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ASCII Dict
(version: 0)
Comparing performance of:
for loop dict vs Array.from dict vs String serialize dict
Created:
4 years ago
by:
Guest
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Tests:
for loop dict
let dictionary = []; for (let i = 0; i < 256; dictionary.push(String.fromCharCode(i++)));
Array.from dict
let dictionary = Array.from({length:256},(i,j)=>String.fromCharCode(j))
String serialize dict
let dictionary = [..."\u0000\u0001\u0002\u0003\u0004\u0005\u0006\u0007\b\t\n\u000b\f\r\u000e\u000f\u0010\u0011\u0012\u0013\u0014\u0015\u0016\u0017\u0018\u0019\u001a\u001b\u001c\u001d\u001e\u001f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ ¡¢£¤¥¦§¨©ª«¬®¯°±²³´µ¶·¸¹º»¼½¾¿ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüýþÿ"]
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
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Test case name
Result
for loop dict
Array.from dict
String serialize dict
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
**Benchmark Overview** MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark definition represents a series of tests aimed at measuring the performance of different approaches for creating an array or object representing ASCII characters. **Script Preparation Code Analysis** The script preparation code is empty in this case, which means that the test cases will be executed from scratch without any pre-initialized variables or objects. **Test Cases** There are three test cases: 1. **"for loop dict"** This test creates an array of 256 strings using a `for` loop, where each iteration pushes the corresponding ASCII character to the array. 2. **"Array.from dict"** This test uses the `Array.from()` method with a callback function that returns a string representing an ASCII character at index `i`. 3. **"String serialize dict"** This test creates an object (not explicitly declared as an array) from a string containing 256 unique ASCII characters, one per line. **Library and Functionality** * None of the test cases use any external libraries. * The `Array.from()` method is used in Test Case 2. This method takes an iterable as an argument and returns a new array populated with the elements of that iterable. In this case, it's used to create an array of ASCII characters. **Special JavaScript Features** The tests do not use any special JavaScript features or syntax. However, it's worth noting that the `String.fromCharCode()` function is used in Test Case 1 to convert numbers to their corresponding ASCII characters. **Benchmark Results Analysis** The latest benchmark results show three test cases with different performance characteristics: * **"for loop dict"**: The fastest execution rate (786948.3125 executions per second) on a desktop device running Windows and Chrome 92. * **"Array.from dict"**: A slower execution rate (28860.12109375 executions per second) compared to the `for` loop approach, likely due to the overhead of creating an array with a callback function. * **"String serialize dict"**: The slowest execution rate (20963.849609375 executions per second) on the same device and browser. **Alternatives** Other alternatives for creating arrays or objects representing ASCII characters include: * Using a `Map` object instead of an array, which could provide better performance for large datasets. * Using a library like Lodash's `fromPairs()` method to create an object from an array of key-value pairs. * Using a language-agnostic approach, such as using binary data or a compact format like JSON. **Pros and Cons** Each approach has its pros and cons: * **For Loop Approach**: + Pros: Easy to understand, low overhead. + Cons: May be slower due to array push operations. * **Array.from() Method**: + Pros: Concise, efficient. + Cons: May have higher overhead due to callback function creation. * **String Serialize Dictionary**: + Pros: Simple, easy to understand. + Cons: Slowest execution rate among the three approaches. These alternatives can be considered when dealing with large datasets or performance-critical code.
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