Benchmark: Sum vs Reduce - MeasureThat.net

HTML Preparation code:

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

Tests:

lodash sum
const tabs = [100, 200, 300, 400, 500]; console.log( _.sum(tabs) );
javascript reduce
const tabs = [100, 200, 300, 400, 500]; console.log( tabs.reduce((a,b) => a+b) );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash sum
javascript reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36

Browser/OS: Chrome 141 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
lodash sum	427062.9 Ops/sec
javascript reduce	424546.3 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 JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

MeasureThat.net provides a platform for comparing the performance of different JavaScript approaches to solve the same problem. The provided JSON represents two benchmark definitions:

1. **Lodash sum**: This test compares the execution time of Lodash's `sum` function versus a simple addition loop.
2. **JavaScript reduce**: This test measures the execution time of a custom implementation using the `reduce` method.

**Benchmark Options Compared**

In both cases, we have two options to compare:

1. **Lodash sum**
	* Use the built-in Lodash library to perform the summation.
	* Implement the summation manually without relying on a library.
2. **JavaScript reduce**
	* Use the `reduce` method provided by JavaScript.
	* Implement the summation manually without using `reduce`.

**Pros and Cons of Each Approach**

1. **Lodash sum:**
	* Pros:
		+ Easy to implement and understand, as it leverages a well-known library.
		+ Provides a reliable and efficient solution.
	* Cons:
		+ Requires an external dependency (the Lodash library).
		+ May not provide the most optimal performance for all use cases.
2. **JavaScript reduce:**
	* Pros:
		+ Provides direct access to the core JavaScript functionality, reducing overhead.
		+ Can be more efficient than using a library.
	* Cons:
		+ Requires manual implementation and optimization, which can add complexity.
		+ May not be as readable or maintainable for complex calculations.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides various functions to simplify tasks, such as array manipulation, functional programming, and more. In this benchmark, we use the `sum` function from Lodash to perform the summation. Other useful functions in Lodash include `pipe`, `map`, `filter`, and many others.

**Special JS Feature/Syntax**

In this benchmark, there is no specific JavaScript feature or syntax used that would be unique or noteworthy. Both tests rely on standard JavaScript concepts, such as loops, array manipulation, and the use of libraries like Lodash.

**Other Alternatives**

If you're looking for alternatives to MeasureThat.net, here are a few options:

1. **Benchmarking frameworks**: Frameworks like Benchmark.js, Benchpress, or Microbenchmark allow you to create and run benchmarks in your own projects.
2. **JavaScript performance testing tools**: Tools like Perf, Chrome DevTools' Performance panel, or Node.js Inspector can help you analyze and optimize the performance of your JavaScript applications.
3. **Benchmarks on GitHub**: Many open-source projects on GitHub have benchmarking scripts that compare different approaches to solving a specific problem.

I hope this explanation helps you understand the basics of MeasureThat.net and its usage!

Related benchmarks:

Sum vs Reduce (version: 0)

Comparing performance of: lodash sum vs javascript reduce

Created: 6 years ago by: Guest

Jump to the latest result

lodash sum

javascript reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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