I'm using a DDD/CQRS/ES approach and I have some questions about modeling my aggregate(s) and queries. As an example consider the following scenario:
A User can create a WorkItem, change its title and associate other users to it. A WorkItem has participants (associated users) and a participant can add Actions to a WorkItem. Participants can execute Actions.
Let's just assume that Users are already created and I only need userIds.
I have the following WorkItem commands:
CreateWorkItem
ChangeTitle
AddParticipant
AddAction
ExecuteAction
These commands must be idempotent, so I cant add twice the same user or action.
And the following query:
WorkItemDetails (all info for a work item)
Queries are updated by handlers that handle domain events raised by WorkItem aggregate(s) (after they're persisted in the EventStore). All these events contain the WorkItemId. I would like to be able to rebuild the queries on the fly, if needed, by loading all the relevant events and processing them in sequence. This is because my users usually won't access WorkItems created one year ago, so I don't need to have these queries processed. So when I fetch a query that doesn't exist, I could rebuild it and store it in a key/value store with a TTL.
Domain events have an aggregateId (used as the event streamId and shard key) and a sequenceId (used as the eventId within an event stream).
So my first attempt was to create a large Aggregate called WorkItem that had a collection of participants and a collection of actions. Participant and Actions are entities that live only within a WorkItem. A participant references a userId and an action references a participantId. They can have more information, but it's not relevant for this exercise. With this solution my large WorkItem aggregate can ensure that the commands are idempotent because I can validate that I don't add duplicate participants or actions, and if I want to rebuild the WorkItemDetails query, I just load/process all the events for a given WorkItemId.
This works fine because since I only have one aggregate, the WorkItemId can be the aggregateId, so when I rebuild the query I just load all events for a given WorkItemId.
However, this solution has the performance issues of a large Aggregate (why load all participants and actions to process a ChangeTitle command?).
So my next attempt is to have different aggregates, all with the same WorkItemId as a property but only the WorkItem aggregate has it as an aggregateId. This fixes the performance issues, I can update the query because all events contain the WorkItemId but now my problem is that I can't rebuild it from scratch because I don't know the aggregateIds for the other aggregates, so I can't load their event streams and process them. They have a WorkItemId property but that's not their real aggregateId. Also I can't guarantee that I process events sequentially, because each aggregate will have its own event stream, but I'm not sure if that's a real problem.
Another solution I can think of is to have a dedicated event stream to consolidate all WorkItem events raised by the multiple aggregates. So I could have event handlers that simply append the events fired by the Participant and Actions to an event stream whose id would be something like "{workItemId}:allevents". This would be used only to rebuild the WorkItemDetails query. This sounds like an hack.. basically I'm creating an "aggregate" that has no business operations.
What other solutions do I have? Is it uncommon to rebuild queries on the fly? Can it be done when events for multiple aggregates (multiple event streams) are used to build the same query? I've searched for this scenario and haven't found anything useful. I feel like I'm missing something that should be very obvious, but I haven't figured what.
Any help on this is very much appreciated.
Thanks
I don't think you should design your aggregates with querying concerns in mind. The Read side is here for that.
On the domain side, focus on consistency concerns (how small can the aggregate be and the domain still remain consistent in a single transaction), concurrency (how big can it be and not suffer concurrent access problems / race conditions ?) and performance (would we load thousands of objects in memory just to perform a simple command ? -- exactly what you were asking).
I don't see anything wrong with on-demand read models. It's basically the same as reading from a live stream, except you re-create the stream when you need it. However, this might be quite a lot of work for not an extraordinary gain, because most of the time, entities are queried just after they are modified. If on-demand becomes "basically every time the entity changes", you might as well subscribe to live changes. As for "old" views, the definition of "old" is that they are not modified any more, so they don't need to be recalculated anyways, regardless of if you have an on-demand or continuous system.
If you go the multiple small aggregates route and your Read Model needs information from several sources to update itself, you have a couple of options :
Enrich emitted events with additional data
Read from multiple event streams and consolidate their data to build the read model. No magic here, the Read side needs to know which aggregates are involved in a particular projection. You could also query other Read Models if you know they are up-to-date and will give you just the data you need.
