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map vs for vs for (init array)
(version: 0)
Comparing performance of:
map vs for vs for (init array)
Created:
3 years ago
by:
Registered User
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Script Preparation code:
function generateTestArray() { const result = []; for (let i = 0; i < 1000000; ++i) { result.push({ a: i, b: i / 2, r: 0, }); } return result; }
Tests:
map
const array = generateTestArray(); array.map(x => x.a + x.b)
for
const array = generateTestArray(); const r = []; for (let i = 0, len = array.length; i < len; ++i) { r[i] = array[i].a + array[i].b; }
for (init array)
const array = generateTestArray(); const r = new Array(array.length); for (let i = 0, len = array.length; i < len; ++i) { r[i] = array[i].a + array[i].b; }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
map
for
for (init array)
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'd be happy to explain the provided benchmark and its different approaches. **Benchmark Overview** The provided benchmark measures the performance of three different ways to sum up elements in an array: 1. Using the `map()` method 2. Using a traditional `for` loop 3. Using a `for` loop with initialization (i.e., creating a new array before the loop) Each test case is designed to generate a large array of 1 million elements, where each element has three properties: `a`, `b`, and `r`. The benchmark compares the execution times of these three approaches on various browsers and devices. **Approach 1: Using the `map()` method** The `map()` method is a built-in JavaScript function that applies a given function to each element in an array, returning a new array with the results. In this test case, the `map()` method is used to sum up the values of `a` and `b` for each element in the array. **Pros:** * Concise and readable code * No need to worry about indexing or bounds checking **Cons:** * Can be slower than traditional loops due to function call overhead * May not be suitable for very large arrays, as it creates a new array with twice the size of the original array **Approach 2: Traditional `for` loop** This approach uses a traditional `for` loop to iterate over each element in the array and sum up its values. **Pros:** * Fast execution time * Suitable for large arrays, as it only creates a new array with one element larger than the original array **Cons:** * More verbose code compared to the `map()` method * Requires manual indexing and bounds checking **Approach 3: `for` loop with initialization (i.e., creating a new array before the loop)** This approach is similar to the traditional `for` loop, but it creates a new array before the loop to store the results. **Pros:** * Similar performance characteristics to the traditional `for` loop * More readable code compared to the traditional `for` loop **Cons:** * Creates an additional array with one element larger than the original array * May not be suitable for very large arrays, as it creates unnecessary memory allocation **Library Usage** None of the test cases use any external libraries. However, the benchmark itself uses a custom `generateTestArray()` function to generate the test arrays. **Special JavaScript Features/Syntax** There are no special JavaScript features or syntax used in these benchmarks. They only rely on standard JavaScript language constructs and built-in functions like `map()`, `for` loops, and array indexing. **Alternative Approaches** If you want to benchmark other approaches for summing up elements in an array, here are a few ideas: * Using `reduce()`: This method is similar to the traditional `for` loop approach but uses a more concise syntax. * Using `forEach()`: This method applies a given function to each element in an array without returning a new array with results. * Using Closures or higher-order functions: These approaches can be used to create more efficient and expressive solutions for summing up elements in an array. Keep in mind that these alternative approaches may have different performance characteristics, readability, and memory usage compared to the benchmarks presented.
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