GTLT - on the production & consumption of resources

November 1, 2020


Programming languages rarely provide high-level patterns for the curation of in-memory resources. Low-level provisions always exist and have strong conventions ready for programmer consumption such as module systems, configuration utilities, or asset packaging [1, 2]. However, even though these resource primitives may exist at the root level of a language's programming environment, they are often unique to that programming context, and not abstract enough to provide guidance for programming-at-large. The provisioning and access to in-program resources appears to be studied, but with a narrow focus on specific resources, such as configuration model approaches or dependency injection [3,4].

I postulate that a new heuristic, hereby GTLT or ><, can be applied during application development such that programs shall tend to yield more desirable programming characteristics in contrast to applications that apply no correlated heuristic. These desirable characteristics should be lauded as near-universally desirable, across various programming communities (e.g. web, gaming, systems, data-science, etc). Examples of such desirable traits may be increased tendency of pure functions or reduced coupling between sub-systems.

GTLT is not intrinsically new, nor revolutionary. GTLT is a refined mechanism to discuss and quantify a concatenation of various existing design patterns, allowing for easier conversation and application of higher-level design between engineers & developers.

The following article is often abstract, verbose, and redundant as the same topics are discussed from different viewpoints. The impatient reader is invited to skip directly to "Understanding GTLT", then processed to the Case Study.

gtlt contrast GT vs LT: resource preparation during application startup


GTLT principles don't apply everywhere.


  • One-off applications/scripts
  • Domains where general purpose programming patterns do not generally apply, such as
    • GPU programs/shaders
    • Assembly programs
    • Globals-centric programs, like R apps or Jupyter notebooks


  • Applications supporting tunable runtime configuration
  • Applications producing or consuming external datas, or more plainly, doing I/O
  • Applications concerned with the abstraction of resources, internal or external
  • Applications expected to be tested via code


GTLT concepts are centered around the definition of a Resource.

What is a resource in the context of GTLT?

GTLT resources are values and procedures consumed in-memory by the application.

What about:

  • CPU? Memory? L1/L2 caches? Disk?
    • No. These machine resources definitely play a role, but not in this discussion. Certain communities, like GPU programmers, have to orient their entire applications around these constraints. GTLT is not for them.
  • The .NET definition of resources, "any non-executable data that is logically deployed with an app."
    • Yes, these resources do count, but GTLT considers a resource to be a superset of this definition
  • A RPC call?
    • The local binding code to execute the call counts as a resource here, but not literally the remote procedure.
  • Functions, modules, packages, primitives, constants, env, datas.
    • Yes.

Understanding GTLT

All programs are GT, LT, or GTLT programs. Every program, ever. Punchcard programs, even.

>< can be broken down into parts. Programs can be either:

  • LT, <
  • GTLT, ><
  • GT, >

GTLT implies that a program may be a blend of GT & LT. Like the classic OOP versus FP, there are not a rigid set of absolutist traits that make something one or the other. However, when studying application code, versed programmers should have little difficulty classifying the style of the program under question.

GTLT programs are defined by where runtime resources are bound. When classifying something in the GTLT range, you inspect whether resources wired together early in execution then accepted by functions later, or if the are resources bound later by modules or standalone statements themselves.


  • GT programs express intent at leaf nodes of the execution graph, with no direct preparation of resources. Resources (instances, factories, handles, config, etc) are hoisted towards the root of the program, such that by the time leafy nodes execute, the required resource (eager or lazy) is ready for direct consumption by the callee.
  • LT programs tend to express implementation details at all levels of the program, often by means of self supplying (importing) resources to fulfill their execution.
  • GTLT programs attempt to practice GT as much as feasible. GT programs allow for LT, ideally only for global or runtime primitive access.
    • Purists could take this further, which is discussed below.
    • When classifying a program as GTLT, it implies that GT principles are attempted with intention, with affordances for some LT occurrences. A program that by happenstance has some GT principles at work should almost always still be classified as LT. Classification is art here, not science, like much of software development.

