Benchmark: areas reduce vs map

Script Preparation code:

var areas = [
          "cloud",
          "line-cloud",
          "edge-1",
          "line-edge-1",
          "chiller-1",
        ]

Tests:

reduce
[...new Array(areas[0].length).keys()] .map((index) => areas.map((item) => item[index]).join(" ")) .reduce((acc, row) => acc + `"${row}"`, "");
map
[...new Array(areas[0].length).keys()] .map((index) => areas.map((item) => item[index]).join(" ")) .map(row => `"${row}"`).join('')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.0 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 17 on iOS 17.0

View result in a separate tab

Test name	Executions per second
reduce	536331.4 Ops/sec
map	493760.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Purpose**

The provided benchmark measures the performance difference between two approaches: `reduce` and `map`. The test case uses an array of strings, "areas", which is used to create a string of words separated by spaces. The goal is to measure how efficient each approach is in concatenating and formatting this string.

**Options Compared**

There are two options compared:

1. **`reduce`**: This method applies a function to each element of the array, reducing it to a single value. In this case, it concatenates the words separated by spaces.
2. **`map`**: This method creates a new array with the results of applying a given function to each element in the original array. In this case, it maps over the "areas" array, formatting each word as a string.

**Pros and Cons**

* `reduce`:
	+ Pros: More concise and expressive code.
	+ Cons: Can be slower than `map` due to the overhead of function calls and concatenations.
* `map`:
	+ Pros: Faster and more efficient for large datasets, since it avoids the overhead of function calls and concatenations.
	+ Cons: Requires an additional step (formatting) that can increase code complexity.

**Library Usage**

There is no explicit library usage in this benchmark. However, the use of `new Array(areas[0].length).keys()` suggests using the `Array.prototype.keys()` method, which was introduced in ECMAScript 2015 (ES6).

**Special JS Feature/Syntax**

The benchmark uses ES6 features:

* Arrow functions (`() => ...`)
* Template literals (`"..."`)

These features are used to make the code more concise and readable.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. `forEach`: Instead of using `map`, you could use `forEach` to iterate over the array and format each word as a string.
2. Loops: You could use traditional loops (e.g., `for` or `while`) to iterate over the array and concatenate the strings manually.
3. Libraries: Depending on your specific needs, you might consider using libraries like Lodash or Underscore.js, which provide functions for manipulating arrays and strings.

Keep in mind that these alternatives may have different performance characteristics and code complexity compared to the original `reduce` and `map` approaches.

Related benchmarks:

areas reduce vs map (version: 0)

Comparing performance of: reduce vs map

Created: one year ago by: Guest

Jump to the latest result

reduce

map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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