Benchmark: hashCode reduce vs while

Script Preparation code:

function makeid(length) {
    var result           = '';
    var characters       = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
    var charactersLength = characters.length;
    for ( var i = 0; i < length; i++ ) {
      result += characters.charAt(Math.floor(Math.random() * 
 charactersLength));
   }
   return result;
}
window.data = new Array(1000).fill(null).map(()=>makeid(50));

Tests:

reduce
function hashCode(s) { return s.split('').reduce((acc, c) => (acc = ((acc << 5) - acc) + c.charCodeAt(0), acc & acc), 0); } window.data.map(input => hashCode(input));
while
function hashCode(s) { let i = 0; let acc = 0; const len = s.length; while (i < len) { acc = ((acc << 5) - acc) + s.charCodeAt(i++); acc &= acc; } return acc; } window.data.map(input => hashCode(input));

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:

No previous run results

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is designed to compare two approaches for calculating the hash code of a string: using the `reduce()` method versus a traditional `while` loop.

**Script Preparation Code**

The script preparation code generates an array of 1000 random strings, each with a length of 50 characters. This creates a large dataset that will be used to calculate the hash code for each string.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmarking process only involves JavaScript execution on the client-side or server-side.

**Individual Test Cases**

The test cases are defined as two separate functions: `hashCode()` using `reduce()` and another function (not shown) using a traditional `while` loop. Both functions take a string input `s` and return its corresponding hash code.

**Library Usage**

Both test cases use the `window.data.map()` method to apply the `hashCode()` function to each element of the `data` array. This suggests that the benchmark is using the JavaScript built-in `Array.prototype.map()` method, which applies a given function to each element of an array and returns a new array with the results.

**Special JS Features**

There are no special JS features or syntax mentioned in the provided code, so we can assume that this benchmark is focused on the comparison of these two approach only.

Now, let's discuss the pros/cons of each approach:

**Reduce() Approach:**

Pros:

* More concise and readable code
* Less prone to errors due to its functional programming nature

Cons:

* Can be slower than a traditional loop due to function call overhead and potential array resizing

**While Loop Approach:**

Pros:

* Often faster due to the elimination of function call overhead
* Can be more efficient in terms of memory usage, as it avoids creating intermediate arrays

Cons:

* More verbose code that may lead to errors
* Less readable and maintainable due to its imperative programming nature

In this benchmark, the results suggest that the `while` loop approach is faster than the `reduce()` approach, with an execution rate of 5378.17 executions per second compared to 3232.14 executions per second.

**Other Alternatives**

Some alternative approaches for calculating hash codes include:

* Using a cryptographic hash function like SHA-256 or MD5
* Implementing a custom hash function using bitwise operations and arithmetic
* Using a library like `crypto-js` or `hash` which provides implementations of various hash algorithms

Related benchmarks:

hashCode reduce vs while (version: 0)

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Comparing performance of: reduce vs while

Created: 4 years ago by: Guest

Jump to the latest result

reduce

while

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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