1 / 78

Relational Algebra (end) SQL Queries

Relational Algebra (end) SQL Queries. Lecture #9. Expression Trees. Leaves are operands --- either variables standing for relations or particular, constant relations. Interior nodes are operators, applied to their child or children. Example.

jola
Download Presentation

Relational Algebra (end) SQL 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. Relational Algebra (end)SQL Queries Lecture #9

  2. Expression Trees • Leaves are operands --- either variables standing for relations or particular, constant relations. • Interior nodes are operators, applied to their child or children.

  3. Example • Using the relations Bars(name, addr) and Sells(bar, beer, price), find the names of all the bars that are either on Maple St. or sell Bud for less than $3.

  4. UNION RENAMER(name) PROJECTname PROJECTbar SELECTaddr = “Maple St.” SELECT price<3 AND beer=“Bud” As a Tree: • Using the relations Bars(name, addr) and Sells(bar, beer, price), find the names of all the bars that are either on Maple St. or sell Bud for less than $3. Bars Sells

  5. Example • Using Sells(bar, beer, price), find the bars that sell two different beers at the same price. • Strategy: by renaming, define a copy of Sells, called S(bar, beer1, price). The natural join of Sells and S consists of quadruples (bar, beer, beer1, price) such that the bar sells both beers at this price.

  6. PROJECTbar SELECTbeer != beer1 JOIN RENAMES(bar, beer1, price) The Tree Sells Sells

  7. Complex Queries Product ( pid, name, price, category, maker-cid) Purchase (buyer-ssn, seller-ssn, store, pid) Company (cid, name, stock price, country) Person(ssn, name, phone number, city) Note: • in Purchase: buyer-ssn, seller-ssn are foreign keys in Person, pid is foreign key in Product; • in Product maker-cid is a foreign key in Company Find phone numbers of people who bought gizmos from Fred. Find telephony products that somebody bought

  8. seller-ssn=ssn pid=pid buyer-ssn=ssn Expression Tree Person Purchase Person Product Pname Pssn Ppid sname=fred sname=gizmo

  9. Exercises Product ( pid, name, price, category, maker-cid) Purchase (buyer-ssn, seller-ssn, store, pid) Company (cid, name, stock price, country) Person(ssn, 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.

  10. 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 the book for more detail

  11. Summary of Relational Algebra • Why bother ? Can write any RA expression directly in C++/Java, seems easy. • Two reasons: • Each operator admits sophisticated implementations (think of , sC) • Expressions in relational algebra can be rewritten: optimized

  12. Glimpse Ahead: Efficient Implementations of Operators • s(age >= 30 AND age <= 35)(Employees) • Method 1: scan the file, test each employee • Method 2: use an index on age • Which one is better ? Depends a lot… • Employees Relatives • Iterate over Employees, then over Relatives • Iterate over Relatives, then over Employees • Sort Employees, Relatives, do “merge-join” • “hash-join” • etc

  13. Glimpse Ahead: Optimizations Product ( pid, name, price, category, maker-cid) Purchase (buyer-ssn, seller-ssn, store, pid) Person(ssn, name, phone number, city) • Which is better: sprice>100(Product) (Purchase scity=seaPerson) (sprice>100(Product) Purchase) scity=seaPerson • Depends ! This is the optimizer’s job…

  14. Finally: RA has Limitations ! • Cannot compute “transitive closure” • Find all direct and indirect relatives of Fred • Cannot express in RA !!! Need to write C program

  15. SQLSelect-From-Where StatementsMeaning of QueriesSubqueries

  16. SQL Introduction Standard language for querying and manipulating data Structured Query Language Many standards out there: SQL92, SQL2, SQL3, SQL99 Vendors support various subsets of these, but all of what we’ll be talking about.

  17. Why SQL? • SQL is a very-high-level language, in which the programmer is able to avoid specifying a lot of data-manipulation details that would be necessary in languages like C++. • What makes SQL viable is that its queries are “optimized” quite well, yielding efficient query executions.

  18. Select-From-Where Statements • The principal form of a query is: SELECT desired attributes FROM one or more tables WHERE condition about tuples of the tables

  19. Single-Relation Queries

  20. 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)

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

  22. Result of Query name ‘Bud’ ‘Bud Lite’ ‘Michelob’ The answer is a relation with a single attribute, name, and tuples with the name of each beer by Anheuser-Busch, such as Bud.

  23. Meaning of Single-Relation Query • Begin with the relation in the FROM clause. • Apply the selection indicated by the WHERE clause. • Apply the extended projection indicated by the SELECT clause.

  24. Operational Semantics • To implement this algorithm think of a tuple variable ranging over each tuple of the relation mentioned in FROM. • Check if the “current” tuple satisfies the WHERE clause. • If so, compute the attributes or expressions of the SELECT clause using the components of this tuple.

  25. * In SELECT clauses • When there is one relation in the FROM clause, * in the SELECT clause stands for “all attributes of this relation.” • Example using Beers(name, manf): SELECT * FROM Beers WHERE manf = ‘Anheuser-Busch’;

  26. Result of Query: name manf ‘Bud’ ‘Anheuser-Busch’ ‘Bud Lite’ ‘Anheuser-Busch’ ‘Michelob’ ‘Anheuser-Busch’ Now, the result has each of the attributes of Beers.

