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Indices

Indices. Advanced Database Systems Dr. Fatemeh Ahmadi-Abkenari. Mechanism for locating Index Entries. Indices. Definition:.

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Indices

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  1. Indices Advanced Database Systems Dr. Fatemeh Ahmadi-Abkenari

  2. Mechanism for locating Index Entries Indices Definition: An index on a database table provides a convenient mechanism for locating a row (data record) without scanning the entire table and thus greatly reduces the time it takes to process a query. Index Entries

  3. Clustered versus Unclustered Indices A Clustered index or Main index is a sorted index in which the index entries and the data records are sorted on the same search key (So there is a single clustered index); Otherwise it is said to be Unclustered or Secondaryindex that could be several. Mechanism for locating Index Entries Index File Data File Index Entries Data Records

  4. Clustered versus Unclustered Indices AnotherDefinition: In a Clustered index, the physical proximity of index entries in the index implies some degree of proximity among the corresponding data records in the data file. Such indices enable certain queries to be executed more efficiently than with unclustered indices. (Indices created with CREATE TABLE statement) One Advantage: In retrieving a particular data record in the range, the probability of a cache hit is high.

  5. Inverted File and Fully Inverted File A file is said to be Inverted on a column if a secondary index exists with that column. A file is Fully Invertedif a secondary index exists on all columns that are not contained in the primary key.

  6. Sparse versus Dense Indices A Dense index is the one whose entries are one-to-one correspondence with the records in the data file. A secondary or unclustered index must be dense but a clustered index need not be. 009406321 Jacob Taylor MGT Anita Cohen Sanjay Sen 101202303 John Smyth CS Marry Brown 121232348 David Jones EE John Smyth 131141151 Anita Cohen CS Jacob Taylor 234567891 Marry Brown ECO Ann White David Jones 444555666 Sanjay Sen ENG Dense Matrix 666777888 Ann White MAT

  7. Sparse versus Dense Indices A Sparse index over a sorted file is one in which there is a one-to-one correspondence between index entries and pages of that data file. For having a Sparse matrix, it is essential that the data file be ordered on the same key as the index. 009406321 Jacob Taylor MGT 009406321 101202303 John Smyth CS 131141151 121232348 David Jones EE 666777888 Sparse Matrix 131141151 Anita Cohen CS 234567891 Marry Brown ECO 444555666 Sanjay Sen ENG 666777888 Ann White MAT

  8. Multilevel Indexing Focusing on Location Mechanism not only index entries Separator Entry 89 89 7 19 7 19 28 92 11 99 12 42 33 94 97 34 15 77 Leaf Entry A two-level index (Sparse Index) with at most four entries fit in a page • The leaf entries contains pointers to the data records in a separate file. • The leaf entries contain the data records == A storage structure Interpretation:

  9. Multilevel Indexing Examples: ISAM B+ Trees Terminology: • Index Level ===Any level of a tree index (Separator or Leaf) • Separator Level===Location Mechanism • Leaf Level === Index Entries • Q: Number of Pages of Index Entries • F: Number of Pages of Data Records • Q < F

  10. Index Sequential Access Method (ISAM) Characteristics: • ISAM is based on multi level indexing. • Generally, the data records are contained in leaf level, so ISAM==A storage structure for the data file. • ISAM is a main clustered index over the ordered records on the search key. • Inserting and deleting a row cause a serious problem in ISAM structure. • Suitable index structure for a relatively static table. • Insertion problem could be temporarily avoided by using Fillfactor<1.

  11. Index Sequential Access Method (ISAM) P0 K1 P1 K2 ---- Kn Pn P0 P2 Jane Bob Tom Mike Bob Tom Judy Rick Abe Edie Joe Rick Pete Jane Al Sol P1 P0 P0 P1 P2 P1

  12. Constructing ISAM Index Structure P0 P2 Judy Tom Mike Bob Tom Jane Bob Abe Rick Joe Edie Sol Jane Al Pete Rick P1 P0 P0 P1 P2 P1 1- Allocating pages sequentially in the storage structure for the leaf pages. 2- Constructing the separator levels from bottom up. 3- The root is the top most index built. Search-key values appear more than once in the tree

