Benchmark: reduce+concat vs. for loops

Script Preparation code:

DATA = Array.from({length:10}, (_, idx) => ({
  idx, 
  containers: Array.from({length: 10}, (_, container) => ({ title: 'hello', container }))
}))

Tests:

reduce+concat+forEach
DATA .reduce((prev, cur) => prev.concat(cur.containers), []) .forEach((element, idx) => { Object.assign(element, { idx }); });
for loops
for (const page of DATA) { for (let i = 0; i < page.containers.length; ++i) { Object.assign(page.containers[i], { idx: i }); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce+concat+forEach
for loops

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 is tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark measures the performance of two approaches to manipulate an array: using `reduce` with concatenation (`reduce+concat`) versus using traditional `for` loops (`for loops`). The dataset used for testing consists of an array of objects, each containing an index and a container array with 10 elements.

**Options Compared**

1. **Reduce with Concat**: This approach uses the `reduce` method to concatenate arrays together. It starts with an initial value (an empty array) and iterates through the input data, appending each element's container array to the accumulator.
2. **For Loops**: This approach uses traditional `for` loops to iterate through the input data and update individual elements.

**Pros and Cons**

**Reduce with Concat**

Pros:

* Concise and readable code
* Eliminates unnecessary iterations

Cons:

* May incur additional overhead due to array concatenation
* Can be slower for large datasets

**For Loops**

Pros:

* Well-known, familiar pattern
* Allows for more control over iteration and updates

Cons:

* More verbose and less readable code
* Involves unnecessary iterations (e.g., iterating through the container array)

**Other Considerations**

Both approaches have their trade-offs. The `reduce+concat` approach is generally faster and more concise, but may incur additional overhead due to array concatenation. The traditional `for` loop approach provides more control over iteration and updates, but is often slower and less readable.

**Library Used**

There is no specific library used in this benchmark. The JavaScript standard library is sufficient for the demonstration.

**Special JS Feature or Syntax**

No special features or syntax are used beyond standard JavaScript.

**Other Alternatives**

If you'd like to explore alternative approaches, here are a few options:

1. **Array.prototype.map()**: This method applies a transformation function to each element of an array and returns a new array with the transformed elements.
2. **Array.prototype.forEach()**: This method executes a callback function once for each element in an array without providing an index.
3. **Iterators and Generators**: These constructs allow you to iterate over data structures in a more flexible and efficient way.

Keep in mind that these alternatives may not be suitable for every use case, and the choice of approach depends on your specific requirements and performance needs.

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Windows

View result in a separate tab

Test name	Executions per second
reduce+concat+forEach	31878.6 Ops/sec
for loops	47613.5 Ops/sec

Related benchmarks:

reduce+concat vs. for loops (version: 0)

Comparing performance of: reduce+concat+forEach vs for loops

Created: 7 months ago by: Guest

Jump to the latest result

reduce+concat+forEach

for loops

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):