流式事件处理器
Streaming Event Processors
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Streaming Event Processors
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The StreamingEventProcessor
, or Streaming Processor for short, is a type of . As any Event Processor, it serves as the technical aspect to handle events by invoking the event handlers written in an Axon application.
The Streaming Processor defines itself by receiving the events from a StreamableMessageSource
. The StreamableMessageSource
is an infrastructure component through which we can open a stream of events. The source can also specify positions on the event stream, so-called , used as start positions when opening an event stream. An example of a StreamableMessageSource
is the , like for example or an .
Furthermore, Streaming Processors use separate threads to process the events retrieved from the StreamableMessageSource
. Using separate threads decouples the StreamingEventProcessor
from other operations (e.g., event publication or command handling), allowing for cleaner separation within any application.
Using separate threads allows for of the event load, either within a single JVM or between several.
When starting a Streaming Processor, it will open an event stream through the configured StreamableMessageSource
. The first time a stream has started, it, by default, will begin at the tail (the oldest/the very first token) of the stream. It keeps track of the event processing progress while traversing the stream. It does so by storing the Tracking Tokens, or tokens for short, accompanying the events. This solution works towards tracking the progress since the tokens specify the event's position on the stream.
Head or Tail?
The oldest (very first) token is located at the tail of the stream, and the latest (newest) token is positioned at the head of the stream.
Maintaining the progress through tokens makes a Streaming Processor
able to deal with stopping and starting the processor,
more resilient against unintended shutdowns, and
the token provides a means to events by adjusting the position of tokens.
All combined, the Streaming Processor allows for decoupling, parallelization, resiliency, and replay-ability. It is these features that make the Streaming Processor the logical choice for the majority of applications. Due to this, the "Tracking Event Processor," a type of Streaming Processor, is the default Event Processor.
Default Event Processor
Which
EventProcessor
type becomes the default processor depends on the event message source available in your application. In the majority of use cases, an Event Store is present. As the Event Store is a type ofStreamableMessageSource
, the default will switch to the Tracking Event Processor.If the application only has an Event Bus configured, the framework will lack a
StreamableMessageSource
. It will fall back to the as the default in these scenarios. This implementation will use the configuredEventBus
as itsSubscribableMessageSource
.
There are two implementations of Streaming Processor available in Axon Framework:
the Tracking Event Processor (TEP for short), and
the Pooled Streaming Event Processor (PSEP for short).
Firstly, to specify that new event processors should default to a TrackingEventProcessor
, you can invoke the usingTrackingEventProcessors
method:
For a specific Event Processor to be a Tracking instance, registerTrackingEventProcessor
is used:
For more fine-grained control when configuring a Tracking Processor, the TrackingEventProcessorConfiguration
can be used. When invoking the registerTrackingEventProcessor
method, you can provide a tracking processor configuration object, or you can register the configuration instance explicitly:
Firstly, to specify that every new processors should default to a PooledStreamingEventProcessor
, you can invoke the usingPooledStreamingProcessors
method:
For a specific Event Processor to be a Pooled Streaming instance, registerPooledStreamingProcessor
is used:
For more fine-grained control when configuring a Pooled Streaming Processor, the PooledStreamingProcessorConfiguration
can be used. When invoking the registerPooledStreamingEventProcessor
method, you can provide a pooled streaming processor configuration object, or you can register the configuration instance explicitly:
The error mode differs between the Tracking- and Pooled Streaming Event Processor.
A vital attribute of the Streaming Event Processor is its capability to keep and maintain the processing progress. It does so through the TrackingToken
, the "token" for short. Such a token accompanies each message a streaming processor receives through its event stream. It's this token that:
specifies the position of the event on the overall stream, and
is used by the Streaming Processor to open the event stream at the desired position on start-up.
Using tokens gives the Streaming Event Processor several benefits, like:
Being able to reopen the stream at any later point, picking up where it left off with the last event.
Dealing with unintended shutdowns without losing track of the last events they've handled.
