摘自Apache官网

一、State的基本概念

什么叫State?搜了一把叫做状态机制。可以用作以下用途。为了保证 at least once, exactly once,Flink引入了State和Checkpoint

  • 某个task/operator某时刻的中间结果
  • 快照(snapshot)
  • 程序一旦crash,恢复用的
  • 机器学习模型的参数

二、Flink中包含的State

Keyed State和Opreator State

1、Keyed State基于KeyedStream的状态。这个状态是跟特定的key绑定的。对KeyedStream流上的每一个key,可能都对应一个state。

2、Operator State。于Keyed State不同。Operator State根一个特定的Operator绑定。整个Operator对应一个State。相比较而言一个State上有多个Keyed State。举例子来说,在Flink中的Kafka Connector就使用了Operator State。会在每个connector实例中,保存该实例中消费topic的所有(partition, offset)映射。

一些原子State

  • ValueState:即类型为T的单值状态。这个状态与对应的key绑定,是最简单的状态了。它可以通过update方法更新状态值,通过value()方法获取状态值。

  • ListState:即key上的状态值为一个列表。可以通过add方法往列表中附加值;也可以通过get()方法返回一个Iterable<T>来遍历状态值。

  • ReducingState:这种状态通过用户传入的reduceFunction,每次调用add方法添加值的时候,会调用reduceFunction,最后合并到一个单一的状态值。

  • MapState:即状态值为一个map。用户通过put或putAll方法添加元素。

上述的State仅仅与状态进行交互。而真正的状态值,有可能是存在内存,磁盘、或者其它分布式系统中。相当于只是我们持有了这个句柄。那么我们如何得到这个状态的句柄呢?Flink通过StateDescriptor来定义一个状态。这是一个抽象类,内部定义了状态名称、类型、序列化器等基础信息。与上面的状态对应、从StateDescriptor派生了ValueStateDescriptor,ListStateDescriptor等descriptor。

