I was looking at 2 scenario's: A is ok, B not sure.
Scenario A: Simulate application restart after commit, before dispatch
Start EventStore
Commit change
Event not dispatched
Stop Event store
Start event store
De commited event is send again in step 5. This works fine and I see this also in the dispatcher code.
Scenario B: Simulate bus error
Start EventStore
Commit change 1
Exception in dispatcher
Commit change 2
Dispatch ok
In this case I cannot find the behavior and also wonder if it is a valid case:
This could only happen if there was a bug in the bus code.
Are there trigger which will retry to dispatch or do I need to write code to handle this or is my reasoning faulty?
Your assessment of Scenario A is correct, in a failure condition such as an application or machine restart/process termination, when the process starts up again it will discover the undispatched commits and push them to the dispatcher.
Scenario B is somewhat more tricky. The issue is that the EventStore is not a bus so the question of how to handle errors in the bus isn't something that cannot be handled in the EventStore itself. Furthermore, because there are a number of bus implementations, I don't want to couple the EventStore to any particular implementation. Some users may not even use a message bus; they may decide to use RPC calls instead.
The question that you really have then is, how should bus failures--and by extension, queue failures--be handled? The EventStore has an interface IPublishCommits. When an event is committed it's then pushed to a dispatcher. The dispatchers are simply responsible for marking an event as dispatched once it has been properly and successfully handled by the implementation of IPublishCommits.
The best way to handle transient bus and queue failures would be to implement the circuit breaker pattern in your IPublishCommits implementation that retries until things start working again. For bigger issues, such as serialization failures, you may want to log some kind of critical failure that will notify an administrator immediately. Again, the sticky problem in all of this is that the EventStore cannot know about all of the specifics of your situation.
Related
I'm trying to find a messaging system that supports the following use case.
Producer registers topic namespace
client subscribes to topic
first client triggers notification on producer to start producing
new client with subscription to the same topic receives data (potentially conflated, similar to hot/cold observables in RX world)
When the last client goes away, unsubscribe or crash, notify the producer to stop producing to said topic
I am aware that according to the pub/sub pattern A producer is defined to be blissfully unaware of the existence of consumers, meaning that my use-case simply does not fit the pub/sub paradigm.
So far I have looked into Kafka, Redis, NATS.io and Amazon SQS, but without much success. I've been thinking about a few possible ways to solve this, Haven't however found anything that would satisfy my needs yet.
One option that springs to mind, for bullet 2) is to model a request/reply pattern as amongs others detailed on the NATS page to have the producer listen to clients. A client would then publish a 'subscribe' message into the system that the producer would pick up on a wildcard subscription. This however leaves one big problem, which is unsubscribing. Assuming the consumer stops as it should, publishing an unsubscribe message just like the subscribe would work. But in the case of a crash or similar this won't work.
I'd be grateful for any ideas, references or architectural patterns/best practices that satisfy the above.
I've been doing quite a bit of research over the past week but haven't come across any satisfying Q&A or articles. Either I'm approaching it entirely wrong, or there just doesn't seem to be much out there which would surprise me as to me, this appears to be a fairly common scenario that applies to many domains.
thanks in advance
Chris
//edit
An actual simple use-case that I have at hand is stock quote distribution.
Quotes come from external source
subscribe to stock A quotes from external system when the first end-user looks at stock A
Stop receiving quotes for stock A from external system when no more end-users look at said stock
RabbitMQ has internal events you can use with the Event Exchange Plugin. Event such as consumer.created or consumer.deleted could be use to trigger some actions at your server level: for example, checking the remaining number of consumers using RabbitMQ Management API and takes action such as closing a topic, based on your use cases.
I don't have any messaging consumer present based publishing in mind. Got ever worst because you'll need kind of heartbeat mechanism to handle consumer crashes.
So here are my two cents, not sue if you're looking for an out of the box solution, but if not, you could wrap your application around a zookeeper cluster to handle all your use cases.
Simply use watchers on ephemeral nodes to check when you have no more consumers ( including crashes) and put some watcher around a 'consumers' path to be advertised when you get consumers.
Consumers side, you would have to register your zk node ID whenever you start it.
It's not so complicated to do, and zk is not the only solution for this, you might use other consensus techs as well.
A start for zookeeper :
https://zookeeper.apache.org/doc/r3.1.2/zookeeperStarted.html
( strongly advise to use curator api, which handle lot of recipes in a smooth way)
Yannick
Unfortunately you haven't specified your use business use case that you try to solve with such requirements. From the sound of it you want not the pub/sub system, but an orchestration one.
I would recommend checking out the Cadence Workflow that is capable of supporting your listed requirements and many more orchestration use cases.
Here is a strawman design that satisfies your requirements:
Any new subscriber sends an event to a workflow with a TopicName as a workflowID to subscribe. If workflow with given ID doesn't exist it is automatically started.