For a Streaming Processor to process any events, it needs "a claim" on a TrackingToken
. The processor will update this claim every time it has finished handling a batch of events. This so-called "claim extension" is, just as updating and saving of tokens, delegated to the Token Store. Hence, the Streaming Processors achieves collaboration among instances/threads through token claims.
In the absence of a claim, a processor will actively try to retrieve one. If a token claim is not extended for a configurable time window, other processor threads are able to "steal" the claim. Token stealing can, for example, happen if event processing is slow or encountered some exceptions.
The Streaming Processor uses a StreamableMessageSource
to retrieve a stream of events that will open on start-up. It requires a TrackingToken
to open this stream, which it will fetch from the TokenStore
. However, if a Streaming Processor starts for the first time, there is no TrackingToken
present to open the stream with yet.
A Saga's Streaming Processor initial position
Conceptually there are a couple of scenarios when a processor builds an initial token on application startup. The obvious one is already shared, namely when a processor starts for the first time. There are, however, also other situations when a token is built that might be unexpected, like:
The TokenStore
has (accidentally) been cleared between application runs, thus losing the stored tokens.
The application running the processor starts in a new environment (e.g., test or acceptance) for the first time.
An InMemoryTokenStore
was used, and hence the processor could never persist the token to begin with.
The application is (accidentally) pointing to another storage solution than expected.
Whenever a Streaming Processor's event handlers show unexpected behavior in the form of missed or reprocessed events, a new initial token might have been triggered. In those cases, we recommend to validate if any of the above situations occurred.
There are a couple of things we can configure when it comes to tokens. We can separate these options in "initial token" and "token claim" configuration, as described in the following sections:
Initial Token
createHeadToken()
- Creates a token from the head of the event stream.
createTailToken()
- Creates a token from the tail of the event stream. Creating tail tokens is the default value for most Streaming Processors.
createTokenAt(Instant)
/ createTokenSince(Duration)
- Creates a token that tracks all events after a given time. If there is an event precisely at that given moment in time, it will also be taken into account.
Of course, you can completely disregard the StreamableMessageSource
input parameter and create a token by yourself. Consider the following snippets if you want to configure a different initial token:
Token Claims
In those scenarios, another processor can steal a token claim to proceed with processing. There are a couple of configurable values that influence this process:
tokenClaimInterval
- Defines how long to wait between attempts to claim a segment. A processor uses this value to steal token claims from other processor threads. This value defaults to 5000 milliseconds.
eventAvailabilityTimeout
- Defines the time to "wait for events" before extending the claim. Only the Tracking Event Processor uses this. The value defaults to 1000 milliseconds.
claimExtensionThreshold
- Threshold to extend the claim in the absence of events. Only the Pooled Streaming Event Processor uses this. The value defaults 5000 milliseconds.
Consider the following snippets if you want to configure any of these values:
The TokenStore
provides the CRUD operations for the StreamingEventProcessor
to interact with TrackingTokens
. The streaming processor will use the store to construct, fetch and claim tokens.
The framework provides a couple of TokenStore
implementations:
InMemoryTokenStore
- A TokenStore
implementation that keeps the tokens in memory. This implementation does not suffice as a production-ready store in most applications.
JpaTokenStore
- A TokenStore
implementation using JPA to store the tokens with. Expects that a table is constructed based on the org.axonframework.eventhandling.tokenstore.jpa.TokenEntry
. It is easily auto-configurable with, for example, Spring Boot.
JdbcTokenStore
- A TokenStore
implementation using JDBC to store the tokens with. Expects that the schema is constructed through the JdbcTokenStore#createSchema(TokenTableFactory)
method. Several TokenTableFactory
can be chosen here, like the GenericTokenTableFactory
, PostgresTokenTableFactory
or Oracle11TokenTableFactory
implementation.
MongoTokenStore
- A TokenStore
implementation using Mongo to store the tokens with.
Where to store Tokens?
Where possible, we recommend using a token store that stores tokens in the same database as to where the event handlers update the view models. This way, changes to the view model can be stored atomically with the changed tokens. Furthermore, it guarantees exactly-once processing semantics.
