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_r379575432
########## 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). + +In case of a program failure (due to machine-, network-, or software failure), +Flink stops the distributed streaming dataflow. The system then restarts the +operators and resets them to the latest successful checkpoint. The input +streams are reset to the point of the state snapshot. Any records that are +processed as part of the restarted parallel dataflow are guaranteed to not have +been part of the previously checkpointed state. + +{% info Note %} By default, checkpointing is disabled. See [Checkpointing]({{ +site.baseurl }}{% link dev/stream/state/checkpointing.md %}) for details on how +to enable and configure checkpointing. + +{% info Note %} For this mechanism to realize its full guarantees, the data +stream source (such as message queue or broker) needs to be able to rewind the +stream to a defined recent point. [Apache Kafka](http://kafka.apache.org) has +this ability and Flink's connector to Kafka exploits this ability. See [Fault +Tolerance Guarantees of Data Sources and Sinks]({{ site.baseurl }}{% link +dev/connectors/guarantees.md %}) for more information about the guarantees +provided by Flink's connectors. + +{% info Note %} Because Flink's checkpoints are realized through distributed +snapshots, we use the words *snapshot* and *checkpoint* interchangeably. + +### Checkpointing + +The central part of Flink's fault tolerance mechanism is drawing consistent +snapshots of the distributed data stream and operator state. These snapshots +act as consistent checkpoints to which the system can fall back in case of a +failure. Flink's mechanism for drawing these snapshots is described in +"[Lightweight Asynchronous Snapshots for Distributed +Dataflows](http://arxiv.org/abs/1506.08603)". It is inspired by the standard +[Chandy-Lamport +algorithm](http://research.microsoft.com/en-us/um/people/lamport/pubs/chandy.pdf) +for distributed snapshots and is specifically tailored to Flink's execution +model. + + +### Barriers + +A core element in Flink's distributed snapshotting are the *stream barriers*. +These barriers are injected into the data stream and flow with the records as +part of the data stream. Barriers never overtake records, they flow strictly in +line. A barrier separates the records in the data stream into the set of +records that goes into the current snapshot, and the records that go into the +next snapshot. Each barrier carries the ID of the snapshot whose records it +pushed in front of it. Barriers do not interrupt the flow of the stream and are +hence very lightweight. Multiple barriers from different snapshots can be in +the stream at the same time, which means that various snapshots may happen +concurrently. + +<div style="text-align: center"> + <img src="{{ site.baseurl }}/fig/stream_barriers.svg" alt="Checkpoint barriers in data streams" style="width:60%; padding-top:10px; padding-bottom:10px;" /> +</div> + +Stream barriers are injected into the parallel data flow at the stream sources. +The point where the barriers for snapshot *n* are injected (let's call it +<i>S<sub>n</sub></i>) is the position in the source stream up to which the +snapshot covers the data. For example, in Apache Kafka, this position would be +the last record's offset in the partition. This position <i>S<sub>n</sub></i> +is reported to the *checkpoint coordinator* (Flink's JobManager). + +The barriers then flow downstream. When an intermediate operator has received a +barrier for snapshot *n* from all of its input streams, it emits a barrier for +snapshot *n* into all of its outgoing streams. Once a sink operator (the end of +a streaming DAG) has received the barrier *n* from all of its input streams, it +acknowledges that snapshot *n* to the checkpoint coordinator. After all sinks +have acknowledged a snapshot, it is considered completed. + +Once snapshot *n* has been completed, the job will never again ask the source +for records from before <i>S<sub>n</sub></i>, since at that point these records +(and their descendant records) will have passed through the entire data flow +topology. + +<div style="text-align: center"> + <img src="{{ site.baseurl }}/fig/stream_aligning.svg" alt="Aligning data streams at operators with multiple inputs" style="width:100%; padding-top:10px; padding-bottom:10px;" /> +</div> + +Operators that receive more than one input stream need to *align* the input +streams on the snapshot barriers. The figure above illustrates this: + + - As soon as the operator receives snapshot barrier *n* from an incoming + stream, it cannot process any further records from that stream until it has + received the barrier *n* from the other inputs as well. Otherwise, it would + mix records that belong to snapshot *n* and with records that belong to + snapshot *n+1*. + - Streams that report barrier *n* are temporarily set aside. Records that are + received