Serialize and Deserialize Binary Tree
Problem Description
Serialization is the process of converting a data structure or object into a sequence of bits so that it can be stored in a file or memory buffer, or transmitted across a network connection link to be reconstructed later in the same or another computer environment.
Design an algorithm to serialize and deserialize a binary tree. There is no restriction on how your serialization/deserialization algorithm should work. You just need to ensure that a binary tree can be serialized to a string and this string can be deserialized to the original tree structure.
Clarification: The input/output format is the same as how LeetCode serializes a binary tree. You do not necessarily need to follow this format, so please be creative and come up with different approaches yourself.
Example 1:
Input: root = [1,2,3,null,null,4,5]
Output: [1,2,3,null,null,4,5]
Example 2:
Input: root = []
Output: []
Constraints:
- The number of nodes in the tree is in the range [0, 104].
-1000 <= Node.val <= 1000
Solve it on LeetCode
Approaches
1. BFS with Queue
Intuition:
This approach uses a queue to perform a level-order traversal (BFS) to serialize and deserialize a binary tree. The BFS approach processes nodes level by level, which suits the serialization of nodes where it can record the tree's structure, including null children, by utilizing a delimiter.
Serialize:
- Start: Initialize a queue and add the root to it.
- Traverse: While the queue is not empty:
- Dequeue an element. If it is not null, add its value to the result string and enqueue its children. If it is null, add a special marker (e.g., "null") to the result string.
- Output: Join all results with a comma.
Deserialize:
- Start: Split the serialized string on commas and initialize a queue.
- Initialize: Create the root node and add it to the queue.
- Reconstruct: For each node, check its children from the serialized data:
- If it is "null", move to the next node.
- Otherwise, create a new node, link it as a child, and add it to the queue.
Code:
- Time Complexity: O(N), where N is the number of nodes in the tree, as each node is processed once during serialization and deserialization.
- Space Complexity: O(N), due to the storage of nodes in the queue and the string building process for serialization.
2. DFS with Preorder Traversal
Intuition:
In this approach, the idea is to use a depth-first search (DFS) technique with a recursive method that uses preorder traversal. This involves capturing each node followed by its left and right children. By marking null references explicitly, reconstruction is facilitated.
Serialize:
- Use a recursive function to append node values to a
StringBuilderin preorder (root → left → right). - Append a special marker ("null") for null nodes to denote absent children.
- Join all elements to generate the final serialized string.
Deserialize:
- Split the serialized data using a delimiter.
- Use recursive functions to construct tree nodes in preorder, consuming values sequentially:
- Return null when a "null" string is found.
- Create a tree node using the current value and recursively handle its left and right subtrees.
Code:
- Time Complexity: O(N), where N is the number of nodes in the tree. Each node is visited once.
- Space Complexity: O(N), due to the recursion stack in the worst case (when the tree is skewed) and the storage for serialization strings.