Note that you can configure the token store to use for a streaming processor in the EventProcessingConfigurer
:
To configure a TokenStore
for all processors:
Alternatively, to configure a TokenStore
for a specific processor, use:
Thus, to be able to parallelize the load, we require several tokens per processor. To that end, each token instance represents a segment of the event stream, wherein each segment is identified through a number. The stream segmentation approach ensures events aren't handled twice (or more), as that would otherwise introduce unintentional duplication. Due to this, the Streaming Processor's API references segment claims instead of token claims throughout.
The default number of segments for a TrackingEventProcessor
is one.
Parallel Processing and Subscribing Event Processors
The Event Handling Components a processor is in charge of may have specific expectations on the event order. The ordering is guaranteed when only a single thread is processing events. Maintaining the ordering requires additional work when the stream is segmented for parallel processing, however. When this is the case, the processor must ensure it sends the events to these handlers in that specific order.
Even though events are processed asynchronously from their publisher, it is often desirable to process certain events in their publishing order. In Axon, the SequencingPolicy
controls this order. The SequencingPolicy
defines whether events must be handled sequentially, in parallel, or a combination of both. Policies return a sequence identifier of a given event.
If the policy returns the same identifier for two events, they must be handled sequentially by the Event Handling Component. Thus, if the SequencingPolicy
returns a different value for two events, they may be processed concurrently. Note that if the policy returns a null
sequence identifier, the event may be processed in parallel with any other events.
** Parallel Processing and Sagas**
The framework provides several policies you can use out of the box:
SequentialPerAggregatePolicy
- The default policy. It will force domain events that were raised from the same aggregate to be handled sequentially. Thus, events from different aggregates may be handled concurrently. This policy is typically suitable for Event Handling Components that update details from aggregates in databases.
FullConcurrencyPolicy
- This policy will tell Axon that this Event Processor may handle all events concurrently. This means that there is no relationship between the events that require them to be processed in a particular order.
SequentialPolicy
- This policy tells Axon that it can process all events sequentially. Handling of an event will start when the handling of a previous event has finished.
PropertySequencingPolicy
- When configuring this policy, the user is required to provide a property name or property extractor function. This implementation provides a flexible solution to set up a custom sequencing policy based on a standard value present in your events. Note that this policy only reacts to properties present in the event class.
MetaDataSequencingPolicy
- When configuring this policy, the user is required to provide a metaDataKey
to be used. This implementation provides a flexible solution to set up a custom sequencing policy based on a standard value present in your events' metadata.
Consider the following snippets when configuring a (custom) SequencingPolicy
:
If the available policies do not suffice, you can define your own. To that end, we should implement the SequencingPolicy
interface. This interface defines a single method, getSequenceIdentifierFor(T)
, that returns the sequence identifier for a given event:
Thread and Segment Count
Since the tracking processor can only claim a single segment per thread, segments may go unprocessed if there are more segments than threads. Hence, we recommend setting the number of threads (on every node) higher than or equal to the total number of segments.
To increase event handling throughput, we recommend changing the number of threads. How to do this is shown in the following sample:
The PooledStreamingEventProcessor
uses two threads pools instead of the single fixed set of threads used by the TrackingEventProcessor
. The first thread pool is in charge of opening a stream with the event source, claiming as many segments as possible, and delegating all the work.
The second thread pool deals with all the segments the Coordinator
of the pooled streaming processor could claim. The Coordinator
starts a WorkPackage
for each segment and provides them the events to handle. The work package will, in turn, invoke the Event Handling Components to process the events. These packages run within the second thread pool, the so-called "worker executor" pool. The worker-pool also defaults to ScheduledExecutorService
with a single thread.
When you want to increase event handling throughput, we recommend changing the number of threads for the worker thread pool. How to do this is shown in the following sample:
Open Event Streams - The tracking processor will open a stream per segment it claims. The pooled streaming processor will always open a single event stream and delegate the events to the segment workers. Due to this, the tracking processor will use more I/O resources than the pooled streaming processor. However, the TEP's segments can move at their own speed as they open a dedicated event stream. The PSEP's segments will at least process as fast as the slowest segment in the set.