Any subscribe sends another signal to unsubscribe.
When no subscribers are left workflow exits.
Publisher sends an event to the workflow to deliver to subscribers.
Workflow delivers the event to the subscribers using an activity.
If workflow with given TopicName doesn't run the publish event to it is going to fail.
Cadence offers a lot of other advantages over using queues for task processing.
Built it exponential retries with unlimited expiration interval
Failure handling. For example it allows to execute a task that notifies another service if both updates couldn't succeed during a configured interval.
Support for long running heartbeating operations
Ability to implement complex task dependencies. For example to implement chaining of calls or compensation logic in case of unrecoverble failures (SAGA)
Gives complete visibility into current state of the update. For example when using queues all you know if there are some messages in a queue and you need additional DB to track the overall progress. With Cadence every event is recorded.
Ability to cancel an update in flight.
Distributed CRON support
See the presentation that goes over Cadence programming model.
I'm new to Service Fabric Reliable Actors technology and trying to figure out best practices for this specific scenario:
Let's say we have some legacy code that we want to run new code built on SF Reliable Actors. Actors of certain type "ActorExecutor" are going to asynchronously call some third-party service that sometimes could stuck for pretty long time, longer than actor's calling client is ready to wait, or even experience some prolonged underling communication issues. We do not want client (legacy code) to get blocked by any sort of issues in ActorExecutor, it does not expect to receive any value or status back from actor. Should we use SF ReliableQueue for that? Should we use some sort of actor-broker to receive requests from client and storing them to queue: Client->ActorBroker->ActorExecutor? Are reminders could be helpful here?
One more question in this regard: Giving the situation is possible when many thousands of actors might stuck in 'third-party incomplete call' in the same time, and we want to reactivate and repeat the very last call for them, should we write a new tool for that? In NServiceBus you can create an error queue in MSMQ where all failed like 'unable to process' messages to be landed, and then we were able to simply re-process them anytime in the future. From my understanding, there is no such thing in Service Fabric and it's something we need to built on our own.
An event driven approach can help you here. Instead of waiting for the Actor to return from the call to a service, you can enqueue some task on it, to request it to perform some action. The service calling Actor would function autonomously, processing items from it's task queue. This will allow it to perform retries and error handling. After a successful call, a new event can notify the rest of the system.
Maybe this project can help you to get started.
edits:
At this time, I don't believe you can use reliable collections in Actors. So a queue inside the state of an Actor, is a regular (read-only) collection.
Process the queue using an Actor Timer. Don't use the threadpool, as it's not persistent and won't survive crashes and Actor garbage collections.
I have a method on ServiceA that I need to call from ServiceB. The method takes upwards of 5 minutes to execute and I don't care about its return value. (Output from the method is handled another way)
I have setup my method in IServiceA like this:
[OneWay]
Task LongRunningMethod(int param1);
However that doesn't appear to run, because I am getting System.TimeoutException: This can happen if message is dropped when service is busy or its long running operation and taking more time than configured Operation Timeout.
One choice is to increase the timeout, but it seems that there should be a better way.
Is there?
For fire and forget or long running operations the best solution is using a message bus as a middle-ware that will handle this dependency between both process.
To do what you want without a middle-ware, your caller would have to worry about many things, like: Timeouts (like in your case), delivery guarantee(confirmation), Service availability, Exceptions and so on.
With the middle-ware the only worry your application logic need is the delivery guarantee, the rest should be handled by the middle-ware and the receiver.
There are many options, like:
Azure Service Bus
Azure Storage Queue
MSMQ
Event Hub
and so on.
I would not recommend using the SF Communication, Task.Run(), Threads workarounds as many places suggests, because they will just bring you extra work and wont run as smooth as the middle-ware approach.
There are various example applications and frameworks that implement a CQRS + Event Sourcing architecture and most describe use of an event handler to create a denormalized view from domain events stored in an event store.
One example of hosting this architecture is as a web api that accepts commands to the write side and supports querying the denormalized views. This web api is likely scaled out to many machines in a load balanced farm.
My question is where are the read model event handlers hosted?
Possible scenarios:
Hosted in a single windows service on a separate host.
If so, wouldn't that create a single point of failure? This probably complicates deployment too but it does guarantee a single thread of execution. Downside is that the read model could exhibit increased latency.
Hosted as part of the web api itself.
If I'm using EventStore, for example, for the event storage and event subscription handling, will multiple handlers (one in each web farm process) be fired for each single event and thereby cause contention in the handlers as they try to read/write to their read store? Or are we guaranteed for a given aggregate instance all its events will be processed one at a time in event version order?
I'm leaning towards scenario 2 as it simplifies deployment and also supports process managers that need to also listen to events. Same situation though as only one event handler should be handling a single event.
