I’m a beginner at Camunda/BPMN and I want to use it to control what is going on in nodejs, mostly likely using a REST API, at least for now. (Unless folks have a better idea for how nodejs should talk to Camunda.) My goal is to deliver systems where non-programmers can update the business logic in very practical ways.
I'd like to trigger the start of perhaps more-than-one process by sending a REST message, say to reflect that "a new insurance policy has been sold" and that might trigger the instantiation of say 2 processes on Monday but perhaps on Tuesday we add a third and now the same REST API call should now trigger more activity on Wednesday. (I figure it is better for nodejs to know about events but not about the process definitions. After all, my goal is to use Camunda as a sort of business logic server for my application. The less the nodejs code needs to know, the better.)
Which REST API should I be using to express the message that, say "a new insurance policy has been sold"? When I look at:
https://docs.camunda.org/manual/develop/reference/rest/signal/post-signal/
I find it very confusing. What should "name" match in the biz process definitions? I assume I don't need an executionId? I assume I can leave out tenantId?
Would some string in the message match the ID of a start event in one or more process definitions (or what has to match what)?
When I look at a process, is there an easy way to tell what variables I need to supply to start that process running?
Should I perhaps avoid using this event-oriented style of kicking off processes and just use the POST /process-definition/key/{key}/start? It would seem to me to be better form to trigger activity with events or signals or something like that rather than to have my nodejs code know about the specific process definition by name.
Should I be using events or signals in this case?
I gather that the start event should not be a "None Start Event" but I'm not clear on what type of start event TO use if I want automatic triggering based on events or signals or something? Would a "Non-interrupting - Message Start Event" be the right sort? I'm finding this confusing.
Once I have triggered the process to start, what does nodejs need to send to step the process forward from one task in that instance to the next?
Thanks!
In order to instantiate a new workflow instance you have the following possibilities:
Start exactly one instance:
Start a workflow instance by its known "key": https://docs.camunda.org/manual/develop/reference/rest/process-definition/post-start-process-instance/
Start a workflow by a message start event: https://docs.camunda.org/manual/develop/reference/rest/message/post-message/. A message can only start one specific workflow instance, it is not allowed that this is not a unique relationship. The message start event is the one you have to use in your BPMN process model. See also https://docs.camunda.org/manual/develop/reference/bpmn20/events/message-events/. This might indeed be the better approach to make your client independent of the process definition key.
Start multiple instances:
- Start a workflow instance by a BPMN signal event: https://docs.camunda.org/manual/develop/reference/rest/signal/post-signal/. The signal name could start many instances as once.
The name of the message or name of signal would be configured in the BPMN model. Both could work for your use case.
Once a process instance is started it will move automatically execute the next steps.
Probably following this example (https://blog.bernd-ruecker.com/use-camunda-without-touching-java-and-get-an-easy-to-use-rest-based-orchestration-and-workflow-7bdf25ac198e) step by step can give you some better idea?
Related
I am attempting to accomplish something along these lines with Quarkus, and Naryana:
client calls service to start a process that takes a while: /lra/start
This call sets off an LRA, and returns an LRA id used to track the status of the action
client can keep polling some endpoint to determine status
service eventually finishes and marks the action done through the coordinator
client sees that the action has completed, is given the result or makes another request to get that result
Is this a valid use case? Am I visualizing the correct way this tool can work? Based on how the linked guide reads, it seems that the endpoints are more of a passthrough to the coordinator, notifying it that we start and end an LRA. Is there a more programmatic way to interact with the coordinator?
Yes, it might be a valid use case, but in every case please read the MicroProfile LRA specification - https://github.com/eclipse/microprofile-lra.
The idea you describe is more or less one LRA participant executing in a new LRA and polling the status of this execution. This is not totally what the LRA is intended for, but surely can be used this way.
The main idea of LRA is the composition of distributed transactions based on the saga pattern. Basically, the point is to coordinate multiple services to achieve consistent results with an eventual consistency guarantee. So you see that the main benefit arises when you can propagate LRA through different services that either all complete their actions or all of their compensation callbacks will be called in case of failures (and, of course, only for the services that executed their actions in the first place). Here is also an example with the LRA propagation https://github.com/xstefank/quarkus-lra-trip-example.
