Database 2 Lecture II. Alessandro Artale
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1 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (1) Database 2 Lecture II Alessandro Artale Faculty of Computer Science Free University of Bolzano Room: 221 artale@inf.unibz.it artale/ 2003/2004 First Semester
2 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (2) Summary of Lecture II Indexing. Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
3 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (3) Indexing Indexing is the principal technique used to efficiently answering a given query. An Index for a DB is like an Index in a book: 1. It is smaller that the book; 2. The words are in sorted order; 3. If we are looking for a particular topic we first search on the index, find the pages where it is discussed, go to the actual pages in the book. Example. MovieStar(Name,Address,Gender,Birthdate) SELECT * FROM MovieStar WHERE Name = Jim Carrey ; All the blocks for the MovieStar relation should be inspected if there is no index on Name.
4 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (4) Index An Index is a data structure that facilitates the query answering process by minimizing the number of disk accesses. An index structure is usually defined on a single Attribute of a Relation, called the Search Key; An Index takes as input a Search Key value and returns the address of the record(s) (block physical address offset of the record) holding that value. Index structure: Search Key-Pointer pairs Search Key Pointer to a data-file record The Searck Key values stored in the Index are Sorted and a binary search can be done on the Index. Only a small part of the records of a relation have to be inspected: Appropriate indexes can speed up query processing passing from minutes to seconds.
5 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (5) Index Structures Different data structures give rise to different indexes: 1. Indexes on Sequential Files (Primary Index); 2. Secondary Indexes on Unsorted Files; 3. B-Trees; 4. Hash Tables.
6 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (6) Evaluating Different Index Structures No one technique is the best. Each has to evaluated w.r.t. the following criteria: Access Type. Finding records either with a particular search key, or with the search key falling in a given range. Access Time. The time it takes to find item(s) using the index in question. Insertion Time. The time to insert an item in the data file, as well as the time to update the index. Deletion Time. The time to delete the item from the data file (which include the time to find the item), and the time to update the index. Space Overhead. Additional space for the index.
7 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (7) Summary Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
8 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (8) Indexes on Sequential Files Index on Sequential File, also called Primary Index, when the Index is associated to a Data File which is in turn sorted with respect to the search key. 1. A Primary Index forces a sequential file organization on the Data File; 2. Since a Data File can have just one order there can be just one Primary Index for Data File. Usually used when the search key is also the primary key of the relation. Usually, these indexes fit in main memory. Indexes on sequential files can be: 1. Dense: One entry in the index file for every record in the data file; 2. Sparse: One entry in the index file for each block of the data file.
9 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (9) Dense Indexes Every value of the search key has a representative in a Dense Index. The index maintains the keys in the same order as in the data file. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
10 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (10) Queries with Dense Indexes Algorithm for Lookup: Searching a data record with a given search key value. Given a search key, the index is scanned and when is found the associated pointer to the data file record is followed and the record (block containing it) is read in main memory. Dense indexes support also range queries: The minimum value is located first, if needed, consecutive blocks are loaded in main memory until a search key greater than the maximum value is found. Query-answering using dense indexes is efficient: 1. Since the index is usually kept in main memory, just 1 disk I/O has to be performed during lookup; 2. Since the index is sorted we can use binary search: If there are keys then at most search steps are required to locate a given search key.
11 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (11) Sparse Indexes Used when dense indexes are too large: A Sparse Index uses less space at the expense of more time to find a record given a key. A sparse index holds one key-pointer pair per data block, usually the first record on the data block. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
12 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (12) Queries with Sparse Indexes Algorithm for Lookup. Given a search key : 1. Search the sparse index for the greatest key to using binary search; 2. We retrieve the pointed block to main memory to look for the record with search key (always using binary search). With respect to dense indexes we need to start two different binary searches: the first on the sparse index, and the second on the retrieved data block. Still disk I/O for lookup. In conclusion, a Sparse Index is more efficient in space at the cost of a worst computing time in Main Memory.
