I read through the Lagom documentation, and already wrote a few small services that interact with each other. But because this is my first foray into CQRS i still have a few conceptual issues about the persistent read side that i don't really understand.
For instance, i have a user-service that keeps a list of users (as aggregates) and their profile data like email addresses, names, addresses, etc.
The questions i have now are
if i want to retrieve the users profile given a certain email-address, should i query the read side for the users id, and then query the event-store using this id for the profile data? or should the read side already keep all profile information?
If the read side has all information, what is the reason for the event-store? If its truly write-only, it's not really useful is it?
Should i design my system that i can use the event-store as much as possible or should i have a read side for everything? what are the scalability implications?
if the user-model changes (for instance, the profile now includes a description of the profile) and i use a read-side that contains all profile data, how do i update this read side in lagom to now also contain this description?
Following that question, should i keep different read-side tables for different fields of the profile instead of one table containing the whole profile
if a different service needs access to the data, should it always ask the user-service, or should it keep its own read side as needed? In case of the latter, doesn't that violate the CQRS principle that the service that owns the data should be the only one reading and writing that data?
As you can see, this whole concept hasn't really 'clicked' yet, and i am thankful for answers and/or some pointers.
if i want to retrieve the users profile given a certain email-address, should i query the read side for the users id, and then query the event-store using this id for the profile data? or should the read side already keep all profile information?
You should use a specially designed ReadModel for searching profiles using the email address. You should query the Event-store only to rehydrate the Aggregates, and you rehydrate the Aggregates only to send them commands, not queries. In CQRS an Aggregate may not be queried.
If the read side has all information, what is the reason for the event-store? If its truly write-only, it's not really useful is it?
The Event-store is the source of truth for the write side (Aggregates). It is used to rehydrate the Aggregates (they rebuild their internal & private state based on the previous emitted events) before the process commands and to persist the new events. So the Event-store is append-only but also used to read the event-stream (the events emitted by an Aggregate instance). The Event-store ensures that an Aggregate instance (that is, identified by a type and an ID) processes only a command at a time.
if the user-model changes (for instance, the profile now includes a description of the profile) and i use a read-side that contains all profile data, how do i update this read side in lagom to now also contain this description?
I don't use any other framework but my own but I guess that you rewrite (to use the new added field on the events) and rebuild the ReadModel.
Following that question, should i keep different read-side tables for different fields of the profile instead of one table containing the whole profile
You should have a separate ReadModel (with its own table(s)) for each use case. The ReadModel should be blazing fast, this means it should be as small as possible, only with the fields needed for that particular use case. This is very important, it is one of the main benefits of using CQRS.
if a different service needs access to the data, should it always ask the user-service, or should it keep its own read side as needed? In case of the latter, doesn't that violate the CQRS principle that the service that owns the data should be the only one reading and writing that data?
Here depends on you, the architect. It is preferred that each ReadModel owns its data, that is, it should subscribe to the right events, it should not depend on other ReadModels. But this leads to a lot of code duplication. In my experience I've seen a desire to have some canonical ReadModels that own some data but also can share it on demand. For this, in CQRS, there is also the term query. Just like commands and events, queries can travel in your system, but only from ReadModel to ReadModel.
Queries should not be sent during a client's request. They should be sent only in the background, as an asynchronous synchronization mechanism. This is an important aspect that influences the resilience and responsiveness of your system.
I've use also live queries, that are pushed from the authoritative ReadModels to the subscribed ReadModels in real time, when the answer changes.
In case of the latter, doesn't that violate the CQRS principle that the service that owns the data should be the only one reading and writing that data?
No, it does not. CQRS does not specify how the R (Read side) is updated, only that the R should not process commands and C should not be queried.
Related
Being a developer with solid experience, i am only entering the world of microservices and event-driven architecture. Things like loose coupling, independent scalability and proper implementation of asynchronous business processes is something that i feel should get simplified as compared with traditional monolith approach. So giving it a try, making a simple PoC for myself.
