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JS insert at index spread vs Splice
(version: 0)
Comparing performance of:
Array update with spread operator vs Array update with slice and assign
Created:
5 years ago
by:
Guest
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Script Preparation code:
const ITERATIONS = 500000; var index = ITERATIONS/2; var n = Math.random(); var list = []; for (let i = 0; i < length; i += 1) { list.push(Math.random()); }
Tests:
Array update with spread operator
const clone = [ ...list.slice(0, index), n, ...list.slice(index + 1), ];
Array update with slice and assign
const clone = list.slice(); clone[index] = n;
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Array update with spread operator
Array update with slice and assign
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36
Browser/OS:
Chrome 130 on Windows
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Test name
Executions per second
Array update with spread operator
2640167.2 Ops/sec
Array update with slice and assign
2628476.8 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of JavaScript microbenchmarks! **What is being tested?** MeasureThat.net is testing two different approaches for inserting an element at a specific index in an array: 1. **Spread operator (`...`)**: This approach uses the spread operator to create a new array by spreading elements from two ranges: before and after the insertion point. 2. **`slice()` and assignment**: This approach uses the `slice()` method to create a shallow copy of the original array, then assigns a value to the specified index. **Options being compared** The benchmark is comparing the performance of these two approaches on an array of random elements. The options are: * Insertion point: The element is inserted at a randomly chosen position within the array. * Array size: The length of the array is randomly chosen between 100,000 and 1,000,000 elements. **Pros and cons of each approach** **Spread operator (`...`)** Pros: * More concise and expressive code * Creates a new array without modifying the original one Cons: * May have higher overhead due to creating a new array * Can be slower for very large arrays **`slice()` and assignment** Pros: * May be faster for very large arrays since it only creates a shallow copy * Allows for more control over the insertion point (e.g., copying all elements before the index) Cons: * More verbose code * Modifies the original array **Other considerations** When choosing between these approaches, consider the following factors: * Code readability: If you need to insert an element at a specific position frequently, the spread operator might be more concise and readable. * Performance: For very large arrays, the `slice()` and assignment approach might be faster due to reduced overhead. **Library usage** There is no explicit library mentioned in the benchmark definition or test cases. However, JavaScript's built-in `Array.prototype` methods are used, such as `slice()`, `push()`, and `...` (spread operator). **Special JS features or syntax** The spread operator (`...`) was introduced in ECMAScript 2015 (ES6). If you're using an older version of JavaScript, this approach might not work. **Benchmark result interpretation** The latest benchmark results show that the `Array update with spread operator` test performed better than the `Array update with slice and assign` test. This suggests that for this specific use case, the spread operator approach is more efficient. **Other alternatives** If you're looking for alternative approaches or optimization techniques, consider: * Using a custom implementation for array insertion, potentially using a cache to optimize performance. * Exploring other JavaScript libraries or modules that provide optimized array manipulation functions (e.g., `Array.prototype.slice()`, `Array.prototype.splice()`). * Investigating parallel execution or multi-threading to take advantage of multiple CPU cores. Keep in mind that the choice of approach ultimately depends on your specific use case, performance requirements, and personal coding preferences.
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