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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2021/01/27 11:43:00 UTC
[GitHub] [flink] HuangXingBo opened a new pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
HuangXingBo opened a new pull request #14775:
URL: https://github.com/apache/flink/pull/14775
## What is the purpose of the change
*This pull request will Introduce `PythonStreamGroupWindowAggregateOperator` for supporting Python Stream Group Window agg*
## Brief change log
- *Introduce `PythonStreamGroupWindowAggregateOperator`*
## Verifying this change
This change added tests and can be verified as follows:
- *UT in `PythonStreamGroupWindowAggregateOperatorTest`*
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): (no)
- The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (no)
- The serializers: (no)
- The runtime per-record code paths (performance sensitive): (no)
- Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn/Mesos, ZooKeeper: (no)
- The S3 file system connector: (no)
## Documentation
- Does this pull request introduce a new feature? (no)
- If yes, how is the feature documented? (not applicable)
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
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[GitHub] [flink] WeiZhong94 commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
WeiZhong94 commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r568385212
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
Review comment:
If we do not plan to support early fire/late fire in the first version of the Python group window aggregate, the `allowedLateness` field is unnecessary/
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569215914
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
Review comment:
Yes. It can remove much deduplicate code in the test.
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] WeiZhong94 commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
WeiZhong94 commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r582663613
##########
File path: flink-python/pyflink/proto/flink-fn-execution.proto
##########
@@ -194,8 +194,11 @@ message UserDefinedAggregateFunctions {
// True if the count(*) agg is inserted by the planner.
bool count_star_inserted = 11;
+ // Whether has Group Window.
+ bool has_group_window = 12;
Review comment:
We can use `HasField` method in Python to check if the field exists.
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
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* f2494304b2c16c02b89ee96864ff1e61f446f203 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=13697)
* d8f3c75d291d0050ee56f82aa019418718bf87a5 UNKNOWN
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
* 3f0036901cb06dd1f641d0e7243c6da486602b86 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=12861)
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569214315
##########
File path: flink-python/pyflink/proto/flink-fn-execution.proto
##########
@@ -147,6 +147,30 @@ message UserDefinedAggregateFunction {
bool takes_row_as_input = 6;
}
+message GroupWindow {
+ enum WindowType {
+ TUMBLING_GROUP_WINDOW = 0;
+ SLIDING_GROUP_WINDOW = 1;
+ SESSION_GROUP_WINDOW = 2;
+ }
+
+ WindowType window_type = 1;
+
+ bool is_time_window = 2;
+
+ int64 window_slide = 3;
+
+ int64 window_size = 4;
+
Review comment:
Good suggestion. Schema information will only be transmitted once, adding one more field will not affect performance.
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[GitHub] [flink] HuangXingBo commented on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-788549281
The failed test has been reported in [FLINK-21147](https://issues.apache.org/jira/browse/FLINK-21147) which is not related to this PR
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
* 9cdc750c7db649b3ce4571c0d80fcdd3eb2acfdf Azure: [SUCCESS](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=12896)
* f2494304b2c16c02b89ee96864ff1e61f446f203 Azure: [PENDING](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=13697)
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[GitHub] [flink] WeiZhong94 commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
WeiZhong94 commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r568352971
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/AbstractPythonStreamAggregateOperator.java
##########
@@ -76,8 +61,7 @@
*/
Review comment:
The java doc needs to be changed.
##########
File path: flink-python/pyflink/proto/flink-fn-execution.proto
##########
@@ -147,6 +147,30 @@ message UserDefinedAggregateFunction {
bool takes_row_as_input = 6;
}
+message GroupWindow {
+ enum WindowType {
+ TUMBLING_GROUP_WINDOW = 0;
+ SLIDING_GROUP_WINDOW = 1;
+ SESSION_GROUP_WINDOW = 2;
+ }
+
+ WindowType window_type = 1;
+
+ bool is_time_window = 2;
+
+ int64 window_slide = 3;
+
+ int64 window_size = 4;
+
Review comment:
Currently the gap of the session window stores in the `window_size` field. I think we can add a new `window_gap` field to make it easier to understand.
