Dbms
- 2. Introduction • Purpose of Database Systems • View of Data • Database Languages • Relational Databases • Database Design • Data Storage and Querying • Transaction Management • Database Architecture • Database Users and Administrators • History of Database Systems
- 3. Database Management System • 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
- 4. 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
- 5. • 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 accessed 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
- 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 table account ( account-number char(10), balance integer) • 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 • 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- 83-7465 select customer.customer_name from customer where customer.customer_id = ‘192-83-7465’ – Example: Find the balances of all accounts held by the customer with customer-id 192-83-7465 select account.balance from depositor, account where depositor.customer_id = ‘192-83-7465’ and depositor.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. Object-Relational Data Models • Extend the relational data model by including object orientation and constructs to deal with added data types. • Allow attributes of tuples to have complex types, including non-atomic values such as nested relations. • Preserve relational foundations, in particular the declarative access to data, while extending modeling power. • Provide upward compatibility with existing relational languages.
- 17. 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
- 18. Storage Management • Storage manager is a program module that provides the interface between the low-level data stored in the database and the application programs and queries submitted to the system. • The storage manager is responsible to the following tasks: – Interaction with the file manager – Efficient storing, retrieving and updating of data • Issues: – Storage access – File organization – Indexing and hashing
- 19. Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation
- 20. • Alternative ways of evaluating a given query – Equivalent expressions – Different algorithms for each operation • Cost difference between a good and a bad way of evaluating a query can be enormous • Need to estimate the cost of operations – Depends critically on statistical information about relations which the database must maintain – Need to estimate statistics for intermediate results to compute cost of complex expressions
- 21. Transaction Management • A transaction is a collection of operations that performs a single logical function in a database application • Transaction-management component ensures that the database remains in a consistent (correct) state despite system failures (e.g., power failures and operating system crashes) and transaction failures. • Concurrency-control manager controls the interaction among the concurrent transactions, to ensure the consistency of the database.
- 22. Database Architecture The architecture of a database systems is greatly influenced by the underlying computer system on which the database is running: • Centralized • Client-server • Parallel (multi-processor) • Distributed
- 23. Database Users Users are differentiated by the way they expect to interact with the system • Application programmers – interact with system through DML calls • Sophisticated users – form requests in a database query language • Specialized users – write specialized database applications that do not fit into the traditional data processing framework • Naïve users – invoke one of the permanent application programs that have been written previously – Examples, people accessing database over the web, bank tellers, clerical staff
- 24. Database Administrator • Coordinates all the activities of the database system; the database administrator has a good understanding of the enterprise’s information resources and needs. • Database administrator's duties include: – Schema definition – Storage structure and access method definition – Schema and physical organization modification – Granting user authority to access the database – Specifying integrity constraints – Acting as liaison with users – Monitoring performance and responding to changes in requirements
- 26. History of Database Systems • 1950s and early 1960s: – Data processing using magnetic tapes for storage • Tapes provide only sequential access – Punched cards for input • Late 1960s and 1970s: – Hard disks allow direct access to data – Network and hierarchical data models in widespread use – Ted Codd defines the relational data model • Would win the ACM Turing Award for this work • IBM Research begins System R prototype • UC Berkeley begins Ingres prototype – High-performance (for the era) transaction processing
- 27. • 1980s: – Research relational prototypes evolve into commercial systems • SQL becomes industrial standard – Parallel and distributed database systems – Object-oriented database systems • 1990s: – Large decision support and data-mining applications – Large multi-terabyte data warehouses – Emergence of Web commerce • 2000s: – XML and XQuery standards – Automated database administration