I am considering making a simple application where user can register, login and change the customer details. However, i want to react on certain events asynchronously:
customer logs in - we send them an email, if the IP address used is new to the system.
customer changes their name, we send them an email notifying of the change.
The idea is to make a separate application that reacts on "CustomerLoggedIn", "CustomerChangeName" events.
Here i can think of three approaches, how to implement this simple functionality, with each of them having some drawbacks. So, when a customer submits their name change:
Store change name Changed name is stored in the DB + an event is sent to Kafkas when the DB transaction is completed. One of the big problems that arise here is that if a customer had 2 tabs open and almost simultaneously submits a change from initial name "Bob" to "Alice" in one tab and from "Bob" to "Jim" in another one, on a database level one of the updates overwrites the other, which is ok, however we cannot guarantee the order of the events to be the same. We can use some checks to ensure that DB update is only done when "the last version" has been seen, thus preventing the second update at all, so only one event will be emitted. But in general case, this pattern will not allow us to preserve the same order of events in the DB as in Kafka, unless we do DB change + Kafka event sending in one distributed transaction, which is anti-pattern afaik.
Change the name in the DB, and use Debezium or similar DB CDC to capture the event and stream it. Here we get a single event source, so ordering problem is solved, however what bothers me is that i lose the ability to enrich the events with business information. Another related drawback is that CDC will stream all the updates in the "customer" table regardless of the business meaning of the event. So, in this case, i will probably need to build a Kafka Streams application to convert the DB CDC events to business events and decouple the DB structure from event structure. The potential benefit of this approach is that i will be able to capture "direct" DB changes in the same manner as those originated in the application.
Emit event from the application, without storing it in the DB. One of the subscribers might to the DB persistence, another will do email sending, etc. The biggest problem i see here is - what do i return to the client? I cannot say "Ok, your name is changed", it's more like "Ok, you request has been recorded and will be processed". In case if the customer quickly hits refresh - he expects to see his new name, as we don't want to explain to the customers what's eventual consistency, do we? Also the order of processing the same event by "email sender" and "db updater" is not guaranteed, so i can send an email before the change is persisted.
I am looking for advices regarding any of these three approaches (and maybe some others i am missing), maybe the usecases when one can be preferrable over others?
It sounds to me like you want event sourcing. In event sourcing, all you need to store is the event: the current state of a customer is derived from replaying the events (either from the beginning of time, or since a snapshot: the snapshot is just an optional optimization). Some other process (there are a few ways to go about this) can then project the events to Kafka for consumption by interested parties. Since every event has a sequence number, you can use the sequence number to prevent concurrent modification (alternatively, the more actor modely event-sourcing implementations can use techniques like cluster sharding in Akka to achieve the same ends).
Doing this, you can have a "write-side" which processes the updates in a strongly consistent manner and can respond to queries which only involve a single customer having seen every update to that point (the consistency boundary basically makes customer in this case an aggregate in domain-driven-design terms). "Read-sides" consuming events are eventually consistent: the latencies are typically fairly short: in this case your services sending emails are read-sides (as would be a hypothetical panel showing names of all customers), but the customer's view of their own data could be served by the write-side.
(The separation into read-sides and write-side (the pluralization is significant) is Command Query Responsibility Segregation, which sometimes gets interpreted as "reads can only be served by a read-side". This is not totally accurate: for one thing a write-side's model needs to be read in order for the write-side to perform its task of validating commands and synchronizing updates, so nearly any CQRS-using project violates that interpretation. CQRS should instead be interpreted as "serve reads from the model that makes the most sense and avoid overcomplicating a model (including that model in the write-side) to support a new read".)
I think I qualify to answer this, having extensively used debezium for simplifying the architecture.
I would prefer Option 2:
Every transaction always results in an event emitted in correct order
Option 1/3 has a corner case, what if transaction succeeds, but application fails to emit the event?
To your point:
Another related drawback is that CDC will stream all the updates in
the "customer" table regardless of the business meaning of the event.
So, in this case, i will probably need to build a Kafka Streams
application to convert the DB CDC events to business events and
decouple the DB structure from event structure.
