Transcript Structured data and Databases

Functionality of a DBMS
• Data Dictionary Management
• Storage management
– Data storage Definition Language (DDL)
• High level query and data manipulation language
– SQL/XQuery etc.
– May tell us what we are missing in text-based search
• Efficient query processing
– May change in the internet scenario
• Transaction processing
• Resiliency: recovery from crashes,
• Different views of the data, security
– May be useful to model a collection of databases together
• Interface with programming languages
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Building an Application with a Database
System
• Requirements modeling (conceptual, pictures)
– Decide what entities should be part of the application and
how they should be linked.
• Schema design and implementation
– Decide on a set of tables, attributes.
– Define the tables in the database system.
– Populate database (insert tuples).
• Write application programs using the DBMS
– Now much easier, with data management API
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Conceptual Modeling
name
category
name
ssn
Takes
Course
Student
quarter
Advises
Teaches
Professor
address
name
Slides adapted from Rao (ASU) & Franklin (Berkeley)
field
Data Models
• A data model is a collection of concepts for
describing data.
• A schema is a description of a particular collection
of data, using a given data model.
• The relational model of data is the most widely used
model today.
– Main concept: relation, basically a table with rows and
columns.
– Every relation has a schema, which describes the columns, or
fields.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Levels of Abstraction
• Views describe how
users see the data.
• Conceptual schema
defines logical structure
• Physical schema
describes the files and
indexes used.
View 1
View 2
View 3
Conceptual Schema
Physical Schema
Slides adapted from Rao (ASU) & Franklin (Berkeley)
DB
Example: University Database
• Conceptual schema:
– Students(sid: string, name: string,
login: string, age: integer, gpa:real)
– Courses(cid: string, cname:string, View 1
credits:integer)
• External Schema (View):
– Course_info(cid:string,enrollment:in
teger)
• Physical schema:
– Relations stored as unordered files.
– Index on first column of Students.
View 2
View 3
Conceptual Schema
Physical Schema
DB
If fiveadapted
peoplefrom
are Rao
asked
to come
up with
a schema for the data,
Slides
(ASU)
& Franklin
(Berkeley)
what are the odds that they will come up with the same schema?
Data Independence
• Applications insulated from
how data is structured and stored.
• Logical data independence:
Protection from changes in
logical structure of data.
View 1
View 2
View 3
Conceptual Schema
Physical Schema
• Physical data independence:
Protection from changes in
physical structure of data.
• Q: Why are these particularly
important for DBMS?
Slides adapted from Rao (ASU) & Franklin (Berkeley)
DB
Schema Design & Implementation
• Table Students
Student
Course
Quarter
Charles
CS 444
Fall, 1997
Dan
CS 142
…
…
Winter,
1998
…
• Separates the logical view from the physical
view of the data.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Terminology
Attribute names
tuples
Students
Student
Course
Quarter
Charles
CS 444
Fall, 1997
Dan
CS 142
…
…
Winter,
1998
…
(Arity=3)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Querying a Database
• Find all the students taking CSE594 in Q1, 2004
• S(tructured) Q(uery) L(anguage)
select E.name
from Enroll E
where E.course=CS490i and
E.quarter=“Winter, 2000”
• Query processor figures out how to answer the query
efficiently.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Example: Projection Onto SSN, Name
Employee
SSN
999999999
777777777
888888888
Name
John
Tony
Alice
SSN
999999999
777777777
888888888
Name
John
Tony
Alice
DepartmentID
1
1
2
Salary
30,000
32,000
45,000
Slides adapted from Rao (ASU) & Franklin (Berkeley)
X
Cartesian Product
• Binary Operation
• Result is set of tuples
combining all elements of
R1 with all elements of
R2, for R1  R2
• Schema is union of
Schema(R1) &
Schema(R2)
• Notice we could do
selection on result to get
meaningful info!
