Level order traversal is a tree traversal technique that visits nodes level by level, from left to right. Unlike depth-first traversals (preorder, inorder, postorder) that go deep into one branch before exploring others, level order traversal explores all nodes at the current depth before moving to the next level.
This traversal pattern naturally maps to Breadth-First Search (BFS), which explores nodes in order of their distance from the starting point. For trees, this distance translates to the node's level or depth.
The key insight is using a queue data structure. Queues follow First-In-First-Out (FIFO) ordering, which perfectly matches how we want to process tree nodes: we visit a parent node, add its children to our "to-visit" list, then process those children in the order we encountered them.
Here's the general algorithm:
The challenge in level order traversal is grouping nodes by their level. We need to know when one level ends and the next begins. The solution is tracking how many nodes belong to the current level before we start processing them.
This pattern appears frequently in graph algorithms, web crawling, and any scenario where you need to explore data in layers or by proximity.
1from collections import deque
2
3def simple_bfs_traversal(root):
4 """Basic BFS traversal that visits all nodes."""
5 if not root:
6 return []
7
8 visited = []
9 queue = deque([root])
10
11 while queue:
12 node = queue.popleft() # Remove from front
13 visited.append(node.val) # Process current node
14
15 # Add children to back of queue
16 if node.left:
17 queue.append(node.left)
18 if node.right:
19 queue.append(node.right)
20
21 return visited