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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2020/10/12 09:09:11 UTC
[GitHub] [flink] dianfu commented on a change in pull request #13504: [FLINK-19404][python] Support Pandas Stream Over Window Aggregation
dianfu commented on a change in pull request #13504:
URL: https://github.com/apache/flink/pull/13504#discussion_r503085577
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,85 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause event-time bounded
+ * OVER window.
+ */
+@Internal
+public class StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ public StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ long minRetentionTime,
+ long maxRetentionTime,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, minRetentionTime, maxRetentionTime, pandasAggFunctions,
+ inputType, outputType, inputTimeFieldIndex, lowerBoundary, groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void bufferInput(RowData input) throws Exception {
+ // register state-cleanup timer
+ registerProcessingCleanupTimer(timerService.currentProcessingTime());
+
+ // triggering timestamp for trigger calculation
+ long triggeringTs = input.getLong(inputTimeFieldIndex);
+
+ Long lastTriggeringTs = lastTriggeringTsState.value();
+ if (lastTriggeringTs == null) {
+ lastTriggeringTs = 0L;
+ }
+
+ // check if the data is expired, if not, save the data and register event time timer
+ if (triggeringTs > lastTriggeringTs) {
+ List<RowData> data = inputState.get(triggeringTs);
+ if (null != data) {
+ data.add(input);
+ inputState.put(triggeringTs, data);
Review comment:
```suggestion
```
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/StreamArrowPythonRowTimeRangeBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,77 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.streaming.api.TimeDomain;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause event-time bounded
+ * OVER window.
+ */
+@Internal
+public class StreamArrowPythonRowTimeRangeBoundedOverWindowAggregateFunctionOperator<K>
Review comment:
```suggestion
public class StreamArrowPythonRowTimeBoundedRangeOperator<K>
```
What about simplify the class name a bit? Same for the other classes.
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,226 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.runtime.functions.CleanupState;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.ListIterator;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for ROWS clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> implements CleanupState {
+
+ private static final long serialVersionUID = 1L;
+
+ private final long minRetentionTime;
+
+ private final long maxRetentionTime;
+
+ private final boolean stateCleaningEnabled;
+
+ /**
+ * list to sort timestamps to access rows in timestamp order.
+ */
+ transient LinkedList<Long> sortedTimestamps;
+
+ public AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ long minRetentionTime,
+ long maxRetentionTime,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ this.minRetentionTime = minRetentionTime;
+ this.maxRetentionTime = maxRetentionTime;
+ this.stateCleaningEnabled = minRetentionTime > 1;
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ sortedTimestamps = new LinkedList<>();
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ if (stateCleaningEnabled) {
+
+ Iterator<Long> keysIt = inputState.keys().iterator();
+ Long lastProcessedTime = lastTriggeringTsState.value();
+ if (lastProcessedTime == null) {
+ lastProcessedTime = 0L;
+ }
+
+ // is data left which has not been processed yet?
+ boolean noRecordsToProcess = true;
+ while (keysIt.hasNext() && noRecordsToProcess) {
+ if (keysIt.next() > lastProcessedTime) {
+ noRecordsToProcess = false;
+ }
+ }
+
+ if (noRecordsToProcess) {
+ inputState.clear();
+ cleanupTsState.clear();
+ } else {
+ // There are records left to process because a watermark has not been received yet.
+ // This would only happen if the input stream has stopped. So we don't need to clean up.
