/**
* 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 storm.trident;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import backtype.storm.generated.Grouping;
import backtype.storm.generated.NullStruct;
import backtype.storm.grouping.CustomStreamGrouping;
import backtype.storm.topology.ResourceDeclarer;
import backtype.storm.topology.base.BaseWindowedBolt;
import backtype.storm.tuple.Fields;
import backtype.storm.utils.Utils;
import storm.trident.fluent.ChainedAggregatorDeclarer;
import storm.trident.fluent.GlobalAggregationScheme;
import storm.trident.fluent.GroupedStream;
import storm.trident.fluent.IAggregatableStream;
import storm.trident.operation.Aggregator;
import storm.trident.operation.Assembly;
import storm.trident.operation.CombinerAggregator;
import storm.trident.operation.Consumer;
import storm.trident.operation.Filter;
import storm.trident.operation.FlatMapFunction;
import storm.trident.operation.Function;
import storm.trident.operation.MapFunction;
import storm.trident.operation.ReducerAggregator;
import storm.trident.operation.builtin.ComparisonAggregator;
import storm.trident.operation.builtin.Max;
import storm.trident.operation.builtin.MaxWithComparator;
import storm.trident.operation.builtin.Min;
import storm.trident.operation.builtin.MinWithComparator;
import storm.trident.operation.impl.CombinerAggStateUpdater;
import storm.trident.operation.impl.ConsumerExecutor;
import storm.trident.operation.impl.FilterExecutor;
import storm.trident.operation.impl.FlatMapFunctionExecutor;
import storm.trident.operation.impl.GlobalBatchToPartition;
import storm.trident.operation.impl.IndexHashBatchToPartition;
import storm.trident.operation.impl.MapFunctionExecutor;
import storm.trident.operation.impl.ReducerAggStateUpdater;
import storm.trident.operation.impl.SingleEmitAggregator.BatchToPartition;
import storm.trident.operation.impl.TrueFilter;
import storm.trident.partition.GlobalGrouping;
import storm.trident.partition.IdentityGrouping;
import storm.trident.partition.IndexHashGrouping;
import storm.trident.planner.Node;
import storm.trident.planner.NodeStateInfo;
import storm.trident.planner.PartitionNode;
import storm.trident.planner.ProcessorNode;
import storm.trident.planner.processor.AggregateProcessor;
import storm.trident.planner.processor.EachProcessor;
import storm.trident.planner.processor.MapProcessor;
import storm.trident.planner.processor.PartitionPersistProcessor;
import storm.trident.planner.processor.ProjectedProcessor;
import storm.trident.planner.processor.StateQueryProcessor;
import storm.trident.state.QueryFunction;
import storm.trident.state.StateFactory;
import storm.trident.state.StateSpec;
import storm.trident.state.StateUpdater;
import storm.trident.tuple.TridentTuple;
import storm.trident.util.TridentUtils;
import storm.trident.windowing.InMemoryWindowsStoreFactory;
import storm.trident.windowing.WindowTridentProcessor;
import storm.trident.windowing.WindowsStateFactory;
import storm.trident.windowing.WindowsStateUpdater;
import storm.trident.windowing.WindowsStoreFactory;
import storm.trident.windowing.config.SlidingCountWindow;
import storm.trident.windowing.config.SlidingDurationWindow;
import storm.trident.windowing.config.TumblingCountWindow;
import storm.trident.windowing.config.TumblingDurationWindow;
import storm.trident.windowing.config.WindowConfig;
// TODO: need to be able to replace existing fields with the function fields (like Cascading Fields.REPLACE)
public class Stream implements IAggregatableStream, ResourceDeclarer<Stream> {
final Node _node;
final String _name;
private final TridentTopology _topology;
protected Stream(TridentTopology topology, String name, Node node) {
_topology = topology;
_node = node;
_name = name;
}
/**
* Applies a label to the stream. Naming a stream will append the label to the name of the bolt(s) created by
* Trident and will be visible in the Storm UI.
