mediocre-blog/_drafts/program-structure-and-composability.md

title	description
Program Structure and Composability	Discussing the nature of program structure, the problems presented by complex structures, and a pattern which helps in solving those problems.

Part 0: Introduction

This post is focused on a concept I call "program structure", which I will try to shed some light on before discussing complex program structures, then discussing why complex structures can be problematic to deal with, and finally discussing a pattern for dealing with those problems.

My background is as a backend engineer working on large projects that have had many moving parts; most had multiple services interacting with each other, using many different databases in various contexts, and facing large amounts of load from millions of users. Most of this post will be framed from my perspective, and will present problems in the way I have experienced them. I believe, however, that the concepts and problems I discuss here are applicable to many other domains, and I hope those with a foot in both backend systems and a second domain can help to translate the ideas between the two.

Also note that I will be using Go as my example language, but none of the concepts discussed here are specific to Go. To that end, I've decided to favor readable code over "correct" code, and so have elided things that most gophers hold near-and-dear, such as error checking and comments on all public types, in order to make the code as accessible as possible to non-gophers as well. As with before, I trust someone with a foot in Go and another language can translate help me translate between the two.

Part 1: Program Structure

In this section I will discuss the difference between directory and program structure, show how global state is antithetical to compartmentalization (and therefore good program structure), and finally discuss a more effective way to think about program structure.

Directory Structure

For a long time I thought about program structure in terms of the hierarchy present in the filesystem. In my mind, a program's structure looked like this:

// The directory structure of a project called gobdns.
src/
    config/
    dns/
    http/
    ips/
    persist/
    repl/
    snapshot/
    main.go

What I grew to learn was that this conflation of "program structure" with "directory structure" is ultimately unhelpful. While I won't deny that every program has a directory structure (and if not, it ought to), this does not mean that the way the program looks in a filesystem in any way corresponds to how it looks in our mind's eye.

The most notable way to show this is to consider a library package. Here is the structure of a simple web-app which uses redis (my favorite database) as a backend:

src/
    redis/
    http/
    main.go

If I were to ask you, based on that directory strucure, what the program does, in the most abstract terms, you might say something like: "The program establishes an http server which listens for requests, as well as a connection to the redis server. The program then interacts with redis in different ways, based on the http requests which are received on the server."

And that would be a good guess. Here's a diagram which depicts the program structure, wherein the root node, main.go, takes in requests from http and processes them using redis.

TODO diagram

This is certainly a viable guess for how a program with that directory structure operates, but consider another: "A component of the program called server establishes an http server which listens for requests, as well as a connection to a redis server. server then interacts with that redis connection in different ways, based on the http requests which are received on the http server. Additionally, server tracks statistics about these interactions and makes them available to other components. The root component of the program establishes a connection to a second redis server, and stores those statistics in that redis server."

TODO diagram

The directory structure could apply to either description; redis is just a library which allows for interacting with a redis server, but it doesn't specify which server, or how many. And those are extremely important factors which are definitely reflected in our concept of the program's structure, and yet not in the directory structure. What the directory structure reflects are the different kinds of components available to use, but it does not reflect how a program will use those components.

Global State vs. Compartmentalization

The directory-centric approach to structure often leads to the use of global singletons to manage access to external resources like RPC servers and databases. In the above example the redis library might contain code which looks something like:

// A mapping of connection names to redis connections.
var globalConns = map[string]redisConnection

func Get(name string) redisConnection {
    if globalConns[name] == nil {
        globalConns[name] = makeConnection(name)
    }
    return globalConns[name]
}

Even though this pattern would work, it breaks with our conception of the program structure in the more complex case shown above. Rather than having the server component own the redis server it uses, the root component would be the owner of it, and server would be borrowing it. Compartmentalization has been broken, and can only be held together through sheer human discipline.

This is the problem with all global state. It's shareable amongst all components of a program, and so is owned by none of them. One must look at an entire codebase to understand how a globally held component is used, which might not even be possible for a large codebase. And so the maintainers of these shared components rely entirely on the discipline of their fellow coders when making changes, usually discovering where that discipline broke down once the changes have been pushed live.

