Index Principle Comparison
MongoDB B-Tree
- Each node stores both actual data and index information
- For queries, once a matching key is found in the node, data can be retrieved directly
- Very efficient for exact queries (such as find({_id:123}))
- Relatively lower efficiency for range queries
MySQL B+Tree
- All data is stored in leaf nodes
- Leaf nodes are connected via bidirectional pointers forming an ordered linked list
- Very efficient for range queries
- Non-leaf nodes only store index keys, not data
Key Differences
Data Storage Method
- MySQL: Uses row-based storage, data stored in fixed table structure
- MongoDB: Uses document storage, data stored in BSON document format
Data Association Method
- MySQL: Establishes relationships between tables through foreign keys, supports JOIN operations
- MongoDB: Implements data association through references or embedded documents
B+Tree vs B-Tree Range Query Performance Comparison
B+Tree Advantages:
- Pointer connections between leaf nodes can efficiently support range queries
- Only need to locate the starting key node, then traverse sequentially along pointers
B-Tree Disadvantages:
- No additional link pointers between nodes
- Range queries require repeated tree traversal operations
External Storage Structure Optimization
B+Tree Advantages:
- Non-leaf nodes only store index keys, single node can hold more index entries
- Disk read-ahead can be better utilized
Tree Depth and I/O Efficiency:
- B-tree is suitable for random point queries, worst case only needs 3-4 disk I/O operations
- B+tree has excellent sequential access performance, suitable for full table scans and range queries