04_Flink Streaming DataStream
欧照
DataStream对象，简称ds，作为流计算编程的核心上下文。提供多种功能
//1:接入数据源
1：ds由env和SourceFunction产生，完成接入数据源的功能
//2:数据处理
2：filter。实现过滤功能
3：map，实现1->1的映射转换
4：flatmap，实现拆解字符串等复杂的转换功能
5：project，实现字段裁剪
6：transform实现复杂需求的功能
//3:join流
7：coGroup，流合并
8：join，实现流join
0：union，merge两个流
//4:分组，种类太多。也没整明白。大多只用keyBy就行。其他都有特殊场景，或者性能优化用处的
9：keyBy，实现按指定key的value值的hash，实现数据分组
10：shuffle，实现数据随机分组
11：broadcast，实现数据的广播
12：forward，实现本地task分组
13：rebalance，使用随机函数，进行数据分组
14：rescale，数据随机的分组到一个instance子集
15：global，将数据发送到，下个操作的第一个instance
15：partitionCustom，实现数据的自定义分组
//5:窗口函数，跳动窗口，滑动窗口两种类型
16：timeWindowAll，实现时间的滑动窗口和跳动窗口
17：countWindowAll，实现事件个数的滑动窗口和跳动窗口
18：windowAll，自定义窗口。时间和事件个数的窗口就是基于这个实现的。
//6:事件时间
19：assignTimestampsAndWatermarks，为事件指定时间，和WATERMARK类型。
//：输出
20：addSink，根据SinkFunction的实现，实现数据处理的结点，一般是数据落地
21：writeUsingOutputFormat，通过OutputFormatSinkFunction，封装了addSink，根据OutputFormat实现类，实现数据落地。
org.apache.flink.streaming.api.datastream.DataStream.union(DataStream<T>...)
org.apache.flink.streaming.api.datastream.DataStream.split(OutputSelector<T>)
org.apache.flink.streaming.api.datastream.DataStream.connect(DataStream<R>)
org.apache.flink.streaming.api.datastream.DataStream.keyBy(KeySelector<T, K>)
org.apache.flink.streaming.api.datastream.DataStream.keyBy(int...)
org.apache.flink.streaming.api.datastream.DataStream.keyBy(String...)
org.apache.flink.streaming.api.datastream.DataStream.keyBy(Keys<T>)
org.apache.flink.streaming.api.datastream.DataStream.partitionCustom(Partitioner<K>, int)
org.apache.flink.streaming.api.datastream.DataStream.partitionCustom(Partitioner<K>, String)
org.apache.flink.streaming.api.datastream.DataStream.partitionCustom(Partitioner<K>, KeySelector<T, K>)
org.apache.flink.streaming.api.datastream.DataStream.partitionCustom(Partitioner<K>, Keys<T>)
org.apache.flink.streaming.api.datastream.DataStream.broadcast()
org.apache.flink.streaming.api.datastream.DataStream.shuffle()
org.apache.flink.streaming.api.datastream.DataStream.forward()
org.apache.flink.streaming.api.datastream.DataStream.rebalance()
org.apache.flink.streaming.api.datastream.DataStream.rescale()
org.apache.flink.streaming.api.datastream.DataStream.global()
org.apache.flink.streaming.api.datastream.DataStream.map(MapFunction<T, R>)
org.apache.flink.streaming.api.datastream.DataStream.flatMap(FlatMapFunction<T, R>)
org.apache.flink.streaming.api.datastream.DataStream.filter(FilterFunction<T>)
org.apache.flink.streaming.api.datastream.DataStream.project(int...)
org.apache.flink.streaming.api.datastream.DataStream.coGroup(DataStream<T2>)
org.apache.flink.streaming.api.datastream.DataStream.join(DataStream<T2>)
org.apache.flink.streaming.api.datastream.DataStream.timeWindowAll(Time)
org.apache.flink.streaming.api.datastream.DataStream.timeWindowAll(Time, Time)
org.apache.flink.streaming.api.datastream.DataStream.countWindowAll(long)
org.apache.flink.streaming.api.datastream.DataStream.countWindowAll(long, long)
org.apache.flink.streaming.api.datastream.DataStream.windowAll(WindowAssigner<? super T, W>)
org.apache.flink.streaming.api.datastream.DataStream.assignTimestamps(TimestampExtractor<T>)
org.apache.flink.streaming.api.datastream.DataStream.assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks<T>)
org.apache.flink.streaming.api.datastream.DataStream.assignTimestampsAndWatermarks(AssignerWithPunctuatedWatermarks<T>)
org.apache.flink.streaming.api.datastream.DataStream.writeUsingOutputFormat(OutputFormat<T>)
org.apache.flink.streaming.api.datastream.DataStream.transform(String, TypeInformation<R>, OneInputStreamOperator<T, R>)
org.apache.flink.streaming.api.datastream.DataStream.addSink(SinkFunction<T>)
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* 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,
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package org.apache.flink.streaming.api.datastream;
import java.util.ArrayList;
import java.util.List;
import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.annotation.Internal;
import org.apache.flink.annotation.Public;
import org.apache.flink.api.common.ExecutionConfig;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.Partitioner;
import org.apache.flink.api.common.functions.RichFilterFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.io.OutputFormat;
import org.apache.flink.api.common.typeinfo.BasicArrayTypeInfo;
import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.Utils;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.io.CsvOutputFormat;
import org.apache.flink.api.java.io.TextOutputFormat;
import org.apache.flink.api.common.operators.Keys;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.typeutils.InputTypeConfigurable;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.core.fs.FileSystem.WriteMode;
import org.apache.flink.core.fs.Path;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.collector.selector.OutputSelector;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks;
