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SQL (cont.)

SQL (cont.). Lecture #11. Single-Relation Queries. Our Running Example. Most of our SQL queries will be based on the following database schema. Underline indicates key attributes. Beers( name , manf) Bars( name , addr, license) Drinkers( name , addr, phone)

JasminFlorian
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SQL (cont.)

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  1. SQL (cont.) Lecture #11

  2. Single-Relation Queries

  3. Our Running Example • Most of our SQL queries will be based on the following database schema. • Underline indicates key attributes. Beers(name, manf) Bars(name, addr, license) Drinkers(name, addr, phone) Likes(drinker, beer) Sells(bar, beer, price) Frequents(drinker, bar)

  4. Example • Using Beers(name, manf), what beers are made by Anheuser-Busch? SELECT name FROM Beers WHERE manf = ‘Anheuser-Busch’;

  5. Null Values

  6. Testing for Null Can test for NULL explicitly: • x IS NULL • x IS NOT NULL SELECT * FROM Person WHERE age < 25 OR age >= 25 OR age IS NULL Now it includes all Persons

  7. Multi-Relation Queries

  8. Multirelation Queries • Interesting queries often combine data from more than one relation. • We can address several relations in one query by listing them all in the FROM clause. • Distinguish attributes of the same name by “<relation>.<attribute>”

  9. Example • Using relations Likes(drinker, beer) and Frequents(drinker, bar), find the beers liked by at least one person who frequents Joe’s Bar. SELECT beer FROM Likes, Frequents WHERE bar = ‘Joe’’s Bar’ AND Frequents.drinker = Likes.drinker;

  10. Another Example Product (pname, price, category, maker) Purchase (buyer, seller, store, product) Company (cname, stockPrice, country) Person(pname, phoneNumber, city) Find names of people living in Champaign that bought gizmo products, and the names of the stores they bought from SELECT pname, storeFROM Person, PurchaseWHERE pname=buyer AND city=“Champaign” AND product=“gizmo”

  11. Disambiguating Attributes Find names of people buying telephony products: Product (name, price, category, maker) Purchase (buyer, seller, store, product) Person(name, phoneNumber, city) SELECT Person.name FROM Person, Purchase, Product WHERE Person.name=Purchase.buyer AND Product=Product.name AND Product.category=“telephony”

  12. Formal Semantics • Almost the same as for single-relation queries: • Start with the product of all the relations in the FROM clause. • Apply the selection condition from the WHERE clause. • Project onto the list of attributes and expressions in the SELECT clause.

  13. Operational Semantics • Imagine one tuple-variable for each relation in the FROM clause. • These tuple-variables visit each combination of tuples, one from each relation. • If the tuple-variables are pointing to tuples that satisfy the WHERE clause, send these tuples to the SELECT clause.

  14. to output check for Joe check these are equal Example drinker bar drinker beer tv1 tv2 Sally Bud Sally Joe’s Likes Frequents

  15. Explicit Tuple-Variables • Sometimes, a query needs to use two copies of the same relation. • Distinguish copies by following the relation name by the name of a tuple-variable, in the FROM clause. • It’s always an option to rename relations this way, even when not essential.

  16. Example • From Beers(name, manf), find all pairs of beers by the same manufacturer. • Do not produce pairs like (Bud, Bud). • Produce pairs in alphabetic order, e.g. (Bud, Miller), not (Miller, Bud). SELECT b1.name, b2.name FROM Beers b1, Beers b2 WHERE b1.manf = b2.manf AND b1.name < b2.name;

  17. Tuple Variables Find pairs of companies making products in the same category SELECT product1.maker, product2.maker FROM Product AS product1, Product AS product2 WHERE product1.category=product2.category AND product1.maker <> product2.maker Product ( name, price, category, maker)

  18. Tuple Variables Tuple variables introduced automatically by the system: Product ( name, price, category, maker) Becomes: Doesn’t work when Product occurs more than once: In that case the user needs to define variables explicitly. SELECT name FROM Product WHERE price > 100 SELECT Product.name FROM Product AS Product WHERE Product.price > 100

  19. Meaning (Semantics) of SQL Queries SELECT a1, a2, …, ak FROM R1 AS x1, R2 AS x2, …, Rn AS xn WHERE Conditions 1. Nested loops: Answer = {} for x1 in R1 do for x2 in R2 do ….. for xn in Rn do if Conditions then Answer = Answer U {(a1,…,ak) return Answer

