Benchmark: lodash intersectionBy vs array filtering with includes

HTML Preparation code:

<script src="lodash.js"></script>

Script Preparation code:

var listOfObjects_1 = [{id: 1, name: '1'}, {id: 2, name: '2'}, {id: 5, name: '5'}]
var listOfObjects_2 = [{id: 1, name: '1'}, {id: 2, name: '2'}, {id: 5, name: '4'}]

Tests:

lodash intersectionBy
_.intersectionBy(listOfObjects_1, listOfObjects_2, 'id')
array filtering with includes
var ids_1 = listOfObjects_1.map(o => o.id) listOfObjects_2.filter(o => ids_1.includes(o.id))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash intersectionBy
array filtering with includes

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36

Browser/OS: Chrome 132 on Linux

View result in a separate tab

Test name	Executions per second
lodash intersectionBy	852457.6 Ops/sec
array filtering with includes	14334470.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is being tested.

**Benchmark Definition**

The benchmark definition specifies two test cases:

1. `_.intersectionBy(listOfObjects_1, listOfObjects_2, 'id')`: This test case uses the `lodash` library to perform an intersection by key on two arrays of objects (`listOfObjects_1` and `listOfObjects_2`) based on the `'id'` key.
2. `var ids_1 = listOfObjects_1.map(o => o.id)\nlistOfObjects_2.filter(o => ids_1.includes(o.id))`: This test case manually implements an array filtering with includes method using JavaScript's built-in `map` and `filter` functions.

**Options Compared**

The two options being compared are:

1. Using the `lodash` library's `intersectionBy` function.
2. Implementing the intersection by key manually using JavaScript's built-in `map` and `filter` functions.

**Pros and Cons of Each Approach**

**Lodash `intersectionBy` Function:**

Pros:

* Convenient and concise way to perform intersections by key on arrays of objects.
* Often more readable and maintainable than manual implementations.

Cons:

* Introduces an additional dependency on the `lodash` library.
* May have performance overhead due to the need to load the library.

**Manual Implementation using `map` and `filter`:**

Pros:

* No additional dependencies or overhead.
* Can be more flexible and customizable for specific use cases.

Cons:

* Often more verbose and less readable than using a library function.
* Requires manual error handling and edge case consideration.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, object transformation, and functional programming. The `intersectionBy` function is designed to find the common elements between two arrays of objects based on a specified key.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntax used in this benchmark that would require additional explanation.

**Other Alternatives**

If you wanted to implement an intersection by key manually without using `lodash`, you could use other libraries like `underscore` or implement it yourself using a custom function. Alternatively, you could also consider using other built-in functions like `Set` and `forEach` to achieve the same result.

It's worth noting that both approaches have their trade-offs in terms of performance, readability, and maintainability. The choice between them ultimately depends on your specific use case, personal preference, and project requirements.

Related benchmarks:

lodash intersectionBy vs array filtering with includes (version: 0)

Return only objects with same id

Comparing performance of: lodash intersectionBy vs array filtering with includes

Created: 4 years ago by: Guest

Jump to the latest result

lodash intersectionBy

array filtering with includes

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):