LT < programs

The defining characteristic of a LT program is that it tends to obtain resources physically adjacent to where they are needed using language specific programming constructs, versus obtaining them via functional constructs. I assert that functions are unique from general programming constructs in that they are predominantly a mathematical construct, but conveniently materialized via programming constructs.

Here are examples of very basic LT modules:

// my-worker.ts
import Resource from "./resource";
// ^ my-worker's resource is obtained from a programming
// construct--a direct consumption of the module system
const doWork = (a: number) =>;
// ^ Resource is freely consumed within functions & statements in the module

Another basic example:

// my-worker.ts
const doWork = (a: number) => process.env.RESOURCE.match(a);
//  ^ Direct consumption of global runtime modules. This example also \
// demonstrates impurity by means of an I/O read. (Don't heckle me
//  on this, the claim isn't perfectly crisp :))

GT >

GT programs have a single runtime entry point from which all downstream effects originate on behalf of the programmer, as opposed to on behalf of the language or runtime. No library or module inclusion implicitly induces effects. All procedures accept resources as input, with the exception of module local resources, and possibly, global primitive resources from the programming environment tooling.

// my-module.ts
// a GT compliant module
export const doX (coll: Collection<A>) => [ => / Math.PI)]
export const doY (resource: A, payload: B) => resource(payload, doX)
  • Resources imported? None.
  • Resources provided via functions, or within the local module scope? Check.

Global primitives, such as primitive literals (eg [], array literal), were not passed in in the above example. However, they are welcome to be. Some foundational permissions with respect to accessing to global primitives ought be made to make programming a GT module tenable. Here's what a stricter GT implementation of the same module may look like:

// my-module.ts
// a GT strictly compliant module
export const createModule ({ map }) => {
  const doX (coll: Collection<A>) => map(coll, it => / Math.PI)
  const doY (resource: A, payload: B) => resource(payload, doX)
  return { doX, doY }

The above implementation removes the implicit global resource, array literals, and instead passes in a map resource, which is compatible with the passed collection. In our earlier example, the Collection type was concretely mapped to an Array, but the new interface in GT strict mode allows for a different implementation, such as hypothetically taking a Collection and mapping to Record<string, T> type. The reduced coupling often allows for such increased flexibility.

Math.PI is also an implicit global resource, and a great example of a permitted GT violation, given that there is no tangible benefit to applying abstraction to such a well known, universal constant.

Is this just Dependency Injection?

Yes and no. Culturally, DI systems do not universally consider any and all programming references or values as injectable resources. GTLT is explicitly widened in this regard. Further, GTLT is tooling agnostic, and suggests that a FP-aligned pattern can fully shift all of the "injection" responsibility to the developer, versus 3rd party tooling. GT accelerates DI objectives far enough such that DI programs, which otherwise are still often quite LT, should look drastically different after a GT refactor. DI is a fantastic idea applied with too narrow of scope. DI is also talked about with such ambiguity that its significance and impact may be reduced to only a reflection of the tools implementing its ideas. I am not ready to make the prior claim with certainty because I do not have sufficient experience with enough DI tools in enough languages. My experience using GUICE, Angular 1.x, and others certainly mold my opinion on the topic.

Is this just the Dependency Inversion Principle?

No. It's wider scoped, but they undoubtedly share the similar ideas. See more on DIP, the D in SOLID.


GTLT is the union of GT & LT. A program can exhibit both GT & LT traits.

Why is it called GTLT

Because of the geometric visualization of the preparation of resources.

After the entrypoint to a LT program, resources are bound on downstream, logically, on evaluation of the resource tree.

