1 / 22

Correlated Queries

Correlated Queries. A nested query that requires the subquery to be evaluated many times; once for each value in the outer query. Example: Find movies whose title appears more than once. SELECT title FROM Movie AS Old WHERE year < ANY ( SELECT year

whitley
Télécharger la présentation

Correlated Queries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Correlated Queries A nested query that requires the subquery to be evaluated many times; once for each value in the outer query Example: Find movies whose title appears more than once. SELECT title FROM Movie AS Old WHERE year < ANY (SELECT year FROM Movie WHERE title = Old.title); Movie (title, year, director, length) Movie titles are not unique (titles may reappear in a later year). Note scope of variables

  2. Removing Duplicates SELECTDISTINCT Company.name FROM Company, Product WHERE Company.name=maker AND (Product.name,price) IN (SELECT product, price) FROM Purchase WHERE buyer = “Joe Blow”);

  3. Conserving Duplicates The UNION, INTERSECTION and EXCEPT operators operate as sets, not bags. (SELECT name FROM Person WHERE City=“Seattle”) UNION ALL (SELECT name FROM Person, Purchase WHERE buyer=name AND store=“The Bon”)

  4. Aggregation SELECT Sum(price) FROM Product WHERE manufacturer=“Toyota” SQL supports several aggregation operations: SUM, MIN, MAX, AVG, COUNT Except COUNT, all aggregations apply to a single attribute SELECT Count(*) FROM Purchase

  5. Grouping and Aggregation Usually, we want aggregations on certain parts of the relation. Find how much we sold of every product SELECT product, Sum(price) FROM Product, Purchase WHERE Product.name = Purchase.product GROUPBY Product.name 1. Compute the relation (I.e., the FROM and WHERE). 2. Group by the attributes in the GROUPBY 3. Select one tuple for every group (and apply aggregation) SELECT can have (1) grouped attributes or (2) aggregates.

  6. HAVING Clause The HAVING clause contains conditions on aggregates. Same query, except that we consider only products that had at least 100 buyers. SELECT product, Sum(price) FROM Product, Purchase WHERE Product.name = Purchase.product GROUPBY Product.name HAVING Count(buyer) > 100

  7. Modifying the Database We have 3 kinds of modifications: insertion, deletion, update. Insertion: general form -- INSERT INTO R(A1,…., An) VALUES (v1,…., vn) If we don’t provide all the attributes of R, they will be filled with NULL. We can drop the attribute names if we’re providing all of them in order. Insert a new purchase to the database: INSERT INTO Purchase(buyer, seller, product, store) VALUES (Joe, Fred, wakeup-clock-espresso-machine, “The Sharper Image”)

  8. More Interesting Insertions INSERT INTO Product(name) SELECT DISTINCT product FROM Purchase WHERE product NOT IN (SELECT name FROM Product) The query replaces the VALUES keyword. Note the order of querying and inserting.

  9. Deletions General Form: DELETE FROM RWHERE <condition> Example: DELETE FROM PURCHASE WHERE seller = “Joe” AND product = “Brooklyn Bridge” Factoid about SQL: there is no way to delete only a single occurrence of a tuple that appears twice in a relation.

  10. Updates General Form: UPDATE R <new assignment> WHERE <condition> Example: UPDATE PRODUCT SET price = price/2 WHERE Product.name IN (SELECT product FROM Purchase WHERE Date = today);

  11. Data Definition in SQL • So far we’ve seen SQL operations on the data. • Data definition: defining the schema. • Create tables • Delete tables • Modify table schema • But first: • Define data types. • Finally: define indexes.

  12. Data Types in SQL • Character strings- fixed or varying length - CHAR(n) • Bit strings- fixed or varying length - BIT(n) or BIT VARYING(n) • Integer and short integers- INTEGER or INT and SHORTINT • Floating point - FLOAT, REAL, and DOUBLE PRECISION • Dates and times - DATE and TIME • Declare a data type: • name VARCHAR(30)

  13. Creating Tables CREATE TABLE Person( name VARCHAR(30), social-security-number INTEGER, age SHORTINT, city VARCHAR(30), gender BIT(1), Birthdate DATE );

  14. Deleting or Modifying a Table Deleting:DROP Person; Altering: ALTER TABLE Person ADD phone CHAR(16); ALTER TABLE Person DROP age;

  15. Default Values The default of defaults: NULL Specifying default values: CREATE TABLE Person( name VARCHAR(30) DEFAULT ‘Bob’, social-security-number INTEGER, age SHORTINT DEFAULT 100, city VARCHAR(30) DEFAULT ‘Seattle’, gender CHAR(1) DEFAULT ‘?’, Birthdate DATE

  16. Domains Domains will be used in table declarations. Domains are used to simplify writing and to enforce logical types Example: CREATE DOMAIN PersonName AS VARCHAR(30) Now use in Person: name PersonName

  17. Indexes REALLY important to speed up query processing time. Take our Person relation. An index on “social-security-number” enables us to fetch a tuple for a given ssn very efficiently (not have to scan the whole relation). The problem of deciding which indexes to put on the relations is very hard! (it’s called: physical database design).

  18. Creating Indexes CREATE INDEX ssnIndex ON Person(social-security-number) Indexes can be created on more than one attribute: CREATE INDEX doubleindex ON Person (name, social-security-number) Why not create indexes on everything?

  19. Defining Views Views are relations, except that they are not physically stored. They are used mostly in order to simplify complex queries and to define conceptually different views of the database to different classes of users. View: purchases of telephony products: CREATE VIEW telephony-purchases AS SELECT product, buyer, seller, store FROM Purchase, Product WHERE Purchase.product = Product.name AND Product.category = “telephony”

  20. A Different View CREATE VIEW Seattle-view AS SELECT buyer, seller, product, store FROM Person, Purchase WHERE Person.city = “Seattle” AND Person.name = Purchase.buyer We can later use the views: SELECT name, store FROM Seattle-view, Product WHERE Seattle-view.product = Product.name AND Product.category = “shoes” What’s really happening when we query a view??

  21. Updating Views How can I insert a tuple into a table that doesn’t exist? CREATE VIEW bon-purchase AS SELECT store, seller, product FROM Purchase WHERE store = “The Bon Marche” If we make the following insertion: INSERT INTO bon-purchase VALUES (“the Bon Marche”, Joe, “Denby Mug”) We can simply add a tuple (“the Bon Marche”, Joe, NULL, “Denby Mug”) to relation Purchase.

  22. Non-Updatable Views CREATE VIEW Seattle-view AS SELECT seller, product, store FROM Person, Purchase WHERE Person.city = “Seattle” AND Person.name = Purchase.buyer How can we add the following tuple to the view? (Joe, “Shoe Model 12345”, “Nine West”)

More Related