knaufk commented on a change in pull request #11092: [FLINK-15999] Extract “Concepts” material from API/Library sections and start proper concepts section URL: https://github.com/apache/flink/pull/11092#discussion_r379571150
########## File path: docs/concepts/stateful-stream-processing.md ########## @@ -0,0 +1,412 @@ +--- +title: Stateful Stream Processing +nav-id: stateful-stream-processing +nav-pos: 2 +nav-title: Stateful Stream Processing +nav-parent_id: concepts +--- +<!-- +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. +--> + +While many operations in a dataflow simply look at one individual *event at a +time* (for example an event parser), some operations remember information +across multiple events (for example window operators). These operations are +called **stateful**. + +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. + +For example: + + - 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. + +Flink needs to be aware of the state in order to make state fault tolerant +using [checkpoints]({{ site.baseurl}}{% link dev/stream/state/checkpointing.md +%}) and to allow [savepoints]({{ site.baseurl }}{%link ops/state/savepoints.md +%}) of streaming applications. + +Knowledge about the state also allows for rescaling Flink applications, meaning +that Flink takes care of redistributing state across parallel instances. + +The [queryable state]({{ site.baseurl }}{% link +dev/stream/state/queryable_state.md %}) 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]({{ site.baseurl }}{% link ops/state/state_backends.md %}). 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. + +* This will be replaced by the TOC +{:toc} + +## What is State? + +`TODO: expand this section` + +There are different types of state in Flink, the most-used type of state is +*Keyed State*. For special cases you can use *Operator State* and *Broadcast +State*. *Broadcast State* is a special type of *Operator State*. + +{% top %} + +## State in Stream & Batch Processing + +`TODO: What is this section about? Do we even need it?` + +{% top %} + +## Keyed State + +Keyed state is maintained in what can be thought of as an embedded key/value +store. The state is partitioned and distributed strictly together with the +streams that are read by the stateful operators. Hence, access to the key/value +state is only possible on *keyed streams*, after a *keyBy()* function, and is +restricted to the values associated with the current event's key. Aligning the +keys of streams and state makes sure that all state updates are local +operations, guaranteeing consistency without transaction overhead. This +alignment also allows Flink to redistribute the state and adjust the stream +partitioning transparently. + +<img src="{{ site.baseurl }}/fig/state_partitioning.svg" alt="State and Partitioning" class="offset" width="50%" /> + +Keyed State is further organized into so-called *Key Groups*. Key Groups are +the atomic unit by which Flink can redistribute Keyed State; there are exactly +as many Key Groups as the defined maximum parallelism. During execution each +parallel instance of a keyed operator works with the keys for one or more Key +Groups. + +`TODO: potentially leave out Operator State and Broadcast State from concepts documentation` + +## Operator State + +*Operator State* (or *non-keyed state*) is state that is is bound to one +parallel operator instance. The [Kafka Connector]({{ site.baseurl }}{% link +dev/connectors/kafka.md %}) is a good motivating example for the use of +Operator State in Flink. Each parallel instance of the Kafka consumer maintains +a map of topic partitions and offsets as its Operator State. + +The Operator State interfaces support redistributing state among parallel +operator instances when the parallelism is changed. There can be different +schemes for doing this redistribution. + +## Broadcast State + +*Broadcast State* is a special type of *Operator State*. It was introduced to +support use cases where some data coming from one stream is required to be +broadcasted to all downstream tasks, where it is stored locally and is used to +process all incoming elements on the other stream. As an example where +broadcast state can emerge as a natural fit, one can imagine a low-throughput +stream containing a set of rules which we want to evaluate against all elements +coming from another stream. Having the above type of use cases in mind, +broadcast state differs from the rest of operator states in that: + 1. it has a map format, + 2. it is only available to specific operators that have as inputs a + *broadcasted* stream and a *non-broadcasted* one, and + 3. such an operator can have *multiple broadcast states* with different names. + +{% top %} + +## State Persistence + +Flink implements fault tolerance using a combination of **stream replay** and +**checkpointing**. A checkpoint is related to a specific point in each of the +input streams along with the corresponding state for each of the operators. A +streaming dataflow can be resumed from a checkpoint while maintaining +consistency *(exactly-once processing semantics)* by restoring the state of the +operators and replaying the events from the point of the checkpoint. + +The checkpoint interval is a means of trading off the overhead of fault +tolerance during execution with the recovery time (the number of events that +need to be replayed). + +The fault tolerance mechanism continuously draws snapshots of the distributed +streaming data flow. For streaming applications with small state, these +snapshots are very light-weight and can be drawn frequently without much impact +on performance. The state of the streaming applications is stored at a +configurable place (such as the master node, or HDFS). Review comment: usually in a distributed filesystem? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
