Benchmark: array vs onject - MeasureThat.net

Tests:

obj
let data = [40, 30, 20, 10]; let labels = ["D", "C", "B", "A"]; const res = data.map((n, i) => ({ [labels[i]]: n })) .sort((a, b) => Object.values(a)[0] - Object.values(b)[0]) .map(o => Object.keys(o)[0]);
arr
let data = [40, 30, 20, 10]; let labels = ["D", "C", "B", "A"]; const res = data.map((n, i) => [labels[i], n]) .sort((a, b) => a[1] - b[1]) .map(a => a[0])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
obj
arr

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.77 Safari/537.36

Browser/OS: Chrome 91 on Windows

View result in a separate tab

Test name	Executions per second
obj	403234.5 Ops/sec
arr	4150945.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this benchmark.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: using an array (`arr`) versus using an object (`obj`).

**Approach 1: Using an Array (`arr`)**

In this approach, the `data` array is mapped to a new array with objects, where each object has a key-value pair with the label as the key and the value from the data array as the value. The resulting array is then sorted by the values in ascending order and mapped to an array of just the keys.

**Approach 2: Using an Object (`obj`)**

In this approach, the `data` array is mapped to a new array with arrays, where each array has a label at the beginning followed by the value from the data array. The resulting array is then sorted by the values in ascending order and mapped to an array of just the first element of each inner array.

**Pros and Cons**

* **Array Approach (`arr`)**:
	+ Pros: might be faster since it uses a simple array manipulation, which can be optimized by JavaScript engines.
	+ Cons: requires extra memory allocation for the objects, which might impact performance on systems with limited memory.
* **Object Approach (`obj`)**:
	+ Pros: might be slower due to the overhead of creating and accessing objects, but can also lead to better cache locality and reduced memory allocation.
	+ Cons: requires more complex object manipulation, which can be harder to optimize.

**Other Considerations**

* The benchmark uses a fixed dataset with 4 elements, which is relatively small. As the dataset grows, the differences in performance between these two approaches might become less pronounced.
* Both approaches use `sort()` and `map()`, which are generally efficient functions in JavaScript. However, the specific sorting and mapping operations used here might have different performance characteristics.

**Library Usage**

The benchmark does not explicitly mention using any libraries beyond vanilla JavaScript.

**Special JS Feature or Syntax**

There doesn't appear to be any special features or syntax being tested in this benchmark.

**Alternatives**

If you were to write a similar benchmark, you could consider adding more test cases with varying dataset sizes or using different sorting and mapping algorithms (e.g., `reduce()`, `forEach()`). You might also want to explore other optimization techniques, such as:

* Using `Int32Array` or other specialized arrays for numerical computations
* Leveraging GPU acceleration (e.g., WebGPU) for parallel processing
* Utilizing JavaScript engine-specific features like SIMD instructions

Keep in mind that the performance differences between these approaches will likely be relatively small and may depend on the specific hardware, browser version, and JavaScript engine being used.

Related benchmarks:

array vs onject (version: 0)

Comparing performance of: obj vs arr

Created: 3 years ago by: Guest

Jump to the latest result

obj

arr

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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