EDIT: Sorry, I forgot to add the programmatic API that allows same interactions as annotations - https://github.com/jbosstm/narayana/blob/master/rts/lra/client/src/main/java/io/narayana/lra/client/NarayanaLRAClient.java. However, note that is not in the specification and is only specific to Narayana.
Suppose, I have an api POST /order which invokes PlaceOrder lambda and expects response from this. PlaceOrder lambda does some works, invokes another lambda ProcessPayment lambda and expects response. Also, ProcessPayment invokes CreateInvoice lambda expecting response. Whole architecture is like a RequestResponse cycle. I woulde like to achieve that without lambda invoking another lambda as it is considered as anti-pattern. My question is what is the best design pattern to achieve this behavior within 29 seconds with event-driven architecture.
What AWS suggests: As per this official documentation, they suggests to use SQS. But regarding using SQS, I have some thoughts.
My thoughts:
At event sources architecture, I can orchestrate these lambdas with SQS, SNS etc other event sources, but in that case, the nature would not be synchronous and thus I would not get response from the api.
My other solution:
Using Step Function: I can orchestrate this workflow with step function, and I think it is more elegant solution in this synchronous calling case. But I would like to achieve
this via event sources.
How can I design this scenerio with best practices using event-based achitecture?
In an Event-Driven Architecture, the communication between producers and consumers is asynchronous by design, that's the way the architecture scales.
You can get nearly synchronous communication between 2 services in an EDA, by creating dedicated queues / channels to communicate between them, make sure they're scaled up to a level where the latency is acceptable (close to synchronous values).
This adds some complexity, because the services which need responses, have to wait in a hot-loop to get them as soon as possible, and also if messages are lost, you need to have retry policies, etc.
I think you need to focus more on the mechanics of your program and a bit less on design patterns. You need to use the design patterns that fit your use-case, the other way around will not work. In the end, you build a program to fulfill a certain task or set of tasks, so that should be your end goal.
You’re stating that you have a process order Lambda, a create invoice Lambda and a process payment Lambda. I’d say the most interesting question is what you need to get done before you return a response to the user. Maybe you can process the order, respond to the user that it is done and handle the invoicing and payments on a later moment. Typically that would mean you put a message in a SQS queue or on an SNS topic.
It could be that you need your payment to be processed before you respond to the user, because they need to be informed about the status of the payment. You could then combine both actions in a single Lambda, because there is no way to spit the two tasks from one another. Keep in mind that often another option exist where you process the order first, put a message in a queue for the process payment (as it typically is a process that involves a third party) and the front end will poll for an update on the payment status. This way you can return a response quickly and still give an update on the payment as soon as possible.
The create invoice process is typically something you would never want to synchronously invoke during order confirmation. What if your invoicing application (intern or extern) is down? Theoretically you could still process orders as long as you create the invoice at some later moment in time. If you couple everything together you make order confirmation dependent on your invoice creation process, which I would regard as an unnecessary dependency.
I would really advice against step functions for this use-case. They can be utilized for long running processes that need to keep state and ‘wake up’ at specific moments, but for this specific flow I would say they do not help and are unnecessarily complex. If you have 3 things you need to do that you cannot separate from
one another, just run them in the same Lambda.
I was reading the accepted answer on this SO post: Orchestrating microservices and my question is, how does one monitor a process using the choreographed approach? The author of the quoted book writes:
One approach I like for dealing with this is to build a monitoring system that explicitly matches the view of the business process in [the workflow], but then tracks what each of the services does as independent entities, letting you see odd exceptions mapped onto the more explicit process flow.
What I would like to know is, how exactly does this monitoring system work? I have tried to research this but wasn't able to find anywhere that properly describes what I am trying to understand.
The way I am thinking of this is that, we store some kind of representation of the process like "here is the work that needs to be done" and then, as that is done, have each service update it accordingly. We can then have something like a cron that monitors this and sends another message if has not been completed. After trying five times say, and it has still not been done, we can deduce that the process has failed and reply accordingly to the caller. Is this an accurate interpretation of what the author is alluding to?
I have a MassTransit system that will consume 2 message types, one for a batch process, the other for CRUD operations on a single entity. Whilst the batch process is running, the CRUD operations should not be de-queued.
Is this possible to achieve using MassTransit? It seems the exchange binding -> type name, would potentially make this behavior difficult.
A solution would be to use one message type to denote both operations and then interrogate the message contents to discern between single and batch but this feels like a code smell. Also, this would require concurrency configuration to ensure only one consumer is ever active.