*
* @param name - The label to apply to the stream
* @return
*/
public Stream name(String name) {
return new Stream(_topology, name, _node);
}
/**
* Applies a parallelism hint to a stream.
*
* @param hint
* @return
*/
public Stream parallelismHint(int hint) {
_node.parallelismHint = hint;
return this;
}
/**
* Sets the CPU Load resource for the current operation
*/
@Override
public Stream setCPULoad(Number load) {
_node.setCPULoad(load);
return this;
}
/**
* Sets the Memory Load resources for the current operation.
* offHeap becomes default
*/
@Override
public Stream setMemoryLoad(Number onHeap) {
_node.setMemoryLoad(onHeap);
return this;
}
/**
* Sets the Memory Load resources for the current operation.
*/
@Override
public Stream setMemoryLoad(Number onHeap, Number offHeap) {
_node.setMemoryLoad(onHeap, offHeap);
return this;
}
/**
* Filters out fields from a stream, resulting in a Stream containing only the fields specified by `keepFields`.
*
* For example, if you had a Stream `mystream` containing the fields `["a", "b", "c","d"]`, calling"
*
* ```java
* mystream.project(new Fields("b", "d"))
* ```
*
* would produce a stream containing only the fields `["b", "d"]`.
*
*
* @param keepFields The fields in the Stream to keep
* @return
*/
public Stream project(Fields keepFields) {
projectionValidation(keepFields);
return _topology.addSourcedNode(this, new ProcessorNode(_topology.getUniqueStreamId(), _name, keepFields, new Fields(), new ProjectedProcessor(keepFields)));
}
/**
* ## Grouping Operation
*
* @param fields
* @return
*/
public GroupedStream groupBy(Fields fields) {
projectionValidation(fields);
return new GroupedStream(this, fields);
}
/**
* ## Repartitioning Operation
*
* @param fields
* @return
*/
public Stream partitionBy(Fields fields) {
projectionValidation(fields);
return partition(Grouping.fields(fields.toList()));
}
/**
* ## Repartitioning Operation
*
* @param partitioner
* @return
*/
public Stream partition(CustomStreamGrouping partitioner) {
return partition(Grouping.custom_serialized(Utils.javaSerialize(partitioner)));
}
/**
* ## Repartitioning Operation
*
* Use random round robin algorithm to evenly redistribute tuples across all target partitions
*
* @return
*/
public Stream shuffle() {
return partition(Grouping.shuffle(new NullStruct()));
}
/**
* ## Repartitioning Operation
*
* Use random round robin algorithm to evenly redistribute tuples across all target partitions, with a preference
* for local tasks.
*
* @return
*/
public Stream localOrShuffle() {
return partition(Grouping.local_or_shuffle(new NullStruct()));
}
/**
* ## Repartitioning Operation
*
* All tuples are sent to the same partition. The same partition is chosen for all batches in the stream.
* @return
*/
public Stream global() {
// use this instead of storm's built in one so that we can specify a single emit batch to partition
// without knowledge of storm's internals
return partition(new GlobalGrouping());
}
/**
* ## Repartitioning Operation
*
* All tuples in the batch are sent to the same partition. Different batches in the stream may go to different
* partitions.
*
* @return
*/
public Stream batchGlobal() {
// the first field is the batch id
return partition(new IndexHashGrouping(0));
}
/**
* ## Repartitioning Operation
*
* Every tuple is replicated to all target partitions. This can useful during DRPC – for example, if you need to do
* a stateQuery on every partition of data.
*
* @return
*/
public Stream broadcast() {
return partition(Grouping.all(new NullStruct()));
}
/**
* ## Repartitioning Operation
*
* @return
*/
public Stream identityPartition() {
return partition(new IdentityGrouping());
}
/**
* ## Repartitioning Operation
*
* This method takes in a custom partitioning function that implements
* {@link org.apache.storm.grouping.CustomStreamGrouping}
*
* @param grouping
* @return
*/
public Stream partition(Grouping grouping) {
if (_node instanceof PartitionNode) {
return each(new Fields(), new TrueFilter()).partition(grouping);
} else {
return _topology.addSourcedNode(this, new PartitionNode(_node.streamId, _name, getOutputFields(), grouping));
}
}
/**
* Applies an `Assembly` to this `Stream`.
