Benchmark: map vs reduce - MeasureThat.net

Script Preparation code:

function generateTestArray() {
    const result = [];
    for (let i = 0; i < 10000; ++i) {
        result.push(i);
    }
    return result;
}

Tests:

map
const testArray = generateTestArray() testArray.map(i => { i })
reduce
const testArray = generateTestArray() testArray.reduce((acc, i) => [...acc, { i }], [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map
reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gemma2:9b, generated one month ago):

LLMs can make mistakes. Check important info.

This benchmark compares the performance of two approaches to process an array in JavaScript: `map` and `reduce`.

**What's being tested:**

* **`map`**: This method iterates over each element in the array and applies a given function to each element, returning a new array with the transformed values. In this case, the function simply returns the original element (`i`), essentially creating a copy of the input array.
* **`reduce`**: This method also iterates over the array, but it accumulates a single value by applying a function that takes two arguments: the accumulated value and the current element.  Here, the provided `reduce` implementation creates a new array containing each element from the original array.

**Options Compared:**

* **Functionality:** Both approaches ultimately achieve similar results – creating a new array with the same values as the input. However, their underlying mechanisms differ significantly. `map` is designed for transforming elements, while `reduce` is more general-purpose and often used for calculating a single value from an array (e.g., sum, average).
* **Performance:** This benchmark aims to measure how many times each function can process the array per second.

**Pros/Cons:**

* **`map`**:
    * **Pros:** Often simpler to read and understand, especially for straightforward transformations. Can be highly optimized by JavaScript engines.
    * **Cons:**  Not as versatile as `reduce` when you need to perform complex calculations or accumulate a single value.
* **`reduce`**:
    * **Pros:** Extremely powerful for tasks involving accumulation and aggregation. 
    * **Cons:**  Can be more complex to write, especially if the logic involves multiple steps or side effects. Might not be as performant for simple transformations compared to `map`.

**Other Considerations:**

* **Context Matters:** The best choice depends heavily on your specific use case. If you need to transform each element in a predictable way, `map` is usually a good starting point. If you're performing calculations or building up a result iteratively, `reduce` offers more flexibility.
* **Optimization:** JavaScript engines are constantly being improved. While `map` often performs well, it's possible that future optimizations could change the performance landscape.

**Alternatives:**

*  There are other libraries like Lodash that provide similar functionality to `map` and `reduce`. These can sometimes offer additional features or optimizations.
*  For specific use cases, you might explore more specialized algorithms or data structures, depending on your requirements.

Let me know if you have any more questions!

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36

Browser/OS: Chrome 108 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
map	10376.9 Ops/sec
reduce	14.9 Ops/sec

Related benchmarks:

map vs reduce (version: 0)

Comparing performance of: map vs reduce

Created: one year ago by: Guest

Jump to the latest result

map

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gemma2:9b, generated one month ago):