  20. Meaning (Semantics) of SQL Queries SELECT a1, a2, …, ak FROM R1 AS x1, R2 AS x2, …, Rn AS xn WHERE Conditions 2. Parallel assignment Doesn’t impose any order ! Answer = {} for all assignments x1 in R1, …, xn in Rn do if Conditions then Answer = Answer U {(a1,…,ak)} return Answer

  21. Meaning (Semantics) of SQL Queries SELECT a1, a2, …, ak FROM R1 AS x1, R2 AS x2, …, Rn AS xn WHERE Conditions 3. Translation to Relational algebra: • a1,…,ak ( sConditions (R1 x R2 x … x Rn)) Select-From-Where queries are precisely Select-Project-Join

  22. Exercises Product ( pname, price, category, maker) Purchase (buyer, seller, store, product) Company (cname, stock price, country) Person( per-name, phone number, city) Ex #1: Find people who bought telephony products. Ex #2: Find names of people who bought American products Ex #3: Find names of people who bought American products and did not buy French products Ex #4: Find names of people who bought American products and they live in Champaign. Ex #5: Find people who bought stuff from Joe or bought products from a company whose stock prices is more than $50.

  23. SubqueriesBoolean Operators IN, EXISTS, ANY, ALL

  24. Subqueries • A parenthesized SELECT-FROM-WHERE statement (subquery) can be used as a value in a number of places, including FROM and WHERE clauses. • Example: in place of a relation in the FROM clause, we can place another query, and then query its result. • Better use a tuple-variable to name tuples of the result.

  25. Subqueries That Return One Tuple • If a subquery is guaranteed to produce one tuple, then the subquery can be used as a value. • Usually, the tuple has one component. • Also typically, a single tuple is guaranteed by keyness of attributes. • A run-time error occurs if there is no tuple or more than one tuple.

  26. Example • From Sells(bar, beer, price), find the bars that serve Miller for the same price Joe charges for Bud. • Two queries would surely work: • Find the price Joe charges for Bud. • Find the bars that serve Miller at that price.

  27. Query + Subquery Solution SELECT bar FROM Sells WHERE beer = ‘Miller’ AND price = (SELECT price FROM Sells WHERE bar = ‘Joe’’s Bar’ AND beer = ‘Bud’); The price at which Joe sells Bud

  28. The IN Operator • <tuple> IN <relation> is true if and only if the tuple is a member of the relation. • <tuple> NOT IN <relation> means the opposite. • IN-expressions can appear in WHERE clauses. • The <relation> is often a subquery.

  29. Example • From Beers(name, manf) and Likes(drinker, beer), find the name and manufacturer of each beer that Fred likes. SELECT * FROM Beers WHERE name IN (SELECT beer FROM Likes WHERE drinker = ‘Fred’); The set of beers Fred likes

  30. The Exists Operator • EXISTS( <relation> ) is true if and only if the <relation> is not empty. • Being a boolean-valued operator, EXISTS can appear in WHERE clauses. • Example: From Beers(name, manf), find those beers that are the unique beer by their manufacturer.

  31. Notice the SQL “not equals” operator Example Query with EXISTS SELECT name FROM Beers b1 WHERE NOT EXISTS( SELECT * FROM Beers WHERE manf = b1.manf AND name <> b1.name); Notice scope rule: manf refers to closest nested FROM with a relation having that attribute. Set of beers with the same manf as b1, but not the same beer

  32. The Operator ANY • x = ANY( <relation> ) is a boolean condition meaning that x equals at least one tuple in the relation. • Similarly, = can be replaced by any of the comparison operators. • Example: x >= ANY( <relation> ) means x is not smaller than all tuples in the relation. • Note tuples must have one component only.

  33. The Operator ALL • Similarly, x <> ALL( <relation> ) is true if and only if for every tuple t in the relation, x is not equal to t. • That is, x is not a member of the relation. • The <> can be replaced by any comparison operator. • Example: x >= ALL( <relation> ) means there is no tuple larger than x in the relation.

  34. Example • From Sells(bar, beer, price), find the beer(s) sold for the highest price. SELECT beer FROM Sells WHERE price >= ALL( SELECT price FROM Sells); price from the outer Sells must not be less than any price.

