Benchmark: reduce vs for custom_222

Script Preparation code:

var strings = [];
for (var i = 0; i < 1000; i++) {
    strings[i] = {
        name: i
    };
}

Tests:

reduce
strings.reduce((a,n) => a+n.name, '');
for
let result = '' for (let i = 0; i < strings.length; i++) { result += strings[i].name }
for of
let result = '' for (const string of strings) { result += string.name }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for
for of

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	175050.1 Ops/sec
for	12895.4 Ops/sec
for of	166984.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark.

**Benchmark Definition JSON:**

The provided JSON defines a benchmark named "reduce vs for custom_222". This benchmark is designed to compare the performance of three approaches:

1. Using the `Array.prototype.reduce()` method.
2. Using a traditional `for` loop.
3. Using an array iterator with the `for...of` loop.

**Script Preparation Code:**

The script preparation code creates an array of 1000 objects, each containing a unique `name` property. This array is used to test the three approaches.

**Html Preparation Code:**

There is no HTML preparation code provided, which suggests that this benchmark only cares about JavaScript performance and does not include any additional overhead from HTML rendering or CSS computations.

**Individual Test Cases:**

The individual test cases are defined in a separate JSON array. Each test case specifies the `Benchmark Definition`, which corresponds to one of the three approaches mentioned earlier:

1. `strings.reduce((a,n) => a+n.name, '');`: This is an example of using the `Array.prototype.reduce()` method.
2. `let result = ''\r\nfor (let i = 0; i < strings.length; i++) {\r\n    result += strings[i].name\r\n}`: This is an example of using a traditional `for` loop.
3. `let result = ''\r\nfor (const string of strings) {\r\n    result += string.name\r\n}`: This is an example of using an array iterator with the `for...of` loop.

**Pros and Cons:**

Here are some pros and cons of each approach:

1. **Array.prototype.reduce()**: Pros:
	* More concise and expressive code.
	* Can be more efficient than traditional loops for certain use cases.
Cons:
	* May have performance issues if the input array is large or if the callback function is complex.
2. **Traditional `for` loop**: Pros:
	* Wide support across browsers and platforms.
	* Easy to understand and maintain.
Cons:
	* Can be less concise and expressive than other approaches.
3. **Array iterator with `for...of` loop**: Pros:
	* More concise and expressive code compared to traditional loops.
	* Can be more efficient than `reduce()` for certain use cases.
Cons:
	* May have performance issues if the input array is large or if the callback function is complex.

**Libraries:**

There are no libraries explicitly mentioned in the benchmark definition. However, the `Array.prototype.reduce()` method is a built-in JavaScript method that uses the ArrayReduce algorithm.

**Special JS Features or Syntax:**

The benchmark makes use of two special features:

1. **Arrow functions**: The `reduce()` method uses an arrow function as its callback.
2. **Template literals**: The `for...of` loop and traditional `for` loop examples use template literals to concatenate strings.

In summary, this benchmark is designed to compare the performance of three approaches for concatenating an array of objects: using the `Array.prototype.reduce()` method, a traditional `for` loop, and an array iterator with the `for...of` loop. The results provide insight into the relative performance characteristics of each approach.

Related benchmarks:

reduce vs for custom_222 (version: 0)

Comparing performance of: reduce vs for vs for of

Created: one year ago by: Guest

Jump to the latest result

reduce

for

for of

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):