Benchmark: Tree Traversals - MeasureThat.net

Script Preparation code:

root = {val: 1001}
let i = 0, node = root
while (++i < 1000) {
  while (node) {
    if (i < node.val) {
      if (!node.left) {
        node.left = { val: i }
        break
      }
      node = node.left
    } else {
      if (!node.right) {
        node.right = { val: i }
        break
      }
      node = node.right
    }
  }
}

Tests:

DFS
function dfs(node) { if (!node) return dfs(node.left) dfs(node.right) } dfs(root)
BFS (shift)
let q = [root] while (q.length) { const node = q.shift() if (node.left) q.push(node.left) if (node.right) q.push(node.right) }
BFS (pop)
let q = [root] while (q.length) { const node = q.pop() if (node.right) q.push(node.right) if (node.left) q.push(node.left) }
BFS (reassign queue)
let q = [root] while (q.length) { const level = [] const node = q.pop() if (node.left) level.push(node.left) if (node.right) level.push(node.right) q = level }
BFS (reassign node, shift)
let q = [], node = root while (node) { if (node.left) q.push(node.left) if (node.right) q.push(node.right) node = q.shift() }
BFS (reassign node, pop)
let q = [], node = root while (node) { if (node.right) q.push(node.right) if (node.left) q.push(node.left) node = q.pop() }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
DFS
BFS (shift)
BFS (pop)
BFS (reassign queue)
BFS (reassign node, shift)
BFS (reassign node, pop)

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):

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I'll break down the benchmark and its test cases for you.

**Benchmark Overview**

The benchmark measures the performance of different tree traversal algorithms: Depth-First Search (DFS) and Breadth-First Search (BFS). The algorithm is designed to traverse a binary tree, where each node has a value and optional left and right child nodes. The goal is to insert 1000 nodes into the tree, with values ranging from 0 to 999.

**Test Cases**

There are four test cases:

1. **DFS**: Uses recursion to traverse the tree.
2. **BFS (shift)**: Uses a queue data structure to enqueue nodes and then dequeue them in order.
3. **BFS (pop)**: Similar to BFS (shift), but uses a pop operation instead of shift.
4. **BFS (reassign queue)**: Reassigns the queue after each level is processed.
5. **BFS (reassign node, shift)**: Shifts the node pointer before enqueueing it into the queue.
6. **BFS (reassign node, pop)**: Pops the node pointer from the queue before enqueueing it.

**Library and Special Features**

None of these test cases use external libraries or special JavaScript features. They are designed to demonstrate the performance differences between various tree traversal algorithms.

**Approach Comparison**

The approaches can be summarized as follows:

* **Recursion (DFS)**: Uses function calls to traverse the tree, which can lead to stack overflows for large trees.
* **Queue-based (BFS)**: Uses a queue data structure to enqueue and dequeue nodes, which avoids recursion but may have higher overhead due to queue operations.
* **Reassigning queue or node**: These approaches modify the queue or node pointer after each operation, which can lead to unexpected behavior if not implemented correctly.

**Pros and Cons**

Here are some pros and cons of each approach:

* **DFS (Recursion)**:
	+ Pros: Easy to implement, natural recursive structure.
	+ Cons: Risk of stack overflows, slower performance for large trees.
* **BFS (Queue-based)**:
	+ Pros: Avoids recursion, can be more efficient with queue operations.
	+ Cons: May have higher overhead due to queue operations, more complex implementation.
* **Reassigning queue or node**:
	+ Pros: Can lead to improved performance by avoiding recursive function calls or queue operations.
	+ Cons: Risk of unexpected behavior if not implemented correctly.

**Alternatives**

Other tree traversal algorithms that could be used as alternatives include:

* Iterative DFS (using loops instead of recursion)
* Level-order traversal (processing nodes at each level before moving to the next level)
* Morris Traversal (a self-balancing algorithm for traversing binary search trees)

These alternative algorithms may offer different trade-offs in terms of performance, complexity, and memory usage.

Related benchmarks:

Tree Traversals (version: 0)

Comparing performance of: DFS vs BFS (shift) vs BFS (pop) vs BFS (reassign queue) vs BFS (reassign node, shift) vs BFS (reassign node, pop)

Created: 3 years ago by: Guest

Jump to the latest result

DFS

BFS (shift)

BFS (pop)

BFS (reassign queue)

BFS (reassign node, shift)

BFS (reassign node, pop)

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):