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Array loop vs foreach vs map forsk2
(version: 0)
Comparing performance of:
foreach vs for vs map vs for prepare vs foreach prepare
Created:
2 years ago
by:
Guest
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Script Preparation code:
var arr = []; for (var i = 0; i < 1000; i++) { arr[i] = i; } function someFn(i) { return i * 3 * 8; }
Tests:
foreach
const arrayResult = [] arr.forEach(function (item){ arrayResult.push(someFn(item)); })
for
const arrayResult = [] for (var i = 0, len = arr.length; i < len; i++) { arrayResult.push(someFn(arr[i])); }
map
const arrayResult = arr.map(item => someFn(item))
for prepare
const arrayResult = new Array(arr.length) for (var i = 0, len = arr.length; i < len; i++) { arrayResult.push(someFn(arr[i])); }
foreach prepare
const arrayResult = new Array(arr.length) arr.forEach(function (item){ arrayResult.push(someFn(item)); })
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (5)
Previous results
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Test case name
Result
foreach
for
map
for prepare
foreach prepare
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 provided JSON and explain what's being tested, compared, and their pros/cons. **Benchmark Definition** The benchmark is designed to compare the performance of three different approaches for executing a loop in JavaScript: 1. `Array.forEach()` 2. `For` loop 3. `Array.map()` The script preparation code defines an array `arr` with 1000 elements, each containing a function `someFn()` that takes an integer `i` and returns the result of `i * 3 * 8`. The `someFn()` function is not optimized for performance, making it an ideal candidate for benchmarking. **Individual Test Cases** Each test case has a unique benchmark definition, which specifies how to execute the `arr` array using one of the three approaches: 1. `foreach`: Uses `Array.forEach()` to iterate over the array and push the result of `someFn(item)` into a new array `arrayResult`. 2. `for`: Uses a traditional `For` loop to iterate over the array and push the result of `someFn(arr[i])` into the same `arrayResult`. 3. `map`: Utilizes `Array.map()` to create a new array `arrayResult` with the results of applying `someFn(item)` to each element in the original `arr` array. 4. `foreach prepare`: Similar to the previous one, but uses `new Array(arr.length)` instead of an existing array for `arrayResult`. 5. `for prepare`: Similar to the previous one, but creates a new empty array for `arrayResult`. **Comparison and Pros/Cons** The comparison aims to determine which approach is faster in terms of executions per second. Pros and cons of each approach: * `Array.forEach()`: Can be slower due to the overhead of function invocation and the need to push elements into an array. However, it's often considered more concise and easier to read. * `For` loop: Generally fast but requires manual memory management (assigning values to variables). It can lead to performance issues if not implemented correctly. * `Array.map()`: Fast and elegant way to apply a function to every element in an array. However, it may incur additional overhead due to the need to create a new array. **Library Usage** In this benchmark, the following library is used: * `Array.prototype.forEach()`: A built-in JavaScript method for iterating over arrays. * `Array.prototype.map()`: Another built-in JavaScript method for creating a new array with transformed elements. These libraries are part of the ECMAScript standard and are implemented in most modern browsers. **Special JS Features or Syntax** None mentioned in this benchmark. If there were any, it would be worth noting, but not present here. **Alternatives** Other approaches that could have been used instead of `Array.forEach()`, `For` loop, and `Array.map()` include: * `Set.prototype.forEach()`: An alternative iteration method for sets. * `Array.prototype.reduce()`: A method that applies a function to each element in an array and reduces it to a single output value. * Custom loops using nested functions or regular expressions. Keep in mind that these alternatives might introduce additional overhead, depending on the specific use case and browser support.
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