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All transformations in Flink may look like functions (in the functional processing terminology), but are in fact stateful operators.
You can make every transformation (map, filter, etc) stateful by using Flink’s state interface or checkpointing instance fields of your function.
You can register any instance field as managed state by implementing an interface.
In this case, and also in the case of using Flink’s native state interface, Flink will automatically take consistent snapshots of your state periodically, and restore its value in the case of a failure.
讨论如何使用Flink的state接口来管理状态数据，对于这些状态数据，Flink会自动的定期做snapshots，并且当failure后，会自动restore这些状态
The Key/Value state interface provides access to different types of state that are all scoped to the key of the current input element.
This means that this type of state can only be used on a KeyedStream, which can be created via stream.keyBy(…).
Key/Value state 只能用于KeyedStream
Now, we will first look at the different types of state available and then we will see how they can be used in a program. The available state primitives are:
ValueState<T>: This keeps a value that can be updated and retrieved (scoped to key of the input element, mentioned above, so there will possibly be one value for each key that the operation sees). The value can be set using update(T) and retrieved using T value().
ListState<T>: This keeps a list of elements. You can append elements and retrieve an Iterable over all currently stored elements. Elements are added using add(T), the Iterable can be retrieved using Iterable<T> get().
ReducingState<T>: This keeps a single value that represents the aggregation of all values added to the state. The interface is the same as for ListState but elements added using add(T) are reduced to an aggregate using a specifiedReduceFunction.
All types of state also have a method clear() that clears the state for the currently active key (i.e. the key of the input element).
3种不同类型的state，
ValueState，单值的state，可以通过update(T)或T value()来操作
ListState<T>， 多只的state，通过add(T)或Iterable<T> get()来操作和访问
ReducingState<T>，多值状态，但是只保留reduce的结果
并且所有的state，都有clear，来清除状态数据
It is important to keep in mind that these state objects are only used for interfacing with state. The state is not necessarily stored inside but might reside on disk or somewhere else.
The second thing to keep in mind is that the value you get from the state depend on the key of the input element.
So the value you get in one invocation of your user function can be different from the one you get in another invocation if the key of the element is different.
这些state对象只能被状态接口使用，
并且取出的状态对象，取决于input element的key；所以不同的调用user function 得到的state value是不一样的，因为element的key 可能不同
To get a state handle you have to create a StateDescriptor this holds the name of the state (as we will later see you can create several states, and they have to have unique names so that you can reference them), the type of the values that the state holds and possibly a user-specified function, such as a ReduceFunction. Depending on what type of state you want to retrieve you create one of ValueStateDescriptor, ListStateDescriptor or ReducingStateDescriptor.
对于state，需要一个StateDescriptor ，作为name用于reference这个state，如果你定义多个state，他们的StateDescriptor 必须是unique的。
不同类型的state，有不同类型的StateDescriptor
State is accessed using the RuntimeContext, so it is only possible in rich functions.
Please see here for information about that but we will also see an example shortly.
The RuntimeContext that is available in a RichFunction has these methods for accessing state:
ValueState<T> getState(ValueStateDescriptor<T>)ReducingState<T> getReducingState(ReducingStateDescriptor<T>)ListState<T> getListState(ListStateDescriptor<T>)
State对象通过RuntimeContext的接口获取到，当然不同类型的state，对应于不同的接口；
关键是，如果要使用state，必须要使用rich function，用普通的function是无法获取到的
This is an example FlatMapFunction that shows how all of the parts fit together:
public class CountWindowAverage extends RichFlatMapFunction<Tuple2<Long, Long>, Tuple2<Long, Long>> { /** * The ValueState handle. The first field is the count, the second field a running sum. */ private transient ValueState<Tuple2<Long, Long>> sum; @Override public void flatMap(Tuple2<Long, Long> input, Collector<Tuple2<Long, Long>> out) throws Exception { // access the state value Tuple2<Long, Long> currentSum = sum.value(); // update the count currentSum.f0 += 1; // add the second field of the input value currentSum.f1 += input.f1; // update the state sum.update(currentSum); // if the count reaches 2, emit the average and clear the state if (currentSum.f0 >= 2) { out.collect(new Tuple2<>(input.f0, currentSum.f1 / currentSum.f0)); sum.clear(); } } @Override public void open(Configuration config) { ValueStateDescriptor<Tuple2<Long, Long>> descriptor = new ValueStateDescriptor<>( "average", // the state name TypeInformation.of(new TypeHint<Tuple2<Long, Long>>() {}), // type information Tuple2.of(0L, 0L)); // default value of the state, if nothing was set sum = getRuntimeContext().getState(descriptor); } } // this can be used in a streaming program like this (assuming we have a StreamExecutionEnvironment env) env.fromElements(Tuple2.of(1L, 3L), Tuple2.of(1L, 5L), Tuple2.of(1L, 7L), Tuple2.of(1L, 4L), Tuple2.of(1L, 2L)) .keyBy(0) .flatMap(new CountWindowAverage()) .print();
Instance fields can be checkpointed by using the Checkpointed interface.
除了使用Key/Value state interface，还可以用Checkpointed interface，去实现snapshotState(…) 和 restoreState(…)
When the user-defined function implements the Checkpointed interface, the snapshotState(…) and restoreState(…) methods will be executed to draw and restore function state.
In addition to that, user functions can also implement the CheckpointNotifier interface to receive notifications on completed checkpoints via the notifyCheckpointComplete(long checkpointId) method. Note that there is no guarantee for the user function to receive a notification if a failure happens between checkpoint completion and notification.
