Introduction Susan B. Davidson University of Pennsylvania CIS330 – Database & Information Systems Some slide content courtesy of Tova Milo

2 Welcome to CIS 330, Database & Information Systems! Instructor: Susan B. Davidson,  305 Levine Hall  Office hours: MW 4-5 pm (or by appointment) TAs: : Saurabh Garg, Vikrant Goel, Aaditya Shirodkar, Nan Course web site: Online discussion forum: Piazza Texts and readings:  Ramakrishnan & Gerke, Database Systems, 3 rd ed.  For SQL: Greenspun, SQL for Nerds (online)  Other books may be useful, such as Sunderraman’s ORACLE Programming: A Primer and Brundage’s XQuery: The XML Query Language Prerequisites: CIS121, CIS160 (Java, data structures, discrete math)

3 Course Format and Grading  Roughly one major topic area per week to two weeks  Readings in the text  6 homework assignments (35%)  Two midterms (20% each)  Project (20%) – groups of 4  General participation, discussion, intangibles (5%)

Cheating  Cheating is a serious offense.  Cheating includes copying on exams or written assignments; obtaining advance copies of exams; outsourcing homeworks or project work; and copying material from the web and including on homeworks without proper attribution.  You may discuss concepts with your classmates or anyone else, and you are encouraged to do so. However, when it comes to writing the program (even just the 'pseudo-code'), you must do it yourself  Aiding someone else's cheating also constitutes cheating. 4

5 Outline for Today’s Lecture  Overview of database systems  Recommended reading: Introduction of SQL for Web Nerds, by Philip Greenspun  (or you can read Chapter 1 of the textbook, but it’s less fun)  Course outline  What the course is about

6 What Is a Relational Database Management System ? DataBase Management System = DBMS Relational DBMS = RDBMS  A collection of files that store the data  A big C program written by someone else that accesses and updates those files for you

7 Where are RDBMS used ?  Backend for traditional “database” applications  Backend for large Web sites  Backend for Web services

Example of a Traditional Database Application  Suppose we are building a system to store information about:  students  courses  professors  who takes what, who teaches what 8

9 Can we do it without a DBMS ? Sure we can! Start by storing the data in files: students.txt courses.txt professors.txt Now write C or Java programs to implement specific tasks

10 Doing it without a DBMS...  Enroll “Mary Johnson” in “CSE444”: Read ‘students.txt’ Read ‘courses.txt’ Find&update the record “Mary Johnson” Find&update the record “CSE444” Write “students.txt” Write “courses.txt” Read ‘students.txt’ Read ‘courses.txt’ Find&update the record “Mary Johnson” Find&update the record “CSE444” Write “students.txt” Write “courses.txt” Write a C program to do the following:

11 Problems without an DBMS...  System crashes:  What is the problem ?  Large data sets (say 50GB)  What is the problem ?  Simultaneous access by many users  Need locks: we know about them from OS, but now data is on disk; and is it any fun to re-implement them ? Read ‘students.txt’ Read ‘courses.txt’ Find&update the record “Mary Johnson” Find&update the record “CSE444” Write “students.txt” Write “courses.txt” Read ‘students.txt’ Read ‘courses.txt’ Find&update the record “Mary Johnson” Find&update the record “CSE444” Write “students.txt” Write “courses.txt” CRASH !

12 Enter a DMBS Data files Database server (someone else’s C program) Applications connection (ODBC, JDBC) “Two tier database system”

13 Functionality of a DBMS The programmer sees SQL, which has two components:  Data Definition Language - DDL  Data Manipulation Language - DML  query language Behind the scenes the DBMS has:  Query engine  Query optimizer  Storage management  Transaction Management (concurrency, recovery)

14 Functionality of a DBMS Two things to remember:  Client-server architecture  Slow, cumbersome connection  But good for the data  It is just someone else’s C program  In the beginning we may be impressed by its speed  But later we discover that it can be frustratingly slow  We can do any particular task faster outside the DBMS  But the DBMS is general and convenient

15 How the Programmer Sees the DBMS  Start with DDL to create tables:  Continue with DML to populate tables: CREATE TABLE Students ( Name CHAR(30) SSN CHAR(9) PRIMARY KEY NOT NULL, Category CHAR(20) )... CREATE TABLE Students ( Name CHAR(30) SSN CHAR(9) PRIMARY KEY NOT NULL, Category CHAR(20) )... INSERT INTO Students VALUES(‘Charles’, ‘ ’, ‘undergraduate’).. INSERT INTO Students VALUES(‘Charles’, ‘ ’, ‘undergraduate’)..