Can EventStore handle this scenario? How are others handling processing of events in eventually consistent architectures?
EDIT:
To clarify, I'm talking about the process of extracting event data into the denormalized tables rather than the reading of those tables for the "Q" in CQRS.
I guess what I'm looking for are options for how we "should" implement and deploy the event processing for read models/sagas/etc that can support redundancy and scale, assuming of course the processing of events is handled in an idempotent way.
I've read of two possible solutions for processing data saved as events in an event store but I don't understand which one should be used over another.
Event bus
An event bus/queue is used to publish messages after an event is saved, usually by the repository implementation. Interested parties (subscribers), such as read models, or sagas/process managers, use the bus/queue "in some way" to process it in an idempotent way.
If the queue is pub/sub this implies that each downstream dependency (read model, sagas, etc) can only support one process each to subscribe to the queue. More than one process would mean each processing the same event and then competing to make the changes downstream. Idempotent handling should take care of consistency/concurrency issues.
If the queue is competing consumer we at least have the possibility of hosting subscribers in each web farm node for redundancy. Though this requires a queue for each downstream dependency; one for sagas/process managers, one for each read model, etc, and so the repository would have to publish to each for eventual consistency.
Subscription/feed
A subscription/feed where interested parties (subscriber) read an event stream on demand and get events from a known checkpoint for processing into a read model.
This looks great for recreating read models if necessary. However, as per the usual pub/sub pattern, it would seem only one subscriber process per downstream dependency should be used. If we register multiple subscribers for the same event stream, one in each web farm node for example, they will all attempt to process and update the same respective read model.
In our project we use subscription-based projections. The reasons for this are:
Committing to the write-side must be transactional and if you use two pieces of infrastructure (event store and message bus), you have to start using DTC or otherwise you risk your events to be saved to the store but not published to the bus, or the other way around, depending on your implementation. DTC and two-phase commits are nasty things and you do not want to go this way
Events are usually published in the message bus anyway (we do it via subscriptions too) for event-driven communication between different bounded contexts. If you use message subscribers to update your read model, when you decide to rebuilt the read model, your other subscribers will get these messages too and this will bring the system to invalid state. I think you have thought about this already when saying you must only have one subscriber for each published message type.
Message bus consumers have no guarantee on message order and this can bring your read model to mess.
Message consumers usually handle retries by sending the message back to the queue, and usually by the end of the queue, for retrying. This means that your events can become heavily out of order. In addition, usually after some number of retries, message consumer gives up on the poison message and puts it to some DLQ. If this would be your projection, this will mean that one update will be ignored whilst others will be processed. This means that your read model will be in inconsistent (invalid) state.
Considering these reasons, we have single-threaded subscription-based projections that can do whatever. You can do different type of projections with own checkpoints, subscribing to the event store using catch-up subscriptions. We host them in the same process as many other things for the sake of simplicity but this process only runs on one machine. Should we want to scale-out this process, we will have to take the subscriptions/projections out. It can easily be done since this part has virtually no dependencies to other modules, except the read model DTOs itself, which can be shared as an assembly anyway.
By using subscriptions you always project events that have been already committed. If something goes wrong with the projections, the write side is definitely the source of truth and remains so, you just need to fix the projection and run it again.
We have two separate ones - one for projecting to the read model and another one for publishing events to the message bus. This construct has proven to work very well.
Specifically for EventStore, they now have competing consumers, which are server based subscriptions where many clients can subscribe to the subscription group but only one client gets the message.
It sounds like that is what you are after, each node in the farm can subscribe to the subscription group and the node that receives the message does the projection
I've been using Akka's event stream in a Play app as an event bus where I can publish events and subscribe listeners and I wanted to know what are the gotchas I should take into account. Specifically there are two things:
Each Listener is implemented via an actor which receives the published events and processes them. What if the actor's message queue starts to get big? How can I implement back-pressure safely, guaranteeing that each event is eventually processed?
Related to the previous one: how can I persist the unprocessed events so, in the case of a failure the application can start again and process them? I'm aware of the existence of akka-persistence but I'm not sure if that would be the right thing to do in this case: the Listener actors aren't stateful, they don't need to replay past events, I only want to store unprocessed events and delete them once they have been processed.
Considering constraints I would not use Akka's event bus for this purpose.
Main reasons are:
Delivery - You have no guarantees that event listeners are in fact listening (no ACK). It's possible to lose some events on the way.
Persistance - There is no built in way of preserving event bus state.
Scaling - Akka's event bus is a local facility, meaning it's not suitable if in future you would like to create a cluster.
Easiest way to deal with that would be to use message queue such as RabbitMQ.
While back I was using sstone/amqp-client. MQ can provide you with persistent queues (queue for each listener/listener type).