Benchmark: reduce spread operator vs flat

Tests:

spread operator
var params = [[ "hello", true, 7 ],[ "hello", true, 7 ]] var other = params.reduce((acc,curr)=>{return [...acc, ...curr]},[]);
Array.prototype.flat
var params = [[ "hello", true, 7 ],[ "hello", true, 7 ]] var other = params.flat()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread operator
Array.prototype.flat

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36 Edg/147.0.0.0

Browser/OS: Chrome 147 on Windows

View result in a separate tab

Test name	Executions per second
spread operator	18956272.0 Ops/sec
Array.prototype.flat	9043279.0 Ops/sec

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

LLMs can make mistakes. Check important info.

**What is being tested:**

MeasureThat.net is testing two different approaches for flattening an array of arrays in JavaScript:

1. The new ES6 spread operator (`...`).
2. The traditional `Array.prototype.flat()` method.

The benchmark aims to compare the performance and efficiency of these two methods in terms of execution speed, which is likely related to the number of operations required, cache behavior, or other factors that might affect the runtime.

**Options compared:**

There are only two options being tested:

1. **Spread Operator (`...`)**: This is a new syntax introduced in ES6 that allows you to expand an array into individual elements using the spread operator. In this case, it's used to flatten an array of arrays.
2. **Array.prototype.flat()**: This is a built-in method of the Array prototype that flattens an array by recursively calling itself on each sub-array.

**Pros and Cons:**

**Spread Operator (`...`):**

Pros:

* Concise and readable syntax
* Can be used to flatten arrays in a single line of code
* Might have better cache locality due to its compact nature

Cons:

* May not be optimized for performance, as it's still an interpreted language feature
* Could be slower than `flat()` due to the overhead of interpreting the spread operator
* Limited browser support (pre-ES6 browsers won't understand this syntax)

**Array.prototype.flat():**

Pros:

* Optimized for performance by the JavaScript engine
* Well-established and widely supported, making it more likely to be executed efficiently
* Can handle large arrays and nested structures with ease

Cons:

* May have a higher memory footprint due to its recursive nature
* Less readable syntax compared to the spread operator

**Other considerations:**

Both methods can have varying levels of performance depending on factors such as array size, nesting depth, and browser implementation. The JavaScript engine might optimize `flat()` more aggressively than the spread operator, but this could also lead to slower execution times due to increased overhead.

**Library usage:**

None of the benchmark test cases use any external libraries.

**Special JS features or syntax:**

The benchmark tests the use of two new ES6 features:

1. **Spread Operator (`...`)**: Introduced in ES6, this operator allows you to expand an array into individual elements.
2. **Array.prototype.flat()**: Also introduced in ES6, this method is specifically designed for flattening arrays.

**Alternatives:**

Other methods for flattening arrays could include:

1. `concat()` with looping
2. Using a library like Lodash (which provides a `flatten()` function)
3. Using a custom recursive function

These alternatives might offer different trade-offs in terms of performance, readability, and maintainability compared to the spread operator and `Array.prototype.flat()`.

Related benchmarks:

reduce spread operator vs flat (version: 0)

Compare the new ES6 spread operator with the traditional concat() method

Comparing performance of: spread operator vs Array.prototype.flat

Created: 4 years ago by: Guest

Jump to the latest result

spread operator

Array.prototype.flat

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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