I have a situation where a main orchestration is responsible for processing a convoy of messages. These messages belong to a set of customers, the orchestration will read the messages as they come in, and for each new customer id it finds, it will spin up a new orchestration that is responsible for processing the messages of a particular customer. I have to preserve the order of messages as they come in, so the newly created orchestrations should process the message it has and wait for additional messages from the main orchestration.
Tried different ways to tackle this, but was not able to successfuly implement it.
I would like to hear your opinions on how this could be done.
Thanks.
It sounds like what you want is a set of nested convoys. While it might be possible to get that working, it's going to... well, hurt. In particular, my first worry would be maintenance: any changes to the process would be a pain in the neck to make, and, much worse, deployment would really, really suck.
Personally, I would really try to find an alternative way to implement this and avoid the convoys if possible, but that would depend a lot on your specific scenario.
A few questions, if you don't mind:
What are your ordering requirements? For example, do you only need ordered processing for each customer on a single incoming batch, or across batches? If the latter, could you make do without the master orchestration and just force a single convoy'd instance per customer? Still not great, but would likely simplify things a lot.
What are you failure requirements with respect to ordering? Should it completely stop processing? Save message and keep going? What about retries?
Is ordering based purely on the arrival time of the message? Is there anything in the message that you could use to force ordering internally instead of relying purely on the arrival time?
What does the processing of the individual messages do? Is the ordering requirement only to ensure that certain preconditions are met when a specific message is processed (for example, messages represent some tree structure that requires parents are processed before children).
I don't think you need a master orchestration to start up the sub-orchestrations. I am assumin you are not talking about the master orchestration implmenting a convoy pattern. So, if that's the case, here's what I might do.
There is a brief example here on how to implment a singleton orchestration. This example shows you how to setup an orchestration that will only ever exist once. All the messages going to it will be lined up in order of receipt and processed one at a time. Your example differs in that you want to have this done by customer ID. This is pretty simple. Promote the customer ID in the inbound message and add it to the correlation type. Now, there will only ever be one instance of the orchestration per customer.
The problem with singletons is this. You have to kill them at some point or they will live forever as dehydrated orchestrations. So, you need to have them end. You can do this if there is a way for the last message for a given customer to signal the orchestration that it's time to die through an attribute or such. If this is not possible, then you need to set a timer. If no messags are received in x seconds, terminate the orch. This is all easy to do, but it can introduce Zombies. Zombies occur when that orchestration is in the process of being shut down when another message for that customer comes in. this can usually be solved by tweeking the time to wait. Regardless, it will cause the occasional Zombie.
A note fromt he field. We've done this and it's really not a great long term solution. We were receiving customer info updates and we had to ensure ordered processing. We did this singleton approach and it's been problematic from the Zombie issue and the exeption issue. If the Singleton orchestration throws an exception, it will block the processing for a all future messages for that customer. So - handle every single possible exception. The real solution would have been to have the far end system check the time stamps from the update messages and discard ones that were older than the last update. We wanted to go this way, but the receiving system didn't want to do this extra work.
Related
I'm maintaining an event-sourced application that went far off the road I'm afraid.
In one case a command is received by an aggregate root that publishes an event that is handled by an event handler that needs to do 2 things:
send a command (cmd1) to another aggregate root that will publish an event that will create a number of sagas each firing of some commands that are eventually handled by a number of aggregates
send a second command (cmd2) that will also lead to all sorts of command/event/command sequences.
In schematic form:
cmd0 -> AR0 -> evt0 -> evtHandler -> cmd1 -> AR1 -> evt1 -> saga stuff and more cmds and evts
|-> cmd2 -> AR2 -> evt2 -> more saga stuff, cmds and evts
Everything happens in the same thread and everything happens in 1 transaction started at the first command handling.
Now the goal: all events, saga's, aggregate calls originated from the first command (cmd1) should happen first and then all events, saga's and aggregate calls originated from the second command (cmd2) should happen.
Here's the observation: cmd1 calls AR1 that published evt1 but after that cmd2 calls AR2 publishing evt2. All other events and commands originating from cmd1 are mingled with those from cmd2.
First I thought I could get away with it using the UnitOfWork but even explicitly creating a separate unit of work for handling cmd1 didn't solve the problem. Looking at the implementation in AbstractEventBus I see that the events are simply merged in the parent unit of work and thus end up being merged with the ones originating from cmd2.
Here's the question: Is there a way to first call cmd1 and wait until all effects originating from that command are handled before calling cmd2 while still preserving the transactional atomicity that I currently have?