See CQRS events do not contain details needed for updating read model
Related
When and who should be responsible for caching data into local data store from API GET requests in DDD architecture with CQS based use cases?
First thing that comes to mind:
Initiate a Query to get some data from local data store and if it is empty, fetch required data from API -> cache it into local data store -> return it
This solution does not seem to follow CQS correctly because Queries should not alter data store (or can they?).
Second thing that comes to mind:
Execute a Command to fetch fresh data from API -> update data store -> raise a data updated event -> event handler listens for data updated events and executes new Query to get fresh data
Second solution seems to follow CQS pattern better, tho I am not sure if any of these solutions are by any means correct way of handling data caching in CQS based architecture.
The first option isn't, to my mind, any "bigger" of a violation of CQS/CQRS as the second. The query isn't altering authoritative state (e.g. a DDD aggregate), it's just copying it into a cache. It does require cache invalidation.
The second is questionable because a query results in a command (it's sometimes reasonable to treat a query as a read-only command (provided the query limits itself to a single aggregate) in order to have a stronger consistency guarantee).
A better approach to my mind is to have a hybrid of the two:
Queries are served from the cache if possible
When commands result in updates to aggregates, events are published (ideally listing the aggregates which changed)
Event handler listens for update events and invalidates cache based on which aggregates have changed.
A further evolution of this would be event sourcing, where the updates are the events and the queries only get served by a read model fed by the events.
I have domain concept of Product.
Product have some GeneralDetails, lets say: sku, name, description.
At the same time, Product have some ProductCalculations part where accountants can put different values like purchasePrice, stockLevelExpenses, wholeSalesPrice, retailPrice.
So, so far, Product would look something like:
class Product{
GeneralDetails Details;
ProductCalculations Calculations;
ChangeDetails(GeneralDetails details){}
Recalculate(ProductCalculations calculations{}
}
This setup would make Product an aggregate root. But now, i want to split it in a way that Product manager can input/update product details but then that accountant can step in and intependently change calculations for given product without concurrency issues.
That would suggest splitting it into 2 separate aggregate roots.
But then, deleting ProductDetails aggregate must mean deleting ProductCalculations too and it should happen in transactional way.
Assuming they are 2 aggregate roots, meaning they have 2 separate repositories with corresponding Delete methods, how to implement this as an atomic transaction?
The only thing i can think about is to raise event when ProductDetails gets deleted, have a handler (DomainService) that uses some special repository that handles transactions over multiple aggregate roots.
Is there some problem with that approach and/or is there some better way to handle it?
PS.
I cannot allow eventual consistency when ProductDetails is deleted.
PS2.
Based on comments from #Jon, Details and Calculations create&delete should be synced in a way that when Details are created/deleted, Calculations should also be created/deleted.
On the other hand, their updates should be completely independent.
I think the answer to your question depends somewhat on what data storage technology you're using and your data storage model, because if you can push operation transactionality to the data layer, things get much easier.
If you're using a document-oriented database (Cosmos DB, MongoDB, etc...), I would model and store your Product aggregate (including Details and Calculations) as a single document and you get the atomic transaction and concurrency checking for free from the database.
If you must store these as separate documents/records in your data store, then providing atomic transactions and concurrency checking becomes your concern. For years folks (especially those using Entity Framework) have been using the Unit of Work pattern to batch up multiple repository operations and submit them to the database as a single operation (EF-specific UoW implementation). Rob Conery suggests here that a better option is to use Command objects to encapsulate a multi-part operation that needs to be executed as a single transaction.
In any event, I would encourage you to keep the management of this operation within Product, so that consumers of Product are unaware of what's going on during the save - they just blissfully call product.SaveAsync() and they don't need to know whether that's causing one record update or ten. As long as Product is injected with the repositories it needs to get the job done, there's no need to have a separate domain service to coordinate this operation. There's nothing wrong with Product listening for events that its children raise and responding appropriately.
