Issue related to AXON EXTENSION TRACING: TracingAutoConfiguration.configureEventHandler method printing same event (i.e. GenericDomainEventMessage) in log multiple times from given interceptor o.a.e.t.OpenTraceHandlerInterceptor.
How to avoid/stop multiple times printing for same event please suggest
click here for reference
The multiple log lines is not per definition wrong. As Lucas also states, the GenericDomainEventMesasge is the object which Axon uses (at all times) to wrap your events in. Without doing so, the framework cannot pull apart whether it is dealing with an Event, Command or Query messages to begin with.
Furthermore, as a TrackingEventProcessor can run in parallel, with multiple threads, you could see a bunch of loglines going through.
On any note, the given description at this stage is too minimal to deduce where the exact problem arises if it is a problem at all.
Hence, I would like to ask you to provide the following, based on which I/we can update this answer:
What version of Axon Framework are you using?
What version of Axon's Tracing Extension are you using?
What do your Event Handling classes look like?
Is there any specific configuration in place for these Event Handling classes?
Related
I am implementing an example of spring-boot and axon. I have two events
(deposit and withdraw account balance). I want to know is there any way to get the state of the Account Aggregate by a given date ?
I want to get not just the final state, but to replay events in a range of dates.
I think I can help with this.
In the context of Axon Framework, you can start a replay of events by telling a given TrackingEventProcessor to 'reset' it's Tokens. By the way, the current description on this in the Reference Guide can be found here.
These TrackingTokens are the objects which know how far a given TrackingEventProcessor is in terms of handling events from the Event Stream. Thus resetting/adjusting these TrackingTokens is what will issue a Replay of events.
Knowing all these, the second step is to look at the methods the TrackingEventProcessor provides to 'reset tokens', which is threefold:
TrackingEventProcessor#resetTokens()
TrackingEventProcessor#resetTokens(Function<StreamableMessageSource, TrackingToken>)
TrackingEventProcessor#resetTokens(TrackingToken)
Option one will reset your tokens to the beginning of the event stream, which will thus replay everything.
Option two and three however give you the opportunity to provide a TrackingToken.
Thus, you could provide a TrackingToken starting from several points on the Event Stream. So, how do you go about to creating such a TrackingToken at a specific point in time? To that end, you should take a look at the StreamableMessageSource interface, which has the following operations:
StreamableMessageSource#createTailToken()
StreamableMessageSource#createHeadToken()
StreamableMessageSource#createTokenAt(Instant)
StreamableMessageSource#createTokenSince(Duration)
Option 1 is what's used to create a token at the start of the stream, whilst 2 will create a token at the head of the stream.
Option 3 and 4 will however allow you to create a token at a specific point in time, thus allowing you to replay all the events since the defined instance up to now.
There is one caveat in this scenario however. You're asking to replay an Aggregate. From Axon's perspective by default the Aggregate is the Command Model in a CQRS set up, thus dealing with Commands going in to your system. In the majority of the applications, you want Commands (e.g. the requests to change something) to occur on the current state of the application. As such, the Repository provided to retrieve an Aggregate does not allow specifying a point in time.
The above described solution in regards to replaying is thus solely tied to Query Model creation, as the TrackingEventProcessor is part of the Event Handling side in your application most often used to create views. This idea also ties in with your questions, that you want to know the "state of the Account Aggregate" at a given point in time. That's not a command, but a query, as you have 'a request for data' instead of 'the request to change state'.
Hope this helps you out #Safe!
So, I'm working on a CQRS/ES project in which we are having some doubts about how to handle trivial problems that would be easy to handle in other architectures
My scenario is the following:
I have a customer CRUD REST API and each customer has unique document(number), so when I'm registering a new customer I have to verify if there is another customer with that document to avoid duplicity, but when it comes to a CQRS/ES architecture where we have eventual consistency, I found out that this kind of validations can be very hard to address.
It is important to notice that my problem is not across microservices, but between the command application and the query application of the same microservice.
