B+ Tree Basics 3 Video Lecture | Database Management System (DBMS) - Computer Science Engineering (CSE)

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FAQs on B+ Tree Basics 3 Video Lecture - Database Management System (DBMS) - Computer Science Engineering (CSE)

1. What is a B+ tree and how is it different from a B tree?
Ans. A B+ tree is a type of self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. The key difference between a B+ tree and a B tree is that in a B+ tree, all data is stored in the leaf nodes, while the internal nodes only contain keys for navigation.
2. How does a B+ tree handle overflow and underflow during insertions and deletions?
Ans. In a B+ tree, overflow can occur when a node becomes full during an insertion. To handle overflow, the node is split into two nodes, and the median key is moved up to the parent node. Underflow can occur when a node falls below a certain minimum number of keys during deletion. To handle underflow, redistribution or merging of nodes is done to maintain the minimum number of keys.
3. What is the benefit of using a B+ tree in database systems?
Ans. B+ trees are commonly used in database systems because they provide efficient insertion, deletion, and retrieval operations in logarithmic time. Additionally, the structure of a B+ tree allows for range queries, as data is stored sequentially in the leaf nodes, making it ideal for applications that require range searches like databases.
4. How does the height of a B+ tree affect its performance?
Ans. The height of a B+ tree directly affects its performance. A shorter tree height means fewer levels to traverse during search operations, resulting in faster retrieval times. To maintain a low height, a B+ tree must be properly balanced through node splitting and merging operations.
5. Can a B+ tree be used for in-memory data structures or is it only suitable for disk-based storage?
Ans. B+ trees can be used for both in-memory and disk-based storage. While they are commonly used in databases that store data on disk due to their efficiency in handling large datasets, B+ trees can also be implemented in memory to efficiently manage and search in-memory data structures.
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