Benchmark: _.fm vs native.fm latest lodash

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min.js"></script>

Script Preparation code:

Tests:

lodash.flatMap
_.flatMap(x,x => [x-1, x, x+1])
Vanilla js flatmap
x.flatMap(x => [x-1, x, x+1])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash.flatMap
Vanilla js flatmap

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
lodash.flatMap	62815.8 Ops/sec
Vanilla js flatmap	30905.5 Ops/sec

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

LLMs can make mistakes. Check important info.

It seems like you're describing a set of benchmarking test cases for comparing the performance of two JavaScript functions: one using Lodash's `flatMap` function and another implementing vanilla JavaScript `flatMap`. The tests seem to be comparing the execution speed (in terms of executions per second) between these two approaches, likely with a specific input or dataset to measure their relative efficiency.

Given this context, I'll provide a structured response that would help in identifying the performance difference and possibly optimizing one of the functions for better performance. However, without access to the actual JavaScript code being benchmarked, we can only speculate based on general principles of functional programming and optimization techniques.

### Benchmark Analysis

1. **Lodash's `flatMap`**: This function is part of a larger library that likely includes optimizations such as memoization or caching for repeated operations. The performance of Lodash's `flatMap` also depends on the input data size, its structure, and how often it's called with the same arguments.

2. **Vanilla JavaScript `flatMap`**: Vanilla JavaScript implementations might not include these optimizations out of the box. However, they can be optimized by memoizing intermediate results or by using methods that reduce memory allocations and copying.

### Potential Optimizations

1. **Memoization/Memo**: If your `flatMap` function is called with similar inputs multiple times (e.g., a large array or object), consider adding a cache to store the results of these computations. This can significantly speed up repeated calls.

2. **Looping vs Iteration**: For very large datasets, vanilla JavaScript looping might be slower than using built-in array methods that are optimized for performance because they have direct access to the underlying array's implementation details.

3. **Avoid Unnecessary Copies**: In both implementations, ensure you're not creating unnecessary intermediate arrays or objects that need to be copied around. Optimize your data structure choices and use `const` where possible to prevent reassignment.

4. **Native Execution**: Consider using methods that are part of the ECMAScript standard (e.g., `Array.prototype.flatMap()`), which might execute more natively on the browser's or Node.js's JavaScript engine, potentially offering better performance compared to Lodash or a custom vanilla implementation.

### Example Optimization

For simplicity, let's assume we're comparing these two functions with a large dataset:

```javascript
// Using Lodash
const _ = require('lodash');
function lodashExample(arr) {
    return _.flatMap(arr, x => [x-1, x, x+1]);
}

// Vanilla Implementation
function vanillaExample(arr) {
    const result = [];
    for (let x of arr) {
        for (let i of [x-1, x, x+1]) {
            if (!result.includes(i)) result.push(i);
        }
    }
    return result;
}
```

In terms of performance optimization specifically tailored to these functions, I would recommend writing benchmarking tests using a library like `Benchmark.js` or `jsbench`, which can help you understand the performance differences between your two implementations more accurately.

Always remember that the best approach depends on your specific use case, data size, and requirements. Always profile your application under real-world conditions to identify bottlenecks.

Related benchmarks:

_.fm vs native.fm latest lodash (version: 0)

Comparing performance of: lodash.flatMap vs Vanilla js flatmap

Created: 2 months ago by: Guest

Jump to the latest result

lodash.flatMap

Vanilla js flatmap

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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