Benchmark: lodash partition vs reduce with array push vs two 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 tabl = Array.from({ length: 10000 }).map((value, i) => i)

Tests:

Lodash partition
const [a, b] = _.partition(tabl, e => e % 2)
reduce with array push
const partition = (a, p) => a.reduce((result, e, i) => (result[p(e) ? 0 : 1].push(e), result), [[], []]); const predicate = e => e % 2; const [a, b] = partition(tabl, predicate);
double filter
const a = tabl.filter(e => e % 2); const b = tabl.filter(e => !e % 2);

Historical Performance Data aggregated from 15 community runs

Desktop Results:

Chrome 15 runs

reduce with array push

26K/s

Lodash partition

24K/s

double filter

22K/s

Fastest: reduce with array push (1.1x faster)

View exact numbers

✓ reduce with array push: 25,607 ops/sec
• Lodash partition: 23,866 ops/sec
• double filter: 22,451 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash partition
reduce with array push
double filter

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36

Browser/OS: Chrome 140 on Mac OS X 10.15.7

View result in a separate tab

Lodash partition

25K/s

reduce with array push

19K/s

double filter

16K/s

View exact numbers

Test name	Executions per second
✓ Lodash partition	24,560 Ops/sec
reduce with array push	18,856 Ops/sec
double filter	16,050 Ops/sec

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 JavaScript microbenchmarks.

**Benchmark Definition**

The benchmark is designed to compare three approaches for partitioning an array:

1. Lodash `partition` function
2. Reducing an array with a push accumulator
3. Two separate filter loops

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

**Options Compared**

Here's a brief overview of each option:

* **Lodash `partition` function**: This method takes two arguments: the input array and a predicate function that determines whether an element belongs in one group (e.g., even indices) or another (e.g., odd indices). The function returns an array with two subarrays, where elements are grouped according to their index value.
* **Reduce with array push**: This approach uses a `reduce` method to iterate over the input array and accumulate results. It iterates over the array, pushing elements into separate arrays based on whether they meet the predicate condition.
* **Two separate filter loops**: This option involves two separate filter functions that are chained together. The first filter loop checks if an element is even (i.e., meets the predicate condition) and pushes it to one array; the second loop filters out odd indices.

**Pros and Cons**

Here's a brief summary of each approach:

* **Lodash `partition` function**:
	+ Pros: Efficient, concise, and well-tested.
	+ Cons: May not be familiar to developers without Lodash experience.
* **Reduce with array push**:
	+ Pros: Can be more flexible and adaptable than filter loops.
	+ Cons: May require more boilerplate code and is less straightforward for beginners.
* **Two separate filter loops**:
	+ Pros: Straightforward, easy to understand, and no dependencies on Lodash or other libraries.
	+ Cons: Less efficient due to the overhead of filtering and pushing elements.

**Library: Lodash**

Lodash is a popular JavaScript library that provides utility functions for tasks like array manipulation. The `partition` function is one such utility that helps you separate an array into two groups based on a predicate condition. In this benchmark, Lodash is used as a reference implementation to compare the performance of alternative approaches.

**Special JS Feature: Spread Operator**

The spread operator (`...`) is used in the `Script Preparation Code` to create a new array with 10,000 elements. The array is created using an arrow function that maps over an object with a length property set to 10,000. This creates an array of numbers from 0 to 9,999.

**Benchmark Results**

The latest benchmark results show the executions per second for each test case:

* **Double filter**: 20049.39
* **Lodash partition**: 18858.46
* **Reduce with array push**: 18559.94

These results indicate that the double filter approach is the fastest, followed by Lodash `partition` and then reduce with array push.

**Alternatives**

If you're looking for alternative approaches or ways to optimize your own code, consider:

1. Using a more efficient data structure, like a binary search tree.
2. Leveraging modern JavaScript features, such as iterators and generators.
3. Investigating other libraries or frameworks that offer similar functionality to Lodash.

Keep in mind that the best approach will depend on the specific requirements of your project and your personal coding style.

lodash partition vs reduce with array push vs two filter loops (version: 0)

Comparing performance of: `Lodash partition vs reduce with array push vs double filter`

Created: 5 years ago by: Guest

Go to the latest result

Lodash partition

reduce with array push

double filter

Historical Performance Data aggregated from 15 community runs

Desktop Results:

Chrome 15 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

lodash partition vs reduce with array push vs two filter loops (version: 0)

Comparing performance of: Lodash partition vs reduce with array push vs double filter

Created: 5 years ago by: Guest

Go to the latest result

Lodash partition

reduce with array push

double filter

Historical Performance Data aggregated from 15 community runs

Desktop Results:

Chrome 15 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

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Comparing performance of: `Lodash partition vs reduce with array push vs double filter`