Segment Claims per Thread - The tracking processor can only claim a single segment per thread. The pooled streaming processor can claim any amount of segments, regardless of the number of threads configured. The maxClaimedSegments
is configurable if required (the defaults is Short.MAX
). The fact the TEP can only claim a single segment per thread highlights a problem of that implementation. Events will go unprocessed if there are more segments than threads when using the tracking processor since events belong to a single segment. Furthermore, it makes dynamic scaling tougher since you cannot adjust the number of threads at runtime. Here we see significant benefits for using the PSEP instead of the TEP since it completely drops the "one segment per thread" policy. As such, partial processing is never a problem, the PooledStreamingEventProcessor
would encounter.
Thread Pool Configuration - The tracking processor does not allow sharing a thread pool between different instances. For the pooled streaming processor, a ScheduledExecutorService
is configurable, which allows sharing the executor between different processor instances. Thus, the PSEP provides a higher level of flexibility towards optimizing the total amount of threads used within an application. The freedom in thread pool configuration is helpful when, for example, the number of different Event Processors in a single application increases.
Which Streaming Processor should I use?
In most scenarios, the
PooledStreamingEventProcessor
is the recommended processor implementation. We conclude this based on the segment-to-thread-count ratio, its ability to share thread pools, and the lower amount of opened event streams.The
TrackingEventProcessor
will still be ideal if you anticipate the processing speed between segments to differ significantly. Also, if the application does not have too many processor instances, the need to share thread pools is loosened.
For streaming processors, it doesn't matter whether the threads handling the events are all running on the same node or on different nodes hosting the same (logical) processor. When two (or more) instances of a streaming processor with the same name are active on different machines, they are considered two instances of the same logical processor. Hence, it is not just a processor's own threads that compete for segments but also the processors on different application instances.
When in a multi-node scenario, often a fair distribution of the segments is desired. Otherwise, the event processing load could be distributed unequally over the active instances. There are roughly two approaches towards balancing the number of segments claimed per node:
Directly on a StreamingEventProcessor
, with the releaseSegment(int segmentId)
or releaseSegment(int segmentId, long releaseDuration, TimeUnit unit)
method
When Axon Server is in place, we recommend using option one, as it is easiest to use. Whenever Axon Server is not used, we can achieve load balancing by having a streaming processor release its segments. Releasing segments is done by calling the releaseSegment
method. When invoking releaseSegment
, the StreamingEventProcessor
will "let go of" the segment for some time.
For those required to take the second approach, consider the following snippet as a form of guidance on how to release segments:
When there is a high event load, ideally, we increase the number of segments. In turn, we can reduce the number of segments again if the load on the streaming processor decreases. To change the number of segments at runtime, the split and merge operations should be used. Splitting and merging allow you to control the number of segments dynamically.
There are roughly two approaches to adjust the number of segments for a streaming processor:
Directly on a StreamingEventProcessor
, with the splitSegment(int segmentId)
and mergeSegment(int segmentId)
methods
When Axon Server is in place, we recommend using option one since it is easiest to use. Whenever Axon Server is not used, and you want to adjust the number of segments, the split and merge methods should be accessible from within your application. For those required to take the second approach, consider the following snippet as a form of guidance:
Note that if you are moving towards a solution using the StreamingProcessorController
, there are a couple of points to consider. When invoking the split/merge operation on a StreamingEventProcessor
, that processor should be in charge of the segment you want to split or merge. Thus, either the streaming processor already has a claim on the segment(s) or can claim the segment(s). Without the claims, the processor will simply fail the split or merge operation.
When doing a merge, the streaming processor should be in charge of both the provided segmentId
and the segment the framework will merge it with. We can calculate the segment identifier the provided segmentId will be merged with through the
Segment#mergeableSegmentId` method.
