Benchmark: test reduce mapper

Script Preparation code:

// [0, 1, 2, ....9999]
a = Array(10000).fill(0).map((c, i) => i);
var filter = (n) => n%2
var mapper = (n) => n + 5
var reduce = (agg, cur) => {
 if(cur % 2) {
   agg.push(cur + 5)
 }
 return agg;
}

Tests:

reduce style
a.reduce(reduce, []);
filter map
a.filter(filter).map(mapper)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce style
filter map

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 the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark, hosted on MeasureThat.net, allows users to create and run JavaScript microbenchmarks. The benchmark in question tests two different approaches for processing an array of numbers: `reduce` style and `filter` + `map`.

**What is being compared?**

Two test cases are compared:

1. **Reduce Style**: This approach uses the `reduce` method directly on the array, as shown in the "Benchmark Definition" code: `"a.reduce(reduce, [])";`. The `reduce` function takes three arguments: the accumulator (`agg`), the current element (`cur`), and the initial value (in this case, an empty array `[]`).
2. **Filter + Map**: This approach uses the `filter`, `map`, and `reduce` methods sequentially, as shown in the "Benchmark Definition" code: `"a.filter(filter).map(mapper)";`. The `filter` method creates a new array with elements that pass the test implemented by the provided function (`filter`). The `map` method creates a new array with the results of applying the provided function to each element of the array. Finally, the resulting array is passed to the `reduce` method.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Reduce Style**: Pros:
	+ Typically faster, as it avoids creating intermediate arrays.
	+ Can be more efficient for large datasets, as it doesn't require additional memory allocation.
*   Cons:
	+ Can be less readable or maintainable, especially for complex logic.
	+ May not work correctly with certain edge cases or data structures (e.g., non-numeric values).
* **Filter + Map**: Pros:
	+ Often more readable and maintainable, as it separates concerns into distinct steps.
	+ Can handle a wider range of data types and edge cases.
*   Cons:
	+ Typically slower than the `reduce` style, due to the creation of intermediate arrays.
	+ May require additional memory allocation.

**Library and Purpose**

In this benchmark, no specific libraries are used. The code relies on built-in JavaScript methods (e.g., `Array.prototype.reduce`, `Array.prototype.filter`, `Array.prototype.map`).

**Special JS Feature or Syntax**

No special features or syntax are being tested in this benchmark.

**Other Alternatives**

If the developers of MeasureThat.net were to expand their benchmarks, they might consider adding more approaches, such as:

* Using a different array method (e.g., `forEach`, `every`, `some`).
* Adding more complex logic using closures, async/await, or other advanced features.
* Comparing performance with different JavaScript engines (e.g., V8, SpiderMonkey) or platforms.

Keep in mind that this is just an example benchmark, and the possibilities for expansion are endless!

Related benchmarks:

test reduce mapper (version: 0)

Comparing performance of: reduce style vs filter map

Created: 4 years ago by: Guest

Jump to the latest result

reduce style

filter map

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):