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[{key, fn}, ...] vs {[key]: fn, ...}
(version: 0)
Iterating over array of {key, fn} vs object of {[key]: fn}
Comparing performance of:
Array vs Object vs Reflect.ownKeys
Created:
9 years ago
by:
Guest
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Script Preparation code:
var data = {}; var array = []; var object = {}; for (var i = 0; i < 1000000; i++) { var key = Math.random().toString(16).slice(2, 10); var fn = function(k) {return data[k];} data[key] = Math.random(); array.push({ key: key, fn: fn }); object[key] = fn; }
Tests:
Array
var sum = 0; for (var i = 0; i < array.length; i++) { var item = array[i]; sum += item.fn(item.key); } sum;
Object
var sum = 0; var keys = Object.keys(object); for (var i = 0; i < keys.length; i++) { sum += object[key](keys[i]); } sum;
Reflect.ownKeys
var sum = 0; var keys = Reflect.ownKeys(object); for (var i = 0; i < keys.length; i++) { sum += object[key](keys[i]); } sum;
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
Array
Object
Reflect.ownKeys
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of JavaScript microbenchmarks! **Benchmark Definition** The provided JSON represents a benchmark definition, which outlines two test cases: iterating over an array of objects with a function as its value versus iterating over an object where each key maps to that same function. **Options Compared** In this benchmark, we're comparing three approaches: 1. **Iterating over an array**: The first approach uses the `array` variable, which contains an array of objects. Each object has a `key` property and a `fn` (function) property. 2. **Iterating over an object**: The second approach uses the `object` variable, which is an object where each key maps to a function. In this case, the functions are the same as those in the array. 3. **Using Reflect.ownKeys**: The third approach uses the `Reflect.ownKeys()` method to iterate over the own enumerable properties of the object. **Pros and Cons** Here's a brief summary of each approach: * **Iterating over an array**: * Pros: This approach is simple and easy to implement. * Cons: It may not be as efficient as using objects, especially for large datasets, since it requires creating an intermediate array. * **Iterating over an object**: * Pros: Using objects can be more memory-efficient than iterating over arrays and can provide better cache locality. However, the overhead of accessing each property through a key (e.g., `object[key]`) might negate some benefits. * Cons: This approach requires extra computations when traversing the object's properties. * **Using Reflect.ownKeys**: * Pros: This approach is more memory-efficient than iterating over an array or directly accessing object keys, and it can provide better cache locality since `Reflect.ownKeys()` returns an iterator that yields own enumerable property names directly. * Cons: It may require additional imports and setup, as well as understanding of the `Reflect` API. **Library/Functionality Descriptions** In this benchmark, we're using: * **Array**: The built-in JavaScript array data structure. * **Object**: The built-in JavaScript object data structure. * **Reflect.ownKeys**: A method introduced in ECMAScript 2018 (ES10), which returns an iterator that yields own enumerable property names of an object. **Special JS Feature/Syntax** In this benchmark, we're not using any specific JavaScript feature or syntax beyond the standard language features provided by ES10. **Other Alternatives** If you'd like to explore alternative approaches, consider these options: * **Using a library**: Libraries like `lodash` or `Ramda` can provide functions and methods that abstract away the details of iteration, but this might come at the cost of additional dependencies. * **Iterating over an array using a different method**: You could use `map()` or `forEach()` to iterate over the array, which would simplify the code but might not be as efficient as direct iteration. When writing microbenchmarks like this one, it's essential to consider factors such as cache locality, memory allocation, and the overhead of accessing properties. By understanding these aspects and choosing the right approach for your use case, you can create more accurate benchmarks that help you identify performance bottlenecks in your code.
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