Benchmark: Reduce (object reuse) vs For loop

Script Preparation code:

var data = { ...Array.from(Array(10000).keys()) };

Tests:

Reduce (reuse object)
Object.entries(data).reduce((acc, [k, v]) => { acc[k] = v.toString(); return acc; }, {});
For loop
const entries = Object.entries(data); const len = entries.length; const acc = {}; for (let i = 0; i < len; i += 1) { acc[entries[i][0]] = entries[i][1]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce (reuse object)
For loop

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 break down the provided JSON and explain what's being tested.

**Benchmark Definition**

The benchmark is comparing two approaches for iterating over an array of objects: `Object.entries(data).reduce()` and a traditional `for` loop.

**Script Preparation Code**

The script preparation code creates an object `data` with 10,000 properties, where each property has a string value. This object is used as the input for both benchmarking approaches.

**Html Preparation Code**

There is no HTML preparation code provided, which suggests that this benchmark focuses on JavaScript performance and does not involve any web page rendering or DOM manipulation.

**Test Cases**

The two test cases are:

1. **Reduce (reuse object)**: This approach uses `Object.entries(data)` to get an array of key-value pairs, and then applies the `reduce()` method to accumulate the values in a new object.
2. **For loop**: This approach iterates over the `entries` array using a traditional `for` loop, and assigns each value to a property in the `acc` object.

**Library Usage**

Neither of these approaches uses any libraries beyond JavaScript's built-in `Object` methods and `Array`.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in this benchmark. Both test cases only use standard JavaScript constructs.

Now, let's discuss the pros and cons of each approach:

**Reduce (reuse object)**

Pros:

* Concise and expressive code
* Eliminates the need for manual array indexing and iteration
* Can be more efficient if the accumulator object is reused

Cons:

* May have higher memory overhead due to the accumulation of values in an object
* Can be slower for very large arrays, as it needs to allocate a new object for each iteration

**For loop**

Pros:

* Generally faster than `reduce()` for very large arrays, since it avoids object allocation and garbage collection
* More straightforward and intuitive code structure

Cons:

* Requires manual array indexing and iteration, which can be error-prone
* More verbose code compared to the concise `reduce()` approach

**Other Alternatives**

If you wanted to test different approaches, here are some alternatives:

1. **Array.prototype.forEach()**: This method iterates over an array without accumulating values in an object.
2. **Map**: Instead of using an object to accumulate values, you could use a `Map` data structure, which provides better performance and memory efficiency for large arrays.
3. **Array.prototype.reduceRight()**: This method is similar to `reduce()` but iterates from right to left, starting with the first element.

Keep in mind that these alternatives might not provide significant performance differences unless you're dealing with extremely large arrays or specific use cases.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/118.0

Browser/OS: Firefox 118 on Linux

View result in a separate tab

Test name	Executions per second
Reduce (reuse object)	1396.4 Ops/sec
For loop	2725.5 Ops/sec

Related benchmarks:

Reduce (object reuse) vs For loop (version: 0)

Comparing performance of: Reduce (reuse object) vs For loop

Created: one year ago by: Guest

Jump to the latest result

Reduce (reuse object)

For loop

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):