// LT, psuedo-code program graph
    export fn_b
    import module::b
    import module::config
    fn_a -> f(fn_b,config)

In a GT program, resources are prepped and bound up-front, then passed into the entrypoint, who will certainly execute routines embedded within those bound resources:

// GT, psuedo-code program graph
import module::a
import module::b
import config

|> entry

Case Study

The citrus app will be put under scrutiny. Citrus is a fake app all about fruits. Delicious sweet citrusy fruits 🍊. Citrus is a dummy server that hosts a tiny GQL & REST(-like) API to do I/O with a SQL fruit database. Fruit records are quite basic:

# fruit.graphql
{ id: ID!, name: String!, tastes: String }

Application overview

The citrus#lt branch looks and feels like a common typescript or node.js application. The LT patterns demonstrated are present in nearly all programming languages, not just the node community. The source code hosted in the citrus repository is intentionally minimal--just four files--such that readers can follow a full transformation from LT to GT. A downside of transforming such a small app is that the impacts of GT may not be as readily apparent. To mitigate such risk, commentary shall accompany and reinforce the principles behind the actions.

Let's look at an abbreviated snapshot of the LT source. Much of the files will be truncated, with only a few interesting tid-bits left intact to demonstrate how the application works.

// config.ts
// read config from env, assert schema

const { PORT, POSTGRES_PASSWORD } = process.env;
export const serverPort = Num.withConstraint(
  (i) => Number.isInteger(i) && i > 1000,
).check(PORT ? parseInt(PORT) : 7777);
/* ... snip snip */
export const dbPassword = Str.withConstraint((x) => !!x.length).check(
// db.ts
// prepare a database client pool

import pg from "pg";
import { dbConfig } from "./config";
export const pool = new pg.Pool(dbConfig);
// server.ts

import Koa from "koa";
import { createServer } from "http";
import { serverPort, serverHostname } from "./config";
import { postgraphile } from "postgraphile";
import { pool } from "./db";
import { promisify } from "util";

// create an empty server
const app = new Koa();
export const server = createServer(app.callback());

// setup a GET /fruits/:id route
app.use((async (ctx, next) => {
  const [_, id] = ctx.path.match(/fruits\/(\d+)/i) || [];
  if (id)
    ctx.body = await pool
        text: `select name from fruits where id = $1 limit 1`,
        values: [id],
      .then((r) => r.rows[0].name);
  return next();
}) as Koa.Middleware);
// setup a graphql handler, with graphiql instance,
// reflecting the database schema
app.use(postgraphile(pool, ["public"], { graphiql: true }));

export const listen = () =>
  promisify((port: number, hostname: string, cb: () => void) =>
    server.listen(port, hostname, cb),
  )(serverPort, serverHostname);

// start & announce the server
if (!module.parent)
  listen().then(() =>
    console.log(`started on ${serverHostname}:${serverPort}`),

// client.ts - omitted

In less than 50 lines, a very basic application has been expressed.

If the database and server were both started, one could open http://localhost:7777/graphiql, execute a basic query:

  allFruits {
    edges {
      node {

and get a rather unamusing result:

  "data": {
    "allFruits": {
      "edges": [
          "node": {
            "id": 1,
            "name": "strawberry"
          "node": {
            "id": 2,
            "name": "apple"
          "node": {
            "id": 3,
            "name": "orange"

Migrating the config module

GT does not allow for effects as part of module load. Remember, GT states that execution begins explicitly after the programmer commisions the app to start, after resource preparation. Reading runtime resources and actively operating with them during module evaluation both constitute violations. Consumption of such resources should only occur after the program has been explicitly started in earnest by programmer intent.

- dotenv.config()
+ export const loadEnvConfig = () => dotenv.config()

- export const serverPort = process.env.PORT ? /* ... */
+ export const getConfig = (env: Record<string, string>) => {
+   return {
+     serverPort: env.PORT ? /* ... */
+   }
+ }

dotenv.config() is a popular API that mutates the node.js VM's process.env memory with key-value pairs from a text file. Eager effects like this mutation make software modules extremely rigid. Such an effect limits code reuse opportunity. More importantly, the effect was not expressed directly by the programmer to execute. Like most patterns found in GT, things happen as a result of function invocations, and thus should be associated with verbs. GT naturally promotes explicit expression of intent, and demotes implicit behaviors like these. Thus, eager invocations during module sourcing is considered a GT violation.

