So I have a background in working with event sourcing and microservices. And usually the best way to enforce bounded context yet be able to make your aggregate communicate is to have either some kind of Choreography or some kind of Orchestration.
In a Choreography Aggregate A will raise eventA that Aggregate B will listen to and handle and then after doing whatever needed to be done, it will raise eventB and A will listen and handle it and proceed. It's effective, respects event-sourcing and DDD rules.
In an Orchestration, Aggregate A will raise eventA that the orchestrator O will listen and handle and then issue a command B to Aggregate B which in return will run what's needed and raise Event B, orchestrator O will handle that event and issue a command A and so on... It adds a level of complexity but it's great for an added level of segregation, also this way Aggregate A and B are not listening/handling each other events.
Obvs these 2 methods have their own pros and cons, but both work perfectly in a microservice context.
The issue I'm facing is that for me there is no context. I'm working with AWS lambdas, whenever an event is pushed to the store I will have a lambda listening to db(event store) changes and then do something. It was working perfectly until I needed to add a second aggregate.
And now to achieve a choreography or an orchestration, I either need a context(which is not a thing for lambdas) and an event bus, or I need to add a lambda for every event, that would lead for total chaos.
Like if Aggregate A needs something from Agg B before continuing its flow it will push an event to the event store and I will have to handle the event with a new lambda so for every type of interaction between Agg A and Agg B, I will need 2 lambdas.
Maybe I'm missing something, after all I'm new in AWS lambdas and more used to working with microservices.
Perhaps what you're after is a Process Manager:
(…) a process manager is a class that coordinates the behavior of the aggregates in the domain. A process manager subscribes to the events that the aggregates raise, and then follow a simple set of rules to determine which command or commands to send. The process manager does not contain any business logic; it simply contains logic to determine the next command to send. The process manager is implemented as a state machine, so when it responds to an event, it can change its internal state in addition to sending a new command.
The above definition can be found in Microsoft's Exploring CQRS and Event Sourcing. This is a way of having orchestration in an event-driven system. The original definition, AFAIK, can be found in Gregor Hohpe's Enterprise Integration Patterns.
In AWS land, you'd have your lambda(s) reacting to those events and firing off commands (either via a command bus, if you have such concept in your system, or by directly invoking other lambdas).
Related
Problem Statement
Informal State
We have some scenarios where the integration layer (a combination of AWS SNS/SQS components and etc.) is also responsible for the data distribution to target systems. Those are mostly async flows. In this case, we send a confirmation to a caller that we have received the data and will take a responsibility for the data delivery. Here, although the data is not originated from the integration layer we are still holding it and need to make sure that the data is not lost, for example, if the consumers are down or if messages, on-error, are sent to the DLQs and hence being automatically deleted after the retention period.
Solution Design
Currently my idea was to proceed with a back-up of the SQS/DLQ queues based upon CloudWatch configured alerts using ApproximateAgeOfOldestMessage metric with some applied threshold (something like the below):
Msg Expiration Event if ApproximateAgeOfOldestMessage / Message retention > Threshold
Now, more I go forward with this idea and more I doubt that this might be actually the right approach…
In particular, I would like to build something unobtrusive that can be "attached" to our SQS queues and dump the messages that are about to expire in some repository, like for example the AWS S3. Then have a procedure to recover the messages from S3 to the same original queue.
The above procedure contains many challenges like: message identification and consumption (receive message is not design to "query" for specific messages), message dump in the repository with a reference to the source queue, etc. which would suggest to me that the above approach might be a complex over-kill.
That being said, I'm aware of other "alternatives" (such as this) but I would appreciate if you could answer to the specific technical details described above, without trying to challenge the "need" instead.
Similar to Mark B's suggestion, you can use the SQS extended client (https://github.com/awslabs/amazon-sqs-java-extended-client-lib) to send all your messages through S3 (which is a configuration knob: https://github.com/awslabs/amazon-sqs-java-extended-client-lib/blob/master/src/main/java/com/amazon/sqs/javamessaging/ExtendedClientConfiguration.java#L189).
The extended client is a drop-in replacement for the AmazonSQS interface so it minimizes the intrusion on business logic - usually it's a matter of just changing your dependency injection.
I am looking into building a simple solution where producer services push events to a message queue and then have a streaming service make those available through gRPC streaming API.
