Benchmark: lodash partition vs forEach with array push vs two "some" filter loops

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var indexTbl = Array.from({ length: 50 }).map((value, i) => Math.ceil(Math.random()*1000));
var tabl = Array.from({ length: 1000000 }).map((value, i) => i);

Tests:

Lodash partition
let result = _.partition(tabl, v => indexTbl.some(i=>i==v))
forEach
let a = [], b = []; let result = tabl.forEach(item => {if (indexTbl.some(i=>i==item)) {a.push(item)} else {b.push(item)}})
double filter
const a = tabl.filter(item => indexTbl.some(i=>i==item)); const b = tabl.filter(item => !indexTbl.some(i=>i==item));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash partition
forEach
double filter

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the provided benchmark.

**What is being tested?**

The test measures the performance of three different approaches to filter an array in JavaScript:

1. Using `lodash.partition` function
2. Using `Array.prototype.forEach` with a custom filtering logic
3. Using two separate `Array.prototype.filter` methods with custom filtering logic

These approaches are compared to determine which one is the most efficient.

**Options being compared**

The options being compared are:

* `lodash.partition`: A utility function from the Lodash library that partitions an array into two parts based on a predicate.
* `forEach` loop: A traditional JavaScript loop using `Array.prototype.forEach` with a custom filtering logic.
* Two separate `filter` methods: Two separate calls to `Array.prototype.filter` with different filtering logic.

**Pros and cons of each approach**

1. **Lodash partition**: This approach is concise and elegant, but it requires the use of an external library (Lodash). It also modifies the original array.
2. **forEach loop**: This approach is straightforward and easy to understand, but it can be less efficient due to the overhead of calling `push` on the `Array.prototype`.
3. **Two separate filter methods**: This approach provides more control over the filtering logic, but it requires two separate calls to `filter`, which can lead to unnecessary array creation.

**Other considerations**

* The use of `Array.from()` to create arrays with random data can introduce additional overhead.
* The use of Lodash library may not be desirable for all developers, especially those who prefer to write pure JavaScript code.

**Library used**

The test uses the Lodash library version 4.17.5, which provides the `partition` function used in one of the test cases.

**Special JS feature or syntax**

None mentioned in this benchmark.

**Benchmark preparation code explanation**

The script preparation code creates two arrays:

* `indexTbl`: An array with random integers between 0 and 1000.
* `tabl`: An array with random integers between 0 and 1,000,000.

These arrays are used to simulate a large dataset that will be filtered by the different approaches.

**Individual test cases**

Each test case measures the performance of one of the three approaches:

1. Lodash partition
2. forEach loop
3. Two separate filter methods

The benchmark result shows the average executions per second for each approach on a Chrome 103 browser on a Mac OS X 10.15.7 device.

**Alternatives**

Other alternatives to these approaches include:

* Using `Array.prototype.reduce()` with a custom accumulator function.
* Using `Array.prototype.map()` and then filtering the resulting array using `Array.prototype.filter()`.
* Using a streaming library like Lodash's `iterate` function or the `stream` API in Node.js.

Related benchmarks:

lodash partition vs forEach with array push vs two "some" filter loops (version: 0)

Comparing performance of: Lodash partition vs forEach vs double filter

Created: 4 years ago by: Guest

Jump to the latest result

Lodash partition

forEach

double filter

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):