Benchmark: python len vs cache

HTML Preparation code:

<script src='https://cdn.jsdelivr.net/pyodide/v0.26.3/full/pyodide.js'></script>

Script Preparation code:

async function globalMeasureThatScriptPrepareFunction() {
  	window.globalPyodide = await loadPyodide();
	await globalPyodide.runPython(`
		def test_len(n):
			arr = [0] * n
			for i in range(n):
				arr[i] = i
			return len(arr)

		def test_cache(n):
			arr = [0] * n
			l = 0
			for i in range(n):
				arr[i] = i
				l += 1
			return l

		def run(fct, n):
			for i in range(n):
				fct(n)
	`);    
}

Tests:

use len built-in
window.globalPyodide.runPython(` run(test_len, 1000) `);
use variable to cache size
window.globalPyodide.runPython(` run(test_cache, 1000) `);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
use len built-in
use variable to cache size

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 18 hours ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
use len built-in	21.8 Ops/sec
use variable to cache size	12.5 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 days ago):

LLMs can make mistakes. Check important info.

The benchmark "python len vs cache" is comparing the performance of two different approaches to determine the length of an array in Python, executed within a JavaScript environment using Pyodide, which allows running Python code in the browser.

### Benchmark Overview

The benchmark involves two test cases:

1. **Use len built-in**:
   This approach utilizes Python’s built-in `len()` function to find the length of an array. The function `test_len(n)` creates an array of size `n`, populates it with sequential integers, and then calls `len(arr)` to return its length.

**Performance Metric**: 
   The execution speed of this test is represented by `ExecutionsPerSecond`, which in the benchmark result was found to be approximately 4.47 executions per second.

**Pros**:
   - **Optimized method**: Built-in functions in Python are usually optimized for performance, leading to faster execution.
   - **Readability**: Using `len(arr)` is generally simpler and more intuitive to read and understand.

**Cons**:
   - Depending on the context (for very specific sizes or conditions), `len()` might require the array to be fully constructed or can lead to overhead if implemented inefficiently in other scenarios (though this is less common for built-ins).

2. **Use variable to cache size**:
   This method involves manually counting the elements in the array using a variable `l` to keep track of the size. The function `test_cache(n)` creates the same array, iterates through it, and increments `l` for each element processed.

**Performance Metric**: 
   This test provided a lower performance metric, with an execution speed of approximately 1.93 executions per second.

**Pros**:
   - **Customizability**: This approach can be modified for specific counting conditions or manipulations of the array size without calling built-ins.
   
   **Cons**:
   - **Performance Overhead**: This method usually incurs higher computation costs as it involves explicit looping through the array, and incrementing the length manually can be slower than calling the optimized built-in function.
   - **Complexity**: The logic is more complex, which can potentially introduce errors or performance pitfalls in more intricate scenarios.

### Libraries and Features
- **Pyodide**: The benchmark utilizes the Pyodide library, which is a Python interpreter implemented in WebAssembly. It enables the execution of Python code in web browsers, leveraging JavaScript for loading and runtime management. This allows engineers who prefer Python or require Python libraries to perform data manipulation in a web environment.

### Alternative Approaches
When evaluating performance and capability for determining the length of an array in Python, additional alternatives to the two methods tested might include:

- **Using Collections**: Depending on the use case, specialized collection types like `collections.deque` could also be employed for performance optimizations in specific scenarios.
- **NumPy**: If dealing with numeric data, using libraries such as NumPy can significantly optimize operations due to lower-level implementations and contiguous memory storage.
  
- **Cython**: For performance-critical applications, Cython can transform Python code to optimized C-level performance, facilitating greater efficiency in array handling.

### Conclusion
Overall, the benchmark effectively highlights the difference in performance between using Python's built-in capabilities versus implementing a manual counting strategy. The results clearly favor the built-in `len()` function, revealing how native language features often offer optimized solutions for common operations like array length calculation.

Related benchmarks:

python len vs cache (version: 2)

Comparing performance of: use len built-in vs use variable to cache size

Created: 2 days ago by: Registered User

Jump to the latest result

use len built-in

use variable to cache size

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 days ago):