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(version: 0)
Comparing performance of:
Array.split vs Substring vs Substr vs Slice
Created:
3 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var image = 'data:image/png;base64,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';
Tests:
Array.split
var base64 = image.split(",").pop();
Substring
var base64 = image.substring(image.indexOf(',') + 1);
Substr
var base64 = image.substr(image.indexOf(',') + 1);
Slice
var base64 = image.slice(image.indexOf(',') + 1);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (4)
Previous results
Fork
Test case name
Result
Array.split
Substring
Substr
Slice
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year 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: ```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'); 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.
Related benchmarks:
copyWithin vs iterate
copyWithin vs iterate (Attempt 2)
my-test-test1
drawImage: Copy from image or canvas fixed
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