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_r379570828
 
 

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 File path: docs/concepts/stateful-stream-processing.md
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+---
+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
 
 Review comment:
   ```suggestion
   tolerance during execution with the recovery time (the number of records that
   ```
   glossary

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