What are the differences between a B-tree and a B+ tree?

B-trees and B+ trees are both self-balancing search trees used for efficient data storage and retrieval, but they have distinct differences in their structure and application:

Key Differences

1. Data Storage:

   • B-tree: Stores keys and data in both internal and leaf nodes. This allows for faster access to data since it can be retrieved from any node45.
   • B+ tree: Stores data only in leaf nodes. Internal nodes contain only keys and pointers to other nodes, which helps in reducing the tree's height and improving search efficiency45.

2. Leaf Node Linking:

   • B-tree: Leaf nodes are not linked together. This makes sequential access less efficient compared to B+ trees4.
   • B+ tree: Leaf nodes are linked together in a linked list structure, enabling efficient sequential access and range queries45.

3. Insertion and Deletion:

   • B-tree: Insertion and deletion can be more complex due to data being stored in all nodes. This may require more adjustments to maintain balance5.
   • B+ tree: Insertion and deletion are generally more efficient due to the predictable structure and the fact that data is only stored in leaf nodes45.

4. Search Efficiency:

   • B-tree: Search operations can be faster for random access because data can be found at any node4.
   • B+ tree: Search operations always end at a leaf node, making the process more predictable. However, it may require more traversals to reach the leaf nodes5.

5. Applications:

   • B-tree: Suitable for systems where quick access to records is necessary, such as database indexing4.
   • B+ tree: Ideal for file systems and databases that require efficient range searching and sequential access to data45.

Summary

• B-trees are versatile and allow for quick access to data at any node but can be less efficient for sequential access.
• B+ trees optimize for sequential access and range queries by storing data only in leaf nodes and linking these nodes together. They are particularly efficient for disk storage optimization due to their ability to reduce the number of disk reads required for operations.