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Database Management System (DBMS)
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

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

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.

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




IBM Sequel language developed as part of System R projects at the IBM San Jose Research Laboratory
Renamed Structured Query Language (SQL)
ANSI and ISO standard SQL:
SQL:1999 (language name became Y2K compliant!)
Commercial systems offer most, if not all, SQL-92 features, plus varying feature sets from later standards and special proprietary features.
Not all examples here may work on your particular system.

Data Definition Language

The schema for each relation, including attribute types.
Integrity constraints
Authorization information for each relation.
Non-standard SQL extensions also allow specification of
The set of indices to be maintained for each relations.
The physical storage structure of each relation on disk.

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 are covered in Chapter 4.

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 and amount for all customers having a loan at some branch.

Conceptual Evaluation Strategy

Semantics of an SQL query defined in terms of the following conceptual evaluation strategy:
Compute the cross-product of relation-list.
Discard resulting tuples if they fail qualifications.
Delete attributes that are not in target-list.
If DISTINCT is specified, eliminate duplicate rows.
This strategy is probably the least efficient way to compute a query! An optimizer will find more efficient strategies to compute the same answers.

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