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Introduction to Database System Concepts

Introduction to Database System Concepts. Database Management System (DBMS). DBMS contains information about a particular enterprise Collection of interrelated data Set of programs to access the data An environment that is both convenient and efficient to use Database Applications:

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Introduction to Database System Concepts

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  1. Introduction to Database System Concepts

  2. Database Management System (DBMS) • DBMS contains information about a particular enterprise • Collection of interrelated data • Set of programs to access the data • An environment that is both convenient and efficient to use • Database Applications: • Banking: all transactions • Airlines: reservations, schedules • Universities: registration, grades • Sales: customers, products, purchases • Online retailers: order tracking, customized recommendations • Manufacturing: production, inventory, orders, supply chain • Human resources: employee records, salaries, tax deductions • Databases touch all aspects of our lives

  3. Purpose of Database Systems • In the early days, database applications were built directly on top of file systems • Drawbacks of using file systems to store data: • Data redundancy and inconsistency • Multiple file formats, duplication of information in different files • Difficulty in accessing data • Need to write a new program to carry out each new task • Data isolation — multiple files and formats • Integrity problems • Integrity constraints (e.g. account balance > 0) become “buried” in program code rather than being stated explicitly • Hard to add new constraints or change existing ones

  4. Purpose of Database Systems (Cont.) • Drawbacks of using file systems (cont.) • Atomicity of updates • Failures may leave database in an inconsistent state with partial updates carried out • Example: Transfer of funds from one account to another should either complete or not happen at all • Concurrent access by multiple users • Concurrent access needed for performance • Uncontrolled concurrent accesses can lead to inconsistencies • Example: Two people reading a balance and updating it at the same time • Security problems • Hard to provide user access to some, but not all, data • Database systems offer solutions to all the above problems

  5. Levels of Abstraction • Physical level: describes how a record (e.g., customer) is stored. • Logical level: describes data stored in database, and the relationships among the data. typecustomer = record customer_id : string; customer_name : string;customer_street : string;customer_city : integer; end; • View level: application programs hide details of data types. Views can also hide information (such as an employee’s salary) for security purposes.

  6. View of Data An architecture for a database system

  7. Instances and Schemas • Similar to types and variables in programming languages • Schema – the logical structure of the database • Example: The database consists of information about a set of customers and accounts and the relationship between them) • Analogous to type information of a variable in a program • Physical schema: database design at the physical level • Logical schema: database design at the logical level • Instance – the actual content of the database at a particular point in time • Analogous to the value of a variable • Physical Data Independence – the ability to modify the physical schema without changing the logical schema • Applications depend on the logical schema • In general, the interfaces between the various levels and components should be well defined so that changes in some parts do not seriously influence others.

  8. Data Models • A collection of tools for describing • Data • Data relationships • Data semantics • Data constraints • Relational model • Entity-Relationship data model (mainly for database design) • Object-based data models (Object-oriented and Object-relational) • Semistructured data model (XML) • Other older models: • Network model • Hierarchical model

  9. Data Manipulation Language (DML) • Language for accessing and manipulating the data organized by the appropriate data model • DML also known as query language • Two classes of languages • Procedural – user specifies what data is required and how to get those data • Declarative (nonprocedural) – user specifies what data is required without specifying how to get those data • SQL is the most widely used query language

  10. Data Definition Language (DDL) • Specification notation for defining the database schema Example: create tableaccount (account-numberchar(10),balanceinteger) • DDL compiler generates a set of tables stored in a data dictionary • Data dictionary contains metadata (i.e., data about data) • Database schema • Data storage and definition language • Specifies the storage structure and access methods used • Integrity constraints • Domain constraints • Referential integrity (references constraint in SQL) • Assertions • Authorization

  11. Relational Model Attributes • Example of tabular data in the relational model

  12. A Sample Relational Database

  13. SQL • SQL: widely used non-procedural language • Example: Find the name of the customer with customer-id 192-83-7465select customer.customer_namefrom customerwherecustomer.customer_id = ‘192-83-7465’ • Example: Find the balances of all accounts held by the customer with customer-id 192-83-7465selectaccount.balancefromdepositor, accountwheredepositor.customer_id = ‘192-83-7465’ anddepositor.account_number = account.account_number • Application programs generally access databases through one of • Language extensions to allow embedded SQL • Application program interface (e.g., ODBC/JDBC) which allow SQL queries to be sent to a database

