Lecture11-Tree-Structured-Indexing

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SOEN 363 - Data Systems for Software Engineers Lecture 12: DBMS Internals- Part II Essam Mansour
Today… Last Session: DBMS Internals- Part I Buffer Management Files and Access Methods (file organizations) Today’s Session: DBMS Internals- Part II Tree-based indexes: ISAM and B+ trees
DBMS Layers Query Optimization and Execution Relational Operators Files and Access Methods Buffer Management Disk Space Management DB Queries Transaction Manager Lock Manager Recovery Manager
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Outline Why Indexing? Storing Data Records and Index Types Indexed Static Access Method (ISAM) Trees B+ Trees
Motivation Consider a file of student records sorted by GPA How can we answer a range selection ( E.g., “Find all students with a GPA higher than 3.0” )? What about doing a binary search followed by a scan ? Yes, but… What if the file becomes “very” large? Cost is proportional to the number of pages fetched Hence, may become very slow! Page 1 Page 2 Page N Page 3 Data File
Motivation What about creating an index file (with one entry per page) and do binary search there? But, what if the index file becomes also “very” large? Page 1 Page 2 Page N … Data File Index File K 1 P 1 K 2 P 2 K N P N Index Entry = <first key on the page, pointer to the page>
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Motivation Repeat recursively! Non-leaf Pages Pages Leaf Each tree page is a disk block and all data records reside ( if chosen to be part of the index ) in ONLY leaf pages How else data records can be stored?
Outline Why Indexing? Storing Data Records and Index Types Indexed Static Access Method (ISAM) Trees B+ Trees
Where to Store Data Records? In general, 3 alternatives for “data records” (each referred to as K* ) can be pursued: Alternative (1): K* is an actual data record with key k Alternative (2): K* is a < k , rid> pair, where rid is the record id of a data record with search key k Alternative (3): K* is a < k , rid-list> pair, where rid-list is a list of rids of data records with search key k
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Where to Store Data Records? In general, 3 alternatives for “data records” (each referred to as K* ) can be pursued: Alternative (1): K* is an actual data record with key k Alternative (2): K* is a < k , rid> pair, where rid is the record id of a data record with search key k Alternative (3): K* is a < k , rid-list> pair, where rid-list is a list of rids of data records with search key k Alternative (1) : Leaf pages contain the actual data (i.e., the data records) Alternative (2) : Leaf pages contain the <key, rid> pairs and actual data records are stored in a separate file Alternative (3) : Leaf pages contain the <key, rid-list> pairs and actual data records are stored in a separate file The choice among these alternatives is orthogonal to the indexing technique
Clustered vs. Un-clustered Indexes Indexes can be either clustered or un-clustered Clustered Indexes: When the ordering of data records is the same as (or close to) the ordering of entries in some index Un-clustered Indexes: When the ordering of data records differs from the ordering of entries in some index
Clustered vs. Un-clustered Indexes Is an index that uses Alternative (1) clustered or un-clustered? Clustered Is an index that uses Alternative (2) or (3) clustered or un-clustered? Clustered “only” if data records are sorted on the search key field In practice: A clustered index is an index that uses Alternative (1) Indexes that use Alternatives (2) or (3) are un-clustered
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Clustered and Nonclustered Indexes
Clustered and Nonclustered Indexes CREATE INDEX myIndex ON SalesData (ProductID, OrderQty)
Clustered and Nonclustered Indexes CREATE INDEX myIndex ON SalesData (ProductID, OrderQty)
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Outline Why Indexing? Storing Data Records and Index Types Indexed Static Access Method (ISAM) Trees B+ Trees
ISAM Trees Indexed Sequential Access Method (ISAM) trees are static E.g., 2 Entries Per Page 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root Non-Leaf Pages Leaf Pages
ISAM Trees: Page Overflows What if there are a lot of insertions after creating the tree? Non-leaf Pages Pages Leaf Overflow page Primary pages
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ISAM: Searching for Entries Search begins at root, and key comparisons direct it to a leaf Search for 27* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root
ISAM: Inserting Entries The appropriate page is determined as for a search, and the entry is inserted (with overflow pages added if necessary) Insert 23* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23*
ISAM: Inserting Entries The appropriate page is determined as for a search, and the entry is inserted (with overflow pages added if necessary) Insert 48* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48*
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ISAM: Inserting Entries The appropriate page is determined as for a search, and the entry is inserted (with overflow pages added if necessary) Insert 41* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41*
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ISAM: Inserting Entries The appropriate page is determined as for a search, and the entry is inserted (with overflow pages added if necessary) Insert 42* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41* 42* Chains of overflow pages can easily develop!
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ISAM: Deleting Entries The appropriate page is determined as for a search, and the entry is deleted ( with ONLY overflow pages removed when becoming empty ) Delete 42* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41* 42*
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ISAM: Deleting Entries The appropriate page is determined as for a search, and the entry is deleted ( with ONLY overflow pages removed when becoming empty ) Delete 42* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41*
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ISAM: Deleting Entries The appropriate page is determined as for a search, and the entry is deleted ( with ONLY overflow pages removed when becoming empty ) Delete 42* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41*
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ISAM: Deleting Entries The appropriate page is determined as for a search, and the entry is deleted ( with ONLY overflow pages removed when becoming empty ) Delete 51* 10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41* Note that 51 still appears in an index entry, but not in the leaf!
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ISAM: Deleting Entries The appropriate page is determined as for a search, and the entry is deleted ( with ONLY overflow pages removed when becoming empty ) Delete 55* 10* 15* 20* 27* 33* 37* 40* 46* 55* 63* 97* 20 33 51 63 40 Root 23* 48* 41* Primary pages are NOT removed, even if they become empty!
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ISAM: Some Issues Once an ISAM file is created, insertions and deletions affect only the contents of leaf pages (i.e., ISAM is a static structure !) Since index-level pages are never modified, there is no need to lock them during insertions/deletions Critical for concurrency! Long overflow chains can develop easily The tree can be initially set so that ~20% of each page is free If the data distribution and size are relatively static, ISAM might be a good choice to pursue!
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Next Lecture Why Indexing? Storing Data Records in Indexes and Index Types Indexed Static Access Method (ISAM) Trees B+ Trees
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