we are migrating from a legacy monolith application to a microservice architecture. we use CQRS and event sourcing pattern and message broker (rabbitmq) for communication mechanism. now We are facing a challenge about how can convert the old database to new architecture and how can use event sourcing for these? Assuming the old database did not have events, can we do the data conversion without creating events? what is the start point of our old database data in the event sourcing pattern?
One important thing to remember is that many databases internally event source: every write goes to a log and that log is used to update tables, replicate etc., after which the log is truncated. It's equivalent to event sourcing with a lot of snapshots and very little retention of events and old snapshots.
In these databases (which include the likes of Postgres, MySQL, Oracle, SQL Server, Cassandra, CosmosDB, to name ones I know from experience do this), there's a technique called Change Data Capture which essentially taps into the log and exposes a stream of changes to the database which can be treated as events from the database (or by extension as commands: "one service's events are another service's commands"). Debezium can be used to write CDC records to Kafka; for RabbitMQ you may need to roll something yourself, in which case you'll want to get acquainted with how CDC is exposed in your database.
Even if the database doesn't support CDC, if the data isn't that large, you can often turn it into an ersatz event stream by periodically dumping its data (if the records are timestamped, this can even work if the data is particularly slow moving) and implementing a service to track what changed: this won't tell you about changes that netted out, but it's often better than nothing. This sort of dump is also likely to be required if you need a "genesis" event to ensure that your initial state is current to when you moved to event-sourcing or CDC.
This whole broad family of techniques has limitations compared to full event sourcing: reifying what changed is not as valuable as reifying what changed and why it changed. But it can be a useful middle ground in migrating to event-sourcing.
By referring #alexey-zimarev's answer at this post, it's essential to have the starting event in your event sourced database. You can not configure an event-sourced aggregate without replaying its events. Therefore, you need to map the legacy snapshot to an individual domain event of your relevant aggregate.
Either the way, considering event souring definition by Martin Fowler:
The fundamental idea of Event Sourcing is that of ensuring every
change to the state of an application is captured in an event object,
and that these event objects are themselves stored in the sequence
they were applied for the same lifetime as the application state
itself.
So that, it's not an appropriate solution to migrate legacy snapshots into the newer one without extracting and storing domain events. It will turn your event-sourced project into a semi-event-sourced project which is not considered as a paradigm to design and develop.
You have an event store that is a database for events. you can create event data that you need for the old database and insert into the event store. After that, do event replaying for creating read models.
Related
I have state stored aggregates in a PostgreSQL database. I'm testing replay by deleting the state stored tables and the token_entry table and restarting the application. All events will be replayed and aggregates are restored in-memory. However, my state stored tables stay empty. I was thinking that they would also be restored?
I'm using SpringBoot and latest Axon. The code, at this moment, is as simple as it can get.
Whenever you're using Axon's State-Stored Aggregates, they'll only be stored as-is. Hence, throwing away the stored instances and starting the application will not trigger a replay.
When removing TrackingTokens, or initiating a replay by invoking StreamingEventProcessor#resetTokens (this is the recommended approach, by the way), you're effectively telling the Event Processors of your application to start event streaming from scratch.
This part of Axon Framework supports the so-called Query Side of CQRS. The Aggregate support in Axon Framework is specifically for the Command Side of an application.
Long story short, State-Stored Aggregates don't have replay support. If you want your Aggregates (Command Models) to be replayed, you will have to use Event Sourcing Aggregates instead.
I hope the above clarifies a little about the misconception between Token and Aggregates from your question. And by the way, if you feel Axon's Reference Guide should be adjusted to clarify your situation, you're always free to file an issue.
We have several services that publishes and subscribes to Domain Events. What we usually do is log events whenever we publish and log events whenever we process events. We basically use this to apply choreography pattern.
We are not doing Event Sourcing in these systems, and there's no programmatic use for them after publishing/processing. That's the main driver we opted not to store these in a durable container, like a database or event store.
Question is, are we missing some fundamental thing by doing this?
Is storing Events a must?
I consider queued messages as system messages, even if they represent some domain event in an event-driven architecture (pub/sub messaging).
There is absolutely no hard-and-fast rule about their storage. If you would like to keep them around you could have your messaging mechanism forward them to some auditing endpoint for storage and then remove them after some time (if necessary).
You are not missing anything fundamental by not storing them.
You're definitely not missing out on anything (but there is a catch) especially if that's not a need by the business. An Event-Sourced System would definitely store all the events generated by the system into a database (or any other event-store)
The main use of an event store is to be able to restore the state of the system to the current state in case of a failure by replaying messages. To make this process of recovery faster we have snapshots.