To be completely honest with you Jan, the best would be if the components within your application don't rely to much on that order.
It essentially means you have distinct message handling components, which in essence could be different micro service, but they are all tied together as the order is important.
Ideally, you'd set up your components to work on their own.
So, aggregates handle a command and publish the result, done.
Sagas react to events, regardless of where they come from, and react on them with actions (e.g. command dispatching).
Embracing the eventuality would help here, as it will drop the entire requirement of waiting for one process to complete.
From a theoretical stance, that would be my response.
From a more pragmatic corner looking at your question, I'd like to point out that it sounds like a rabbit hole you are going in to. You don't only want cmd1 handling to be done, you want event handling on all sagas to be resolved, including commands coming out of that too, correct? Who's here to tell what the number of Sagas is? Or what the number of commands those saga dispatch need to be taken into account? These criteria will likely change over time, adding more an more stuff which needs to happen "in a single transaction".
Well, yes there are way to wait for processing from some parts, to pull them all in a single transaction. But to be honest with you, I wouldn't recommend taking that route, as it will only make using such a message based system more and more complex.
The crux is what all effects are. From the point of dispatching that command, you should only care if that exact command handles successfully yes or no, and that's where the concerns should end.
I know this does not give you a simple programmatic solution, as you need to adjust the design. But I think decoupling is the only right way to go hear.
That's my two cents to the situation, hope this helps you further in any way Jan.
Message Anticipation explanation update
In essence, the messages you'd use in an Axon application form a boundary. A boundary after which the components essentially don't have a clue what is going to handle those messages. The behaviour per message differs a little, but might clarify what opens you have too:
Commands - Commands are consistently routed to a single handle, on a single instance. Furthermore, you can anticipate a response, in the form of an OK or NOK. OK's mean the handler is void or the identifier of a created entity (like the aggregate itself). NOK's typically are the exceptions you throw from your command handling methods, which signal something went wrong or the command simply couldn't be executed and it should be let know to the dispatching end.
Events - Events will be broadcast to any component which has subscribed itself to the EventBus as being capable to handle a given event. Note that event handling is segregated in time from the actual publication point of the event. This means there is no way there are results from event handling which could (or should) be returned to the dispatcher of an event.
Queries - Query messages can be routed in several forms. Either a single component is best suited to answer the query (called Point-to-Point queries). You can also dispatch a query to several handlers and aggregate the results (called Scatter-Gather queries). Lastly, you can subscribe to query models by doing a "Subscription query", which is essentially a combination of a point-to-point followed up by a Flux of updates. Clearly, query dispatching would mean you are receiving a result from some component. It's just that you have freedom in the type of query you do. If any assurance is required about the "up-to-date"-ness of a query response should be part of the implementation of the query being sent and how it is handled by a #QueryHandler annotated method.
Hope this provides some additional clarity at what each of the messages do in an Axon application!
Currently I use the AcknoledgingMessageListener to implement a Kafka consumer using spring-Kafka. This implementation helps me listen on a specific topic and process messages with a manual ack.
I now need to build the following capability:
Let us assume that for an some environmental exception or some entry of bad data via this topic, I need to replay data on a topic from and to a specific offset. This would be a manual trigger (mostly via the execution of a Java class).
It would be ideal if I can retrieve the messages between those offsets and feed it is a replay topic so that a new consumer can process those messages thus keeping the offsets intact on the original topic.
CosumerSeekAware interface - if this is the answer how can I trigger this externally? Via let say a mvn -Dexec. I am not sure if this is even possible
Also let say that I have an crash time stamp with me, is it possible to introspect the topic to find the offset corresponding to the crash so that I can replay from that offset?
Can I find offsets corresponding to some specific data so that I can replay those specific offsets?
All of these requirements are towards building a resilience layer around our Kafka capabilities. I need all of these to be managed by a separate executable class that can be triggered manually providing the relevant data (like time stamps etc). This class should determine offsets and then seek to that offset, retrieve the messages corresponding to those offsets and post them to a separate topic. Can someone please point me in the right direction? I’m afraid I’m going around in circles.
so that a new consumer can process those messages thus keeping the offsets intact on the original topic.
Just create a new listener container with a different group id (new consumer) and use a ConsumerAwareRebalanceListener (or ConsumerSeekAware) to perform the seeks when the partitions are assigned.
Here is a sample CARL that seeks all assigned topics based on a timestamp.
