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C20.0046: Database Management Systems Lecture #1

C20.0046: Database Management Systems Lecture #1. M.P. Johnson Stern School of Business, NYU Spring, 2008. What Is a Database?. A large, integrated collection of data which models a real-world enterprise: Entities students, courses, instructors, TAs Relationships

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C20.0046: Database Management Systems Lecture #1

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  1. C20.0046: Database Management SystemsLecture #1 M.P. Johnson Stern School of Business, NYU Spring, 2008 M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  2. What Is a Database? • A large, integrated collection of data • which models a real-world enterprise: • Entities • students, courses, instructors, TAs • Relationships • Hillary is currently taking C20.0046 • Barack is currently teaching C20.0046 • John is currently TA-ing C20.0046 but took it last semester • A Database Management System (DBMS)is a software package that stores and manages DBs M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  3. Databases are everywhere: non-web • criminal/terrorist: TIA • NYPD’s CompStat • Tracking crime stats by precinct • airline bookings • Retailers: Wal-Mart, etc. • when to re-order, purchase patterns, data-mining • Genomics M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  4. Databases are everywhere: web • retail: Amazon, etc. • data-mining: Page You Made • search engines • searchable DBs: IMDB, tvguide.com, etc. • Web2.0 sites: • flickr = images + tags • CMS systems (Wikis, blog & forum software, etc.) M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  5. Databases involved in ordering a pizza? • Pizza Hut’s DB • Credit card records • CC  approval by credit agencies • phone company’s records • (“Pull his LUDs, Lenny.”) • Caller ID • Error-checking, anticrime M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  6. Your wallet is full of DB records… • Driver’s license • Credit cards • NYUCard • Medical insurance card • Social security card • Money (serial numbers) • Photos (ids on back) • Etc… “You may not be interested in databases, but databases are interested in you.” - Trotsky M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  7. Example of a Traditional DB App • Suppose we build a system • We store: • checking accounts • savings accounts • account holders • state of each of each person’s accounts M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  8. Can we do without a DBMS? Sure! Start by storing the data in files: checking.txt savings.txt customers.txt Now write C or Java programs to implement specific tasks… M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  9. Doing it without a DBMS... • Transfer $100 from George’s savings to checking: Write a C program to do the following: • Read savings.txt • Find&update the line w/“George” • balance -= 100 • Write savings.txt • Read checking.txt • Find&update the line w/“George” • balance += 100 • Write checking.txt M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  10. Problems without an DBMS... 1. System crashes: • Same problem even if reordered • High-volume  (Rare  frequent) 2. Simultaneous access by many users • George and Dick visit ATMs at same time • Lock checking.txt before each use–what is the problem? • Read savings.txt • Find&update the line w/ “George.” • Write savings.txt • Read checking.txt • Find&update the line w/ “George” • Write checking.txt CRASH! M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  11. Problems without a DBMS... 3. Large data sets (100s of GBs, or TBs, …) • No indices • Finding “George” in huge flatfile is expensive • Modifications intractable without better data structures • “George”  “Georgie” is very expensive • Deletions are very expensive M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  12. Problems without an DBMS... 5. Security? • File system may lack security features • File system security may be coarse 6. Application programming interface (API)? • Interfaces, interoperability 7. How to query the data? M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  13. In homebrew system, must support • failover/rovery • concurrent use • deal with large datasets? • security • interop? • querying in what? •  DBMS as application • Q: How does a DBMS solve these problems? • A: See third part of course, but for now… M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  14. One big issue: Transaction processing • Grouping of several queries (or other DB operations) into one transaction • ACID test properties • Atomicity • all or nothing • Consistency • constraints on relationships • Isolation • concurrency control • simulated solipsism • Durability • Crash recovery M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  15. Atomicity & Durability • Avoiding inconsistent state • A DBMS prevents this outcome • xacts are all or nothing • One simple idea: log progress of and plans for each xact • Durability: changes stay made (with log…) • Atomicity: entire xact is committed at once M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  16. Isolation • Many users  concurrent execution • Disk access is slow (compared to CPU) •  don’t waste CPU – keep running • Interweaving actions of different user programs •  but can lead to inconsistency: • e.g., two programs simultaneously withdraw from the same account • For each user, should look like a single-user system • Simulated solipsism M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  17. Isolation • Contrast with a file in two Notepads • Strategy: ignore multiple users • whichever saves last wins • first save is overwritten • Contrast with a file in two Words • Strategy: blunt isolation • One can edit • To the other it’s read-only M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  18. Consistency • Each xact (on a consistent DB) must leave it in a consistent state • can define integrity constraints • checks that the defined claims about the data • Only xacts obeying them are allowed M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  19. A level up: data models • Any DBMS uses a data model: collection of concepts for describing data • Relational data model: basically universal • Oracle, DB2, SQLServer, other SQL DBMSs • Relations: table of rows & columns • a rel’s schema defines its fields • Though some have OO extensions… M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  20. Data Schemas • Schema: description of partic set of data, using some data model • “Physical schema” • Physical