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序
本文主要研究一下 flink 的 Broadcast State
实例
@Test
public void testBroadcastState() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStreamSource<String> originStream = env.addSource(new RandomWordSource());
MapStateDescriptor<String, String> descriptor = new MapStateDescriptor(“dynamicConfig”, BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO);
BroadcastStream<Tuple2<String,String>> configStream = env.addSource(new DynamicConfigSource()).broadcast(descriptor);
BroadcastConnectedStream<String, Tuple2<String,String>> connectStream = originStream.connect(configStream);
connectStream.process(new BroadcastProcessFunction<String, Tuple2<String,String>, Void>() {
@Override
public void processElement(String value, ReadOnlyContext ctx, Collector<Void> out) throws Exception {
ReadOnlyBroadcastState<String,String> config = ctx.getBroadcastState(descriptor);
String configValue = config.get(“demoConfigKey”);
//do some process base on the config
LOGGER.info(“process value:{},config:{}”,value,configValue);
}
@Override
public void processBroadcastElement(Tuple2<String, String> value, Context ctx, Collector<Void> out) throws Exception {
LOGGER.info(“receive config item:{}”,value);
//update state
ctx.getBroadcastState(descriptor).put(value.getField(0),value.getField(1));
}
});
env.execute(“testBroadcastState”);
}
public class DynamicConfigSource implements SourceFunction<Tuple2<String,String>> {
private volatile boolean isRunning = true;
@Override
public void run(SourceContext<Tuple2<String, String>> ctx) throws Exception {
long idx = 1;
while (isRunning){
ctx.collect(Tuple2.of(“demoConfigKey”,”value” + idx));
idx++;
TimeUnit.SECONDS.sleep(10);
}
}
@Override
public void cancel() {
isRunning = false;
}
}
这里模拟了一个配置的 source，定时去刷新配置，然后 broadcast 到每个 task
MapStateDescriptor
flink-core-1.7.0-sources.jar!/org/apache/flink/api/common/state/MapStateDescriptor.java
@PublicEvolving
public class MapStateDescriptor<UK, UV> extends StateDescriptor<MapState<UK, UV>, Map<UK, UV>> {
private static final long serialVersionUID = 1L;
/**
* Create a new {@code MapStateDescriptor} with the given name and the given type serializers.
*
* @param name The name of the {@code MapStateDescriptor}.
* @param keySerializer The type serializer for the keys in the state.
* @param valueSerializer The type serializer for the values in the state.
*/
public MapStateDescriptor(String name, TypeSerializer<UK> keySerializer, TypeSerializer<UV> valueSerializer) {
super(name, new MapSerializer<>(keySerializer, valueSerializer), null);
}
/**
* Create a new {@code MapStateDescriptor} with the given name and the given type information.
*
* @param name The name of the {@code MapStateDescriptor}.
* @param keyTypeInfo The type information for the keys in the state.
* @param valueTypeInfo The type information for the values in the state.
*/
public MapStateDescriptor(String name, TypeInformation<UK> keyTypeInfo, TypeInformation<UV> valueTypeInfo) {
super(name, new MapTypeInfo<>(keyTypeInfo, valueTypeInfo), null);
}
/**
* Create a new {@code MapStateDescriptor} with the given name and the given type information.
*
* <p>If this constructor fails (because it is not possible to describe the type via a class),
* consider using the {@link #MapStateDescriptor(String, TypeInformation, TypeInformation)} constructor.
*
* @param name The name of the {@code MapStateDescriptor}.
* @param keyClass The class of the type of keys in the state.
* @param valueClass The class of the type of values in the state.
*/
public MapStateDescriptor(String name, Class<UK> keyClass, Class<UV> valueClass) {
super(name, new MapTypeInfo<>(keyClass, valueClass), null);
}
@Override
public Type getType() {
return Type.MAP;
}
/**
* Gets the serializer for the keys in the state.
*
* @return The serializer for the keys in the state.