One may have observed that onload we were previously reading directly from the process.env references. This is a paramount GT violation. Resources should be passed, or minimally support being passed. One can see that after the diff is applied, the application's configuration is no longer implicitly generated by the effect of module load. Instead, the configuration provided by the module must be explicitly requested via function. By acknowledging that the application configuration value is truly a function of the environment (in the mathematical sense), code comprehension should increase after the refactor by means of reducing implicitness. Why is this simple refactor powerful? Other modules in the system are now much less likely to couple themselves to the config module. Even if some coupling remains, the coupling will be reduced to a single edge (calls to getConfig), vs. N edges, where N = count of configuration values. Applications with fewer runtime edges between module nodes are intrinsically simpler. Additionally, authoring tests becomes more tenable, as explored next.

Migrating the config module tests

What are some challenges we may encounter testing the LT version of the configuration module?

  • If env var inputs are evaluated on module load, how do we test different inputs?
  • If we do test different env inputs, how do we do so without meddling with our actual testing process?

Unfortunately (that's right--unfortunately), languages support mechanisms for addressing the above. Let's break down the full LT test module for our config.ts file.

// config.test.ts - (jest testing framework)
const KNOWN_GOOD_ENV = { PORT: "7890" /* snip */ };

describe("config", () => {
  const OLD_ENV = process.env;
  beforeEach(() => {
    // :|
    // :|
    process.env = { ...OLD_ENV, ...KNOWN_GOOD_ENV };

  afterAll(() => {
    // :|
    process.env = OLD_ENV;

  it("should not error on import", () => {
    expect(() => require("../config")).not.toThrow();
      case: "bad db pw",
      envPatch: {
        DB_PASSWORD: "",
      errorMatch: /env.example/,
      case: "bad port",
      envPatch: {
        PORT: "100",
      errorMatch: /constraint check/,
  ].map((opts) =>
    it(`should error on ${}`, () => {
      // :|
      process.env = { ...process.env, ...opts.envPatch };
      expect(() => require("../config")).toThrowError(opts.errorMatch);

We overcame all of our aforementioned challenges! But we sacrificed many values in order to do so. What is undesirable about this code?

  1. Mutations of the running process. Almost never is it ok to meddle with your running process in order to test a module. Absolutism is generally unwelcome in programming, however, there are severe drawbacks to this. First, how can you safely run tests in parallel, which such mutations? Plainly, you cannot! Reads to your dynamically mutated world will yield non-deterministic values as inputs for scenarios under test, causing unpredictable failures. Synchronization problems are common in code like this. Secondarily, when you mutate a process that is not ultimately owned by you, what guarantees do you have that such mutations are safe? From a user's perspective, jest owns this process--it is the entrypoint and control flow executor. It is intrusive for the tester to mutate the owner's process without permission.
  2. Dropped static analysis & typing on the module under test. In our case, we have to use require over import, such that we could do a dynamic require versus a static import. We have to do this, again, because the module evaluation is effectful, so we need to call require to get a readable output. This indirect use of require to execute configuration evaluation is a naive and indirect design. Correctness is incredibly valuable. Static analysis is often a major component on the road to correctness. Sacrificing it must be justified. The sacrifice is not justified here, especially because it is so easy to avoid, as we will see shortly.
  3. Overhead of managing the module system. See usage of jest.resetModules(). Much of the code in this test suite is oriented around managing test state, specifically around the VM's module system cache. This effort onboards cost and detracts from focus on the module under test.
  4. State resets. A consequence of the undesirable mutation discussed above is that we now must clean the ephemeral, dirtied state. What happens when fallible programmers forget to reset their state? What happens when there is an error, and state reset logic is not triggered? If these sound like unlikely errors to the reader, rest assured that optimistically managed mutable state is a still a common programming behavior in 2020. Managed mutable state is undeniably the modus operandi for many professional programmers. The most popular languages in the world right now are dynamic, and allow for this behavior by default. Even those languages that do have state of the art typesytems allow for such behavior. I do particularly like how rust-lang forces users to opt-in to mutability via a mut keyword. This makes mutable memory exceptional, markes it almost as a wart, and hopefully prompts the user to reduce usages as much as feasible.
  5. Evaluation order negatively impacts test case specificity. In one test case, when dotenv.config() reads the filesystem, it throws because POSTGRES_PASSWORD is non-truthy. However, I did not want to test dotenv.config() logic at all. Instead, I wanted to test the password schema validator beneath it. To work around this, the test now needs to know about the internal module structure and mock out the dotenv module. How cumbersome.

