Benchmark: filter + map vs reduce 1234515335

Script Preparation code:

var values = []
for(i=0; i<1000; i++){ values.push([i, i]) }

Tests:

flatMap
values.flatMap(e => [e, e * 2])
reduce
values.reduce((acc, x) => { acc.push(x, x * 2); return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:104.0) Gecko/20100101 Firefox/104.0

Browser/OS: Firefox 104 on Windows

View result in a separate tab

Test name	Executions per second
flatMap	2520.4 Ops/sec
reduce	2640.6 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to explain what's being tested in the provided JSON benchmark definition.

**Benchmark Overview**

The benchmark measures the performance of two different approaches: using `reduce` and `flatMap` functions on an array of values. The goal is to determine which approach is faster for this specific use case.

**Script Preparation Code**

The script preparation code generates an array `values` with 1000 elements, each containing a pair of numbers `[i, i]`.

**Html Preparation Code**

There is no HTML preparation code provided, so we can assume that the benchmark only tests the JavaScript execution.

**Library Usage**

In both test cases, there is no explicit library usage. However, it's worth noting that `reduce` and `flatMap` are built-in Array methods in JavaScript, so they don't require any external libraries to work.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in these benchmark definitions. They only rely on standard JavaScript functionality.

**Options Compared**

The two options being compared are:

1. **`reduce`**: This method applies a callback function to each element of the array, accumulating a result. In this case, it pushes each element and its doubled value onto the accumulator array.
2. **`flatMap`**: This method creates a new array with the results of applying an provided function on every element in this array.

**Pros and Cons**

**Reduce:**

Pros:

* Can be more flexible than `flatMap`, as it allows for accumulating multiple values in the accumulator.
* Can be used when the accumulator needs to hold multiple values.

Cons:

* Can lead to higher memory usage, especially if the accumulator grows large.
* The callback function may need to handle complex logic, which can make it harder to read and maintain.

**flatMap:**

Pros:

* Reduces memory usage compared to `reduce`, as only one value is pushed onto the new array at a time.
* Can be more concise and easier to understand than `reduce`.

Cons:

* Requires an additional function call for each element, which can lead to slower performance.
* The callback function may need to handle complex logic, similar to `reduce`.

**Other Considerations**

When deciding between `reduce` and `flatMap`, consider the following factors:

* **Memory usage**: If you need to accumulate multiple values in the accumulator, `reduce` might be a better choice. However, if memory efficiency is crucial, `flatMap` is likely a better option.
* **Conciseness and readability**: If simplicity and ease of understanding are more important than raw performance, `flatMap` might be a better fit.

**Alternatives**

Other alternatives to consider for this use case include:

1. Using `forEach` instead of `reduce` or `flatMap`. This can provide similar functionality but is generally less efficient.
2. Utilizing other libraries or frameworks that offer optimized array processing functions, such as Lodash or RxJS.
3. Implementing a custom iterative solution using loops and variables to accumulate values.

Keep in mind that the choice of approach ultimately depends on your specific requirements, performance constraints, and personal preference.

Related benchmarks:

filter + map vs reduce 1234515335 (version: 0)

Comparing performance of: flatMap vs reduce

Created: 2 years ago by: Guest

Jump to the latest result

flatMap

reduce

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):