Cartesian Product Example
Employee
Name
John
Tony
Dependents
EmployeeSSN
999999999
777777777
SSN
999999999
777777777
Dname
Emily
Joe
Employee_Dependents
Name SSN
EmployeeSSN
John
999999999 999999999
John
999999999 777777777
Tony
777777777 999999999
Tony
777777777 777777777
3/19/2001 12:13 PM
Copyright © 2000 D.S.Weld (modified by Rao)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Dname
Emily
Joe
Emily
Joe
14
Cartesian Product Example
Employee
Name
John
Tony
Dependents
EmployeeSSN
999999999
777777777
SSN
999999999
777777777
Dname
Emily
Joe
Employee_Dependents
Name SSN
EmployeeSSN
John
999999999 999999999
John
999999999 777777777
Tony
777777777 999999999
Tony
777777777 777777777
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Dname
Emily
Joe
Emily
Joe
Join
• Most common (and exciting!) operator…
• Combines 2 relations
– Selecting only related tuples
• Result has all attributes of the two relations
• Equivalent to
– Cross product followed by selection followed by Projection
• Equijoin
– Join condition is equality between two attributes
• Natural join
– Equijoin on attributes of same name
– result has only one copy of join condition attribute
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Example: Natural Join
Employee
Name
John
Tony
Dependents
SSN
999999999
777777777
Employee
SSN
999999999
777777777
Dname
Emily
Joe
Dependents
Employee_Dependents
Name SSN
Dname
John
999999999 Emily
Tony
777777777 Joe
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Complex Queries
Product ( pname, price, category, maker)
Purchase (buyer, seller, store, prodname)
Company (cname, stock price, country)
Person( per-name, phone number, city)
Find phone numbers of people who bought gizmos from
Fred.
Find telephony products that somebody bought
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Exercises
Product ( pname, price, category, maker)
Purchase (buyer, seller, store, prodname)
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 Seattle.
Ex #5: Find people who bought stuff from Joe or bought products
from a company whose stock prices is more than $50.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
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 we’ll only discuss a subset of what they support)
Basic form = syntax on relational algebra (but many other features too)
Select attributes
From relations (possibly multiple, joined)
Where conditions (selections)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Selections
s
SELECT *
FROM Company
WHERE country=“USA” AND stockPrice > 50
You can use:
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.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Projection
p
Select only a subset of the attributes
SELECT name, stock price
FROM Company
WHERE country=“USA” AND stockPrice > 50
Rename the attributes in the resulting table
SELECT name AS company, stockprice AS price
FROM Company
WHERE country=“USA” AND stockPrice > 50
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Ordering the Results
SELECT name, stock price
FROM Company
WHERE country=“USA” AND stockPrice > 50
ORDERBY country, name
Ordering is ascending, unless you specify the DESC keyword.
Ties are broken by the second attribute on the ORDERBY list, etc.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Join
SELECT name, store
FROM
Person, Purchase
WHERE per-name=buyer AND city=“Seattle”
AND product=“gizmo”
Product ( pname, price, category, maker)
Purchase (buyer, seller, store, product)
Company (cname, stock price, country)
Person( per-name, phone number, city)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
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)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Defining Views
(Virtual) 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”
Slides adapted from Rao (ASU) & Franklin (Berkeley)
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??
Slides adapted from Rao (ASU) & Franklin (Berkeley)
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.
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Non-Updatable Views
Given
Purchase (buyer, seller, store, product)
Person( name, phone-num, city)
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”)
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Materialized Views
• Views whose corresponding queries have been executed
and the data is stored in a separate database
– Uses: Caching
• Issues
– Using views in answering queries
• Normally, the views are available in addition to database
– (so, views are local caches)
• In information integration, views may be the only things we have access to.
– An internet source that specializes in woody allen movies can be seen as a view
on a database of all movies. Except, there is no database out there which
contains all movies..
– Maintaining consistency of materialized views
Slides adapted from Rao (ASU) & Franklin (Berkeley)
Query Optimization
Goal:
Declarative SQL query
SELECT S.buyer
FROM Purchase P, Person Q
WHERE P.buyer=Q.name AND
Q.city=‘seattle’ AND
Q.phone > ‘5430000’
Imperative query execution plan:
buyer
sCity=‘seattle’
phone>’5430000’
Buyer=name
Inputs:
• the query
• statistics about the data
(indexes, cardinalities,
selectivity factors)
• available memory
Purchase
(Table scan)
(Simple Nested Loops)
Person
(Index scan)
Ideally: Want to find best plan.
Practically: Avoid worst plans!
Slides adapted from Rao (ASU) & Franklin (Berkeley)