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Array slice vs array filter 1
(version: 0)
Comparing performance of:
slice vs filter
Created:
4 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20];
Tests:
slice
var copy = data.slice(5);
filter
var copy = data.filter(d => d < 5);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
slice
filter
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36
Browser/OS:
Chrome 131 on Mac OS X 10.15.7
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Test name
Executions per second
slice
30169384.0 Ops/sec
filter
11863145.0 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'll break down the provided benchmark and explain what's being tested, compared options, pros and cons of those approaches, and other considerations. **Benchmark Overview** The provided benchmark compares two ways to create a subset of an array in JavaScript: 1. `Array.prototype.slice()` 2. `Array.prototype.filter()` with a callback function **Options Compared** In the benchmark, we have two test cases: * `slice`: This test case creates a copy of the array using `data.slice(5)`. * `filter`: This test case creates a new array containing only elements that satisfy the condition `d < 5` using `data.filter(d => d < 5)`. **Pros and Cons** 1. **Slice()** * Pros: + Fast and efficient, as it returns a shallow copy of the original array. + Creates a new array object, which can be more memory-efficient than modifying the original array. * Cons: + Only creates a subset of the original array; if the slice is too large, it may cause performance issues due to memory allocation and garbage collection. + Does not preserve the original array's structure or properties (e.g., `length`, `indexOf`). 2. **Filter()** * Pros: + Preserves the original array's structure and properties. + Can create an arbitrary subset of the original array, making it more flexible than `slice`. * Cons: + Generally slower and less efficient than `slice`, especially for large datasets, as it iterates over the entire array to find matching elements. + May consume more memory than `slice` if the filter condition returns a large number of matching elements. **Library Usage** There is no explicit library usage in this benchmark. However, some libraries (e.g., Lodash) provide similar functionality for creating subsets or filtering arrays. **Special JS Features/Syntax** The `filter()` method uses an arrow function (`d => d < 5`), which is a shorthand syntax introduced in ECMAScript 2015 (ES6). This syntax allows for concise and expressive code, but it's not necessary for this specific benchmark. **Other Considerations** * The benchmark uses a relatively small array size (21 elements) to test performance. Larger datasets may reveal more pronounced differences between `slice()` and `filter()`. * The benchmark does not account for edge cases, such as empty arrays or arrays with non-numeric values. * If the original array is very large, other factors like memory allocation, garbage collection, and caching may become significant contributors to performance overhead. **Alternatives** If you're looking for alternative ways to create subsets of an array in JavaScript, consider: 1. `Array.prototype.map()` with a callback function: Similar to `filter()`, but returns a new array with transformed values. 2. `Array.prototype.reduce()`: Can be used to accumulate elements into a new array, but may not be as efficient or concise as `slice()` or `filter()`. 3. Library functions like Lodash's `_.take()` or `_.pick()`: Provide more flexible and expressive ways to create subsets of arrays, but may come with performance overhead. Remember that the choice of method depends on your specific use case, performance requirements, and personal coding style.
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