Benchmark: native find vs lodash _.find hey joe

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 users = [
  { 'user': 'joey',  'age': 32 },
  { 'user': 'ross',    'age': 41 },
  { 'user': 'chandler', 'age': 39 }
]

Tests:

array find
// Native users.find(function (o) { return o.age === 41; })
_.find
_.find(users, { age: 41 })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array find
_.find

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the benchmark.

**What is being tested?**

The provided JSON represents two test cases: `array find` using native JavaScript and `_.find` using the Lodash library. Both tests aim to measure the performance difference between using the traditional method (`users.find`) for searching an array based on a specific condition, versus the new ES6 spread operator with the `concat()` method.

**Options compared**

The benchmark compares two approaches:

1. **Native JavaScript**: The native approach uses the built-in `find()` method of arrays in JavaScript.
2. **Lodash _.find**: The Lodash library provides a utility function called `_find`, which is used to find the first element in an array that satisfies a given condition.

**Pros and Cons**

*   **Native JavaScript**:
    *   Pros:
        *   No additional dependencies required (native implementation).
        *   Simple and concise code.
    *   Cons:
        *   May have performance overhead due to the iteration process.
        *   Limited control over optimization techniques.
*   **Lodash _.find**:
    *   Pros:
        *   Optimized for performance, thanks to its internal implementation.
        *   Provides a more explicit and readable code structure (using Lodash's curried functions).
    *   Cons:
        *   Requires an additional dependency (the Lodash library).
        *   May introduce overhead due to the addition of dependencies.

**Library explanation**

In this benchmark, the Lodash library is used for its `_.find` function. The `_.find()` function takes two arguments: the array to search and a predicate function that defines the condition for finding the desired element. This allows for a more concise and readable way of expressing the search logic.

**Special JS feature or syntax**

The benchmark uses ES6's spread operator (`...`) in the native implementation, but it does not specifically highlight this feature as being used for performance comparison purposes.

However, note that the use of `find()` method is indeed a fundamental concept introduced by ES6 and has been widely adopted in modern JavaScript development. It allows developers to write more concise code while maintaining readability.

**Other alternatives**

Some alternative approaches to searching arrays include:

*   Using `forEach` or `reduce` methods
*   Employing custom iteration mechanisms (e.g., using a for loop)
*   Leveraging the `some()` method, although it's typically used with a predicate function rather than an array search

It is worth noting that Lodash has many other useful functions in addition to `_find`, such as _some()_, _.filter()_, and _.map()_, which can simplify common JavaScript tasks.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.152 Safari/537.36

Browser/OS: Chrome 88 on Linux

View result in a separate tab

Test name	Executions per second
array find	14038311.0 Ops/sec
_.find	2721985.8 Ops/sec

Related benchmarks:

native find vs lodash _.find hey joe (version: 0)

Compare the new ES6 spread operator with the traditional concat() method

Comparing performance of: array find vs _.find

Created: 3 years ago by: Guest

Jump to the latest result

array find

_.find

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):