Module systems in many languages allow for eager binding and execution of resources. Even OCaml, my new crush. The consequences of this capability cannot be understated. There is not much code in this test file, yet even so, we've enumerated quite a few macro consequences induced by simply programming in common LT style.

To be fair, it is worth confessing that the creation of functions on module evaluation is certainly eager evaluation, effectually creating resources. However, we make exceptions for functions as they are mathematical in nature and naively assumed uninvoked on module evaluation by GT. The effect of creating a function resource serves the purpose of creating a resource flow graph, defining all possible future flows of resources, including functions themselves, after the entrypoint function is formally invoked.

Back to tests. When we GT-ified our config module, how did those tests look afterwards?

// config.test.ts - (jest testing framework)
import { getConfig } from "../config";
const KNOWN_GOOD_ENV = { PORT: "7890" /* snip */ };

describe("config", () => {
  it(`should accept valid env`, () => {
    expect(() => getConfig(KNOWN_GOOD_ENV)).not.toThrowError();
  const failureCases = [
    /* snip */
  failureCases.forEach((opts) =>
    it(`should error on ${}`, () => {
      const env = { ...KNOWN_GOOD_ENV, ...opts.envPatch };
      expect(() => getConfig(env)).toThrowError(/constraint check/);

What has changed? We'll, all of our configuration is now the result of a function call. We removed all of our hacks, and all of the raised concerns immediately vanish.

Pure functions.

Thanks, pure functions. I skipped out on testing logic that exercised dotenv.config() for brevity, which is also a key difference. However, we didn't explicitly test it previously. It could be further be argued that such a test would be frivolous. But let's pause that discussion and keep focused on GTLT!

Migrating the db module

// db.ts - lt
import pg from "pg";
import { dbHost, dbPassword, dbPort, dbUser } from "./config";

export const pool = new pg.Pool({
  host: dbHost,
  port: dbPort,
  user: dbUser,
  password: dbPassword,
// db.ts - gt
import pg, { PoolConfig } from "pg";
export const createPool = (opts: PoolConfig, db = pg) => new db.Pool(opts);

Config resources are now passed, not self-sourced via direct import. The database module resource is now passed, not self-sourced via direct import.

However, was it worth keeping this module at all?

// flip the createPool args
export const createPool = (db, opts) => new db.Pool(opts);
// remove the extra call
export const createPool = (db) => db.Pool;

Omitting the fact that we dropped the new keyword, createPool has been reduced to function that just does an object field lookup. That does not warrant a software module! For this reason, our GT transition removes this full module, outright.

At this point, we need to move our db pool creation inline to wherever it is needed and adjust tests to accommodate. One place that uses the db pool is server.ts. However, because server.ts is effectful on eval, just like config.ts above, we need to update our test mocks during this incremental transition.

-jest.mock("../db", () => {
+jest.mock("pg", () => {
-  return {
-    pool: {
-      query: async () => {
-        return queryResult;
-      },
-    },
-  };
+  function Pool() {}
+  Pool.prototype.query = async () => {
+    return queryResult;
+  };
+  return { Pool };

Migrating the server module

In a nod to functional core, imperative shell, let us move the effectful entities of server.ts to bin.ts, and study the new file's roles and responsibilities.

// bin.ts
/* import module resources */
/* prepare resources */
/* execute GT entry point */

This psuedo-code concisely represents the heart of GT. General resources are curated then passed into a logical beginning of general execution. After we have globbed up resources, we thread them through a needle, a single entrypoint representing the vertex of the greater-than symbol > in GT. When execution reaches that sharp point, it is reasoned that the program now begins executing raw intent, verses the LT execution of intent+resource orchestration.