+ // We leave the state as it is and schedule a new cleanup timer
+ registerProcessingCleanupTimer(timerService.currentProcessingTime());
+ }
+ }
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+
+ Iterable<Long> keyIter = inputState.keys();
+ for (Long dataTs : keyIter) {
+ insertToSortedList(dataTs);
+ }
+ int index = sortedTimestamps.indexOf(timestamp);
+ for (int i = 0; i < inputs.size(); i++) {
+ forwardedInputQueue.add(inputs.get(i));
+ triggerWindowProcess(inputs, i, index);
+ }
+ sortedTimestamps.clear();
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ RowData key = forwardedInputQueue.poll();
+ reuseJoinedRow.setRowKind(key.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(key, data));
+ }
+ }
+
+ void registerProcessingCleanupTimer(long currentTime) throws Exception {
+ if (stateCleaningEnabled) {
+ registerProcessingCleanupTimer(
+ cleanupTsState,
+ currentTime,
+ minRetentionTime,
+ maxRetentionTime,
+ timerService);
+ }
+ }
+
+ void triggerWindowProcess(List<RowData> inputs, int i, int index) throws Exception {
+ int startIndex;
+ int startPos = 0;
+ if (i >= lowerBoundary) {
+ for (int j = (int) (i - lowerBoundary); j <= i; j++) {
+ arrowSerializer.write(getFunctionInput(inputs.get(j)));
+ }
+ currentBatchCount += lowerBoundary;
+ } else {
+ Long previousTimestamp;
+ List<RowData> previousData = null;
+ int length = 0;
+ startIndex = index - 1;
+ long remainingDataCount = lowerBoundary - i;
+ ListIterator<Long> iter = sortedTimestamps.listIterator(index);
+ while (remainingDataCount > 0 && iter.hasPrevious()) {
+ previousTimestamp = iter.previous();
+ previousData = inputState.get(previousTimestamp);
+ length = previousData.size();
+ if (remainingDataCount <= length) {
+ startPos = (int) (length - remainingDataCount);
+ remainingDataCount = 0;
+ } else {
+ remainingDataCount -= length;
+ startIndex--;
+ }
+ }
+ if (previousData != null) {
+ for (int j = startPos; j < length; j++) {
+ arrowSerializer.write(getFunctionInput(previousData.get(j)));
+ currentBatchCount++;
+ startIndex++;
Review comment:
I guess ``startIndex+`` should be moved out of the for loop
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/StreamArrowPythonProcTimeRowsBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,89 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Stream Arrow Python {@link AggregateFunction} Operator for ROWS clause proc-time bounded
+ * OVER window.
+ */
+@Internal
+public class StreamArrowPythonProcTimeRowsBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ private transient long currentTime;
+
+ public StreamArrowPythonProcTimeRowsBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ long minRetentionTime,
+ long maxRetentionTime,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, minRetentionTime, maxRetentionTime, pandasAggFunctions,
+ inputType, outputType, inputTimeFieldIndex, lowerBoundary, groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void bufferInput(RowData input) throws Exception {
+ currentTime = timerService.currentProcessingTime();
+ // register state-cleanup timer
+ registerProcessingCleanupTimer(currentTime);
+
+ // buffer the event incoming event
+
+ // add current element to the window list of elements with corresponding timestamp
+ List<RowData> rowList = inputState.get(currentTime);
+ // null value means that this is the first event received for this timestamp
+ if (rowList == null) {
+ rowList = new ArrayList<>();
+ }
+ rowList.add(input);
Review comment:
Make rowList as an instance variable, then there is no need to read inputState again in *processElementInternal*
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,85 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause event-time bounded
+ * OVER window.
+ */
+@Internal
+public class StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ public StreamArrowPythonRowTimeRowsBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ long minRetentionTime,
+ long maxRetentionTime,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, minRetentionTime, maxRetentionTime, pandasAggFunctions,
+ inputType, outputType, inputTimeFieldIndex, lowerBoundary, groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void bufferInput(RowData input) throws Exception {
+ // register state-cleanup timer
+ registerProcessingCleanupTimer(timerService.currentProcessingTime());
+
+ // triggering timestamp for trigger calculation
+ long triggeringTs = input.getLong(inputTimeFieldIndex);
+
+ Long lastTriggeringTs = lastTriggeringTsState.value();
+ if (lastTriggeringTs == null) {
+ lastTriggeringTs = 0L;
+ }
+
+ // check if the data is expired, if not, save the data and register event time timer
+ if (triggeringTs > lastTriggeringTs) {
+ List<RowData> data = inputState.get(triggeringTs);
+ if (null != data) {
+ data.add(input);
+ inputState.put(triggeringTs, data);
+ } else {
+ data = new ArrayList<>();
+ data.add(input);
+ inputState.put(triggeringTs, data);
Review comment:
```suggestion
```
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,226 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.runtime.functions.CleanupState;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.ListIterator;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for ROWS clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> implements CleanupState {
+
+ private static final long serialVersionUID = 1L;
+
+ private final long minRetentionTime;
+
+ private final long maxRetentionTime;
+
+ private final boolean stateCleaningEnabled;
+
+ /**
+ * list to sort timestamps to access rows in timestamp order.
+ */
+ transient LinkedList<Long> sortedTimestamps;
+
+ public AbstractStreamArrowPythonRowsBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ long minRetentionTime,
+ long maxRetentionTime,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ this.minRetentionTime = minRetentionTime;
+ this.maxRetentionTime = maxRetentionTime;
+ this.stateCleaningEnabled = minRetentionTime > 1;
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ sortedTimestamps = new LinkedList<>();
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ if (stateCleaningEnabled) {
+
+ Iterator<Long> keysIt = inputState.keys().iterator();
+ Long lastProcessedTime = lastTriggeringTsState.value();
+ if (lastProcessedTime == null) {
+ lastProcessedTime = 0L;
+ }
+
+ // is data left which has not been processed yet?