Cloud Pub/Sub seems well suited for the job however scaling the streaming service means that each copy of that service would need to create its own subscription and delete it before scaling down and that seems unnecessarily complicated and not what the platform was intended for.
On the other hand Kafka seems to work well for something like this but I'd like to avoid having to manage the underlying platform itself and instead leverage the cloud infrastructure.
I should also mention that the reason for having a streaming API is to allow for streaming towards a frontend (who may not have access to the underlying infrastructure)
Is there a better way to go about doing something like this with the GCP platform without going the route of deploying and managing my own infrastructure?
If you essentially want ephemeral subscriptions, then there are a few things you can set on the Subscription object when you create a subscription:
Set the expiration_policy to a smaller duration. When a subscriber is not receiving messages for that time period, the subscription will be deleted. The tradeoff is that if your subscriber is down due to a transient issue that lasts longer than this period, then the subscription will be deleted. By default, the expiration is 31 days. You can set this as low as 1 day. For pull subscribers, the subscribers simply need to stop issuing requests to Cloud Pub/Sub for the timer on their expiration to start. For push subscriptions, the timer starts based on when no messages are successfully delivered to the endpoint. Therefore, if no messages are published or if the endpoint is returning an error for all pushed messages, the timer is in effect.
Reduce the value of message_retention_duration. This is the time period for which messages are kept in the event a subscriber is not receiving messages and acking them. By default, this is 7 days. You can set it as low as 10 minutes. The tradeoff is that if your subscriber disconnects or gets behind in processing messages by more than this duration, messages older than that will be deleted and the subscriber will not see them.
Subscribers that cleanly shut down could probably just call DeleteSubscription themselves so that the subscription goes away immediately, but for ones that shut down unexpectedly, setting these two properties will minimize the time for which the subscription continues to exist and the number of messages (that will never get delivered) that will be retained.
Keep in mind that Cloud Pub/Sub quotas limit one to 10,000 subscriptions per topic and per project. Therefore, if a lot of subscriptions are created and either active or not cleaned up (manually, or automatically after expiration_policy's ttl has passed), then new subscriptions may not be able to be created.
I think your original idea was better than ephemeral subscriptions tbh. I mean it works, but it feels totally unnatural. Depending on what your requirements are. For example, do clients only need to receive messages while they're connected or do they all need to get all messages?
Only While Connected
Your original idea was better imo. What I probably would have done is to create a gRPC stream service that clients could connect to. The implementation is essentially an observer pattern. The consumer will receive a message and then iterate through the subscribers to do a "Send" to all of them. From there, any time a client connects to the service, it just registers itself with that observer collection and unregisters when it disconnects. Horizontal scaling is passive since clients are sticky to whatever instance they've connected to.
Everyone always get the message, if eventually
The concept is similar to the above but the client doesn't implicitly un-register from the observer on disconnect. Instead, it would register and un-register explicitly (through a method/command designed to do so). Modify the 'on disconnected' logic to tell the observer list that the client has gone offline. Then the consumer's broadcast logic is slightly different. Now it iterates through the list and says "if online, then send, else queue", and send the message to a ephemeral queue (that belongs to the client). Then your 'on connect' logic will send all messages that are in queue to the client before informing the consumer that it's back online. Basically an inbox. Setting up ephemeral, self-deleting queues is really easy in most products like RabbitMQ. I think you'll have to do a bit of managing whether or not it's ok to delete a queue though. For example, never delete the queue unless the client explicitly unsubscribes or has been inactive for so long. Fail to do that, and the whole inbox idea falls apart.
The selected answer above is most similar to what I'm subscribing here in that the subscription is the queue. If I did this, then I'd probably implement it as an internal bus instead of an observer (since it would be unnecessary) - You create a consumer on demand for a connecting client that literally just forwards the message. The message consumer subscribes and unsubscribes based on whether or not the client is connected. As Kamal noted, you'll run into problems if your scale exceeds the maximum number of subscriptions allowed by pubsub. If you find yourself in that position, then you can unshackle that constraint by implementing the pattern above. It's basically the same pattern but you shift the responsibility over to your infra where the only constraint is your own resources.
gRPC makes this mechanism pretty easy. Alternatively, for web, if you're on a Microsoft stack, then SignalR makes this pretty easy too. Clients connect to the hub, and you can publish to all connected clients. The consumer pattern here remains mostly the same, but you don't have to implement the observer pattern by hand.