13 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (13) Primary Dense Index: Example Example of a Primary Dense Index with Search Key=Account#. A-101 A-102 A-110 A-201 A-215 A-217 A-218 A-222 A-305 Account Branch Balance A-101 Downtown 500 A-102 Perryridge 400 A-110 Downtown 600 A-201 Perryridge 900 A-215 Mianus 700 A-217 Brighton 750 A-218 Perryridge 700 A-222 Redwood 700 A-305 Round Hill 350
14 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (14) Primary Sparse Index: Example Example of a Primary Sparse Index with Search Key=Account#. Account Branch Balance A-101 Downtown 500 A-102 Perryridge 400 A-101 A-201 A-218 A-110 Downtown 600 A-201 Perryridge 900 A-215 Mianus 700 A-217 Brighton 750 A-218 Perryridge 700 A-222 Redwood 700 A-305 Round Hill 350
15 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (15) Summary Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
16 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (16) Primary Indexes with Duplicate Keys Indexes for non key attributes: More than one record with the same search key. As usual, the data file should be sorted w.r.t the search key to speak of primary indexes. Techniques for dense indexes: 1. One entry for each record in the data file: Duplicate key-pointer pairs (not used); 2. Just a single entry for each record in the data file with search key no duplicate key-pointer pairs: Pointer to the first record with search key (more efficient).
17 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (17) Dense Index with Duplicate Search Keys Single Entry Index Lookup. Find the search key on the index, read the pointed disk block, possibly read successive blocks. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
18 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (18) Primary Dense Index with Duplicates: Example Example of a Primary Dense Index with Duplicates with Search Key=Branch. Account Branch Balance A-217 Brighton 750 Brighton Downtown Mianus Perryridge Redwood Round Hill A-101 Downtown 500 A-110 Downtown 600 A-215 Mianus 700 A-102 Perryridge 400 A-201 Perryridge 900 A-218 Perryridge 700 A-222 Redwood 700 A-305 Round Hill 350
19 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (19) Analysis of Primary indexes Advantages. Efficient access of tuples with a given search key. Very few blocks should be read (also in case of duplicate keys); Range Queries looking for search key values in a certain range are answered efficiently.
20 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (20) Analysis of Primary indexes (cont.) Disadvantages. Expensive maintenance of the physical records storage to maintain the sorted order. Technique used for insertion based on Overflow Blocks. 1. If there is space in the block insert the new record there in the right place; 2. Otherwise, insert the new record in an Overflow Blocks. In order to maintain the order, records are linked by means of pointers: The pointer in each record points to the next record in search-key order. In general, performance degrades as far as the relation grows. The file is reorganized when the system load is low. An optimal solution is to implement primary indexes as B-Tree structures (presented soon).
21 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (21) Insertion in Sequential Files: Example A-215 Mianus 700 A-102 Perryridge 400 A-201 Perryridge 900 A-306 Mianus 650 Overflow Block
22 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (22) Summary Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
23 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (23) Secondary Indexes A primary index is an index on a file sorted w.r.t. the search key. Then a primary index controls the storage of records in the data file. Indexes on Sequential files and Hash Tables are examples of primary indexes. Since a file can have at most one physical order then it can have at most one primary index. Secondary Indexes facilitate query-answering on attributes other than primary keys or, more generally, on non-ordering attributes. A file can have several secondary indexes. Secondary indexes do not determine the placement of records in the data file.
24 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (24) Secondary Index: An Example Let us consider the MovieStar relation: MovieStar(Name,Address,Gender,Birthdate) and a query involving the non-key Birthdate attribute: SELECT Name, Address FROM MovieStar WHERE Birthdate = ; A secondary index on the MovieStar relation w.r.t. the Birthdate attribute would reduce the answering time.
25 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (25) Structure of Secondary Indexes Secondary Indexes are always Dense: Sparse secondary indexes make no sense! Secondary indexes are sorted w.r.t. the search key Binary search. The Data File IS NOT sorted w.r.t. the Secondary Index Search Key! More than one data block may be needed for a given search key more disk I/O to answer queries: in general Secondary Indexes are less efficient than Primary Indexes.