+ boolean noRecordsToProcess = true;
+ while (keysIt.hasNext() && noRecordsToProcess) {
+ if (keysIt.next() > lastProcessedTime) {
+ noRecordsToProcess = false;
+ }
+ }
+
+ if (noRecordsToProcess) {
+ inputState.clear();
+ cleanupTsState.clear();
+ } else {
+ // There are records left to process because a watermark has not been received yet.
+ // This would only happen if the input stream has stopped. So we don't need to clean up.
+ // We leave the state as it is and schedule a new cleanup timer
+ registerProcessingCleanupTimer(timerService.currentProcessingTime());
+ }
+ }
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+
+ Iterable<Long> keyIter = inputState.keys();
+ for (Long dataTs : keyIter) {
+ insertToSortedList(dataTs);
+ }
+ int index = sortedTimestamps.indexOf(timestamp);
+ for (int i = 0; i < inputs.size(); i++) {
+ forwardedInputQueue.add(inputs.get(i));
+ triggerWindowProcess(inputs, i, index);
+ }
+ sortedTimestamps.clear();
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ RowData key = forwardedInputQueue.poll();
+ reuseJoinedRow.setRowKind(key.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(key, data));
+ }
+ }
+
+ void registerProcessingCleanupTimer(long currentTime) throws Exception {
+ if (stateCleaningEnabled) {
+ registerProcessingCleanupTimer(
+ cleanupTsState,
+ currentTime,
+ minRetentionTime,
+ maxRetentionTime,
+ timerService);
+ }
+ }
+
+ void triggerWindowProcess(List<RowData> inputs, int i, int index) throws Exception {
+ int startIndex;
+ int startPos = 0;
+ if (i >= lowerBoundary) {
+ for (int j = (int) (i - lowerBoundary); j <= i; j++) {
+ arrowSerializer.write(getFunctionInput(inputs.get(j)));
+ }
+ currentBatchCount += lowerBoundary;
+ } else {
+ Long previousTimestamp;
+ List<RowData> previousData = null;
+ int length = 0;
+ startIndex = index - 1;
+ long remainingDataCount = lowerBoundary - i;
+ ListIterator<Long> iter = sortedTimestamps.listIterator(index);
+ while (remainingDataCount > 0 && iter.hasPrevious()) {
+ previousTimestamp = iter.previous();
+ previousData = inputState.get(previousTimestamp);
+ length = previousData.size();
+ if (remainingDataCount <= length) {
+ startPos = (int) (length - remainingDataCount);
+ remainingDataCount = 0;
+ } else {
+ remainingDataCount -= length;
+ startIndex--;
+ }
+ }
+ if (previousData != null) {
+ for (int j = startPos; j < length; j++) {
+ arrowSerializer.write(getFunctionInput(previousData.get(j)));
+ currentBatchCount++;
+ startIndex++;
+ }
+ while (startIndex < index) {
+ previousTimestamp = iter.next();
+ previousData = inputState.get(previousTimestamp);
Review comment:
The data stored in the *inputState* will be read multiple times: 2 * inputs.size().
What about buffering them in memory for performance optimization?
