Benchmark: every and _every

HTML Preparation code:

<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.core.js"></script>

Script Preparation code:

var elements = ["cat", "dog", "bat"]

Tests:

_.every
_.every(elements, el => el.length == 3)
elements.every
elements.every(el => el.length == 3)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.every
elements.every

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and explore what's being tested in this benchmark.

**Benchmark Overview**

The provided JSON represents two test cases, `elements.every` and `_.every`, which are variations of a microbenchmarking test. The goal is to compare the performance differences between using a vanilla JavaScript function (`elements.every`) versus a utility library function (`_.`). Let's break down what each part does:

**Script Preparation Code**

The script preparation code is a simple JavaScript snippet that defines an array `elements` containing three strings: `"cat"`, `"dog"`, and `"bat"`. This code is executed before running the benchmark.

**Html Preparation Code**

The HTML preparation code includes a link to load the Lodash library (version 4.17.11) from a CDN. Lodash is a popular utility library for functional programming in JavaScript. It provides various functions, including `every`, which is used in one of our test cases.

**Benchmark Test Cases**

There are two individual test cases:

1. **`_.every(elements, el => el.length == 3)`**: This test case uses the Lodash library to call the `every` function on the `elements` array. The callback function checks if each element's length is equal to 3.
2. **`elements.every(el => el.length == 3)`**: This test case calls the `every` function directly on the `elements` array, without using Lodash.

**Options Compared**

In this benchmark, two options are being compared:

* Using the Lodash library (`_.every`)
* Not using a utility library (`elements.every`)

**Pros and Cons**

Using Lodash (`_.every`) offers several benefits:

Pros:

* Simplifies code: `_.every` makes it easy to write functional programming code.
* Performance: Modern browsers have optimized JavaScript engines that can handle the overhead of function calls, making Lodash a good choice for performance-critical code.

Cons:

* Adds dependency: Using an external library adds an extra dependency and potential maintenance burden.
* Potential overhead: Even with modern engines, there might be some overhead due to function call and garbage collection.

Not using Lodash (`elements.every`) offers the following benefits:

Pros:

* No additional dependencies
* Lightweight implementation

Cons:

* More verbose code
* Potential performance issues if not optimized correctly

**Other Considerations**

Keep in mind that the results of this benchmark might depend on specific use cases, such as:

* Array size and data distribution
* Performance-critical sections of code
* Additional optimizations or workarounds

**Special JavaScript Features**

There are no special JavaScript features or syntax used in these test cases. The code is standard vanilla JavaScript.

**Alternatives**

Other alternatives for implementing the `every` function include:

* Writing a custom implementation from scratch.
* Using another utility library, such as Lo-Dash or Ramda.
* Leveraging modern JavaScript features like `reduce()` and iterating over arrays directly.

Keep in mind that these alternatives might have their own trade-offs in terms of performance, readability, and maintainability.

Related benchmarks:

every and _every (version: 0)

Comparing performance of: _.every vs elements.every

Created: 7 years ago by: Registered User

Jump to the latest result

_.every

elements.every

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):