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continued work on program structure, pretty much done with Part 1

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Brian Picciano 2019-05-19 13:07:02 -06:00
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_drafts/program-structure-and-composability.md
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@ -6,24 +6,39 @@ description: >-
complex structures, and a pattern which helps in solving those problems.
---
## Part 0: Intro
## Part 0: Introduction
This post is focused on a concept I call "program structure", which I will try
to shed some light on before moving on to discussing complex program structures,
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, used many different
databases in various contexts, and faced large amounts of load from millions of
users. Most of this post will be framed from my perspective, and 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.
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:
@ -40,10 +55,10 @@ src/
main.go
```
What I grew to learn was that this consolidation of "program structure" with
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 anyway corresponds to how it
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
@ -57,30 +72,39 @@ src/
main.go
```
(Note that I use go as my example language throughout this post, but none of the
ideas I'll referring to are go specific.)
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. But consider another case: "The program
establishes an http server which listens for requests, as well as connections to
_two different_ redis servers. The program then interacts with one redis server
or the other in different ways, based on the http requests which are received
from 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. Even worse, thinking of structure in terms of
directories might (and, I claim, often does) cause someone to assume that
program only _could_ interact with one redis server, which is obviously untrue.
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 and Microservices
### 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
@ -88,70 +112,157 @@ databases. In the above example the `redis` library might contain code which
looks something like:
```go
// For the non-gophers, redisConnection is variable type which has been made up
// for this example.
var globalConn redisConnection
// A mapping of connection names to redis connections.
var globalConns = map[string]redisConnection
func Get() redisConnection {
if globalConn == nil {
globalConn = makeConnection()
func Get(name string) redisConnection {
if globalConns[name] == nil {
globalConns[name] = makeConnection(name)
}
return globalConn
return globalConns[name]
}
```
Ignoring that the above code is not thread-safe, the above pattern has some
serious drawbacks. For starters, it does not play nicely with a microservices
oriented system, or any other system with good separation of concerns between
its components.
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.
I have been a part of building several large products with teams of various
sizes. In each case we had a common library which was shared amongst all
components of the system, and contained functionality which was desired to be
kept the same across those components. For example, configuration was generally
done through that library, so all components could be configured in the same
way. Similarly, an RPC framework is usually included in the common library, so
all components can communicate in a shared language. The common library also
generally contains domain specific types, for example a `User` type which all
components will need to be able to understand.
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.
Most common libraries also have parts dedicated to databases, such as the
`redis` library example we've been using. In a medium-to-large sized system,
with many components, there are likely to be multiple running instances of any
database: multiple SQLs, different caches for each, different queues set up for
different asynchronous tasks, etc... And this is good! The ideal
compartmentalized system has components interact with each other directly, not
via their databases, and so each component ought to, to the extent possible,
keep its own databases to itself, with other components not touching them.
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.
The singleton pattern breaks this separation, by forcing the configuration of
_all_ databases through the common library. If one component in the system adds
a database instance, all other components have access to it. While this doesn't
necessarily mean the components will _use_ it, that will only be accomplished
through sheer discipline, which will inevitably break down once management
decides it's crunch time.
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.
To be clear, I'm not suggesting that singletons make proper compartmentalization
impossible, they simply add friction to it. In other words, compartmentalization
is not the default mode of singletons.
### Program Structure
Another problem with singletons, as mentioned before, is that they don't handle
multiple instances of the same thing very well. In order to support having
multiple redis instances in the system, the above code would need to be modified
to give every instance a name, and track the mapping of between that name, its
singleton, and its configuration. For large projects the number of different
instances can be enormous, and often the list which exists in code does not stay
fully up-to-date.
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:
```go
// 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:
```go
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
This might all sound petty, but I think it has a large impact. Ultimately, when
a component is using a singleton which is housed in a common library, that
component is borrowing the instance, rather than owning it. Put another way, the
component's structure is partially held by the common library, and since all
components are going to use the common library, all of their structures are
incorporated together. The separation between components is less solidified, and
systems become weaker.
What I'm going to propose is an alternative way to think about program structure
which still allows for all the useful aspects of a common library, without
compromising on component separation, and therefore giving large teams more
freedom to act independently of each other.