In your case since these events are just are only relevant until the process is completed, it would not make sense to store them until you have a failure. (this is the catch) especially in a Distributed Transaction case scenario.
What I would suggest?
Don't store the event themselves but log the relevant details about these events and maybe use an ELK stack or Grafana to store these logs.
Use either the Saga Pattern or the Routing Slip pattern in case of a Distributed Transaction and log them as well.
In case a failure occurs while processing an event, put that event into an exception queue and handle it. If it's a part of a distributed transaction make sure either they all have the same TransactionId or they have a CorrelationId so you can lookup for logs and save your system.
For reliably performing your business transactions in a distributed archicture you somehow need to make sure that your events are published at least once.
So a service that publishes events needs to persist such an event within the same transaction that causes it to get created.
Considering you are publishing an event via infrastructure services (e.g. a messaging service) you can not rely on it being available all the time.
Also, your own service instance could go down after persisting your newly created or changed aggregate but before it had the chance to publish the event via, for instance, a messaging service.
Question is, are we missing some fundamental thing by doing this? Is storing Events a must?
It doesn't matter that you are not doing event sourcing. Unless it is okay from the business perspective to sometimes lose an event forever you need to temporarily persist your event with your local transaction until it got published.
You can look into the Transactional Outbox Pattern to achieve reliable event publishing.
Note: Logging/tracking your events somehow for monitoring or later analyzing/reporting purpose is a different thing and has another motivation.
I've been aware of event sourcing, CQRS, DDD and micro services for a little while and I'm now at that point where I want to try and start implementing stuff and giving something a go.
I've been looking into the technical side of CQRS and I understand the DDD concepts in there. How both the write side handles commands from the UI and publishes events from it, and how the read side handles events and creates projections on them.
The difficulty I'm having is the communication & a handling events from service-to-service (both from a write to read service and between micro services).
So I want to focus on eventstore (this one: https://eventstore.com/ to be less ambiguous). This is what I want to use as I understand it is a perfect for event sourcing and the simple nature of storing the events means I can use this for a message bus as well.
So my issue falls into two questions:
Between the write and the read, in order for the read side to receive/fetch the events created from the write side, am i right in thinking something like a catch up subscription can be used to subscribe to a stream to receive any events written to it or do i use something like polling to fetch events from a given point?
Between micro services, I am having an even harder time... So when looking at CQRS tutorials/talks etc... they always seem to talk with an example of an isolated service which receives commands from the UI/API. This is fine. I understand the write side will have an API attached to it so the user can interact with it to perform commands. E.g. create a customer. However... say if I have two micro services, e.g. a order micro service and an shipping micro service, how does the shipping micro service get the events published from the order micro service. Specifically, how does those customer events, translate to commands for the shipping service.
So let's take a simple example of: - Command created from the order's API to place an order. - A OrderPlacedEvent is published to the event store. How does the shipping service listen and react to this is it need to then DispatchOrder and create ain turn an OrderDispatchedEvent.
Does the write side of the shipping microservice then need to poll or also have a catch up subscription to the order stream? If so how does an event get translated to an command using DDD approach?
something like a catch up subscription can be used to subscribe to a stream to receive any events written to it
Yes, using catch-up subscriptions is the right way of doing it. You need to keep the stream position of your subscription persisted somewhere as well.
Here you can find some sample code that works. I am not posting the whole snippet since it is too long.
The projection service startup flow is:
Load the checkpoint (first time ever it would be the stream start)
Subscribe to the stream from that checkpoint
The runtime flow will then be:
The subscription will then call the function you provide when it receives an event. There's some plumbing there to do, like if you subscribe to $all, you need to filter out system events (it will be easier in the next version of Event Store)
Project the event
Store the new checkpoint
If you make your projections idempotent, you can store the checkpoint from time to time and save some IO.
how does the shipping micro service get the events published from the order micro service
When you build a brand new system and you have a small team working on all the components, you can make a shortcut and subscribe to domain events from another service, as you'd do with projections. Within the integration context (between the boxes), ordering should not be important so you can use persistent subscriptions so you won't need to think about checkpoints. Event Store will do it for you.
Be aware that it introduces tight coupling on the domain event schema of the originating service. Your contexts will have the Partnership relationship or the downstream service will be a Conformist.
When you move forward with your system, you might decide to decouple those contexts properly. So, you introduce a stable event API for the service that publishes events for others to consume. The same subscription that you used for integration can now instead take care of translating domain (internal) events to integration (external) events. The consuming context would then use the stable API and the domain model of the upstream service will be free in iterating on their domain model, as soon as they keep the conversion up-to-date.