You will need some mechanism to know when the new consumer should stop consuming (at which time you can stop() the new container). Maybe set max.poll.records=1 on the new consumer so he doesn't prefetch past the failure point.
I am not sure what you mean by #3.
Say I want to start friendship between A and B.
Say I want to end friendship between A and B.
Those are two tasks I want to send to a queue having multiple consumers (workers).
I want to guarantee processing order so, how to avoid the second task to be performed before the first?
My solution: make tasks sticky (tasks about A are always sent to the same consumer).
Implementation: use RabbitMQ's exchanges and map tasks to the available consumers.
How do I map A to its consumer? I'm thinking about nginx's ip_hash. I think I need something similar.
I don't know if it is relevant but A and B are uuid.v4() UUIDs.
Can you point me out to the algorithm I need to accomplish mapping, please?
Well, there are two options:
make one exchange / queue for all events and guarantee that they're gonna be inserted in proper order. Create one worker for them. This costs more on inserting data (and doesn't give you option of scalability).
prepare your app for such situation, e.g. when you get message destroyFriendship and friendship does not exist - save message to db containing future friendship ending. Then you can have multiple workers making and destroying friendship and do not have to care about proper order. Simply do your job, make friends and if there's row in db about ending of friendship - destroy it (or simply do not create). Of course you need to check timestamp of creation/destroying time and check if destroying time was after creation time!
Of course you can count somehow hash of A/B, but it would be IMO more costfull then preparing app. Scalling app using excahnges/queues is not really good - you're going to create more and more queues and it's going to end up in too many queues/exchanges in rabbitmq.
If you have to use solution you specified - you can for example count crc32 from A and B, and using it's value calcalate to which queue task should be send. But having multiple consumers might result wrong here - what if one of consumers is blocked somehow and other receive message with destroying friendship? Using this solution I'd say that it's dangerous to have more than 1 worker per group of A/B.
Looking into examples of priority queues online it seems the standard is that the publisher sets the priority of the message. This makes sense if the message itself has meaning that dictates that "Fire detected!".
What if the priority is more dictated by the consumer? my scenario is this, I have 100s of customers generating messages, customer A wants me to have a dedicated message consumer for their messages. I don't want to have to change all my producers everytime this scenario comes into play. (perhaps they stop paying, perhaps someone else wants this also, perhaps we use this as a way of relieving some devops issues...)
Is there a way I could do this? Could the consumer "scan" all messages in a queue and "pass" on handling a message?
There no way, really, for Rebus to be able to "scan" messages or in any other way know beforehand which messages are available in order to pick one to handle.
Generally, with message queues, you just get the next message when you receive one.
If you need some kind of intelligence in how messages are picked when they need to be handled (and it sounds like that to me), I suggest you just handle messages by queueing them up as "work items" in a database, and then you periodically or continually pick the next work item to work on.
This way, your worker can perform arbitrarily complex logic when it picks the next work item - e.g. by skipping those that did not pay, ordering by customer plan (e.g. prioritizing "gold customers" over "silver customers"), etc.
This would have the added advantage IMO that it would make it a more prominent part of the application's logic, which I feel would be nice in this case.
Does that sound reasonable?
Assume that i have function called PlaceOrder, which when called inserts the order details into local DB and puts a message(order details) into a TIBCO EMS Queue.
Once message received, a TIBCO BW will then invoke some other system(say ExternalSystem) to pass on the order details.
Now the way i wrote my integration tests is
Call the Place Order
Sleep, and check details exists in local DB
Sleep and check details exists in ExternalSystem.
Is the above approach correct? Above test gives me confidence that, End to End integration is working, but are there any better way to test above scenario?
The problem you describe is quite common, and your approach is a very typical solution.
The problem with this solution is that if the delay is too short, your tests may sometimes pass and sometimes fail, but if the delay is very long, then your just wasteing time waiting, and with many tests, it can add a lot of delay. But unless you can get some signal to tell you the order arrived in the database, then you just have to wait.
You can reduce the delay by doing lots of checks with short intervals. If you're order is not there after timeout, then you would fail the test.
In "Growing Object-Oriented Software, Guided by Tests"*, there is a chapter on this very subject, so you might want to get a copy if you will be doing a lot of this sort of testing.
"There are two ways a test can observe the system: by sampling its observable
state or by listening for events that it sends out. Of these, sampling is
often the only option because many systems don’t send any monitoring
events. It’s quite common for a test to include both techniques to interact
with different “ends” of its system"
(*) http://my.safaribooksonline.com/book/software-engineering-and-development/software-testing/9780321574442