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
Review comment:
It would be better if we extract some utility method to create these stream record in one line.
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(Long.MAX_VALUE));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredOnCheckpoint() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(10000L));
+ // checkpoint trigger finishBundle
+ testHarness.prepareSnapshotPreBarrier(0L);
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByCount() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 4);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+
+ testHarness.processWatermark(new Watermark(10000L));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByTime() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ conf.setLong(PythonOptions.MAX_BUNDLE_TIME_MILLS, 1000L);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(20000L));
+ assertOutputEquals(
+ "FinishBundle should not be triggered.", expectedOutput, testHarness.getOutput());
+
+ testHarness.setProcessingTime(1000L);
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.close();
+ }
+
+ @Override
+ public LogicalType[] getOutputLogicalType() {
+ return new LogicalType[] {
+ DataTypes.STRING().getLogicalType(), DataTypes.BIGINT().getLogicalType()
+ };
+ }
+
+ @Override
+ public RowType getInputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new VarCharType()),
+ new RowType.RowField("f3", new BigIntType()),
+ new RowType.RowField("rowTime", new BigIntType())));
+ }
+
+ @Override
+ public RowType getOutputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new BigIntType()),
+ new RowType.RowField("windowStart", new TimestampType(3)),
+ new RowType.RowField("windowEnd", new TimestampType(3))));
+ }
+
+ @Override
+ OneInputStreamOperator getTestOperator(Configuration config) {
+ long size = 10000L;
+ long slide = 5000L;
+ SlidingWindowAssigner windowAssigner =
+ SlidingWindowAssigner.of(Duration.ofMillis(size), Duration.ofMillis(slide))
+ .withEventTime();
+ PlannerWindowReference windowRef =
+ new PlannerWindowReference("w$", new Some<>(new TimestampType(3)));
+ LogicalWindow window =
+ new SlidingGroupWindow(
+ windowRef,
+ new FieldReferenceExpression(
+ "rowtime",
+ new AtomicDataType(
+ new TimestampType(true, TimestampKind.ROWTIME, 3)),
+ 0,
+ 3),
+ intervalOfMillis(size),
+ intervalOfMillis(slide));
+ return new PassThroughPythonStreamGroupWindowAggregateOperator(
+ config,
+ getInputType(),
+ getOutputType(),
+ new PythonAggregateFunctionInfo[] {
+ new PythonAggregateFunctionInfo(
+ PythonScalarFunctionOperatorTestBase.DummyPythonFunction.INSTANCE,
+ new Integer[] {2},
+ -1,
+ false)
+ },
+ getGrouping(),
+ -1,
+ false,
+ false,
+ 3,
+ windowAssigner,
+ window,
+ 0,
+ new int[] {0, 1});
+ }
+
+ /** PassThroughPythonStreamGroupWindowAggregateOperator. */
+ public static class PassThroughPythonStreamGroupWindowAggregateOperator<K>
Review comment:
It would be better if we extract this class to a independent java file. It is so large.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
+
+ private int timerDataLength;
+
+ private int keyLength;
+
+ public PythonStreamGroupWindowAggregateOperator(
+ Configuration config,
+ RowType inputType,
+ RowType outputType,
+ PythonAggregateFunctionInfo[] aggregateFunctions,
+ DataViewUtils.DataViewSpec[][] dataViewSpecs,
+ int[] grouping,
+ int indexOfCountStar,
+ boolean generateUpdateBefore,
+ boolean countStarInserted,
+ int inputTimeFieldIndex,
+ WindowAssigner<W> windowAssigner,
+ LogicalWindow window,
+ long allowedLateness,
+ int[] namedProperties) {
+ super(
+ config,
+ inputType,
+ outputType,
+ aggregateFunctions,
+ dataViewSpecs,
+ grouping,
+ indexOfCountStar,
+ generateUpdateBefore);
+ this.countStarInserted = countStarInserted;
+ this.inputTimeFieldIndex = inputTimeFieldIndex;
+ this.windowAssigner = windowAssigner;
+ this.allowedLateness = allowedLateness;
+ this.namedProperties = namedProperties;
+ buildWindow(window);
+ }
+
+ @Override
+ public void open() throws Exception {
+ windowSerializer = windowAssigner.getWindowSerializer(new ExecutionConfig());
+ internalTimerService = getInternalTimerService("window-timers", windowSerializer, this);
+ if (isTimeWindow) {
Review comment:
How about add some explanation about the structure of the timer data？
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
Review comment:
It would be better if we extract some utility method to create these stream record in one line.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
Review comment:
If we do not plan to support early fire/late fire in the first version of the Python group window aggregate, the `allowedLateness` field is unnecessary/
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
+
+ private int timerDataLength;
+
+ private int keyLength;
Review comment:
ditto
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
Review comment:
This field can also be transient.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
Review comment:
Why not 3?
