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Introduction to SQL

Select-From-Where StatementsSubqueries

Grouping and AggregationSource: slides by Jeffrey Ullman

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Why SQL?

SQL is a very-high-level language. Say “what to do” rather than “how to do it.” Avoid a lot of data-manipulation details

needed in procedural languages like C++ or Java.

Database management system figures out “best” way to execute query. Called “query optimization.”

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Select-From-Where Statements

SELECT desired attributesFROM one or more tablesWHERE condition about tuples of

the tables

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Our Running Example

All our SQL queries will be based on the following database schema. Underline indicates key attributes.

Candies(name, manf)Stores(name, addr, license)Consumers(name, addr, phone)Likes(consumer, candy)Sells(store, candy, price)Frequents(consumer, store)

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Example

Using Candies(name, manf), what candies are made by Hershey?

SELECT name

FROM Candies

WHERE manf = ’Hershey’;

Notice SQL uses single-quotes for strings.SQL is case-insensitive, except inside strings.

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Result of Query

nameTwizzlerKitkatAlmondJoy . . .

The answer is a relation with a single attribute,name, and tuples with the name of each candyby Hershey, such as Twizzler.

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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.

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Operational Semantics

To implement this algorithm think of a tuple variable (tv) 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.

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Operational Semantics

Check ifHershey

name manf

Twizzler Hershey

tv

Include tv.name in the result

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* 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 Candies(name, manf):SELECT *

FROM Candies

WHERE manf = ’Hershey’;

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Result of Query:

name manfTwizzler HersheyKitkat HersheyAlmondJoy Hershey . . . . . .

Now, the result has each of the attributesof Candies.

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Renaming Attributes

If you want the result to have different attribute names, use “AS <new name>” to rename an attribute.

Example based on Candies(name, manf):

SELECT name AS candy, manf

FROM Candies

WHERE manf = ’Hershey’

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Result of Query:

candy manfTwizzler HersheyKitkat HersheyAlmondJoy Hershey . . . . . .

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Expressions in SELECT Clauses

Any expression that makes sense can appear as an element of a SELECT clause.

Example: from Sells(store, candy, price):SELECT store, candy,

price * 114 AS priceInYen

FROM Sells;

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Result of Query

store candy priceInYen7-11 Twizzler 285Kroger Snickers 342 … … …

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Another Example: Constant Expressions

From Likes(consumer, candy) :

SELECT consumer,’likes Kitkats’ AS whoLikesKitkats

FROM Likes

WHERE candy = ’Kitkat’;

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Result of Query

consumerwhoLikesKitkats

Sally likes KitkatsFred likes Kitkats … …

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Complex Conditions in WHERE Clause

From Sells(store, candy, price), find the price that 7-11 charges for Twizzlers:

SELECT price

FROM Sells

WHERE store = ’7-11’ AND

candy = ’Twizzler’;

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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.”

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Example

From Consumers(name, addr, phone) find the consumers with exchange 555:

SELECT name

FROM Consumers

WHERE phone LIKE ’%555-_ _ _ _’;

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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 7-11 has some

address, but we don’t know what it is. Inapplicable : e.g., the value of attribute

spouse for an unmarried person.

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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).

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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 = UNKNOWN.

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Surprising Example

From the following Sells relation:store candy price7-11 Twizzler NULL

SELECT storeFROM SellsWHERE price < 2.00 OR price >= 2.00; UNKNOWN UNKNOWN

UNKNOWN

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Reason: 2-Valued Laws != 3-Valued Laws

Some common laws, like commutativity of AND, hold in 3-valued logic.

But not others, e.g., the “law of the excluded middle”: p OR NOT p = TRUE. When p = UNKNOWN, the left side is

MAX( ½, (1 – ½ )) = ½ != 1.

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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>”

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Example

Using relations Likes(consumer, candy) and Frequents(consumer, store), find the candies liked by at least one person who frequents 7-11.SELECT candyFROM Likes, FrequentsWHERE store = ’7-11’ AND

Frequents.consumer = Likes.consumer;

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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.