import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks;
import org.apache.flink.streaming.api.functions.TimestampExtractor;
import org.apache.flink.streaming.api.functions.sink.OutputFormatSinkFunction;
import org.apache.flink.streaming.api.functions.sink.PrintSinkFunction;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.streaming.api.functions.sink.SocketClientSink;
import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
import org.apache.flink.streaming.api.operators.StreamFilter;
import org.apache.flink.streaming.api.operators.StreamFlatMap;
import org.apache.flink.streaming.api.operators.StreamMap;
import org.apache.flink.streaming.api.operators.StreamSink;
import org.apache.flink.streaming.api.transformations.OneInputTransformation;
import org.apache.flink.streaming.api.transformations.PartitionTransformation;
import org.apache.flink.streaming.api.transformations.StreamTransformation;
import org.apache.flink.streaming.api.transformations.UnionTransformation;
import org.apache.flink.streaming.api.windowing.assigners.GlobalWindows;
import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.assigners.WindowAssigner;
import org.apache.flink.streaming.api.windowing.evictors.CountEvictor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.CountTrigger;
import org.apache.flink.streaming.api.windowing.triggers.PurgingTrigger;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.api.windowing.windows.Window;
import org.apache.flink.streaming.runtime.operators.ExtractTimestampsOperator;
import org.apache.flink.streaming.runtime.operators.TimestampsAndPeriodicWatermarksOperator;
import org.apache.flink.streaming.runtime.operators.TimestampsAndPunctuatedWatermarksOperator;
import org.apache.flink.streaming.runtime.partitioner.BroadcastPartitioner;
import org.apache.flink.streaming.runtime.partitioner.CustomPartitionerWrapper;
import org.apache.flink.streaming.runtime.partitioner.ForwardPartitioner;
import org.apache.flink.streaming.runtime.partitioner.RescalePartitioner;
import org.apache.flink.streaming.runtime.partitioner.RebalancePartitioner;
import org.apache.flink.streaming.runtime.partitioner.GlobalPartitioner;
import org.apache.flink.streaming.runtime.partitioner.ShufflePartitioner;
import org.apache.flink.streaming.runtime.partitioner.StreamPartitioner;
import org.apache.flink.streaming.util.keys.KeySelectorUtil;
import org.apache.flink.streaming.util.serialization.SerializationSchema;
import com.google.common.base.Preconditions;
/**
* A DataStream represents a stream of elements of the same type. A DataStream
* can be transformed into another DataStream by applying a transformation as
* for example:
* <ul>
* <li>{@link DataStream#map}
* <li>{@link DataStream#filter}
* </ul>
*
* @param <T> The type of the elements in this stream.
*/
@Public
public class DataStream<T> {
protected final StreamExecutionEnvironment environment;
protected final StreamTransformation<T> transformation;
/**
* Create a new {@link DataStream} in the given execution environment with
* partitioning set to forward by default.
*
* @param environment The StreamExecutionEnvironment
*/
public DataStream(StreamExecutionEnvironment environment, StreamTransformation<T> transformation) {
this.environment = Preconditions.checkNotNull(environment, "Execution Environment must not be null.");
this.transformation = Preconditions.checkNotNull(transformation, "Stream Transformation must not be null.");
}
/**
* Returns the ID of the {@link DataStream} in the current {@link StreamExecutionEnvironment}.
*
* @return ID of the DataStream
*/
@Internal
public int getId() {
return transformation.getId();
}
/**
* Gets the parallelism for this operator.
*
* @return The parallelism set for this operator.
*/
public int getParallelism() {
return transformation.getParallelism();
}
/**
* Gets the type of the stream.
*
* @return The type of the datastream.
*/
public TypeInformation<T> getType() {
return transformation.getOutputType();
}
/**
* Invokes the {@link org.apache.flink.api.java.ClosureCleaner}
* on the given function if closure cleaning is enabled in the {@link ExecutionConfig}.
*
* @return The cleaned Function
*/
protected <F> F clean(F f) {
return getExecutionEnvironment().clean(f);
}
/**
* Returns the {@link StreamExecutionEnvironment} that was used to create this
* {@link DataStream}
*
* @return The Execution Environment
*/
public StreamExecutionEnvironment getExecutionEnvironment() {
return environment;
}
public ExecutionConfig getExecutionConfig() {
return environment.getConfig();
}
/**
* Creates a new {@link DataStream} by merging {@link DataStream} outputs of
* the same type with each other. The DataStreams merged using this operator
* will be transformed simultaneously.
*
* @param streams
* The DataStreams to union output with.
* @return The {@link DataStream}.
