Benchmark: ChildNodes vs NextSibling

Script Preparation code:

var t = document.createElement('template');
t.innerHTML = '<tr><td class="col-md-1"></td><td class="col-md-4"><a></a></td><td class="col-md-1"><a><span class="glyphicon glyphicon-remove" aria-hidden="true" /></a></td><td class="col-md-6" /></tr>';
var template = t.content.firstChild;

function childNodes(tree) {
  let index = 0;
  const result = {};
  const walk = (node) => {
    result[index++] = node;
    node.childNodes.forEach(walk);
  };
  walk(tree);
  return result;
}

function nextSibling(tree) {
  let index = 0;
  const result = {};
  const walk = (node) => {
    result[index++] = node;
    let child = node.firstChild;
    while(child) {
      walk(child);
      child = child.nextSibling;
    }
  };
  walk(tree);
  return result;
}

Tests:

ChildNodes
childNodes(template)
NextSibling
nextSibling(template)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
ChildNodes
NextSibling

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Measuring performance in JavaScript microbenchmarks can be complex, especially when comparing different approaches. Let's break down the provided benchmark definition and test cases.

**Benchmark Definition Json:**

The `Script Preparation Code` section defines two functions:

*   `childNodes(tree)`: This function creates a template element with some nested HTML content and then recursively traverses its child nodes, storing them in an object (`result`). The goal is to measure the time it takes to collect all child nodes of a given node (`tree`) using this approach.
*   `nextSibling(tree)`: This function also creates the same template element but uses a different strategy. It starts with the first child of the current node and then recursively visits its siblings, storing them in the `result` object. The objective here is to measure the time taken to collect all next sibling nodes of a given node (`tree`) using this approach.

**Options Compared:**

The two functions differ in their traversal strategy:

*   `childNodes(tree)` uses a recursive descent approach, starting with each child node and then exploring its own children.
*   `nextSibling(tree)` employs an iterative descent strategy, traversing the sibling chain of each node until all next siblings are visited.

**Pros and Cons:**

### Recursive Descent (ChildNodes)

Pros:

*   Efficient in terms of memory usage, as only the immediate child nodes need to be stored in memory.
*   Easy to implement and understand for developers familiar with recursive data structures.

Cons:

*   May lead to stack overflows for very deep tree structures or large amounts of nested elements due to the recursive call stack size limit.
*   Could result in slower performance if the tree structure is highly unbalanced, as each node's children need to be visited individually.

### Iterative Descent (NextSibling)

Pros:

*   Less susceptible to stack overflows since it only uses a fixed amount of memory for storing sibling indices.
*   Can provide better cache locality and potentially faster execution times due to the linear traversal pattern.

Cons:

*   Requires more complex code implementation, especially when dealing with edge cases like empty siblings or node removals.
*   Might lead to increased memory usage if the tree is very large or has many nodes with multiple children.

**Library Usage:**

In this benchmark, no specific JavaScript libraries are used outside of the built-in `document.createElement` and `Element.prototype.childNodes` APIs. However, some implementations might opt for library-assisted data structures or algorithms for better performance.

**Special JS Features/Syntax:**

The provided code snippet does not include any special JavaScript features or syntax. It is standard ECMAScript 5 (ES5) compliant and should be run in most modern browsers without issues.

**Other Alternatives:**

When dealing with large-scale microbenchmarks, you might consider the following alternatives:

*   **Benchmarking frameworks:** Utilize dedicated benchmarking libraries like `benchmark.js`, `jsperf`, or others that provide a structured approach to writing benchmarks.
*   **Web Workers:** Consider using web workers for tasks that can be parallelized or offloaded from the main thread to improve performance and reduce system overhead.
*   **Async/await and Promises:** When optimizing asynchronous operations, explore the use of `async/await` and `Promises` for better readability and performance.

Keep in mind that choosing the right approach depends on your specific requirements and problem domain.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:105.0) Gecko/20100101 Firefox/105.0

Browser/OS: Firefox 105 on Windows

View result in a separate tab

Test name	Executions per second
ChildNodes	780502.7 Ops/sec
NextSibling	1746324.0 Ops/sec

Related benchmarks:

ChildNodes vs NextSibling (version: 0)

Comparing performance of: ChildNodes vs NextSibling

Created: 2 years ago by: Guest

Jump to the latest result

ChildNodes

NextSibling

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):