Benchmark: Remove MIME type from encoded image URL

Script Preparation code:

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';

AخA
 
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';

Tests:

Array.split
var base64 = image.split(",").pop();
var base64 = image.split(",").pop();
Substring
var base64 = image.substring(image.indexOf(',') + 1);
var base64 = image.substring(image.indexOf(',') + 1);
Substr
var base64 = image.substr(image.indexOf(',') + 1);
var base64 = image.substr(image.indexOf(',') + 1);

Slice

var base64 = image.slice(image.indexOf(',') + 1);

 
var base64 = image.slice(image.indexOf(',') + 1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
Array.split
Substring
Substr
Slice

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/103.0.0.0 Safari/537.36

Browser/OS: Chrome 103 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Array.split	10424551.0 Ops/sec
Substring	12703480.0 Ops/sec
Substr	12672813.0 Ops/sec
Slice	12704022.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 7 months ago):

The task is to write a function that takes an image URL, splits it into an array using the comma (,) character as the delimiter, and then extracts the last element of this array.

Here's how you can do it in JavaScript:

function getBase64(imageUrl) {
    const image = imageUrl.split(',').pop();
    return image;
}

// Example usage:
const imageUrl = '...your base64 encoded image string here}';
console.log(getBase64(imageUrl));

However, this is a bit naive as it assumes that the URL will always be in the format base64,string1,string2 and only contains commas. In reality, there might be other characters or spaces in the URL.

To make it more robust, we can use regular expressions to extract the base64 string from the URL:

function getBase64(imageUrl) {
    const regex = /base64,(.*)/;
    const match = imageUrl.match(regex);
    if (match && match[1]) {
        return match[1];
    } else {
        throw new Error('Invalid base64 encoded image URL');
    }
}

// Example usage:
const imageUrl = '...your base64 encoded image string here>';
console.log(getBase64(imageUrl));

This function uses a regular expression to find the first base64 followed by a comma and then captures any characters until it finds another comma. If there is no match, or if the captured group is empty, an error is thrown.

Now, let's assume that we have this getBase64 function implemented correctly, and we need to benchmark its performance against different methods of extracting the base64 string from the URL. We can do this by writing a test suite like the one provided in the question:

const getBase64 = require('./get-base64');

function arraySplitTest() {
    const imageUrl = 'base64,string1,string2';
    const start = performance.now();
    for (let i = 0; i < 10000; i++) {
        getBase64(imageUrl);
    }
    const end = performance.now();
    console.log(`Array.split: ${end - start}ms`);
}

function substringTest() {
    const imageUrl = 'base64,string1,string2';
    const start = performance.now();
    for (let i = 0; i < 10000; i++) {
        getBase64(imageUrl.substring(imageUrl.indexOf(',') + 1));
    }
    const end = performance.now();
    console.log(`Substring: ${end - start}ms`);
}

function substrTest() {
    const imageUrl = 'base64,string1,string2';
    const start = performance.now();
    for (let i = 0; i < 10000; i++) {
        getBase64(imageUrl.substr(imageUrl.indexOf(',') + 1));
    }
    const end = performance.now();
    console.log(`Substr: ${end - start}ms`);
}

function sliceTest() {
    const imageUrl = 'base64,string1,string2';
    const start = performance.now();
    for (let i = 0; i < 10000; i++) {
        getBase64(imageUrl.slice(imageUrl.indexOf(',') + 1));
    }
    const end = performance.now();
    console.log(`Slice: ${end - start}ms`);
}

arraySplitTest();
substringTest();
substrTest();
sliceTest();

This code defines four test functions, each testing a different method of extracting the base64 string from the URL. It uses performance.now() to measure the time it takes to execute each function 10,000 times.

You can modify this code to suit your needs and add more tests as required.

LLMs can make mistakes. Check important info.

The task is to write a function that takes an image URL, splits it into an array using the comma (`,`) character as the delimiter, and then extracts the last element of this array.

Here's how you can do it in JavaScript:
```javascript
function getBase64(imageUrl) {
    const image = imageUrl.split(',').pop();
    return image;
}

// Example usage:
const imageUrl = '...your base64 encoded image string here}';
console.log(getBase64(imageUrl));
```
However, this is a bit naive as it assumes that the URL will always be in the format `base64,string1,string2` and only contains commas. In reality, there might be other characters or spaces in the URL.

To make it more robust, we can use regular expressions to extract the base64 string from the URL:
```javascript
function getBase64(imageUrl) {
    const regex = /base64,(.*)/;
    const match = imageUrl.match(regex);
    if (match && match[1]) {
        return match[1];
    } else {
        throw new Error('Invalid base64 encoded image URL');
    }
}

// Example usage:
const imageUrl = '...your base64 encoded image string here>';
console.log(getBase64(imageUrl));
```
This function uses a regular expression to find the first `base64` followed by a comma and then captures any characters until it finds another comma. If there is no match, or if the captured group is empty, an error is thrown.

Now, let's assume that we have this `getBase64` function implemented correctly, and we need to benchmark its performance against different methods of extracting the base64 string from the URL. We can do this by writing a test suite like the one provided in the question:
```javascript
const getBase64 = require('./get-base64');

arraySplitTest();
substringTest();
substrTest();
sliceTest();
```
This code defines four test functions, each testing a different method of extracting the base64 string from the URL. It uses `performance.now()` to measure the time it takes to execute each function 10,000 times.

You can modify this code to suit your needs and add more tests as required.

Related benchmarks:

Remove MIME type from encoded image URL (version: 0)

Comparing performance of: Array.split vs Substring vs Substr vs Slice

Created: 2 years ago by: Guest

Jump to the latest result

Array.split

Substring

Substr