Benchmark: pedroac Fizz Buzz reusing strings 2

Tests:

not reusing strings
const fizzBuzz = function(n) { const result = new Array(n); for (let i = 1; i <= n; ++i) { if (i % 15 === 0) { result[i-1] = 'FizzBuzz'; } else if (i % 3 === 0) { result[i-1] = 'Fizz'; } else if (i % 5 === 0) { result[i-1] = 'Buzz'; } else { result[i-1] = i.toString(); } } return result; }; fizzBuzz(100);
reusing strings
const fizzBuzz = function(n) { const result = new Array(n); const values = ['Fizz', 'Buzz', 'FizBuzz']; for (let i = 1; i <= n; ++i) { if (i % 15 === 0) { result[i-1] = values[2]; } else if (i % 3 === 0) { result[i-1] = values[0]; } else if (i % 5 === 0) { result[i-1] = values[1]; } else { result[i-1] = i.toString(); } } return result; }; fizzBuzz(100);

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Test case name	Result
not reusing strings
reusing strings

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Slowest: N/A

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

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Let's dive into the provided benchmark and explain what's being tested.

**Benchmark Description**

The benchmark tests two different approaches to creating the Fizz Buzz sequence in JavaScript:

1. **Not Reusing Strings**: This approach creates a new string literal for each value ('Fizz', 'Buzz', 'FizBuzz') inside the loop, which is then assigned to the corresponding index of the `result` array.
2. **Reusing Strings**: In this approach, an array of strings (`values`) is created outside the loop and reused throughout the iteration. This eliminates the need for creating new string literals in each iteration.

**Comparison of Options**

The main difference between these two approaches lies in memory allocation and management:

* **Not Reusing Strings**: Each time a new string literal is needed, a new object is created, which can lead to:
	+ Increased memory usage due to the accumulation of short-lived objects.
	+ Higher overhead for garbage collection.
* **Reusing Strings**: By reusing an array of strings, this approach eliminates the need for frequent creation and deallocation of temporary objects, leading to:
	+ Reduced memory usage.
	+ Lower overhead for garbage collection.

**Pros and Cons**

**Not Reusing Strings:**

Pros:

* Easier to understand and maintain code.
* Less memory-intensive.

Cons:

* Creates new string literals in each iteration, which can lead to increased memory allocation and garbage collection overhead.

**Reusing Strings:**

Pros:

* Reduces memory usage by reusing an array of strings.
* Can improve performance due to reduced garbage collection overhead.

Cons:

* Requires careful management of the shared array of strings to avoid potential issues like:
	+ Overwriting existing values with new ones.
	+ Increasing memory usage if the array is not properly managed.

**Library and Special Features**

Neither of the benchmark test cases uses a specific library, but it does use JavaScript's built-in `Array` and string manipulation features. There are no special JS features or syntax used in these benchmarks that would affect their execution.

**Other Alternatives**

In addition to these two approaches, there might be other ways to optimize the Fizz Buzz sequence creation in JavaScript, such as:

* Using a `Map` or `WeakMap` instead of an array to store the values.
* Utilizing `template literals` for creating strings.
* Employing `string pooling` techniques to reduce string allocation.

However, these alternatives are not explicitly tested in this benchmark.

In conclusion, this benchmark provides a straightforward comparison between two approaches to creating the Fizz Buzz sequence in JavaScript: reusing or not reusing strings. The choice of approach affects memory usage and garbage collection overhead, making it an interesting microbenchmark for measuring performance differences in JavaScript applications.

Related benchmarks:

pedroac Fizz Buzz reusing strings 2 (version: 4)

Comparing performance of: not reusing strings vs reusing strings

Created: 4 years ago by: Registered User

Jump to the latest result

not reusing strings

reusing strings

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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