Benchmark: String looping test

Script Preparation code:

function randomDigits() {
	return Math.floor(Math.random() * (2**64)).toString();
}

String.prototype.reduce = Array.prototype.reduce

Tests:

for loop
let digits = randomDigits(); let sum = 0; for (let i = 0; i < digits.length; ++i) { sum += +digits[i]; }
Array.prototype.reduce.call
let digits = randomDigits(); let sum = 0; sum = Array.prototype.reduce.call(digits, (prev, curr) => prev + +curr, 0);
String.prototype.reduce
let digits = randomDigits(); let sum = 0; sum = digits.reduce((prev, curr) => prev + +curr, 0);
Array.prototype.forEach
let digits = randomDigits(); let sum = 0; Array.prototype.forEach.apply(digits, [(i) => sum += +i]);
.split.reduce
let digits = randomDigits(); let sum = 0; sum = digits.split('').reduce((prev, curr) => prev + +curr, 0);
.split.forEach
let digits = randomDigits(); let sum = 0; digits.split('').forEach((i) => sum += +i);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for loop
Array.prototype.reduce.call
String.prototype.reduce
Array.prototype.forEach
.split.reduce
.split.forEach

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36

Browser/OS: Chrome 87 on Windows

View result in a separate tab

Test name	Executions per second
for loop	948124.1 Ops/sec
Array.prototype.reduce.call	298208.6 Ops/sec
String.prototype.reduce	269954.4 Ops/sec
Array.prototype.forEach	271579.4 Ops/sec
.split.reduce	769837.9 Ops/sec
.split.forEach	678940.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is a complex task, and the provided benchmark definition and test cases provide valuable insights into the optimization strategies used by developers.

**Overview of the Benchmark**

The benchmark measures the performance of different ways to sum up digits in a randomly generated 64-bit integer string. The tests compare various approaches:

1. **For loop**: A traditional loop-based approach.
2. **Array.prototype.reduce.call**: Using `reduce()` method on an array, followed by a callback function.
3. **String.prototype.reduce**: Using `reduce()` method directly on the string.
4. **Array.prototype.forEach.apply**: Iterating over the array elements using `forEach()`, and accumulating the sum in another variable.
5. **Split().reduce**: Splitting the string into an array, applying `.reduce()` to it, and summing up the digits.

**Options Compared**

Each test case measures a specific optimization strategy:

*   **For loop**: A straightforward approach that accumulates the sum directly within the loop.
*   **Array.prototype.reduce.call** and **String.prototype.reduce**: These methods use a cumulative reduction approach, which can be more efficient than accumulating the sum in a separate variable using a for loop or array iteration. However, they may incur overhead due to function call and property access operations.
*   **Array.prototype.forEach.apply**: This method uses `forEach()` followed by an accumulation operation outside the callback, similar to a traditional loop-based approach.

**Pros and Cons of Each Approach**

Here's a brief summary of each test case:

1.  **For Loop**
    *   Pros: Simple, intuitive implementation, suitable for small datasets.
    *   Cons: May become slow with large datasets due to repeated calculations.

2.  **Array.prototype.reduce.call**
    *   Pros: Efficient use of `reduce()` method, less prone to errors compared to traditional loops or array iteration.
    *   Cons: Requires more memory allocations and function calls, may be slower for very small datasets.

3.  **String.prototype.reduce**
    *   Pros: Similar efficiency benefits as `Array.prototype.reduce.call`, eliminates the need for an intermediate array.
    *   Cons: May incur higher overhead due to string property access and method call operations.

4.  **Array.prototype.forEach.apply**
    *   Pros: Combines the benefits of `forEach()` with direct accumulation, suitable for large datasets.
    *   Cons: More complex implementation than traditional loop-based approaches or using `reduce()`. May be slower due to function call overhead.

**Special Considerations**

*   The benchmark uses a random 64-bit integer string generator (`randomDigits`), which ensures that the results are not skewed by fixed-size input data.
*   The `String.prototype.reduce` test case modifies the global scope by assigning a temporary implementation to `reduce`. This can lead to unexpected behavior in other parts of the code, making it less suitable for production environments.

**Other Alternatives**

Additional optimization strategies might be explored using modern JavaScript features like:

1.  **Arrow functions**: Simplifying function implementations and reducing overhead.
2.  **Template literals**: Potentially improving string manipulation performance.
3.  **Cache-friendly data structures**: Optimizing memory layout to reduce access patterns.
4.  **Parallelization or concurrency**: Leveraging multi-core processors for improved performance.

However, these alternatives are not directly tested in the provided benchmark and should be evaluated on a case-by-case basis.

By understanding the different optimization strategies and their trade-offs, developers can better choose the most suitable approach for their specific use cases.

Related benchmarks:

String looping test (version: 4)

Comparing performance of: for loop vs Array.prototype.reduce.call vs String.prototype.reduce vs Array.prototype.forEach vs .split.reduce vs .split.forEach

Created: 4 years ago by: Registered User

Jump to the latest result

for loop

Array.prototype.reduce.call

String.prototype.reduce

Array.prototype.forEach

.split.reduce

.split.forEach

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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