I really dont think that is a roadblock. The benefit you get is potentially other usecases may crop up where another consumer to this topic may want to read other columns of the table.
Option 1 and 3 are only going to tie this to your core application logic, and that is not doing any favor from simplifying PoV. With option 2, with zero code changes to core application APIs, a developer can independently work on the events, with no need to understand that core logic.
In CQRS when we need to create a custom-tailored projections for our read-models, we usually prefer a "denormalized" projections (assume we are talking about projecting onto a DB). It is not uncommon to have the information need by the application/UI come from different aggregates (possibly from different BCs).
Imagine we need a projected table to contain customer's information together with her full address and that Customer and Address are different aggregates in our system (possibly in different BCs). Meaning that, addresses are generated and maintained independently of customers. Or, in other words, when a new customer is created, there is no guarantee that there will be an AddressCreatedEvent subsequently produced by the system, this event may have already been processed prior to the creation of the customer. All we have at the time of CreateCustomerCommand is an UUID of an existing address.
We have several solutions here.
Enrich CreateCustomerCommand and the subsequent CustomerCreatedEvent to contain full address of the customer (looking up this information on the fly from the UI or the controller). This way the projection handler will just update the table directly upon receiving CustomerCreatedEvent.
Use the addrUuid provided in CustomerCreatedEvent to perform an ad-hoc query in the projection handler to get the missing part of the address information before updating the table.
These are commonly discussed solution to this problem. However, as noted by many others, there are problems with each approach. Enriching events can be difficult to justify as well described by Enrico Massone in this question, for example. Querying other views/projections (kind of JOINs) will work but introduces coupling (see the same link).
I would like describe another method here, which, as I believe, nicely addresses these concerns. I apologize beforehand for not giving a proper credit if this is a known technique. Sincerely, I have not seen it described elsewhere (at least not as explicitly).
"A picture speaks a thousand words", as they say:
The idea is that :
We keep CreateCustomerCommand and CustomerCreatedEvent simple with only addrUuid attribute (no enriching).
In API controller we send two commands to the command handler (aggregates): the first one, as usual, - CreateCustomerCommand to create customer and project customer information together with addrUuid to the table leaving other columns (full address, etc.) empty for time being. (Warning: See the update, we may have concurrency issue here and need to issue the probe command from a Saga.)
Right after this, and after we have obtained custUuid of the newly created customer, we issue a special ProbeAddrressCommand to Address aggregate triggering an AddressProbedEvent which will encapsulate the full state of the address together with the special attribute probeInitiatorUuid which is, of course our custUuid from the previous command.
The projection handler will then act upon AddressProbedEvent by simply filling in the missing pieces of the information in the table looking up the required row by matching the provided probeInitiatorUuid (i.e. custUuid) and addrUuid.
So we have two phases: create Customer and probe for the related Address. They are depicted in the diagram with (1) and (2) correspondingly.
Obviously, we can send as many such "probe" commands (in parallel) as needed by our projection: ProbeBillingCommand, ProbePreferencesCommand, etc. effectively populating or "filling in" the denormalized projection with missing data from each handled "probe" event.
The advantages of this method is that we keep the commands/events in the first phase simple (only UUIDs to other aggregates) all the while avoiding synchronous coupling (joining) of the projections. The whole approach has a nice EDA feeling about it.
My question is then: is this a known technique? Seems like I have not seen this... And what can go wrong with this approach?
I would be more then happy to update this question with any references to other sources which describe this method.
UPDATE 1:
There is one significant flaw with this approach that I can see already: command ProbeAddrressCommand cannot be issued before the projection handler had a chance to process CustomerCreatedEvent. But this is impossible to know from the API gateway (or controller).
The solution would probably involve a Saga, say CustomerAddressJoinProjectionSaga with will start upon receiving CustomerCreatedEvent and which will only then issue ProbeAddrressCommand. The Saga will end upon registering AddressProbedEvent. Or, if many other aggregates are involved in probing, when all such events have been received.
So here is the updated diagram.