16 How the Programmer Sees the DBMS  Tables:  Still implemented as files, but behind the scenes can be quite complex Students:Takes: Courses: “data independence” = separate logical view from physical implementation

17 Transactions  Enroll “Mary Johnson” in “CSE444”: BEGIN TRANSACTION; INSERT INTO Takes SELECT Students.SSN, Courses.CID FROM Students, Courses WHERE Students.name = ‘Mary Johnson’ and Courses.name = ‘Databases’ -- More updates here.... IF everything-went-OK THEN COMMIT; ELSE ROLLBACK BEGIN TRANSACTION; INSERT INTO Takes SELECT Students.SSN, Courses.CID FROM Students, Courses WHERE Students.name = ‘Mary Johnson’ and Courses.name = ‘Databases’ -- More updates here.... IF everything-went-OK THEN COMMIT; ELSE ROLLBACK If system crashes, the transaction is still either committed or aborted

18 Transactions  A transaction = sequence of statements that either all succeed, or all fail  Transactions have the ACID properties: A = atomicity C = consistency I = independence D = durability

19 Queries  Find all courses that “Mary” takes  What happens behind the scene ?  Query processor figures out how to answer the query efficiently. SELECT C.name FROM Students S, Takes T, Courses C WHERE S.name=“Mary” and S.ssn = T.ssn and T.cid = C.cid

20 Queries, behind the scene Imperative query execution plan: SELECT C.name FROM Students S, Takes T, Courses C WHERE S.name=“Mary” and S.ssn = T.ssn and T.cid = C.cid SELECT C.name FROM Students S, Takes T, Courses C WHERE S.name=“Mary” and S.ssn = T.ssn and T.cid = C.cid Declarative SQL query Students Takes sid=sid c.name name=“Mary” cid=cid Courses The optimizer chooses the “best” execution plan for a query.

21 Database Systems  The big commercial database vendors:  Oracle  IBM (with DB2)  Microsoft (SQL Server)  Sybase  Some free database systems :  MySQL (acquired by Oracle..)  PostgreSQL  We will use Oracle since the University provides it…

22 An Issue: 80% of the World’s Data is Not in a DB! Examples:  scientific data (large images, complex programs that analyze the data)  personal data  WWW and (some of it is stored in something resembling a DBMS) Data management is expanding to tackle these problems  Flexibility – data management imposes many constraints to make problems solvable  Must deal with entities outside our control In this course, we’ll start by focusing on databases, but eventually look “outside the box” at the Web and at bringing together data from many places

23 New Trends in Databases  Object-relational databases  Main memory database systems  XML XML XML !  Relational databases with XML support  Middleware between XML and relational databases  Native XML database systems  Lots of research on XML and databases  Data integration  Peer to peer, stream data management – still research  BIG DATA  * Hbase / Hive / Hadoop/ PigLatin

24 Course Outline (may vary slightly) Part I  SQL (Chapter 5)  The relational data model (Chapter 3)  Database design (Chapters 2, 3, 19) Part II  Data storage, indexes (Chapters )  Query execution and optimization (Chapter 12, 14, 15)  XML, XPath, XQuery (Chapter 27)  RDF  Security (Chapter 21)

25 So what is this course about, really ?  SQL:  An old language, but still cute  Newer, XML stuff  Unfortunately tools are still primitive here  Theory !  Implementation: hacking and thinking!  And you need to learn a lot while you go Most CS courses concentrate on code – our interest is managing and representing data – BIG DATA is BIG!

Size is relative Are databases Big Data?