Working with State describes operator state which upon restore is either evenly distributed among the parallel tasks of an operator, or unioned, with the whole state being used to initialize the restored parallel tasks.
A third type of supported operator state is the Broadcast State. Broadcast state 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:
To show the provided APIs, we will start with an example before presenting their full functionality. As our running example, we will use the case where we have a stream of objects of different colors and shapes and we want to find pairs of objects of the same color that follow a certain pattern, e.g. a rectangle followed by a triangle. We assume that the set of interesting patterns evolves over time.
In this example, the first stream will contain elements of type
Item with a
Color and a
Shape property. The other
stream will contain the
Starting from the stream of
Items, we just need to key it by
Color, as we want pairs of the same color. This will
make sure that elements of the same color end up on the same physical machine.
Moving on to the
Rules, the stream containing them should be broadcasted to all downstream tasks, and these tasks
should store them locally so that they can evaluate them against all incoming
Items. The snippet below will i) broadcast
the stream of rules and ii) using the provided
MapStateDescriptor, it will create the broadcast state where the rules
will be stored.
Finally, in order to evaluate the
Rules against the incoming elements from the
Item stream, we need to:
Connecting a stream (keyed or non-keyed) with a
BroadcastStream can be done by calling
connect() on the
non-broadcasted stream, with the
BroadcastStream as an argument. This will return a
which we can call
process() with a special type of
CoProcessFunction. The function will contain our matching logic.
The exact type of the function depends on the type of the non-broadcasted stream:
Given that our non-broadcasted stream is keyed, the following snippet includes the above calls:
As in the case of a
CoProcessFunction, these functions have two process methods to implement; the
which is responsible for processing incoming elements in the broadcasted stream and the
processElement() which is used
for the non-broadcasted one. The full signatures of the methods are presented below:
The first thing to notice is that both functions require the implementation of the
for processing elements in the broadcast side and the
processElement() for elements in the non-broadcasted side.
The two methods differ in the context they are provided. The non-broadcast side has a
ReadOnlyContext, while the
broadcasted side has a
Both of these contexts (
ctx in the following enumeration):
ctx.getBroadcastState(MapStateDescriptor<K, V> stateDescriptor)
ctx.output(OutputTag<X> outputTag, X value).
stateDescriptor in the
getBroadcastState() should be identical to the one in the
The difference lies in the type of access each one gives to the broadcast state. The broadcasted side has read-write access to it, while the non-broadcast side has read-only access (thus the names). The reason for this is that in Flink there is no cross-task communication. So, to guarantee that the contents in the Broadcast State are the same across all parallel instances of our operator, we give read-write access only to the broadcast side, which sees the same elements across all tasks, and we require the computation on each incoming element on that side to be identical across all tasks. Ignoring this rule would break the consistency guarantees of the state, leading to inconsistent and often difficult to debug results.
Finally, due to the fact that the
KeyedBroadcastProcessFunction is operating on a keyed stream, it
exposes some functionality which is not available to the
BroadcastProcessFunction. That is:
processElement()method gives access to Flink’s underlying timer service, which allows to register event and/or processing time timers. When a timer fires, the
onTimer()(shown above) is invoked with an
OnTimerContextwhich exposes the same functionality as the
processBroadcastElement()method contains the method
applyToKeyedState(StateDescriptor<S, VS> stateDescriptor, KeyedStateFunction<KS, S> function). This allows to register a
KeyedStateFunctionto be applied to all states of all keys associated with the provided
Coming back to our original example, our
KeyedBroadcastProcessFunction could look like the following:
After describing the offered APIs, this section focuses on the important things to keep in mind when using broadcast state. These are:
There is no cross-task communication: As stated earlier, this is the reason why only the broadcast side of a
(Keyed)-BroadcastProcessFunction can modify the contents of the broadcast state. In addition, the user has to make
sure that all tasks modify the contents of the broadcast state in the same way for each incoming element. Otherwise,
different tasks might have different contents, leading to inconsistent results.
Order of events in Broadcast State may differ across tasks: Although broadcasting the elements of a stream guarantees that all elements will (eventually) go to all downstream tasks, elements may arrive in a different order to each task. So the state updates for each incoming element MUST NOT depend on the ordering of the incoming events.
All tasks checkpoint their broadcast state: Although all tasks have the same elements in their broadcast state
when a checkpoint takes place (checkpoint barriers do not overpass elements), all tasks checkpoint their broadcast state,
and not just one of them. This is a design decision to avoid having all tasks read from the same file during a restore
(thus avoiding hotspots), although it comes at the expense of increasing the size of the checkpointed state by a factor
of p (= parallelism). Flink guarantees that upon restoring/rescaling there will be no duplicates and no missing data.
In case of recovery with the same or smaller parallelism, each task reads its checkpointed state. Upon scaling up, each
task reads its own state, and the remaining tasks (
p_old) read checkpoints of previous tasks in a round-robin
No RocksDB state backend: Broadcast state is kept in-memory at runtime and memory provisioning should be done accordingly. This holds for all operator states.