*/
@SafeVarargs
public final DataStream<T> union(DataStream<T>... streams) {
List<StreamTransformation<T>> unionedTransforms = new ArrayList<>();
unionedTransforms.add(this.transformation);
for (DataStream<T> newStream : streams) {
if (!getType().equals(newStream.getType())) {
throw new IllegalArgumentException("Cannot union streams of different types: "
+ getType() + " and " + newStream.getType());
}
unionedTransforms.add(newStream.getTransformation());
}
return new DataStream<>(this.environment, new UnionTransformation<>(unionedTransforms));
}
/**
* Operator used for directing tuples to specific named outputs using an
* {@link org.apache.flink.streaming.api.collector.selector.OutputSelector}.
* Calling this method on an operator creates a new {@link SplitStream}.
*
* @param outputSelector
* The user defined
* {@link org.apache.flink.streaming.api.collector.selector.OutputSelector}
* for directing the tuples.
* @return The {@link SplitStream}
*/
public SplitStream<T> split(OutputSelector<T> outputSelector) {
return new SplitStream<>(this, clean(outputSelector));
}
/**
* Creates a new {@link ConnectedStreams} by connecting
* {@link DataStream} outputs of (possible) different types with each other.
* The DataStreams connected using this operator can be used with
* CoFunctions to apply joint transformations.
*
* @param dataStream
* The DataStream with which this stream will be connected.
* @return The {@link ConnectedStreams}.
*/
public <R> ConnectedStreams<T, R> connect(DataStream<R> dataStream) {
return new ConnectedStreams<>(environment, this, dataStream);
}
/**
*
* It creates a new {@link KeyedStream} that uses the provided key for partitioning
* its operator states.
*
* @param key
* The KeySelector to be used for extracting the key for partitioning
* @return The {@link DataStream} with partitioned state (i.e. KeyedStream)
*/
public <K> KeyedStream<T, K> keyBy(KeySelector<T, K> key) {
return new KeyedStream<>(this, clean(key));
}
/**
* Partitions the operator state of a {@link DataStream} by the given key positions.
*
* @param fields
* The position of the fields on which the {@link DataStream}
* will be grouped.
* @return The {@link DataStream} with partitioned state (i.e. KeyedStream)
*/
public KeyedStream<T, Tuple> keyBy(int... fields) {
if (getType() instanceof BasicArrayTypeInfo || getType() instanceof PrimitiveArrayTypeInfo) {
return keyBy(KeySelectorUtil.getSelectorForArray(fields, getType()));
} else {
return keyBy(new Keys.ExpressionKeys<>(fields, getType()));
}
}
/**
* Partitions the operator state of a {@link DataStream}using field expressions.
* A field expression is either the name of a public field or a getter method with parentheses
* of the {@link DataStream}S underlying type. A dot can be used to drill
* down into objects, as in {@code "field1.getInnerField2()" }.
*
* @param fields
* One or more field expressions on which the state of the {@link DataStream} operators will be
* partitioned.
* @return The {@link DataStream} with partitioned state (i.e. KeyedStream)
**/
public KeyedStream<T, Tuple> keyBy(String... fields) {
return keyBy(new Keys.ExpressionKeys<>(fields, getType()));
}
private KeyedStream<T, Tuple> keyBy(Keys<T> keys) {
return new KeyedStream<>(this, clean(KeySelectorUtil.getSelectorForKeys(keys,
getType(), getExecutionConfig())));
}
/**
* Partitions a tuple DataStream on the specified key fields using a custom partitioner.
* This method takes the key position to partition on, and a partitioner that accepts the key type.
* <p>
* Note: This method works only on single field keys.
*
* @param partitioner The partitioner to assign partitions to keys.
* @param field The field index on which the DataStream is to partitioned.
* @return The partitioned DataStream.
*/
public <K> DataStream<T> partitionCustom(Partitioner<K> partitioner, int field) {
Keys.ExpressionKeys<T> outExpressionKeys = new Keys.ExpressionKeys<>(new int[]{field}, getType());
return partitionCustom(partitioner, outExpressionKeys);
}
/**
* Partitions a POJO DataStream on the specified key fields using a custom partitioner.
* This method takes the key expression to partition on, and a partitioner that accepts the key type.
* <p>
* Note: This method works only on single field keys.
*
* @param partitioner The partitioner to assign partitions to keys.
* @param field The expression for the field on which the DataStream is to partitioned.
* @return The partitioned DataStream.
*/
public <K> DataStream<T> partitionCustom(Partitioner<K> partitioner, String field) {
Keys.ExpressionKeys<T> outExpressionKeys = new Keys.ExpressionKeys<>(new String[]{field}, getType());
return partitionCustom(partitioner, outExpressionKeys);
}
/**
* Partitions a DataStream on the key returned by the selector, using a custom partitioner.
* This method takes the key selector to get the key to partition on, and a partitioner that
* accepts the key type.
* <p>
* Note: This method works only on single field keys, i.e. the selector cannot return tuples
* of fields.
*
* @param partitioner
* The partitioner to assign partitions to keys.
* @param keySelector
* The KeySelector with which the DataStream is partitioned.
* @return The partitioned DataStream.
* @see KeySelector
*/
public <K> DataStream<T> partitionCustom(Partitioner<K> partitioner, KeySelector<T, K> keySelector) {
return setConnectionType(new CustomPartitionerWrapper<>(clean(partitioner),
clean(keySelector)));
}
// private helper method for custom partitioning
private <K> DataStream<T> partitionCustom(Partitioner<K> partitioner, Keys<T> keys) {
KeySelector<T, K> keySelector = KeySelectorUtil.getSelectorForOneKey(keys, partitioner, getType(), getExecutionConfig());
return setConnectionType(
new CustomPartitionerWrapper<>(
clean(partitioner),
clean(keySelector)));
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are broadcasted to every parallel instance of the next operation.