*/
public TypeSerializer<UK> getKeySerializer() {
final TypeSerializer<Map<UK, UV>> rawSerializer = getSerializer();
if (!(rawSerializer instanceof MapSerializer)) {
throw new IllegalStateException(“Unexpected serializer type.”);
}
return ((MapSerializer<UK, UV>) rawSerializer).getKeySerializer();
}
/**
* Gets the serializer for the values in the state.
*
* @return The serializer for the values in the state.
*/
public TypeSerializer<UV> getValueSerializer() {
final TypeSerializer<Map<UK, UV>> rawSerializer = getSerializer();
if (!(rawSerializer instanceof MapSerializer)) {
throw new IllegalStateException(“Unexpected serializer type.”);
}
return ((MapSerializer<UK, UV>) rawSerializer).getValueSerializer();
}
}
MapStateDescriptor 继承了 StateDescriptor，其中 state 为 MapState 类型，value 为 Map 类型
DataStream.broadcast
flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are broadcasted to every parallel instance of the next operation. In addition,
* it implicitly as many {@link org.apache.flink.api.common.state.BroadcastState broadcast states}
* as the specified descriptors which can be used to store the element of the stream.
*
* @param broadcastStateDescriptors the descriptors of the broadcast states to create.
* @return A {@link BroadcastStream} which can be used in the {@link #connect(BroadcastStream)} to
* create a {@link BroadcastConnectedStream} for further processing of the elements.
*/
@PublicEvolving
public BroadcastStream<T> broadcast(final MapStateDescriptor<?, ?>… broadcastStateDescriptors) {
Preconditions.checkNotNull(broadcastStateDescriptors);
final DataStream<T> broadcastStream = setConnectionType(new BroadcastPartitioner<>());
return new BroadcastStream<>(environment, broadcastStream, broadcastStateDescriptors);
}
/**
* 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));
}
/**
* Sets the partitioning of the {@link DataStream} so that the output elements
* are broadcast to every parallel instance of the next operation.
*
* @return The DataStream with broadcast partitioning set.
*/
public DataStream<T> broadcast() {
return setConnectionType(new BroadcastPartitioner<T>());
}
DataStream 的 broadcast 方法，首先调用 setConnectionType，然后使用 MapStateDescriptor 作为参数创建 BroadcastStream 返回；DataStream 也有一个无参的 broadcast 方法，它直接调用 setConnectionType 返回 DataStream
DataStream.connect
flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java
/**
* 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);
}
/**
* Creates a new {@link BroadcastConnectedStream} by connecting the current
* {@link DataStream} or {@link KeyedStream} with a {@link BroadcastStream}.
*
* <p>The latter can be created using the {@link #broadcast(MapStateDescriptor[])} method.
*
* <p>The resulting stream can be further processed using the {@code BroadcastConnectedStream.process(MyFunction)}
* method, where {@code MyFunction} can be either a
* {@link org.apache.flink.streaming.api.functions.co.KeyedBroadcastProcessFunction KeyedBroadcastProcessFunction}
* or a {@link org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction BroadcastProcessFunction}
* depending on the current stream being a {@link KeyedStream} or not.
*
* @param broadcastStream The broadcast stream with the broadcast state to be connected with this stream.
* @return The {@link BroadcastConnectedStream}.
*/
@PublicEvolving
public <R> BroadcastConnectedStream<T, R> connect(BroadcastStream<R> broadcastStream) {
return new BroadcastConnectedStream<>(
environment,
this,
Preconditions.checkNotNull(broadcastStream),
broadcastStream.getBroadcastStateDescriptor());
}
DataStream 的 connect 方法参数可以是 DataStream 类型，也可以是 BroadcastStream 类型，如果是 BroadcastStream 类型则返回的是 BroadcastConnectedStream，否则是普通的 ConnectedStreams
BroadcastConnectedStream.process
flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/BroadcastConnectedStream.java
@PublicEvolving
public class BroadcastConnectedStream<IN1, IN2> {
private final StreamExecutionEnvironment environment;
private final DataStream<IN1> inputStream1;
private final BroadcastStream<IN2> inputStream2;
private final List<MapStateDescriptor<?, ?>> broadcastStateDescriptors;
protected BroadcastConnectedStream(
final StreamExecutionEnvironment env,
final DataStream<IN1> input1,
final BroadcastStream<IN2> input2,
final List<MapStateDescriptor<?, ?>> broadcastStateDescriptors) {
this.environment = requireNonNull(env);
this.inputStream1 = requireNonNull(input1);
this.inputStream2 = requireNonNull(input2);
this.broadcastStateDescriptors = requireNonNull(broadcastStateDescriptors);
}
public StreamExecutionEnvironment getExecutionEnvironment() {
return environment;
}
/**
* Returns the non-broadcast {@link DataStream}.