*
* @see org.apache.storm.trident.operation.Assembly
* @param assembly
* @return
*/
public Stream applyAssembly(Assembly assembly) {
return assembly.apply(this);
}
@Override
public Stream each(Fields inputFields, Function function, Fields functionFields) {
projectionValidation(inputFields);
return _topology.addSourcedNode(this,
new ProcessorNode(_topology.getUniqueStreamId(),
_name,
TridentUtils.fieldsConcat(getOutputFields(), functionFields),
functionFields,
new EachProcessor(inputFields, function)));
}
//creates brand new tuples with brand new fields
@Override
public Stream partitionAggregate(Fields inputFields, Aggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return _topology.addSourcedNode(this,
new ProcessorNode(_topology.getUniqueStreamId(),
_name,
functionFields,
functionFields,
new AggregateProcessor(inputFields, agg)));
}
public Stream stateQuery(TridentState state, Fields inputFields, QueryFunction function, Fields functionFields) {
projectionValidation(inputFields);
String stateId = state._node.stateInfo.id;
Node n = new ProcessorNode(_topology.getUniqueStreamId(),
_name,
TridentUtils.fieldsConcat(getOutputFields(), functionFields),
functionFields,
new StateQueryProcessor(stateId, inputFields, function));
_topology._colocate.get(stateId).add(n);
return _topology.addSourcedNode(this, n);
}
public TridentState partitionPersist(StateFactory stateFactory, Fields inputFields, StateUpdater updater, Fields functionFields) {
return partitionPersist(new StateSpec(stateFactory), inputFields, updater, functionFields);
}
public TridentState partitionPersist(StateSpec stateSpec, Fields inputFields, StateUpdater updater, Fields functionFields) {
projectionValidation(inputFields);
String id = _topology.getUniqueStateId();
ProcessorNode n = new ProcessorNode(_topology.getUniqueStreamId(),
_name,
functionFields,
functionFields,
new PartitionPersistProcessor(id, inputFields, updater));
n.committer = true;
n.stateInfo = new NodeStateInfo(id, stateSpec);
return _topology.addSourcedStateNode(this, n);
}
public TridentState partitionPersist(StateFactory stateFactory, Fields inputFields, StateUpdater updater) {
return partitionPersist(stateFactory, inputFields, updater, new Fields());
}
public TridentState partitionPersist(StateSpec stateSpec, Fields inputFields, StateUpdater updater) {
return partitionPersist(stateSpec, inputFields, updater, new Fields());
}
public Stream each(Function function, Fields functionFields) {
return each(null, function, functionFields);
}
public Stream each(Fields inputFields, Filter filter) {
return each(inputFields, new FilterExecutor(filter), new Fields());
}
/**
* Returns a stream consisting of the elements of this stream that match the given filter.
*
* @param filter the filter to apply to each trident tuple to determine if it should be included.
* @return the new stream
*/
public Stream filter(Filter filter) {
return each(getOutputFields(), filter);
}
/**
* Returns a stream consisting of the elements of this stream that match the given filter.
*
* @param inputFields the fields of the input trident tuple to be selected.
* @param filter the filter to apply to each trident tuple to determine if it should be included.
* @return the new stream
*/
public Stream filter(Fields inputFields, Filter filter) {
return each(inputFields, filter);
}
/**
* Returns a stream consisting of the result of applying the given mapping function to the values of this stream.
*
* @param function a mapping function to be applied to each value in this stream.