We can conceptualize the execution of a LT program by imagining that the entrypoint opens module X, X opens Y, and Y executes to produce value Z". GT pulls all of that imperative behavior inside out. It's analogous to reaching into a sock, pinching the toe area, and pulling it inside out. When you conceptualize execution in GT, you think of the LT flow, but with an inverted perspective. "To get the final produced value Z, we previously bound Y as the producer of Z, and we bound system X to implement Y". > vs <.


Here's our actual bin "shell":

// bin.ts
import { getConfig, loadDotEnv } from "./config";
import { Pool } from "pg";
import { bindMiddlewares, createServer, listen } from "./server";
import Koa from "koa";
import http from "http";

async function start() {
  /* prepare resources */
  const config = getConfig(process.env);
  const { serverHostname, serverPort, dbHost, dbPassword, dbPort, dbUser } =
  const pool = new Pool({
    host: dbHost,
    password: dbPassword,
    port: dbPort,
    user: dbUser,
  const [app, server] = createServer(Koa, http);
  bindMiddlewares(app, pool);

  /* execute GT entry point */
  await listen(server, config);
  console.log(`started on ${serverHostname}:${serverPort}`);


Let's take a peek at the full server.ts file. Unsurprisingly, server.ts is now a small set of functions.

// server.ts
type GqlMiddleware = typeof postgraphile;
type Http = typeof http;
type KoaConstructor = typeof Koa;
type WithApp = { app: Koa };
type WithGql = { gqlMiddleware: GqlMiddleware };
type WithPool = { pool: Pool };

export const createServer = (App: KoaConstructor, { createServer }: Http) => {
  const app = new App();
  const server = createServer(app.callback());
  return [app, server] as const;

export const createFruitMiddleware: (pool: Pool) => Koa.Middleware =
  (pool) => async (ctx, next) => {
    const [_, id] = ctx.path.match(/fruits\/(\d+)/i) || [];
    if (id)
      ctx.body = await pool
          text: `select name from fruits where id = $1 limit 1`,
          values: [id],
        .then((r) => r.rows[0].name);
    return next();

export const createMiddlewares = ({
}: WithPool & WithGql) => [
  gqlMiddleware(pool, ["public"], { graphiql: true }),

export const bindMiddlewares = ({
}: WithApp & WithPool & WithGql) => createMiddlewares(rest).forEach(app.use);

export const listen = (
  server: Server,
  { serverPort, serverHostname }: Config,
) => new Promise((res) => server.listen(serverPort, serverHostname, res));

Something readers may notice is that, plainly, the GT version is more code. That's a proper downside to GT.

  • To accept resources, logic must all be nested in functions. More functions => more LOC.
  • To author functions, additional structs and type definintions may need to be authored. More defintions => more LOC.
  • To use resources, some LOCs may be added to support unpacking values or renaming symbols. More statements => more LOC.

Extra content absolutely hinders readability in comparison to LT. A flat imperative instruction set, as seen in the LT program, reads comfortably like a good book. This code does not exhibit that trait. However, keep in mind that server.ts is not supposed to read like a book--the imperative, readable nature of the old version was ported into to bin.ts. The readability trait is still present and active in that file. There's a readability upside here now too. Individual server.ts functions tend to do very little. Even more so, these functions tend to all be verb oriented. How did this come to be? By happenstance? By hoisting resource preparation upstream, keeping resource coupling out of server.ts, the actual steps within functions are quite terse, and often realizable in simple expressions. The unwelcome union of resource manipulation and expression-of-intent within function bodies yields complexity, common to LT applications. Remove the former, and functions take a clear & expressive form.

Aside: If you are thinking that algebraic effects are the next logical evolution in this process, such that we can just express intent without passing resources--me too. In 2020, we're not yet in an effects enabled world. I intend future writings to drill down on this.

bindMiddlewares is a great example of the advantages we just discussed. It vaguely has the form of:

resources => iterator(f, collection)

Simple, as promised! Let's study the real version:

export const bindMiddlewares = ({
}: WithApp & WithPool & WithGql) => createMiddlewares(rest).forEach(app.use);

This function is small and easily grokable. Ironically, applying GT rapidly assists in revealing outstanding GT violations quite well. Was createMiddlewares passed in as an argument? Was the logic that wired createMiddlewares(...) to app.use passed in? They are not! Due to these violations, can we even claim this to be a GT function? Yes, but justification is warranted.

