Benchmark: Lodash sumBy vs reduce

HTML Preparation code:

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

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}

var arr = [];
for(var i = 0; i < 100000; i++){
  arr.push({value:getRandomInt(100)});
}

Tests:

_.sumBy
_.sumBy(arr,"value");
map and reduce
arr.reduce((prev, next) => prev + next.value, 0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.sumBy
map and reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and its results.

**Benchmark Overview**

The MeasureThat.net benchmark compares the performance of two approaches: using Lodash's `sumBy` function versus using the built-in array `reduce()` method in JavaScript. The benchmark generates an array of 100,000 objects with random integer values and then tests both approaches to sum up all the values.

**Options Compared**

The benchmark compares two options:

1. **Lodash's `sumBy`**: This approach uses Lodash's `sumBy` function to iterate over the array and sum up the values.
2. **Built-in array `reduce()` method**: This approach uses the `reduce()` method to iterate over the array and sum up the values.

**Pros and Cons**

Here are some pros and cons of each approach:

**Lodash's `sumBy`**

Pros:

* Easy to read and understand
* Lodash provides a convenient way to perform common operations like summing up values

Cons:

* Adds an additional dependency on Lodash, which may slow down the benchmark due to the overhead of loading the library
* May not be as efficient as native JavaScript code due to the added abstraction layer

**Built-in array `reduce()` method**

Pros:

* Native JavaScript code means no dependencies or overhead
* Can potentially be faster due to the absence of an abstraction layer

Cons:

* Requires more code and is less readable than Lodash's `sumBy`
* May not be as convenient for complex operations like summing up values with different functions

**Other Considerations**

The benchmark also takes into account the following factors:

* **Hardware**: The test is run on a desktop computer with Chrome 129, which suggests that the results may not be representative of mobile devices or other platforms.
* **Browser engine**: The use of Safari as the browser engine (alongside Chrome) may affect the results due to differences in rendering and JavaScript execution.

**Library: Lodash**

Lodash is a popular JavaScript library that provides a collection of functional programming helpers. In this case, `sumBy` is used to sum up the values in an array while applying a given function to each element. The library provides several other useful functions for data manipulation, such as `filter()`, `map()`, and `compact()`.

**Special JavaScript Feature or Syntax**

There are no special JavaScript features or syntaxes mentioned in this benchmark. However, it's worth noting that the use of Lodash may be considered a "feature" in some cases, but in this context, it's simply used as a convenient dependency to simplify the code.

**Alternatives**

If you want to run a similar benchmark on your own, you can try using other libraries or approaches, such as:

* Using only native JavaScript methods like `forEach()` and `Array.prototype.reduce()`
* Implementing your own custom summing function
* Using a different library like `lodash` with a different version (e.g., `4.17.2`)

Latest run results:

Run details: (Test run date: yesterday)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Linux

View result in a separate tab

Test name	Executions per second
_.sumBy	749.9 Ops/sec
map and reduce	875.6 Ops/sec

Related benchmarks:

Lodash sumBy vs reduce (version: 0)

Comparing performance of: _.sumBy vs map and reduce

Created: 6 years ago by: Guest

Jump to the latest result

_.sumBy

map and reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):