*
* @return The DataStream with broadcast partitioning set.
*/
public DataStream<T> broadcast() {
return setConnectionType(new BroadcastPartitioner<T>());
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are shuffled uniformly randomly to the next operation.
*
* @return The DataStream with shuffle partitioning set.
*/
@PublicEvolving
public DataStream<T> shuffle() {
return setConnectionType(new ShufflePartitioner<T>());
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are forwarded to the local subtask of the next operation.
*
* @return The DataStream with forward partitioning set.
*/
public DataStream<T> forward() {
return setConnectionType(new ForwardPartitioner<T>());
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are distributed evenly to instances of the next operation in a round-robin
* fashion.
*
* @return The DataStream with rebalance partitioning set.
*/
public DataStream<T> rebalance() {
return setConnectionType(new RebalancePartitioner<T>());
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are distributed evenly to a subset of instances of the next operation in a round-robin
* fashion.
*
* <p>The subset of downstream operations to which the upstream operation sends
* elements depends on the degree of parallelism of both the upstream and downstream operation.
* For example, if the upstream operation has parallelism 2 and the downstream operation
* has parallelism 4, then one upstream operation would distribute elements to two
* downstream operations while the other upstream operation would distribute to the other
* two downstream operations. If, on the other hand, the downstream operation has parallelism
* 2 while the upstream operation has parallelism 4 then two upstream operations will
* distribute to one downstream operation while the other two upstream operations will
* distribute to the other downstream operations.
*
* <p>In cases where the different parallelisms are not multiples of each other one or several
* downstream operations will have a differing number of inputs from upstream operations.
*
* @return The DataStream with rescale partitioning set.
*/
@PublicEvolving
public DataStream<T> rescale() {
return setConnectionType(new RescalePartitioner<T>());
}
/**
* Sets the partitioning of the {@link DataStream} so that the output values
* all go to the first instance of the next processing operator. Use this
* setting with care since it might cause a serious performance bottleneck
* in the application.
*
* @return The DataStream with shuffle partitioning set.
*/
@PublicEvolving
public DataStream<T> global() {
return setConnectionType(new GlobalPartitioner<T>());
}
/**
* Initiates an iterative part of the program that feeds back data streams.
* The iterative part needs to be closed by calling
* {@link IterativeStream#closeWith(DataStream)}. The transformation of
* this IterativeStream will be the iteration head. The data stream
* given to the {@link IterativeStream#closeWith(DataStream)} method is
* the data stream that will be fed back and used as the input for the
* iteration head. The user can also use different feedback type than the
* input of the iteration and treat the input and feedback streams as a
* {@link ConnectedStreams} be calling
* {@link IterativeStream#withFeedbackType(TypeInformation)}
* <p>
* A common usage pattern for streaming iterations is to use output
* splitting to send a part of the closing data stream to the head. Refer to
* {@link #split(OutputSelector)} for more information.
* <p>
* The iteration edge will be partitioned the same way as the first input of
* the iteration head unless it is changed in the
* {@link IterativeStream#closeWith(DataStream)} call.
* <p>
* By default a DataStream with iteration will never terminate, but the user
* can use the maxWaitTime parameter to set a max waiting time for the
* iteration head. If no data received in the set time, the stream
* terminates.
*
* @return The iterative data stream created.
*/
@PublicEvolving
public IterativeStream<T> iterate() {
return new IterativeStream<>(this, 0);
}
/**
* Initiates an iterative part of the program that feeds back data streams.
* The iterative part needs to be closed by calling
* {@link IterativeStream#closeWith(DataStream)}. The transformation of
* this IterativeStream will be the iteration head. The data stream
* given to the {@link IterativeStream#closeWith(DataStream)} method is
* the data stream that will be fed back and used as the input for the
* iteration head. The user can also use different feedback type than the
* input of the iteration and treat the input and feedback streams as a
* {@link ConnectedStreams} be calling
* {@link IterativeStream#withFeedbackType(TypeInformation)}
* <p>
* A common usage pattern for streaming iterations is to use output
* splitting to send a part of the closing data stream to the head. Refer to
* {@link #split(OutputSelector)} for more information.
* <p>
* The iteration edge will be partitioned the same way as the first input of
* the iteration head unless it is changed in the
* {@link IterativeStream#closeWith(DataStream)} call.
* <p>
* By default a DataStream with iteration will never terminate, but the user
* can use the maxWaitTime parameter to set a max waiting time for the
* iteration head. If no data received in the set time, the stream
* terminates.
*
* @param maxWaitTimeMillis
* Number of milliseconds to wait between inputs before shutting
* down
*
* @return The iterative data stream created.
*/
@PublicEvolving
public IterativeStream<T> iterate(long maxWaitTimeMillis) {
return new IterativeStream<>(this, maxWaitTimeMillis);
}
/**
* Applies a Map transformation on a {@link DataStream}. The transformation
* calls a {@link MapFunction} for each element of the DataStream. Each
* MapFunction call returns exactly one element. The user can also extend
* {@link RichMapFunction} to gain access to other features provided by the
* {@link org.apache.flink.api.common.functions.RichFunction} interface.
