Benchmark: reduce vs while - MeasureThat.net

Tests:

Reduce
const charOccurances = str => str .split("") .reduce((p, c) => ({ ...p, [c]: (p[c] || 0) + 1 }), {}); console.log(charOccurances('abac'));
while
const charOccurances = (str = "") => { const response = {}; while(str.length !== 0) { const char = str.charAt(); const reg = new RegExp(char, "g"); response[char] = str.match(reg).length; str = str.replace(reg, ""); } return response } console.log(charOccurances('abac'));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
while

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36 Edg/130.0.0.0

Browser/OS: Chrome 130 on Linux

View result in a separate tab

Test name	Executions per second
Reduce	144840.2 Ops/sec
while	139828.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is crucial in ensuring efficient coding practices. Let's break down the provided benchmark.

**Benchmark Overview**

The benchmark tests two approaches to count character occurrences in a string: the `reduce()` method and a `while` loop. The goal is to determine which approach is faster, considering various factors such as browser, device platform, operating system, and execution frequency.

**Options Compared**

The two options being compared are:

1. **Reduce**: This method uses the `Array.prototype.reduce()` function to iterate over the string, accumulating the count of each character in a target object.
2. **While Loop**: This approach uses a `while` loop to repeatedly extract characters from the string using regular expressions, updating the count in a target object.

**Pros and Cons**

Here are the pros and cons of each approach:

* **Reduce**:
	+ Pros:
		- More concise and expressive code.
		- Potential for better performance due to optimized Array.prototype methods.
	+ Cons:
		- May have higher memory overhead due to creating a new object for each iteration.
		- Less intuitive for beginners, as it requires understanding of the reduction function.
* **While Loop**:
	+ Pros:
		- Lower memory overhead since only a temporary object is created.
		- More straightforward and easier to understand for beginners.
	+ Cons:
		- More verbose code.
		- Potential performance overhead due to repeated string manipulation.

**Library Usage**

Neither of the provided options uses any external libraries. However, if we were to extend this benchmark to include other approaches, we might consider using libraries like `lodash` (for `reduce()` alternatives) or `regex` (for more efficient regular expression-based solutions).

**Special JS Features/Syntax**

There is no specific JavaScript feature or syntax being utilized in these benchmarks beyond the standard language features.

**Other Alternatives**

Some alternative approaches to solving this problem include:

1. **Iterating over an array**: Using a traditional `for` loop or `forEach()` method, iterating over an array of characters and updating a counter.
2. **String methods**: Leveraging built-in string methods like `split()`, `indexOf()`, or `match()` to extract character counts.
3. **Higher-order functions**: Applying higher-order functions like `map()` or `filter()` to process the string.

These alternatives might not be as efficient or concise as the provided options but could serve as interesting variations for further exploration.

Keep in mind that measuring performance is just one aspect of coding best practices. Code readability, maintainability, and scalability should also be considered when solving problems like this one.

Related benchmarks:

reduce vs while (version: 0)

Comparing performance of: Reduce vs while

Created: 8 years ago by: Guest

Jump to the latest result

Reduce

while

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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