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,162 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.TimeDomain;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ private transient LinkedList<List<RowData>> inputData;
+
+ public AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ inputData = new LinkedList<>();
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ lastTriggeringTsState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+ triggerWindowProcess(timestamp, inputs);
+ lastTriggeringTsState.update(timestamp);
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+
+ // we consider the original timestamp of events
+ // that have registered this time trigger 1 ms ago
+
+ long currentTime = timestamp - 1;
+
+ // get the list of elements of current proctime
+ List<RowData> currentElements = inputState.get(currentTime);
+ triggerWindowProcess(timestamp, currentElements);
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ List<RowData> input = inputData.poll();
+ for (RowData ele : input) {
+ reuseJoinedRow.setRowKind(ele.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(ele, data));
+ }
+ }
+ }
+
+ void registerCleanupTimer(long timestamp, TimeDomain domain) throws Exception {
+ long minCleanupTimestamp = timestamp + lowerBoundary + 1;
+ long maxCleanupTimestamp = timestamp + (long) (lowerBoundary * 1.5) + 1;
+ // update timestamp and register timer if needed
+ Long curCleanupTimestamp = cleanupTsState.value();
+ if (curCleanupTimestamp == null || curCleanupTimestamp < minCleanupTimestamp) {
+ // we don't delete existing timer since it may delete timer for data processing
+ if (domain == TimeDomain.EVENT_TIME) {
+ timerService.registerEventTimeTimer(maxCleanupTimestamp);
+ } else {
+ timerService.registerProcessingTimeTimer(maxCleanupTimestamp);
+ }
+ cleanupTsState.update(maxCleanupTimestamp);
+ }
+ }
+
+ private void triggerWindowProcess(long timestamp, List<RowData> inputs) throws Exception {
+ long limit = timestamp - lowerBoundary;
Review comment:
```suggestion
long lowerLimit = timestamp - lowerBoundary;
```
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,162 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.TimeDomain;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ private transient LinkedList<List<RowData>> inputData;
+
+ public AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ inputData = new LinkedList<>();
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ lastTriggeringTsState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+ triggerWindowProcess(timestamp, inputs);
+ lastTriggeringTsState.update(timestamp);
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+
+ // we consider the original timestamp of events
+ // that have registered this time trigger 1 ms ago
+
+ long currentTime = timestamp - 1;
+
+ // get the list of elements of current proctime
+ List<RowData> currentElements = inputState.get(currentTime);
+ triggerWindowProcess(timestamp, currentElements);
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ List<RowData> input = inputData.poll();
+ for (RowData ele : input) {
+ reuseJoinedRow.setRowKind(ele.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(ele, data));
+ }
+ }
+ }
+
+ void registerCleanupTimer(long timestamp, TimeDomain domain) throws Exception {
+ long minCleanupTimestamp = timestamp + lowerBoundary + 1;
+ long maxCleanupTimestamp = timestamp + (long) (lowerBoundary * 1.5) + 1;
+ // update timestamp and register timer if needed
+ Long curCleanupTimestamp = cleanupTsState.value();
+ if (curCleanupTimestamp == null || curCleanupTimestamp < minCleanupTimestamp) {
+ // we don't delete existing timer since it may delete timer for data processing
+ if (domain == TimeDomain.EVENT_TIME) {
+ timerService.registerEventTimeTimer(maxCleanupTimestamp);
+ } else {
+ timerService.registerProcessingTimeTimer(maxCleanupTimestamp);
+ }
+ cleanupTsState.update(maxCleanupTimestamp);
+ }
+ }
+
+ private void triggerWindowProcess(long timestamp, List<RowData> inputs) throws Exception {
+ long limit = timestamp - lowerBoundary;
+ if (inputs != null) {
+ for (long dataTs : inputState.keys()) {
Review comment:
What about using inputState.entries to avoid reading the same state twice.
##########
File path: flink-table/flink-table-planner-blink/src/main/java/org/apache/flink/table/planner/plan/rules/physical/stream/StreamExecPythonOverAggregateRule.java
##########
@@ -0,0 +1,112 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.table.planner.plan.rules.physical.stream;
+
+import org.apache.flink.table.api.TableException;
+import org.apache.flink.table.functions.python.PythonFunctionKind;
+import org.apache.flink.table.planner.plan.nodes.FlinkConventions;
+import org.apache.flink.table.planner.plan.nodes.logical.FlinkLogicalOverAggregate;
+import org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecPythonOverAggregate;
+import org.apache.flink.table.planner.plan.trait.FlinkRelDistribution;
+import org.apache.flink.table.planner.plan.utils.PythonUtil;
+
+import org.apache.calcite.plan.RelOptRule;
+import org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.calcite.plan.RelTraitSet;
+import org.apache.calcite.rel.RelNode;
+import org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.calcite.rel.core.AggregateCall;
+import org.apache.calcite.util.ImmutableBitSet;
+
+import java.util.List;
+
+/**
+ * The physical rule is responsible for converting {@link FlinkLogicalOverAggregate} to
+ * {@link StreamExecPythonOverAggregate}.
