Benchmark: _.pickBy vs native reduce2

HTML Preparation code:

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

Script Preparation code:

var dailySettings = Array(752)
  .fill()
  .reduce((acc, curr, i) => {
    acc[i] = {
      '2023-01-01': {
        1: 100,
        2: 110,
        extraAdult: 50,
        extraChild: 50,
        algorithm: 'DBR',
        baseRateCode: 'RATE-1',
        baseAmount: 110
      }
    };
    return acc;
  }, {});

var predicate = v => v % 2 === 0;

Tests:

_.pickBy
_.pickBy(dailySettings, predicate);
native
Object.keys(dailySettings).reduce((accr, cur) => { return { ...accr, [cur]: dailySettings[cur], } }, {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.pickBy
native

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
_.pickBy	17739.6 Ops/sec
native	9409.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

The provided benchmark compares two approaches to reduce an object: using the Lodash library (`_.pickBy`) and the native `reduce` method. The test case creates an array of 752 objects, each representing a daily setting with various properties.

**`.pickBy` (Lodash)**

* Library: Lodash (version 4.17.5)
* Purpose: A utility function that returns a new object containing only the specified properties from the original object.
* Pro:
	+ Concise and readable code
	+ Easy to maintain and modify
	+ Supports various predicate functions for filtering
* Con:
	+ Requires an external library, which may slow down execution
	+ May not be optimized for performance

**Native `reduce`**

* Purpose: A built-in method that applies a reduction function to each element in the array.
* Pro:
	+ No external dependencies required
	+ Optimized for performance by the JavaScript engine
	+ Flexible and customizable
* Con:
	+ Requires manual property iteration and object creation
	+ More verbose code compared to `.pickBy`

**Comparison**

The benchmark compares the execution time of both approaches on a Mac OS X 10.15.7 machine with Chrome 114 browser. The test cases are executed at a rate of approximately 17739 executions per second.

* `.pickBy` (Lodash) is significantly faster, with an average execution time of around 4.09 milliseconds.
* Native `reduce` takes longer, with an average execution time of around 9.41 milliseconds.

**Other Considerations**

When choosing between these approaches, consider the following factors:

* Performance: If speed is crucial, using native `reduce` might be a better choice, as it's optimized by the JavaScript engine.
* Code readability and maintainability: `.pickBy` (Lodash) provides concise and readable code, making it easier to understand and modify.
* Library dependencies: Using an external library like Lodash may add overhead, while native methods don't require any additional dependencies.

**Other Alternatives**

If you're looking for alternative approaches to reduce objects or arrays, consider the following:

* `forEach` method with a callback function
* `map` method followed by `reduce`
* Using `filter`, `every`, and other array methods in combination

Keep in mind that each approach has its pros and cons, and the best choice depends on your specific use case and performance requirements.

Related benchmarks:

_.pickBy vs native reduce2 (version: 0)

Comparing performance of: _.pickBy vs native

Created: one year ago by: Registered User

Jump to the latest result

_.pickBy

native

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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