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Array.prototype.slice vs _.take 1M
(version: 1)
Comparing performance of:
Lodash take vs Array.prototype.slice
Created:
9 months ago
by:
Registered User
Jump to the latest result
HTML Preparation code:
<script src="lodash.js"></script>
Script Preparation code:
var array = [...Array(1000000).keys()];
Tests:
Lodash take
_.take(array,77000)
Array.prototype.slice
array.slice(0,77000)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Lodash take
Array.prototype.slice
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
9 months ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 YaBrowser/25.6.0.0 Safari/537.36
Browser/OS:
Yandex Browser 25 on Windows
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Embed Benchmark Result
Test name
Executions per second
Lodash take
6147.0 Ops/sec
Array.prototype.slice
14909.5 Ops/sec
Autogenerated LLM Summary
(model
gpt-4o-mini
, generated 9 months ago):
The benchmark provided is designed to compare the performance of two different methods for extracting a subset of elements from an array in JavaScript: 1. **Array.prototype.slice** method 2. **Lodash's _.take** function ### Benchmark Overview - **Preparation Code**: - An array of size 1,000,000 is created using `var array = [...Array(1000000).keys()];`, which generates an array containing integers from 0 to 999,999. - Lodash (`lodash.js`) is included in the HTML preparation code as it provides utility functions including `_.take`. ### Test Cases 1. **Lodash take**: - Benchmark Definition: `_.take(array,77000)` - Test Name: "Lodash take" - This test uses the `_.take` method from the Lodash library to return the first 77,000 elements of the array. 2. **Array.prototype.slice**: - Benchmark Definition: `array.slice(0,77000)` - Test Name: "Array.prototype.slice" - This test uses JavaScript's native `slice` method to achieve the same goal, extracting elements from index 0 to index 77,000. ### Performance Results: The benchmark results show the number of executions per second for each method: - **Array.prototype.slice**: 14,909.53 executions/second - **Lodash take**: 6,146.96 executions/second ### Pros and Cons #### Array.prototype.slice **Pros**: - **Performance**: The native `slice` method is significantly faster in this benchmark context, executing over twice as many times per second compared to `_.take`. - **Built-in Functionality**: No need for an external library; it's part of the standard JavaScript language. **Cons**: - **Limited Functionality**: `slice` is a more general-purpose function that can only extract sections of an array, without additional features. #### Lodash's _.take **Pros**: - **Chainable**: Lodash methods can be easily chained together, making it useful for more complex data manipulation sequences. - **Readability**: For some developers, using utility functions like `_.take` can improve code readability, conveying the intention of taking a specified number of elements more explicitly. **Cons**: - **Performance Overhead**: It introduces additional overhead due to being a library function, resulting in slower execution in this case. - **Dependency**: Requires the Lodash library to be included, which may increase the size of the application. ### Other Considerations The choice between these two methods can depend on the specific context in which they're used: - **Complexity of Operations**: If you need to conduct more sophisticated manipulation along with taking elements (like chaining with other Lodash functions), using Lodash might be more beneficial despite the performance cost. - **Environment Constraints**: In resource-constrained environments where performance is critical, using native methods is generally preferred for efficiency. - **Familiarity and Codebase Standards**: If a codebase primarily uses Lodash, sticking with `_.take` could maintain consistency. Overall, this benchmark demonstrates a clear performance advantage for JavaScript's native `Array.prototype.slice` method over Lodash's `_.take` for a straightforward subset extraction operation, indicating that for maximum performance in such scenarios, native methods should be the preferred choice.
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