xuyangzhong commented on code in PR #23886: URL: https://github.com/apache/flink/pull/23886#discussion_r1423461099
########## flink-table/flink-table-planner/src/test/java/org/apache/flink/table/planner/plan/nodes/exec/stream/WindowAggregateEventTimeRestoreTest.java: ########## @@ -0,0 +1,60 @@ +/* + * 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.planner.plan.nodes.exec.stream; + +import org.apache.flink.table.planner.plan.nodes.exec.testutils.RestoreTestBase; +import org.apache.flink.table.test.program.TableTestProgram; + +import java.util.Arrays; +import java.util.List; + +/** Restore tests for {@link StreamExecWindowAggregate}. */ +public class WindowAggregateEventTimeRestoreTest extends RestoreTestBase { + + public WindowAggregateEventTimeRestoreTest() { + super(StreamExecWindowAggregate.class); + } + + @Override + public List<TableTestProgram> programs() { + return Arrays.asList( + WindowAggregateTestPrograms.GROUP_TUMBLE_WINDOW_EVENT_TIME, Review Comment: Just wonder why add prefix 'GROUP_' each test? ########## flink-table/flink-table-planner/src/test/java/org/apache/flink/table/planner/plan/nodes/exec/stream/WindowAggregateTestPrograms.java: ########## @@ -0,0 +1,494 @@ +/* + * 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.planner.plan.nodes.exec.stream; + +import org.apache.flink.table.api.config.OptimizerConfigOptions; +import org.apache.flink.table.planner.utils.AggregatePhaseStrategy; +import org.apache.flink.table.test.program.SinkTestStep; +import org.apache.flink.table.test.program.SourceTestStep; +import org.apache.flink.table.test.program.TableTestProgram; +import org.apache.flink.types.Row; + +import java.math.BigDecimal; + +/** {@link TableTestProgram} definitions for testing {@link StreamExecWindowAggregate}. */ +public class WindowAggregateTestPrograms { + + static final Row[] BEFORE_DATA = { + Row.of("2020-10-10 00:00:01", 1, 1d, 1f, new BigDecimal("1.11"), "Hi", "a"), + Row.of("2020-10-10 00:00:02", 2, 2d, 2f, new BigDecimal("2.22"), "Comment#1", "a"), + Row.of("2020-10-10 00:00:03", 2, 2d, 2f, new BigDecimal("2.22"), "Comment#1", "a"), + Row.of("2020-10-10 00:00:04", 5, 5d, 5f, new BigDecimal("5.55"), null, "a"), + Row.of("2020-10-10 00:00:07", 3, 3d, 3f, null, "Hello", "b"), + // out of order + Row.of("2020-10-10 00:00:06", 6, 6d, 6f, new BigDecimal("6.66"), "Hi", "b"), + Row.of("2020-10-10 00:00:08", 3, null, 3f, new BigDecimal("3.33"), "Comment#2", "a"), + // late event + Row.of("2020-10-10 00:00:04", 5, 5d, null, new BigDecimal("5.55"), "Hi", "a"), + Row.of("2020-10-10 00:00:16", 4, 4d, 4f, new BigDecimal("4.44"), "Hi", "b"), + Row.of("2020-10-10 00:00:32", 7, 7d, 7f, new BigDecimal("7.77"), null, null), + Row.of("2020-10-10 00:00:34", 1, 3d, 3f, new BigDecimal("3.33"), "Comment#3", "b") + }; + + static final Row[] AFTER_DATA = { + Row.of("2020-10-10 00:00:40", 10, 3d, 3f, new BigDecimal("4.44"), "Comment#4", "a"), + Row.of("2020-10-10 00:00:42", 11, 4d, 4f, new BigDecimal("5.44"), "Comment#5", "d"), + Row.of("2020-10-10 00:00:43", 12, 5d, 5f, new BigDecimal("6.44"), "Comment#6", "c"), + Row.of("2020-10-10 00:00:44", 13, 6d, 6f, new BigDecimal("7.44"), "Comment#7", "d") + }; + + static final SourceTestStep SOURCE = + SourceTestStep.newBuilder("source_t") + .addSchema( + "ts STRING", + "a_int INT", + "b_double DOUBLE", + "c_float FLOAT", + "d_bigdec DECIMAL(10, 2)", + "`comment` STRING", + "name STRING", + "`rowtime` AS TO_TIMESTAMP(`ts`)", + "`proctime` AS PROCTIME()", + "WATERMARK for `rowtime` AS `rowtime` - INTERVAL '1' SECOND") + .producedBeforeRestore(BEFORE_DATA) + .producedAfterRestore(AFTER_DATA) + .build(); + + static final String[] TUMBLE_EVENT_TIME_BEFORE_ROWS = { + "+I[a, 2020-10-10T00:00, 2020-10-10T00:00:05, 4, 10, 2]", + "+I[a, 2020-10-10T00:00:05, 2020-10-10T00:00:10, 1, 3, 1]", + "+I[b, 2020-10-10T00:00:05, 2020-10-10T00:00:10, 2, 9, 2]", + "+I[b, 2020-10-10T00:00:15, 2020-10-10T00:00:20, 1, 4, 1]" + }; + + public static final