A function must offer intrinsic value. If logic for doing work™ is considered a resource, and we continuously declare that something above the function under study is responsible for providing the resource, we have a non-halting condition where nothing is ever permitted to own necessary work. Thus,

  • GTLT permits local function resources that are supportive of the intrinsic nature of the function
  • GTLT is permissive of local module function references

With these permissions, createMiddlewares doesn't need to be passed in. Further, bindMiddlewares can itself create ephemeral resources to execute whatever is needed for binding middlewares. An interesting exercise is to ask "What if we tried hoisting these entities from bindMiddlewares" upwards? See the #gt/3-fn-instrinsics branch. Here's what I suggest it could look like:

// server.ts
type WithCreateMiddlewares = { createMiddlewares: typeof createMiddlewares };
export const bindMiddlewares = ({
}: /* snip */
WithCreateMiddlewares & {
  bind: (mws: Koa.Middleware[]) => void;
}) => bind(createMiddlewares(rest));
// bin.ts
  gqlMiddleware: postgraphile,
  bind: (mw) => app.use(mw),

Pause for a moment to study what's really going on here. bin.ts now knows of createMiddlewares, and specifies the binding implementation. For what? To call a function named bindMiddlewares? If bin.ts knows how to create and bind middlewares, what's the point of bindMiddlewares at all? Candidly, not a lot! bindMiddlewares has been stripped of its intrinsic value. It does read nicely, but bindMiddlewares intrinsically should own binding middlewares! Instead, in pursuit of extreme GT principles, idealism has defeated common sense. For this reason, the prior discussed affordances are made. We allow local module references, and we allow functions to define inline resources to carry out their purpose. Without these affordances, programs would have to represent intent & implementation details entirely during the resource preparation phase, undermining the value of functions and module systems outright.

Migrating server test code

The interesting elements of study for the server test code are not what is included, as much as what is not included. No module patching required. No hard coupling of the server config module to the the server module. No state resets. No constrained parallelism, beyond those constraints from the test framework. Our fixture creates a configuration, passes it to the server module functions, enabling clean preparation and removal of resources, pre and post test. Without a doubt, GT code produces better testing code than LT, even if it comes with tradeoffs discussed previously.

let fixture: ServerFixture | null = null;

beforeEach(async () => {
  fixture = await createFixture();
afterEach(() => fixture?.server?.close());

describe("server", () => {
  it("should return a fruit on GET /fruit/:id ", async () => {
      rows: [{ name: "teststrawberry" }],
    const fruit = await fixture?.client?.getFruitById(1).then((r) => r.text());

Case Study Conclusion

The case study showed a brief translation between LT & GT code. The primary takeaways were that:

  • GT code produced cleaner tests
  • GT yielded terse, expressive functions
  • GT code moved imperative code towards the program root, and left function oriented (not strictly FP) code to modules
  • GT promoted a breakup between logical intent expression, embodied by the function verb, and resource curation to satisfy that intent
  • LT code was more compact
  • LT code was less portable
  • LT code demonstrated more per-module coupling, observed by the count of symbol import edges between local modules

Feel free to head to the citrus source to study further.


Isn't this just ...

DI? DIP? FunciontonalCoreImperativeShell? As discussed above, yes and no. GTLT is a sum of various of these ideas, wrapped in new language. Again, GTLT as a geometric concept should enable design discussions, using prior art as embedded talking points.

This (program|module) could be more GT if you apply DI in function X.

This file exhibits some GT violations. 3rd party modules are being imported and used directly by functions Y & Z.

This module is not side-effect free on load. If you apply some GT refactors here, it would be easier to test. What do you think?

If GTLT had a function signature, what would it be

Maybe something like resources => resources => work => ()? It's not really a valid question anyway :).