Hope this helps!
" I cannot allow eventual consistency when ProductDetails is deleted"
Why not? What would be the business cost of having Inventory.Product exist while Finance.Product doesn't or vice-versa?
"but then that accountant can step in and intependently change calculations for given product"
That's pretty much what eventual consistency is, no?
If you really can't have eventual consistency then use a domain service to create/delete two distinct aggregate roots in a single transaction, but ask yourself how you are going to do that if the information is not entirely provided by the same end user?
I agree with #plalx in almost every point. However I want to do my bit to the discussion.
I've found that there is usually a very little cost in creating two or more related aggregates inside a single transaction (inside a single bounded context). After all, if those aggregates don't exist yet there cannot be a concurrency conflict, there is no contention and no much difference. Furher, you don't need to deal with partially created state (thinking that state is split between aggregates). It is possible to do that using eventual consistency, and there are situations where that is a better approach, but most of the time there is no great benefit. Even Vernon in his book Implementing Domain-Driven Design mentions this use case as "valid reason to break the rules".
Deleting more than one aggregate is a different story. What should happen if you delete and aggregate that another user is updating at the same time? The probability of such a conflict increases as more aggregates you try to modify/delete in the same transaction. Is there always an upstream/downstream relationship between those aggregates? I mean, if an user deletes A and B must be also deleted, have the user that is updating B no "power" or "voice" to cancel that deletion since she is providing more information to the state of the aggregate?
Those are a very tricky questions and most of the time it is something you need to discuss with a domain expert, and there are very few real scenarios when the answer is something you can't afford with eventual consistency. I discovered that in many cases is preferable to put a "flag" marking the aggregate as "inactive", notifying that will be deleted after some period of time. If no user with enough permission request that aggregate to become active again, then it gets deleted. That helped users to not kill themselves when they delete some aggregate by mistake.
You've mentioned that you don't want a user to spend hours modifying one aggregate if there is a deletion, but that is something that a transaction doesn't contribute much. This is very dependent in the whole architecture, though. That user could have loaded the aggregate into her own memory space and then a deletion occurs. It doesn't matter if you delete inside a transaction, the user is still wasting time. A better solution could be to publish a domain event that triggers some sort of push notification to the user, so she knows that a deletion happened and can stop working (or request a cancellation of that deletion, if you follow such approach).
For the reports and calculations, there are many cases when those "scripts" can skip records where the sibling aggregate is gone, so users doesn't notice there is a missing part or there is no complete consistency yet.
If for some reason you still need to delete several aggregates in the same transaction you just start a transaction in an application service and use repositories to perform the deletion, analogous to the creation case.
So, to summarize:
The rule of "modify one aggregate per transaction" is not that important when there is a creation of aggregates.
Deletion of many aggregates works quite well (most of the time) with eventual consistency, and very often just disabling those aggregates, one at a time, is better than performing the deletion immediately.
Preventing an user from wasting time is better achieved with proper notifications than transactions.
If there is a real need to perform those actions inside a single transaction, then manage that transaction in the application an be explicit. Using a domain service to perform all the required operations (except for the transaction that is mostly an application concern) brings that logic back to the domain layer.
I am looking for a good, up to date and "decision helping" explanation on how to choose a NoSQL database engine for storing all the events in a CQRS designed application.
I am currently a newcomer to all things around NoSQL (but learning): please be clear and do not hesitate to explain your point of view in an (almost too much) precise manner. This post may deserve other newcomers like me.
This database will:
Be able to insert 2 to 10 rows per updates asked by the front view (in my case, updates are frequent). Think of thousand of updates per minute, how would it scale?
