“A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines." - Emerson
Continuing with my ideas from the other day, I wanted to move from the abstract to the more concrete, which is to say that I wanted to talk about what such a system would look like.
To begin with, let's assume that this system is a fairly large-scale distributed system, so that, were we to design it the way such things are typically designed these days, we would probably have some sort of cache between our business logic and the data layer (say, Redis or Memcached). The back-end data model is probably normalized and very relational, but since we're having to cache the data for the business layer, the data is mostly accessed via a key which is derived from the primary indexes of the table(s) being used. In other words, from the point of view of the business layer, the data layer is just a key/value store.
This becomes even more true if the data becomes large enough to require sharding the database. Now, to find the proper data store/cache for the data, you take the key you use for the cache, hash it, and then use some algorithm to take you from the hash to the actual data store.
So now we have the business layer dealing with the data in terms of key/value pairs, talking directly only with the cache in most cases (except the case of a cache miss, which you want to be rare and handled transparently anyway), but we're still having to send the data to our relational database, paying the network and transactional cost so that we have a "system of record" (and, of course, can do our reports).
Nonsense and Non-sensibility
This doesn't really make much sense to me, especially when part of the cost of this "system of record" is strong coupling within the system.
Let's think instead about where we want to get to, from a scalability perspective. Ultimately, what we want, I think, is a system that has been componentized at the architectural level - where our component is an instance of our front-end/business/data layer, and we scale by adding new instances of these. Further, we stop worrying about the whole thing being consistent, and instead accept that it may be eventually consistent instead.
To do this, we'd need to decouple our persistent data store from each of those instances, so that each instance's data layer can read/write/update the underlying data without being coupled to it.
So, let's try this: a persistent data store of some sort linked to the instance's data layer via a pub/sub framework of some sort. Each instance receives updates about the data from the framework, and in turn updates the data store via the framework. We would need to guarantee eventual delivery via the framework, but it wouldn't be synchronous, and, in cases of network fragmentation, might not arrive for quite a while.
Each instance would use the cache key as the subscription topic, and, once they subscribed, would receive all updates from all components posted to the topic, so that, for instance, if another instance updated the data they would get that message directly, rather than having to access the persistent data store to get it.
There's only one rub: how do we bootstrap such a system? How do we seed the initial data in the pub/sub framework on startup? We probably don't want something publishing every "row" of our persistent store on startup, for instance, so how do we manage getting the initial data out.
What we need is the ability for some component to be able to detect when someone first subscribes to a new topic, and then populate that topic with the data from the persistent store when that happens.
One way to do this is to use something like Apache Kafka. I won't give a thorough description of how it works, but, basically, it uses Zookeeper to manage the subscriptions, which means that you can ask Zookeeper to let you know when a new topic is created.
So now, we have our persistent data store (which can be, really, anything) fronted by a component whose whole job is to listen for new topics and publish the corresponding initial data. Each instance's data layer simply subscribes to the proper topic when a new request from the business layer comes in, and updates its cache of the current state of the data from the updates on the topic, as well as publishing any changes it makes to the data. As instances come online, they bootstrap from the system so that loss of any individual instance isn't catastrophic.
This architecture becomes very interesting, because it demotes the persistent data store from being the be-all and end-all of the system to being just another component. Because it's just another component, just another client of the pub/sub framework, things become much more flexible. Now, it can be multiple data stores, not just one. They can be replicated, or sharded, or whatever you want. Since the data layers of our application are decoupled from them, changing it no longer means changing the application.
Even better, it means that the persistent data store can be changed while the system is running. You want to shard an existing database? Bring up a new instance of the database, but have the data publisher front end only listen for certain keys. Let it populate itself from the pub/sub network, and then change the data publisher on the original store to exclude those keys. Voila! You're sharded!