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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 | ||
--- | ||
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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**. | ||
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Some examples of stateful operations: | ||
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- 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. | ||
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Flink needs to be aware of the state in order to make it fault tolerant using | ||
[checkpoints]({{ site.baseurl}}{% link dev/stream/state/checkpointing.zh.md %}) | ||
and [savepoints]({{ site.baseurl }}{%link ops/state/savepoints.zh.md %}). | ||
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Knowledge about the state also allows for rescaling Flink applications, meaning | ||
that Flink takes care of redistributing state across parallel instances. | ||
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[Queryable state]({{ site.baseurl }}{% link dev/stream/state/queryable_state.zh.md | ||
%}) allows you to access state from outside of Flink during runtime. | ||
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When working with state, it might also be useful to read about [Flink's state | ||
backends]({{ site.baseurl }}{% link ops/state/state_backends.zh.md %}). Flink | ||
provides different state backends that specify how and where state is stored. | ||
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* This will be replaced by the TOC | ||
{:toc} | ||
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## What is State? | ||
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`TODO: expand this section` | ||
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{% top %} | ||
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## State in Stream & Batch Processing | ||
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`TODO: What is this section about? Do we even need it?` | ||
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{% top %} | ||
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## Keyed State | ||
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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*, i.e. after a keyed/partitioned data | ||
exchange, 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. | ||
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<img src="{{ site.baseurl }}/fig/state_partitioning.svg" alt="State and Partitioning" class="offset" width="50%" /> | ||
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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. | ||
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## State Persistence | ||
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Flink implements fault tolerance using a combination of **stream replay** and | ||
**checkpointing**. A checkpoint marks 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 records from the point of the checkpoint. | ||
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The checkpoint interval is a means of trading off the overhead of fault | ||
tolerance during execution with the recovery time (the number of records that | ||
need to be replayed). | ||
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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, usually in a distributed file system. | ||
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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 | ||
affected the previously checkpointed state. | ||
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{% info Note %} By default, checkpointing is disabled. See [Checkpointing]({{ | ||
site.baseurl }}{% link dev/stream/state/checkpointing.zh.md %}) for details on how | ||
to enable and configure checkpointing. | ||
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{% 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:https://kafka.apache.org) has | ||
this ability and Flink's connector to Kafka exploits this. See [Fault | ||
Tolerance Guarantees of Data Sources and Sinks]({{ site.baseurl }}{% link | ||
dev/connectors/guarantees.zh.md %}) for more information about the guarantees | ||
provided by Flink's connectors. | ||
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{% info Note %} Because Flink's checkpoints are realized through distributed | ||
snapshots, we use the words *snapshot* and *checkpoint* interchangeably. Often | ||
we also use the term *snapshot* to mean either *checkpoint* or *savepoint*. | ||
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### Checkpointing | ||
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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:https://arxiv.org/abs/1506.08603)". It is inspired by the standard | ||
[Chandy-Lamport | ||
algorithm](http:https://research.microsoft.com/en-us/um/people/lamport/pubs/chandy.pdf) | ||
for distributed snapshots and is specifically tailored to Flink's execution | ||
model. | ||
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Keep in mind that everything to do with checkpointing can be done | ||
asynchronously. The checkpoint barriers don't travel in lock step and | ||
operations can asynchronously snapshot their state. | ||
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#### Barriers | ||
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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. | ||
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<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> | ||
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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). | ||
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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. | ||
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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. | ||
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<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> | ||
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Operators that receive more than one input stream need to *align* the input | ||
streams on the snapshot barriers. The figure above illustrates this: | ||
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- 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. | ||
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#### Snapshotting Operator State | ||
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When operators contain any form of *state*, this state must be part of the | ||
snapshots as well. | ||
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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.zh.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. | ||
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The resulting snapshot now contains: | ||
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- 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 | ||
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<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> | ||
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#### Recovery | ||
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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>. | ||
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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. | ||
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See [Restart Strategies]({{ site.baseurl }}{% link dev/task_failure_recovery.zh.md | ||
%}#restart-strategies) for more information. | ||
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### State Backends | ||
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`TODO: expand this section` | ||
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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.zh.md %}). One state backend stores data in an in-memory | ||
hash map, another state backend uses [RocksDB](http:https://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. State backends can be configured without changing your application | ||
logic. | ||
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<img src="{{ site.baseurl }}/fig/checkpoints.svg" alt="checkpoints and snapshots" class="offset" width="60%" /> | ||
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{% top %} | ||
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### Savepoints | ||
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`TODO: expand this section` | ||
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All programs that use checkpointing can resume execution from a **savepoint**. | ||
Savepoints allow both updating your programs and your Flink cluster without | ||
losing any state. | ||
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[Savepoints]({{ site.baseurl }}{% link ops/state/savepoints.zh.md %}) are | ||
**manually triggered checkpoints**, which take a snapshot of the program and | ||
write it out to a state backend. They rely on the regular checkpointing | ||
mechanism for this. | ||
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Savepoints are similar to checkpoints except that they are | ||
**triggered by the user** and **don't automatically expire** when newer | ||
checkpoints are completed. | ||
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{% top %} | ||
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### Exactly Once vs. At Least Once | ||
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The alignment step may add latency to the streaming program. Usually, this | ||
extra latency is on the order of a few milliseconds, but we have seen cases | ||
where the latency of some outliers increased noticeably. For applications that | ||
require consistently super low latencies (few milliseconds) for all records, | ||
Flink has a switch to skip the stream alignment during a checkpoint. Checkpoint | ||
snapshots are still drawn as soon as an operator has seen the checkpoint | ||
barrier from each input. | ||
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When the alignment is skipped, an operator keeps processing all inputs, even | ||
after some checkpoint barriers for checkpoint *n* arrived. That way, the | ||
operator also processes elements that belong to checkpoint *n+1* before the | ||
state snapshot for checkpoint *n* was taken. On a restore, these records will | ||
occur as duplicates, because they are both included in the state snapshot of | ||
checkpoint *n*, and will be replayed as part of the data after checkpoint *n*. | ||
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{% info Note %} Alignment happens only for operators with multiple predecessors | ||
(joins) as well as operators with multiple senders (after a stream | ||
repartitioning/shuffle). Because of that, dataflows with only embarrassingly | ||
parallel streaming operations (`map()`, `flatMap()`, `filter()`, ...) actually | ||
give *exactly once* guarantees even in *at least once* mode. | ||
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{% top %} | ||
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## End-to-end Exactly-Once Programs | ||
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`TODO: add` | ||
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## State and Fault Tolerance in Batch Programs | ||
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Flink executes [batch programs](../dev/batch/index.html) as a special case of | ||
streaming programs, where the streams are bounded (finite number of elements). | ||
A *DataSet* is treated internally as a stream of data. The concepts above thus | ||
apply to batch programs in the same way as well as they apply to streaming | ||
programs, with minor exceptions: | ||
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- [Fault tolerance for batch programs](../dev/batch/fault_tolerance.html) | ||
does not use checkpointing. Recovery happens by fully replaying the | ||
streams. That is possible, because inputs are bounded. This pushes the | ||
cost more towards the recovery, but makes the regular processing cheaper, | ||
because it avoids checkpoints. | ||
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- Stateful operations in the DataSet API use simplified in-memory/out-of-core | ||
data structures, rather than key/value indexes. | ||
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- The DataSet API introduces special synchronized (superstep-based) | ||
iterations, which are only possible on bounded streams. For details, check | ||
out the [iteration docs]({{ site.baseurl }}/dev/batch/iterations.html). | ||
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{% top %} |
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