UPDATE 2:
As noted by Levi Ramsey (see answer below) my example is rather convoluted with respect to the choice of aggregates. Indeed, Customer and Address are often conceptualized as belonging together (same Aggregate Root). So it is a better illustration of the problem to think of something like Student and Course instead, assuming for the sake of simplicity that there is a straightforward relation between the two: a student is taking a course. This way it is more obvious that Student and Course are independent aggregates (students and courses can be created and maintained at different times and different places in the system).
But the question still remains: how can we obtain a projection containing the full information about a student (full name, etc.) and the courses she is registered for (title, credits, the instructor's full name, prerequisites, etc.) all in the same table, if the UI requires it ?
A couple of thoughts:
I question why address needs to be a separate aggregate much less in a different bounded context, in view of the requirement that customers have an address. If in some other bounded context customer addresses are meaningful (e.g. you want to know "which addresses have more customers" etc.), then that context can subscribe to the events from the customer service.
As an alternative, if there's a particularly strong reason to model addresses separately from customers, why not have the read side prospectively listen for events from the address aggregate and store the latest address for a given address UUID in case there's a customer who ends up with that address. The reliability per unit effort of that approach is likely to be somewhat greater, I would expect.
I'm having a hard time understanding the shape of the state that's derived applying that entity's events vs a projection of that entity's data.
Is an Aggregate's state ONLY used for determining whether or not a command can successfully be applied? Or should that state be usable in other ways?
An example - I have a Post entity for a standard blog post. I might have events like postCreated, postPublished, postUnpublished, etc. For my projections that I'll be persisting in my read tables, I need a projection for the base posts (which will include all posts, regardless of status, with lots of detail) as well as published_posts projection (which will only represent posts that are currently published with only the information necessary for rendering.
In the situation above, is my aggregate state ONLY supposed to be used to determine, for example, if a post can be published or unpublished, etc? If this is the case, is the shape of my state within the aggregate purely defined by what's required for these validations? For example, in my base post projection, I want to have a list of all users that have made a change to the post. In terms of validation for the aggregate/commands, I couldn't care less about the list of users that have made changes. Does that mean that this list should not be a part of my state within my aggregate?
TL;DR: yes - limit the "state" in the aggregate to that data that you choose to cache in support of data change.
In my aggregates, I distinguish two different ideas:
the history , aka the sequence of events that describes the changes in the lifetime of the aggregate
the cache, aka the data values we tuck away because querying the event history every time kind of sucks.
There's not a lot of value in caching results that we are never going to use.
One of the underlying lessons of CQRS is that we don't need aggregates everywhere
An AGGREGATE is a cluster of associated objects that we treat as a unit for the purpose of data changes. -- Evans, 2003
If we aren't changing the data, then we can safely work directly with immutable copies of the data.
The only essential purpose of the aggregate is to determine what events, if any, need to be applied to bring the aggregate's state in line with a command (if the aggregate can be brought so in line). All state that's not needed for that purpose can be offloaded to a read-side, which can be thought of as a remix of the event stream (with each read-side only maintaining the state it needs).
That said, there are in practice, reasons to use the aggregate state directly, with the primary one being a desire for a stronger consistency for the aggregate: CQRS is inherently eventually consistent. As with all questions of consistent updates, it's important to recognize that consistency isn't free and very often isn't even cheap; I tend to think of a project as having a consistency budget and I'm pretty miserly about spending it.
In your case, there's probably no reason to include the list of users changing a post in the aggregate state, unless e.g. there's something like "no single user can modify a given post more than n times".
My team and I we are refactoring a REST-API and I have come to a question.
For terms of brevity, let us assume that we have an SQL database with 4 tables: Teachers, Students, Courses and Classrooms.
Right now all the relations between the items are represented in the REST-API through referencing the URL of the related item. For example for a course we could have the following
{ "id":"Course1", "teacher": "http://server.com/teacher1", ... }
In addition, if ask a list of courses thought a call GET call to /courses, I get a list of references as shown below:
{
... //pagination details
"items": [
{"href": "http://server1.com/course1"},
{"href": "http://server1.com/course2"}...
