Database Management System, Lecture-1
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This is prepared from the book- Database System Concepts, written by ©Silberschatz, Korth and Sudarshan.
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Database Management System, Lecture-1
- 1. Database Management System Chapter 1: INTRODUCTION Prepared By- Sonia Akther Mim soniamim08@gmail.com
- 2. Outline Data and Information DB and DBMS Applications of DBMS File System Level of abstraction Database Language Database Design Data Models Relational Databases Database Design Storage Manager Query Processing Transaction Manager
- 3. Data & Information Data: It is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized. Example: Each student's test score is one piece of data. Information: When data is processed, organized, structured or presented in a given context so as to make it useful, it is called information. Example: The average score of a class or of the entire school is information that can be derived from the given data.
- 4. Database Management System (DBMS) Database (DB): The collection of data, usually referred to as the database, contains information relevant to an enterprise Database Management System (DBMS): A database-management system (DBMS) is a collection of interrelated data and a set of programs to access those data. 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
- 5. Database Management System (DBMS) Database Applications: Banking: 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 can be very large. Databases touch all aspects of our lives
- 6. University Database Example Application program examples Add new students, instructors, and courses Register students for courses, and generate class rosters Assign grades to students, compute grade point averages (GPA) and generate transcripts
- 7. 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
- 8. Drawbacks of using file systems to store data (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 (say 100) and updating it by withdrawing money (say 50 each) 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
- 9. Levels of Abstraction Physical level: describes how a record (e.g., instructor) is stored. Logical level: describes data stored in database, and the relationships among the data. type instructor = record ID : string; name : string; dept_name : string; salary : 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.
- 10. View of Data An architecture for a database system
- 11. Instances and Schemas Similar to types and variables in programming languages Logical Schema – the overall logical structure of the database Example: The database consists of information about a set of customers and accounts in a bank and the relationship between them Analogous to type information of a variable in a program Physical schema– the overall physical structure of the database 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.
- 12. 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
- 13. Relational Model All the data is stored in various tables. Example of tabular data in the relational model Columns Rows
- 14. A Sample Relational Database
- 15. Data Definition Language (DDL) Specification notation for defining the database schema Example: create table instructor ( ID char(5), name varchar(20), dept_name varchar(20), salary numeric(8,2)) DDL compiler generates a set of table templates stored in a data dictionary Data dictionary contains metadata (i.e., data about data) Database schema Integrity constraints Primary key (ID uniquely identifies instructors) Authorization Who can access what
- 16. 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 Pure – used for proving properties about computational power and for optimization Relational Algebra Tuple relational calculus Domain relational calculus Commercial – used in commercial systems SQL is the most widely used commercial language
- 17. SQL The most widely used commercial language SQL is NOT a Turing machine equivalent language SQL is NOT a Turing machine equivalent language To be able to compute complex functions SQL is usually embedded in some higher-level language 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
- 18. Database Design 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 The process of designing the general structure of the database:
- 19. Database Design (Cont.) Is there any problem with this relation?
- 20. Design Approaches Need to come up with a methodology to ensure that each of the relations in the database is “good” Two ways of doing so: Entity Relationship Model (Chapter 7) Models an enterprise as a collection of entities and relationships Represented diagrammatically by an entity-relationship diagram: Normalization Theory (Chapter 8) Formalize what designs are bad, and test for them
- 21. Object-Relational Data Models Relational model: flat, “atomic” values 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.
- 22. 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
- 23. Database Engine Storage manager Query processing Transaction manager
- 24. 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 OS file manager Efficient storing, retrieving and updating of data Issues: Storage access File organization Indexing and hashing
- 25. Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation
- 26. Query Processing (Cont.) 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
- 27. Transaction Management What if the system fails? What if more than one user is concurrently updating the same data? 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.
- 30. 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
- 31. History of Database Systems 1950s and early 1960s: Data processing using magnetic tapes for storage Tapes provided only sequential access Punched cards for input Late 1960s and 1970s: Hard disks allowed 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
- 32. History (cont.) 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 Early 2000s: XML and XQuery standards Automated database administration Later 2000s: Giant data storage systems Google BigTable, Yahoo PNuts, Amazon, ..
- 33. End of Chapter 1