你还可以实现CheckpointNotifier ，这样当checkpoint结束的时候会调用这个接口，你可以做些事，不过这个不保证一定触发
public class CountWindowAverage extends RichFlatMapFunction<Tuple2<Long, Long>, Tuple2<Long, Long>> implements Checkpointed<Tuple2<Long, Long>> { private Tuple2<Long, Long> sum = null; @Override public void flatMap(Tuple2<Long, Long> input, Collector<Tuple2<Long, Long>> out) throws Exception { // update the count sum.f0 += 1; // add the second field of the input value sum.f1 += input.f1; // if the count reaches 2, emit the average and clear the state if (sum.f0 >= 2) { out.collect(new Tuple2<>(input.f0, sum.f1 / sum.f0)); sum = Tuple2.of(0L, 0L); } } @Override public void open(Configuration config) { if (sum == null) { // only recreate if null // restoreState will be called before open() // so this will already set the sum to the restored value sum = Tuple2.of(0L, 0L); } } // regularly persists state during normal operation @Override public Serializable snapshotState(long checkpointId, long checkpointTimestamp) { return sum; } // restores state on recovery from failure @Override public void restoreState(Tuple2<Long, Long> state) { sum = state; } }
Stateful sources require a bit more care as opposed to other operators.
In order to make the updates to the state and output collection atomic (required for exactly-once semantics on failure/recovery), the user is required to get a lock from the source’s context.
对于有状态的source，有些不一样的是，在更新state和output时，注意要加锁来保证exactly-once，比如避免多个线程同时更新offset
public static class CounterSource extends RichParallelSourceFunction<Long> implements Checkpointed<Long> { /** current offset for exactly once semantics */ private long offset; /** flag for job cancellation */ private volatile boolean isRunning = true; @Override public void run(SourceContext<Long> ctx) { final Object lock = ctx.getCheckpointLock(); while (isRunning) { // output and state update are atomic synchronized (lock) { ctx.collect(offset); offset += 1; } } } @Override public void cancel() { isRunning = false; } @Override public Long snapshotState(long checkpointId, long checkpointTimestamp) { return offset; } @Override public void restoreState(Long state) { offset = state; } }
Flink currently only provides processing guarantees for jobs without iterations. Enabling checkpointing on an iterative job causes an exception. In order to force checkpointing on an iterative program the user needs to set a special flag when enabling checkpointing:env.enableCheckpointing(interval, force = true).
Please note that records in flight in the loop edges (and the state changes associated with them) will be lost during failure.
Programs written in the Data Stream API often hold state in various forms:
Checkpointed interface to make their local variables fault tolerant主要的state，包含几种，
windows里面gather的elements
Transformation functions中用key/value state interface创建的state
Transformation functions 中通过Checkpointed interface 去对local variables做的state
When checkpointing is activated, such state is persisted upon checkpoints to guard against data loss and recover consistently.
How the state is represented internally, and how and where it is persisted upon checkpoints depends on the chosen State Backend.
关键，state如何和存到何处，还是看具体用什么State Backend
Out of the box, Flink bundles these state backends:
If nothing else is configured, the system will use the MemoryStateBacked.
当前有3种state backends，默认的是用MemoryStateBacked
The MemoryStateBackend
The MemoryStateBacked holds data internally as objects on the Java heap. Key/value state and window operators hold hash tables that store the values, triggers, etc.
Upon checkpoints, this state backend will snapshot the state and send it as part of the checkpoint acknowledgement messages to the JobManager (master), which stores it on its heap as well.
MemoryStateBackend顾名思义，就是state是存储在Java heap中的；在做checkpoints的时候，state backend 会将state snapshot放入 checkpoint acknowledgement messages 发给JobManager，JobManager 仍然是将它存在heap中。
The FsStateBackend
The FsStateBackend is configured with a file system URL (type, address, path), such as for example “hdfs://namenode:40010/flink/checkpoints” or “file:///data/flink/checkpoints”.
The FsStateBackend holds in-flight data in the TaskManager’s memory. Upon checkpointing, it writes state snapshots into files in the configured file system and directory.
Minimal metadata is stored in the JobManager’s memory (or, in high-availability mode, in the metadata checkpoint).
State snapshot数据是存在文件系统中的，而JobManager的内存中，只是存放最小的元数据
The RocksDBStateBackend
只是用RocksDB来替换文件系统，
NOTE: To use the RocksDBStateBackend you also have to add the correct maven dependency to your project:
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-statebackend-rocksdb_2.10</artifactId>
<version>1.0.3</version>
</dependency>The backend is currently not part of the binary distribution. See here for an explanation of how to include it for cluster execution.
State backends can be configured per job. In addition, you can define a default state backend to be used when the job does not explicitly define a state backend.
Setting the Per-job State Backend
The per-job state backend is set on the StreamExecutionEnvironment of the job, as shown in the example below:
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStateBackend(new FsStateBackend("hdfs://namenode:40010/flink/checkpoints"));
A default state backend can be configured in the flink-conf.yaml, using the configuration key state.backend.
Possible values for the config entry are jobmanager (MemoryStateBackend), filesystem (FsStateBackend), or the fully qualified class name of the class that implements the state backend factory FsStateBackendFactory.
In the case where the default state backend is set to filesystem, the entry state.backend.fs.checkpointdir defines the directory where the checkpoint data will be stored.
A sample section in the configuration file could look as follows:
# The backend that will be used to store operator state checkpoints
state.backend: filesystem
# Directory for storing checkpoints
state.backend.fs.checkpointdir: hdfs://namenode:40010/flink/checkpoints标签：
原文地址：http://www.cnblogs.com/fxjwind/p/5633302.html