  27. Another Example Company(sticker, name, country, stockPrice) Find all US companies whose stock is > 50: Output schema: R(sticker, name, country, stockPrice) SELECT *FROM CompanyWHERE country=“USA” AND stockPrice > 50

  28. Renaming Attributes • If you want the result to have different attribute names, use “AS <new name>” to rename an attribute. • Example based on Beers(name, manf): SELECT name AS beer, manf FROM Beers WHERE manf = ‘Anheuser-Busch’

  29. Result of Query: beer manf ‘Bud’ ‘Anheuser-Busch’ ‘Bud Lite’ ‘Anheuser-Busch’ ‘Michelob’ ‘Anheuser-Busch’

  30. Expressions in SELECT Clauses • Any expression that makes sense can appear as an element of a SELECT clause. • Example: from Sells(bar, beer, price): SELECT bar, beer, price * 120 AS priceInYen FROM Sells;

  31. Result of Query bar beer priceInYen Joe’s Bud 300 Sue’s Miller 360 … … …

  32. Another Example: Constant Expressions • From Likes(drinker, beer): SELECT drinker, ‘likes Bud’ AS whoLikesBud FROM Likes WHERE beer = ‘Bud’;

  33. Result of Query drinker whoLikesBud ‘Sally’ ‘likes Bud’ ‘Fred’ ‘likes Bud’ … …

  34. Complex Conditions in WHERE Clause • From Sells(bar, beer, price), find the price Joe’s Bar charges for Bud: SELECT price FROM Sells WHERE bar = ‘Joe’’s Bar’ AND beer = ‘Bud’;

  35. Selections What you can use in WHERE: attribute names of the relation(s) used in the FROM. comparison operators: =, <>, <, >, <=, >= apply arithmetic operations: stockprice*2 operations on strings (e.g., “||” for concatenation). Lexicographic order on strings. Pattern matching: s LIKE p Special stuff for comparing dates and times.

  36. Important Points • Two single quotes inside a string represent the single-quote (apostrophe). • Conditions in the WHERE clause can use AND, OR, NOT, and parentheses in the usual way boolean conditions are built. • SQL is case-insensitive. In general, upper and lower case characters are the same, except inside quoted strings.

  37. Patterns • WHERE clauses can have conditions in which a string is compared with a pattern, to see if it matches. • General form: <Attribute> LIKE <pattern> or <Attribute> NOT LIKE <pattern> • Pattern is a quoted string with % = “any string”; _ = “any character.”

  38. Example • From Drinkers(name, addr, phone) find the drinkers with exchange 555: SELECT name FROM Drinkers WHERE phone LIKE ‘%555-_ _ _ _’;

  39. The LIKE operator • s LIKE p: pattern matching on strings • p may contain two special symbols: • % = any sequence of characters • _ = any single character Company(sticker, name, address, country, stockPrice) Find all US companies whose address contains “Mountain”: SELECT *FROM CompanyWHERE country=“USA” ANDaddress LIKE “%Mountain%”

  40. Motivating Example for Next Few Slides • From the following Sells relation: bar beer price .... .... ... SELECT bar FROM Sells WHERE price < 2.00 OR price >= 2.00;

  41. Null Values

  42. NULL Values • Tuples in SQL relations can have NULL as a value for one or more components. • Meaning depends on context. Two common cases: • Missing value : e.g., we know Joe’s Bar has some address, but we don’t know what it is. • Inapplicable : e.g., the value of attribute spouse for an unmarried person.

  43. Comparing NULL’s to Values • The logic of conditions in SQL is really 3-valued logic: TRUE, FALSE, UNKNOWN. • When any value is compared with NULL, the truth value is UNKNOWN. • But a query only produces a tuple in the answer if its truth value for the WHERE clause is TRUE (not FALSE or UNKNOWN).

  44. Three-Valued Logic • To understand how AND, OR, and NOT work in 3-valued logic, think of TRUE = 1, FALSE = 0, and UNKNOWN = ½. • AND = MIN; OR = MAX, NOT(x) = 1-x. • Example: TRUE AND (FALSE OR NOT(UNKNOWN)) = MIN(1, MAX(0, (1 - ½ ))) = MIN(1, MAX(0, ½ ) = MIN(1, ½ ) = ½.

  45. UNKNOWN UNKNOWN UNKNOWN Surprising Example • From the following Sells relation: bar beer price Joe’s Bar Bud NULL SELECT bar FROM Sells WHERE price < 2.00 OR price >= 2.00;

  46. Reason: 2-Valued Laws != 3-Valued Laws • Some common laws, like the commutativity of AND, hold in 3-valued logic. • But others do not; example: the “law of excluded middle,” p OR NOT p = TRUE. • When p = UNKNOWN, the left side is MAX( ½, (1 – ½ )) = ½ != 1.

  47. Null Values • If x=Null then 4*(3-x)/7 is still NULL • If x=Null then x=“Joe” is UNKNOWN • Three boolean values: • FALSE = 0 • UNKNOWN = 0.5 • TRUE = 1

  48. Null Value Logic • C1 AND C2 = min(C1, C2) • C1 OR C2 = max(C1, C2) • NOT C1 = 1 – C1 SELECT * FROM Person WHERE (age < 25) AND (height > 6 OR weight > 190) Semantics of SQL: include only tuples that yield TRUE

  49. Null Values Unexpected behavior: SELECT * FROM Person WHERE age < 25 OR age >= 25 Some Persons are not included !

  50. 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

More Related