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CMPT 354 Views and Indexes Spring 2012 Instructor: Hassan Khosravi.

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Presentation on theme: "CMPT 354 Views and Indexes Spring 2012 Instructor: Hassan Khosravi."— Presentation transcript:

1 CMPT 354 Views and Indexes Spring 2012 Instructor: Hassan Khosravi

2 1.2 Three level vision of a database

3 1.3 What are views Relations that are defined with a create table statement exist in the physical layer and  do not change unless explicitly told so Virtual views do not physically exist, they are defined by expression over the tables.  Can be queries (most of the time) as if they were tables. Why use views? Hide some data from users Make some queries easier Modularity of database  When not specified exactly based on tables.

4 1.4 Defining and using Views Create View As View definition is defined in SQL From now on we can use the view almost as if it is just a normal table View V (R 1,…R n ) query Q involving V Conceptually  V (R 1,…R n ) is used to evaluate Q In reality  The evaluation is performed over R 1,…R n

5 1.5 Example of views

6 1.6 Views Conceptually Reality Query optimizer Users do not need to worry about any of this

7 1.7 Views on Views

8 1.8 Views make life easy

9 1.9 Deleting views Drop View Dropping a view does not affect any tuples of the in the underlying relation. Dropping a table would remove all of the tuples of the table and views that use that table. What would happen if CSaccept is dropped?  Some DBMS would not allow it because it has dependencies  Some would delete it and the be unable to answer queries on CSberk

10 1.10 Modifying views Can views be modified? Doesn’t make sense to modify a view since it doesn’t physically exist Should be possible since many users can only observe views and do not have access to tables, so they should be able to alter tables using views. Solution Modifications to the view rewritten to modify base tables.

11 1.11 Example R(A,B) which has (1, 2) V is just the A attribute of V = ∏ A (R) n Insert (3) into A l What would be insert into R(A,B)?  (3,1)?  (3,2)?  (3,null)? n R(N) n V = avg(N) Update

12 1.12 Example

13 1.13 Example

14 1.14 Approaches to View Modification Rewriting process specified explicitly by view creator (user) + Can handle all modification - No guarantee of correctness - More work Restrict views and modifications so that translation to base tables can be done automatically, correctly, and unambiguously + No User intervention - Restrictions are significant

15 1.15 Rewriting process specified explicitly by view creator Instead of Triggers are mainly used We can write a trigger to perform deletion on this view

16 1.16 This will produce an error without having a trigger for it There is nothing stopping you from making a mistake in your trigger.

17 1.17 Restricting modifications in Views Showing students that are either in CS or EE Inserting new students

18 1.18 Automatic View Modification Restrict views and modifications so that translation to base tables can be done automatically, correctly, and unambiguously The view should be on one table and not join of tables Attributes that are not in the view can be null or have default values Sub-queries must not refer to T You cannot have group by or aggregation MYSQL supports automatic updates on views Postgress and SQLite do not support automatic updates on views and triggers should be used

19 1.19 Deletion Example The deletion will be done in the apply table and would show in the CSaccept view

20 1.20 Insertion Example You should not be able to add to view tuples that do not belong to the view

21 1.21 Updatable Views -- Aggregation What would this deletion mean? The system disallows this deletion.

22 1.22 Updatable views -- Sub-queries This is disallowed since it is vague It would have surprising effects on the base table

23 1.23 Updatable views-- Joins What would insertion or deletion to this table mean? Updates the apply table but not the student table so the first row would not match any more.

24 1.24 Indexes in SQL Primary mechanism to get improved performance on database Data structure stored in the database Implementation issues Full scan vs. immediate tuple location

25 1.25

26 1.26 Implementation of Indexes Underlying data structure Balance Trees  A = v  A < v  A > v  Logarithm time Hash tables  Only A = v  If designed properly, constant time

27 1.27

28 1.28

29 1.29

30 1.30 Downside of Indexes Extra space Local hard space Over head in Index creation Index maintenance

31 1.31 Calculating the best indexes to create Typically a relation is stored over many disk blocks Principle cost of a query or modification is often the number of disks that have to be brought into the main memory Indexes store where tuples are stored, so we know which block to restore instead of full scan of the table However indexes themselves have to be stored and modified Modification in the actual relation needs one page access to modify the relation and one page access to modify the index Assuming we have history of the previous queries and modifications how can we decide what to have indexes on?

32 1.32 Example StarIn(movieTitle, movieYear, starName) Three database operations are performed (Q1)Look for title and year of movies in which a given star appeared (p1) (Q2) Look for stars that appear in a given movie (p2) (I) Insert a new tuple into the starIn table (1-p1-p2) StarIn occupies 10 pages On average a star has appeared in 3 movies On average a movie has 3 stars Movies of a star and stars of a movie are speared over all pages One disk access is needed to read a page in the index Insertion requires 1 access to find page to write to and 1 to actually write it ActionNo IndexStar IndexMovie Index Both Indexes Q Q I2446 Average 2 + 8p 1 +8p p p p 1 - 2p 2

33 1.33 ActionNo IndexStar IndexMovie Index Both Indexes Q Q I2446 Average 2 + 8p 1 +8p p p p 1 - 2p 2 P1=0.33 P2= P1=0.1 p2= P1=0.1 p2=

34 1.34 Automatic Selection of Indexes to create

35 1.35 Materialized views Periodic Maintenance of Materialized views Common for databases to serve two purposes  Record current inventory  Analysts to study patterns Analyst’s queries involve many aggregation and joins Not as sensitive to modifications They are updated (reconstructed) periodically  Once a day or week

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