* @return the new stream
*/
public Stream map(MapFunction function) {
projectionValidation(getOutputFields());
return _topology.addSourcedNode(this,
new ProcessorNode(
_topology.getUniqueStreamId(),
_name,
getOutputFields(),
getOutputFields(),
new MapProcessor(getOutputFields(), new MapFunctionExecutor(function))));
}
/**
* Returns a stream consisting of the results of replacing each value of this stream with the contents
* produced by applying the provided mapping function to each value. This has the effect of applying
* a one-to-many transformation to the values of the stream, and then flattening the resulting elements into a new stream.
*
* @param function a mapping function to be applied to each value in this stream which produces new values.
* @return the new stream
*/
public Stream flatMap(FlatMapFunction function) {
projectionValidation(getOutputFields());
return _topology.addSourcedNode(this,
new ProcessorNode(
_topology.getUniqueStreamId(),
_name,
getOutputFields(),
getOutputFields(),
new MapProcessor(getOutputFields(), new FlatMapFunctionExecutor(function))));
}
/**
* Returns a stream consisting of the trident tuples of this stream, additionally performing the provided action on
* each trident tuple as they are consumed from the resulting stream. This is mostly useful for debugging
* to see the tuples as they flow past a certain point in a pipeline.
*
* @param action the action to perform on the trident tuple as they are consumed from the stream
* @return the new stream
*/
public Stream peek(Consumer action) {
projectionValidation(getOutputFields());
return _topology.addSourcedNode(this,
new ProcessorNode(
_topology.getUniqueStreamId(),
_name,
getOutputFields(),
getOutputFields(),
new MapProcessor(getOutputFields(), new ConsumerExecutor(action))));
}
public ChainedAggregatorDeclarer chainedAgg() {
return new ChainedAggregatorDeclarer(this, new BatchGlobalAggScheme());
}
public Stream partitionAggregate(Aggregator agg, Fields functionFields) {
return partitionAggregate(null, agg, functionFields);
}
public Stream partitionAggregate(CombinerAggregator agg, Fields functionFields) {
return partitionAggregate(null, agg, functionFields);
}
public Stream partitionAggregate(Fields inputFields, CombinerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return chainedAgg()
.partitionAggregate(inputFields, agg, functionFields)
.chainEnd();
}
public Stream partitionAggregate(ReducerAggregator agg, Fields functionFields) {
return partitionAggregate(null, agg, functionFields);
}
public Stream partitionAggregate(Fields inputFields, ReducerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return chainedAgg()
.partitionAggregate(inputFields, agg, functionFields)
.chainEnd();
}
/**
* This aggregator operation computes the minimum of tuples by the given {@code inputFieldName} and it is
* assumed that its value is an instance of {@code Comparable}. If the value of tuple with field {@code inputFieldName} is not an
* instance of {@code Comparable} then it throws {@code ClassCastException}
*
* @param inputFieldName input field name
* @return the new stream with this operation.
*/
public Stream minBy(String inputFieldName) {
Aggregator<ComparisonAggregator.State> min = new Min(inputFieldName);
return comparableAggregateStream(inputFieldName, min);
}
/**
* This aggregator operation computes the minimum of tuples by the given {@code inputFieldName} in a stream by
* using the given {@code comparator}. If the value of tuple with field {@code inputFieldName} is not an
* instance of {@code T} then it throws {@code ClassCastException}
*
* @param inputFieldName input field name
* @param comparator comparator used in for finding minimum of two tuple values of {@code inputFieldName}.
* @param <T> type of tuple's given input field value.
* @return the new stream with this operation.
*/
public <T> Stream minBy(String inputFieldName, Comparator<T> comparator) {
Aggregator<ComparisonAggregator.State> min = new MinWithComparator<>(inputFieldName, comparator);
return comparableAggregateStream(inputFieldName, min);
}
/**
* This aggregator operation computes the minimum of tuples in a stream by using the given {@code comparator} with
* {@code TridentTuple}s.
*
* @param comparator comparator used in for finding minimum of two tuple values.
* @return the new stream with this operation.