Segment Selection Considerations
When splitting or merging through Axon Server, it chooses the most appropriate segment to split or merge for you. When using the Axon Framework API directly, the developer should deduce the segment to split or segments to merge by themselves:
Split: for fair balancing, a split is ideally performed on the largest segment
Merge: for fair balancing, a merge is ideally performed on the smallest segment
The reset API revolves around the resetTokens()
method and provides a couple of options:
resetTokens(Function<StreamableMessageSource<TrackedEventMessage<?>>, TrackingToken> initialTrackingTokenSupplier)
- Resets the TrackingToken
to the results of the initialTrackingTokenSupplier
resetTokens(TrackingToken startPosition)
- Resets the TrackingToken
to the provided startPosition
Partial Replays
A replay does not always have to start "from the beginning of time." Partially replaying the event stream suffices for a lot of applications.
If creating tokens based on time is not sufficient, but creating tokens based on the exact position is something that is more convenient, you could create a
TrackingToken
providing the position and give it toresetTokens(TrackingToken startPosition)
orresetTokens(TrackingToken startPosition, R resetContext)
methods. The concrete implementation ofTrackingToken
to provide depends on theToken Store
being used.Be mindful that when initiating a partial replay, the event handlers may handle an event in the middle of model construction. Hence, event handlers need to be "aware" that some events might not have been handled at all. Making the event handlers lenient (e.g., deal with missing data) or performing ad-hoc manual replays would help in that area.
To achieve this, the streaming event processor must be inactive when starting a reset. Hence, it is required to be shut down first before invoking the resetTokens
operation. Once the reset was successful, the processor can be started up again.
Consider the following sample on how to trigger a reset within an application:
Resets in multi-node environments
When Axon Server is not used, you should construct a custom endpoint in your application. The
StreamingProcessorService
sample shared above would be ideal for adding a start and stop method.
Initiating a replay through the StreamingEventProcessor
opens up an API to tap into the process of replaying. It is, for example, possible to define a @ResetHandler
. A processor will invoke ResetHandler
annotated methods as a result of StreamingEventProcessor#resetTokens
. It provides a hook to prepare an Event Handling Component before the replay begins.
The following sample Event Handling Component shows the available replay API:
The CardSummaryProjection
shows a couple of interesting things to take note of when it comes to "being aware" of a replay in progress:
An @AllowReplay
can be used, situated either on an entire class or an @EventHandler
annotated method. It defines whether the processor should invoke the given class or method when a replay is in transit.
In addition to allowing a replay, @DisallowReplay
can also be used. Similar to @AllowReplay
, you can place it on class level and methods. It serves to define whether a processor should not invoke the class or method when a replay is in transit.
To have more fine-grained control on what (not) to do during a replay, we can use the ReplayStatus
parameter. The ReplayStatus
is an additional parameter that we can add to @EventHandler
annotated methods. It allows conditional operations in the event handlers based on whether a replay is taking place.
If it is necessary to perform certain pre-replay logic, such as clearing out a projection table, we can use the @ResetHandler
annotation. It allows adding a "reset context" to provide more information on why the reset is taking place. To include a resetContext
the resetTokens(R resetContext)
method (or other methods containing the resetContext
parameter) should be invoked. The type of the resetContext
is up to the user.
You can configure a Streaming Event Processor to use multiple sources to process events from. When required to process events from several sources, we can configure a specific type of StreamableMessageSource
: the MultiStreamableMessageSource
. The MultiStreamableMessageSource
is useful when a streaming processor should act on the events from:
several event stores,
from different storage types (e.g., an Event Store and a Kafka Stream)
Having multiple sources means that there might be a choice of multiple events that the processor could consume at any given instant. Therefore, you can specify a Comparator
to choose between them. The default implementation chooses the event with the oldest timestamp (i.e., the event waiting for the longest).
Using multiple sources also means that the streaming processor's polling interval needs to be divided between sources. Some sources might use a strategy to optimize event discovery, thus minimizing overhead in establishing costly connections to the data sources. To that end, you can choose which source the processor does most of the polling on using the longPollingSource()
method in the builder. This operation ensures one source consumes most of the polling interval while also checking intermittently for events on the other sources. The MultiStreamableMessageSource
defaults the longPollingSource
to the last configured source.