  14. Database Design The process of designing the general structure of the database: • Logical Design – Deciding on the database schema. Database design requires that we find a “good” collection of relation schemas. • Business decision – What attributes should we record in the database? • Computer Science decision – What relation schemas should we have and how should the attributes be distributed among the various relation schemas? • Physical Design – Deciding on the physical layout of the database

  15. The Entity-Relationship Model • Models an enterprise as a collection of entities and relationships • Entity: a “thing” or “object” in the enterprise that is distinguishable from other objects • Described by a set of attributes • Relationship: an association among several entities • Represented diagrammatically by an entity-relationship diagram:

  16. XML: Extensible Markup Language • Defined by the WWW Consortium (W3C) • Originally intended as a document markup language not a database language • The ability to specify new tags, and to create nested tag structures made XML a great way to exchange data, not just documents • XML has become the basis for all new generation data interchange formats. • A wide variety of tools is available for parsing, browsing and querying XML documents/data

  17. Chapter 3: SQL

  18. Data Definition Language • The schema for each relation. • The domain of values associated with each attribute. • Integrity constraints • The set of indices to be maintained for each relations. • Security and authorization information for each relation. • The physical storage structure of each relation on disk. Allows the specification of not only a set of relations but also information about each relation, including:

  19. Domain Types in SQL • char(n). Fixed length character string, with user-specified length n. • varchar(n). Variable length character strings, with user-specified maximum length n. • int.Integer (a finite subset of the integers that is machine-dependent). • smallint. Small integer (a machine-dependent subset of the integer domain type). • numeric(p,d). Fixed point number, with user-specified precision of p digits, with n digits to the right of decimal point. • real, double precision. Floating point and double-precision floating point numbers, with machine-dependent precision. • float(n). Floating point number, with user-specified precision of at least n digits. • More ...

  20. Create Table Construct • An SQL relation is defined using thecreate tablecommand: create table r (A1D1, A2D2, ..., An Dn,(integrity-constraint1), ..., (integrity-constraintk)) • r is the name of the relation • each Ai is an attribute name in the schema of relation r • Di is the data type of values in the domain of attribute Ai • Example: create table branch (branch_name char(15) not null,branch_city char(30),assets integer)

  21. Integrity Constraints in Create Table • not null • primary key (A1, ..., An ) Example: Declare branch_name as the primary key for branch . create table branch(branch_name char(15),branch_city char(30),assets integer,primary key (branch_name)) primary key declaration on an attribute automatically ensures not null

  22. Drop and Alter Table Constructs • The drop tablecommand deletes all information about the dropped relation from the database. • The alter table command is used to add attributes to an existing relation: alter table r add A D where A is the name of the attribute to be added to relation r and D is the domain of A. • All tuples in the relation are assigned null as the value for the new attribute. • The alter table command can also be used to drop attributes of a relation: alter table r drop A where A is the name of an attribute of relation r • Dropping of attributes not supported by many databases

  23. Basic Query Structure • SQL is based on set and relational operations with certain modifications and enhancements • A typical SQL query has the form:select A1, A2, ..., AnfromR1, R2, ..., Rmwhere P • Ai represents an attribute • Ri represents a relation • P is a predicate. • The result of an SQL query is a relation.

  24. The select Clause • The select clause list the attributes desired in the result of a query • Example: find the names of all branches in the loan relation: • select branch_namefrom loan • NOTE: SQL names are case insensitive (i.e., you may use upper- or lower-case letters.) • E.g. Branch_Name ≡ BRANCH_NAME ≡ branch_name

  25. The select Clause (Cont.) • SQL allows duplicates in relations as well as in query results. • To force the elimination of duplicates, insert the keyword distinct after select. • Find the names of all branches in the loan relations, and remove duplicates select distinct branch_namefrom loan • The keyword all specifies that duplicates not be removed. select allbranch_namefrom loan

  26. The select Clause (Cont.) • An asterisk in the select clause denotes “all attributes” select *from loan • The select clause can contain arithmetic expressions involving the operation, +, –, , and /, and operating on constants or attributes of tuples. • The query: selectloan_number, branch_name, amount  100from loan would return a relation that is the same as the loan relation, except that the value of the attribute amount is multiplied by 100.

