1 FROM range(10) WHERE true`  and returns 10 rows. This violates SQL standard, and has been fixed in Spark 2.4. Since Spark 2.4, HAVING without GROUP BY is treated as a global aggregate, which means `SELECT 1 FROM range(10) HAVING true` will return only one row. To restore the previous behavior, set `spark.sql.legacy.parser.havingWithoutGroupByAsWhere` to `true`.
 
 ## Upgrading From Spark SQL 2.3.0 to 2.3.1 and above
 

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala

@@ -394,6 +394,17 @@ class AstBuilder(conf: SQLConf) extends SqlBaseBaseVisitor[AnyRef] with Logging
       Filter(expression(ctx), plan)
     }
 
+    def withHaving(ctx: BooleanExpressionContext, plan: LogicalPlan): LogicalPlan = {
+      // Note that we add a cast to non-predicate expressions. If the expression itself is
+      // already boolean, the optimizer will get rid of the unnecessary cast.
+      val predicate = expression(ctx) match {
+        case p: Predicate => p
+        case e => Cast(e, BooleanType)
+      }
+      Filter(predicate, plan)
+    }
+
+
     // Expressions.
     val expressions = Option(namedExpressionSeq).toSeq
       .flatMap(_.namedExpression.asScala)
@@ -446,30 +457,34 @@ class AstBuilder(conf: SQLConf) extends SqlBaseBaseVisitor[AnyRef] with Logging
           case e: NamedExpression => e
           case e: Expression => UnresolvedAlias(e)
         }
-        val withProject = if (aggregation != null) {
-          withAggregation(aggregation, namedExpressions, withFilter)
-        } else if (namedExpressions.nonEmpty) {
+
+        def createProject() = if (namedExpressions.nonEmpty) {
           Project(namedExpressions, withFilter)
         } else {
           withFilter
         }
 
-        // Having
-        val withHaving = withProject.optional(having) {
-          // Note that we add a cast to non-predicate expressions. If the expression itself is
-          // already boolean, the optimizer will get rid of the unnecessary cast.
-          val predicate = expression(having) match {
-            case p: Predicate => p
-            case e => Cast(e, BooleanType)
+        val withProject = if (aggregation == null && having != null) {
+          if (conf.getConf(SQLConf.LEGACY_HAVING_WITHOUT_GROUP_BY_AS_WHERE)) {
+            // If the legacy conf is set, treat HAVING without GROUP BY as WHERE.
+            withHaving(having, createProject())
+          } else {
+            // According to SQL standard, HAVING without GROUP BY means global aggregate.
+            withHaving(having, Aggregate(Nil, namedExpressions, withFilter))
           }
-          Filter(predicate, withProject)
+        } else if (aggregation != null) {
+          val aggregate = withAggregation(aggregation, namedExpressions, withFilter)
+          aggregate.optionalMap(having)(withHaving)
+        } else {
+          // When hitting this branch, `having` must be null.
+          createProject()
         }
 
         // Distinct
         val withDistinct = if (setQuantifier() != null && setQuantifier().DISTINCT() != null) {
-          Distinct(withHaving)
+          Distinct(withProject)
         } else {
-          withHaving
+          withProject
         }
 
         // Window

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -1567,6 +1567,14 @@ object SQLConf {
     .internal()
     .booleanConf
     .createWithDefault(false)
+
+  val LEGACY_HAVING_WITHOUT_GROUP_BY_AS_WHERE =
+    buildConf("spark.sql.legacy.parser.havingWithoutGroupByAsWhere")
+      .internal()
+      .doc("If it is set to true, the parser will treat HAVING without GROUP BY as a normal " +
+        "WHERE, which does not follow SQL standard.")
+      .booleanConf
+      .createWithDefault(false)
 }
 
 /**

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/PlanParserSuite.scala

@@ -108,7 +108,7 @@ class PlanParserSuite extends AnalysisTest {
     assertEqual("select a, b from db.c where x < 1", table("db", "c").where('x < 1).select('a, 'b))
     assertEqual(
       "select a, b from db.c having x < 1",
-      table("db", "c").select('a, 'b).where('x < 1))
+      table("db", "c").groupBy()('a, 'b).where('x < 1))
     assertEqual("select distinct a, b from db.c", Distinct(table("db", "c").select('a, 'b)))
     assertEqual("select all a, b from db.c", table("db", "c").select('a, 'b))
     assertEqual("select from tbl", OneRowRelation().select('from.as("tbl")))

sql/core/src/test/resources/sql-tests/inputs/group-by.sql

@@ -73,3 +73,10 @@ where b.z != b.z;
 -- SPARK-24369 multiple distinct aggregations having the same argument set
 SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
   FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y);
+
+-- SPARK-25708 HAVING without GROUP BY means global aggregate
+SELECT 1 FROM range(10) HAVING true;
+
+SELECT 1 FROM range(10) HAVING MAX(id) > 0;
+
+SELECT id FROM range(10) HAVING id > 0;

sql/core/src/test/resources/sql-tests/results/group-by.sql.out

@@ -1,5 +1,5 @@
 -- Automatically generated by SQLQueryTestSuite
--- Number of queries: 27
+-- Number of queries: 30
 
 
 -- !query 0
@@ -250,3 +250,28 @@ SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
 struct<corr(DISTINCT CAST(x AS DOUBLE), CAST(y AS DOUBLE)):double,corr(DISTINCT CAST(y AS DOUBLE), CAST(x AS DOUBLE)):double,count(1):bigint>
 -- !query 26 output
 1.0	1.0	3
+
+
+-- !query 27
+SELECT 1 FROM range(10) HAVING true
+-- !query 27 schema
+struct<1:int>
+-- !query 27 output
+1
+
+
+-- !query 28
+SELECT 1 FROM range(10) HAVING MAX(id) > 0
+-- !query 28 schema
+struct<1:int>
+-- !query 28 output
+1
+
+
+-- !query 29
+SELECT id FROM range(10) HAVING id > 0
+-- !query 29 schema
+struct<>
+-- !query 29 output
+org.apache.spark.sql.AnalysisException
+grouping expressions sequence is empty, and '`id`' is not an aggregate function. Wrap '()' in windowing function(s) or wrap '`id`' in first() (or first_value) if you don't care which value you get.;

sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala

@@ -740,10 +740,6 @@ class HiveQuerySuite extends HiveComparisonTest with SQLTestUtils with BeforeAnd
     sql("select key, count(*) c from src group by key having c").collect()
   }
 
-  test("SPARK-2225: turn HAVING without GROUP BY into a simple filter") {
-    assert(sql("select key from src having key > 490").collect().size < 100)
-  }
-
   test("union/except/intersect") {
     assertResult(Array(Row(1), Row(1))) {
       sql("select 1 as a union all select 1 as a").collect()