Also we are using eventstore.
My current solution:
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%. That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
Altough this works, there are 2 things that bother me here, the first thing is my command application relying on the query application, so if my query application is down, my command is affected (today I just return false on my validation if query is down but still...) and second thing is, should a query/read model really be able to emit events? And if so, what is the correct way of doing it? Should the command have some kind of API for that? Or should the query emit the event directly to eventstore using some common shared library? And if I have more than one view/read? Which one should I choose to handle this?
Really hope someone could shine a light into these questions and help me this these matters.
For reference, you may want to be reviewing what Greg Young has written about Set Validation.
I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right?
That's exactly right - your read model is stale copy, and may not have all of the information collected by the write model.
That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
This spelling doesn't quite match the usual designs. The more common implementation is that, if we detect a problem when reading data, we send a command message to the write model, telling it to straighten things out.
This is commonly referred to as a process manager, but you can think of it as the automation of a human supervisor of the system. Conceptually, a process manager is an event sourced collection of messages to be sent to the command model.
You might also want to consider whether you are modeling your domain correctly. If documents are supposed to be unique, then maybe the command model should be using the document number as a key in the book of record, rather than using the customer. Or perhaps the document id should be a function of the customer data, rather than being an arbitrary input.
as far as I know, eventstore doesn't have transactions across different streams
Right - one of the things you really need to be thinking about in general is where your stream boundaries lie. If set validation has significant business value, then you really need to be thinking about getting the entire set into a single stream (or by finding a way to constrain uniqueness without using a set).
How should I send a command message to the write model? via API? via a message broker like Kafka?
That's plumbing; it doesn't really matter how you do it, so long as you are sure that the command runs within its own transaction/unit of work.
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%.
No, you cannot safely rely on the query side, which is eventually consistent, to prevent the system to step into an invalid state.
You have two options:
You permit the system to enter in a temporary, pending state and then, eventually, you will bring it into a valid permanent state; for this you could allow the command to pass, yield CustomerRegistered event and using a Saga/Process manager you verify against a uniquely-indexed-by-document-collection and issue a compensating command (not event!), i.e. UnregisterCustomer.
Instead of sending a command, you create&start a Saga/Process that preallocates the document in a uniquely-indexed-by-document-collection and if successfully then send the RegisterCustomer command. You can model the Saga as an entity.
So, in both solution you use a Saga/Process manager. In order for the system to be resilient you should make sure that RegisterCustomer command is idempotent (so you can resend it if the Saga fails/is restarted)
You've butted up against a fairly common problem. I think the other answer by VoicOfUnreason is worth reading. I just wanted to make you aware of a few more options.
A simple approach I have used in the past is to create a lookup table. Your command tries to register the key in a unique constraint table. If it can reserve the key the command can go ahead.
Depending on the nature of the data and the domain you could let this 'problem' occur and raise additional events to mark it. If it is something that's important to the business/the way the application works then you can deal with it either manually or at the time via compensating commands. if the latter then it would make sense to use a process manager.
In some (rare) cases where speed/capacity is less of an issue then you could consider old-fashioned locking and transactions. Admittedly these are much better suited to CRUD style implementations but they can be used in CQRS/ES.
I have more detail on this in my blog post: How to Handle Set Based Consistency Validation in CQRS
I hope you find it helpful.
I have recently been building an application on top of Greg Young EventStore as my peristance layer and I have been pondering how big should I allow an event to get?
For example I have an UK Address Aggregate with the following fields
UK_Address
-BuildingName
-Street
-Locality
-Town
-Postcode
Now I'm building the UI using React/Redux and was thinking should I create a single FAT addressUpdated Event contatining all the above fields?
Or should I Create a event for each of the different fields? and batch them within the client until the Save event is fired? buildingNameUpdated Event, streetUpdated Event, localityUpdated Event.
I'm not sure if the answer is as black and white ask I have asked it what I really would like to know is what conditions/constraints could you use to make the decision?
should I create a event for each of the different fields?