+ */
+public class StreamExecPythonOverAggregateRule extends ConverterRule {
+ public static final StreamExecPythonOverAggregateRule INSTANCE = new StreamExecPythonOverAggregateRule();
+
+ private StreamExecPythonOverAggregateRule() {
+ super(FlinkLogicalOverAggregate.class,
+ FlinkConventions.LOGICAL(),
+ FlinkConventions.STREAM_PHYSICAL(),
+ "StreamExecPythonOverAggregateRule");
+ }
+
+ @Override
+ public boolean matches(RelOptRuleCall call) {
+ FlinkLogicalOverAggregate logicWindow = call.rel(0);
+ List<AggregateCall> aggCalls = logicWindow.groups.get(0).getAggregateCalls(logicWindow);
+
+ boolean existGeneralPythonFunction =
+ aggCalls.stream().anyMatch(x -> PythonUtil.isPythonAggregate(x, PythonFunctionKind.GENERAL));
+ boolean existPandasFunction =
+ aggCalls.stream().anyMatch(x -> PythonUtil.isPythonAggregate(x, PythonFunctionKind.PANDAS));
+ boolean existJavaFunction =
+ aggCalls.stream().anyMatch(x -> !PythonUtil.isPythonAggregate(x, null));
+ if (existPandasFunction || existGeneralPythonFunction) {
+ if (existGeneralPythonFunction) {
+ throw new TableException("non-Pandas UDAFs are not supported in stream mode currently.");
Review comment:
```suggestion
throw new TableException("Non-Pandas Python UDAFs are not supported in stream mode currently.");
```
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,162 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.TimeDomain;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ private transient LinkedList<List<RowData>> inputData;
+
+ public AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ inputData = new LinkedList<>();
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ lastTriggeringTsState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+ triggerWindowProcess(timestamp, inputs);
+ lastTriggeringTsState.update(timestamp);
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+
+ // we consider the original timestamp of events
+ // that have registered this time trigger 1 ms ago
+
+ long currentTime = timestamp - 1;
+
+ // get the list of elements of current proctime
+ List<RowData> currentElements = inputState.get(currentTime);
+ triggerWindowProcess(timestamp, currentElements);
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ List<RowData> input = inputData.poll();
+ for (RowData ele : input) {
+ reuseJoinedRow.setRowKind(ele.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(ele, data));
+ }
+ }
+ }
+
+ void registerCleanupTimer(long timestamp, TimeDomain domain) throws Exception {
+ long minCleanupTimestamp = timestamp + lowerBoundary + 1;
+ long maxCleanupTimestamp = timestamp + (long) (lowerBoundary * 1.5) + 1;
+ // update timestamp and register timer if needed
+ Long curCleanupTimestamp = cleanupTsState.value();
+ if (curCleanupTimestamp == null || curCleanupTimestamp < minCleanupTimestamp) {
+ // we don't delete existing timer since it may delete timer for data processing
+ if (domain == TimeDomain.EVENT_TIME) {
+ timerService.registerEventTimeTimer(maxCleanupTimestamp);
+ } else {
+ timerService.registerProcessingTimeTimer(maxCleanupTimestamp);
+ }
+ cleanupTsState.update(maxCleanupTimestamp);
+ }
+ }
+
+ private void triggerWindowProcess(long timestamp, List<RowData> inputs) throws Exception {
+ long limit = timestamp - lowerBoundary;
+ if (inputs != null) {
+ for (long dataTs : inputState.keys()) {
+ if (dataTs >= limit && dataTs <= timestamp) {
+ List<RowData> dataList = inputState.get(dataTs);
Review comment:
The state in *inputState* are never cleared. I think we should also remove outdated state in *triggerWindowProcess*.
##########
File path: flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/plan/nodes/physical/stream/StreamExecPythonOverAggregate.scala
##########
@@ -0,0 +1,312 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.flink.table.planner.plan.nodes.physical.stream
+
+import org.apache.flink.api.dag.Transformation
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.core.memory.ManagedMemoryUseCase
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator
+import org.apache.flink.streaming.api.transformations.OneInputTransformation
+import org.apache.flink.table.api.TableException
+import org.apache.flink.table.data.RowData
+import org.apache.flink.table.functions.python.PythonFunctionInfo
+import org.apache.flink.table.planner.calcite.FlinkTypeFactory
+import org.apache.flink.table.planner.delegation.StreamPlanner
+import org.apache.flink.table.planner.plan.nodes.common.CommonPythonAggregate
+import org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecPythonOverAggregate.{
+ ARROW_PYTHON_OVER_WINDOW_RANGE_ROW_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_RANGE_PROC_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_ROWS_ROW_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_ROWS_PROC_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME
+}
+import org.apache.flink.table.planner.plan.utils.{KeySelectorUtil, OverAggregateUtil}
+import org.apache.flink.table.runtime.typeutils.InternalTypeInfo
+import org.apache.flink.table.types.logical.RowType
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.RelFieldCollation.Direction.ASCENDING
+import org.apache.calcite.rel.RelNode
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rel.core.Window.Group
+import org.apache.calcite.rel.core.{AggregateCall, Window}
+
+import java.util
+
+import scala.collection.JavaConverters._
+
+/**
+ * Stream physical RelNode for python time-based over [[Window]].
