Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.flink.table.planner.plan.nodes.exec.stream;
import org.apache.flink.api.dag.Transformation;
import org.apache.flink.api.java.tuple.Tuple2;
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.TableConfig;
import org.apache.flink.table.api.TableException;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo;
import org.apache.flink.table.planner.delegation.PlannerBase;
import org.apache.flink.table.planner.plan.nodes.exec.ExecEdge;
import org.apache.flink.table.planner.plan.nodes.exec.ExecNode;
import org.apache.flink.table.planner.plan.nodes.exec.ExecNodeBase;
import org.apache.flink.table.planner.plan.nodes.exec.InputProperty;
import org.apache.flink.table.planner.plan.nodes.exec.SingleTransformationTranslator;
import org.apache.flink.table.planner.plan.nodes.exec.utils.CommonPythonUtil;
import org.apache.flink.table.planner.plan.utils.AggregateInfoList;
import org.apache.flink.table.planner.plan.utils.AggregateUtil;
import org.apache.flink.table.planner.plan.utils.KeySelectorUtil;
import org.apache.flink.table.planner.typeutils.DataViewUtils;
import org.apache.flink.table.planner.utils.JavaScalaConversionUtil;
import org.apache.flink.table.runtime.keyselector.RowDataKeySelector;
import org.apache.flink.table.runtime.typeutils.InternalTypeInfo;
import org.apache.flink.table.types.logical.RowType;
import org.apache.calcite.rel.core.AggregateCall;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.lang.reflect.Constructor;
import java.util.Arrays;
import java.util.Collections;
/** Stream {@link ExecNode} for Python unbounded group aggregate. */
public class StreamExecPythonGroupAggregate extends ExecNodeBase
implements StreamExecNode, SingleTransformationTranslator {
private static final Logger LOG = LoggerFactory.getLogger(StreamExecPythonGroupAggregate.class);
private static final String PYTHON_STREAM_AGGREAGTE_OPERATOR_NAME =
"org.apache.flink.table.runtime.operators.python.aggregate.PythonStreamGroupAggregateOperator";
private final int[] grouping;
private final AggregateCall[] aggCalls;
private final boolean[] aggCallNeedRetractions;
private final boolean generateUpdateBefore;
private final boolean needRetraction;
public StreamExecPythonGroupAggregate(
int[] grouping,
AggregateCall[] aggCalls,
boolean[] aggCallNeedRetractions,
boolean generateUpdateBefore,
boolean needRetraction,
InputProperty inputProperty,
RowType outputType,
String description) {
super(Collections.singletonList(inputProperty), outputType, description);
this.grouping = grouping;
this.aggCalls = aggCalls;
this.aggCallNeedRetractions = aggCallNeedRetractions;
this.generateUpdateBefore = generateUpdateBefore;
this.needRetraction = needRetraction;
}
@SuppressWarnings("unchecked")
@Override
protected Transformation translateToPlanInternal(PlannerBase planner) {
TableConfig tableConfig = planner.getTableConfig();
if (grouping.length > 0 && 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.");
}
final ExecEdge inputEdge = getInputEdges().get(0);
final Transformation inputTransform =
(Transformation) inputEdge.translateToPlan(planner);
final RowType inputRowType = (RowType) inputEdge.getOutputType();
final AggregateInfoList aggInfoList =
AggregateUtil.transformToStreamAggregateInfoList(
inputRowType,
JavaScalaConversionUtil.toScala(Arrays.asList(aggCalls)),
aggCallNeedRetractions,
needRetraction,
true, // isStateBackendDataViews
true); // needDistinctInfo
final int inputCountIndex = aggInfoList.getIndexOfCountStar();
final boolean countStarInserted = aggInfoList.countStarInserted();
Tuple2
aggInfosAndDataViewSpecs =
CommonPythonUtil.extractPythonAggregateFunctionInfos(aggInfoList, aggCalls);
PythonAggregateFunctionInfo[] pythonFunctionInfos = aggInfosAndDataViewSpecs.f0;
DataViewUtils.DataViewSpec[][] dataViewSpecs = aggInfosAndDataViewSpecs.f1;
Configuration config = CommonPythonUtil.getMergedConfig(planner.getExecEnv(), tableConfig);
final OneInputStreamOperator operator =
getPythonAggregateFunctionOperator(
config,
inputRowType,
InternalTypeInfo.of(getOutputType()).toRowType(),
pythonFunctionInfos,
dataViewSpecs,
tableConfig.getMinIdleStateRetentionTime(),
tableConfig.getMaxIdleStateRetentionTime(),
inputCountIndex,
countStarInserted);
// partitioned aggregation
OneInputTransformation transform =
new OneInputTransformation<>(
inputTransform,
getDescription(),
operator,
InternalTypeInfo.of(getOutputType()),
inputTransform.getParallelism());
if (CommonPythonUtil.isPythonWorkerUsingManagedMemory(config)) {
transform.declareManagedMemoryUseCaseAtSlotScope(ManagedMemoryUseCase.PYTHON);
}
// set KeyType and Selector for state
final RowDataKeySelector selector =
KeySelectorUtil.getRowDataSelector(grouping, InternalTypeInfo.of(inputRowType));
transform.setStateKeySelector(selector);
transform.setStateKeyType(selector.getProducedType());
return transform;
}
@SuppressWarnings("unchecked")
private OneInputStreamOperator getPythonAggregateFunctionOperator(
Configuration config,
RowType inputType,
RowType outputType,
PythonAggregateFunctionInfo[] aggregateFunctions,
DataViewUtils.DataViewSpec[][] dataViewSpecs,
long minIdleStateRetentionTime,
long maxIdleStateRetentionTime,
int indexOfCountStar,
boolean countStarInserted) {
Class clazz = CommonPythonUtil.loadClass(PYTHON_STREAM_AGGREAGTE_OPERATOR_NAME);
try {
Constructor ctor =
clazz.getConstructor(
Configuration.class,
RowType.class,
RowType.class,
PythonAggregateFunctionInfo[].class,
DataViewUtils.DataViewSpec[][].class,
int[].class,
int.class,
boolean.class,
boolean.class,
long.class,
long.class);
return (OneInputStreamOperator)
ctor.newInstance(
config,
inputType,
outputType,
aggregateFunctions,
dataViewSpecs,
grouping,
indexOfCountStar,
countStarInserted,
generateUpdateBefore,
minIdleStateRetentionTime,
maxIdleStateRetentionTime);
} catch (Exception e) {
throw new TableException(
"Python Stream Aggregate Function Operator constructed failed.", e);
}
}
}
