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Questions and Answers
What happens to a B+ tree when a leaf node is split during an insert operation?
What happens to a B+ tree when a leaf node is split during an insert operation?
Which of the following actions can be taken if a delete operation causes a node to have low occupancy?
Which of the following actions can be taken if a delete operation causes a node to have low occupancy?
What is a potential outcome of deleting a key from a B+ tree?
What is a potential outcome of deleting a key from a B+ tree?
After deleting elements from a B+ tree, what should be done if the tree needs to maintain its balance?
After deleting elements from a B+ tree, what should be done if the tree needs to maintain its balance?
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What does it mean for a B+ tree to 'grow wider and deeper' after an insert operation?
What does it mean for a B+ tree to 'grow wider and deeper' after an insert operation?
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Which type of file organization allows for efficient range searches?
Which type of file organization allows for efficient range searches?
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What is the primary function of indexes in a database?
What is the primary function of indexes in a database?
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When inserting a value into a B-tree index, what happens if there is not enough space at the leaf level?
When inserting a value into a B-tree index, what happens if there is not enough space at the leaf level?
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What type of query would benefit most from the use of an index?
What type of query would benefit most from the use of an index?
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Which statement best describes heap files in the context of databases?
Which statement best describes heap files in the context of databases?
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What is the primary drawback of using heap files for data organization?
What is the primary drawback of using heap files for data organization?
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What happens to a B-tree when a new key is inserted and the existing leaf node is full?
What happens to a B-tree when a new key is inserted and the existing leaf node is full?
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Which aspect of database organization optimizes search speed and allows for fast updates?
Which aspect of database organization optimizes search speed and allows for fast updates?
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Study Notes
INF2003: Database Systems - Indexing
- Course: INF2003 Database Systems, Indexing
- Semester: AY24/25 Trimester 1
- Instructor: Zhang Wei, SIT
- Date: Oct. 22, 2024
About the Instructor
- Email: [email protected]
- Website: https://www.singaporetech.edu.sg/directory/faculty/wei-zhang
- Google Homepage: https://sites.google.com/view/sit-zhang-wei/
- PhD in Computer Science from NTU
- Research: applied AI and energy efficiency
- Teaching databases since 2019
- Part of A*STAR database tasks (2017-2019)
- Customized content for different programs (e.g., CS/SE)
- Current course INF2003 harmonized for lower difficulty
- Interested in inter-program collaboration and activities
- Interested in applied learning projects
Course Outline (2nd Half Topics)
- Week 8: Indexing (mainly centralized)
- Week 9: Transactions and Concurrency
- Week 10: NoSQL
- Week 11: Blockchain
- Week 12: Data Warehouse
Files and File Organization
- Database: Consists of one or more tables.
- Table: Contains one or more tuples (rows).
- Data File: Contains data chunks.
- Data Pages: Individual chunks within the data file.
- Query Processing: Database Management Systems (DBMS) determine which data pages contain the requested data.
- File Organization:
- Heap files (random order): Used for scanning all records.
- Sorted files: Used for order-sensitive data retrieval and range queries.
- Indexes: Additional data structures that speed up specific retrievals.
- Index Advantages
- Speed up search
- Speed up updates
Discussion Points
- SQL Query Examples:
-
SELECT * FROM Students;Basic query -
SELECT * FROM Students WHERE sid = 2001234;Query by student ID -
SELECT * FROM Students WHERE year >= 2013 AND year <= 2015;Query by year range
-
- Indexing and Query Speed: Certain queries can be substantially faster when indexes are used.
- Index Creation: Creating appropriate indexes can significantly improve query performance.
Index Data Structure - Hash
- Index: Collection of buckets (possibly with overflow pages).
- Buckets: Contain data entries.
-
Hash Function (h): Same for all records, maps record's key to a specific bucket. h(r) = bucket for record r.
- h(r) = Surname
- Suitable for equality search only.
- Potential Issues: Multiple records in the same bucket, one record in different buckets.
Index Data Structure - Tree
- Hash: Primarily equality search.
- Tree: Both equality and range searches are supported
- Structure: Hierarchical (non-flat)
- B+ tree: Most popular tree-based index structure.
B+ Tree: Basic Elements
- Root: Topmost node of the tree.
- Internal Nodes: Nodes that store keys and pointers to child nodes.
- Leaves: Nodes that store data entries.
- High Fanout: Typically a large number of pointers to child nodes (e.g., 100 or more) for fast navigation.
- Philosophy: All data located in leaf nodes and all leaves on same level.
- Balance: The tree is balanced ensuring all leaf nodes at the same depth.
B+ Tree: Search, Insert and Delete
- Search: A series of comparisons that operate in O(log n) time.
-
Insert:
- Locate insertion place in leaf node
- If space available, insert value.
- Otherwise, split leaf node and propagate changes up the tree.
- May require splitting parent nodes also
-
Delete:
- Locate item.
- If low occupancy in leaf, borrow from neighboring node.
- Merge if borrowing not possible.
- Propagation up tree to root if necessary
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