]
}
All this is nice and clean but if I want a list of all the courses titles with the teachers' names and I have 2000 courses and 500 teachers I have to do the following:
Approximately 2500 queries just to read the data.
Implement the join between the teachers and courses
Optimize with caching etc, so that I will do it as fast as possible.
My problem is that this method creates a lot of network traffic with thousands of REST-API calls and that I have to re-implement the natural join that the database would do way more efficiently.
Colleagues say that this is approach is the standard way of implementing a REST-API but then a relatively simple query becomes a big hassle.
My question therefore is:
1. Is it wrong if we we nest the teacher information in the courses.
2. Should the listing of items e.g. GET /courses return a list of references or a list of items?
Edit: After some research I would say the model I have in mind corresponds mainly to the one shown in jsonapi.org. Is this a good approach?
My problem is that this method creates a lot of network traffic with thousands of REST-API calls and that I have to re-implement the natural join that the database would do way more efficiently. Colleagues say that this is approach is the standard way of implementing a REST-API but then a relatively simple query becomes a big hassle.
Your colleagues have lost the plot.
Here's your heuristic - how would you support this use case on a web site?
You would probably do it by defining a new web page, that produces the report you need. You'd run the query, you the result set to generate a bunch of HTML, and ta-da! The client has the information that they need in a standardized representation.
A REST-API is the same thing, with more emphasis on machine readability. Create a new document, with a schema so that your clients can understand the semantics of the document you return to them, tell the clients how to find the target uri for the document, and voila.
Creating new resources to handle new use cases is the normal approach to REST.
Yes, I totally think you should design something similar to jsonapi.org. As a rule of thumb, I would say "prefer a solution that requires less network calls". It's especially true if amount of network calls will be less by order of magnitude.
Of course it doesn't eliminate the need to limit the request/response size if it becomes unreasonable.
Real life solutions must have a proper balance. Clean API is nice as long as it works.
So in your case I would so something like:
GET /courses?include=teachers
Or
GET /courses?includeTeacher=true
Or
GET /courses?includeTeacher=brief|full
In the last one the response can have only the teacher's id for brief and full teacher details for full.
My problem is that this method creates a lot of network traffic with thousands of REST-API calls and that I have to re-implement the natural join that the database would do way more efficiently. Colleagues say that this is approach is the standard way of implementing a REST-API but then a relatively simple query becomes a big hassle.
Have you actually measured the overhead generated by each request? If not, how do you know that the overhead will be too intense? From an object-oriented programmers perspective it may sound bad to perform each call on their own, your design, however, lacks one important asset which helped the Web to grew to its current size: caching.
Caching can occur on multiple levels. You can do it on the API level or the client might do something or an intermediary server might do it. Fielding even mad it a constraint of REST! So, if you want to comply to the REST architecture philosophy you should also support caching of responses. Caching helps to reduce the number of requests having to be calculated or even processed by a single server. With the help of stateless communication you might even introduce a multitude of servers that all perform calculations for billions of requests that act as one cohesive system to the client. An intermediary cache may further help to reduce the number of requests that actually reach the server significantly.
A URI as a whole (including any path, matrix or query parameters) is actually a key for a cache. Upon receiving a GET request, i.e., an application checks whether its current cache already contains a stored response for that URI and returns the stored response on behalf of the server directly to the client if the stored data is "fresh enough". If the stored data already exceeded the freshness threshold it will throw away the stored data and route the request to the next hop in line (might be the actual server, might be a further intermediary).
Spotting resources that are ideal for caching might not be easy at times, though the majority of data doesn't change that quickly to completely neglect caching at all. Thus, it should be, at least, of general interest to introduce caching, especially the more traffic your API produces.