3、研究下Keyed State内部的结构

在StreamingRuntimeContext这个类中可以看到各个State的get方法

/*
* 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.streaming.api.operators; import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.api.common.accumulators.Accumulator;
import org.apache.flink.api.common.functions.BroadcastVariableInitializer;
import org.apache.flink.api.common.functions.util.AbstractRuntimeUDFContext;
import org.apache.flink.api.common.state.AggregatingState;
import org.apache.flink.api.common.state.AggregatingStateDescriptor;
import org.apache.flink.api.common.state.FoldingState;
import org.apache.flink.api.common.state.FoldingStateDescriptor;
import org.apache.flink.api.common.state.KeyedStateStore;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.state.MapState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ReducingState;
import org.apache.flink.api.common.state.ReducingStateDescriptor;
import org.apache.flink.api.common.state.StateDescriptor;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.runtime.execution.Environment;
import org.apache.flink.runtime.jobgraph.tasks.InputSplitProvider;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.graph.StreamConfig;
import org.apache.flink.streaming.runtime.tasks.ProcessingTimeService;
import org.apache.flink.util.Preconditions; import java.util.List;
import java.util.Map; /**
* Implementation of the {@link org.apache.flink.api.common.functions.RuntimeContext},
* for streaming operators.
*/
@PublicEvolving
public class StreamingRuntimeContext extends AbstractRuntimeUDFContext { /** The operator to which this function belongs. */
private final AbstractStreamOperator<?> operator; /** The task environment running the operator. */
private final Environment taskEnvironment; private final StreamConfig streamConfig; public StreamingRuntimeContext(AbstractStreamOperator<?> operator,
Environment env, Map<String, Accumulator<?, ?>> accumulators) {
super(env.getTaskInfo(),
env.getUserClassLoader(),
operator.getExecutionConfig(),
accumulators,
env.getDistributedCacheEntries(),
operator.getMetricGroup()); this.operator = operator;
this.taskEnvironment = env;
this.streamConfig = new StreamConfig(env.getTaskConfiguration());
} // ------------------------------------------------------------------------ /**
* Returns the input split provider associated with the operator.
*
* @return The input split provider.
*/
public InputSplitProvider getInputSplitProvider() {
return taskEnvironment.getInputSplitProvider();
} public ProcessingTimeService getProcessingTimeService() {
return operator.getProcessingTimeService();
} // ------------------------------------------------------------------------
// broadcast variables
// ------------------------------------------------------------------------ @Override
public boolean hasBroadcastVariable(String name) {
throw new UnsupportedOperationException("Broadcast variables can only be used in DataSet programs");
} @Override
public <RT> List<RT> getBroadcastVariable(String name) {
throw new UnsupportedOperationException("Broadcast variables can only be used in DataSet programs");
} @Override
public <T, C> C getBroadcastVariableWithInitializer(String name, BroadcastVariableInitializer<T, C> initializer) {
throw new UnsupportedOperationException("Broadcast variables can only be used in DataSet programs");
} // ------------------------------------------------------------------------
// key/value state
// ------------------------------------------------------------------------ @Override
public <T> ValueState<T> getState(ValueStateDescriptor<T> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getState(stateProperties);
} @Override
public <T> ListState<T> getListState(ListStateDescriptor<T> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getListState(stateProperties);
} @Override
public <T> ReducingState<T> getReducingState(ReducingStateDescriptor<T> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getReducingState(stateProperties);
} @Override
public <IN, ACC, OUT> AggregatingState<IN, OUT> getAggregatingState(AggregatingStateDescriptor<IN, ACC, OUT> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getAggregatingState(stateProperties);
} @Override
public <T, ACC> FoldingState<T, ACC> getFoldingState(FoldingStateDescriptor<T, ACC> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getFoldingState(stateProperties);
} @Override
public <UK, UV> MapState<UK, UV> getMapState(MapStateDescriptor<UK, UV> stateProperties) {
KeyedStateStore keyedStateStore = checkPreconditionsAndGetKeyedStateStore(stateProperties);
stateProperties.initializeSerializerUnlessSet(getExecutionConfig());
return keyedStateStore.getMapState(stateProperties);
} private KeyedStateStore checkPreconditionsAndGetKeyedStateStore(StateDescriptor<?, ?> stateDescriptor) {
Preconditions.checkNotNull(stateDescriptor, "The state properties must not be null");
KeyedStateStore keyedStateStore = operator.getKeyedStateStore();
Preconditions.checkNotNull(keyedStateStore, "Keyed state can only be used on a 'keyed stream', i.e., after a 'keyBy()' operation.");
return keyedStateStore;
} // ------------------ expose (read only) relevant information from the stream config -------- // /**
* Returns true if checkpointing is enabled for the running job.
*
* @return true if checkpointing is enabled.
*/
public boolean isCheckpointingEnabled() {
return streamConfig.isCheckpointingEnabled();
} /**
* Returns the checkpointing mode.
*
* @return checkpointing mode
*/
public CheckpointingMode getCheckpointMode() {
return streamConfig.getCheckpointMode();
} /**
* Returns the buffer timeout of the job.
*
* @return buffer timeout (in milliseconds)
*/
public long getBufferTimeout() {
return streamConfig.getBufferTimeout();
} }

所有的State都继承自StateDescpritor这个类。简单看一下构造函数。实际上包含了三个参数,

  • 名称
  • 类型--属于哪一类state
  • 默认值
 protected StateDescriptor(String name, Class<T> type, T defaultValue) {
this.name = requireNonNull(name, "name must not be null");
requireNonNull(type, "type class must not be null"); try {
this.typeInfo = TypeExtractor.createTypeInfo(type);
} catch (Exception e) {
throw new RuntimeException(
"Could not create the type information for '" + type.getName() + "'. " +
"The most common reason is failure to infer the generic type information, due to Java's type erasure. " +
"In that case, please pass a 'TypeHint' instead of a class to describe the type. " +
"For example, to describe 'Tuple2<String, String>' as a generic type, use " +
"'new PravegaDeserializationSchema<>(new TypeHint<Tuple2<String, String>>(){}, serializer);'", e);
} this.defaultValue = defaultValue;
}

再具体的可以自己研究研究,建议工作遇到或者对源码有兴趣可以读读。或者结合实际应用理解下会更快

三、容错机制

1、Flink的核心容错机制是不断的给数据流绘制Snapshots。当系统回滚的时候,这些snapshots就扮演了checkpoints的作用。快照机制受Chandy-Lamport 算法的启发。

读了下论文研究了下这个算法。英文的看不太懂。找了个中文的。

大概就是通过token 和marker来判断是哪里出了问题和需要恢复。

具体可以参考:

https://www.jianshu.com/p/938001e998f5