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Substr vs startsWith GUL
(version: 0)
Benchmarking usage of comparaison of substr result and starts with
Comparing performance of:
startsWith vs substr
Created:
5 years ago
by:
Guest
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HTML Preparation code:
<div></div>
Script Preparation code:
window.test = '$meta-'; window.testLen = window.test.length; var data = window.data = []; const possible = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"; var TOTAL_STRINGS = window.TOTAL_STRINGS = 100000; function getRandomInt(max) { return Math.floor(Math.random() * max); } function makeRandomString(len) { var text = ""; if (getRandomInt(2) === 1) { text += window.test; } for( var i=0; i < len; i++ ) { text += possible.charAt(getRandomInt(possible.length)); } return text; } while (data.length < TOTAL_STRINGS) { data.push(makeRandomString(getRandomInt(20))); }
Tests:
startsWith
var x = 0; var TOTAL_STRINGS = window.TOTAL_STRINGS; var data = window.data; var test = window.test; while (x < TOTAL_STRINGS) { const str = data[x]; str.startsWith(test); x += 1; }
substr
var x = 0; var TOTAL_STRINGS = window.TOTAL_STRINGS; var data = window.data; var test = window.test; var testLen = window.testLen; while (x < TOTAL_STRINGS) { const str = data[x]; str.length >= testLen && str.substr(0, testLen) === test; x += 1; }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
startsWith
substr
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and its components. **Benchmark Definition JSON** The provided `Benchmark Definition` JSON represents two test cases: `substr` and `startsWith`. The main difference between these two tests is how they compare strings: 1. **Substring Comparison (substr)**: * In this test, a random string `str` is generated from the dataset. * The length of `str` is compared to the length of the predefined substring `test`. * If the lengths match and the first `testLen` characters of `str` are equal to `test`, the comparison is considered successful. 2. **Substring Comparison with Start (startsWith)**: * Similar to the previous test, a random string `str` is generated from the dataset. * The test checks if the substring starting from the beginning of `str` and having the same length as `test` matches `test`. **Comparison Options** There are two comparison options being tested: 1. **Substring Comparison (substr)**: This approach compares a portion of one string (`str`) with another fixed-length substring (`test`). 2. **Substring Comparison with Start (startsWith)**: In this approach, the test checks if the start of a randomly generated string matches a predefined substring. **Pros and Cons** Here are some pros and cons of each approach: 1. **Substring Comparison (substr)** * Pros: + More flexible, as it allows for different types of comparisons (e.g., prefix, suffix). + Can be optimized for performance. * Cons: + May require more memory to store the intermediate results of substring extraction. 2. **Substring Comparison with Start (startsWith)** * Pros: + More predictable and consistent behavior. + Reduces the number of potential collisions between substrings. * Cons: + Less flexible than the `substr` approach. **Library** There is no explicit library mentioned in the benchmark definition, but it's likely that the built-in JavaScript `String.prototype.startsWith()` method is being used in both test cases. **Special JS Features/Syntax** None of the provided benchmarks use special JavaScript features or syntax. The focus appears to be on the comparison logic and performance optimization. **Other Alternatives** Some alternative approaches for comparing strings could include: 1. **Regular Expressions**: Instead of using substrings, regular expressions can provide more flexibility and power for string matching. 2. **Hash Tables**: Storing the strings in a hash table and then performing lookups could potentially improve performance, especially for large datasets. 3. **Approximate String Matching**: If exact matches are not required, algorithms like Levenshtein distance or Jaro-Winkler distance can provide more efficient string comparison. Keep in mind that these alternative approaches may have their own trade-offs and require additional resources (e.g., memory, computational power).
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