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
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flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
* 3f0036901cb06dd1f641d0e7243c6da486602b86 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=12861)
* 9cdc750c7db649b3ce4571c0d80fcdd3eb2acfdf Azure: [PENDING](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=12896)
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569215043
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
Review comment:
Yes. I will remove the allowedLateness.
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[GitHub] [flink] HuangXingBo closed pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo closed pull request #14775:
URL: https://github.com/apache/flink/pull/14775
----------------------------------------------------------------
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569215043
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
Review comment:
Yes. I will remove the allowedLateness.
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569216546
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(Long.MAX_VALUE));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredOnCheckpoint() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(10000L));
+ // checkpoint trigger finishBundle
+ testHarness.prepareSnapshotPreBarrier(0L);
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByCount() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 4);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+
+ testHarness.processWatermark(new Watermark(10000L));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByTime() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ conf.setLong(PythonOptions.MAX_BUNDLE_TIME_MILLS, 1000L);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(20000L));
+ assertOutputEquals(
+ "FinishBundle should not be triggered.", expectedOutput, testHarness.getOutput());
+
+ testHarness.setProcessingTime(1000L);
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.close();
+ }
+
+ @Override
+ public LogicalType[] getOutputLogicalType() {
+ return new LogicalType[] {
+ DataTypes.STRING().getLogicalType(), DataTypes.BIGINT().getLogicalType()
+ };
+ }
+
+ @Override
+ public RowType getInputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new VarCharType()),
+ new RowType.RowField("f3", new BigIntType()),
+ new RowType.RowField("rowTime", new BigIntType())));
+ }
+
+ @Override
+ public RowType getOutputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new BigIntType()),
+ new RowType.RowField("windowStart", new TimestampType(3)),
+ new RowType.RowField("windowEnd", new TimestampType(3))));
+ }
+
+ @Override
+ OneInputStreamOperator getTestOperator(Configuration config) {
+ long size = 10000L;
+ long slide = 5000L;
+ SlidingWindowAssigner windowAssigner =
+ SlidingWindowAssigner.of(Duration.ofMillis(size), Duration.ofMillis(slide))
+ .withEventTime();
+ PlannerWindowReference windowRef =
+ new PlannerWindowReference("w$", new Some<>(new TimestampType(3)));
+ LogicalWindow window =
+ new SlidingGroupWindow(
+ windowRef,
+ new FieldReferenceExpression(
+ "rowtime",
+ new AtomicDataType(
+ new TimestampType(true, TimestampKind.ROWTIME, 3)),
+ 0,
+ 3),
+ intervalOfMillis(size),
+ intervalOfMillis(slide));
+ return new PassThroughPythonStreamGroupWindowAggregateOperator(
+ config,
+ getInputType(),
+ getOutputType(),
+ new PythonAggregateFunctionInfo[] {
+ new PythonAggregateFunctionInfo(
+ PythonScalarFunctionOperatorTestBase.DummyPythonFunction.INSTANCE,
+ new Integer[] {2},
+ -1,
+ false)
+ },
+ getGrouping(),
+ -1,
+ false,
+ false,
+ 3,
+ windowAssigner,
+ window,
+ 0,
+ new int[] {0, 1});
+ }
+
+ /** PassThroughPythonStreamGroupWindowAggregateOperator. */
+ public static class PassThroughPythonStreamGroupWindowAggregateOperator<K>
Review comment:
Yes, it is really bad for reading
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569213485
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
+
+ private int timerDataLength;
+
+ private int keyLength;
+
+ public PythonStreamGroupWindowAggregateOperator(