  27. The where Clause • The whereclause specifies conditions that the result must satisfy • To find all loan number for loans made at the Perryridge branch with loan amounts greater than $1200. select loan_numberfrom loanwhere branch_name ='Perryridge'and amount > 1200 • Comparison results can be combined using the logical connectives and, or, and not. • Comparisons can be applied to results of arithmetic expressions.

  28. The where Clause (Cont.) • SQL includes a between comparison operator • Example: Find the loan number of those loans with loan amounts between $90,000 and $100,000 (that is,  $90,000 and  $100,000) select loan_numberfrom loanwhere amountbetween 90000 and 100000

  29. The from Clause • The fromclause lists the relations involved in the query • Find the Cartesian product borrower X loan select from borrower, loan • Find the name, loan number and loan amount of all customers having a loan at the Perryridge branch. select customer_name, borrower.loan_number, amountfrom borrower, loanwhere borrower.loan_number = loan.loan_number andbranch_name = 'Perryridge'

  30. The Rename Operation • The SQL allows renaming relations and attributes using the as clause: old-name as new-name • Find the name, loan number and loan amount of all customers; rename the column name loan_number as loan_id. select customer_name, borrower.loan_number as loan_id, amountfrom borrower, loanwhere borrower.loan_number = loan.loan_number

  31. Tuple Variables • Tuple variables are defined in the from clause via the use of the as clause. • Find the customer names and their loan numbers for all customers having a loan at some branch. select customer_name, T.loan_number, S.amountfrom borrower as T, loan as Swhere T.loan_number = S.loan_number • Find the names of all branches that have greater assets than some branch located in Brooklyn. select distinct T.branch_namefrom branch as T, branch as Swhere T.assets > S.assets and S.branch_city = 'Brooklyn' • Keyword as is optional and may be omittedborrower as T ≡ borrowerT

  32. String Operations • SQL includes a string-matching operator for comparisons on character strings. The operator “like” uses patterns that are described using two special characters: • percent (%). The % character matches any substring. • underscore (_). The _ character matches any character. • Find the names of all customers whose street includes the substring “Main”. select customer_namefrom customerwherecustomer_street like '% Main%' • SQL supports a variety of string operations

  33. Ordering the Display of Tuples • List in alphabetic order the names of all customers having a loan in Perryridge branch select distinct customer_namefrom borrower, loanwhere borrower loan_number = loan.loan_number and branch_name = 'Perryridge' order by customer_name • We may specify desc for descending order or asc for ascending order, for each attribute; ascending order is the default. • Example: order bycustomer_namedesc

  34. Set Operations • The set operations union, intersect, and exceptoperate on relations and correspond to the relational algebra operations  • Each of the above operations automatically eliminates duplicates; to retain all duplicates use the corresponding multiset versions union all, intersect alland except all.Suppose a tuple occurs m times in r and n times in s, then, it occurs: • m + n times in r union all s • min(m,n) times in rintersect all s • max(0, m – n) times in rexcept all s

  35. Set Operations • Find all customers who have a loan, an account, or both: (selectcustomer_name from depositor)union(selectcustomer_name from borrower) • Find all customers who have both a loan and an account. (selectcustomer_name from depositor)intersect(selectcustomer_name from borrower) • Find all customers who have an account but no loan. (selectcustomer_name from depositor)except(selectcustomer_name from borrower)

  36. Aggregate Functions • These functions operate on the multiset of values of a column of a relation, and return a value avg: average valuemin: minimum valuemax: maximum valuesum: sum of valuescount: number of values

