[
https://issues.apache.org/jira/browse/FLINK-3475?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15834275#comment-15834275
]
ASF GitHub Bot commented on FLINK-3475:
---------------------------------------
Github user twalthr commented on a diff in the pull request:
https://github.com/apache/flink/pull/3111#discussion_r97295864
--- Diff:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/api/scala/batch/sql/DistinctAggregateTest.scala
---
@@ -0,0 +1,275 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.table.api.scala.batch.sql
+
+import org.apache.flink.table.utils.TableTestBase
+import org.apache.flink.api.scala._
+import org.apache.flink.table.api.scala._
+import org.apache.flink.table.utils.TableTestUtil._
+import org.junit.Test
+
+class DistinctAggregateTest extends TableTestBase {
+
+ @Test
+ def testSingleDistinctAggregate(): Unit = {
+ val util = batchTestUtil()
+ util.addTable[(Int, Long, String)]("MyTable", 'a, 'b, 'c)
+
+ val sqlQuery = "SELECT COUNT(DISTINCT a) FROM MyTable"
+
+ val expected = unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "a")
+ ),
+ term("groupBy", "a"),
+ term("select", "a")
+ ),
+ tuples(List(null)),
+ term("values", "a")
+ ),
+ term("union", "a")
+ ),
+ term("select", "COUNT(a) AS EXPR$0")
+ )
+
+ util.verifySql(sqlQuery, expected)
+ }
+
+ @Test
+ def testMultiDistinctAggregateOnSameColumn(): Unit = {
+ val util = batchTestUtil()
+ util.addTable[(Int, Long, String)]("MyTable", 'a, 'b, 'c)
+
+ val sqlQuery = "SELECT COUNT(DISTINCT a), SUM(DISTINCT a),
MAX(DISTINCT a) FROM MyTable"
+
+ val expected = unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "a")
+ ),
+ term("groupBy", "a"),
+ term("select", "a")
+ ),
+ tuples(List(null)),
+ term("values", "a")
+ ),
+ term("union", "a")
+ ),
+ term("select", "COUNT(a) AS EXPR$0", "SUM(a) AS EXPR$1", "MAX(a) AS
EXPR$2")
+ )
+
+ util.verifySql(sqlQuery, expected)
+ }
+
+ @Test
+ def testSingleDistinctAggregateAndOneOrMultiNonDistinctAggregate(): Unit
= {
+ val util = batchTestUtil()
+ util.addTable[(Int, Long, String)]("MyTable", 'a, 'b, 'c)
+
+ // case 0x00: DISTINCT on COUNT and Non-DISTINCT on others
+ val sqlQuery0 = "SELECT COUNT(DISTINCT a), SUM(b) FROM MyTable"
+
+ val expected0 = unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "a", "b")
+ ),
+ term("groupBy", "a"),
+ term("select", "a", "SUM(b) AS EXPR$1")
+ ),
+ tuples(List(null, null)),
+ term("values", "a", "EXPR$1")
+ ),
+ term("union", "a", "EXPR$1")
+ ),
+ term("select", "COUNT(a) AS EXPR$0", "SUM(EXPR$1) AS EXPR$1")
+ )
+
+ util.verifySql(sqlQuery0, expected0)
+
+ // case 0x01: Non-DISTINCT on COUNT and DISTINCT on others
+ val sqlQuery1 = "SELECT COUNT(a), SUM(DISTINCT b) FROM MyTable"
+
+ val expected1 = unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "a", "b")
+ ),
+ term("groupBy", "b"),
+ term("select", "b", "COUNT(a) AS EXPR$0")
+ ),
+ tuples(List(null, null)),
+ term("values", "b", "EXPR$0")
+ ),
+ term("union", "b", "EXPR$0")
+ ),
+ term("select", "$SUM0(EXPR$0) AS EXPR$0", "SUM(b) AS EXPR$1")
+ )
+
+ util.verifySql(sqlQuery1, expected1)
+ }
+
+ @Test
+ def testMultiDistinctAggregateOnDifferentColumn(): Unit = {
+ val util = batchTestUtil()
+ util.addTable[(Int, Long, String)]("MyTable", 'a, 'b, 'c)
+
+ val sqlQuery = "SELECT COUNT(DISTINCT a), SUM(DISTINCT b) FROM MyTable"
+
+ val expected = binaryNode(
+ "DataSetSingleRowJoin",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "a")
+ ),
+ term("groupBy", "a"),
+ term("select", "a")
+ ),
+ tuples(List(null)),
+ term("values", "a")
+ ),
+ term("union", "a")
+ ),
+ term("select", "COUNT(a) AS EXPR$0")
+ ),
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetUnion",
+ unaryNode(
+ "DataSetValues",
+ unaryNode(
+ "DataSetAggregate",
+ unaryNode(
+ "DataSetCalc",
+ batchTableNode(0),
+ term("select", "b")
+ ),
+ term("groupBy", "b"),
+ term("select", "b")
+ ),
+ tuples(List(null)),
+ term("values", "b")
+ ),
+ term("union", "b")
+ ),
+ term("select", "SUM(b) AS EXPR$1")
+ ),
+ term("where", "true"),
+ term("join", "EXPR$0", "EXPR$1"),
+ term("joinType", "NestedLoopJoin")
--- End diff --
This join type is not supported. It would fail during execution.
> DISTINCT aggregate function support for SQL queries
> ---------------------------------------------------
>
> Key: FLINK-3475
> URL: https://issues.apache.org/jira/browse/FLINK-3475
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: Chengxiang Li
> Assignee: Zhenghua Gao
>
> DISTINCT aggregate function may be able to reuse the aggregate function
> instead of separate implementation, and let Flink runtime take care of
> duplicate records.
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