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Efficient Array Storage for Binary Tree

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Can binary tree be stored in array?

An array can store the tree's data values efficiently, placing each data value in the array position corresponding to that node's position within the tree. The table lists the array indices for the children, parent, and siblings of each node in Figure 12.16. 1.

How do you store a binary tree?

How to store a general Binary Tree? A simple solution is to store both Inorder and Preorder traversals. This solution requires space twice the size of Binary Tree. We can save space by storing Preorder traversal and a marker for NULL pointers.

Which is the most efficient data structure for creation of tree?

Array is a good static data structure that can be accessed randomly and is fairly easy to implement. Linked Lists on the other hand is dynamic and is ideal for application that requires frequent operations such as add, delete, and update.


One method which I like is to store the preorder traversal, but also include the 'null' nodes in there. Storing the 'null' nodes removes the need for also storing the inorder of the tree.

Some advantages of this method

  • You can do better storage than pre/post + inorder method in most practical cases.
  • Serialization just takes one traversal
  • Deserialization can be done in one pass.
  • The inorder traversal can be gotten in one pass without constructing the tree, which might be useful if the situation calls for it.

For example say you had a binary tree of 64 bit integers, you can store an extra bit after each node saying whether the next is a null node or not (the first node is always the root). Null nodes, you can represent by a single bit.

So if there are n nodes, the space usage would be 8n bytes + n-1 indicator bits + n+1 bits for null nodes = 66*n bits.

In the pre/post + inorder you will end up using 16n bytes= 128*n bits.

So you save a space of 62*n bits over this pre/post + inorder method.

Consider the tree

       100
      /   \
     /     \
    /       \
   10       200
  / \       /  \
 .   .     150  300
          / \    / \
         .   .   .  .

where the '.' are the null nodes.

You will serialize it as 100 10 . . 200 150 . . 300 . .

Now each (including subtrees) 'preorder traversal with null' has the property that number of null nodes = number of nodes + 1.

This allows you to create the tree, given the serialized version in one pass, as the first node is the root of the tree. Nodes that follow are the left subtree followed by right, which can be viewed to be like this:

100 (10 . .) (200 (150 . .) (300 . .))

To create the inorder traversal, you use a stack and push when you see a node and pop (onto a list) when you see a null. The resulting list is the inorder traversal (a detailed explanation for this can be found here: C++/C/Java: Anagrams - from original string to target;).


Think about XML. It's a kind of tree serialization. For example:

<node id="1">
    <node id="2">                                   1
    </node>                                       /   \
    <node id="3">                                2     3
        <node id="4">                                 / \
        </node>                                      4   5
        <node id="5">
        </node>
    </node>
</node>

Then, why the spaces and tags ? We can omit them, step by step:

<1>
   <2></>
   <3>
     <4></>
     <5></>
   </>
</>

Remove the spaces: <1><2></2><3><4></><5></></></>.

Remove the angle brackets: 12/34/5///

Now the problem is: what if a node has a empty left subtree and non-empty right subtree? Then we can use another special charactor, '#' to represent an empty left sub-tree.

For example:

    1
  /   \
      2
     /  \
    3

This tree can be serialized as: 1#23///.


The 2i, 2i+1 (Binary Heap) method is indeed the best way if you have a (nearly) complete tree.

Otherwise you won't escape storing a ParentId (parent Index) with each node.


You can save the in-order and pre/post-order traversal of the binary tree in the file and reconstruct the tree from these traversals.