  37. Aggregate Functions (Cont.) • Find the average account balance at the Perryridge branch. select avg (balance)from accountwhere branch_name = 'Perryridge' • Find the number of tuples in the customer relation. select count (*)from customer • Find the number of depositors in the bank. select count (distinct customer_name)from depositor

  38. Aggregate Functions – Group By • Find the number of depositors for each branch. select branch_name, count (distinctcustomer_name)from depositor, accountwhere depositor.account_number = account.account_numbergroup by branch_name Note: Attributes in select clause outside of aggregate functions must appear in group by list

  39. Aggregate Functions – Having Clause • Find the names of all branches where the average account balance is more than $1,200. select branch_name, avg (balance)from accountgroup by branch_namehaving avg(balance) > 1200 Note: predicates in the having clause are applied after the formation of groups whereas predicates in the where clause are applied before forming groups

  40. Null Values • It is possible for tuples to have a null value, denoted by null, for some of their attributes • null signifies an unknown value or that a value does not exist. • The predicate is null can be used to check for null values. • Example: Find all loan number which appear in the loan relation with null values for amount. select loan_numberfrom loanwhere amount is null • The result of any arithmetic expression involving null is null • Example: 5 + null returns null • However, aggregate functions simply ignore nulls

  41. Null Values and Aggregates • Total all loan amounts select sum (amount )from loan • Above statement ignores null amounts • Result is null if there is no non-null amount • All aggregate operations except count(*) ignore tuples with null values on the aggregated attributes.

  42. Nested Subqueries • SQL provides a mechanism for the nesting of subqueries. • A subquery is a select-from-where expression that is nested within another query. • A common use of subqueries is to perform tests for set membership, set comparisons, and set cardinality.

  43. Example Query • Find all customers who have both an account and a loan at the bank. select distinct customer_namefrom borrowerwhere customer_name in (select customer_namefromdepositor ) • Find all customers who have a loan at the bank but do not have an account at the bank select distinct customer_namefrom borrowerwhere customer_name not in (select customer_namefrom depositor )

  44. Example Query • Find all customers who have both an account and a loan at the Perryridge branch select distinctcustomer_namefrom borrower, loanwhere borrower.loan_number = loan.loan_number andbranch_name = 'Perryridge' and(branch_name, customer_name )in (select branch_name, customer_namefrom depositor, accountwhere depositor.account_number = account.account_number ) • Note: Above query can be written in a much simpler manner. The formulation above is simply to illustrate SQL features.

  45. Set Comparison • Find all branches that have greater assets than some branch located in Brooklyn. select distinct T.branch_namefrom branch as T, branch as Swhere T.assets > S.assets andS.branch_city = 'Brooklyn' • Same query using > some clause select branch_namefrom branchwhere assets > some (select assetsfrom branchwhere branch_city = 'Brooklyn')

  46. Example Query • Find the names of all branches that have greater assets than all branches located in Brooklyn. select branch_namefrom branchwhere assets > all (select assetsfrom branchwhere branch_city = 'Brooklyn')

  47. Test for Empty Relations • The exists construct returns the value true if the argument subquery is nonempty. • exists r  r  Ø • not exists r  r = Ø

  48. Example Query • Find all customers who have an account at all branches located in Brooklyn. select distinct S.customer_namefrom depositor as Swhere not exists ((select branch_namefrom branchwhere branch_city = 'Brooklyn') except(select R.branch_namefrom depositor as T, account as Rwhere T.account_number = R.account_number andS.customer_name = T.customer_name ))

  49. Test for Absence of Duplicate Tuples • The unique construct tests whether a subquery has any duplicate tuples in its result. • Find all customers who have at most one account at the Perryridge branch. select T.customer_name from depositor as T where unique ( select R.customer_namefrom account, depositor as Rwhere T.customer_name = R.customer_name andR.account_number = account.account_number andaccount.branch_name = 'Perryridge')

  50. Example Query • Find all customers who have at least two accounts at the Perryridge branch. select distinct T.customer_name from depositor as T where not unique ( select R.customer_name from account, depositor as R where T.customer_name = R.customer_name and R.account_number = account.account_number and account.branch_name ='Perryridge') • Variable from outer level is known as a correlation variable

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