References:

Problem Statement

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:[]

**Example 3:**

Input:root = [1]Output:[1]

**Example 4:**

Input:root = [1,2]Output:[1,2]

**Constraints:**

- The number of nodes in the tree is in the range
`[0, 10`

.^{4}] `-1000 <= Node.val <= 1000`

## Solution

#### Approach 1: Depth First Search (DFS)

**Intuition**

The **serialization** of a `Binary Search Tree`

is essentially to encode its values and more importantly its structure. One can traverse the tree to accomplish the above task. And it is well know that we have two general strategies to do so:

*Breadth First Search*(`BFS`

)We scan through the tree level by level, following the order of height, from top to bottom. The nodes on higher level would be visited before the ones with lower levels.*Depth First Search*(`DFS`

)In this strategy, we adopt the`depth`

as the priority, so that one would start from a root and reach all the way down to certain leaf, and then back to root to reach another branch.The DFS strategy can further be distinguished as`preorder`

,`inorder`

, and`postorder`

depending on the relative order among the root node, left node and right node.

In this task, however, the `DFS`

strategy is more adapted for our needs, since the linkage among the adjacent nodes is naturally encoded in the order, which is rather helpful for the later task of **deserialization**.

Therefore, in this solution, we demonstrate an example with the `preorder`

DFS strategy. One can check out more tutorial about `Binary Search Tree`

on the LeetCode Explore.

**Algorithm**

First of all, here is the definition of the `TreeNode`

which we would use in the following implementation.

class TreeNode(object): """ Definition of a binary tree node.""" def __init__(self, x): self.val = x self.left = None self.right = None

The preorder DFS traverse follows *recursively* the order of`root -> left subtree -> right subtree`

.

As an example, let’s serialize the following tree. Note that serialization contains information about the node values as well as the information about the tree structure.

We start from the root, node `1`

, the serialization string is `1,`

. Then we jump to its left subtree with the root node `2`

, and the serialization string becomes `1,2,`

. Now starting from node `2`

, we visit its left node `3`

(`1,2,3,None,None,`

) and right node `4`

(`1,2,3,None,None,4,None,None`

) sequentially. Note that `None,None,`

appears for each leaf to mark the absence of left and right child node, this is how we save the tree structure during the serialization. And finally, we get back to the root node `1`

and visit its right subtree which happens to be a leaf node `5`

. Finally, the serialization string is done as `1,2,3,None,None,4,None,None,5,None,None,`

.

# Serialization class Codec: def serialize(self, root): """ Encodes a tree to a single string. :type root: TreeNode :rtype: str """ def rserialize(root, string): """ a recursive helper function for the serialize() function.""" # check base case if root is None: string += 'None,' else: string += str(root.val) + ',' string = rserialize(root.left, string) string = rserialize(root.right, string) return string return rserialize(root, '')

Now let’s deserialize the serialization string constructed above `1,2,3,None,None,4,None,None,5,None,None,`

. It goes along the string, initiate the node value and then calls itself to construct its left and right child nodes.

# Deserialization class Codec: def deserialize(self, data): """Decodes your encoded data to tree. :type data: str :rtype: TreeNode """ def rdeserialize(l): """ a recursive helper function for deserialization.""" if l[0] == 'None': l.pop(0) return None root = TreeNode(l[0]) l.pop(0) root.left = rdeserialize(l) root.right = rdeserialize(l) return root data_list = data.split(',') root = rdeserialize(data_list) return root

**Complexity Analysis**

- Time complexity : in both serialization and deserialization functions, we visit each node exactly once, thus the time complexity is , where is the number of nodes,
*i.e.*the size of tree. - Space complexity : in both serialization and deserialization functions, we keep the entire tree, either at the beginning or at the end, therefore, the space complexity is .

The solutions with BFS or other DFS strategies normally will have the same time and space complexity.

**Further Space Optimization**

In the above solution, we store the node value and the references to `None`

child nodes, which means complexity, where is the size of value. That is called *natural serialization*, and has was implemented above.

The component here is the encoding of values, can’t be optimized further, but there is a way to reduce part which is the encoding of the tree structure.

The number of unique binary tree structures that can be constructed using `n`

nodes is , where is the `nth`

Catalan number. Please refer to this article for more information.

There are possible structural configurations of a binary tree with n nodes, so the largest index value that we might need to store is . That means storing the index value could require up to 1 bit for , or bits for .

In this way one could reduce the encoding of the tree structure by . More precisely, the Catalan numbers grow as and hence the theoretical minimum of storage for the tree structure that could be achieved is .

#### Approach 2: Recursive DFS Preorder Traversal using Tuples

class Codec: def serialize(self, root): # take care of base cases # if a node is empty, add 'x' to string # you can set 'x' to any mark as you want if not root: return 'x' # preoder(Root->left->right) # ex, # 1 # / \ # 2 3 # / \ # 4 5 # # return (1, (2, 'x', 'x'), (3, (4, 'x', 'x'), (5, 'x', 'x'))) # if you look at the return statement very closely, it is actually very intuitive # for value 1, you have 2 as left child and 3 as right child # for value 2, you have 'x'(None) as left child and 'x'(None) as right child which indicates it is a leaf node return root.val, self.serialize(root.left), self.serialize(root.right) def deserialize(self, data): #######################INTUITION######################### # The initial data string will be something like below: # (1, (2, 'x', 'x'), (3, (4, 'x', 'x'), (5, 'x', 'x'))) # if you loop through string: # 1 -> this is node value # (2, 'x', 'x') -> this is node left # (3, (4, 'x', 'x'), (5, 'x', 'x')) -> this is node right ######################################################## # always take care of base case: if the node's value is 'x' then return None if data[0] == 'x': return None # create new treenode for node value node = TreeNode(data[0]) # do the recursive to unpack string value node.left = self.deserialize(data[1]) node.right = self.deserialize(data[2]) # return the new TreeNode that we just created return node ''' def serialize(self, root): if not root: return 'x' return root.val, self.serialize(root.left), self.serialize(root.right) def deserialize(self, data): if data[0] == 'x': return None node = TreeNode(data[0]) node.left = self.deserialize(data[1]) node.right = self.deserialize(data[2]) return node '''