Benchmark: stage1 collect varients v5

Script Preparation code:

var Z=()=>{},_=()=>document.createDocumentFragment(),$=(j)=>document.createTextNode(j),Q=(j)=>document.createElement(j),z=(j)=>j.cloneNode(!0),F=(j,q)=>q.appendChild(j),R=(j,q)=>q.insertBefore(j,q.firstChild),U=(j,q)=>q.parentNode.insertBefore(j,q.nextSibling),W=(j,q)=>(q.parentNode.replaceChild(j,q),j),C=(j,q)=>{new MutationObserver((A,B)=>{for(let I of A)for(let G of I.removedNodes)if(G.contains(j)){q(),B.disconnect();return}}).observe(document,{childList:!0,subtree:!0})};var P=Q("template"),Y=document.createTreeWalker(P),M=(j)=>{return P.innerHTML=j,P.content.firstChild},T=(j,q,A)=>{let B=Y,I={},G=q.length,J=0,H;B.currentNode=j;for(;J<G;J++){H=A[J];while(H--)B.nextNode();I[q[J]]=B.currentNode}return I},N=(j,q,A)=>{let B={},I=q.length,G=0,J,H=j,L;for(;G<I;G++){J=A[G];while(J--){L=H,H=H.firstChild||H.nextSibling;while(!H)L=L.parentNode,H=L.nextSibling}B[q[G]]=H}return B};var V={},X=(j)=>{let q="__"+j.type,A=j.target;while(A){if(A[q])return A[q](j);A=A.parentNode}},E=(j)=>{V[j]??=(document.addEventListener(j,X),!0)},S=(j)=>{V[j]=null,document.removeEventListener(j,X)};var w=(j)=>{let q={};return new Proxy({...j,on(A,B){return(q[A]??=[]).push(B),()=>{q[A]?.splice(q[A].indexOf(B)>>>0,1)}}},{set(A,B,I){return q[B]?.forEach((G)=>G(I,A[B])),A[B]=I,!0}})};
var h = M;
var collect = T; // original
var collect2 = N; // custom
var view=h('<div class=d1><span></span><div class=d2><span></span><div class=d3><span></span><div class=d4><span></span><div class=d5><span></span><div class=d6><span></span><div class=d7><span></span><div class=d8><span></span><div class=d9><span></span><div class=d10><span></span><div class=d11><!--></div></div></div></div></div></div></div></div></div></div></div></div>');
var view2=h('<div class=d1><span><div class=d2><span><div class=d3><span><div class=d4><span><div class=d5><span><div class=d6><span><div class=d7><span><div class=d8><span><div class=d9><span><div class=d10><span><div class=d11><!--></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div>');
var view3=h('<div class=d1><span></span><div class=d2><span></span><div class=d3><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><div class=d4><span></span><div class=d5><span></span><div class=d6><span></span><div class=d7><span></span><div class=d8><span></span><div class=d9><span></span><div class=d10><span></span><div class=d11><!--></div></div></div></div></div></div></div></div></div></div></div></div>');

Tests:

collect mixed depth
collect(view,['a','b','c','d'],[2,6,10,3])
collect2 mixed depth
collect2(view,['a','b','c','d'],[2,6,10,3])
collect nested
collect(view2,['a','b','c','d'],[2,6,10,3])
collect2 nested
collect2(view2,['a','b','c','d'],[2,6,10,3])
collect with flat
collect(view3,['a','b','c','d'],[2,16,10,3])
collect2 with flat
collect2(view3,['a','b','c','d'],[2,16,10,3])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
collect mixed depth
collect2 mixed depth
collect nested
collect2 nested
collect with flat
collect2 with flat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: yesterday)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:136.0) Gecko/20100101 Firefox/136.0

Browser/OS: Firefox 136 on Linux

View result in a separate tab

Test name	Executions per second
collect mixed depth	1452357.2 Ops/sec
collect2 mixed depth	3062285.0 Ops/sec
collect nested	1506599.8 Ops/sec
collect2 nested	3568188.5 Ops/sec
collect with flat	1115576.8 Ops/sec
collect2 with flat	2033902.0 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):

LLMs can make mistakes. Check important info.