+ */
+class StreamExecPythonOverAggregate(
+ cluster: RelOptCluster,
+ traitSet: RelTraitSet,
+ inputRel: RelNode,
+ outputRowType: RelDataType,
+ inputRowType: RelDataType,
+ logicWindow: Window)
+ extends StreamExecOverAggregateBase(
+ cluster,
+ traitSet,
+ inputRel,
+ outputRowType,
+ inputRowType,
+ logicWindow)
+ with CommonPythonAggregate {
+
+ override def copy(traitSet: RelTraitSet, inputs: util.List[RelNode]): RelNode = {
+ new StreamExecPythonOverAggregate(
+ cluster,
+ traitSet,
+ inputs.get(0),
+ outputRowType,
+ inputRowType,
+ logicWindow
+ )
+ }
+
+ override protected def translateToPlanInternal(
+ planner: StreamPlanner): Transformation[RowData] = {
+ val tableConfig = planner.getTableConfig
+
+ val overWindow: Group = logicWindow.groups.get(0)
+
+ val orderKeys = overWindow.orderKeys.getFieldCollations
+
+ if (orderKeys.size() != 1) {
+ throw new TableException(
+ "The window can only be ordered by a single time column.")
+ }
+ val orderKey = orderKeys.get(0)
+
+ if (!orderKey.direction.equals(ASCENDING)) {
+ throw new TableException(
+ "The window can only be ordered in ASCENDING mode.")
+ }
+
+ val inputDS = getInputNodes.get(0).translateToPlan(planner)
+ .asInstanceOf[Transformation[RowData]]
+
+ if (!logicWindow.groups.get(0).keys.isEmpty && tableConfig.getMinIdleStateRetentionTime < 0) {
+ LOG.warn(
+ "No state retention interval configured for a query which accumulates state. " +
+ "Please provide a query configuration with valid retention interval to prevent " +
+ "excessive state size. You may specify a retention time of 0 to not clean up the state.")
+ }
+
+ val timeType = outputRowType.getFieldList.get(orderKey.getFieldIndex).getType
+
+ // check time field
+ if (!FlinkTypeFactory.isRowtimeIndicatorType(timeType)
+ && !FlinkTypeFactory.isProctimeIndicatorType(timeType)) {
+ throw new TableException(
+ "OVER windows' ordering in stream mode must be defined on a time attribute.")
+ }
+
+ // identify window rowtime attribute
+ val rowTimeIdx: Option[Int] = if (FlinkTypeFactory.isRowtimeIndicatorType(timeType)) {
+ Some(orderKey.getFieldIndex)
+ } else if (FlinkTypeFactory.isProctimeIndicatorType(timeType)) {
+ None
+ } else {
+ throw new TableException(
+ "OVER windows can only be applied on time attributes.")
+ }
+
+ if (overWindow.lowerBound.isPreceding
+ && overWindow.lowerBound.isUnbounded) {
+ throw new TableException(
+ "OVER PRECEDING windows are not supported yet."
+ )
+ } else if (!overWindow.upperBound.isCurrentRow) {
+ throw new TableException(
+ "OVER FOLLOWING windows are not supported yet."
Review comment:
```suggestion
"UNBOUNDED FOLLOWING OVER windows are not supported yet for Python UDAF."
```
##########
File path: flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/plan/nodes/physical/stream/StreamExecPythonOverAggregate.scala
##########
@@ -0,0 +1,312 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.flink.table.planner.plan.nodes.physical.stream
+
+import org.apache.flink.api.dag.Transformation
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.core.memory.ManagedMemoryUseCase
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator
+import org.apache.flink.streaming.api.transformations.OneInputTransformation
+import org.apache.flink.table.api.TableException
+import org.apache.flink.table.data.RowData
+import org.apache.flink.table.functions.python.PythonFunctionInfo
+import org.apache.flink.table.planner.calcite.FlinkTypeFactory
+import org.apache.flink.table.planner.delegation.StreamPlanner
+import org.apache.flink.table.planner.plan.nodes.common.CommonPythonAggregate
+import org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecPythonOverAggregate.{
+ ARROW_PYTHON_OVER_WINDOW_RANGE_ROW_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_RANGE_PROC_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_ROWS_ROW_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME,
+ ARROW_PYTHON_OVER_WINDOW_ROWS_PROC_TIME_AGGREGATE_FUNCTION_OPERATOR_NAME
+}
+import org.apache.flink.table.planner.plan.utils.{KeySelectorUtil, OverAggregateUtil}
+import org.apache.flink.table.runtime.typeutils.InternalTypeInfo
+import org.apache.flink.table.types.logical.RowType
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.RelFieldCollation.Direction.ASCENDING
+import org.apache.calcite.rel.RelNode
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rel.core.Window.Group
+import org.apache.calcite.rel.core.{AggregateCall, Window}
+
+import java.util
+
+import scala.collection.JavaConverters._
+
+/**
+ * Stream physical RelNode for python time-based over [[Window]].