*
* @return The stream which, by convention, is not broadcasted.
*/
public DataStream<IN1> getFirstInput() {
return inputStream1;
}
/**
* Returns the {@link BroadcastStream}.
*
* @return The stream which, by convention, is the broadcast one.
*/
public BroadcastStream<IN2> getSecondInput() {
return inputStream2;
}
/**
* Gets the type of the first input.
*
* @return The type of the first input
*/
public TypeInformation<IN1> getType1() {
return inputStream1.getType();
}
/**
* Gets the type of the second input.
*
* @return The type of the second input
*/
public TypeInformation<IN2> getType2() {
return inputStream2.getType();
}
/**
* Assumes as inputs a {@link BroadcastStream} and a {@link KeyedStream} and applies the given
* {@link KeyedBroadcastProcessFunction} on them, thereby creating a transformed output stream.
*
* @param function The {@link KeyedBroadcastProcessFunction} that is called for each element in the stream.
* @param <KS> The type of the keys in the keyed stream.
* @param <OUT> The type of the output elements.
* @return The transformed {@link DataStream}.
*/
@PublicEvolving
public <KS, OUT> SingleOutputStreamOperator<OUT> process(final KeyedBroadcastProcessFunction<KS, IN1, IN2, OUT> function) {
TypeInformation<OUT> outTypeInfo = TypeExtractor.getBinaryOperatorReturnType(
function,
KeyedBroadcastProcessFunction.class,
1,
2,
3,
TypeExtractor.NO_INDEX,
getType1(),
getType2(),
Utils.getCallLocationName(),
true);
return process(function, outTypeInfo);
}
/**
* Assumes as inputs a {@link BroadcastStream} and a {@link KeyedStream} and applies the given
* {@link KeyedBroadcastProcessFunction} on them, thereby creating a transformed output stream.
*
* @param function The {@link KeyedBroadcastProcessFunction} that is called for each element in the stream.
* @param outTypeInfo The type of the output elements.
* @param <KS> The type of the keys in the keyed stream.
* @param <OUT> The type of the output elements.
* @return The transformed {@link DataStream}.
*/
@PublicEvolving
public <KS, OUT> SingleOutputStreamOperator<OUT> process(
final KeyedBroadcastProcessFunction<KS, IN1, IN2, OUT> function,
final TypeInformation<OUT> outTypeInfo) {
Preconditions.checkNotNull(function);
Preconditions.checkArgument(inputStream1 instanceof KeyedStream,
“A KeyedBroadcastProcessFunction can only be used on a keyed stream.”);
TwoInputStreamOperator<IN1, IN2, OUT> operator =
new CoBroadcastWithKeyedOperator<>(clean(function), broadcastStateDescriptors);
return transform(“Co-Process-Broadcast-Keyed”, outTypeInfo, operator);
}
/**
* Assumes as inputs a {@link BroadcastStream} and a non-keyed {@link DataStream} and applies the given
* {@link BroadcastProcessFunction} on them, thereby creating a transformed output stream.
*
* @param function The {@link BroadcastProcessFunction} that is called for each element in the stream.
* @param <OUT> The type of the output elements.
* @return The transformed {@link DataStream}.
*/
@PublicEvolving
public <OUT> SingleOutputStreamOperator<OUT> process(final BroadcastProcessFunction<IN1, IN2, OUT> function) {
TypeInformation<OUT> outTypeInfo = TypeExtractor.getBinaryOperatorReturnType(
function,
BroadcastProcessFunction.class,
0,
1,
2,
TypeExtractor.NO_INDEX,
getType1(),
getType2(),
Utils.getCallLocationName(),
true);
return process(function, outTypeInfo);
}
/**
* Assumes as inputs a {@link BroadcastStream} and a non-keyed {@link DataStream} and applies the given
* {@link BroadcastProcessFunction} on them, thereby creating a transformed output stream.
