I was trying to understanding ES+CQRS and tech stack can be used.
As per my understanding flow should be as below.
UI sends a request to Controller(HTTP Adapter)
Controller calls application service by passing Request Object as parameter.
Application Service creates Command from Request Object passed from controller.
Application Service pass this Command to Message Consumer.
Message Consumer publish Command to message broker(RabbitMQ)
Two Subscriber will be listening for above command
a. One subscriber will generate Aggregate from eventStore using command
and will apply command than generated event will be stored in event store.
b. Another subscriber will be at VIEW end,that will populate data in view database/cache.
Kindly suggest my understanding is correct.
Kindly suggest my understanding is correct
I think you've gotten a bit tangled in your middleware.
As a rule, CQRS means that the writes happen to one data model, and reads in another. So the views aren't watching commands, they are watching the book of record.
So in the subscriber that actually processes the command, the command handler will load the current state from the book of record into memory, update the copy in memory according to the domain model, and then replace the state in the book of record with the updated version.
Having update the book of record, we can now trigger a refresh of the data model that backs the view; no business logic is run here, this is purely a transform of the data from the model we use for writes to the model we use for reads.
When we add event sourcing, this pattern is the same -- the distinction is that the data model we use for writes is a history of events.
How atomicity is achieved in writing data in event store and writing data in VIEW Model?
It's not -- we don't try to make those two actions atomic.
how do we handle if event is stored in EventStrore but System got crashed before we send event in Message Queue
The key idea is to realize that we typically build new views by reading events out of the event store; not by reading the events out of the message queue. The events in the queue just tell us that an update is available. In the absence of events appearing in the message queue, we can still poll the event store watching for updates.
Therefore, if the event store is unreachable, you just leave the stale copy of the view in place, and wait for the system to recover.
If the event store is reachable, but the message queue isn't, then you update the view (if necessary) on some predetermined schedule.
This is where the eventual consistency part comes in. Given a successful write into the event store, we are promising that the effects of that write will be visible in a finite amount of time.
Related
I have a situation where data is coming from a third party service. It is being passed through to a function that formats the data and then saves it to a view model in a way that I can visualize for my system.
In an Event driven approach, should I save the payload of the request (as this can easily be repayable) in the Event stream, or the formatted changes it produces to the view model (a more accurate representation of the current state of the data)?
Or something else completely?
Thanks
The incoming data can be viewed as a command expressing the intent to ultimately update some state. In this case the command is from outside our system, but commands can also be internal to our system. Especially for external commands, one critical thing to remember is that a command can be rejected.
In event sourcing, however, events are internal and express that the change has occurred and cannot be denied (at most it can be ignored). Thus it's probably best to store them in the format that is the most convenient for that internal use.
I would characterize the requests as commands and the formatted changes as events. Saving the payload is command sourcing, saving the formatted changes is event sourcing (confusingly, Fowler's earliest descriptions of event sourcing are more like command sourcing) and both are valid approaches. Event sourcing tends to imply a commitment to replay to a similar state while command sourcing leaves open the ability for replay to depend on something in the outside world. I've seen (and developed even) applications which used both techniques (e.g. incoming data is dumped to Kafka, a consumer treats those messages as commands against aggregates whose state is persisted as a stream of events, which gets projected back into Kafka).
If you (in CQRS/ES fashion) consider the read-side of your application to be a separate autonomous component from the write-side, then you reach the interesting conclusion that when the write-side publishes events, from the read-side's perspective it's publishing commands to the read-side. "One component's events are often another component's commands".
I am trying to make sense of which one should be called before and which one later between wl_display_dispatch and wl_display_roundtrip. I have seen both order so wondering which one is correct.
1st order:
wl_display_get_registry(display); wl_registry_add_listener() // this call is just informational
wl_display_dispatch();
wl_display_roundtrip();
what i think : wl_display_dispatch() will read and dispatch events from display fd, whatever is sent by server but in between server might be still processing requests and for brief time fd might be empty.
wl_display_dispatch returns assuming all events are dispatched. Then wl_display_roundtrip() is called and will block until server has processed all request and put then in event queue. So after this, event queue still has pending events, but there is no call to wl_display_dispatch(). How those pending events will be dispatched ? Is that wl_display_dispatch() wait for server to process all events and then dispatch all events?
2nd order:
wl_display_get_registry(display); wl_registry_add_listener() // this call is just informational
wl_display_roundtrip();
wl_display_dispatch();
In this case, wl_display_roundtrip() wait for server to process all events and put them in event queue, So once this return we can assume all events sent from server are available in queue. Then wl_display_dispatch() is called which will dispatch all pending events.
Order 2nd looks correct and logical to me, as there is no chance of leftover pending events in queue. but I have seen Order 1st in may places including in weston client examples code so I am confused whats the correct order of calling.
It would be great if someone could clarify here.
Thanks in advance
2nd order is correct.
client can't do much without getting proxy(handle for global object). what i mean is client can send request by binding to the global object advertised by server so for this client has to block until all global object are bind in registry listener callback.
for example for client to create surface you need to bind wl_compositor interface then to shell interface to give role and then shm(for share memory) and so on.wl_display_dispatch cannot guaranty all the events are processed if your lucky it may dispatch all events too but cannot guarantee every-time. so you should use wl_display_roundtrip for registry at-least.
As to my understanding, in event sourcing, events are recorded. However that would also mean a state changed first happened and thereafter we record the event. For example, assuming:
A Client sends a command to a server to "Create user".
The server validates the command and creates user i.e. stores new
user in a database.