*/
public Stream min(Comparator<TridentTuple> comparator) {
Aggregator<ComparisonAggregator.State> min = new MinWithComparator<>(comparator);
return comparableAggregateStream(null, min);
}
/**
* This aggregator operation computes the maximum of tuples by the given {@code inputFieldName} and it is
* assumed that its value is an instance of {@code Comparable}. If the value of tuple with field {@code inputFieldName} is not an
* instance of {@code Comparable} then it throws {@code ClassCastException}
*
* @param inputFieldName input field name
* @return the new stream with this operation.
*/
public Stream maxBy(String inputFieldName) {
Aggregator<ComparisonAggregator.State> max = new Max(inputFieldName);
return comparableAggregateStream(inputFieldName, max);
}
/**
* This aggregator operation computes the maximum of tuples by the given {@code inputFieldName} in a stream by
* using the given {@code comparator}. If the value of tuple with field {@code inputFieldName} is not an
* instance of {@code T} then it throws {@code ClassCastException}
*
* @param inputFieldName input field name
* @param comparator comparator used in for finding maximum of two tuple values of {@code inputFieldName}.
* @param <T> type of tuple's given input field value.
* @return the new stream with this operation.
*/
public <T> Stream maxBy(String inputFieldName, Comparator<T> comparator) {
Aggregator<ComparisonAggregator.State> max = new MaxWithComparator<>(inputFieldName, comparator);
return comparableAggregateStream(inputFieldName, max);
}
/**
* This aggregator operation computes the maximum of tuples in a stream by using the given {@code comparator} with
* {@code TridentTuple}s.
*
* @param comparator comparator used in for finding maximum of two tuple values.
* @return the new stream with this operation.
*/
public Stream max(Comparator<TridentTuple> comparator) {
Aggregator<ComparisonAggregator.State> max = new MaxWithComparator<>(comparator);
return comparableAggregateStream(null, max);
}
private <T> Stream comparableAggregateStream(String inputFieldName, Aggregator<T> aggregator) {
if(inputFieldName != null) {
projectionValidation(new Fields(inputFieldName));
}
return partitionAggregate(getOutputFields(), aggregator, getOutputFields());
}
public Stream aggregate(Aggregator agg, Fields functionFields) {
return aggregate(null, agg, functionFields);
}
public Stream aggregate(Fields inputFields, Aggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return chainedAgg()
.aggregate(inputFields, agg, functionFields)
.chainEnd();
}
public Stream aggregate(CombinerAggregator agg, Fields functionFields) {
return aggregate(null, agg, functionFields);
}
public Stream aggregate(Fields inputFields, CombinerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return chainedAgg()
.aggregate(inputFields, agg, functionFields)
.chainEnd();
}
public Stream aggregate(ReducerAggregator agg, Fields functionFields) {
return aggregate(null, agg, functionFields);
}
public Stream aggregate(Fields inputFields, ReducerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return chainedAgg()
.aggregate(inputFields, agg, functionFields)
.chainEnd();
}
/**
* Returns a stream of tuples which are aggregated results of a tumbling window with every {@code windowCount} of tuples.
*
* @param windowCount represents number of tuples in the window
* @param windowStoreFactory intermediary tuple store for storing windowing tuples
* @param inputFields projected fields for aggregator
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream tumblingWindow(int windowCount, WindowsStoreFactory windowStoreFactory,
Fields inputFields, Aggregator aggregator, Fields functionFields) {
return window(TumblingCountWindow.of(windowCount), windowStoreFactory, inputFields, aggregator, functionFields);
}
/**
* Returns a stream of tuples which are aggregated results of a sliding window with every {@code windowCount} of tuples
* and slides the window after {@code slideCount}.
