Benchmark: Fizbuzz v2 - MeasureThat.net

Script Preparation code:

function fizzBuzzA(n) {
    if (n % 15 == 0) return 'FizzBuzz'
    if (n % 3 == 0) return 'Fizz'
    if (n % 5 == 0) return 'Buzz'
    return n.toString()
}

function fizzBuzzB(n) {
    const test = (d, s, x) => (n % d == 0 ? (_) => s + x('') : x)
    const fizz = (x) => test(3, 'Fizz', x)
    const buzz = (x) => test(5, 'Buzz', x)
    return fizz(buzz((x) => x))(n.toString())
}

function fizzBuzzC(n) {
    let res = ''
    if (n % 3 == 0) res += 'Fizz'
    if (n % 5 == 0) res += 'Buzz'
    if (!res) res += n.toString()
    return res
}

function fizzBuzzD(n) {
    return n % 3 == 0 ? 'Fizz' + (n % 5 == 0 ? 'Buzz' : '') :
        n % 5 == 0 ? 'Buzz' : n.toString()
}

Tests:

A
const iterations = 1000000; for (let i = 0; i < iterations; i++) { fizzBuzzA(i + 1) }
B
const iterations = 1000000; for (let i = 0; i < iterations; i++) { fizzBuzzB(i + 1) }
C
const iterations = 1000000; for (let i = 0; i < iterations; i++) { fizzBuzzC(i + 1) }
D
const iterations = 1000000; for (let i = 0; i < iterations; i++) { fizzBuzzD(i + 1) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
A
B
C
D

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmarking data to explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

The FizzBuzz benchmark is a classic coding challenge that involves generating numbers from 1 to n and replacing multiples of 3 with "Fizz", multiples of 5 with "Buzz", and multiples of both with "FizzBuzz". The goal is to find the most efficient implementation for this task.

**Benchmark Definitions**

There are four benchmark definitions, each representing a different approach:

1. **A**: `fizzBuzzA(n)`

This implementation uses a simple if-else statement to check for multiples of 3 and 5. It's straightforward but may not be the most efficient.

Pros: Easy to understand, minimal logic.
Cons: May have higher overhead due to repeated checks.

2. **B**: `fizzBuzzB(n)`

This implementation uses a more functional programming style with a recursive function (`test`) that takes three arguments (divisor, string, and callback). The `fizz` and `buzz` functions are used to generate the corresponding strings.

Pros: More concise, potentially faster due to reduced overhead.
Cons: May be harder to understand for those unfamiliar with functional programming.

3. **C**: `fizzBuzzC(n)`

This implementation uses a more traditional approach with multiple if-else statements to check for multiples of 3 and 5.

Pros: Easy to understand, minimal logic.
Cons: Similar to A, may have higher overhead due to repeated checks.

4. **D**: `fizzBuzzD(n)`

This implementation uses a ternary operator to concisely generate the strings based on the conditions.

Pros: Most concise, potentially fastest due to reduced overhead.
Cons: May be harder to understand for those unfamiliar with ternary operators.

**Library and Syntax**

The benchmark doesn't explicitly mention any libraries or syntax features. However, it's worth noting that functional programming (e.g., `fizzBuzzB`) is a popular approach in modern JavaScript.

**Other Considerations**

* **Preparation Code**: The preparation code is included as part of the benchmark definition, which means the compiler/interpreter must execute this code before running the actual benchmark.
* **Html Preparation Code**: This field is empty for all benchmarks, suggesting that no additional HTML preparation is required.

**Alternatives**

If you're looking to improve or optimize these implementations, consider the following alternatives:

1. Using a more efficient data structure (e.g., an array or object) to store multiples of 3 and 5.
2. Applying memoization or caching techniques to reduce repeated checks.
3. Exploring different algorithmic approaches, such as using a single loop with multiple conditions or utilizing bitwise operations.

Keep in mind that these alternatives may require more complex code and potentially impact readability.

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/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
A	24.1 Ops/sec
B	14.4 Ops/sec
C	23.8 Ops/sec
D	25.4 Ops/sec

Related benchmarks:

Fizbuzz v2 (version: 1)

Comparing performance of: A vs B vs C vs D

Created: one year ago by: Registered User

Jump to the latest result

A

B

C

D

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):