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Multilevel Indexes
In RDBMS, indexes are data structures that speed up data retrieval by reducing disk accesses. As databases grow, single-level indexes become too large for main memory. Multilevel indexes solve this by dividing the index into a hierarchy of smaller, manageable blocks.
Index Components
Multilevel Index Structure
Multilevel indexes create a hierarchy where each level provides progressively more detailed references to data. Most implementations use B-tree or B+ tree structures ?
B-Tree vs B+ Tree
| Feature | B-Tree | B+ Tree |
|---|---|---|
| Data Storage | All nodes store key-value pairs | Only leaf nodes store data |
| Fanout | Lower (internal nodes store data) | Higher (internal nodes store only keys) |
| Range Queries | Needs access to internal nodes | Efficient traverse linked leaf nodes |
| Point Queries | Faster (data found at any level) | Always traverses to leaf level |
| Disk Space | More (data in all nodes) | Less (data only in leaves) |
| Best For | Point queries, small datasets | Range queries, large datasets |
Conclusion
Multilevel indexes organize index data hierarchically to reduce disk I/O for large databases. B-trees offer simplicity and efficient point queries, while B+ trees excel at range queries and space utilization with their leaf-only data storage. The choice depends on query patterns and dataset size.
Updated on: 2026-03-14T22:03:33+05:30
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