*
* @param windowCount represents tuples count of a window
* @param inputFields projected fields for aggregator
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream slidingWindow(int windowCount, int slideCount, WindowsStoreFactory windowStoreFactory,
Fields inputFields, Aggregator aggregator, Fields functionFields) {
return window(SlidingCountWindow.of(windowCount, slideCount), windowStoreFactory, inputFields, aggregator, functionFields);
}
/**
* Returns a stream of tuples which are aggregated results of a window that tumbles at duration of {@code windowDuration}
*
* @param windowDuration represents tumbling window duration configuration
* @param windowStoreFactory intermediary tuple store for storing windowing tuples
* @param inputFields projected fields for aggregator
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream tumblingWindow(BaseWindowedBolt.Duration windowDuration, WindowsStoreFactory windowStoreFactory,
Fields inputFields, Aggregator aggregator, Fields functionFields) {
return window(TumblingDurationWindow.of(windowDuration), windowStoreFactory, inputFields, aggregator, functionFields);
}
/**
* Returns a stream of tuples which are aggregated results of a window which slides at duration of {@code slidingInterval}
* and completes a window at {@code windowDuration}
*
* @param windowDuration represents window duration configuration
* @param inputFields projected fields for aggregator
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream slidingWindow(BaseWindowedBolt.Duration windowDuration, BaseWindowedBolt.Duration slidingInterval,
WindowsStoreFactory windowStoreFactory, Fields inputFields, Aggregator aggregator, Fields functionFields) {
return window(SlidingDurationWindow.of(windowDuration, slidingInterval), windowStoreFactory, inputFields, aggregator, functionFields);
}
/**
* Returns a stream of aggregated results based on the given window configuration which uses inmemory windowing tuple store.
*
* @param windowConfig window configuration like window length and slide length.
* @param inputFields input fields
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream window(WindowConfig windowConfig, Fields inputFields, Aggregator aggregator, Fields functionFields) {
// this store is used only for storing triggered aggregated results but not tuples as storeTuplesInStore is set
// as false int he below call.
InMemoryWindowsStoreFactory inMemoryWindowsStoreFactory = new InMemoryWindowsStoreFactory();
return window(windowConfig, inMemoryWindowsStoreFactory, inputFields, aggregator, functionFields, false);
}
/**
* Returns stream of aggregated results based on the given window configuration.
*
* @param windowConfig window configuration like window length and slide length.
* @param windowStoreFactory intermediary tuple store for storing tuples for windowing
* @param inputFields input fields
* @param aggregator aggregator to run on the window of tuples to compute the result and emit to the stream.
* @param functionFields fields of values to emit with aggregation.
*
* @return the new stream with this operation.
*/
public Stream window(WindowConfig windowConfig, WindowsStoreFactory windowStoreFactory, Fields inputFields,
Aggregator aggregator, Fields functionFields) {
return window(windowConfig, windowStoreFactory, inputFields, aggregator, functionFields, true);
}
private Stream window(WindowConfig windowConfig, WindowsStoreFactory windowStoreFactory, Fields inputFields, Aggregator aggregator,
Fields functionFields, boolean storeTuplesInStore) {
projectionValidation(inputFields);
windowConfig.validate();
Fields fields = addTriggerField(functionFields);
// when storeTuplesInStore is false then the given windowStoreFactory is only used to store triggers and
// that store is passed to WindowStateUpdater to remove them after committing the batch.
Stream stream = _topology.addSourcedNode(this,
new ProcessorNode(_topology.getUniqueStreamId(),
_name,
fields,
fields,
new WindowTridentProcessor(windowConfig, _topology.getUniqueWindowId(), windowStoreFactory,
inputFields, aggregator, storeTuplesInStore)));
Stream effectiveStream = stream.project(functionFields);
// create StateUpdater with the given windowStoreFactory to remove triggered aggregation results form store
// when they are successfully processed.
