Benchmark: reduce vs map & join

Script Preparation code:

var strings = [],
    prefix = "abc";
for (var i = 0; i < 1000; i++) {
    strings.push("" + i + i);
}

Tests:

for-i
let result = ""; const len = strings.length; for (let i = 0; i < len; i++) { result += prefix + strings[i]; }
reduce
const result = strings.reduce((accum, el) => accum + prefix + el, "");
map & join
const result = strings.map(el => prefix + el).join("");
for-of
let result = ""; for (const el of strings) { result += prefix + el; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for-i
reduce
map & join
for-of

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested.

**Benchmark Overview**

The benchmark compares four different approaches to concatenate an array of strings:

1. Using a traditional `for` loop (`for-i`)
2. Using a `for...of` loop with a string concatenation (`for-of`)
3. Using the `reduce()` method to concatenate the strings (`reduce`)
4. Using the `map()` and `join()` methods together to concatenate the strings (`map & join`)

**Script Preparation Code**

The script preparation code creates an array of 1000 strings, where each string is a concatenation of two numbers (i and i) prefixed with "abc". This array will be used as input for the benchmark.

**Test Cases**

Each test case has a unique `Benchmark Definition` JSON object that defines how to perform the concatenation. The main difference between these definitions is:

*   **for-i**: Uses a traditional `for` loop to iterate over the strings and concatenate them.
*   **reduce**: Uses the `reduce()` method, which applies a function to each element of the array (in this case, concatenating two strings).
*   **map & join**: Uses the `map()` method to create an array with modified versions of the strings (prefixing each string with "abc"), and then uses the `join()` method to concatenate these strings.
*   **for-of**: Uses a `for...of` loop to iterate over the strings and concatenate them, similar to the traditional `for-i` approach.

**Library: String**

Both the `reduce()` and `map()` methods use the built-in `String` class in JavaScript. The `join()` method also uses the `String` class.

**JavaScript Feature/Syntax: Closures**

The `reduce()` method uses a closure to accumulate the result of each iteration. This is a fundamental concept in JavaScript, where a closure is created when a function has access to its own scope and the scope of its outer functions.

**Benchmark Result Interpretation**

The benchmark results show the number of executions per second (in millions) for each test case on different browsers and devices. The browser with the highest execution rate for each test case will likely be the fastest in terms of performance.

In summary, this benchmark compares four approaches to concatenate an array of strings:

*   Traditional `for` loop (`for-i`)
*   `for...of` loop with string concatenation (`for-of`)
*   `reduce()` method
*   `map()` and `join()` methods together

Each approach has its pros and cons. For example, the traditional `for` loop is straightforward but may be less efficient than other approaches. The `map()` and `join()` methods are often more concise and expressive, but may have additional overhead due to function calls.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36

Browser/OS: Chrome 91 on Windows 7

View result in a separate tab

Test name	Executions per second
for-i	2434.8 Ops/sec
reduce	4411.9 Ops/sec
map & join	3505.4 Ops/sec
for-of	4356.9 Ops/sec

Related benchmarks:

reduce vs map & join (version: 0)

Comparing performance of: for-i vs reduce vs map & join vs for-of

Created: 3 years ago by: Guest

Jump to the latest result

for-i

reduce

map & join

for-of

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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