26 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (26) Secondary Indexes: An Example The example shows that 3 data blocks (i.e., 3 disk I/O) are needed to retrieve all the tuples with search key using the Secondary Index. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
27 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (27) Indirect Buckets To avoid repeating keys in secondary index, use a level of indirection, called Buckets. The index maintains only one key-pointer pair for each search key : The pointer for goes to a position in the bucket which contains pointers to records with search key till the next position pointed by the index. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
28 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (28) Summary Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
29 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (29) Document Retrieval and Inverted Indexes Problem: Given a set of text documents we need to retrieve that ones where a particular word(s) occurs. Given the success of the Web this has become an urgent database problem. A document is thought as a tuple in the relation Doc(ID,cat,dog, with one attribute for each possible word. ), Each attribute has a Boolean value, eg, the value of cat is TRUE if and only if the word cat appears in the document. An Inverted Index is a form of secondary index with indirect bucket containing as search keys all the attribute names of the Doc relation. Pointers are stored in a bucket file and consider only the TRUE occurrences of a search key.
30 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (30) Inverted Indexes: An Example Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
31 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (31) Additional Information in Buckets Buckets can be extended to include Type (e.g., specify whether the word appears in the title, abstract or body), Position of word, etc. Database System Implementation, H. Garcia-Molina, J. Ullman, and J. Widom, Prentice-Hall, 2000.
32 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (32) Summary Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
33 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (33) B-Trees A B-Tree is a multilevel index with a Tree structure; When used as primary index (i.e., on a sorted file) maintains efficiency against insertion and deletion of records avoiding file reorganization (the main disadvantage of index on sequential file); Also used to index very-large relations when single-level indexes don t fit in main memory; Commercial systems (DB2, ORACLE) implement indexes with B-Trees; In the following we will present the structure of so called stands for Balanced Tree. -Tree the B
34 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (34) B-Trees (cont.) B-tree is usually a 3 levels tree: the root, an intermediate level, the leaves. All the leaves are at the same level Balanced Tree. The size of each node of the B-tree is equal to a disk block. All nodes have the same format: keys and extra pointer. pointers key-pointer pairs plus To keys To keys To keys To keys Example. Let a block be bytes, a search key be an integer of a pointer be bytes. If there is no additional header in the block then largest integer s.t.. bytes, and is the
35 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (35) B-Tree: An Example Data file where search-keys are all the prime numbers from to. All the keys appear once (in case of a dense index), and in sorted order at the leaves. A pointer points to either a file record (in case of a primary index structure) or to a bucket of pointers (in case of a secondary index structure).
36 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (36) Leaves To next leaf in sequence To record with key To record with key To record with key One pointer to next leaf used to chain leaf nodes in search-key order for efficient sequential processing; At least (round down) key-pointer pairs pointing to either records of the data file (as shown in the Figure) or to a bucket of pointers.
37 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (37) Interior Nodes To keys To keys To keys To keys All pointers can be used to point to B-tree nodes of the inferior level; At least key is used. (round up) pointers must be used, and one more pointer than Exception: the root may have only 2 children, then one key and two pointers, regardless of how large is.
38 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (38) B-Trees as Indexes B-Trees are useful for various types of indexes: 1. The search key is a primary or candidate key (i.e., no duplicates) of the data file and the B-Tree is a dense index with a key-pointer pair in a leaf for every record in the data file. The data file may or may not be sorted by primary key (primary or secondary index). 2. The search key is not a key (i.e., duplicate values) and the data file is sorted by this attribute. The B-Tree is a dense primary index with no duplicate key-pointer pairs: just a single entry for each record in the data file with search key, and pointers to the first record with search key. 3. The data file is sorted by search-key, and the B-Tree is a sparse primary index with a key-pointer pair for each data block of the data file. 4. The search key is not a key (i.e., duplicate values) and the data file is NOT sorted. The B-Tree is a secondary index with indirect bucket: No duplicate key-pointer pairs, just a single entry for each record in the data file with a given search key, and pointers to a bucket of pointers.
39 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (39) Lookup in B-trees Problem: Given a B-tree (dense) index, find a record with search key. Recursive search, starting at the root and ending at a leaf: 1. If we are at a leaf then if is among the keys of the leaf follow the associated pointer to the data file, else fail. 2. If we are at an interior node (included the root) with keys then if second child, and so on. then go to the first child, if then go to the, Note: B-Trees are useful for queries in which a range of values are asked for: Range Queries.
