Benchmark: composition first vs composition after

Script Preparation code:

var composeFirst = (...fns) => fns.reduce((f, g) => a => f(g(a)))

var composeAfter = (...fns) => a => fns.reduceRight((g, f) => f(g), a)

var addOne = x => x + 1

var addOnes = Array.from({ length: 20 }, () => addOne)

calcFirst = composeFirst(...addOnes)

calcAfter = composeAfter(...addOnes)

Tests:

Reduce to composed function
calcFirst()
Iterate through functions
calcAfter()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce to composed function
Iterate through functions

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**What is being tested?**

The provided benchmark defines two approaches to composing functions: `composeFirst` and `composeAfter`. Composing functions means creating a new function that applies multiple existing functions in a specific order. The two approaches differ in how they handle the sequence of functions:

* `composeFirst`: Reduces to a composed function by applying each function in the original order, with the result of one function becoming the input for the next.
* `composeAfter`: Iterates through the functions in reverse order, starting from the last function and moving backwards, passing the return value of each function as input to the previous one.

**Options being compared**

The two approaches are compared on a test case that involves creating an array of 20 functions (`addOnes`) that simply add 1 to their input. The resulting composed function (`calcFirst` or `calcAfter`) is then executed.

**Pros and Cons of each approach:**

* **composeFirst**: Pros:
	+ More intuitive and easier to understand for developers familiar with functional programming.
	+ May perform better due to less overhead from iterating through the functions in reverse order.
* Cons:
	+ Can be slower due to the need to create an intermediate result object on each iteration.
* **composeAfter**: Pros:
	+ May perform better due to the ability to cache results and avoid unnecessary function calls.
	+ More efficient for large arrays or complex composed functions.
* Cons:
	+ Less intuitive and may require more cognitive effort to understand.

**Library usage**

In the provided benchmark, there is no explicit library usage. However, it's worth noting that some JavaScript engines might rely on internal libraries or optimized implementations of basic operations (e.g., array iteration).

**Special JS features or syntax**

There are no special JavaScript features or syntax used in this benchmark. It solely relies on standard ECMAScript 2020 syntax.

**Other alternatives**

If you're interested in exploring alternative approaches, consider the following:

* **Using `Array.prototype.reduce()`**: Instead of defining your own composition function, you can use the built-in `reduce()` method to create a composed function.
* **Using `Function.prototype.bind()`**: You can also use `bind()` to create a new function that "remembers" the context of previous function calls.

Here's an example using `Array.prototype.reduce()`:
```javascript
var composeFirst = (...fns) => fns.reduce((f, g) => a => f(g(a)));
```
And here's an example using `Function.prototype.bind()`:
```javascript
var composeAfter = (...fns) => a => fns.reduceRight((g, f) => f.bind(null)(g), a);
```

Related benchmarks:

composition first vs composition after (version: 0)

Testing to composition functions. One the reduces to a composed function and the second which iterates through the functions passing a return value to the next function.

Comparing performance of: Reduce to composed function vs Iterate through functions

Created: 3 years ago by: Guest

Jump to the latest result

Reduce to composed function

Iterate through functions

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):