  13. Deletion in ISAM Indices e.g. Jane 1- Search for Jane, starts from root, Jane < Judy P0 is followed. Jane== Jane  P2 is followed. 2- Item found and Jane (the corresponding leaf entry) is deleted from the leaf level page but no change are made to the separator level. (The separator levels never change once constructed) P0 Jane Bob Bob Judy Abe Joe Edie Al Jane Rick Because ISAM is a static index 1- A search-key value in separator entry has no corresponding value in a leaf entry. 2- The most serious problem here is the potential waste of space where the deallocated leaf entries reside. P0 P1 P2

  14. Insertion in ISAM Indices e.g. Ivan The new leaf entry is an overflow of the existing leaf-level page, not a new level. Insertion is a serious problem if the appropriate leaf page is full P0 Judy Bob Bob Abe Joe Edie Jane Rick Al Overflowchain Fillfactor < 1 In a dynamic table with frequent insertion, overflow chains can become long, the index structure becomes less efficient since the overflow chains must be searched to satisfy queries. P0 P1 P2 Ivan

  15. B+ Trees Characteristics: • B+ tree is the most commonly used index structure. • B+ tree is based on multilevel indexing. • The data records either could be contained in leaf level, or in a separate data file so, B+ could be both only index or storage structure. • B+ tree has additional sibling pointers in leaf level. Searching at separator level is identical to ISAM technique. • Inserting and deleting a row is easy in B+ tree index structure, so it is a suitable index structure for a dynamic table. • B+tree is a balanced tree so any path from the root to a leaf page has the same length as any other despite the deletion or insertion.

  16. B+ Trees CREATE INDEX Trans ON Transcript (Grade) DROP INDEX Trans Secondary, Unclustered Index , B+ Tree In Insertion, instead of creating overflow chain, the tree structure will be modified. So the number of separators in each page will vary from φ/2 to φ (Fan-out= φ).

  17. B+ Trees - Insertion There is room, so no modification is needed in the tree structure. 1- Vince Judy Tom Tom Tom Judy Tom Rick Rick Vera Rick Sol Sol Vince Rick 2- Vera D Following Rule No. 1 Vince A B C There is no room, so the tree structure is modified and a new leaf page is added.

  18. B+ Trees – Insertion Rules Rule 1: In general, when a full leaf page containing φ entries must accommodate an insertion, two leaf pages are created one containing φ/2+1 entries and the other containing φ/2 entries. A separator at the next upper index level will be inserted equals to the smallest entry at the new leaf page.

  19. B+ Trees - Insertion Fan-out=2. Assuming each node is a page that includes two separator entries. tom D1 D2 3- Rob Vince Judy Tom Tom Tom Sol Rick Vera Vera Sol Rick Vince Rick Rob Sol A1 A2 B C Following Rule No. 2 D Vince Vince A

  20. B+ Trees – Insertion Rules Rule 2: In general, when a page at the separator level must accommodate φ+1 separators (Sol, Tom and Vince), the middle separator (Tom) in the separator sequence is not sorted in either of the two resulting separator pages but instead is pushed up the tree.

  21. B+ Trees Why Sibling Pointers? Sibling pointers in ISAMis not necessary because the leaf pages (that generally contain data records) are sorted in the file when the file is constructed. Since the index is static, the ordering is maintained. Overflow chains supports the dynamically inserted index entries. The B+ tree is a dynamic index structure. Upon deletion and insertion, the order of leaf pages in the file will alter. So sibling pointers link pages at the leaf level in such a way that the link list contains the search-key values of the data records of the table in sorted order.

  22. B+ Trees Fan-Out Fan-out(φ)refers to the number of index separator entry in a page. 1- Fan-out(φ) controls the number of levels in the tree in a way that if φ is a small number, the number of levels would be increased. 2- The number of levels equals the number of I/O operations needed to fetch a leaf entry. Root index occupies one page and could be maintained in main memory for reducing the cost.

  23. Fan-Out Example: There are 106 rows in the data file, 10000 pages at the leaf level, the Fan-out is 100. Assume that the size of leaf and separator entries are the same and leaf entries and data records are not integrated. How many I/Os are necessary to retrieve a particular leaf? The number of I/Os to retrieve a particular leaf page equals to: (Logφ Q) + 1. So Q= 10000 , φ=100 and The number of I/Os= 3

  24. For Further Reading: Database Systems, An application-Oriented Approach Second Edition Chapter 9 Michael Kifer, Arthur Bernstein, Philip M. Lewis Pearson, Addison Wesley Publication 2006

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