Stateful functions and operators store data across the processing of individual elements/events, making state a critical building block for any type of more elaborate operation.
*When an application searches for certain event patterns, the state will store the sequence of events encountered so far.
*When aggregating events per minute/hour/day, the state holds the pending aggregates.
*When training a machine learning model over a stream of data points, the state holds the current version of the model parameters.
*When historic data needs to be managed, the state allows efficient access to events that occurred in the past.
*当应用程序搜索某些事件模式时，状态将存储迄今遇到的事件序列。
*当每分钟/小时/天聚合事件时，状态保存挂起的聚合。
*当在数据流上训练机器学习模型时，状态保存模型参数的当前版本。
*当需要管理历史数据时，状态允许有效访问过去发生的事件。v
Flink needs to be aware of the state in order to make state fault tolerant using [checkpoints](checkpointing.html) and to allow [savepoints](//ci.apache.org/projects/flink/flink-docs-release-1.7/ops/state/savepoints.html) of streaming applications.
Knowledge about the state also allows for rescaling Flink applications, meaning that Flink takes care of redistributing state across parallel instances.
关于状态的知识还允许重新调用flink应用程序，这意味着flink负责在并行实例中重新分配状态。v
The [queryable state](queryable_state.html) feature of Flink allows you to access state from outside of Flink during runtime.
When working with state, it might also be useful to read about [Flink’s state backends](//ci.apache.org/projects/flink/flink-docs-release-1.7/ops/state/state_backends.html). Flink provides different state backends that specify how and where state is stored. State can be located on Java’s heap or off-heap. Depending on your state backend, Flink can also _manage_ the state for the application, meaning Flink deals with the memory management (possibly spilling to disk if necessary) to allow applications to hold very large state. State backends can be configured without changing your application logic.
*[Working with State](state.html): Shows how to use state in a Flink application and explains the different kinds of state.
*[The Broadcast State Pattern](broadcast_state.html): Explains how to connect a broadcast stream with a non-broadcast stream and use state to exchange information between them.
*[Checkpointing](checkpointing.html): Describes how to enable and configure checkpointing for fault tolerance.
*[Queryable State](queryable_state.html): Explains how to access state from outside of Flink during runtime.
*[State Schema Evolution](schema_evolution.html): Shows how schema of state types can be evolved.
*[Custom Serialization for Managed State](custom_serialization.html): Discusses how to implement custom serializers, especially for schema evolution.