+ Configuration config,
+ RowType inputType,
+ RowType outputType,
+ PythonAggregateFunctionInfo[] aggregateFunctions,
+ DataViewUtils.DataViewSpec[][] dataViewSpecs,
+ int[] grouping,
+ int indexOfCountStar,
+ boolean generateUpdateBefore,
+ boolean countStarInserted,
+ int inputTimeFieldIndex,
+ WindowAssigner<W> windowAssigner,
+ LogicalWindow window,
+ long allowedLateness,
+ int[] namedProperties) {
+ super(
+ config,
+ inputType,
+ outputType,
+ aggregateFunctions,
+ dataViewSpecs,
+ grouping,
+ indexOfCountStar,
+ generateUpdateBefore);
+ this.countStarInserted = countStarInserted;
+ this.inputTimeFieldIndex = inputTimeFieldIndex;
+ this.windowAssigner = windowAssigner;
+ this.allowedLateness = allowedLateness;
+ this.namedProperties = namedProperties;
+ buildWindow(window);
+ }
+
+ @Override
+ public void open() throws Exception {
+ windowSerializer = windowAssigner.getWindowSerializer(new ExecutionConfig());
+ internalTimerService = getInternalTimerService("window-timers", windowSerializer, this);
+ if (isTimeWindow) {
Review comment:
Yes. make sense.
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[GitHub] [flink] flinkbot commented on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot commented on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot commented on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot commented on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768230921
Thanks a lot for your contribution to the Apache Flink project. I'm the @flinkbot. I help the community
to review your pull request. We will use this comment to track the progress of the review.
## Automated Checks
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* No documentation files were touched! Remember to keep the Flink docs up to date!
* **This pull request references an unassigned [Jira ticket](https://issues.apache.org/jira/browse/FLINK-20964).** According to the [code contribution guide](https://flink.apache.org/contributing/contribute-code.html), tickets need to be assigned before starting with the implementation work.
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## Review Progress
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* ❓ 2. There is [consensus] that the contribution should go into to Flink.
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* ❓ 4. The change fits into the overall [architecture].
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
* 9cdc750c7db649b3ce4571c0d80fcdd3eb2acfdf Azure: [SUCCESS](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=12896)
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[GitHub] [flink] amaliujia commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
amaliujia commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569195501
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/AbstractPythonStreamGroupAggregateOperator.java
##########
@@ -0,0 +1,189 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.common.state.ValueState;
+import org.apache.flink.api.common.state.ValueStateDescriptor;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.runtime.state.VoidNamespaceSerializer;
+import org.apache.flink.streaming.api.SimpleTimerService;
+import org.apache.flink.streaming.api.TimerService;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.functions.CleanupState;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Base class for {@link PythonStreamGroupAggregateOperator} and {@link
+ * PythonStreamGroupTableAggregateOperator}.
+ */
+@Internal
+public abstract class AbstractPythonStreamGroupAggregateOperator
+ extends AbstractPythonStreamAggregateOperator
+ implements Triggerable<RowData, VoidNamespace>, CleanupState {
+
+ private static final long serialVersionUID = 1L;
+
+ /** The minimum time in milliseconds until state which was not updated will be retained. */
+ private final long minRetentionTime;
+
+ /** The maximum time in milliseconds until state which was not updated will be retained. */
+ private final long maxRetentionTime;
+
+ /**
+ * Indicates whether state cleaning is enabled. Can be calculated from the `minRetentionTime`.
