forked from criteo-forks/flink
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
We expect both sides of the upper-level 'DataStreamGroupWindowAggregate' to have different sliding windows (6hrs and 2hrs), but the planner gives a plan where both sides have the same sliding window.
- Loading branch information
Benoit Hanotte
committed
Jan 13, 2020
1 parent
d76f216
commit 3d388f1
Showing
1 changed file
with
115 additions
and
0 deletions.
There are no files selected for viewing
115 changes: 115 additions & 0 deletions
115
...ble/flink-table-planner/src/test/scala/org/apache/flink/table/plan/UnionWindowsTest.scala
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| /* | ||
| * 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.plan | ||
|
|
||
| import org.apache.flink.api.common.typeinfo.TypeInformation | ||
| import org.apache.flink.table.api.Types | ||
| import org.apache.flink.table.api.scala._ | ||
| import org.apache.flink.table.typeutils.TimeIndicatorTypeInfo | ||
| import org.apache.flink.table.utils.TableTestBase | ||
| import org.apache.flink.types.Row | ||
| import org.junit.Test | ||
|
|
||
| class UnionWindowsTest extends TableTestBase { | ||
|
|
||
| @Test | ||
| def testUnionDifferentWindows(): Unit = { | ||
| val tablesTypeInfo = Types.ROW( | ||
| Array("timestamp"), | ||
| Array[TypeInformation[_]](TimeIndicatorTypeInfo.ROWTIME_INDICATOR) | ||
| ) | ||
|
|
||
| val util = streamTestUtil() | ||
| util.addTable[Row]("table_display", 'timestamp)(tablesTypeInfo) | ||
| util.addTable[Row]("table_click", 'timestamp)(tablesTypeInfo) | ||
|
|
||
|
|
||
| // The SQL is a bit complex, but it seems that removing the lower-levels UNION between clicks and display makes the | ||
| // problem disappear. | ||
| val sqlQuery = | ||
| """ | ||
| |WITH displays AS ( | ||
| | SELECT `timestamp`, 1 as nb_displays, 0 as nb_clicks FROM table_display | ||
| |), | ||
| | | ||
| |clicks AS ( | ||
| | SELECT `timestamp`, 0 as nb_displays, 1 as nb_clicks FROM table_click | ||
| |), | ||
| | | ||
| |counts_2h AS ( | ||
| | SELECT | ||
| | SUM(nb_clicks) / SUM(nb_displays) as ctr, | ||
| | HOP_ROWTIME(`timestamp`, INTERVAL '1' HOUR, INTERVAL '2' HOUR) as `timestamp` | ||
| | FROM ( | ||
| | (SELECT * FROM displays) | ||
| | UNION ALL | ||
| | (SELECT * FROM clicks) | ||
| | ) t | ||
| | GROUP BY HOP(`timestamp`, INTERVAL '1' HOUR, INTERVAL '2' HOUR) | ||
| |), | ||
| | | ||
| |counts_6h AS ( | ||
| | SELECT | ||
| | SUM(nb_clicks) / SUM(nb_displays) as ctr, | ||
| | HOP_ROWTIME(`timestamp`, INTERVAL '1' HOUR, INTERVAL '6' HOUR) as `timestamp` | ||
| | FROM ( | ||
| | (SELECT * FROM displays) | ||
| | UNION ALL | ||
| | (SELECT * FROM clicks) | ||
| | ) t | ||
| | GROUP BY HOP(`timestamp`, INTERVAL '1' HOUR, INTERVAL '6' HOUR) | ||
| |) | ||
| | | ||
| |SELECT | ||
| | TUMBLE_END(`timestamp`, INTERVAL '1' HOUR) as `timestamp`, | ||
| | MAX(ctr) | ||
| |FROM ( | ||
| | (SELECT * FROM counts_6h) | ||
| | UNION ALL | ||
| | (SELECT * FROM counts_2h) | ||
| |) t | ||
| |GROUP BY TUMBLE(`timestamp`, INTERVAL '1' HOUR) | ||
| |""".stripMargin | ||
|
|
||
| // We expect both sides of the upper-level 'DataStreamGroupWindowAggregate' to have different sliding windows (6hrs and 2hrs), | ||
| // but the planner gives a plan where both sides have the same sliding window. | ||
| val expected = | ||
| """DataStreamCalc(select=[w$end AS timestamp, CAST(EXPR$1) AS EXPR$1]) | ||
| | DataStreamGroupWindowAggregate(window=[TumblingGroupWindow('w$, 'timestamp, 3600000.millis)], select=[MAX(ctr) AS EXPR$1, start('w$) AS w$start, end('w$) AS w$end, rowtime('w$) AS w$rowtime, proctime('w$) AS w$proctime]) | ||
| | DataStreamUnion(all=[true], union all=[ctr, timestamp]) | ||
| | DataStreamCalc(select=[/(CAST($f0), CAST($f1)) AS ctr, w$rowtime AS timestamp]) | ||
| | DataStreamGroupWindowAggregate(window=[SlidingGroupWindow('w$, 'timestamp, 21600000.millis, 3600000.millis)], select=[SUM(nb_clicks) AS $f0, SUM(nb_displays) AS $f1, start('w$) AS w$start, end('w$) AS w$end, rowtime('w$) AS w$rowtime, proctime('w$) AS w$proctime]) | ||
| | DataStreamUnion(all=[true], union all=[timestamp, nb_displays, nb_clicks]) | ||
| | DataStreamCalc(select=[timestamp, 1 AS nb_displays, 0 AS nb_clicks]) | ||
| | DataStreamScan(id=[1], fields=[timestamp]) | ||
| | DataStreamCalc(select=[timestamp, 0 AS nb_displays, 1 AS nb_clicks]) | ||
| | DataStreamScan(id=[2], fields=[timestamp]) | ||
| | DataStreamCalc(select=[/(CAST($f0), CAST($f1)) AS ctr, w$rowtime AS timestamp]) | ||
| | DataStreamGroupWindowAggregate(window=[SlidingGroupWindow('w$, 'timestamp, 7200000.millis, 3600000.millis)], select=[SUM(nb_clicks) AS $f0, SUM(nb_displays) AS $f1, start('w$) AS w$start, end('w$) AS w$end, rowtime('w$) AS w$rowtime, proctime('w$) AS w$proctime]) | ||
| | DataStreamUnion(all=[true], union all=[timestamp, nb_displays, nb_clicks]) | ||
| | DataStreamCalc(select=[timestamp, 1 AS nb_displays, 0 AS nb_clicks]) | ||
| | DataStreamScan(id=[1], fields=[timestamp]) | ||
| | DataStreamCalc(select=[timestamp, 0 AS nb_displays, 1 AS nb_clicks]) | ||
| | DataStreamScan(id=[2], fields=[timestamp]) | ||
| |""".stripMargin | ||
|
|
||
| util.verifySql(sqlQuery, expected) | ||
| } | ||
|
|
||
| } |