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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.

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Example

consumer store consumer candy

tv1 tv2Sally Twizzler

Sally 7-11

Likes Frequents to outputcheck these

are equal

checkfor 7-11

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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.

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Example

From Candies(name, manf), find all pairs of candies by the same manufacturer. Do not produce pairs like (Twizzler, Twizzler). Produce pairs in alphabetic order, e.g. (Kitkat,

Twizzler), not (Twizzler, Kitkat).

SELECT c1.name, c2.name

FROM Candies c1, Candies c2

WHERE c1.manf = c2.manf AND

c1.name < c2.name;

tuplevariables

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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. Can use a tuple-variable to name tuples

of the result.

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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. A run-time error occurs if there is no

tuple or more than one tuple.

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Example From Sells(store, candy, price), find

the stores that sell Kitkats for the same price 7-11 charges for Twizzlers.

Two queries would surely work: Find the price 7-11 charges for Twizzlers. Find the stores that sell Kitkats at that

price.

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Query + Subquery Solution

SELECT storeFROM SellsWHERE candy = ’Kitkat’ AND

price = (SELECT price FROM Sells WHERE store= ’7-11’

AND candy = ’Twizzler’);

The price atwhich 7-11sells Twizzlers

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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.

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Example From Candies(name, manf) and

Likes(consumer, candy), find the name and manufacturer of each candy that Fred likes. SELECT * FROM Candies WHERE name IN (SELECT candy

FROM Likes WHERE consumer =

’Fred’);

The set ofcandies Fredlikes

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The Exists Operator

EXISTS( <relation> ) is true if and only if the <relation> is not empty.

Example: From Candies(name, manf) , find those candies that are the unique candy by their manufacturer.

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Example Query with EXISTS

SELECT nameFROM Candies c1WHERE NOT EXISTS(

SELECT *FROM CandiesWHERE manf = c1.manf AND

name <> c1.name);

Set ofcandieswith thesamemanf asc1, butnot thesamecandy

Notice scope rule: manf refersto closest nested FROM witha relation having that attribute.

Notice theSQL “notequals”operator

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The Operator ANY

x = ANY( <relation> ) is a boolean condition true if 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 the smallest tuple in the relation. Note tuples must have one component only.

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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.

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Example

From Sells(store, candy, price), find the candies sold for the highest price.

SELECT candyFROM SellsWHERE price >= ALL(

SELECT priceFROM Sells);

price from the outerSells must not beless than any price.

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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 )

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Example

From relations Likes(consumer, candy), Sells(store, candy, price), and Frequents(consumer, store), find the consumers and candies such that:

The consumer likes the candy, and The consumer frequents at least

one store that sells the candy.

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Solution

(SELECT * FROM Likes)INTERSECT

(SELECT consumer, candy FROM Sells, Frequents WHERE Frequents.store =

Sells.store);

The consumer frequentsa store that sells thecandy.

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Bag Semantics

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.

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Motivation: Efficiency

When doing projection, it is easier to avoid eliminating duplicates. Just work tuple-at-a-time.

For intersection or difference, it is most efficient to sort the relations first. At that point you may as well

eliminate the duplicates anyway.

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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 . . .

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Example: DISTINCT

From Sells(store, candy, price), find all the different prices charged for candies:

SELECT DISTINCT priceFROM Sells;

Notice that without DISTINCT, each price would be listed as many times as there were store/candy pairs at that price.

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Example: ALL

Using relations Frequents(consumer, store) and Likes(consumer, candy):

(SELECT consumer FROM Frequents)

EXCEPT ALL

(SELECT consumer FROM Likes); Lists consumers who frequent more stores

than they like candies, and does so as many times as the difference of those counts.

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Join Expressions

SQL provides several versions of (bag) joins.

These expressions can be stand-alone queries or used in place of relations in a FROM clause.