Global state also makes it easier for disparate services/components to share datastores for completely unrelated tasks. In the above example, rather than creating a new redis instance for the root component's statistics storage, the coder might have instead said "well, there's already a redis instance available, I'll just use that." And so compartmentalization would have been broken further. Perhaps the two instances could be coalesced into the same one, for the sake of resource efficiency, but that decision would be better made at runtime via the configuration of the program, rather than being hardcoded into the code.

From the perspective of team management, global state-based patterns do nothing except slow teams down. The person/team responsible for maintaining the central library which holds all the shared resources (redis, in the above example) becomes the bottleneck for creating new instances for new components, which will further lead to re-using existing instances rather than create new ones, further breaking compartmentalization. The person/team responsible for the central library often finds themselves as the maintainers of the shared resource as well, rather than the team actually using it.

Program Structure

So what does proper program structure look like? In my mind the structure of a program is a hierarchy of components, or, in other words, a tree. The leaf nodes of the tree are almost always IO related components, e.g. database connections, RPC server frameworks or clients, message queue consumers, etc... The non-leaf nodes will generally be components which bring together the functionalities of their children in some useful way, though they may also have some IO functionality of their own.

Let's look at an even more complex structure, still only using the redis and http component types:

TODO diagram:

    root
        rest-api
            redis
            http
        redis // for stats keeping
        debug
            http

This structure contains the addition of the debug component. Clearly the http and redis components are reusable in different contexts, but for this example the debug endpoint is as well. It creates a separate http server which can be queried to perform runtime debugging of the program, and can be tacked onto virtually any program. The rest-api component is specific to this program and therefore not reusable. Let's dive into it a bit to see how it might be implemented:

// RestAPI is very much not thread-safe, hopefully it doesn't have to handle
// more than one request at once.
type RestAPI struct {
    redisConn *redis.Conn
    httpSrv   *http.Server

    // Statistics exported for other components to see
    RequestCount int
    FooRequestCount int
    BarRequestCount int
}

func NewRestAPI() *RestAPI {
    r := new(RestAPI)
    r.redisConn := redis.NewConn("127.0.0.1:6379")

    // mux will route requests to different handlers based on their URL path.
    mux := http.NewServeMux()
    mux.Handle("/foo", http.HandlerFunc(r.fooHandler))
    mux.Handle("/bar", http.HandlerFunc(r.barHandler))
    r.httpSrv := http.NewServer(mux)

    // Listen for requests and serve them in the background.
    go r.httpSrv.Listen(":8000")

    return r
}

func (r *RestAPI) fooHandler(rw http.ResponseWriter, r *http.Request) {
    r.redisConn.Command("INCR", "fooKey")
    r.RequestCount++
    r.FooRequestCount++
}

func (r *RestAPI) barHandler(rw http.ResponseWriter, r *http.Request) {
    r.redisConn.Command("INCR", "barKey")
    r.RequestCount++
    r.BarRequestCount++
}

As can be seen, rest-api coalesces http and redis into a simple REST api, using pre-made library components. main.go, the root component, does much the same:

func main() {
    // Create debug server and start listening in the background
    debugSrv := debug.NewServer()

    // Set up the RestAPI, this will automatically start listening
    restAPI := NewRestAPI()

    // Create another redis connection and use it to store statistics
    statsRedisConn := redis.NewConn("127.0.0.1:6380")
    for {
        time.Sleep(1 * time.Second)
        statsRedisConn.Command("SET", "numReqs", restAPI.RequestCount)
        statsRedisConn.Command("SET", "numFooReqs", restAPI.FooRequestCount)
        statsRedisConn.Command("SET", "numBarReqs", restAPI.BarRequestCount)
    }
}

One thing which is clearly missing in this program is proper configuration, whether from command-line, environment variables, etc.... As it stands, all configuration parameters, such as the redis addresses and http listen addresses, are hardcoded. Proper configuration actually ends up being somewhat difficult, as the ideal case would be for each component to set up the configuration variables of itself, without its parent needing to be aware. For example, redis could set up addr and pool-size parameters. The problem is that there are two redis components in the program, and their parameters would therefore conflict with each other. An elegant solution to this problem is discussed in the next section.

Part 2: Context, Configuration, and Runtime