Benchmark: quick test - MeasureThat.net

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/lodash/4.16.0/lodash.min.js"></script>

Script Preparation code:

var arr = [{a: 111}, {b: 2222}, {c: 3333}]

Tests:

iodash
var match = 2222; var index = -1; for (var i = arr.length - 1; i >= 0; i--) { if(arr[i] == match) { index = i; replaceElementInArray(arr, index, {d: 3333}) } }
vanilla
var match = 2222; var index = -1; for (var i = arr.length - 1; i >= 0; i--) { if(arr[i] == match) { index = i; arr[i] = {d: 3333} } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
iodash
vanilla

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.

Measuring the performance of different approaches to find an optimal solution is crucial in software development, especially when dealing with JavaScript applications. Let's break down the provided JSON and explain what's being tested.

**Benchmark Definition**

The benchmark definition specifies two scripts that will be executed to measure their performance:

1. **Script Preparation Code**: This code creates a sample array `arr` containing three objects: `{a: 111}, {b: 2222}, {c: 3333}`.
2. **Html Preparation Code**: This line includes a reference to the Lodash library, which provides a set of high-order functions that can be used for functional programming tasks.

**Individual Test Cases**

There are two test cases:

1. **"iodash"`**: This script uses a traditional `for` loop to iterate through the array from right to left (`arr.length - 1; i >= 0`). It finds the index of the object with value `2222`, and then replaces the element at that index with an object containing `d: 3333`.
2. **"vanilla"`**: This script also uses a traditional `for` loop, but it updates the original array by assigning a new object to the found index (`arr[i] = {d: 3333}`). The main difference is that this approach modifies the original data.

**Pros and Cons**

Both approaches have their advantages and disadvantages:

* **"iodash"`**:
	+ Pros: This approach may be more efficient since it doesn't require modifying the original array, which can reduce unnecessary copies.
	+ Cons: It requires using a separate function (`replaceElementInArray`) to update the element at the found index, adding overhead.
* **"vanilla"`**:
	+ Pros: This approach modifies the original data in-place, reducing memory allocations and copying.
	+ Cons: It can be slower due to the extra copying required when updating the array.

Other considerations:

* Using a library like Lodash can provide optimized implementations for certain tasks, such as iterating through arrays or finding elements by value. In this case, the `find` function is used from Lodash.
* The test cases only use basic JavaScript features, so there are no special JS features or syntax to explain.

**Alternatives**

If you wanted to explore alternative approaches, some options could be:

* Using a more modern JavaScript approach like `findIndex` and `map`:
```javascript
const index = arr.findIndex(obj => obj.b === 2222);
arr[index] = {d: 3333};
```
This would eliminate the need for manual iteration and indexing.
* Using a library like `underscore` or `ramda`, which provide optimized implementations for functional programming tasks.
* Using a JavaScript engine that supports Just-In-Time (JIT) compilation, such as V8 in Chrome or SpiderMonkey in Firefox. This could potentially lead to significant performance improvements.

Keep in mind that the best approach will depend on your specific use case and requirements. Measuring performance using tools like MeasureThat.net can help you determine the most efficient solution for your needs.

Related benchmarks:

quick test (version: 0)

Comparing performance of: iodash vs vanilla

Created: 7 years ago by: Guest

Jump to the latest result

iodash

vanilla

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):