+ */
+ private final boolean stateCleaningEnabled;
+
+ private transient TimerService timerService;
+
+ // holds the latest registered cleanup timer
+ private transient ValueState<Long> cleanupTimeState;
+
+ public AbstractPythonStreamGroupAggregateOperator(
+ Configuration config,
+ RowType inputType,
+ RowType outputType,
+ PythonAggregateFunctionInfo[] aggregateFunctions,
+ DataViewUtils.DataViewSpec[][] dataViewSpecs,
+ int[] grouping,
+ int indexOfCountStar,
+ boolean generateUpdateBefore,
+ long minRetentionTime,
+ long maxRetentionTime) {
+ super(
+ config,
Review comment:
nit: indents are a bit wired. Seems `config` can be after `super(`?
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] WeiZhong94 commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
WeiZhong94 commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r568352971
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/AbstractPythonStreamAggregateOperator.java
##########
@@ -76,8 +61,7 @@
*/
Review comment:
The java doc needs to be changed.
##########
File path: flink-python/pyflink/proto/flink-fn-execution.proto
##########
@@ -147,6 +147,30 @@ message UserDefinedAggregateFunction {
bool takes_row_as_input = 6;
}
+message GroupWindow {
+ enum WindowType {
+ TUMBLING_GROUP_WINDOW = 0;
+ SLIDING_GROUP_WINDOW = 1;
+ SESSION_GROUP_WINDOW = 2;
+ }
+
+ WindowType window_type = 1;
+
+ bool is_time_window = 2;
+
+ int64 window_slide = 3;
+
+ int64 window_size = 4;
+
Review comment:
Currently the gap of the session window stores in the `window_size` field. I think we can add a new `window_gap` field to make it easier to understand.
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
Review comment:
It would be better if we extract some utility method to create these stream record in one line.
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6));
+ testHarness.processWatermark(Long.MAX_VALUE);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c3 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(Long.MAX_VALUE));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredOnCheckpoint() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(10000L));
+ // checkpoint trigger finishBundle
+ testHarness.prepareSnapshotPreBarrier(0L);
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByCount() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 4);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+
+ testHarness.processWatermark(new Watermark(10000L));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(new Watermark(10000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.processWatermark(20000L);
+ testHarness.close();
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+ }
+
+ @Test
+ public void testFinishBundleTriggeredByTime() throws Exception {
+ Configuration conf = new Configuration();
+ conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10);
+ conf.setLong(PythonOptions.MAX_BUNDLE_TIME_MILLS, 1000L);
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf);
+
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+
+ testHarness.open();
+
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3));
+ testHarness.processElement(
+ new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4));
+ testHarness.processWatermark(new Watermark(20000L));
+ assertOutputEquals(
+ "FinishBundle should not be triggered.", expectedOutput, testHarness.getOutput());
+
+ testHarness.setProcessingTime(1000L);
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}",
+ 0L,
+ TimestampData.fromEpochMillis(-5000L),
+ TimestampData.fromEpochMillis(5000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c2",
+ 3L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}",
+ 0L,
+ TimestampData.fromEpochMillis(0L),
+ TimestampData.fromEpochMillis(10000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 1L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}",
+ 0L,
+ TimestampData.fromEpochMillis(5000L),
+ TimestampData.fromEpochMillis(15000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "c1",
+ 2L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(
+ new StreamRecord<>(
+ newRow(
+ true,
+ "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}",
+ 0L,
+ TimestampData.fromEpochMillis(10000L),
+ TimestampData.fromEpochMillis(20000L))));
+
+ expectedOutput.add(new Watermark(20000L));
+
+ assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput());
+
+ testHarness.close();
+ }
+
+ @Override
+ public LogicalType[] getOutputLogicalType() {
+ return new LogicalType[] {
+ DataTypes.STRING().getLogicalType(), DataTypes.BIGINT().getLogicalType()
+ };
+ }
+
+ @Override
+ public RowType getInputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new VarCharType()),