(note: arrows in diagram are in the direction of dependency, not data flow)
I am implementing a process in my AWS based hosting business with an event driven architecture on AWS SNS. This is largely a learning experience with a new architecture, programming and hosting paradigm for me.
I have considered AWS Step functions, but have decided to implement a Message Bus with AWS SNS topic(s), because I want to understand the underlying event driven programming model.
Nearly all actions are performed by lambda functions and steps are coupled via SNS and/or SQS.
I am undecided if to implement the process with one or many SNS topics and if I should subscribe the core logic to the message bus(es) with one or many lambda functions.
One or many message buses
My core process currently consist of 9 events which of which 2 sets of 2 can be parallel, the remaining 4 are sequential. Subscribing these all to the same message bus is easier to set up, but requires each lambda function to check if the message is relevant to it, which seems like a waste of resources.
On the other hand I could have 6 message buses and be sure that a notified resource has something to do with the message.
One or many lambda functions
If all lambda functions are subscribed to the same message bus, it may be easier to package them all up with a dispatcher function in a single lambda function. It would also reduce the amount of code to upload to lambda, albeit I don't have to pay for that.
On the other hand I would loose the ability to control the timeout for the lambda function and any changes to the order of events is now dependent on the dispatcher code.
I would still have the ability to scale each process part, as any parts that contain repeating elements are seperated by SQS queues.
You should always emit each type of message to it's own topic, as this allows other services to consume these events without tightly coupling the two services.
Likewise, each worker that wants to consume messages should have it's own queue with it's own subscription to the topic.
Doing the following allows you to add new message consumers for a given event without having to modify the upstream service. Furthermore, responsibility over each component is clear - the service producing messages to a topic owns that topic (and the message format), whereas the consumer owns its queue and event handling semantics.
Your consumer can specify a message filter when subscribing to a topic, so it can only receive messages it cares about (documentation).
For example, a process that sends a customer survey after the customer has received their order would subscribe its queue to the Order Status Changed event with the filter set to only receive events where the new_status field is equal to shipment-received).
The above reflects principles of Service-Oriented architecture - and there's plenty of good material out there elaborating the points above.
I have a problem When I use two or more pools in the same process diagram in Camunda process model, the simulation does not work well. In particular, it seems that tokens do not pass through the message flow and so the two pools work separately and not as well as a collaboration diagram.
How can I do to simulate a collaboration diagram?
Thanks
In Camunda, you need to implement a message throw event like you would implement a service task, that means by providing code that delivers the message. There is no default delivery of messages. The engine ignores message connections in a collaboration diagram.
Resources to read:
https://docs.camunda.org/manual/7.6/reference/bpmn20/events/message-events/#message-api
https://docs.camunda.org/manual/7.6/reference/bpmn20/events/message-events/#message-intermediate-throwing-event
I am wondering how to handle this specific case.
I have two ClientService that I want to provide to to a web application. By clientService, I mean client API that calls some external rest service. So we are in spray client here.
The thing is, one of the two service can be quite intensive and time consuming but less frequently called than the other one, which will be quicker but with very frequent calls.
I was thinking of having two dispatchers for the two clientService. Let's say we have the query API (ClientService1) and the classification API (ClientService2)
Both service shall indeed be based on the same actor system. So in other words, I would like to have two dispatcher in my actor system, then pass them to spray via the client-level api, for instance pipeline.
Is it feasible, scalable and appropriate?
What would you recommend instead to use one dispatcher but with a bigger thread pool?
Also, how can I obtain a dispatcher?
Should I create a threadpool executor myself and get a dispatcher out
of it?
How do I get an actor system to load/create multiple dispatcher, and
how to retrieve them such that to pass them to the pipeline method?
I know how to create an actor with a specific dispatcher, there are example for that, but that is a different scenario. I would not like to have lower than the client level API by the way
Edit
I have found that the system.dispatchers.lookup method can create one. So that should do.
However the thing that is not clear is related to AKK.IO/SPRAY.IO.
The manager IO(HTTP): it is not clear to me on which dispatcher it runs or if it can be configured.
Moreover, let's say I pass a different execution context to the pipeline method. What happens? I will still have IO(HTTP) running on the default execution context or its own (I don't know how it is done internally) ? Also what exactly will be ran on the execution context that I pass ? (in other words, which actors)