from these streams are not processed, but put into an input + buffer. + - Once the last stream has received barrier *n*, the operator emits all + pending outgoing records, and then emits snapshot *n* barriers itself. + - After that, it resumes processing records from all input streams, + processing records from the input buffers before processing the records + from the streams. + +### Snapshotting Operator State + +When operators contain any form of *state*, this state must be part of the +snapshots as well. + +Operators snapshot their state at the point in time when they have received all +snapshot barriers from their input streams, and before emitting the barriers to +their output streams. At that point, all updates to the state from records +before the barriers will have been made, and no updates that depend on records +from after the barriers have been applied. Because the state of a snapshot may +be large, it is stored in a configurable *[state backend]({{ site.baseurl }}{% +link ops/state/state_backends.md %})*. By default, this is the JobManager's +memory, but for production use a distributed reliable storage should be +configured (such as HDFS). After the state has been stored, the operator +acknowledges the checkpoint, emits the snapshot barrier into the output +streams, and proceeds. + +The resulting snapshot now contains: + + - For each parallel stream data source, the offset/position in the stream + when the snapshot was started + - For each operator, a pointer to the state that was stored as part of the + snapshot + +<div style="text-align: center"> + <img src="{{ site.baseurl }}/fig/checkpointing.svg" alt="Illustration of the Checkpointing Mechanism" style="width:100%; padding-top:10px; padding-bottom:10px;" /> +</div> + +### Asynchronous State Snapshots + +Note that the above described mechanism implies that operators stop processing +input records while they are storing a snapshot of their state in the *state +backend*. This *synchronous* state snapshot introduces a delay every time a +snapshot is taken. + +It is possible to let an operator continue processing while it stores its state +snapshot, effectively letting the state snapshots happen *asynchronously* in +the background. To do that, the operator must be able to produce a state object +that should be stored in a way such that further modifications to the operator +state do not affect that state object. For example, *copy-on-write* data +structures, such as are used in RocksDB, have this behavior. + +After receiving the checkpoint barriers on its inputs, the operator starts the +asynchronous snapshot copying of its state. It immediately emits the barrier to +its outputs and continues with the regular stream processing. Once the +background copy process has completed, it acknowledges the checkpoint to the +checkpoint coordinator (the JobManager). The checkpoint is now only complete +after all sinks have received the barriers and all stateful operators have +acknowledged their completed backup (which may be after the barriers reach the +sinks). + +See [State Backends]({{ site.baseurl }}{% link ops/state/state_backends.md %}) +for details on the state snapshots. + +### Recovery + +Recovery under this mechanism is straightforward: Upon a failure, Flink selects +the latest completed checkpoint *k*. The system then re-deploys the entire +distributed dataflow, and gives each operator the state that was snapshotted as +part of checkpoint *k*. The sources are set to start reading the stream from +position <i>S<sub>k</sub></i>. For example in Apache Kafka, that means telling +the consumer to start fetching from offset <i>S<sub>k</sub></i>. + +If state was snapshotted incrementally, the operators start with the state of +the latest full snapshot and then apply a series of incremental snapshot +updates to that state. + +See [Restart Strategies]({{ site.baseurl }}{% link dev/task_failure_recovery.md +%}#restart-strategies) for more information. + +### State Backends + +`TODO: expand this section` + +The exact data structures in which the key/values indexes are stored depends on +the chosen [state backend]({{ site.baseurl }}{% link +ops/state/state_backends.md %}). One state backend stores data in an in-memory +hash map, another state backend uses [RocksDB](http://rocksdb.org) as the +key/value store. In addition to defining the data structure that holds the +state, the state backends also implement the logic to take a point-in-time +snapshot of the key/value state and store that snapshot as part of a +checkpoint. + +<img src="{{ site.baseurl }}/fig/checkpoints.svg" alt="checkpoints and snapshots" class="offset" width="60%" /> + +{% top %} + +### Savepoints + +`TODO: expand this section` + +Programs written in the Data Stream API can resume execution from a Review comment: hence all programs that use checkpointing ---------------------------------------------------------------- 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
