What are the differences between B trees and B+ trees?

Question

In a b-tree you can store both keys and data in the internal and leaf nodes, but in a b+ tree you have to store the data in the leaf nodes only.

Is there any advantage of doing the above in a b+ tree?

Why not use b-trees instead of b+ trees everywhere, as intuitively they seem much faster?

I mean, why do you need to replicate the key (data) in a b+ tree?

Answer 1

The image below helps show the differences between B+ trees and B trees.

Advantages of B+ trees:

• Because B+ trees don't have data associated with interior nodes, more keys can fit on a page of memory. Therefore, it will require fewer cache misses in order to access data that is on a leaf node.
• The leaf nodes of B+ trees are linked, so doing a full scan of all objects in a tree requires just one linear pass through all the leaf nodes. A B tree, on the other hand, would require a traversal of every level in the tree. This full-tree traversal will likely involve more cache misses than the linear traversal of B+ leaves.

Advantage of B trees:

• Because B trees contain data with each key, frequently accessed nodes can lie closer to the root, and therefore can be accessed more quickly.

---

Answer 2

The principal advantage of B+ trees over B trees is they allow you to pack in more pointers to other nodes by removing pointers to data, thus increasing the fanout and potentially decreasing the depth of the tree.

The disadvantage is that there are no early outs when you might have found a match in an internal node. But since both data structures have huge fanouts, the vast majority of your matches will be on leaf nodes anyway, making on average the B+ tree more efficient.