+ new RowType.RowField("f3", new BigIntType()),
+ new RowType.RowField("rowTime", new BigIntType())));
+ }
+
+ @Override
+ public RowType getOutputType() {
+ return new RowType(
+ Arrays.asList(
+ new RowType.RowField("f1", new VarCharType()),
+ new RowType.RowField("f2", new BigIntType()),
+ new RowType.RowField("windowStart", new TimestampType(3)),
+ new RowType.RowField("windowEnd", new TimestampType(3))));
+ }
+
+ @Override
+ OneInputStreamOperator getTestOperator(Configuration config) {
+ long size = 10000L;
+ long slide = 5000L;
+ SlidingWindowAssigner windowAssigner =
+ SlidingWindowAssigner.of(Duration.ofMillis(size), Duration.ofMillis(slide))
+ .withEventTime();
+ PlannerWindowReference windowRef =
+ new PlannerWindowReference("w$", new Some<>(new TimestampType(3)));
+ LogicalWindow window =
+ new SlidingGroupWindow(
+ windowRef,
+ new FieldReferenceExpression(
+ "rowtime",
+ new AtomicDataType(
+ new TimestampType(true, TimestampKind.ROWTIME, 3)),
+ 0,
+ 3),
+ intervalOfMillis(size),
+ intervalOfMillis(slide));
+ return new PassThroughPythonStreamGroupWindowAggregateOperator(
+ config,
+ getInputType(),
+ getOutputType(),
+ new PythonAggregateFunctionInfo[] {
+ new PythonAggregateFunctionInfo(
+ PythonScalarFunctionOperatorTestBase.DummyPythonFunction.INSTANCE,
+ new Integer[] {2},
+ -1,
+ false)
+ },
+ getGrouping(),
+ -1,
+ false,
+ false,
+ 3,
+ windowAssigner,
+ window,
+ 0,
+ new int[] {0, 1});
+ }
+
+ /** PassThroughPythonStreamGroupWindowAggregateOperator. */
+ public static class PassThroughPythonStreamGroupWindowAggregateOperator<K>
Review comment:
It would be better if we extract this class to a independent java file. It is so large.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
+
+ private int timerDataLength;
+
+ private int keyLength;
+
+ public PythonStreamGroupWindowAggregateOperator(
+ Configuration config,
+ RowType inputType,
+ RowType outputType,
+ PythonAggregateFunctionInfo[] aggregateFunctions,
+ DataViewUtils.DataViewSpec[][] dataViewSpecs,
+ int[] grouping,
+ int indexOfCountStar,
+ boolean generateUpdateBefore,
+ boolean countStarInserted,
+ int inputTimeFieldIndex,
+ WindowAssigner<W> windowAssigner,
+ LogicalWindow window,
+ long allowedLateness,
+ int[] namedProperties) {
+ super(
+ config,
+ inputType,
+ outputType,
+ aggregateFunctions,
+ dataViewSpecs,
+ grouping,
+ indexOfCountStar,
+ generateUpdateBefore);
+ this.countStarInserted = countStarInserted;
+ this.inputTimeFieldIndex = inputTimeFieldIndex;
+ this.windowAssigner = windowAssigner;
+ this.allowedLateness = allowedLateness;
+ this.namedProperties = namedProperties;
+ buildWindow(window);
+ }
+
+ @Override
+ public void open() throws Exception {
+ windowSerializer = windowAssigner.getWindowSerializer(new ExecutionConfig());
+ internalTimerService = getInternalTimerService("window-timers", windowSerializer, this);
+ if (isTimeWindow) {
Review comment:
How about add some explanation about the structure of the timer data？
##########
File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java
##########
@@ -0,0 +1,1129 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputDeserializer;
+import org.apache.flink.core.memory.DataOutputSerializer;
+import org.apache.flink.python.PythonFunctionRunner;
+import org.apache.flink.python.PythonOptions;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness;
+import org.apache.flink.table.api.DataTypes;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.data.binary.BinaryRowData;
+import org.apache.flink.table.data.util.RowDataUtil;
+import org.apache.flink.table.data.utils.JoinedRowData;
+import org.apache.flink.table.expressions.FieldReferenceExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator;
+import org.apache.flink.table.planner.expressions.PlannerWindowReference;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.generated.GeneratedProjection;
+import org.apache.flink.table.runtime.generated.Projection;
+import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner;
+import org.apache.flink.table.runtime.utils.PythonTestUtils;
+import org.apache.flink.table.types.AtomicDataType;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+import org.apache.flink.table.types.logical.VarCharType;
+import org.apache.flink.types.RowKind;
+
+import org.junit.Test;
+
+import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentLinkedQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import scala.Some;
+
+import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis;
+
+/**
+ * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that:
+ *
+ * <ul>
+ * <li>Retraction flag is handled correctly
+ * <li>FinishBundle is called when checkpoint is encountered