Critically need to be consistent and failure safe, since events are the source of truth of the application
Not need any link between entities (like RDBMS does) except maybe a user ID/GUID (I don't know if it's critical or needed yet)
Receive events containing 3 to 10 "columns" (a sequence ID, an event name, a datetime, a JSON/binary encoded parameter bag, some context informations..). Without orientating your point of view in a column-oriented type of database, it may be document-oriented if it fits all other requirements
Be used as a queue or sent to/read from an external AMQP system like RabbitMQ or ZeroMQ (didn't worked that part yet, if you could also argument/explain..) since view projections will be built upon events
Need some kind of filtering by sequence ID like SELECT * FROM events WHERE sequence_id > last_sequence_id for subscribers (or queue systems) to be able to synchronize from a given point
I heard of HBase for CQRS event storing, but maybe MongoDB could fit? Or even Elasticsearch (would not bet on that one..)? I'm also open to RDBMS for consistency and availability.. but what about the partition tolerance part..?
Really I'm lost, I need arguments to make a pertinent choice.
https://geteventstore.com/ is a database designed specifically for event streams.
They take consistency and reliability of the source of truth (your events) very seriously and I use it myself to read/write thousands of events a second.
I have a working, in production implementation of MongoDB as an Event store. It is used by a CQRS + Event sourcing web based CRM application.
In order to provide 100% transaction-less but transaction-like guarantee for persisting multiple events in one go (all events or none of them) I use a MongoDB document as an events commit, with events as nested documents. As you know, MongoDB has document level locking.
For concurrency I use optimistic locking, using a version property for each Aggregate steam. An Aggregate stream is identified by the dublet (Aggregate class x Aggregate ID).
The event store also stores the commits in relative order using a sequence on each commit, incremented on each commit, protected using optimistic locking.
Each commit contains the following:
aggregateId : string, probably a GUID,
aggregateClass: string,
version: integer, incremented for each aggregateId x aggregateClass,
sequence, integer, incremented for each commit,
createdAt: UTCDateTime,
authenticatedUserId: string or null,
events: list of EventWithMetadata,
Each EventWithMetadata contains the event class/type and the payload as string (the serialized version of the actual event).
The MongoDB collection has the following indexes:
aggregateId, aggregateClass, version as unique
events.eventClass, sequence
sequence
other indexes for query optimization
These indexes are used to enforce the general event store rules (no events are stored for the same version of an Aggregate) and for query optimizations (the client can select only certain events - by type - from all streams).
You could use sharding by aggregateId to scale, if you strip the global ordering of events (the sequence property) and you move that responsibility to an event publisher but this complicates things as the event publisher needs to stay synchronized (even in case of failure!) with the event store. I recommend to do it only if you need it.
Benchmarks for this implementation (on Intel I7 with 8GB of RAM):
total aggregate write time was: 7.99, speed: 12516 events wrote per second
total aggregate read time was: 1.43, speed: 35036 events read per second
total read-model read time was: 3.26, speed: 30679 events read per second
I've noticed that MongoDB was slow on counting the number of events in the event store. I don't know why but I don't care as I don't need this feature.
I recommend using MongoDB as an event store.
I have an .NET Core event sourcing implementation project https://github.com/jacqueskang/EventSourcing
I started with relational database (SQL Server and MySQL) using entity framework core.
Then moved to AWS so I wrote a DynamoDB extension.
My experience is that relational DB can do the job perfectly but it depends on requirement and your technical stack. If your project is cloud based then the best option might probably be cloud provider's no-sql database like AWS DynamoDB or Azure CosmosDB, which are powerful in proformance and provide additional features (e.g. DynamoDB can trigger a notification or lambda function)
My CouchDB view indexes are being created slower than I would like. Writing the documents is not such a problem but the users can edit them offline and then bulk update, which seems to slow things right down.
This answer helped but I was just wondering is it better to separate out various views into different design documents (eg1) or to store them all in one (eg2).
Eg. 1
_design/posts/_view/id
_design/comments/_view/id
_design/tags/_view/id
Eg.2
_design/webresources/_view/_id?key="posts"
_design/webresources/_view/_id?key="comments"
_design/webresources/_view/_id?key="tags"
*This example is just for illustration purposes. I am only concerned with the time it takes to build the indexes.
You will gain better performance if you read often. Couchdb views are updated and build at read time. So you can can read the view every time the document updates to keep it hot*.
