Benchmark: Ramda lens path vs precompiled lens path

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

function compileFn(source) {
  // eslint-disable-next-line no-eval
  return eval(`(function() {return ${source}})()`);
}

function compiledPathGetter(path) {
  return compileFn(/* typescript */ `function _getter (obj) {
    return obj.${path};
  }`);
}

function ramdaGetter(path) {
  const getter = R.view(R.lensPath(path.split('.')));
  
  return getter;
}

window.theme = { colors: { red: 'red' } };

window.getNestedColor = compiledPathGetter('colors.red');
window.getNestedColorRamda = ramdaGetter('colors.red');

Tests:

Precompiled
getNestedColor(theme);
Ramda
getNestedColorRamda(theme);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Precompiled
Ramda

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

The benchmark compares two approaches to accessing nested properties in an object using JavaScript: Ramda's `lensPath` function and precompiling a custom getter function. The test case uses Ramda version 0.25.0.

**Precompiled Lens Path**

In this approach, the `compileFn` function is used to precompile a custom getter function for accessing nested properties in an object. The `compiledPathGetter` function takes a path as input and returns a compiled getter function that can be called directly on the object.

Here's what happens:

1. The `compileFn` function takes a source code string as input, which defines a JavaScript function with a single method that returns the nested property value.
2. The `compiledPathGetter` function calls `compileFn` with a custom getter function definition, which is created using Ramda's `function` constructor.
3. The compiled getter function is stored in the `window.getNestedColor` object and can be called directly on the `theme` object.

**Ramda Lens Path**

In this approach, the `ramdaGetter` function uses Ramda's `lensPath` function to access nested properties in an object.

Here's what happens:

1. The `ramdaGetter` function takes a path as input and returns a getter function that can be called directly on the object.
2. The `R.view` function is used to create a getter function that extracts the value at the specified path from the object.
3. The getter function is returned by the `ramdaGetter` function and can be called directly on the `theme` object.

**Pros and Cons**

Precompiled Lens Path:

Pros:

* Potential performance improvement due to optimized compilation
* Can lead to better code readability and maintainability

Cons:

* Requires manual configuration of the getter function path
* May introduce additional overhead due to compilation

Ramda Lens Path:

Pros:

* Convenient and concise syntax for accessing nested properties
* Optimized by Ramda's compiler, reducing runtime overhead

Cons:

* May not be as readable or maintainable as precompiled approach
* Limited control over compilation and optimization

**Special Considerations**

None mentioned in this benchmark.

**Other Alternatives**

1. **Destructuring**: Instead of using getter functions or `lensPath`, you can use destructuring to access nested properties directly on the object.
2. **Object.prototype.hasOwnProperty.call**: You can use this method to check if a property exists and then access it if true, instead of relying on getter functions.

These alternatives are not part of the benchmark, but they might be worth considering in similar scenarios.

Related benchmarks:

Ramda lens path vs precompiled lens path (version: 0)

Comparing performance of: Precompiled vs Ramda

Created: 4 years ago by: Guest

Jump to the latest result

Precompiled

Ramda

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):