Benchmark: maping - MeasureThat.net

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var itemsArray = [];
var itemsObj = {};

for (let i = 0; i < 100000; i++) {
	itemsArray.push({
    	id: i,
      	author: 'Robert' + i,
    })
    itemsObj = {...itemsObj, [i]: {id: i, author: 'Robert' + i}}
}

Tests:

item by key
console.log('itemsObj ', itemsObj[59430])
filtered item
console.log(itemsArray.filter(item => item.id === 59430)[0])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
item by key
filtered item

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is tested, compared, pros, cons, and other considerations.

**Benchmark Definition**

The provided JSON defines a benchmark named "maping" with two test cases: "item by key" and "filtered item".

The **Script Preparation Code** initializes an array `itemsArray` and an object `itemsObj`. The script then populates these data structures using nested loops, where each iteration creates a new object in the `itemsArray` with properties `id` and `author`, as well as updates the `itemsObj` with key-value pairs.

The **Html Preparation Code** includes a reference to the Lodash library, which is used for functional programming utilities. In this case, it's loaded via a CDN link.

**Individual Test Cases**

There are two test cases:

1. **"item by key"`**: This test case measures the performance of accessing an item in the `itemsObj` using its key. The benchmark definition uses JavaScript's built-in `console.log()` function to output the value of `itemsObj[59430]`.
2. **"filtered item"`**: This test case measures the performance of filtering an array in JavaScript to find a specific item based on a condition (in this case, `item.id === 59430`). The benchmark definition uses JavaScript's built-in `filter()` method and `console.log()` function to output the first filtered element.

**Comparison**

The two test cases compare the performance of direct object access (`itemsObj[59430]`) versus array filtering (`itemsArray.filter(...)[0]`).

**Pros and Cons**

1. **Direct Object Access**: This approach is simple, straightforward, and typically faster for small to medium-sized datasets.
	* Pros: Easy to understand, fast execution.
	* Cons: May be slower for large datasets or when the object's key is not present.
2. **Array Filtering**: This approach is more flexible and can handle larger datasets or different data structures.
	* Pros: Can handle missing keys, efficient for large datasets.
	* Cons: Typically slower due to the overhead of creating an array and iterating over it.

**Other Considerations**

1. **JavaScript Engine Optimization**: Modern JavaScript engines, like V8 (used in Chrome), are optimized for performance and often use techniques like caching and inlining to improve execution speed.
2. **Library Usage**: The Lodash library is used extensively in modern web development, but its inclusion can introduce additional overhead due to parsing and loading time.
3. **Benchmarking Best Practices**: When benchmarking JavaScript code, it's essential to consider the test environment, device, browser, and operating system. In this case, both tests are run on a Chrome 84 instance on a Windows desktop.

**Alternatives**

If you were to rewrite these benchmarks using alternative approaches:

1. **Use a library like `Map` instead of direct object access**: JavaScript's built-in `Map` data structure provides faster and more efficient key-based lookups.
2. **Use a custom filtering function or sorting algorithm**: Depending on the specific requirements, you might be able to optimize the filtering process using a custom implementation, such as a binary search algorithm.

Keep in mind that rewriting these benchmarks would require significant changes to the code and potentially introduce new performance considerations.

I hope this explanation helps!

Related benchmarks:

maping (version: 0)

Comparing performance of: item by key vs filtered item

Created: 5 years ago by: Guest

Jump to the latest result

item by key

filtered item

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):