Benchmark: boom2213243143134daadfadf

Tests:

reduce
const arr = [...new Array(1000)]; arr.reduce((acc, item) => item ? [...acc, item] : [...acc], [])
map + filter
const arr = [...new Array(1000)]; arr.filter(item => item).map(item => item)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map + filter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to help you understand what's being tested in the MeasureThat.net benchmark.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The benchmark being discussed uses two individual test cases: "reduce" and "map + filter".

**Test Cases**

1. **Reduce**
The first test case measures the performance of the `reduce` method in JavaScript. Specifically, it tests how long it takes to iterate over an array and accumulate a result using the `reduce` method.

In the provided benchmark definition code:
```javascript
const arr = [...new Array(1000)];
arr.reduce((acc, item) => item ? [...acc, item] : [...acc], []);
```
The test case is creating an array of 1000 elements and then applying the `reduce` method to it. The `reduce` method is called with an initial value (`[]`) as its first argument, which means that each iteration will create a new accumulator variable.

**Pros and Cons**

The use of `reduce` in this benchmark has some pros and cons:

Pros:

* Efficient way to accumulate results from an array
* Can be more concise than using loops

Cons:

* May have higher overhead due to the creation of a new accumulator variable on each iteration
* Less intuitive for developers who are not familiar with the method

2. **Map + Filter**
The second test case measures the performance of combining two methods: `map` and `filter`. The benchmark definition code:
```javascript
const arr = [...new Array(1000)];
arr.filter(item => item).map(item => item);
```
This test case is creating an array of 1000 elements, filtering out the empty values using `filter`, and then mapping over the remaining elements using `map`.

**Pros and Cons**

The use of combining `map` and `filter` in this benchmark has some pros and cons:

Pros:

* Efficient way to transform arrays by removing unwanted values
* Can be more intuitive for developers who are familiar with the methods

Cons:

* May have higher overhead due to the creation of new arrays on each iteration (e.g., `new Array()` or array literals)
* Less efficient than using a single method like `reduce`

**Library: None**

Neither test case uses any external libraries.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntax used in either benchmark definition code.

**Other Alternatives**

For the "map + filter" test case, alternative approaches could include:

* Using a single `filter` method followed by a transformation using `map`
* Using an array comprehension (a feature supported by some modern browsers)
* Using a library like Lodash's `filter` and `map` methods

These alternatives might have different performance characteristics or trade-offs in terms of readability.

For the "reduce" test case, alternative approaches could include:

* Using a single loop with conditional statements to accumulate results
* Using a library like Lodash's `reduce` method
* Using a more functional programming approach with arrow functions and higher-order functions

Again, these alternatives might have different performance characteristics or trade-offs in terms of readability.

Overall, the MeasureThat.net benchmark provides a useful way for developers to compare the performance of different JavaScript methods and approaches.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.192 Safari/537.36

Browser/OS: Chrome 88 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	48741.6 Ops/sec
map + filter	247052.1 Ops/sec

Related benchmarks:

boom2213243143134daadfadf (version: 0)

Comparing performance of: reduce vs map + filter

Created: 3 years ago by: Guest

Jump to the latest result

reduce

map + filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):