+ * <li>FinishBundle is called when bundled element count reach to max bundle size
+ * <li>FinishBundle is called when bundled time reach to max bundle time
+ * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered
+ * </ul>
+ */
+public class PythonStreamGroupWindowAggregateOperatorTest
+ extends AbstractPythonStreamAggregateOperatorTest {
+ @Test
+ public void testGroupWindowAggregateFunction() throws Exception {
+ OneInputStreamOperatorTestHarness<RowData, RowData> testHarness =
+ getTestHarness(new Configuration());
+ long initialTime = 0L;
+ ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
+ testHarness.open();
+ testHarness.processElement(
Review comment:
It would be better if we extract some utility method to create these stream record in one line.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
Review comment:
If we do not plan to support early fire/late fire in the first version of the Python group window aggregate, the `allowedLateness` field is unnecessary/
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
+
+ private int timerDataLength;
+
+ private int keyLength;
Review comment:
ditto
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
+
+ /** True if the count(*) agg is inserted by the planner. */
+ private final boolean countStarInserted;
+
+ /** The row time index of the input data. */
+ @VisibleForTesting final int inputTimeFieldIndex;
+
+ /**
+ * The allowed lateness for elements. This is used for:
+ *
+ * <ul>
+ * <li>Deciding if an element should be dropped from a window due to lateness.
+ * <li>Clearing the state of a window if the system time passes the {@code window.maxTimestamp
+ * + allowedLateness} landmark.
+ * </ul>
+ */
+ @VisibleForTesting final long allowedLateness;
+
+ /**
+ * The Infos of the Window. 0 -> start of the Window. 1 -> end of the Window. 2 -> row time of
+ * the Window. 3 -> proc time of the Window.
+ */
+ private final int[] namedProperties;
+
+ /** A {@link WindowAssigner} assigns zero or more {@link Window Windows} to an element. */
+ @VisibleForTesting final WindowAssigner<W> windowAssigner;
+
+ /** Window Type includes Tumble window, Sliding window and Session Window. */
+ private transient FlinkFnApi.GroupWindow.WindowType windowType;
+
+ /** Whether it is a row time window. */
+ private transient boolean isRowTime;
+
+ /** Whether it is a Time Window. */
+ private transient boolean isTimeWindow;
+
+ /** Window size. */
+ private transient long size;
+
+ /** Window slide. */
+ private transient long slide;
+
+ /** For serializing the window in checkpoints. */
+ @VisibleForTesting transient TypeSerializer<W> windowSerializer;
+
+ /** Interface for working with time and timers. */
+ private transient InternalTimerService<W> internalTimerService;
+
+ private UpdatableRowData reuseTimerData;
Review comment:
This field can also be transient.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
Review comment:
Why not 3?
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569215154
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperator.java
##########
@@ -0,0 +1,342 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.UpdatableRowData;
+import org.apache.flink.table.expressions.ValueLiteralExpression;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.plan.logical.LogicalWindow;
+import org.apache.flink.table.planner.plan.logical.SessionGroupWindow;
+import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow;
+import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.operators.window.CountWindow;
+import org.apache.flink.table.runtime.operators.window.TimeWindow;
+import org.apache.flink.table.runtime.operators.window.Window;
+import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** The Python Group Window AggregateFunction operator for the blink planner. */
+@Internal
+public class PythonStreamGroupWindowAggregateOperator<K, W extends Window>
+ extends AbstractPythonStreamAggregateOperator implements Triggerable<K, W> {
+
+ private static final long serialVersionUID = 1L;
+
+ @VisibleForTesting
+ static final String STREAM_GROUP_WINDOW_AGGREGATE_URN =
+ "flink:transform:stream_group_window_aggregate:v1";
+
+ @VisibleForTesting static final byte REGISTER_EVENT_TIMER = 0;
+
+ @VisibleForTesting static final byte REGISTER_PROCESSING_TIMER = 1;
+
+ @VisibleForTesting static final byte DELETE_EVENT_TIMER = 2;
+
+ @VisibleForTesting static final byte DELETE_PROCESSING_TIMER = 4;
Review comment:
Good Catch
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] HuangXingBo commented on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-786441629
@WeiZhong94 Thanks a lot for the review. I have addressed the comment at the latest commit.
