Benchmark: Sliding Window Maximum from Leetcode

Script Preparation code:

const nums = [1, 3, -1, -3, 5, 3, 6, 7],
    k = 3;

Tests:

Original
function slideWindow(nums, k) { let arr = []; let j = k; for (let i = 0; i < nums.length; i++) { if (j <= nums.length) { let slicedArr = nums.slice(i, j); arr.push(Math.max(...slicedArr)); j++ } else { break; } } return arr; } slideWindow([1, 3, -1, -3, 5, 3, 6, 7], 3);
Using Reduce
function slideWindowReduce(nums, k) { return nums.reduce((agg, cur, idx, arr) => { k + idx <= arr.length ? agg = [ ...agg, Math.max( ...arr.slice( idx, idx + k ) ) ] : agg = agg; return agg; }, []); } slideWindowReduce([1, 3, -1, -3, 5, 3, 6, 7], 3);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Original
Using Reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark definition is for the "Sliding Window Maximum" problem, which involves finding the maximum value in a sliding window of size `k` within an array of numbers.

**Options Compared**

Two options are compared:

1. **Original Implementation**: The original implementation of the sliding window algorithm using a traditional loop-based approach.
2. **Using Reduce**: A modified implementation that uses the `reduce()` method to accumulate the maximum values in the sliding window.

**Pros and Cons**

### Original Implementation

Pros:

* Easy to understand and implement
* No additional dependencies or libraries required

Cons:

* May have performance issues due to the use of `slice()` and `Math.max()`
* Can be less efficient than other approaches, especially for large input sizes

### Using Reduce

Pros:

* More concise and elegant implementation using a standard JavaScript method
* Potential for better performance due to reduced function call overhead

Cons:

* May require additional understanding of the `reduce()` method and its behavior
* Could introduce performance issues if not implemented correctly

**Library Used**

In both benchmark definitions, no specific library is used beyond the built-in `Math` and `Array.prototype.slice()` methods.

**Special JS Feature/Syntax**

Neither benchmark definition uses any special JavaScript features or syntax. The implementations are straightforward and use standard JavaScript constructs.

**Other Considerations**

When choosing between these two options, consider the trade-offs between readability, conciseness, and performance. If readability and maintainability are crucial, the original implementation may be a better choice. However, if performance is a top priority and you're comfortable with using `reduce()`, the second option might offer advantages.

**Alternative Approaches**

Other alternative approaches to implement the sliding window algorithm could include:

* **Using Iterators**: Utilize iterators to create an efficient and lazy iterator over the input array.
* **Caching**: Implement caching mechanisms to store intermediate results and avoid redundant computations.
* **Parallelization**: Explore parallelizing the computation using Web Workers or other concurrency techniques for improved performance.

These alternative approaches can offer significant performance improvements, but they may introduce additional complexity and require careful consideration of their trade-offs.

Related benchmarks:

Sliding Window Maximum from Leetcode (version: 0)

Comparing performance of: Original vs Using Reduce

Created: 3 years ago by: Registered User

Jump to the latest result

Original

Using Reduce

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):