Or maybe listen to the changes feed and keep a track of documents updated. Once they reach a certain threshold value read a view.
Another option is use stale parameter.
If stale=ok is set, CouchDB will not refresh the view even if it is stale, the benefit is a an improved query latency. If stale=update_after is set, CouchDB will update the view after the stale result is returned
Every design document is a separate erlang process. So separating your views across different design documents will cause them to be built concurrently. However each view will still be built in a blocking manner. That is the two views across different design documents can start updating at the same time but the time it takes to update the individual views will be the same as if they were in the same design document.
*You don't necessarily have to care about the result. Our goal here is to trick couchdb to update the view. So you can fire off a request in a separate async process and be done with it.
In DDD, an aggregate root can have a repository. Let us take an Order aggregate and it's non-persistant counterpart OrderRepository and persistent counterpart OrderUoW. We have also ProductVariant aggregate which tracks the inventory of the products in the order. It can have a ProductVariantRepository and ProductVariantUoW.
The way the Order and the ProductVariant work is that before the order is persisted, the inventory is checked. If there is inventory, the order will be persisted by calling OrderUoW.Commit(). Yes, the ProductVariantUoW.Commit() will be called next to update the inventory of the products.
UNFORTUNATELY things can go bad, a user bought the same products in that short time (Consider this as a web app where two users are buying the same products). Now the whole transaction for the second user should fail by reverting the order that just created. Should I call the OrderUoW to rollback the changes (the order should be deleted from the db)? Or should I put both UoW.Commit() operations in a transaction scope, so failing of one commit() will rollback the changes? Or both the repositories (Order, ProductVariant) should have only UoW and it needs to have only one transaction scope?
I may be able to make the story short by saying, how the transaction is handled where there are multiple repositories involved?
A question we could ask is who is doing the following:
The way the Order and the ProductVariant work is that before the order
is persisted, the inventory is checked. If there is inventory, the
order will be persisted by calling OrderUoW.Commit(). Yes, the
ProductVariantUoW.Commit() will be called next to update the inventory
of the products.
Some argue that this kind of work belongs in the service layer, which allows the service layer to put things crossing aggregate objects into a single transaction.
According to http://www.infoq.com/articles/ddd-in-practice:
Some developers prefer managing the transactions in the DAO classes
which is a poor design. This results in too fine-grained transaction
control which doesn't give the flexibility of managing the use cases
where the transactions span multiple domain objects. Service classes
should handle transactions; this way even if the transaction spans
multiple domain objects, the service class can manage the transaction
since in most of the use cases the Service class handles the control
flow.
I think as an alternative to using a single transaction, you can claim the inventory using ProductVariant, and, if all the inventory items necessary are available then you can commit the order. Otherwise (i.e. you can't claim all the products you need for the order) you have to return the inventory that was successfully claimed using compensating transactions. The results it that in the case of unsuccessfull commit of an order, some of the inventory will temporarily appear unavailable for other orders, but the advantage is that you can work without a distributed transaction.
None the less, this logic still belongs in the service layer, not the DAO classes.
The way you are using unit of work seems a bit fine-grained. Just in case you haven't read Martin Fowler's take: http://martinfowler.com/eaaCatalog/unitOfWork.html
That being said you want to handle the transaction at the use-case level. The fact that the inventory is checked up-front is simply a convenience (UX) and the stock level should be checked when persisting the various bits also. An exception can be raised for insufficient stock.
The transaction isolation level should be set such that the two 'simultaneous' parts are performed serially. So whichever one gets to update the stock levels first is going to 'win'. The second will then raise the exception.
If you can use a single UoW then do so, because it's easier.
If your repositories are on different DBs (or maybe one is file-based and the others are not) then you may be forced to use multiple UoWs, but then you're writing roll-back commands too, because if UoW1 saves changes to SqlRepo OK, but then UoW2 fails to save changes to FileRepo then you need to rollback SqlRepo. Don't bother writing all that rollback command stuff if you can avoid it!