Benchmark: Lodash vs Native reduce on object on large array

Script Preparation code:

var obj = {}

for (var i = 0, len = 10000; i < len; i++) {
  var arr = []
  obj['key' + i] = arr
  for (var n = 0, len = 1000; n < len; n++) {
    arr.push({ ['key' + n]: n });
  }
}

Tests:

Lodash
_.reduce(obj, (acc, val, key) => { acc[key] = val.map(d => ({ label: key, value: val })); return acc; }, {})
Native
Object.entries(obj).reduce((acc, [key, val]) => { acc[key] = val.map(d => ({ label: key, value: val })); return acc; }, {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Native

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 explaining the benchmark.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark test case, specifically comparing the performance of two approaches: using Lodash's `reduce` method versus the native JavaScript `reduce` method on a large object containing an array with nested objects.

**Options Compared**

Two options are being compared:

1. **Lodash's `reduce` method**: This approach uses the popular utility library Lodash to perform the reduction operation. Lodash provides a convenient and often more concise way to perform common tasks, such as array manipulation and object transformation.
2. **Native JavaScript `reduce` method**: This approach relies on the built-in `reduce` method provided by the JavaScript language itself. It uses a more traditional programming style, where each iteration of the reduction process is executed explicitly.

**Pros and Cons**

Here are some pros and cons for each approach:

### Lodash's `reduce`

Pros:

* **Concise code**: The code using Lodash's `reduce` method is often shorter and easier to read.
* **Reusable utility functions**: Lodash provides a wide range of reusable utility functions that can simplify common tasks.

Cons:

* **Overhead from library usage**: Using an external library like Lodash adds overhead in terms of loading, parsing, and executing the code.
* **Potential performance impact**: While Lodash is often optimized, its inclusion can still introduce a slight performance penalty compared to native JavaScript implementation.

### Native JavaScript `reduce`

Pros:

* **No external library dependencies**: By using only built-in JavaScript functions, this approach eliminates the overhead associated with loading and parsing an external library.
* **Native performance**: The native `reduce` method is typically optimized for performance, making it a good choice when every last bit of speed matters.

Cons:

* **More verbose code**: The code using the native `reduce` method can be more verbose due to the need to explicitly define each iteration step.

**Library Used**

In this case, Lodash is used as a utility library. Its `reduce` function provides a convenient and concise way to perform array reduction operations.

**Special JS Feature or Syntax**

The benchmark does not use any special JavaScript features or syntax that would require explanation. It's focused on showcasing the performance comparison between two existing approaches: using an external library (Lodash) versus native implementation.

**Other Alternatives**

For large-scale JavaScript benchmarking, other alternatives to MeasureThat.net include:

* **jsPerf**: A built-in Chrome DevTools tool for benchmarking JavaScript performance.
* **Benchmark.js**: A popular open-source benchmarking framework that allows you to write and execute benchmarks.
* **WebPageTest**: A comprehensive web testing tool that includes benchmarking capabilities.

These alternatives offer varying degrees of flexibility, customization options, and integration with other tools.

Related benchmarks:

Lodash vs Native reduce on object on large array (version: 0)

Comparing performance of: Lodash vs Native

Created: 5 years ago by: Guest

Jump to the latest result

Lodash

Native

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):