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set add vs map 2
(version: 0)
Comparing performance of:
set add vs map
Created:
6 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var arr = []; var count = 100000; for(var i = 0; i<count; i++) { arr.push(i); }
Tests:
set add
const folderSet = new Set() arr.forEach((f) => { folderSet.add(f) }) var end = [...folderSet]
map
const result = arr.map((f) => f) var end = [...new Set(result)]
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
set add
map
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'll break down the provided benchmark definition and test cases to explain what's being tested, compared, and the pros and cons of each approach. **Benchmark Definition:** The benchmark measures the performance difference between two approaches: 1. Using a `Set` data structure to add elements and then converting it to an array. 2. Using the `map()` function to create a new array with the same elements as the original array, and then using a `Set` to remove duplicates. **Script Preparation Code:** The script creates an empty array `arr` and sets a variable `count` to 100,000. The loop iterates from 0 to `count-1`, pushing each number into the `arr` array. **Html Preparation Code:** There is no HTML preparation code provided. **Individual Test Cases:** The benchmark consists of two test cases: 1. **"set add"`: ```javascript const folderSet = new Set(); arr.forEach((f) => { folderSet.add(f); }); var end = [...folderSet]; ``` This test case creates a new `Set` called `folderSet` and adds all elements from the `arr` array to it using the `forEach()` method. The resulting set is then converted back to an array and assigned to the variable `end`. 2. **"map"`: ```javascript const result = arr.map((f) => f); var end = [...new Set(result)]; ``` This test case uses the `map()` function to create a new array called `result` with the same elements as the original `arr` array, but with each element duplicated (i.e., the identity function is applied). The resulting array is then passed to the `Set` constructor, which removes duplicates, and the resulting set is converted back to an array. **Pros and Cons:** 1. **"set add"`: * Pros: + Faster execution time, as adding elements to a `Set` is an O(1) operation. + More memory-efficient, as sets only store unique values. * Cons: + Requires more code and manual iteration. 2. **"map"`: * Pros: + Easier to read and maintain, as it uses a built-in function. + Can be faster for very large datasets, since `map()` can use parallel processing. * Cons: + Slower execution time, due to the overhead of creating a new array and removing duplicates. + Less memory-efficient, since each element is duplicated in the resulting array. **Library:** The only library used in this benchmark is the built-in `Set` data structure in JavaScript. **Special JS Feature or Syntax:** None. **Other Considerations:** When choosing between these two approaches, consider the trade-off between performance and readability. If you need to perform a simple set operation with a small dataset, "set add" might be a better choice. However, if you're working with very large datasets or need to optimize for readability, "map" might be more suitable. **Alternatives:** Other alternatives to achieve the same goal include: 1. Using `Array.from()` instead of `Set()`: This method creates an array from an iterable object, which can be faster than creating a set and then converting it back to an array. ```javascript const result = arr.map((f) => f); var end = Array.from(new Set(result)); ``` 2. Using `filter()` or `reduce()` instead of `Set()`: These methods allow you to filter or aggregate elements in the original array, which can be faster than creating a set and then removing duplicates. ```javascript const result = arr.map((f) => f); var end = result.filter((f) => true); // no-op, just returns the entire array ``` Keep in mind that these alternatives may have different trade-offs in terms of performance, readability, and memory usage.
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