Benchmark: Map&filter VS reduce

Script Preparation code:


var arr = [];
for(i=0; i<10000; i++){
  arr.push(i);
}

Tests:

map + filter
arr.map((item) => { return { id: item, name: item, } }).filter(item => item.id % 5 !== 0)
reduce
arr.reduce((acc, item) => { if(item % 5 !== 0) { acc.push({ id: item, name: item}); } return acc; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map + filter
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in the provided JSON benchmark.

**Benchmark Overview**

The benchmark is comparing three different approaches to process an array of 10,000 numbers:

1. `map` and `filter` combination
2. `reduce`
3. (Implicitly) A default JavaScript approach using a for loop

**Options Compared**

* **Map**: Applies a transformation function to each element in the array.
* **Filter**: Removes elements from the array based on a condition.
* **Reduce**: Accumulates values in an array by applying a reduction function to each element.

**Pros and Cons of Each Approach**

1. **Map + Filter**
	* Pros:
		+ Easy to understand and implement
		+ Effective for filtering out unwanted data
	* Cons:
		+ Can be slower than `reduce` due to the overhead of creating a new array
2. **Reduce**
	* Pros:
		+ Often faster than `map + filter`
		+ Allows for efficient accumulation of values
	* Cons:
		+ Can be less readable and more error-prone, especially for complex transformations
3. **Default JavaScript Approach (for loop)**
	* This approach is not explicitly compared in the benchmark, but it's likely being used as a baseline.

**Library Usage**

None mentioned explicitly, but the `map`, `filter`, and `reduce` methods are built-in JavaScript functions that don't require any external libraries.

**Special JS Feature/Syntax**

The benchmark uses arrow functions (e.g., `(item) => { ... }`) in both `map` and `reduce` definitions. Arrow functions are a concise way to define small, anonymous functions in JavaScript. They were introduced in ECMAScript 2015 (ES6) and have since become a standard feature of the language.

**Other Alternatives**

If you want to explore alternative approaches or optimize your code further:

* Consider using `filter` with `Array.prototype.every()` instead of `map + filter`.
* Use `reduce()` with an accumulator object to simplify the transformation process.
* Look into using specialized libraries like Lodash for more complex transformations or array operations.
* Experiment with modern JavaScript features like `async/await`, `generators`, or `Promise.all()` for alternative ways to handle asynchronous data processing.

Keep in mind that these alternatives might not necessarily outperform the original benchmark results, but they can help you explore different design choices and optimization strategies.

Related benchmarks:

Map&filter VS reduce (version: 0)

Comparing performance of: map + filter vs reduce

Created: 5 years ago by: Guest

Jump to the latest result

map + filter

reduce

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):