  35. More SQLRelations as BagsGrouping and AggregationDatabase Modification (next lecture)

  36. Relational Algebra: Operations on Bags (and why we care) • Union: {a,b,b,c} U {a,b,b,b,e,f,f} = {a,a,b,b,b,b,b,c,e,f,f} • add the number of occurrences • Difference: {a,b,b,b,c,c} – {b,c,c,c,d} = {a,b,b,d} • subtract the number of occurrences • Intersection: {a,b,b,b,c,c} {b,b,c,c,c,c,d} = {b,b,c,c} • minimum of the two numbers of occurrences • Selection: preserve the number of occurrences • Projection: preserve the number of occurrences (no duplicate elimination) • Cartesian product, join: no duplicate elimination Read Section 5.3 of the book for more detail

  37. Bag Semantics for SFW Queries • The SELECT-FROM-WHERE statement uses bag semantics • Selection: preserve the number of occurrences • Projection: preserve the number of occurrences (no duplicate elimination) • Cartesian product, join: no duplicate elimination

  38. Union, Intersection, and Difference • Union, intersection, and difference of relations are expressed by the following forms, each involving subqueries: • ( subquery ) UNION ( subquery ) • ( subquery ) INTERSECT ( subquery ) • ( subquery ) EXCEPT ( subquery )

  39. Example • From relations Likes(drinker, beer), Sells(bar, beer, price) and Frequents(drinker, bar), find the drinkers and beers such that: • The drinker likes the beer, and • The drinker frequents at least one bar that sells the beer.

  40. The drinker frequents a bar that sells the beer. Solution (SELECT * FROM Likes) INTERSECT (SELECT drinker, beer FROM Sells, Frequents WHERE Frequents.bar = Sells.bar );

  41. Bag Semantics for Set Operations in SQL • Although the SELECT-FROM-WHERE statement uses bag semantics, the default for union, intersection, and difference is set semantics. • That is, duplicates are eliminated as the operation is applied.

  42. Motivation: Efficiency • When doing projection in relational algebra, it is easier to avoid eliminating duplicates. • Just work tuple-at-a-time. • When doing intersection or difference, it is most efficient to sort the relations first. • At that point you may as well eliminate the duplicates anyway.

  43. Controlling Duplicate Elimination • Force the result to be a set by SELECT DISTINCT . . . • Force the result to be a bag (i.e., don’t eliminate duplicates) by ALL, as in . . . UNION ALL . . .

  44. Example: DISTINCT • From Sells(bar, beer, price), find all the different prices charged for beers: SELECT DISTINCT price FROM Sells; • Notice that without DISTINCT, each price would be listed as many times as there were bar/beer pairs at that price.

  45. Example: ALL • Using relations Frequents(drinker, bar) and Likes(drinker, beer): (SELECT drinker FROM Frequents) EXCEPT ALL (SELECT drinker FROM Likes); • Lists drinkers who frequent more bars than they like beers, and does so as many times as the difference of those counts.

  46. Join Expressions • SQL provides a number of expression forms that act like varieties of join in relational algebra. • But using bag semantics, not set semantics. • These expressions can be stand-alone queries or used in place of relations in a FROM clause.

  47. Products and Natural Joins • Natural join is obtained by: R NATURAL JOIN S; • Product is obtained by: R CROSS JOIN S; • Example: Likes NATURAL JOIN Serves; • Relations can be parenthesized subexpressions, as well.

  48. Theta Join • R JOIN S ON <condition> is a theta-join, using <condition> for selection. • Example: using Drinkers(name, addr) and Frequents(drinker, bar): Drinkers JOIN Frequents ON name = drinker; gives us all (d, a, d, b) quadruples such that drinker d lives at address a and frequents bar b.

  49. Motivation for Outerjoins Explicit joins in SQL: Product(name, category) Purchase(prodName, store) SELECT Product.name, Purchase.store FROM Product JOIN Purchase ON Product.name = Purchase.prodName Same as: SELECT Product.name, Purchase.store FROM Product, Purchase WHERE Product.name = Purchase.prodName But Products that never sold will be lost !

  50. Null Values and Outerjoins Left outer joins in SQL: Product(name, category) Purchase(prodName, store) SELECT Product.name, Purchase.store FROM Product LEFT OUTER JOIN Purchase ON Product.name = Purchase.prodName

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