*
* @param mapper
* The MapFunction that is called for each element of the
* DataStream.
* @param <R>
* output type
* @return The transformed {@link DataStream}.
*/
public <R> SingleOutputStreamOperator<R> map(MapFunction<T, R> mapper) {
TypeInformation<R> outType = TypeExtractor.getMapReturnTypes(clean(mapper), getType(),
Utils.getCallLocationName(), true);
return transform("Map", outType, new StreamMap<>(clean(mapper)));
}
/**
* Applies a FlatMap transformation on a {@link DataStream}. The
* transformation calls a {@link FlatMapFunction} for each element of the
* DataStream. Each FlatMapFunction call can return any number of elements
* including none. The user can also extend {@link RichFlatMapFunction} to
* gain access to other features provided by the
* {@link org.apache.flink.api.common.functions.RichFunction} interface.
*
* @param flatMapper
* The FlatMapFunction that is called for each element of the
* DataStream
*
* @param <R>
* output type
* @return The transformed {@link DataStream}.
*/
public <R> SingleOutputStreamOperator<R> flatMap(FlatMapFunction<T, R> flatMapper) {
TypeInformation<R> outType = TypeExtractor.getFlatMapReturnTypes(clean(flatMapper),
getType(), Utils.getCallLocationName(), true);
return transform("Flat Map", outType, new StreamFlatMap<>(clean(flatMapper)));
}
/**
* Applies a Filter transformation on a {@link DataStream}. The
* transformation calls a {@link FilterFunction} for each element of the
* DataStream and retains only those element for which the function returns
* true. Elements for which the function returns false are filtered. The
* user can also extend {@link RichFilterFunction} to gain access to other
* features provided by the
* {@link org.apache.flink.api.common.functions.RichFunction} interface.
*
* @param filter
* The FilterFunction that is called for each element of the
* DataStream.
* @return The filtered DataStream.
*/
public SingleOutputStreamOperator<T> filter(FilterFunction<T> filter) {
return transform("Filter", getType(), new StreamFilter<>(clean(filter)));
}
/**
* Initiates a Project transformation on a {@link Tuple} {@link DataStream}.<br>
* <b>Note: Only Tuple DataStreams can be projected.</b>
*
* <p>
* The transformation projects each Tuple of the DataSet onto a (sub)set of
* fields.
*
* @param fieldIndexes
* The field indexes of the input tuples that are retained. The
* order of fields in the output tuple corresponds to the order
* of field indexes.
* @return The projected DataStream
*
* @see Tuple
* @see DataStream
*/
@PublicEvolving
public <R extends Tuple> SingleOutputStreamOperator<R> project(int... fieldIndexes) {
return new StreamProjection<>(this, fieldIndexes).projectTupleX();
}
/**
* Creates a join operation. See {@link CoGroupedStreams} for an example of how the keys
* and window can be specified.
*/
public <T2> CoGroupedStreams<T, T2> coGroup(DataStream<T2> otherStream) {
return new CoGroupedStreams<>(this, otherStream);
}
/**
* Creates a join operation. See {@link JoinedStreams} for an example of how the keys
* and window can be specified.
*/
public <T2> JoinedStreams<T, T2> join(DataStream<T2> otherStream) {
return new JoinedStreams<>(this, otherStream);
}
/**
* Windows this {@code DataStream} into tumbling time windows.
*
* <p>
* This is a shortcut for either {@code .window(TumblingEventTimeWindows.of(size))} or
* {@code .window(TumblingProcessingTimeWindows.of(size))} depending on the time characteristic
* set using
*
* <p>
* Note: This operation can be inherently non-parallel since all elements have to pass through
* the same operator instance. (Only for special cases, such as aligned time windows is
* it possible to perform this operation in parallel).
*
* {@link org.apache.flink.streaming.api.environment.StreamExecutionEnvironment#setStreamTimeCharacteristic(org.apache.flink.streaming.api.TimeCharacteristic)}
*
* @param size The size of the window.
*/
public AllWindowedStream<T, TimeWindow> timeWindowAll(Time size) {
if (environment.getStreamTimeCharacteristic() == TimeCharacteristic.ProcessingTime) {
return windowAll(TumblingProcessingTimeWindows.of(size));
} else {
return windowAll(TumblingEventTimeWindows.of(size));
}
}
/**
* Windows this {@code DataStream} into sliding time windows.
*
* <p>
* This is a shortcut for either {@code .window(SlidingEventTimeWindows.of(size, slide))} or
* {@code .window(SlidingProcessingTimeWindows.of(size, slide))} depending on the time characteristic
* set using
* {@link org.apache.flink.streaming.api.environment.StreamExecutionEnvironment#setStreamTimeCharacteristic(org.apache.flink.streaming.api.TimeCharacteristic)}
*
* <p>
* Note: This operation can be inherently non-parallel since all elements have to pass through
* the same operator instance. (Only for special cases, such as aligned time windows is
* it possible to perform this operation in parallel).
*
* @param size The size of the window.