*
* @param function The {@link BroadcastProcessFunction} that is called for each element in the stream.
* @param outTypeInfo The type of the output elements.
* @param <OUT> The type of the output elements.
* @return The transformed {@link DataStream}.
*/
@PublicEvolving
public <OUT> SingleOutputStreamOperator<OUT> process(
final BroadcastProcessFunction<IN1, IN2, OUT> function,
final TypeInformation<OUT> outTypeInfo) {
Preconditions.checkNotNull(function);
Preconditions.checkArgument(!(inputStream1 instanceof KeyedStream),
“A BroadcastProcessFunction can only be used on a non-keyed stream.”);
TwoInputStreamOperator<IN1, IN2, OUT> operator =
new CoBroadcastWithNonKeyedOperator<>(clean(function), broadcastStateDescriptors);
return transform(“Co-Process-Broadcast”, outTypeInfo, operator);
}
@Internal
private <OUT> SingleOutputStreamOperator<OUT> transform(
final String functionName,
final TypeInformation<OUT> outTypeInfo,
final TwoInputStreamOperator<IN1, IN2, OUT> operator) {
// read the output type of the input Transforms to coax out errors about MissingTypeInfo
inputStream1.getType();
inputStream2.getType();
TwoInputTransformation<IN1, IN2, OUT> transform = new TwoInputTransformation<>(
inputStream1.getTransformation(),
inputStream2.getTransformation(),
functionName,
operator,
outTypeInfo,
environment.getParallelism());
if (inputStream1 instanceof KeyedStream) {
KeyedStream<IN1, ?> keyedInput1 = (KeyedStream<IN1, ?>) inputStream1;
TypeInformation<?> keyType1 = keyedInput1.getKeyType();
transform.setStateKeySelectors(keyedInput1.getKeySelector(), null);
transform.setStateKeyType(keyType1);
}
@SuppressWarnings({“unchecked”, “rawtypes”})
SingleOutputStreamOperator<OUT> returnStream = new SingleOutputStreamOperator(environment, transform);
getExecutionEnvironment().addOperator(transform);
return returnStream;
}
protected <F> F clean(F f) {
return getExecutionEnvironment().clean(f);
}
}
BroadcastConnectedStream.process 接收两种类型的 function，一种是 KeyedBroadcastProcessFunction，另外一种是 BroadcastProcessFunction；它们都定义了 processElement、processBroadcastElement 抽象方法，只是 KeyedBroadcastProcessFunction 多定义了一个 onTimer 方法，默认是空操作，允许子类重写
小结
对于 broadcast 的使用有几个步骤，1 是建立 MapStateDescriptor，然后通过 DataStream.broadcast 方法返回 BroadcastStream；2 是需要接受 broadcast 的 stream 通过 DataStream.connect 方法跟 BroadcastStream 进行连接返回 BroadcastConnectedStream；3 是通过 BroadcastConnectedStream.process 方法进行 processElement 及 processBroadcastElement 处理
BroadcastConnectedStream.process 接收两种类型的 function，一种是 KeyedBroadcastProcessFunction，另外一种是 BroadcastProcessFunction；它们都定义了 processElement、processBroadcastElement 抽象方法，只是 KeyedBroadcastProcessFunction 多定义了一个 onTimer 方法，默认是空操作，允许子类重写
Broadcast State 为 map format，它会将 state 广播到每个 task，注意该 state 并不会跨 task 传播，对其修改，仅仅是作用在其所在的 task；downstream tasks 接收到 broadcast event 的顺序可能不一样，所以依赖其到达顺序来处理 element 的时候要小心；checkpoint 的时候也会 checkpoint broadcast state；另外就是 Broadcast State 只在内存有，没有 RocksDB state backend
doc
The Broadcast State Pattern
