Benchmark: Reduce Push vs. flatMap with subarrays

Script Preparation code:

var arr = Array(10_000).fill([0])

Tests:

Reduce Push
arr.reduce((acc, x) => { acc.push(...x); return acc; }, [])
flatMap
arr.flatMap(x => x)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce Push
flatMap

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/143.0.0.0 Safari/537.36

Browser/OS: Chrome 143 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Reduce Push	13707.7 Ops/sec
flatMap	9094.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition:**
The benchmark is comparing two approaches:

1. **Reduce Push**: This approach uses the `reduce()` method with an initial value of an empty array (`[]`), and it iterates over each element in the array, pushing all its elements into the accumulator using the spread operator (`...`). The final result is returned by the callback function.
2. **flatMap**: This approach uses the `flatMap()` method, which returns a new array with the results of applying the provided mapping function to each element in the original array.

**Comparison:**
The benchmark aims to compare the performance of these two approaches for large arrays (10,000 elements).

**Pros and Cons:**

* **Reduce Push**:
	+ Pros:
		- Can be more efficient since it doesn't create a new array for each iteration.
		- Might be optimized by some JavaScript engines for this specific pattern.
	+ Cons:
		- Can lead to higher memory usage due to the accumulation of elements in the accumulator.
		- May have higher overhead due to the use of `push()` and the spread operator (`...`).
* **flatMap**:
	+ Pros:
		- Creates a new array with the results, which can be more memory-efficient.
		- Might be optimized by some JavaScript engines for this specific pattern.
	+ Cons:
		- Creates a new array, which can lead to higher memory usage.
		- May have lower performance due to the creation of a new array.

**Library and Purpose:**
There is no external library used in these benchmark definitions. However, it's worth noting that both `reduce()` and `flatMap()` are part of the Array.prototype methods, which are native to JavaScript.

**Special JS Feature/Syntax:**
There are no specific special features or syntaxes mentioned in these benchmark definitions. The focus is on comparing two approaches for array processing.

**Other Alternatives:**
If you want to compare other approaches for array processing, you could consider the following alternatives:

1. **forEach()**: Iterates over each element in the array and performs an action.
2. **for...of loop**: Iterates over each element in the array using a traditional `for` loop.
3. **Array.prototype.map()`: Creates a new array with the results of applying the provided mapping function to each element in the original array.

Keep in mind that these alternatives might have different performance characteristics and may not be directly comparable to `reduce()` and `flatMap()`.

For example, using `forEach()` would require adding an additional callback function to handle the iteration, whereas `for...of loop` would require manual indexing. On the other hand, `Array.prototype.map()` creates a new array with the results, similar to `flatMap()`, but might have different performance characteristics due to the creation of a new array.

These alternatives can be used to create more comprehensive benchmarks and help developers understand the trade-offs between different approaches for array processing in JavaScript.

Related benchmarks:

Reduce Push vs. flatMap with subarrays (version: 0)

Comparing performance of: Reduce Push vs flatMap

Created: 3 years ago by: Guest

Jump to the latest result

Reduce Push

flatMap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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