While certain media-types such as HAL JSON, jsonapi, ... allow you to embed content gathered from related resources into the response, embedding content has some potential drawbacks such as:
Utilization of the cache might be low due to mixing data that changes quickly with data that is more static
Server might calculate data the client wont need
One server calculates the whole response
If related resources are only linked to instead of directly embedded, a client for sure has to fire off a further request to obtain that data, though it actually is more likely to get (partly) served by a cache which, as mentioned a couple times now throughout the post, reduces the workload on the server. Besides that, a positive side effect could be that you gain more insights into what the clients are actually interested in (if an intermediary cache is run by you i.e.).
Is it wrong if we we nest the teacher information in the courses.
It is not wrong, but it might not be ideal as explained above
Should the listing of items e.g. GET /courses return a list of references or a list of items?
It depends. There is no right or wrong.
As REST is just a generalization of the interaction model used in the Web, basically the same concepts apply to REST as well. Depending on the size of the "item" it might be beneficial to return a short summary of the items content and add a link to the item. Similar things are done in the Web as well. For a list of students enrolled in a course this might be the name and its matriculation number and the link further details of that student could be asked for accompanied by a link-relation name that give the actual link some semantical context which a client can use to decide whether invoking such URI makes sense or not.
Such link-relation names are either standardized by IANA, common approaches such as Dublin Core or schema.org or custom extensions as defined in RFC 8288 (Web Linking). For the above mentioned list of students enrolled in a course you could i.e. make use of the about relation name to hint a client that further information on the current item can be found by following the link. If you want to enable pagination the usage of first, next, prev and last can and probably should be used as well and so forth.
This is actually what HATEOAS is all about. Linking data together and giving them meaningful relation names to span a kind of semantic net between resources. By simply embedding things into a response such semantic graphs might be harder to build and maintain.
In the end it basically boils down to implementation choice whether you want to embed or reference resources. I hope, I could shed some light on the usefulness of caching and the benefits it could yield, especially on large-scale systems, as well as on the benefit of providing link-relation names for URIs, that enhance the semantical context of relations used within your API.
From what I read aggregates must only contain properties which are used to protect their invariants.
I also read sagas can be aggregates which makes sense to me.
Now I modeled a registration process using a saga: on RegistrationStarted event it sends a ReserveEmail command which will trigger an EmailReserved or EmailReservationFailed given if the email is free or not. A listener will then either send a validation link or a message telling an account already exists.
I would like to use data from the RegistrationStarted event in this listener (say the IP and user-agent). How should I do it?
Storing these data in the saga? But they’re not used to protect invariants.
Pushing them through ReserveEmail command and the resulting event? Sounds tedious.
Project the saga to the read model? What about eventual consistency?
Another way?
Rinat Abdullin wrote a good overview of sagas / process managers.
The usual answer is that the saga has copies of the events that it cares about, and uses the information in those events to compute the command messages to send.
List[Command] processManager(List[Event] events)
Pushing them through ReserveEmail command and the resulting event?
Yes, that's the usual approach; we get a list [RegistrationStarted], and we use that to calculate the result [ReserveEmail]. Later on, we'll get [RegistrationStarted, EmailReserved], and we can use that to compute the next set of commands (if any).
Sounds tedious.
The data has to travel between the two capabilities somehow. So you are either copying the data from one message to another, or you are copying a correlation identifier from one message to another and then allowing the consumer to decide how to use the correlation identifier to fetch a copy of the data.
Storing these data in the saga? But they’re not used to protect invariants.
You are typically going to be storing events in the sagas (to keep track of what has happened). That gives you a copy of the data provided in the event. You don't have an invariant to protect because you are just caching a copy of a decision made somewhere else. You won't usually have the process manager running queries to collect additional data.
What about eventual consistency?
By their nature, sagas are always going to be "eventually consistent"; the "state" of an instance of a saga is just cached copies of data controlled elsewhere. The data is probably nanoseconds old by the time the saga sees it, there's no point in pretending that the data is "now".
If I understand correctly I could model my saga as a Registration aggregate storing all the events whose correlation identifier is its own identifier?
Udi Dahan, writing about CQRS:
Here’s the strongest indication I can give you to know that you’re doing CQRS correctly: Your aggregate roots are sagas.