The server then logs/stores a Created User event. i.e event
sourcing.
Created User event is propagated to subscribers
In the scenario above, how do we handle scenarios where step (2) succeeded but step (3) failed due to say network failures, database offline etc? The whole system would be in an indeterminate state now that there was a new user created but the event was never logged. How do we mitigate these types of failures? Or are the steps that I've listed above not the way to do event sourcing?
Thanks!
This is not what happens exactly in Event sourcing, not even in plain CQRS.
In Event sourcing, after the command is validated, the domain events are generated by the source (the Aggregate in DDD) and then they are appended to the Event store in the first step. After that the subscribers (read models, projections, Sagas, external systems) receive and process the new domain events.
In CQRS, after the domain events are generated, they are applied to the Aggregate and then the Aggregate's state and the new events are persisted in the same local transaction, as the first step. Only after that the subscribers receive the events.
So you see? Your situation cannot happen: steps 2 and 3 are persisted atomically, they succeed or fail together.
We are using microservices, cqrs, event store using nodejs cqrs-domain, everything works like a charm and the typical flow goes like:
REST->2. Service->3. Command validation->4. Command->5. aggregate->6. event->7. eventstore(transactional Data)->8. returns aggregate with aggregate ID-> 9. store in microservice local DB(essentially the read DB)-> 10. Publish Event to the Queue
The problem with the flow above is that since the transactional data save i.e. persistence to the event store and storage to the microservice's read data happen in a different transaction context if there is any failure at step 9 how should i handle the event which has already been propagated to the event store and the aggregate which has already been updated?
Any suggestions would be highly appreciated.
The problem with the flow above is that since the transactional data save i.e. persistence to the event store and storage to the microservice's read data happen in a different transaction context if there is any failure at step 9 how should i handle the event which has already been propagated to the event store and the aggregate which has already been updated?
You retry it later.
The "book of record" is the event store. The downstream views (the "published events", the read models) are derived from the book of record. They are typically behind the book of record in time (eventual consistency) and are not typically synchronized with each other.
So you might have, at some point in time, 105 events written to the book of record, but only 100 published to the queue, and a representation in your service database constructed from only 98.
Updating a view is typically done in one of two ways. You can, of course, start with a brand new representation and replay all of the events into it as part of each update. Alternatively, you track in the metadata of the view how far along in the event history you have already gotten, and use that information to determine where the next read of the event history begins.
Inside your event store, you could track whether read-side replication was successful.
As soon as step 9 suceeds, you can flag the event as 'replicated'.
That way, you could introduce a component watching for unreplicated events and trigger step 9. You could also track whether the replication failed multiple times.
Updating the read-side (step 9) and flagigng an event as replicated should happen consistently. You could use a saga pattern here.
I think i have now understood it to a better extent.
The Aggregate would still be created, answer is that all the validations for any type of consistency should happen before my aggregate is constructed, it is in case of a failure beyond the purview of the code that a failure exists while updating the read side DB of the microservice which needs to be handled.
So in an ideal case aggregate would be created however the event associated would remain as undispatched unless all the read dependencies are updated, if not it remains as undispatched and that can be handled seperately.
The Event Store will still have all the event and the eventual consistency this way is maintained as is.
I'm developing small CQRS+ES framework and develop applications with it. In my system, I should log some action of the client and use it for analytics, statistics and maybe in the future do something in domain with it. For example, client (on web) download some resource(s) and I need save date, time, type (download, partial,...), from region or country (maybe IP), etc. after that in some view client can see count of download or some complex report. I'm not sure how to implement this feather.
First solution creates analytic context and some aggregate, in each client action send some command like IncreaseDownloadCounter(resourced) them handle the command and raise domain event's and updating view, but in this scenario first download occurred and after that, I send command so this is not really command and on other side version conflict increase.
The second solution is raising event, from client side and update the view model base on it, but in this type of handling my event not store in event store because it's not raise by command and never change any domain context. If is store it in event store, no aggregate to handle it after fetch for some other use.
Third solution is raising event, from client side and I store it on other database may be for each type of event have special table, but in this manner of event handle I have multiple event storage with different schema and difficult on recreating view models and trace events for recreating contexts states so in future if I add some domain for use this type of event's it's difficult to use events.
What is the best approach and solution for this scenario?
First solution creates analytic context and some aggregate
Unquestionably the wrong answer; the event has already happened, so it is too late for the domain model to complain.
What you have is a stream of events. Putting them in the same event store that you use for your aggregate event streams is fine. Putting them in a separate store is also fine. So you are going to need some other constraint to make a good choice.
Typically, reads vastly outnumber writes, so one concern might be that these events are going to saturate the domain store. That might push you towards storing these events separately from your data model (prior art: we typically keep the business data in our persistent book of record, but the sequence of http requests received by the server is typically written instead to a log...)
If you are supporting an operational view, push on the requirement that the state be recovered after a restart. You might be able to get by with building your view off of an in memory model of the event counts, and use something more practical for the representations of the events.
Thanks for your complete answer, so I should create something like the ES schema without some field (aggregate name or type, version, etc.) and collect client event in that repository, some offline process read and update read model or create command to do something on domain space.
Something like that, yes. If the view for the client doesn't actually require any validation by your model at all, then building the read model from the externally provided events is fine.
Are you recommending save some claim or authorization token of the user and sender app for validation in another process?
Maybe, maybe not. The token describes the authority of the event; our own event handler is the authority for the command(s) that is/are derived from the events. It's an interesting question that probably requires more context -- I'd suggest you open a new question on that point.