Can anyone help with an alternative solution here? Essentially, we need to pause all message consumption whilst an event driven process is running.
Thanks in advance.
By pause, do you mean that you want the CRUD operations to be able to occur without being blocked by the batch process? Because if it's only a matter of not having the two separate messages get in the way of each other, the most logical solution is using two separate queues, one receive endpoint for the batch process and another for the CRUD operations.
Now, if you truly need to separate the batch process such that it doesn't happen during the CRUD operations, that will require more work. And what if you receive a CRUD operation while the batch process is already running?
I think the separate queues is your best solution, however.
So, I'm working on a CQRS/ES project in which we are having some doubts about how to handle trivial problems that would be easy to handle in other architectures
My scenario is the following:
I have a customer CRUD REST API and each customer has unique document(number), so when I'm registering a new customer I have to verify if there is another customer with that document to avoid duplicity, but when it comes to a CQRS/ES architecture where we have eventual consistency, I found out that this kind of validations can be very hard to address.
It is important to notice that my problem is not across microservices, but between the command application and the query application of the same microservice.
Also we are using eventstore.
My current solution:
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%. That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
Altough this works, there are 2 things that bother me here, the first thing is my command application relying on the query application, so if my query application is down, my command is affected (today I just return false on my validation if query is down but still...) and second thing is, should a query/read model really be able to emit events? And if so, what is the correct way of doing it? Should the command have some kind of API for that? Or should the query emit the event directly to eventstore using some common shared library? And if I have more than one view/read? Which one should I choose to handle this?
Really hope someone could shine a light into these questions and help me this these matters.
For reference, you may want to be reviewing what Greg Young has written about Set Validation.
I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right?
That's exactly right - your read model is stale copy, and may not have all of the information collected by the write model.
That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
This spelling doesn't quite match the usual designs. The more common implementation is that, if we detect a problem when reading data, we send a command message to the write model, telling it to straighten things out.
This is commonly referred to as a process manager, but you can think of it as the automation of a human supervisor of the system. Conceptually, a process manager is an event sourced collection of messages to be sent to the command model.
You might also want to consider whether you are modeling your domain correctly. If documents are supposed to be unique, then maybe the command model should be using the document number as a key in the book of record, rather than using the customer. Or perhaps the document id should be a function of the customer data, rather than being an arbitrary input.
as far as I know, eventstore doesn't have transactions across different streams
Right - one of the things you really need to be thinking about in general is where your stream boundaries lie. If set validation has significant business value, then you really need to be thinking about getting the entire set into a single stream (or by finding a way to constrain uniqueness without using a set).
How should I send a command message to the write model? via API? via a message broker like Kafka?
That's plumbing; it doesn't really matter how you do it, so long as you are sure that the command runs within its own transaction/unit of work.
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%.
No, you cannot safely rely on the query side, which is eventually consistent, to prevent the system to step into an invalid state.
You have two options:
You permit the system to enter in a temporary, pending state and then, eventually, you will bring it into a valid permanent state; for this you could allow the command to pass, yield CustomerRegistered event and using a Saga/Process manager you verify against a uniquely-indexed-by-document-collection and issue a compensating command (not event!), i.e. UnregisterCustomer.
Instead of sending a command, you create&start a Saga/Process that preallocates the document in a uniquely-indexed-by-document-collection and if successfully then send the RegisterCustomer command. You can model the Saga as an entity.
So, in both solution you use a Saga/Process manager. In order for the system to be resilient you should make sure that RegisterCustomer command is idempotent (so you can resend it if the Saga fails/is restarted)
You've butted up against a fairly common problem. I think the other answer by VoicOfUnreason is worth reading. I just wanted to make you aware of a few more options.
A simple approach I have used in the past is to create a lookup table. Your command tries to register the key in a unique constraint table. If it can reserve the key the command can go ahead.
Depending on the nature of the data and the domain you could let this 'problem' occur and raise additional events to mark it. If it is something that's important to the business/the way the application works then you can deal with it either manually or at the time via compensating commands. if the latter then it would make sense to use a process manager.
In some (rare) cases where speed/capacity is less of an issue then you could consider old-fashioned locking and transactions. Admittedly these are much better suited to CRUD style implementations but they can be used in CQRS/ES.
I have more detail on this in my blog post: How to Handle Set Based Consistency Validation in CQRS
I hope you find it helpful.