Benchmark: Spreading vs Contact in Reduce

Tests:

Reduce + Spread
const array = [[1,2], ["hello",true]]; const result = array.reduce((a, v) => [...a, ...v], []);
Reduce + Concat
const array = [[1,2], ["hello",true]]; const result = array.reduce((a, v) => a.concat(v), []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce + Spread
Reduce + Concat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 15_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.0 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 15 on iOS 15.0

View result in a separate tab

Test name	Executions per second
Reduce + Spread	6573826.0 Ops/sec
Reduce + Concat	8124447.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and test cases for you.

**What is being tested?**

The provided JSON represents two individual test cases for measuring the performance of JavaScript's `reduce()` function with different approaches: spreading the array elements (`...`) vs. using the `concat()` method to concatenate the elements.

**Options compared**

Two options are compared:

1. **Spreading the array elements (`...`)**: This approach uses the spread operator (`...`) to create a new array by copying all elements from the original arrays into a new array.
2. **Using `concat()` method**: This approach uses the `concat()` method to concatenate the elements of the original arrays.

**Pros and Cons**

Here are some pros and cons for each approach:

* **Spreading the array elements (`...`)**:
	+ Pros:
		- More concise and readable code
		- Can be faster due to the optimization provided by the spread operator in modern JavaScript engines
	+ Cons:
		- May not work as expected in older browsers or environments that don't support the spread operator
* **Using `concat()` method**:
	+ Pros:
		- Works consistently across all browsers and environments
	+ Cons:
		- Can be less readable and more verbose code

**Other considerations**

It's worth noting that modern JavaScript engines have optimized the spread operator, which can provide a performance boost. However, older browsers or environments may not support this optimization.

Additionally, the `concat()` method creates a new array on each call, while the spread operator creates a new array only when the original array is modified. This can impact memory usage and performance in certain scenarios.

**Library/dependency**

There are no libraries or dependencies mentioned in the provided benchmark definition.

**Special JS feature/syntax**

None of the test cases use any special JavaScript features or syntax that would require specific handling or interpretation.

**Benchmark preparation code**

The script preparation code is not provided, but it's likely that a simple array is created with some elements and then passed to the `reduce()` function using either the spreading or concatenation approach.

**Alternatives**

There are other alternatives for measuring performance:

1. **Microbenchmarking frameworks**: Tools like Benchmark.js or Fast.js provide more advanced features for creating and running benchmarks.
2. **Browser performance APIs**: Modern browsers have built-in APIs for measuring performance, such as `performance.now()` in Web API or `requestAnimationFrame` with a callback function.
3. **Third-party libraries**: Libraries like Benchmark or PerfTest provide additional features and tools for benchmarking.

Keep in mind that the specific approach used in MeasureThat.net may not be suitable for all use cases, especially those requiring more advanced performance testing or customization.

Related benchmarks:

Spreading vs Contact in Reduce (version: 0)

Comparing performance of: Reduce + Spread vs Reduce + Concat

Created: 2 years ago by: Guest

Jump to the latest result

Reduce + Spread

Reduce + Concat

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):