*/
public AllWindowedStream<T, TimeWindow> timeWindowAll(Time size, Time slide) {
if (environment.getStreamTimeCharacteristic() == TimeCharacteristic.ProcessingTime) {
return windowAll(SlidingProcessingTimeWindows.of(size, slide));
} else {
return windowAll(SlidingEventTimeWindows.of(size, slide));
}
}
/**
* Windows this {@code DataStream} into tumbling count windows.
*
* <p>
* Note: This operation can be inherently non-parallel since all elements have to pass through
* the same operator instance. (Only for special cases, such as aligned time windows is
* it possible to perform this operation in parallel).
*
* @param size The size of the windows in number of elements.
*/
public AllWindowedStream<T, GlobalWindow> countWindowAll(long size) {
return windowAll(GlobalWindows.create()).trigger(PurgingTrigger.of(CountTrigger.of(size)));
}
/**
* Windows this {@code DataStream} into sliding count windows.
*
* <p>
* Note: This operation can be inherently non-parallel since all elements have to pass through
* the same operator instance. (Only for special cases, such as aligned time windows is
* it possible to perform this operation in parallel).
*
* @param size The size of the windows in number of elements.
* @param slide The slide interval in number of elements.
*/
public AllWindowedStream<T, GlobalWindow> countWindowAll(long size, long slide) {
return windowAll(GlobalWindows.create())
.evictor(CountEvictor.of(size))
.trigger(CountTrigger.of(slide));
}
/**
* Windows this data stream to a {@code KeyedTriggerWindowDataStream}, which evaluates windows
* over a key grouped stream. Elements are put into windows by a
* {@link org.apache.flink.streaming.api.windowing.assigners.WindowAssigner}. The grouping of
* elements is done both by key and by window.
*
* <p>
* A {@link org.apache.flink.streaming.api.windowing.triggers.Trigger} can be defined to specify
* when windows are evaluated. However, {@code WindowAssigners} have a default {@code Trigger}
* that is used if a {@code Trigger} is not specified.
*
* <p>
* Note: This operation can be inherently non-parallel since all elements have to pass through
* the same operator instance. (Only for special cases, such as aligned time windows is
* it possible to perform this operation in parallel).
*
* @param assigner The {@code WindowAssigner} that assigns elements to windows.
* @return The trigger windows data stream.
*/
@PublicEvolving
public <W extends Window> AllWindowedStream<T, W> windowAll(WindowAssigner<? super T, W> assigner) {
return new AllWindowedStream<>(this, assigner);
}
// ------------------------------------------------------------------------
// Timestamps and watermarks
// ------------------------------------------------------------------------
/**
* Extracts a timestamp from an element and assigns it as the internal timestamp of that element.
* The internal timestamps are, for example, used to to event-time window operations.
*
* <p>
* If you know that the timestamps are strictly increasing you can use an
* {@link org.apache.flink.streaming.api.functions.AscendingTimestampExtractor}. Otherwise,
* you should provide a {@link TimestampExtractor} that also implements
* {@link TimestampExtractor#getCurrentWatermark()} to keep track of watermarks.
*
* @param extractor The TimestampExtractor that is called for each element of the DataStream.
*
* @deprecated Please use {@link #assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks)}
* of {@link #assignTimestampsAndWatermarks(AssignerWithPunctuatedWatermarks)}
* instread.
* @see #assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks)
* @see #assignTimestampsAndWatermarks(AssignerWithPunctuatedWatermarks)
*/
@Deprecated
public SingleOutputStreamOperator<T> assignTimestamps(TimestampExtractor<T> extractor) {
// match parallelism to input, otherwise dop=1 sources could lead to some strange
// behaviour: the watermark will creep along very slowly because the elements
// from the source go to each extraction operator round robin.
int inputParallelism = getTransformation().getParallelism();
ExtractTimestampsOperator<T> operator = new ExtractTimestampsOperator<>(clean(extractor));
return transform("ExtractTimestamps", getTransformation().getOutputType(), operator)
.setParallelism(inputParallelism);
}
/**
* Assigns timestamps to the elements in the data stream and periodically creates
* watermarks to signal event time progress.
*
* <p>This method creates watermarks periodically (for example every second), based
* on the watermarks indicated by the given watermark generator. Even when no new elements
* in the stream arrive, the given watermark generator will be periodically checked for
* new watermarks. The interval in which watermarks are generated is defined in
* {@link ExecutionConfig#setAutoWatermarkInterval(long)}.
*
* <p>Use this method for the common cases, where some characteristic over all elements
* should generate the watermarks, or where watermarks are simply trailing behind the
* wall clock time by a certain amount.
*
* <p>For cases where watermarks should be created in an irregular fashion, for example
* based on certain markers that some element carry, use the
* {@link AssignerWithPunctuatedWatermarks}.
*
* @param timestampAndWatermarkAssigner The implementation of the timestamp assigner and
* watermark generator.
* @return The stream after the transformation, with assigned timestamps and watermarks.