Consider the following sample when constructing a MultiStreamableMessageSource
:
Assuming a buildMultiStreamableMessageSource(...)
method is present, we can use the outcome to register a processor with the configuring EventProcessingConfigurer
:
Both implementations support the same set of operations. Operations like replaying events through a , and tracking the progress with . They diverge on their threading approach and work separation, as discussed in more detail in section.
The Streaming Processors have several additional components that you can configure, next to the . For other streaming processor features that are configurable, we refer to their respective sections for more details. This chapter will cover how to configure a or Processor respectively.
Whenever the rethrows an exception, a TrackingEventProcessor
will retry processing the event using an incremental back-off period. It will start at 1 second and double after each attempt until it reaches the maximum wait time of 60 seconds per attempt. This back-off time ensures that in a distributed environment, when another node is able to process events, it will have the opportunity to claim the required to process the event.
The PooledStreamingEventProcessor
simply aborts the failed part of the process. The Pooled Streaming Processor can deal with this since the is different from the Tracking Processor. As such, the chance is high the failed process will be picked up quickly by another thread within the same JVM. This chance increases further whenever the PSEP instance is distributed over several application instances.
Collaboration over the event handling load from two perspectives. First, the tokens make sure only a single thread is actively processing specific events. Secondly, it allows of the load over several threads or nodes of a Streaming Processor.
events by adjusting the token position of that processor.
To be able to reopen the stream at a later point, we should keep the progress somewhere. The progress is kept by updating and saving the TrackingToken
after handling batches of events. Keeping the progress requires CRUD operation, for which the Streaming Processor uses the .
Whenever this situation occurs, a Streaming Processor will construct an "initial token." By default, the initial token will start at the tail of the event stream. Thus, the processor will begin at the start and handle every event present in the message source. This start position is configurable, as is described .
A Streaming Processor dedicated to a will default the initial token to the head of the stream. The default initial token position ensures that the Saga does not react to events from the past, as in most cases, this would introduce unwanted side effects.
The for a StreamingEventProcessor
is configurable for every processor instance. When configuring the initial token builder function, the received input parameter is the StreamableMessageSource
. The message source, in turn, gives three possibilities to build a token, namely:
As described , a streaming processor should claim a token before it is allowed to perform any processing work. There are several scenarios where a processor may keep the claim for too long. This can occur when, for example, the event handling process is slow or encountered an exception.
When no token store is explicitly defined, an InMemoryTokenStore
is used. The InMemoryTokenStore
is not recommended in most production scenarios since it cannot maintain the progress through application shutdowns. Unintentionally using the InMemoryTokenStore
counts towards one of the unexpected scenarios where the framework creates an on each application start-up.
Streaming processors can use to process an event stream. Using multiple threads allows the StreamingEventProcessor
to more efficiently process batches of events. As described , a streaming processor's thread requires a claim on a tracking token to process events.
You can define the number of segments used by adjusting the initialSegmentCount
property. Only when a streaming processor starts for the first time can it initialize the number of segments to use. This requirement follows from the fact each token represents a single segment. Tokens, in turn, can only be initialized if they are not present yet, as is explained in more detail .
Whenever the number of segments should be adjusted during runtime, you can use the functionality. To adjust the number of initial segments, consider the following sample:
Note that don't manage their own threads. Therefore, it is not possible to configure how they should receive their events. Effectively, they will always work on a sequential-per-aggregate basis, as that is generally the level of concurrency in the command handling component.
Axon uses the SequencingPolicy
for this. The SequencingPolicy
is a function that returns a value for any given message. If the return value of the SequencingPolicy
function is equal for two distinct event messages, it means that those messages must be processed sequentially. By default, Axon components will use the SequentialPerAggregatePolicy
, making it so that events published by the same aggregate instance will be handled sequentially. Check out section to understand how to influence the sequencing policy.
Each node running a streaming processor will attempt to start its configured amount of threads to start processing events. The number of segments that a single thread can claim differ between the Tracking- and Pooled Streaming Event Processor. A tracking processor can only claim a single segment per thread, whereas the pooled streaming processor can claim any amount of segments per thread. These approaches provide different pros and cons for each implementation, which section explains further.
