Contains keys [K1, K2]
Data / Knowledge library
B-Tree
Stores many ordered keys per node to reduce tree height and storage reads
Why learn B-Tree?
It is effective for database and filesystem indexes backed by pages
The problem it solves
It minimizes random I/O while supporting ordered lookup and updates
Where it came from
B-Tree is a data structure that stores many ordered keys per node to reduce tree height and storage reads. Its guarantees and tradeoffs depend on the concrete implementation and operating context.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define B-Tree and distinguish it from a neighboring concept.
- Choose a realistic task where B-Tree is appropriate.
- Identify a failure mode caused by misusing B-Tree.
- Interpret a small design or result involving B-Tree.
- Explain one tradeoff or limitation of B-Tree.
Documentation and sources
- NIST Dictionary of Algorithms and Data Structures (xlinux.nist.gov)
Connect the ideas
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Real World Example
High-Latency Metadata Indexing
A digital archive service managing millions of document metadata records that must be retrieved by unique identifiers.
The business problem
Standard binary search trees result in excessive disk I/O because the tree height is too large, forcing multiple slow storage reads to traverse from root to leaf.
How this topic solves it
A B-Tree stores many ordered keys per node, which significantly increases the branching factor and reduces the total tree height, ensuring that metadata lookups require fewer disk accesses.
- Define a node capacity that aligns with the underlying storage block size to optimize I/O.
- Insert keys into nodes while maintaining sorted order within each node.
- Split nodes when they reach maximum capacity to maintain balance.
- Promote the median key to the parent node to facilitate efficient searching.
Shows how parent nodes contain multiple keys that act as separators for child nodes.
Keys < K1
K1 < Keys < K2
Keys > K2
- Root Node → pointer → Left Child
- Root Node → pointer → Middle Child
- Root Node → pointer → Right Child
Expected business outcome
Improved retrieval latency for metadata lookups due to a shallower tree structure and minimized disk seek operations.
Limitations and checks
The primary tradeoff is increased memory usage per node and complexity in handling node splits and merges; monitor this by tracking the average node occupancy ratio.