StateFactory stateFactory = new WindowsStateFactory();
StateUpdater stateUpdater = new WindowsStateUpdater(windowStoreFactory);
stream.partitionPersist(stateFactory, new Fields(WindowTridentProcessor.TRIGGER_FIELD_NAME), stateUpdater, new Fields());
return effectiveStream;
}
private Fields addTriggerField(Fields functionFields) {
List<String> fieldsList = new ArrayList<>();
fieldsList.add(WindowTridentProcessor.TRIGGER_FIELD_NAME);
for (String field : functionFields) {
fieldsList.add(field);
}
return new Fields(fieldsList);
}
public TridentState partitionPersist(StateFactory stateFactory, StateUpdater updater, Fields functionFields) {
return partitionPersist(new StateSpec(stateFactory), updater, functionFields);
}
public TridentState partitionPersist(StateSpec stateSpec, StateUpdater updater, Fields functionFields) {
return partitionPersist(stateSpec, null, updater, functionFields);
}
public TridentState partitionPersist(StateFactory stateFactory, StateUpdater updater) {
return partitionPersist(stateFactory, updater, new Fields());
}
public TridentState partitionPersist(StateSpec stateSpec, StateUpdater updater) {
return partitionPersist(stateSpec, updater, new Fields());
}
public TridentState persistentAggregate(StateFactory stateFactory, CombinerAggregator agg, Fields functionFields) {
return persistentAggregate(new StateSpec(stateFactory), agg, functionFields);
}
public TridentState persistentAggregate(StateSpec spec, CombinerAggregator agg, Fields functionFields) {
return persistentAggregate(spec, null, agg, functionFields);
}
public TridentState persistentAggregate(StateFactory stateFactory, Fields inputFields, CombinerAggregator agg, Fields functionFields) {
return persistentAggregate(new StateSpec(stateFactory), inputFields, agg, functionFields);
}
public TridentState persistentAggregate(StateSpec spec, Fields inputFields, CombinerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
// replaces normal aggregation here with a global grouping because it needs to be consistent across batches
return new ChainedAggregatorDeclarer(this, new GlobalAggScheme())
.aggregate(inputFields, agg, functionFields)
.chainEnd()
.partitionPersist(spec, functionFields, new CombinerAggStateUpdater(agg), functionFields);
}
public TridentState persistentAggregate(StateFactory stateFactory, ReducerAggregator agg, Fields functionFields) {
return persistentAggregate(new StateSpec(stateFactory), agg, functionFields);
}
public TridentState persistentAggregate(StateSpec spec, ReducerAggregator agg, Fields functionFields) {
return persistentAggregate(spec, null, agg, functionFields);
}
public TridentState persistentAggregate(StateFactory stateFactory, Fields inputFields, ReducerAggregator agg, Fields functionFields) {
return persistentAggregate(new StateSpec(stateFactory), inputFields, agg, functionFields);
}
public TridentState persistentAggregate(StateSpec spec, Fields inputFields, ReducerAggregator agg, Fields functionFields) {
projectionValidation(inputFields);
return global().partitionPersist(spec, inputFields, new ReducerAggStateUpdater(agg), functionFields);
}
public Stream stateQuery(TridentState state, QueryFunction function, Fields functionFields) {
return stateQuery(state, null, function, functionFields);
}
@Override
public Stream toStream() {
return this;
}
@Override
public Fields getOutputFields() {
return _node.allOutputFields;
}
static class BatchGlobalAggScheme implements GlobalAggregationScheme<Stream> {
@Override
public IAggregatableStream aggPartition(Stream s) {
return s.batchGlobal();
}
@Override
public BatchToPartition singleEmitPartitioner() {
return new IndexHashBatchToPartition();
}
}
static class GlobalAggScheme implements GlobalAggregationScheme<Stream> {
@Override
public IAggregatableStream aggPartition(Stream s) {
return s.global();
}
@Override
public BatchToPartition singleEmitPartitioner() {
return new GlobalBatchToPartition();
}
}
private void projectionValidation(Fields projFields) {
if (projFields == null) {
return;
}
Fields allFields = this.getOutputFields();
for (String field : projFields) {
if (!allFields.contains(field)) {
throw new IllegalArgumentException("Trying to select non-existent field: '" + field + "' from stream containing fields fields: <" + allFields + ">");
}
}
}
}