+ */
+class StreamExecPythonOverAggregate(
+ cluster: RelOptCluster,
+ traitSet: RelTraitSet,
+ inputRel: RelNode,
+ outputRowType: RelDataType,
+ inputRowType: RelDataType,
+ logicWindow: Window)
+ extends StreamExecOverAggregateBase(
+ cluster,
+ traitSet,
+ inputRel,
+ outputRowType,
+ inputRowType,
+ logicWindow)
+ with CommonPythonAggregate {
+
+ override def copy(traitSet: RelTraitSet, inputs: util.List[RelNode]): RelNode = {
+ new StreamExecPythonOverAggregate(
+ cluster,
+ traitSet,
+ inputs.get(0),
+ outputRowType,
+ inputRowType,
+ logicWindow
+ )
+ }
+
+ override protected def translateToPlanInternal(
+ planner: StreamPlanner): Transformation[RowData] = {
+ val tableConfig = planner.getTableConfig
+
+ val overWindow: Group = logicWindow.groups.get(0)
+
+ val orderKeys = overWindow.orderKeys.getFieldCollations
+
+ if (orderKeys.size() != 1) {
+ throw new TableException(
+ "The window can only be ordered by a single time column.")
+ }
+ val orderKey = orderKeys.get(0)
+
+ if (!orderKey.direction.equals(ASCENDING)) {
+ throw new TableException(
+ "The window can only be ordered in ASCENDING mode.")
+ }
+
+ val inputDS = getInputNodes.get(0).translateToPlan(planner)
+ .asInstanceOf[Transformation[RowData]]
+
+ if (!logicWindow.groups.get(0).keys.isEmpty && tableConfig.getMinIdleStateRetentionTime < 0) {
+ LOG.warn(
+ "No state retention interval configured for a query which accumulates state. " +
+ "Please provide a query configuration with valid retention interval to prevent " +
+ "excessive state size. You may specify a retention time of 0 to not clean up the state.")
+ }
+
+ val timeType = outputRowType.getFieldList.get(orderKey.getFieldIndex).getType
+
+ // check time field
+ if (!FlinkTypeFactory.isRowtimeIndicatorType(timeType)
+ && !FlinkTypeFactory.isProctimeIndicatorType(timeType)) {
+ throw new TableException(
+ "OVER windows' ordering in stream mode must be defined on a time attribute.")
+ }
+
+ // identify window rowtime attribute
+ val rowTimeIdx: Option[Int] = if (FlinkTypeFactory.isRowtimeIndicatorType(timeType)) {
+ Some(orderKey.getFieldIndex)
+ } else if (FlinkTypeFactory.isProctimeIndicatorType(timeType)) {
+ None
+ } else {
+ throw new TableException(
+ "OVER windows can only be applied on time attributes.")
+ }
+
+ if (overWindow.lowerBound.isPreceding
+ && overWindow.lowerBound.isUnbounded) {
+ throw new TableException(
+ "OVER PRECEDING windows are not supported yet."
Review comment:
```suggestion
"UNBOUNDED PRECEDING OVER windows are not supported yet for Python UDAF."
```
##########
File path: flink-python/pyflink/table/tests/test_pandas_udaf.py
##########
@@ -504,6 +504,129 @@ def test_tumbling_group_window_over_count(self):
self.assert_equals(actual, ["1,2.5", "1,6.0", "2,2.0", "3,2.5"])
os.remove(source_path)
+ def test_over_range_window_aggregate_function(self):
+ # create source file path
+ import tempfile
+ import os
+ tmp_dir = tempfile.gettempdir()
+ data = [
+ '1,1,2013-01-01 03:10:00',
+ '3,2,2013-01-01 03:10:00',
+ '2,1,2013-01-01 03:10:00',
+ '1,5,2013-01-01 03:10:00',
+ '1,8,2013-01-01 04:20:00',
+ '2,3,2013-01-01 03:30:00'
+ ]
+ source_path = tmp_dir + '/test_over_range_window_aggregate_function.csv'
+ with open(source_path, 'w') as fd:
+ for ele in data:
+ fd.write(ele + '\n')
+ max_add_min_udaf = udaf(lambda a: a.max() + a.min(),
+ result_type=DataTypes.SMALLINT(),
+ func_type='pandas')
+ self.env.set_stream_time_characteristic(TimeCharacteristic.EventTime)
Review comment:
Add test cases for processing time?
