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Home arrow Computer Science arrow Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable and Maintainable Systems

Separation of application code and state

In theory, databases could be deployment environments for arbitrary application code, like an operating system. However, in practice they have turned out to be poorly suited for this purpose. They do not fit well with the requirements of modern application development, such as dependency and package management, version control, rolling upgrades, evolvability, monitoring, metrics, calls to network services, and integration with external systems.

On the other hand, deployment and cluster management tools such as Mesos, YARN, Docker, Kubernetes, and others are designed specifically for the purpose of running application code. By focusing on doing one thing well, they are able to do it much better than a database that provides execution of user-defined functions as one of its many features.

I think it makes sense to have some parts of a system that specialize in durable data storage, and other parts that specialize in running application code. The two can interact while still remaining independent.

Most web applications today are deployed as stateless services, in which any user request can be routed to any application server, and the server forgets everything about the request once it has sent the response. This style of deployment is convenient, as servers can be added or removed at will, but the state has to go somewhere: typically, a database. The trend has been to keep stateless application logic separate from state management (databases): not putting application logic in the database and not putting persistent state in the application [36]. As people in the functional programming community like to joke, “We believe in the separation of Church and state” [37]3

In this typical web application model, the database acts as a kind of mutable shared variable that can be accessed synchronously over the network. The application can read and update the variable, and the database takes care of making it durable, providing some concurrency control and fault tolerance.

However, in most programming languages you cannot subscribe to changes in a mutable variable—you can only read it periodically. Unlike in a spreadsheet, readers of the variable don’t get notified if the value of the variable changes. (You can implement such notifications in your own code—this is known as the observer pattern— but most languages do not have this pattern as a built-in feature.)

Databases have inherited this passive approach to mutable data: if you want to find out whether the content of the database has changed, often your only option is to poll (i.e., to repeat your query periodically). Subscribing to changes is only just beginning to emerge as a feature (see “API support for change streams” on page 456).

i. Explaining a joke rarely improves it, but I don’t want anyone to feel left out. Here, Church is a reference to the mathematician Alonzo Church, who created the lambda calculus, an early form of computation that is the basis for most functional programming languages. The lambda calculus has no mutable state (i.e., no variables that can be overwritten), so one could say that mutable state is separate from Church’s work.

 
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