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.
Some examples of stateful operations:
Knowledge about the state also allows for rescaling Flink applications, meaning that Flink takes care of redistributing state across parallel instances.
Queryable state 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. Flink provides different state backends that specify how and where state is stored.
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.
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.
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.
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).
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.
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.
Note By default, checkpointing is disabled. See Checkpointing for details on how to enable and configure checkpointing.
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 has this ability and Flink’s connector to Kafka exploits this. See Fault Tolerance Guarantees of Data Sources and Sinks for more information about the guarantees provided by Flink’s connectors.
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.
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”. It is inspired by the standard Chandy-Lamport algorithm for distributed snapshots and is specifically tailored to Flink’s execution model.
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.
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.
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 Sn) 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 Sn 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 Sn, since at that point these records (and their descendant records) will have passed through the entire data flow topology.
Operators that receive more than one input stream need to align the input streams on the snapshot barriers. The figure above illustrates this:
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. 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:
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 Sk. For example in Apache Kafka, that means telling the consumer to start fetching from offset Sk.
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 for more information.
The exact data structures in which the key/values indexes are stored depends on the chosen state backend. One state backend stores data in an in-memory hash map, another state backend uses RocksDB 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.
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.
Savepoints 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.
Savepoints are similar to checkpoints except that they are triggered by the user and don’t automatically expire when newer checkpoints are completed.
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.
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.
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 (
filter(), …) actually
give exactly once guarantees even in at least once mode.
Flink executes batch programs 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:
Fault tolerance for batch programs 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.
Stateful operations in the DataSet API use simplified in-memory/out-of-core data structures, rather than key/value indexes.
The DataSet API introduces special synchronized (superstep-based) iterations, which are only possible on bounded streams. For details, check out the iteration docs.