Computer Science Engineering (CSE) Exam > Computer Science Engineering (CSE) Videos > Database Management System (DBMS) > BST Performance: B Trees
BST Performance: B Trees Video Lecture | Database Management System (DBMS) - Computer Science Engineering (CSE)
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FAQs on BST Performance: B Trees Video Lecture - Database Management System (DBMS) - Computer Science Engineering (CSE)
| 1. What is the difference between BST and B Trees? |  |
Ans. BST (Binary Search Tree) is a binary tree data structure where each node has at most two child nodes, while B Trees are multiway search trees that can have more than two children per node. B Trees are designed to work efficiently on disk storage.
| 2. How does the performance of BST compare to B Trees in terms of insertion and deletion operations? | |
Ans. In BST, insertion and deletion operations have an average time complexity of O(log n) but can degrade to O(n) in the worst case scenario. B Trees have a better performance for insertion and deletion operations with an average time complexity of O(log n) due to their ability to maintain balance.
| 3. What is the advantage of using B Trees over BST for large datasets? | |
Ans. B Trees are more suitable for large datasets as they are designed to work efficiently on disk storage. Their ability to have more children per node reduces the depth of the tree, leading to faster access times for operations like insertion, deletion, and search.
| 4. How does the search operation performance differ between BST and B Trees? | |
Ans. In BST, the search operation has an average time complexity of O(log n) but can degrade to O(n) in the worst case scenario. B Trees have a better performance for search operations with an average time complexity of O(log n) due to their balanced structure.
| 5. Can B Trees be used for in-memory data structures or are they only suitable for disk storage? | |
Ans. B Trees can be used for both in-memory data structures and disk storage. While they are commonly used for disk storage due to their efficient access times, they can also be beneficial for in-memory data structures, especially for large datasets where maintaining balance is crucial for performance.
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Dec 31, 2024 Last updated |
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