40 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (40) B-Tree Updates Insertion and Deletion are more complicated that lookup. It may be necessary to either: 1. Split a node that becomes too large as the result of an insertion; 2. Merge nodes (i.e., combine nodes) if a node becomes too small as the result of a deletion.
41 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (41) B-Tree Insertion Algorithm for inserting a new search key in a B-Tree. 1. Start a search for the key being inserted. If there is room for another key-pointer at the reached leaf, insert there; 2. If there is no room in the leaf, split the leaf in two and divide the keys between the two new nodes (each node is at least half full); 3. The splitting of nodes implies that a new key-pointer pair has to be inserted at the level above. If necessary the parent node will be split and we proceed recursively up the tree (including the root).
42 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (42) B-Tree Insertion: Splitting Leaves Let be a leaf whose capacity is keys, and we need to insert the key-pointer pair. 1. Create a new sibling node, to the right of ; 2. The first. key-pointer pairs remain with, while the other move to 3. The first key of the new node is also inserted at the parent node. Note: At least key-pointer pairs for both of the splitted nodes.
43 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (43) B-Tree Insertion: Splitting Interior Nodes Let be an interior node whose capacity is has been assigned the new pointer inferior level. keys and pointers, and because of a node splitting at the 1. Create a new sibling node, to the right of ; 2. Leave at the first pointers, and move the other to ; 3. The first there are keys stay with go with neither nor, while the last keys move to keys there is one key in the middle (say it, but:. Since ) that doesn t is reachable via the first of s children; is used by the common parent of between those two nodes. and to distinguish the search Note: At least pointers for both of the splitted nodes.
44 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (44) Example: B-Tree Insertion of the key 40 Leaf of insertion
45 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (45) Example: Splitting the Leaf for Inserting Key 40
46 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (46) Example: Splitting Interior Nodes
47 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (47) B-Tree Deletion Algorithm for deleting a search key in a B-Tree. 1. Start a search for the key being deleted; 2. Delete the record from the data file and the key-pointer pair from the leaf of the B-tree; 3. If the lower limit of keys and pointers on a leaf is violated then two cases are possible: (a) Look for an adjacent sibling that is above lower limit and steal a key-pointer pair from that leaf, keeping the order of keys intact. Make sure keys for the parent are adjusted to reflect the new situation. (b) Hard case: no adjacent sibling can provide an extra key. Then there must be two adjacent siblings leaves, one at minimum, one below minimum capacity. Just enough to merge nodes deleting one of them. Keys at the parent should be adjusted, and then delete a key and a pointer. If the parent is below the minimum capacity then we recursively apply the deletion algorithm at the parent.
48 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (48) Example: B-Tree Deletion of the Key 7
49 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (49) Example: B-Tree Deletion of the Key 7 (cont.)
50 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (50) Efficiency of B-Trees B-Trees allow lookup, insertion and deletion of records of very large relations using few disk I/O. When the capacity and merging of nodes is rare. of B-Tree nodes is reasonably large ( ) splitting 3 levels are typical: let a block be of bytes, and a pointer be bytes is occupied midway between the minimum ( each node has pointers Root bytes, a search key be an integer children. Suppose that a node ) and the maximum, then million pointers to data file records. If the root is kept in main memory lookup requires the tree and leaves disk I/O for traversing disk I/O for accessing the record, if also second level in main memory a single disk I/O is sufficient for traversing the tree.
51 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (51) Efficiency of B-Trees (cont.) B-Tree maintains its efficiency against relation updates. A relation is physically stored into a B-Tree. The actual records are stored in the leaf level of the B-Tree. Insertion and deletion can cause either node splitting or merging i.e., no need for overflow blocks.
52 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (52) Efficiency of B-Trees: Relation Storage Example A-218 A-110 A-215 A-305 (A-101,Downtown,500) (A-102,Perryridge,400) (A-218,..) (A-222,..) (A-110,Downtown,600) (A-201,Perryridge,900) (A-305,..) (A-306,..) (A-215,Mianus,700) (A-217,Brighton,750)
53 Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (53) Summary of Lecture II Indexing Indexes on Sequential Files: Dense Vs. Sparse Indexes. Primary Indexes with Duplicate Keys. Secondary Indexes. Document Indexing. B-Tree Indexes.
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