files on disk • Schema • Set of relations/tables, with structure • Views (“external schema”) • Virtual tables generated for user types M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  21. Schema e.g.: college registrar • Schema: • Students(ssn: string, name: string, login: string, age: int, gpa: real) • Courses(cid: string, cname: string, credits: int) • Enrolled(sid:string, cid:string, grade: string) • Physical schema: • Relations stored as unordered text files. • Indices on first column of each rel • Views: • My_courses(cname: string, grade: string, credits: int) • Course_info(ssn: string, name: string, status: string) M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  22. How the programmer sees the DBMS • Start with SQL DDL to create tables: • Continue with SQL to populate tables: CREATE TABLE Students ( Name CHAR(30) SSN CHAR(9) PRIMARY KEY NOT NULL, Category CHAR(20) ); INSERT INTO Students VALUES('Hillary', '123456789', 'undergraduate'); M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  23. How the programmer sees the DBMS Takes: Students: • Ultimately files, but complex Courses: M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  24. Querying: Structured Query Language • Find all the students who have taken C20.0046: • Find all the students who C20.0046 previously: • Find the students’ names: SELECT SSN FROM Takes WHERE CID='C20.0046'; SELECT SSN FROM Takes WHERE CID='C20.0046' AND Semester='Fall, 2005'; SELECT Name FROM Students, Takes WHERE Students.SSN=Takes.SSN AND CID='C20.0046' AND Semester='Fall, 2005'; M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  25. Database Industry • Relational databases are based on set theory • Commercial DBMSs: Oracle, IBM’s DB2, Microsoft’s SQL Server, etc. • Opensource: MySQL, PostgreSQL, etc. • DBAs manage these • Programmers write apps (CRUD, etc.) • XML (“semi-structured data”) also important M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  26. The Study of DBMS • Primary aspects: • Data modeling • SQL • DB programming • DBMS implementation • This course covers all four (tho less of #4) • Also will look at some more advanced areas • XML, websearch, column-oriented DBs, RAID, RegExs, MapReduce… M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  27. Course outline • Database design: • Entity/Relationship models • Modeling constraints • The relational model: • Relational algebra • Transforming E/R models to relational schemas • SQL • DDL & query language M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  28. Course outline • Programming for databases • Some DB implementation • Indexes, sorting, xacts • Advanced topics… • May change as course progresses • partly in response to audience • Also “current events” • Slashdot/whatever, Database Blog, etc. M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  29. Textbook: Database Management Systems • by Raghu Ramakrishnan & Johannes Gehrke • 3 edition (August 14, 2002) • Available: • NYU bookstore • Amazon/BN (may be cheaper) • Amazon.co.uk (may be cheaper still) • Links on class page • Difficult but good M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  30. SQL Readings • Many SQL references available online • Good online (free) SQL tutorials include: • A Gentle Introduction to SQL (http://sqlzoo.net/) • SQL for Web Nerds (http://philip.greenspun.com/sql/) M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  31. Communications • M. P. Johnson • mjohnson@stern • Office hours: after class • To receive class mail: • Activate account: http://start.stern.nyu.edu • Forward mail: http://simon.stern.nyu.edu M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  32. Communications • Web page: http://pages.stern.nyu.edu/~mjohnson/dbms/ • syllabus • course policies • antecedent courses • Blackboard web site • Some materials will be available here • Discussion board • send general-interest messages here to benefit all • Go to http://sternclasses.nyu.edu • Click on C20.0046 M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  33. Grading • Prerequisites: • Light programming experience • A bit of “mathematical maturity” • Interest in IT/CS • Requirements & base score: • Homework 15%: O(3) • Project: 30% - see below… • Midterm (closed book/notes): 20% • Final (closed book/notes; likely 2hrs in class): 25% • Class participation/pop-quizzes: 10% • Stern Curve • Consistent class attendance is required • Absences will seriously affect your total grade. • Final score = base score – 2n-1 • where n = # missed quizzes (if n>0) M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  34. The Project: design end-to-end DB web app • data model • Identify entities & their relationships •  relations • creation of DB in Oracle/MySQL • Population with real(alistic) data • web app for accessing/modifying data • Identification of “interesting” questions & actions • Produce DBMS interface • Work in pairs (/threes) • Choose topic on your own • Start forming your group today! M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  35. Collaboration model • Homework and exams done individually • Project done with your team members only, though can in general use any tools • Non-cited use of others’ problem solutions, code, etc. = plagiarism • See Stern’s stern academic honesty policy • Contact me if you’re at all unclear before a particular case • Cite any materials used if you’re at all unclear after M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  36. On-going Feedback • Don’t be afraid to ask questions • Some parts will be abstract/mathematical • Topic selection will be partly based on student interest M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  37. So what is this course about, really? • Languages: SQL (some XML …) • Data modeling • Some theory! (rereading…) • Functional dependencies, normal forms • e.g., how to find most efficient schema for data • Some DBMS implementation (algs & data structs) • Algorithms and data structures (in the latter part) • e.g., indices make data much faster to find – how? • Lots of DB implementation and hacking for the project M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  38. For next time • Get the book • Skim chapter 1 • Start reading chapter 2 M.P. Johnson, DBMS, Stern/NYU, Spring 2008

  39. For right now/tonight: email survey • Send to SOMEWHERE • name • previous cs/is/math/logic courses • previous programming experience • career plans: programmer, DBA, MBA, etc. • why taking class/what you’re interested in learning about M.P. Johnson, DBMS, Stern/NYU, Spring 2008

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