Benchmark: Map vs Preallocation (object)

Script Preparation code:

var array = new Array(1000000).fill(0);

Tests:

Map
const array2 = array.map((item, index) => ({ name: `name${index}`, email: `email${index}`, address: `address${index}`, }));
Preallocation
const array2 = new Array(1000000).fill(0); for (let i = 0; i < array2.length; i++) { array2[i] = { name: `name${i}`, email: `email${i}`, address: `address${i}` }; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map
Preallocation

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 Edg/118.0.2048.1

Browser/OS: Chrome 118 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Map	4.6 Ops/sec
Preallocation	7.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The `Map vs Preallocation (object)` benchmark measures the performance difference between using the `Array.prototype.map()` method versus preallocating an array with a specific number of elements. The purpose is to compare the efficiency of these two approaches when working with large datasets.

**Options being compared:**

1. **`Array.prototype.map()`**: This method creates a new array by transforming each element in the original array using a provided function.
2. **Preallocation**: Creating an array with a specific number of elements (in this case, 1,000,000) and then populating it with data.

**Pros and Cons:**

* **`Array.prototype.map()`**:
	+ Pros:
		- Easy to use and implement.
		- No need to worry about the initial size of the array.
		- Works well for small to medium-sized datasets.
	+ Cons:
		- Can be slower for large datasets due to the overhead of creating a new array.
		- May not be as cache-friendly as preallocation.
* **Preallocation**:
	+ Pros:
		- Can be faster for large datasets since it avoids the overhead of creating a new array.
		- More cache-friendly, which can improve performance.
	+ Cons:
		- Requires more memory to allocate the initial array.
		- May require more manual effort and planning when working with dynamic data.

**Library**

The `Array.prototype.map()` method is a built-in JavaScript method that uses the `V8` engine. The `V8` engine is an open-source JavaScript engine developed by Google.

**Special JS feature/syntax**

None mentioned in the provided benchmark definition.

**Other alternatives**

If you're working with large datasets, other approaches to consider:

1. **Using a library like Lodash**: Lodash provides optimized functions for common array operations, including `map()`. Using Lodash can provide better performance and readability.
2. **Using a library like Ramda**: Ramda is another popular functional programming library that provides optimized versions of various array operations, including `map()`.
3. **Using native Web Assembly (WASM)**: If you're working with very large datasets or require extreme performance, using WASM might be an option.

Keep in mind that these alternatives may add complexity to your project and are typically used for specific use cases where performance is critical.

**Benchmark preparation code**

The provided script preparation code creates an array of 1,000,000 elements filled with zeros:
```javascript
var array = new Array(1000000).fill(0);
```
This code initializes the `array` variable with a large number of elements, which will be used as input for the benchmark.

**Individual test cases**

The two individual test cases compare the performance of `Array.prototype.map()` versus preallocation:

1. **`Map`**: Creates an array using `Array.prototype.map()`, transforming each element in the original array into an object.
```javascript
const array2 = array.map((item, index) => ({ name: `name${index}`, email: `email${index}`, address: `address${index}` }));
```
2. **`Preallocation`**: Creates an array of 1,000,000 elements filled with zeros and then populates it with data using a loop.
```javascript
const array2 = new Array(1000000).fill(0);
for (let i = 0; i < array2.length; i++) {
  array2[i] = { name: `name${i}`, email: `email${i}`, address: `address${i}` };
}
```
These test cases demonstrate the performance difference between using `Array.prototype.map()` versus preallocation when working with large datasets.

Related benchmarks:

Map vs Preallocation (object) (version: 0)

measures performance of map vs preallocation

Comparing performance of: Map vs Preallocation

Created: 2 years ago by: Registered User

Jump to the latest result

Map

Preallocation

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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