##########
File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/stream/AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator.java
##########
@@ -0,0 +1,162 @@
+/*
+ * 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.arrow.stream;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.runtime.state.VoidNamespace;
+import org.apache.flink.streaming.api.TimeDomain;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.functions.AggregateFunction;
+import org.apache.flink.table.functions.python.PythonFunctionInfo;
+import org.apache.flink.table.types.logical.RowType;
+
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Abstract class of Stream Arrow Python {@link AggregateFunction} Operator for RANGE clause
+ * bounded Over Window Aggregation.
+ */
+@Internal
+public abstract class AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator<K>
+ extends AbstractStreamArrowPythonOverWindowAggregateFunctionOperator<K> {
+
+ private static final long serialVersionUID = 1L;
+
+ private transient LinkedList<List<RowData>> inputData;
+
+ public AbstractStreamArrowPythonRangeBoundedOverWindowAggregateFunctionOperator(
+ Configuration config,
+ PythonFunctionInfo[] pandasAggFunctions,
+ RowType inputType,
+ RowType outputType,
+ int inputTimeFieldIndex,
+ long lowerBoundary,
+ int[] groupingSet,
+ int[] udafInputOffsets) {
+ super(config, pandasAggFunctions, inputType, outputType, inputTimeFieldIndex, lowerBoundary,
+ groupingSet, udafInputOffsets);
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ inputData = new LinkedList<>();
+ }
+
+ @Override
+ public void onEventTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ lastTriggeringTsState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+ // gets all window data from state for the calculation
+ List<RowData> inputs = inputState.get(timestamp);
+ triggerWindowProcess(timestamp, inputs);
+ lastTriggeringTsState.update(timestamp);
+ }
+
+ @Override
+ public void onProcessingTime(InternalTimer<K, VoidNamespace> timer) throws Exception {
+ long timestamp = timer.getTimestamp();
+ Long cleanupTimestamp = cleanupTsState.value();
+ // if cleanupTsState has not been updated then it is safe to cleanup states
+ if (cleanupTimestamp != null && cleanupTimestamp <= timestamp) {
+ inputState.clear();
+ cleanupTsState.clear();
+ return;
+ }
+
+ // we consider the original timestamp of events
+ // that have registered this time trigger 1 ms ago
+
+ long currentTime = timestamp - 1;
+
+ // get the list of elements of current proctime
+ List<RowData> currentElements = inputState.get(currentTime);
+ triggerWindowProcess(timestamp, currentElements);
+ }
+
+ @Override
+ @SuppressWarnings("ConstantConditions")
+ public void emitResult(Tuple2<byte[], Integer> resultTuple) throws Exception {
+ byte[] udafResult = resultTuple.f0;
+ int length = resultTuple.f1;
+ bais.setBuffer(udafResult, 0, length);
+ int rowCount = arrowSerializer.load();
+ for (int i = 0; i < rowCount; i++) {
+ RowData data = arrowSerializer.read(i);
+ List<RowData> input = inputData.poll();
+ for (RowData ele : input) {
+ reuseJoinedRow.setRowKind(ele.getRowKind());
+ rowDataWrapper.collect(reuseJoinedRow.replace(ele, data));
+ }
+ }
+ }
+
+ void registerCleanupTimer(long timestamp, TimeDomain domain) throws Exception {
+ long minCleanupTimestamp = timestamp + lowerBoundary + 1;
+ long maxCleanupTimestamp = timestamp + (long) (lowerBoundary * 1.5) + 1;
+ // update timestamp and register timer if needed
+ Long curCleanupTimestamp = cleanupTsState.value();
+ if (curCleanupTimestamp == null || curCleanupTimestamp < minCleanupTimestamp) {
+ // we don't delete existing timer since it may delete timer for data processing
+ if (domain == TimeDomain.EVENT_TIME) {
+ timerService.registerEventTimeTimer(maxCleanupTimestamp);
+ } else {
+ timerService.registerProcessingTimeTimer(maxCleanupTimestamp);
+ }
+ cleanupTsState.update(maxCleanupTimestamp);
+ }
+ }
+
+ private void triggerWindowProcess(long timestamp, List<RowData> inputs) throws Exception {
Review comment:
```suggestion
private void triggerWindowProcess(long upperLimit, List<RowData> inputs) throws Exception {
```
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