Benchmark: Reduce + Spread vs Reduce + Concat

Tests:

Reduce + Concat
const users = { 'user_1': { list: { ids: [...Array(400)].map(e=>Math.random()*40|0) } }, 'user_2': { list: { ids: [...Array(500)].map(e=>Math.random()*40|0) } }, 'user_3': { list: { ids: [...Array(600)].map(e=>Math.random()*40|0) } }, 'user_4': { list: { ids: [...Array(700)].map(e=>Math.random()*40|0) } }, } const result = Object.values(users).reduce((ids, user) => ids.concat(user.list.ids), []);
Reduce + Spread
const users = { 'user_1': { list: { ids: [...Array(400)].map(e=>Math.random()*40|0) } }, 'user_2': { list: { ids: [...Array(500)].map(e=>Math.random()*40|0) } }, 'user_3': { list: { ids: [...Array(600)].map(e=>Math.random()*40|0) } }, 'user_4': { list: { ids: [...Array(700)].map(e=>Math.random()*40|0) } }, } const result = Object.values(users).reduce((ids, user) => [...ids, ...user.list.ids], []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce + Concat
Reduce + Spread

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:86.0) Gecko/20100101 Firefox/86.0

Browser/OS: Firefox 86 on Windows

View result in a separate tab

Test name	Executions per second
Reduce + Concat	3377.7 Ops/sec
Reduce + Spread	2657.7 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript benchmark test case on MeasureThat.net, which compares the performance of two approaches: using `concat()` and using spread operators (`...`) to flatten an array of arrays.

**Approaches Compared**

1. **Reduce + Concat**: This approach uses the `reduce()` method with a callback function that concatenates the inner arrays using `concat()`.
2. **Reduce + Spread**: This approach uses the `reduce()` method with a callback function that flattens the inner arrays using spread operators (`...`).

**Pros and Cons of Each Approach**

1. **Reduce + Concat**:
	* Pros: Can be more efficient for large datasets, as it avoids creating new arrays.
	* Cons: Can lead to performance issues if the input data is not properly optimized, as `concat()` creates a new array.
2. **Reduce + Spread**:
	* Pros: Faster and more memory-efficient than `concat()`, as it avoids creating new arrays.
	* Cons: May have slightly higher overhead due to the creation of temporary arrays.

**Libraries Used**

None are explicitly mentioned in the benchmark JSON. However, it's likely that the benchmark uses a default JavaScript implementation or engine.

**Special JS Features/Syntax**

1. **Spread Operators (`...`)**: Introduced in ECMAScript 2015, spread operators allow for spreading elements of an array into another array.
2. **Reduce() Method**: A built-in Array method that applies a callback function to each element of the array and returns a new array with the results.

**Other Alternatives**

1. **Array.prototype.flat()**: An alternative approach to flatten arrays, introduced in ECMAScript 2017. It can be used instead of `concat()` or spread operators.
2. **for...of Loop**: Another alternative for iterating over arrays and flattening them, which can be more readable and efficient than using `reduce()`.

In conclusion, the benchmark tests two approaches to flatten an array of arrays: one using `concat()` and another using spread operators (`...`). While both approaches have their pros and cons, the use of spread operators is generally faster and more memory-efficient.

Related benchmarks:

Reduce + Spread vs Reduce + Concat (version: 0)

Comparing performance of: Reduce + Concat vs Reduce + Spread

Created: 3 years ago by: Guest

Jump to the latest result

Reduce + Concat

Reduce + Spread

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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