A instance is never invoked concurrently by multiple threads. Therefore, the SequencingPolicy
is irrelevant for a saga. Axon will ensure each saga instance receives the events it needs to process in the order they have been published on the event bus.
Conceptually, the SequencingPolicy
decides whether an event belongs to a given . Furthermore, Axon guarantees that Events that are part of the same segment are processed sequentially.
A Streaming Processor cannot process events in parallel without multiple threads configured. We can process events in parallel by running of an application. Or by configuring a StreamingEventProcessor
to use several threads. The following section describes the threading differences between the Tracking- and Pooled Streaming Event Processor. These sections are followed up with samples on configuring multiple threads for the TEP and PSEP, respectively.
Adjusting the number of threads will not automatically parallelize a Streaming Processor. A segment claim to let a thread process any events. Hence, increasing the thread count should be paired with adjusting the segment count.
The TrackingEventProcessor
uses a ThreadFactory
to start the process of claiming segments. It will use a single thread per segment it is able to claim until the processor exhausts the configured amount of threads. Each thread will open a stream with the StreamableMessageSource
and start processing events at their own speed. Other segment operations, like , are processed by the thread owning the segment operated on.
The work it coordinates is foremost the events to handle. Next to event coordination, it deals with segment operations like . The component coordinating all the work is called the Coordinator
. This coordinator defaults to using a ScheduledExecutorService
with a single thread, which suffices in most scenarios.
Based on the threading approaches of the and , there are a couple of differences to note:
Thus in a multi-node setup, each processor instance will try to , preventing events assigned to that segment from being processed on other nodes. In this process, the processor updates the token by adding a node identifier when it claims a segment to enforce the claim. The node identifier is configurable on the TokenStore
. By default, it will use the JVM's name (usually a combination of the hostname and process ID) as the nodeId
.
Through the Dashboard with the load balancing feature
As a consequence of releasing, another node will be able to claim the segment. Due to this, releasing allows you to balance the load. By default, the segment will be released for twice the .
The Streaming Event Processor provides scalability by supporting . Through this, it is possible to tune the processor's performance by . However, only changing the number of threads is insufficient since the parallelization is dictated through the number of segments.
Through the Dashboard with the split and merge buttons
It is advised to check which segments a streaming processor has a claim on. For that, is used. The status information shows which segments a processor instance owns. This guides which processor to invoke the split or merge on.
A benefit of streaming events is that we can reopen the stream at any point in time. Whenever some event handling components misbehaved, and the view models they update or actions they triggered should happen again, starting anew can be very useful. Handling events again by adjusting the position on the stream is what's called "a replay," a feature supported by the StreamingEventProcessor
. The following sections describe how to and what the framework provides.
resetTokens()
- Simple reset, adjusting the TrackingToken
to the configured
resetTokens(R resetContext)
- Resets the TrackingToken
to the configured , providing the resetContext
to the
resetTokens(Function<StreamableMessageSource<TrackedEventMessage<?>>, TrackingToken> initialTrackingTokenSupplier, R resetContext)
- Resets the TrackingToken
to the results of the initialTrackingTokenSupplier
, providing the resetContext
to the
resetTokens(TrackingToken startPosition, R resetContext)
- Resets the TrackingToken
to the provided startPosition
, providing the resetContext
to the
To perform a so-called "partial replay," you should provide the token at a specific point in time. The StreamableMessageSource
's can be used for this.
As the method name suggests, the reset adjusts the to a new position. When starting a reset, the streaming processor is required to claim all its . All claims are required since the processor needs to update all tokens to their new position to start the replay.
If you are in a scenario, that means all nodes should shut down the StreamingEventProcessor
. Otherwise, another node will pick up the segments released by the inactive processor instance.
Being able to shut down or start up all streaming processor instances is most easily achieved through the Dashboard. The application's dashboard provides a "start" and "stop" button, which will start/stop the processor on every node.
, or