String[] TUMBLE_EVENT_TIME_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:30, 2020-10-10T00:00:35, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:30, 2020-10-10T00:00:35, 1, 7, 0]", + "+I[a, 2020-10-10T00:00:40, 2020-10-10T00:00:45, 1, 10, 1]", + "+I[c, 2020-10-10T00:00:40, 2020-10-10T00:00:45, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:40, 2020-10-10T00:00:45, 2, 24, 2]" + }; + + static final TableTestProgram GROUP_TUMBLE_WINDOW_EVENT_TIME = + getTableTestProgram( + "window-aggregate-tumble-event-time", + "validates group by using tumbling window with event time", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND)", + TUMBLE_EVENT_TIME_BEFORE_ROWS, + TUMBLE_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_TUMBLE_WINDOW_EVENT_TIME_TWO_PHASE = + getTableTestProgram( + "window-aggregate-tumble-event-time-two-phase", + "validates group by using tumbling window with event time with two phase aggregation", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND)", + TUMBLE_EVENT_TIME_BEFORE_ROWS, + TUMBLE_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_TUMBLE_WINDOW_EVENT_TIME_TWO_PHASE_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-tumble-event-time-two-phase-distinct-split", + "validates group by using tumbling window with event time with two phase aggregation", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND)", + TUMBLE_EVENT_TIME_BEFORE_ROWS, + TUMBLE_EVENT_TIME_AFTER_ROWS, + true); + + static final String[] TUMBLE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS = { + "+I[a, 2020-10-10T00:00:01, 2020-10-10T00:00:06, 4, 10, 2]", + "+I[b, 2020-10-10T00:00:06, 2020-10-10T00:00:11, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:06, 2020-10-10T00:00:11, 1, 3, 1]", + "+I[b, 2020-10-10T00:00:16, 2020-10-10T00:00:21, 1, 4, 1]" + }; + + public static final String[] TUMBLE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:31, 2020-10-10T00:00:36, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:31, 2020-10-10T00:00:36, 1, 7, 0]", + "+I[a, 2020-10-10T00:00:36, 2020-10-10T00:00:41, 1, 10, 1]", + "+I[c, 2020-10-10T00:00:41, 2020-10-10T00:00:46, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:41, 2020-10-10T00:00:46, 2, 24, 2]" + }; + + static final TableTestProgram GROUP_TUMBLE_WINDOW_EVENT_TIME_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-tumble-event-time-with-offset", + "validates group by using tumbling window with event time with an offset", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '1' SECOND)", + TUMBLE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + TUMBLE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram GROUP_TUMBLE_WINDOW_EVENT_TIME_TWO_PHASE_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-tumble-event-time-two-phase-with-offset", + "validates group by using tumbling window with event time with two phase aggregation with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '1' SECOND)", + TUMBLE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + TUMBLE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram + GROUP_TUMBLE_WINDOW_EVENT_TIME_TWO_PHASE_WITH_OFFSET_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-tumble-event-time-two-phase-with-offset-distinct-split", + "validates group by using tumbling window with event time with two phase aggregation with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "TUMBLE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '1' SECOND)", + TUMBLE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + TUMBLE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS, + true); + + static final String[] HOP_EVENT_TIME_BEFORE_ROWS = { + "+I[a, 2020-10-09T23:59:55, 2020-10-10T00:00:05, 4, 10, 2]", + "+I[b, 2020-10-10T00:00, 2020-10-10T00:00:10, 2, 9, 2]", + "+I[a, 2020-10-10T00:00, 2020-10-10T00:00:10, 6, 18, 3]", + "+I[b, 2020-10-10T00:00:05, 2020-10-10T00:00:15, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:05, 2020-10-10T00:00:15, 1, 3, 1]", + "+I[b, 