Benchmark: flatMap + reduce vs reduce + reduce

Script Preparation code:

var arr = Array(10_000).fill([...Array(1000)].map(e=>~~(Math.random()*50)))

Tests:

flatMap + reduce
arr.flatMap(inner => inner).reduce((a, b) => Math.max(a, b))
reduce + reduce
arr.reduce((total, inner) => total + inner.reduce((a, b) => Math.max(a, b)))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap + reduce
reduce + reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark compares the performance of two approaches: using `flatMap` followed by `reduce`, versus using `reduce` twice. Both approaches are designed to find the maximum value in an array of random integers.

**Options Compared**

There are two options being compared:

1. **flatMap + reduce**: This approach uses `flatMap` to flatten the array, and then applies `reduce` to find the maximum value.
2. **reduce + reduce**: This approach uses `reduce` twice: first to sum up all elements in the inner arrays, and then to find the maximum of these sums.

**Pros and Cons**

Here are some pros and cons for each approach:

* **flatMap + reduce**:
	+ Pros:
		- More readable code, as the intent is clear.
		- Can be more efficient, since `flatMap` can stop iterating over inner arrays once a certain condition is met.
	+ Cons:
		- May have higher overhead due to function call and argument passing.
* **reduce + reduce**:
	+ Pros:
		- Can be more concise code.
	+ Cons:
		- Less readable, since the intent is not as clear.
		- More likely to lead to errors, since there are two separate reduction operations.

**Library Used**

Neither of these approaches relies on a specific library. However, it's worth noting that `reduce` is a built-in JavaScript method for arrays.

**Special JS Feature or Syntax**

There are no special features or syntax used in this benchmark. The code uses standard JavaScript features and syntax.

**Other Considerations**

* **Data Generation**: The benchmark generates an array of 10,000 random integers using the `~~(Math.random()*50)` expression.
* **Execution Frequency**: Both test cases execute at a rate of 5.359394550323486 executions per second for one test case and 2.3052098751068115 executions per second for the other.

**Alternatives**

Other alternatives to these approaches might include:

* Using `forEach` with an arrow function to iterate over the array, instead of `flatMap`.
* Using a custom loop instead of `reduce`, especially if performance is critical.
* Parallelizing the execution using Web Workers or async/await syntax (not shown in this benchmark).

Overall, this benchmark highlights the importance of considering code readability and maintainability alongside performance considerations when writing JavaScript code.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:91.0) Gecko/20100101 Firefox/91.0 Waterfox/91.4.0

Browser/OS: Firefox 91 on Windows

View result in a separate tab

Test name	Executions per second
flatMap + reduce	2.3 Ops/sec
reduce + reduce	5.4 Ops/sec

Related benchmarks:

flatMap + reduce vs reduce + reduce (version: 0)

Comparing performance of: flatMap + reduce vs reduce + reduce

Created: 2 years ago by: Guest

Jump to the latest result

flatMap + reduce

reduce + reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):