Benchmark: reduce vs plain cycle

Script Preparation code:

var test = []
for(let i=1; i<200000; i++) {
    test.push({
      id: i,
      class: Math.floor(Math.random()*10)
    })
}

Tests:

reduce
test.reduce((acc, item) => { if(item.class==3) { acc.push(item.id) return acc } return acc }, [])
filter map
test.filter(item => item.class ==2).map(item => item.id)
cycle
var test = [] for(let i=1; i<200000; i++) { test.push({ id: i, class: Math.floor(Math.random()*10) }) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
filter map
cycle

Fastest: N/A

Slowest: N/A

Latest 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, the options compared, pros and cons of each approach, and other considerations.

**Benchmark Overview**

The benchmark compares three different approaches for processing an array of objects:

1. `reduce()`
2. `filter()` followed by `map()`
3. A simple loop (`cycle`)

**Script Preparation Code**

The script preparation code creates an array `test` with 200,000 objects, each having a unique `id` and a randomly generated `class` value between 0 and 9.

**Benchmark Definition JSON**

The benchmark definition contains three test cases:

1. `reduce()`: The reduce function is used to iterate over the array and accumulate values in an accumulator array. In this case, it filters out objects with `class == 3` and pushes their `id` values into the accumulator array.
2. `filter()` followed by `map()`: This approach first filters out objects with `class == 2`, then maps each remaining object to its `id` value.
3. `cycle`: A simple loop that iterates over the array, pushing each object's `id` value into a new array.

**Library: Lodash (not explicitly mentioned in the provided JSON)**

Although not explicitly stated in the JSON, it is likely that the benchmark uses the Lodash library for its tests. The `filter()` and `map()` functions are commonly used with Lodash, so it's probable that the benchmark utilizes these utility functions.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntaxes being tested in this benchmark.

**Options Compared**

The three approaches compared are:

* `reduce()`: Iterates over the array, accumulating values in an accumulator array.
	+ Pros:
		- Can be more efficient for small arrays due to caching.
		- Allows for concise code.
	+ Cons:
		- Can be less readable for complex operations.
		- May not perform well with very large arrays.
* `filter()` followed by `map()`: Iterates over the array, filtering out unwanted elements and then mapping each remaining element to a new value.
	+ Pros:
		- More readable than reduce for some use cases.
		- Can be more efficient for small arrays due to caching.
	+ Cons:
		- Requires two separate operations: filtering and mapping.
* `cycle`: A simple loop that iterates over the array, pushing each object's `id` value into a new array.
	+ Pros:
		- Simple and easy to understand.
	+ Cons:
		- Can be less efficient due to the overhead of manual looping.

**Considerations**

When choosing an approach, consider the following factors:

* Readability: Choose the approach that best fits your code's readability goals. `reduce()` can be concise but may be less readable for complex operations. The `filter()` + `map()` approach can be more readable.
* Efficiency: For large arrays, consider the caching behavior of each approach. `reduce()` and `filter()` + `map()` often benefit from caching due to their iterative nature. `cycle` has no caching advantage.

**Alternatives**

Other alternatives for processing arrays include:

* Using a functional programming library like Lodash or Ramda.
* Utilizing streaming algorithms, which process data in chunks rather than loading the entire array into memory.
* Employing parallel processing techniques, such as using Web Workers or Node.js clusters to execute multiple tasks concurrently.

Keep in mind that these alternatives may have different performance characteristics and trade-offs compared to the approaches tested in this benchmark.

Related benchmarks:

reduce vs plain cycle (version: 0)

Comparing performance of: reduce vs filter map vs cycle

Created: 2 years ago by: Guest

Jump to the latest result

reduce

filter map

cycle

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):