2020-10-10T00:00:10, 2020-10-10T00:00:20, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:15, 2020-10-10T00:00:25, 1, 4, 1]" + }; + + public static final String[] HOP_EVENT_TIME_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:25, 2020-10-10T00:00:35, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:25, 2020-10-10T00:00:35, 1, 7, 0]", + "+I[b, 2020-10-10T00:00:30, 2020-10-10T00:00:40, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:30, 2020-10-10T00:00:40, 1, 7, 0]", + "+I[c, 2020-10-10T00:00:35, 2020-10-10T00:00:45, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:35, 2020-10-10T00:00:45, 2, 24, 2]", + "+I[a, 2020-10-10T00:00:35, 2020-10-10T00:00:45, 1, 10, 1]", + "+I[d, 2020-10-10T00:00:40, 2020-10-10T00:00:50, 2, 24, 2]", + "+I[a, 2020-10-10T00:00:40, 2020-10-10T00:00:50, 1, 10, 1]", + "+I[c, 2020-10-10T00:00:40, 2020-10-10T00:00:50, 1, 12, 1]" + }; + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME = + getTableTestProgram( + "window-aggregate-hop-event-time", + "validates group by using a hop window with event time", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND)", + HOP_EVENT_TIME_BEFORE_ROWS, + HOP_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME_TWO_PHASE = + getTableTestProgram( + "window-aggregate-hop-event-time-two-phase", + "validates group by using a hop window with event time with two phase aggregation", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND)", + HOP_EVENT_TIME_BEFORE_ROWS, + HOP_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME_TWO_PHASE_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-hop-event-time-two-phase-distinct-split", + "validates group by using a hop window with event time with two phase aggregation", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND)", + HOP_EVENT_TIME_BEFORE_ROWS, + HOP_EVENT_TIME_AFTER_ROWS, + true); + + static final String[] HOP_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS = { + "+I[a, 2020-10-09T23:59:56, 2020-10-10T00:00:06, 4, 10, 2]", + "+I[b, 2020-10-10T00:00:01, 2020-10-10T00:00:11, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:01, 2020-10-10T00:00:11, 6, 18, 3]", + "+I[b, 2020-10-10T00:00:06, 2020-10-10T00:00:16, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:06, 2020-10-10T00:00:16, 1, 3, 1]", + "+I[b, 2020-10-10T00:00:11, 2020-10-10T00:00:21, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:16, 2020-10-10T00:00:26, 1, 4, 1]" + }; + + public static final String[] HOP_EVENT_TIME_WITH_OFFSET_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:26, 2020-10-10T00:00:36, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:26, 2020-10-10T00:00:36, 1, 7, 0]", + "+I[a, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 10, 1]", + "+I[b, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 7, 0]", + "+I[c, 2020-10-10T00:00:36, 2020-10-10T00:00:46, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:36, 2020-10-10T00:00:46, 2, 24, 2]", + "+I[a, 2020-10-10T00:00:36, 2020-10-10T00:00:46, 1, 10, 1]", + "+I[d, 2020-10-10T00:00:41, 2020-10-10T00:00:51, 2, 24, 2]", + "+I[c, 2020-10-10T00:00:41, 2020-10-10T00:00:51, 1, 12, 1]" + }; + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-hop-event-time-with-offset", + "validates group by using a hop window with event time with an offset", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND, INTERVAL '1' SECOND)", + HOP_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + HOP_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME_TWO_PHASE_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-hop-event-time-two-phase-with-offset", + "validates group by using a hop window with event time with two phase aggregation with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND, INTERVAL '1' SECOND)", + HOP_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + HOP_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram GROUP_HOP_WINDOW_EVENT_TIME_TWO_PHASE_WITH_OFFSET_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-hop-event-time-two-phase-with-offset-distinct-split", + "validates