The benchmark provided tests different methods of collecting elements from the DOM in JavaScript. Specifically, it compares two functions, `collect` and `collect2`, which seem to perform similar tasks but may differ in implementation and performance characteristics. The tests also vary based on the structure of the DOM elements being accessed, which is represented by different `view` variables.

### Function Overview

1. **`collect`**: This function traverses a DOM tree starting from the `view` element and collects nodes based on their depth and specified indices. It utilizes a `TreeWalker`, which allows efficient traversal of a DOM tree.

2. **`collect2`**: This is a custom implementation that appears to collect nodes in a different way, potentially optimizing traversal or node access. The implementation details suggest it uses a loop that navigates through child nodes, potentially making it more flexible at the cost of computational complexity.

### Test Cases and Their Structures

Each test case invokes one of the two collection methods with varying DOM structures defined by different `view` variables:

- **`view`**: Represents a simple nested structure. 
- **`view2`**: Represents a more deeply nested structure but maintains symmetry in content.
- **`view3`**: Represents a flat structure with a mix of elements, which may include duplicate nodes.

### Tests Explained

Here are the tests compared:

1. **`collect(view, [...])` vs. `collect2(view, [...])`**: Comparing both functions on a simple nested structure (view) with varied indices in the collect function.
   
2. **`collect(view2, [...])` vs. `collect2(view2, [...])`**: Both functions are evaluated on a more complicated structure (view2), still maintaining symmetry.

3. **`collect(view3, [...])` vs. `collect2(view3, [...])`**: Testing how both functions collect nodes in a flat structure with potentially more elements.

### Pros and Cons

- **Pros of `collect`**:
  - Uses `TreeWalker`, which is efficient for traversing the DOM.
  - Simplicity in implementation; straightforward traversal of child nodes.

- **Cons of `collect`**:
  - Limited flexibility if the DOM structure varies significantly or requires complex navigation.

- **Pros of `collect2`**:
  - More flexible traversal; can handle varied and complex DOM structures.
  - Potentially optimized for specific use cases, preserving context about previous nodes.

- **Cons of `collect2`**:
  - Could be less efficient due to multiple nested loops, especially with a deep or broad DOM.

### Benchmark Results

The results indicate execution times (number of executions per second) for each test case performed on a specific browser environment (Firefox 136 on Linux):

1. `collect2 (nested)` performed best with 5,680,925 executions per second.
2. `collect2 (mixed depth)` also performed well with 4,629,878 executions.
3. `collect (nested)` showed the least executions per second at 1,873,154.375.

These performance metrics suggest that the custom `collect2` function may have optimizations for certain structures, especially deeper or more complex trees.

### Other Considerations

- **Use Cases**: When selecting between `collect` and `collect2`, it is essential to consider the actual complexity of the DOM structure expected in production. Understanding performance trade-offs is vital for dynamic web applications where real-time updates to the DOM occur frequently.

- **Future Alternatives**: Other alternatives include:
  - Using `querySelectorAll`, which can provide a more universal approach for selecting nodes if they are easily identifiable with CSS selectors.
  - Leveraging libraries like jQuery for cross-browser compatibility in DOM manipulation and collection.
  - Exploring Web Components, which promote encapsulation and may affect how elements are traversed.

In conclusion, the benchmark offers an insightful analysis of DOM traversal methods, encouraging developers to evaluate performance implications based on different structures and access patterns.

Related benchmarks:

stage1 collect varients v5 (version: 1)

Comparing performance of: collect mixed depth vs collect2 mixed depth vs collect nested vs collect2 nested vs collect with flat vs collect2 with flat

Created: 12 days ago by: Guest

Jump to the latest result

collect mixed depth

collect2 mixed depth

collect nested

collect2 nested

collect with flat

collect2 with flat

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):