*
* @see AssignerWithPeriodicWatermarks
* @see AssignerWithPunctuatedWatermarks
* @see #assignTimestampsAndWatermarks(AssignerWithPunctuatedWatermarks)
*/
public SingleOutputStreamOperator<T> assignTimestampsAndWatermarks(
AssignerWithPeriodicWatermarks<T> timestampAndWatermarkAssigner) {
// match parallelism to input, otherwise dop=1 sources could lead to some strange
// behaviour: the watermark will creep along very slowly because the elements
// from the source go to each extraction operator round robin.
final int inputParallelism = getTransformation().getParallelism();
final AssignerWithPeriodicWatermarks<T> cleanedAssigner = clean(timestampAndWatermarkAssigner);
TimestampsAndPeriodicWatermarksOperator<T> operator =
new TimestampsAndPeriodicWatermarksOperator<>(cleanedAssigner);
return transform("Timestamps/Watermarks", getTransformation().getOutputType(), operator)
.setParallelism(inputParallelism);
}
/**
* Assigns timestamps to the elements in the data stream and periodically creates
* watermarks to signal event time progress.
*
* <p>This method creates watermarks based purely on stream elements. For each element
* that is handled via {@link AssignerWithPunctuatedWatermarks#extractTimestamp(Object, long)},
* the {@link AssignerWithPunctuatedWatermarks#checkAndGetNextWatermark(Object, long)}
* method is called, and a new watermark is emitted, if the returned watermark value is
* non-negative and greater than the previous watermark.
*
* <p>This method is useful when the data stream embeds watermark elements, or certain elements
* carry a marker that can be used to determine the current event time watermark.
* This operation gives the programmer full control over the watermark generation. Users
* should be aware that too aggressive watermark generation (i.e., generating hundreds of
* watermarks every second) can cost some performance.
*
* <p>For cases where watermarks should be created in a regular fashion, for example
* every x milliseconds, use the {@link AssignerWithPeriodicWatermarks}.
*
* @param timestampAndWatermarkAssigner The implementation of the timestamp assigner and
* watermark generator.
* @return The stream after the transformation, with assigned timestamps and watermarks.
*
* @see AssignerWithPunctuatedWatermarks
* @see AssignerWithPeriodicWatermarks
* @see #assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks)
*/
public SingleOutputStreamOperator<T> assignTimestampsAndWatermarks(
AssignerWithPunctuatedWatermarks<T> timestampAndWatermarkAssigner) {
// match parallelism to input, otherwise dop=1 sources could lead to some strange
// behaviour: the watermark will creep along very slowly because the elements
// from the source go to each extraction operator round robin.
final int inputParallelism = getTransformation().getParallelism();
final AssignerWithPunctuatedWatermarks<T> cleanedAssigner = clean(timestampAndWatermarkAssigner);
TimestampsAndPunctuatedWatermarksOperator<T> operator =
new TimestampsAndPunctuatedWatermarksOperator<>(cleanedAssigner);
return transform("Timestamps/Watermarks", getTransformation().getOutputType(), operator)
.setParallelism(inputParallelism);
}
// ------------------------------------------------------------------------
// Data sinks
// ------------------------------------------------------------------------
/**
* Writes a DataStream to the standard output stream (stdout).
*
* <p>
* For each element of the DataStream the result of
* {@link Object#toString()} is written.
*
* @return The closed DataStream.
*/
@PublicEvolving
public DataStreamSink<T> print() {
PrintSinkFunction<T> printFunction = new PrintSinkFunction<>();
return addSink(printFunction);
}
/**
* Writes a DataStream to the standard output stream (stderr).
*
* <p>
* For each element of the DataStream the result of
* {@link Object#toString()} is written.
*
* @return The closed DataStream.
*/
@PublicEvolving
public DataStreamSink<T> printToErr() {
PrintSinkFunction<T> printFunction = new PrintSinkFunction<>(true);
return addSink(printFunction);
}
/**
* Writes a DataStream to the file specified by path in text format.
*
* <p>
* For every element of the DataStream the result of {@link Object#toString()}
* is written.
*
* @param path
* The path pointing to the location the text file is written to.
*
* @return The closed DataStream.
*/
@PublicEvolving
public DataStreamSink<T> writeAsText(String path) {
return writeUsingOutputFormat(new TextOutputFormat<T>(new Path(path)));
}
/**
* Writes a DataStream to the file specified by path in text format.
*
* <p>
* For every element of the DataStream the result of {@link Object#toString()}
* is written.
*
* @param path
* The path pointing to the location the text file is written to
* @param writeMode
* Controls the behavior for existing files. Options are
* NO_OVERWRITE and OVERWRITE.
*
* @return The closed DataStream.
*/
@PublicEvolving
public DataStreamSink<T> writeAsText(String path, WriteMode writeMode) {
TextOutputFormat<T> tof = new TextOutputFormat<>(new Path(path));
tof.setWriteMode(writeMode);
return writeUsingOutputFormat(tof);
}
/**
* Writes a DataStream to the file specified by the path parameter.
*
* <p>
* For every field of an element of the DataStream the result of {@link Object#toString()}
* is written. This method can only be used on data streams of tuples.
*
* @param path
* the path pointing to the location the text file is written to
*
* @return the closed DataStream
*/
@PublicEvolving
public DataStreamSink<T> writeAsCsv(String path) {
return writeAsCsv(path, null, CsvOutputFormat.DEFAULT_LINE_DELIMITER, CsvOutputFormat.DEFAULT_FIELD_DELIMITER);
}
/**
* Writes a DataStream to the file specified by the path parameter.
*
* <p>
* For every field of an element of the DataStream the result of {@link Object#toString()}
* is written. This method can only be used on data streams of tuples.