It won't be necessary to use Event Store for the downstream context, they could just as well use a message broker. Integration events usually don't need to be persisted due to their transient nature.
We are running a webinar series about Event Sourcing at Event Store, check our web site to get on-demand access to previous webinars and you might find interesting to join future ones.
The difficulty I'm having is the communication & a handling events from service-to-service (both from a write to read service and between micro services).
The difficulty is not your fault - the DDD literature is really weak when it comes to discussing the plumbing.
Greg Young discusses some of the issues of subscription in the latter part of his Polygot Data talk.
Eventide Project has documentation that does a decent job of explaining the principles behind how the plumbing fits things together.
Between micro services, I am having an even harder time...
The basic idea: your message store is fundamentally a database; when the host of your microservice wakes up, it queries the message store for messages after some checkpoint, and then feeds them to your domain logic (updating its own local copy of the checkpoint as needed).
So the host pulls a document with events in it from the store, and transforms that document into a stream of handle(Event) commands that ultimately get passed to your domain component.
Put another way, you build a host that polls the database for information, parses the response, and then passes the parsed data to the domain model, and writes its own checkpoints.
I have a REST service - all its requests are persisted to its own relational database. So far, good. But, there is also a small business functionality (email notification, sms alert) that should be run on the newly received/updated data. For this process to work on data in background, it requires some way to know about the persisted data - a message queue would fix the problem. Three common ways I see designing this,
The REST service inserts into the database, also, publish to the queue, too.
The problem here is, distributed transaction - combining different types within one transaction - relational database & the queue. Some tools may support, some may not.
As usual REST service persists only to its database. Additionally it also inserts the data into another table to which a scheduled job queries, publishes them to queue (from which the background job should start its work).
The problem I see is the scheduler - not reactive, batchprocessing, limited by the time slot, not realtime, slow and others.
The REST endpoint publishes the data directly to a topic. A consumer persists it to the database, whereas another process it in the background.
Something like eventsourcing. TMU, it is bit complex to implement as the number of services grow. Also, if the db is down, the persistent service would fail to save the data, however the background service (say, the emailer) would send email which is functionaly wrong. This may lead to inconsistency among the services, also functional.
I have also thought of reading database transaction-logs, but it seems more complex, requires tools to configurations to make it work, also, it seems right for data processing systems than for our use case.
What's your thought on this - did I miss anything? How do you manage such scenarios? What should be looked for? Thinking reactive, say Vertx?
Apologies if this looks very naive, but I have to ask.
I think best approach is 2 with a CDC(change data capture) system like debezium.
See [https://microservices.io/patterns/data/transactional-outbox.html][1]
I usually recommend option 3 if you don't need immediate read after write consistency. Background job should retry if the database record is still not updated by the message it processes.
Your post exemplifies why queues shouldn't be used for these types of scenarios. They are good for delivering analytical data or logs, but for task orchestration developers have to reinvent the wheel every time.
The much better approach is to use a task orchestration system like Cadence Workflow that eliminates issues you described and makes multi-service orchestration much simpler.
See this presentation that explains the Cadence programming model.
I've been studying a lot of the common ways that developers design/architect an application on domain driven design (Still trying to understand the concept as a whole). Some of the examples that I saw included the use of events via an event aggregator. I liked the concept because it truly keeps the different elements/domains of an application decoupled.
A concern that I have is: how do you rollback an operation in the case of an error?
For example:
Say I have an order application that has to save an order to the database and also save a copy of the order as a pdf to a CMS. The application fires an event that a new order has been created and the pdf service that subscribes to this event saves the pdf. Meanwhile when committing the order changes to the database an exception is thrown. The problem is that the pdf has been saved but their isn't a matching database record.
Should I cache the previously handled events and fire a new error event that looks to the cache for "undo" operations? Use something like the command pattern for this?
Or... is the event aggregator not a good pattern for this.
Edit
I'm starting to think that maybe events should be used for less "mission critical" items, such as emailing and logging.
My initial thought was to limit dependencies by using the event aggregator pattern.
You want the event to be committed in the same transaction as the operation on your database.
In this particular scenario, you can push the event on a queue, which enlists in your transaction, so that the event will never go out unless the aggregate is persisted. This will make creating the PDF eventual consistent; if creating the PDF fails, you can fix the problem, and have it automatically retried.
Maybe you can get more inspiration in one of my previous posts on eventual consistent domain events with RavenDB and IronMQ.
Handling an event before it actually happened (committed) only works if the event handler participates in the transaction. Make the event handler transactional (for instance by storing the PDF in a database), or publish and handle events after the transaction committed.