No. The representations of your events are part of the API -- so you want to use spellings that make sense at the level of the business, not at the level of the implementation.
Now I'm building the UI using React/Redux and was thinking should I create a single FAT updateAddress Event containing all the above fields?
You don't need to constrain the data that you send to your UI to match that which is in the persistence store. The UI is just a cached representation of a read model; there's no reason that representation needs to have the same form as what is in your event store.
Consider the React model itself -- your code makes changes to the "in memory" representation of your data, and then the library computes the new DOM and replaces it, which in turn causes the browser to update its view, which in turn causes the pixels on the screen to change.
So taking a fat event from the store, and breaking it into field level events for the UI is fine. Taking multiple events from the store and aggregating them into a single message for the UI is also fine. Taking events from the event store and transforming them into a spelling that the UI will recognize is also fine.
Do you have any comment regarding Arien answer regarding keeping fields that need to be consistent together? so regardless of when your snapshop the current state of the world it would be in a valid state?
I don't believe that this makes sense, and I'm not sure if it is possible in general.
It doesn't make sense, because "valid state" is a write model concern only; events are things that have happened, its too late to vote on whether they are valid or not. For instance, if you deploy a new model, with a new invariant, it still needs to respect the history of what happened before. So you can build a snapshot for that new model, but the snapshot may not be "valid". Too bad.
Given that, I don't think it makes sense to worry over whether each individual event in a commit leaves the snapshot in a valid state.
In particular, if a particular transaction involves multiple entities, it is very likely that the domain language will suggest an event for each entity (we "debit cash" and "credit accounts receivable"). The entities themselves, of course, are capable of changing independently of each other -- it's the aggregate that maintains the balance.
You have to bundle al the information together in one event when this data has to be consistent with each other.
So when you update one field of an address you probably get an unwanted address.
This will happen when the client has not processed all the events at a certain time due to eventual consistency.
Example:
Change address (City=1, Street=1, Housenumber=1) to (City=2, Street=2, Housenumber=2)
When you do this with 3 events and you have just processed one at the time of reading you could get the address: (City=2, Street=1, Housenumber=1).
If puzzled, give a try to a solution that is easier to implement. I guess "FAT" event will be easier: you will end up spending less time for implementing/debugging/supporting.
It is usually referred as YAGNI-KISS-Occam's Razor principles.
In theory and I find it to be a good rule of thumb is to have your commands and events reflecting the intent of the user staying true to DDD. You can find a good explanation of the pros and cons about event granularity here: https://medium.com/#hugo.oliveira.rocha/what-they-dont-tell-you-about-event-sourcing-6afc23c69e9a
Is there any reason, other than semantics, to create different dispatch methods for view and server actions? All tutorials and examples I’ve seen (most notably this) ignore the source constant entirely when listening to dispatched payloads in favor of switching on the payload's action type.
I suppose there is a reason why this pattern is pervasive in flux examples, but I have yet to see a concrete example as to why this is useful. Presumably one could add an additional if or switch on the payload source to determine whether to act in stores, but no examples I've seen consider this constant at all. Any thoughts on this would be much appreciated.
Yes, this was cruft/cargo-culting that came over from a particular Flux project at Facebook, but there is no real reason to do this. If you do need to differentiate between server and view actions, you can just give them different types, or have another property of the action itself to help differentiate them.
When I get time, I plan to rewrite all the examples and documentation to reflect this.
I've started looking at MassTransit and am writing the classes that will handle the messages. When I implement the interface from Consumes<T> I get four options: All, Selected, For<T> and Context. What is the difference between the four and when should them be used?
All just gives you all the messages to consume. Context is All but you also get the Context<TMessage> if you need it. Selected allows you to accept or reject messages before it gets to your consumer. For<T> is primarily for Sagas, I don't think there's a good use case for it outside of that.
Starting off, just using All is likely the right answer.