11.Indexing§
- Goals:
- Store large files
- Support multiple search keys
- Support efficient insert, delete, and range queries
Files and Indexing
- Entry sequenced file: Order records by time of insertion.
- Search with sequential search
- Index file: Organized, stores pointers to actual records.
- Could be organized with a tree or other data structure.
Keys and Indexing
- Primary Key : A unique identifier for records. May be inconvenient for search.
- Secondary Key: An alternate search key, often not unique for each record. Often used for search key.
Linear Indexing (1)
- Linear index: Index file organized as a simple sequence of key/record pointer pairs with key values are in sorted order.
- Linear indexing is good for searching variable-length records.
Linear Indexing (2)
- If the index is too large to fit in main memory, a second-level index might be used.
Tree Indexing (1)
- Linear index is poor for insertion/deletion.
- Tree index can efficiently support all desired operations:
- Insert/delete
- Multiple search keys (multiple indices)
- Key range search
Tree Indexing (2)
Tree Indexing (3)
- Difficulties when storing tree index on disk:
- Tree must be balanced.
- Each path from root to leaf should cover few disk pages.
Tree Indexing (4)
2-3 Tree
- A 2-3 Tree has the following properties:
- A node contains one or two keys
- Every internal node has either two children (if it contains one key) or three children (if it contains two keys).
- All leaves are at the same level in the tree, so the tree is always height balanced.
- The 2-3 Tree has a search tree property analogous to the BST.
2-3 Tree Example
- The advantage of the 2-3 Tree over the BST is that it can be updated at low cost.
2-3 Tree Insertion (1)
2-3 Tree Insertion (2)
2-3 Tree Insertion (3)
B-Trees (1)
- The B-Tree is an extension of the 2-3 Tree.
- The B-Tree is now the standard file organization for applications requiring insertion, deletion, and key range searches.
B-Trees (2)
- B-Trees are always balanced.
- B-Trees keep similar-valued records together on a disk page, which takes advantage of locality of reference.
- B-Trees guarantee that every node in the tree will be full at least to a certain minimum percentage. This improves space efficiency while reducing the typical number of disk fetches necessary during a search or update operation.
B-Tree Search
- Generalizes search in a 2-3 Tree.
- Do binary search on keys in current node. If search key is found, then return record. If current node is a leaf node and key is not found, then report an unsuccessful search.
- Otherwise, follow the proper branch and repeat the process.
B+-Trees
- The most commonly implemented form of the B-Tree is the B+-Tree.
- Internal nodes of the B+-Tree do not store record – only key values to guild the search.
- Leaf nodes store records or pointers to records.
- A leaf node may store more or less records than an internal node stores keys.
23+-Tree Build Example
23+-Tree Search Example
23+-Tree Delete Example
B+-Tree Find
B+-Tree Insert
B+-Tree Deletion
B+-Tree Insert (Degree 5)
B-Tree Space Analysis (1)
- B+-Trees nodes are always at least half full.
- The B*-Tree splits two pages for three, and combines three pages into two. In this way, nodes are always 2/3 full.
- Asymptotic cost of search, insertion, and deletion of nodes from B-Trees is \(\Theta(log n)\).
- Base of the log is the (average) branching factor of the tree.
B-Tree Space Analysis (2)
- Example: Consider a B+-Tree of order 100 with leaf nodes containing 100 records.
- 1 level B+-tree:
- 2 level B+-tree:
- 3 level B+-tree:
- 4 level B+-tree:
- Ways to reduce the number of disk fetches:
- Keep the upper levels in memory.
- Manage B+-Tree pages with a buffer pool.
B-Trees: The Big Idea
B-trees are really good at managing a sorted list
- They break the list into manageable chunks
- The leaves of the B+-tree form the list
- The internal nodes of the B+-tree merely help find the right chunk