Benchmark: reduce()+push() vs map()

Script Preparation code:

var data = [ ...Array.from(Array(10000).keys()) ];

Tests:

reduce()+push()
data.reduce((acc, v) => { acc.push(v.toString()); return acc; }, []);
map()
data.map(v => { return v.toString(); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce()+push()
map()

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**What is being tested?**

MeasureThat.net is testing two different approaches to transform an array of numbers and convert them to strings: using the `reduce()` method with `push()` or using the `map()` function.

In both cases, the input data is generated by creating a large array of numbers from 0 to 9999 (using `[ ...Array.from(Array(10000).keys()) ]`).

**Options compared**

Two options are being compared:

1. **reduce()+push()**: This approach uses the `reduce()` method to accumulate an array of strings in a single array, pushing new strings onto the accumulator.
2. **map()**: This approach uses the `map()` function to create a new array with transformed elements (numbers converted to strings).

**Pros and Cons**

*   **reduce()+push()**
    *   Pros:
        *   Can be more efficient for small datasets or when memory is limited, since it only creates one array.
        *   Allows for easy modification of the accumulator by adding or removing elements.
    *   Cons:
        *   May be slower and less scalable for large datasets due to the accumulation step.
        *   Requires more code and understanding of the `reduce()` method.
*   **map()**
    *   Pros:
        *   More concise and readable than using `reduce()+push()`.
        *   Faster and more scalable, as it creates a new array with transformed elements without accumulation.
    *   Cons:
        *   Creates an additional array, which can be memory-intensive for large datasets.

**Library usage**

There is no explicit library usage mentioned in the provided benchmark. However, both `reduce()` and `map()` are native JavaScript methods.

**Special JS feature or syntax**

None of the provided benchmarks use special JavaScript features or syntax beyond the standard language.

**Other alternatives**

For a more comprehensive comparison, additional approaches might include:

*   Using `forEach()` instead of `reduce()+push()`
*   Using `filter()` and concatenating arrays (not as efficient as map)
*   Using `String.prototype.join()` to concatenate strings
*   Using other libraries or frameworks for optimization

Keep in mind that the choice of approach depends on specific requirements, such as performance, readability, memory constraints, or the need for array manipulation capabilities.

Overall, this benchmark provides a simple yet informative comparison of two commonly used methods in JavaScript: `reduce()` with `push()` versus `map()`.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36

Browser/OS: Chrome 88 on Windows

View result in a separate tab

Test name	Executions per second
reduce()+push()	1658.1 Ops/sec
map()	2558.4 Ops/sec

Related benchmarks:

reduce()+push() vs map() (version: 0)

Comparing performance of: reduce()+push() vs map()

Created: 3 years ago by: Guest

Jump to the latest result

reduce()+push()

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):