group by using a hop window with event time with two phase aggregation with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "HOP(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '10' SECOND, INTERVAL '1' SECOND)", + HOP_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + HOP_EVENT_TIME_WITH_OFFSET_AFTER_ROWS, + true); + + static final String[] CUMULATE_EVENT_TIME_BEFORE_ROWS = { + "+I[a, 2020-10-10T00:00, 2020-10-10T00:00:05, 4, 10, 2]", + "+I[b, 2020-10-10T00:00, 2020-10-10T00:00:10, 2, 9, 2]", + "+I[a, 2020-10-10T00:00, 2020-10-10T00:00:10, 6, 18, 3]", + "+I[b, 2020-10-10T00:00, 2020-10-10T00:00:15, 2, 9, 2]", + "+I[a, 2020-10-10T00:00, 2020-10-10T00:00:15, 6, 18, 3]", + "+I[b, 2020-10-10T00:00:15, 2020-10-10T00:00:20, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:15, 2020-10-10T00:00:25, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:15, 2020-10-10T00:00:30, 1, 4, 1]" + }; + + public static final String[] CUMULATE_EVENT_TIME_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:30, 2020-10-10T00:00:35, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:30, 2020-10-10T00:00:35, 1, 7, 0]", + "+I[b, 2020-10-10T00:00:30, 2020-10-10T00:00:40, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:30, 2020-10-10T00:00:40, 1, 7, 0]", + "+I[b, 2020-10-10T00:00:30, 2020-10-10T00:00:45, 1, 1, 1]", + "+I[c, 2020-10-10T00:00:30, 2020-10-10T00:00:45, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:30, 2020-10-10T00:00:45, 2, 24, 2]", + "+I[a, 2020-10-10T00:00:30, 2020-10-10T00:00:45, 1, 10, 1]", + "+I[null, 2020-10-10T00:00:30, 2020-10-10T00:00:45, 1, 7, 0]" + }; + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME = + getTableTestProgram( + "window-aggregate-cumulate-event-time", + "validates group by using cumulate window with event time", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND)", + CUMULATE_EVENT_TIME_BEFORE_ROWS, + CUMULATE_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME_TWO_PHASE = + getTableTestProgram( + "window-aggregate-cumulate-event-time-two-phase", + "validates group by using cumulate window with event time with two phase aggregation", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND)", + CUMULATE_EVENT_TIME_BEFORE_ROWS, + CUMULATE_EVENT_TIME_AFTER_ROWS); + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME_TWO_PHASE_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-cumulate-event-time-two-phase-distinct-split", + "validates group by using cumulate window with event time with two phase aggregation with distinct split", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND)", + CUMULATE_EVENT_TIME_BEFORE_ROWS, + CUMULATE_EVENT_TIME_AFTER_ROWS, + true); + + static final String[] CUMULATE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS = { + "+I[a, 2020-10-10T00:00:01, 2020-10-10T00:00:06, 4, 10, 2]", + "+I[b, 2020-10-10T00:00:01, 2020-10-10T00:00:11, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:01, 2020-10-10T00:00:11, 6, 18, 3]", + "+I[b, 2020-10-10T00:00:01, 2020-10-10T00:00:16, 2, 9, 2]", + "+I[a, 2020-10-10T00:00:01, 2020-10-10T00:00:16, 6, 18, 3]", + "+I[b, 2020-10-10T00:00:16, 2020-10-10T00:00:21, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:16, 2020-10-10T00:00:26, 1, 4, 1]", + "+I[b, 2020-10-10T00:00:16, 2020-10-10T00:00:31, 1, 4, 1]" + }; + + public static final String[] CUMULATE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS = { + "+I[b, 2020-10-10T00:00:31, 2020-10-10T00:00:36, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:31, 2020-10-10T00:00:36, 1, 7, 0]", + "+I[a, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 10, 1]", + "+I[b, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 1, 1]", + "+I[null, 2020-10-10T00:00:31, 2020-10-10T00:00:41, 1, 7, 0]", + "+I[b, 2020-10-10T00:00:31, 2020-10-10T00:00:46, 1, 1, 1]", + "+I[c, 2020-10-10T00:00:31, 2020-10-10T00:00:46, 1, 12, 1]", + "+I[d, 2020-10-10T00:00:31, 