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[GitHub] [flink] HuangXingBo commented on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-772491559
@WeiZhong94 Thanks a lot for the review. I have addressed the comments at the latest commit.
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
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[GitHub] [flink] amaliujia commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
amaliujia commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569194581
##########
File path: flink-python/pyflink/proto/flink-fn-execution.proto
##########
@@ -147,6 +147,30 @@ message UserDefinedAggregateFunction {
bool takes_row_as_input = 6;
}
+message GroupWindow {
+ enum WindowType {
+ TUMBLING_GROUP_WINDOW = 0;
+ SLIDING_GROUP_WINDOW = 1;
+ SESSION_GROUP_WINDOW = 2;
+ }
+
+ WindowType window_type = 1;
+
+ bool is_time_window = 2;
+
+ int64 window_slide = 3;
+
+ int64 window_size = 4;
+
Review comment:
+1
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[GitHub] [flink] HuangXingBo commented on a change in pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
HuangXingBo commented on a change in pull request #14775:
URL: https://github.com/apache/flink/pull/14775#discussion_r569218822
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/AbstractPythonStreamGroupAggregateOperator.java
##########
@@ -0,0 +1,189 @@
+/*
+ * 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.runtime.operators.python.aggregate;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.common.state.ValueState;
+import org.apache.flink.api.common.state.ValueStateDescriptor;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.fnexecution.v1.FlinkFnApi;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.runtime.state.VoidNamespaceSerializer;
+import org.apache.flink.streaming.api.SimpleTimerService;
+import org.apache.flink.streaming.api.TimerService;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.Triggerable;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
+import org.apache.flink.table.planner.typeutils.DataViewUtils;
+import org.apache.flink.table.runtime.functions.CleanupState;
+import org.apache.flink.table.types.logical.BigIntType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TinyIntType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Base class for {@link PythonStreamGroupAggregateOperator} and {@link
+ * PythonStreamGroupTableAggregateOperator}.
+ */
+@Internal
+public abstract class AbstractPythonStreamGroupAggregateOperator
+ extends AbstractPythonStreamAggregateOperator
+ implements Triggerable<RowData, VoidNamespace>, CleanupState {
+
+ private static final long serialVersionUID = 1L;
+
+ /** The minimum time in milliseconds until state which was not updated will be retained. */
+ private final long minRetentionTime;
+
+ /** The maximum time in milliseconds until state which was not updated will be retained. */
+ private final long maxRetentionTime;
+
+ /**
+ * Indicates whether state cleaning is enabled. Can be calculated from the `minRetentionTime`.
+ */
+ private final boolean stateCleaningEnabled;
+
+ private transient TimerService timerService;
+
+ // holds the latest registered cleanup timer
+ private transient ValueState<Long> cleanupTimeState;
+
+ public AbstractPythonStreamGroupAggregateOperator(
+ Configuration config,
+ RowType inputType,
+ RowType outputType,
+ PythonAggregateFunctionInfo[] aggregateFunctions,
+ DataViewUtils.DataViewSpec[][] dataViewSpecs,
+ int[] grouping,
+ int indexOfCountStar,
+ boolean generateUpdateBefore,
+ long minRetentionTime,
+ long maxRetentionTime) {
+ super(
+ config,
Review comment:
This is caused by the format configured by the google-java-format plugin used by flink
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
URL: https://github.com/apache/flink/pull/14775#issuecomment-768234660
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[GitHub] [flink] flinkbot edited a comment on pull request #14775: [FLINK-20964][python] Introduce PythonStreamGroupWindowAggregateOperator
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #14775:
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