*
* @param path
* the path pointing to the location the text file is written to
* @param writeMode
* Controls the behavior for existing files. Options are
* NO_OVERWRITE and OVERWRITE.
*
* @return the closed DataStream
*/
@PublicEvolving
public DataStreamSink<T> writeAsCsv(String path, WriteMode writeMode) {
return writeAsCsv(path, writeMode, CsvOutputFormat.DEFAULT_LINE_DELIMITER, CsvOutputFormat.DEFAULT_FIELD_DELIMITER);
}
/**
* Writes a DataStream to the file specified by the path parameter. The
* writing is performed periodically every millis milliseconds.
*
* <p>
* For every field of an element of the DataStream the result of {@link Object#toString()}
* is written. This method can only be used on data streams of tuples.
*
* @param path
* the path pointing to the location the text file is written to
* @param writeMode
* Controls the behavior for existing files. Options are
* NO_OVERWRITE and OVERWRITE.
* @param rowDelimiter
* the delimiter for two rows
* @param fieldDelimiter
* the delimiter for two fields
*
* @return the closed DataStream
*/
@SuppressWarnings("unchecked")
@PublicEvolving
public <X extends Tuple> DataStreamSink<T> writeAsCsv(
String path,
WriteMode writeMode,
String rowDelimiter,
String fieldDelimiter) {
Preconditions.checkArgument(
getType().isTupleType(),
"The writeAsCsv() method can only be used on data streams of tuples.");
CsvOutputFormat<X> of = new CsvOutputFormat<>(
new Path(path),
rowDelimiter,
fieldDelimiter);
if (writeMode != null) {
of.setWriteMode(writeMode);
}
return writeUsingOutputFormat((OutputFormat<T>) of);
}
/**
* Writes the DataStream to a socket as a byte array. The format of the
* output is specified by a {@link SerializationSchema}.
*
* @param hostName
* host of the socket
* @param port
* port of the socket
* @param schema
* schema for serialization
* @return the closed DataStream
*/
@PublicEvolving
public DataStreamSink<T> writeToSocket(String hostName, int port, SerializationSchema<T> schema) {
DataStreamSink<T> returnStream = addSink(new SocketClientSink<>(hostName, port, schema, 0));
returnStream.setParallelism(1); // It would not work if multiple instances would connect to the same port
return returnStream;
}
/**
* Writes the dataStream into an output, described by an OutputFormat.
*
* The output is not participating in Flink's checkpointing!
*
* For writing to a file system periodically, the use of the "flink-connector-filesystem" is recommended.
*
* @param format The output format
* @return The closed DataStream
*/
@PublicEvolving
public DataStreamSink<T> writeUsingOutputFormat(OutputFormat<T> format) {
return addSink(new OutputFormatSinkFunction<>(format));
}
/**
* Method for passing user defined operators along with the type
* information that will transform the DataStream.
*
* @param operatorName
* name of the operator, for logging purposes
* @param outTypeInfo
* the output type of the operator
* @param operator
* the object containing the transformation logic
* @param <R>
* type of the return stream
* @return the data stream constructed
*/
@PublicEvolving
public <R> SingleOutputStreamOperator<R> transform(String operatorName, TypeInformation<R> outTypeInfo, OneInputStreamOperator<T, R> operator) {
// read the output type of the input Transform to coax out errors about MissingTypeInfo
transformation.getOutputType();
OneInputTransformation<T, R> resultTransform = new OneInputTransformation<>(
this.transformation,
operatorName,
operator,
outTypeInfo,
environment.getParallelism());
@SuppressWarnings({ "unchecked", "rawtypes" })
SingleOutputStreamOperator<R> returnStream = new SingleOutputStreamOperator(environment, resultTransform);
getExecutionEnvironment().addOperator(resultTransform);
return returnStream;
}
/**
* Internal function for setting the partitioner for the DataStream
*
* @param partitioner
* Partitioner to set.
* @return The modified DataStream.
*/
protected DataStream<T> setConnectionType(StreamPartitioner<T> partitioner) {
return new DataStream<>(this.getExecutionEnvironment(), new PartitionTransformation<>(this.getTransformation(), partitioner));
}
/**
* Adds the given sink to this DataStream. Only streams with sinks added
* will be executed once the {@link StreamExecutionEnvironment#execute()}
* method is called.
*
* @param sinkFunction
* The object containing the sink's invoke function.
* @return The closed DataStream.
*/
public DataStreamSink<T> addSink(SinkFunction<T> sinkFunction) {
// read the output type of the input Transform to coax out errors about MissingTypeInfo
transformation.getOutputType();
// configure the type if needed
if (sinkFunction instanceof InputTypeConfigurable) {
((InputTypeConfigurable) sinkFunction).setInputType(getType(), getExecutionConfig() );
}
StreamSink<T> sinkOperator = new StreamSink<>(clean(sinkFunction));
DataStreamSink<T> sink = new DataStreamSink<>(this, sinkOperator);
getExecutionEnvironment().addOperator(sink.getTransformation());
return sink;
}
/**
* Returns the {@link StreamTransformation} that represents the operation that logically creates
* this {@link DataStream}.
*
* @return The Transformation
*/
@Internal
public StreamTransformation<T> getTransformation() {
return transformation;
}
}
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