Benchmark: flatMap vs reduce.concat vs reduce.push

Script Preparation code:

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

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with concat
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 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36

Browser/OS: Chrome 115 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce with concat	14.3 Ops/sec
flatMap	740.2 Ops/sec
reduce with push	22024.4 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what is being tested.

**Benchmark Definition JSON**

The benchmark defines three test cases:

1. `flatMap`: Tests the performance of the `flatMap()` method, which creates a new array with the results of applying a provided function to each element in the original array.
2. `reduce.concat`: Tests the performance of the `concat()` method used inside the `reduce()` method, which combines multiple arrays into one.
3. `reduce.push`: Tests the performance of the `push()` method used inside the `reduce()` method, which adds an element to the end of an array.

**Options Compared**

The benchmark compares the performance of three approaches:

* `flatMap()`: Uses the `flatMap()` method to transform the array.
* `concat()` + `reduce()`: Uses the `concat()` method to combine arrays, and then uses the `reduce()` method to accumulate the results.
* `push()` + `reduce()`: Uses the `push()` method to add elements to the accumulator array inside the `reduce()` method.

**Pros and Cons of Each Approach**

1. **flatMap()**: This approach is generally faster because it avoids creating intermediate arrays using `concat()`. However, it may not be suitable for large datasets due to its memory usage.
2. **concat() + reduce()**: This approach can be slower than `flatMap()` due to the overhead of concatenating arrays and creating temporary arrays. However, it's more memory-efficient and can handle larger datasets.
3. **push() + reduce()**: This approach is generally faster than `concat() + reduce()` because it avoids creating intermediate arrays using `concat()`. However, it may lead to slower performance due to the repeated push operations inside the `reduce()` method.

**Library Used**

There is no specific library used in this benchmark. The methods being tested are part of the JavaScript standard library.

**Special JS Features or Syntax**

The benchmark uses modern JavaScript features such as:

* Arrow functions (`=>`)
* Template literals (`10_000` for a large number)
* `flatMap()`, which is a newer method introduced in ECMAScript 2019

**Other Considerations**

When choosing an approach, consider the following factors:

* **Memory usage**: If memory efficiency is crucial, use `concat()` + `reduce()` or `push()` + `reduce()`.
* **Data size**: For large datasets, `flatMap()` might be a better choice due to its potential for faster execution.
* **Specific requirements**: Depending on your specific use case, one approach might be more suitable than others.

**Alternatives**

If you're interested in exploring alternative approaches, consider the following:

* Use `reduce()` with an array comprehension (e.g., `[...arr, ...arr]`) instead of `concat()`. This can be slower but provides a concise syntax.
* Experiment with other libraries or implementations, such as React's `map` and `reduce` methods, to see if they offer better performance.

Related benchmarks:

flatMap vs reduce.concat vs reduce.push (version: 0)

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

Created: one year ago by: Guest

Jump to the latest result

reduce with concat

flatMap

reduce with push

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):