2020-10-10T00:00:46, 2, 24, 2]", + "+I[a, 2020-10-10T00:00:31, 2020-10-10T00:00:46, 1, 10, 1]", + "+I[null, 2020-10-10T00:00:31, 2020-10-10T00:00:46, 1, 7, 0]" + }; + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-cumulate-event-time-with-offset", + "validates group by using cumulate window with event time with an offset", + AggregatePhaseStrategy.ONE_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND, INTERVAL '1' SECOND)", + CUMULATE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + CUMULATE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME_TWO_PHASE_WITH_OFFSET = + getTableTestProgram( + "window-aggregate-cumulate-event-time-two-phase-with-offset", + "validates group by using cumulate window with event time with two phase aggregation with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND, INTERVAL '1' SECOND)", + CUMULATE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + CUMULATE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS); + + static final TableTestProgram GROUP_CUMULATE_WINDOW_EVENT_TIME_WITH_OFFSET_DISTINCT_SPLIT = + getTableTestProgram( + "window-aggregate-cumulate-event-time-with-offset-distinct-split", + "validates group by using cumulate window with event time with an offset", + AggregatePhaseStrategy.TWO_PHASE.toString(), + "CUMULATE(TABLE source_t, DESCRIPTOR(rowtime), INTERVAL '5' SECOND, INTERVAL '15' SECOND, INTERVAL '1' SECOND)", + CUMULATE_EVENT_TIME_WITH_OFFSET_BEFORE_ROWS, + CUMULATE_EVENT_TIME_WITH_OFFSET_AFTER_ROWS, + true); + + private static TableTestProgram getTableTestProgram( + final String id, + final String description, + final String aggPhaseStrategy, + final String windowSql, + final String[] beforeRows, + final String[] afterRows) { + return getTableTestProgram( + id, description, aggPhaseStrategy, windowSql, beforeRows, afterRows, false); + } + + private static TableTestProgram getTableTestProgram( + final String id, + final String description, + final String aggPhaseStrategy, + final String windowSql, + final String[] beforeRows, + final String[] afterRows, + final Boolean enableDistinctAggSplit) { + final String sql = + String.format( + "INSERT INTO sink_t SELECT " + + "name, " + + "window_start, " + + "window_end, " + + "COUNT(*), " + + "SUM(a_int), " + + "COUNT(DISTINCT `comment`) " + + "FROM TABLE(%s)\n" + + "GROUP BY name, window_start, window_end", + windowSql); + + TableTestProgram.Builder builder = TableTestProgram.of(id, description); + if (enableDistinctAggSplit) { + builder.setupConfig( + OptimizerConfigOptions.TABLE_OPTIMIZER_DISTINCT_AGG_SPLIT_ENABLED, true); + } + + return builder.setupConfig( + OptimizerConfigOptions.TABLE_OPTIMIZER_AGG_PHASE_STRATEGY, aggPhaseStrategy) + .setupTableSource(SOURCE) + .setupTableSink( + SinkTestStep.newBuilder("sink_t") + .addSchema( + "name STRING", + "window_start TIMESTAMP(3)", + "window_end TIMESTAMP(3)", + "cnt BIGINT", + "sum_int INT", + "distinct_cnt BIGINT") + .consumedBeforeRestore(beforeRows) + .consumedAfterRestore(afterRows) + .build()) + .runSql(sql) + .build(); + } + + static final TableTestProgram TUMBLE_WINDOW_PROC_TIME = + TableTestProgram.of( + "window-aggregate-tumble-proc-time", + "validates group by using tumbling window with processing time") + .setupTableSource(SOURCE) + .setupTableSink( + SinkTestStep.newBuilder("sink_t") + .addSchema( + "name STRING", + "cnt BIGINT", + "sum_int INT", + "distinct_cnt BIGINT") + .consumedBeforeRestore( + "+I[a, 6, 18, 3]", + "+I[null, 1, 7, 0]", + "+I[b, 4, 14, 3]") + .consumedAfterRestore( + "+I[a, 1, 10, 1]", "+I[c, 1, 12, 1]", "+I[d, 2, 24, 2]") + .build()) + .runSql( + "INSERT INTO sink_t SELECT " + + "name, " + + "COUNT(*), " + + "SUM(a_int), " + + "COUNT(DISTINCT `comment`) " + + "FROM source_t " + + "GROUP BY name, TUMBLE(proctime, INTERVAL '5' SECOND)") Review Comment: All tests in this class are about window agg, but this test is related about group window agg, right? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
