Event driven architecture pattern is a distributed asynchronous architecture pattern to create highly scalable reactive applications. The pattern suits for on every level application stack from small to complex ones. The main idea is delivering and processing events asynchronously.
There are couple of ways to implement event driven pattern to your stack. One of them is using “Broker Topology”. In broker topology, you send events to a central broker and all subscribers of these broker receive and process events asynchronously.
In this story, I shared four implementations of event bus and with their goods and drawbacks. In all implementations, all event generators sends events to the event bus. Event processors(subscribers) subscribe to the event bus. And subscribers can also create new events and pass it to the event bus.
Event Bus, Subscribers (Event Processor), Event Creators
Whenever the event bus receive any event from event creators, it passes the event to the all subscribers. Subscribers process the event data with their own logic. Subscribers can also create new events and pass it to the event bus too. Event bus does not care about if receiver fails to receive the event message.
Notify, subscribe, unsubscribe, subscribers are enough to implement such event bus.
It is very simple in terms of development, not too much responsibility.
In this approach you are replicating the event data to the all subscribers, even if the subscribers do not have power to consume this event data. This means that your memory demand may increase with the multiplication of the subscribers in time.
It also does not care about the assurance of the delivery. It tries to send the event to the subscriber only once but it does not care about the broken pipes, connection errors and other possible lacks.
This implementation notify all subscriber without filtering. So, filtering should be done in subscribers.
On JS front-end world `reduxjs` library may be given as a sample of this implementation. When an event dispatched by `actions`, all `reducers` receive the same event type and event data and decides what to change.
Such implementation in Elixir lang: https://github.com/mustafaturan/event_bus/commit/08a955294c1d1c5bec2c7fef28ed255400e5f322
Event Bus, Event Creators, Subscribers, Event Store, Event Watcher
Probably, you never heard this term before, do not worry I made it up. Event shadow is basically a reference data to your original event data.
Whenever the event bus receives any event, it save it to an Event Store. Then passes the event shadow to the all subscribers. Subscribers process the event shadow and fetch the event data from Event Store when they start processing.
When an event comes to the event bus, it creates a watcher with the list of subscribers. Event Watcher is responsible for deletion of event data from the Event Store when all subscriber work done with the event data.
Event bus does not care about if receiver fails to receive the event message. But it automatically mark the Event Watcher as ‘skipped’ when any subscriber fails to receive event shadow.
Notify, subscribe, unsubscribe, subscribers, save/delete/fetch_event_data, mark_as_completed/skipped are enough to implement such an event bus.
This approach comes with a less memory consumption. It is possible to implement one more function to watch current status of a specific event or all events.
In this approach each subscriber need to call fetch_event_data, mark_as_completed, and mark_as_skipped functions. You need to implement a read heavy event store. And you need to implement also an ‘event watcher’ to delete the event data when all subscribers processed the data. And sadly, event store write persistence should be blocking.
Like in the implementation i1, this implementation notify all subscriber without filtering. So, filtering should be done in subscribers. Which is not too bad, but if you are using the event bus over networks, this will cause unnecessary network traffic.
Such implementation in Elixir lang: https://github.com/mustafaturan/event_bus/releases/tag/v0.2.1
This implementation is same as the i2 but it is filtering the events before notifying the subscribers. In this approach, you need to register subscribers with their interests of topics. Note: It is good to have a regex filter on topics(event types) like in RabbitMQ.
It has same goods and drawbacks as in the implementation i2, except i3 supports filtering on events.
Such implementation in Elixir lang: https://github.com/mustafaturan/event_bus
To guarantee the ordered event delivery, the idea is partition your data before passing to interested in subscribers. The Kafka paper is a great way to start such an implementation. In basic, the producer decides for the event partition with a custom function like `CRC32`. And only one consumer is responsible only for that partition.
Increasing, decreasing the number of partition might be pain. You need to implement your own partition function on producer. The consumer implementation will be as hard as broker implementation.
In i1 and i2 implementations, all subscribers should match the event type(topic) first in blocking manner to prevent unnecessary stacks in memory.
In i3 and i4 implementations, you should match event type(topic) in non-blocking asynchronous way because only the subscribed topic data comes to the event processors.
In general, all subscribers should process the event asynchronously without any blocking manner. Also subscribers can also create new events and pass those events to the event bus.
In conclusion, event broker topology promises reactive, event driven, asynchronous systems. There are many implementation of event buses like AMQP (RabbitMQ is an implementation of AMQP), ZeroMQ and Kafka. There are all coming with different promises. For example, Kafka promises on the ‘guaranteed and ordered delivery’, AMQP promises on the ‘guaranteed delivery’ and ZeroMQ comes with patterns to develop your own custom broker.
If you are using Elixir or Erlang for the development, try the event_bus library which is an implementation of the i3.
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