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Products and Natural Joins

Natural join:R NATURAL JOIN S;

Product:R CROSS JOIN S;

Example:Likes NATURAL JOIN Sells;

Relations can be parenthesized subqueries, as well.

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Theta Join

R JOIN S ON <condition> Example: using Consumers(name,

addr) and Frequents(consumer, store):Consumers JOIN Frequents ON

name = consumer;gives us all (c, a, c, s) quadruples such that consumer c lives at address a and frequents store s.

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Outerjoins

R OUTER JOIN S is the core of an outerjoin expression. It is modified by:

1. Optional NATURAL in front of OUTER.2. Optional ON <condition> after JOIN.3. Optional LEFT, RIGHT, or FULL before

OUTER. LEFT = pad dangling tuples of R only. RIGHT = pad dangling tuples of S only. FULL = pad both; this choice is the default.

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Aggregations

SUM, AVG, COUNT, MIN, and MAX can be applied to a column in a SELECT clause to produce that aggregation on the column.

Also, COUNT(*) counts the number of tuples.

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Example: Aggregation

From Sells(store, candy, price), find the average price of Twizzlers:

SELECT AVG(price)

FROM Sells

WHERE candy = ’Twizzler’;

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Eliminating Duplicates in an Aggregation

Use DISTINCT inside an aggregation. Example: find the number of different

prices charged for Twizzlers:SELECT COUNT(DISTINCT price)

FROM Sells

WHERE candy = ’Twizzler’;

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NULL’s Ignored in Aggregation

NULL never contributes to a sum, average, or count, and can never be the minimum or maximum of a column.

But if there are no non-NULL values in a column, then the result of the aggregation is NULL.

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Example: Effect of NULL’s

SELECT count(*)FROM SellsWHERE candy = ’Twizzler’;

SELECT count(price)FROM SellsWHERE candy = ’Twizzler’;

The number of storesthat sell Twizzlers.

The number of storesthat sell Twizzlers at aknown price.

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Grouping

We may follow a SELECT-FROM-WHERE expression by GROUP BY and a list of attributes.

The relation that results from the SELECT-FROM-WHERE is grouped according to the values of all those attributes, and any aggregation is applied only within each group.

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Example: Grouping

From Sells(store, candy, price), find the average price for each candy:

SELECT candy, AVG(price)

FROM Sells

GROUP BY candy;

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Example: Grouping

From Sells(store, candy, price) and Frequents(consumer, store), find for each consumer the average price of Twizzlers at the stores they frequent:

SELECT consumer, AVG(price)FROM Frequents, SellsWHERE candy = ’Twizzler’ AND

Frequents.store = Sells.storeGROUP BY consumer;

Computeconsumer-store-price for Twiz.tuples first,then groupby consumer.

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Restriction on SELECT Lists With Aggregation

If any aggregation is used, then each element of the SELECT list must be either:

1. Aggregated, or2. An attribute on the GROUP BY list.

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Illegal Query Example

You might think you could find the store that sells Twizzlers the cheapest by:

SELECT store, MIN(price)FROM SellsWHERE candy = ’Twizzler’;

But this query is illegal in SQL.

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HAVING Clauses

HAVING <condition> may follow a GROUP BY clause.

If so, the condition applies to each group, and groups not satisfying the condition are eliminated.

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Example: HAVING

From Sells(store, candy, price) and Candies(name, manf), find the average price of those candies that are either sold in at least three stores or are manufactured by Nestle.

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Solution

SELECT candy, AVG(price)FROM SellsGROUP BY candyHAVING COUNT(store) >= 3 OR

candy IN (SELECT name FROM Candies WHERE manf = ’Nestle’);

Candies manufacturedby Nestle.

Candy groups with at least3 non-NULL stores and alsocandy groups where themanufacturer is Nestle.

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Requirements on HAVING Conditions

These conditions may refer to any relation or tuple-variable in the FROM clause.

They may refer to attributes of those relations, as long as the attribute makes sense within a group; i.e., it is either:

A grouping attribute, or Aggregated.