Benchmark: flatMap vs reduce spread vs reduce push

Script Preparation code:

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

Tests:

reduce with spread
arr.reduce((acc, x) => [...acc, x, x], [])
flatMap
arr.flatMap(x => [x, x])
reduce with push
arr.reduce((acc, x) => (acc.push(x, x), acc), [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with spread
flatMap
reduce with push

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:137.0) Gecko/20100101 Firefox/137.0

Browser/OS: Firefox 137 on Ubuntu

View result in a separate tab

Test name	Executions per second
reduce with spread	2.0 Ops/sec
flatMap	4469.5 Ops/sec
reduce with push	4153.2 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmarking test created on MeasureThat.net. The benchmark compares three approaches to flatten an array in JavaScript: using `flatMap`, `reduce` with spread, and `reduce` with push.

**Approaches Compared**

1. **`arr.flatMap(x => [x, x])`**: This approach uses the `flatMap` method, which is a built-in method in modern JavaScript that flattens an array of arrays into a single array.
2. **`arr.reduce((acc, x) => [...acc, x, x], [])`**: This approach uses the `reduce` method with spread syntax to flatten the array. The callback function takes the accumulator (`acc`) and the current element (`x`) as arguments, and returns a new array by concatenating `acc` with `[x, x]`.
3. **`arr.reduce((acc, x) => (acc.push(x, x), acc), [])`**: This approach uses the `reduce` method without spread syntax to flatten the array. The callback function takes the accumulator (`acc`) and the current element (`x`) as arguments, and pushes `[x, x]` onto `acc`, returning the updated accumulator.

**Pros and Cons of Each Approach**

1. **`flatMap`**: Pros:
	* Efficient use of built-in method
	* Concise syntax
2. Cons:
	* May not be supported in older browsers or environments that don't support ES6+ features
	* Can be slower than other approaches for large arrays due to the overhead of iterating over each element
3. **`reduce with spread`**: Pros:
	* Clear and concise syntax
	* Works well for large arrays, as it uses a more efficient algorithm
4. Cons:
	* May not be supported in older browsers or environments that don't support ES6+ features
5. **`reduce with push`**: Pros:
	* May be faster than `reduce with spread` due to the optimized implementation of `push`
	* Works well for large arrays, as it uses a more efficient algorithm
Cons:
	* Syntax can be less readable due to the use of an array method

**Library and Special JS Features**

There are no libraries used in this benchmark.

**Special JS Features**

1. **Spread syntax (`...`)**: Used in `reduce with spread` approach to create a new array by concatenating elements.
2. **Arrow functions**: Used in `reduce with spread` approach to define the callback function for `reduce`.
3. **ES6+ features (e.g., `flatMap`, `let`/`const`)**: Used in the benchmark, but not explicitly highlighted as special features.

**Other Alternatives**

1. **Using a loop**: An alternative implementation could use a simple loop to flatten the array, e.g.: `for (var i = 0; i < arr.length; i++) { acc.push(arr[i], arr[i]); }`
2. **Using a library function**: Another alternative could be using a library like Lodash or Ramda to flatten arrays.
3. **Using a custom implementation**: A custom implementation using recursive functions or other approaches could also be used to flatten the array.

These alternatives are not included in the benchmark, but they demonstrate that there are multiple ways to approach this problem in JavaScript.

Related benchmarks:

flatMap vs reduce spread vs reduce push (version: 0)

Comparing performance of: reduce with spread vs flatMap vs reduce with push

Created: 2 years ago by: Guest

Jump to the latest result

reduce with spread

flatMap

reduce with push

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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