B-Tree Visualization
A B-Tree is a self-balancing search tree designed to maintain sorted data and allow efficient insertion, deletion, and search operations. Unlike binary trees, each node can hold multiple keys and have more than two children. B-Trees are widely used in databases and file systems where large blocks of data must be read and written efficiently.
When inserting a new key, it is placed into the appropriate leaf node. If the node overflows by exceeding the maximum number of keys, it splits — the median key is pushed up to the parent, while the remaining keys form two child nodes. This process can propagate upward and may even create a new root. This splitting mechanism is essential for keeping the tree balanced, ensuring that all leaves always remain at the same depth as keys are redistributed.
For simplicity, the order of this B-Tree visualizer is fixed to 3.
AI Summary
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Common Interview Questions
Why are B-Trees and B+ Trees heavily utilized in database disk indexes and File Systems?
Disk I/O is thousands of times slower than RAM access. B-Trees have huge branching factors (hundreds of keys per node), matching disk block sizes. This reduces tree height to 3-4 levels for millions of records, minimizing disk seek reads.
What is the primary difference between a B-Tree and a B+ Tree?
In a B-Tree, keys and data pointers are stored in both internal nodes and leaf nodes. In a B+ Tree, internal nodes store only routing search keys, while all actual data records are stored in leaf nodes. B+ Tree leaf nodes are linked sequentially for fast range queries.
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