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javascriptTest2403191
(version: 0)
Benchmark.js
Comparing performance of:
native vs lodash
Created:
2 years ago
by:
Guest
Go to the latest result
Tests:
native
const numbers = [10, 40, 230, 15, 18, 51, 1221] //native numbers.filter(num => num % 3 === 0)
lodash
const numbers = [10, 40, 230, 15, 18, 51, 1221] // lodash _.filter(numbers, num => num % 3 === 0)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
native
lodash
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 years ago
)
User agent:
Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Mobile Safari/537.36
Browser/OS:
Chrome Mobile 122 on Android
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<iframe src="https://measurethat.net/Embed?id=512858" width="100%" height="500px"></iframe>
native
11.4M/s
lodash
1.8M/s
View exact numbers
Test name
Executions per second
✓
native
11,362,774 Ops/sec
lodash
1,834,273 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested. **Benchmark Overview** MeasureThat.net is a platform for testing JavaScript microbenchmarks. The benchmark you provided involves two test cases, each with a different approach to filter an array of numbers using JavaScript. **Test Cases** The two test cases are: 1. **Native**: This test case uses the native `filter()` method without any external libraries or modifications. 2. **Lodash**: This test case uses the Lodash library, specifically the `_filter()` function, to achieve the same result as the native approach. **Options Compared** In this benchmark, two options are compared: * Native: Uses the built-in JavaScript `filter()` method. * Lodash: Uses the external Lodash library with its `_filter()` function. **Pros and Cons of Each Approach** 1. **Native (filter())** * Pros: + Fastest execution time since it's a native method that doesn't require any external libraries or overhead. + Well-suited for small to medium-sized datasets where the overhead of an external library is negligible. * Cons: + May not be as efficient for very large datasets due to the overhead of creating a new array with the filtered elements. 2. **Lodash (_filter())** * Pros: + Can handle larger datasets more efficiently since it doesn't require creating a new array, but rather modifies the original array. + Provides additional features and utilities that can be useful in other parts of the codebase. * Cons: + Requires an external library (Lodash), which adds overhead and may not be necessary for smaller projects. + May have slower execution times compared to native methods due to the overhead of importing and initializing Lodash. **Other Considerations** When choosing between these two approaches, consider the following factors: * Dataset size: If you're working with very large datasets, the Lodash approach might be more efficient. For smaller datasets, the native approach is likely sufficient. * Performance-critical code: If your code requires extreme performance and is executed frequently, using native methods like `filter()` might be a better choice. * Codebase complexity: If you need to use multiple features from Lodash, it might be worth considering the added complexity and overhead. **Library: Lodash** Lodash is a popular JavaScript utility library that provides a collection of functional programming helpers. The `_filter()` function in Lodash takes two arguments: an array and a callback function that specifies the filtering criteria. In this benchmark, Lodash's `_filter()` function is used to filter the `numbers` array. **Special JS Feature or Syntax** None mentioned in the provided code snippets.
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