CPS 216: Advanced Database Systems
(Data-Intensive Computing Systems, Fall 2010)

Announcements

• We thank Amazon Web Services (AWS) for giving an educational grant for our students to do programming projects on the Amazon Cloud!

Course Description

Database systems are going through very interesting and chaotic times. Popular relational database systems like IBM DB2, Microsoft SQLServer, Oracle, and Sybase are struggling to handle the massive scale of data introduced by the Web. Today, companies have to deal with extremely large datasets. Facebook absorbs 15 TeraBytes of data each day into their 2.5 PetaByte Hadoop-powered data warehouse. eBay maintains a 6.5 PetaByte (i.e., 6.5 x 1,000,000,000,000,000 Bytes!) data warehouse.

A new breed of database systems are emerging to handle data at massive scale. These systems take some of the successful features from conventional relational databases---like run-time query optimization, automated crash recovery, and self-tuning---and make them work at the scale of 100s-1000s of processors and disks. As we move into the world of "big data", many traditional assumptions break, new query and programming interfaces are required, and new computing models will emerge. Did you know that each Google search query can touch up to 2,000 servers that must all execute that query and respond in less than a third of a second.

This course covers a spectrum of topics from core techniques in relational data management to highly-scalable data processing using parallel database systems and MapReduce. The course material will be drawn from textbooks as well as recent research literature. The following topics will be covered this year. The figures in brackets indicate the amount of time devoted to the topic relative to the total duration of the course.

• Principles of query processing (30%)
  • Indexes
  • Query execution plans and operators
  • Query optimization
• Data storage (10%)
  • Databases Vs. filesystems (Google filesystem, Hadoop distributed filesystem)
  • Flash memory and Solid State Drives
• Scalable data processing (35%)
  • Parallel query plans and operators
  • Systems based on MapReduce (Hadoop, Pig, Hive)
  • Scalable key-value stores (Amazon Dynamo, Cassandra)
• Concurrency control and recovery (15%)
  • Consistency models for data (ACID, Serializability)
  • Write-ahead logging
• Information retrieval and Data mining (10%)
  • Web search (Google PageRank, inverted indexes)
  • Association rules and clustering

Prerequisites: An introductory database course will be helpful, but it is not required. If you have not taken an introductory database course before, please talk to the instructor first. A lot of the material that we cover cannot be found in textbooks. Be prepared to do a fair amount of reading.

Time and Place

2:50pm-4:05 PM on Mondays and Wednesdays; D243 LSRC

Books and References

Hadoop: The Definitive Guide, by Tom White. O'Reilly Media. 2009. (First edition of the book at Amazon.com)

Database Systems: The Complete Book, by Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom. Prentice Hall. 2002. (The second edition is also available now.)

Readings will be posted on the readings page.

Instructor

Shivnath Babu
Web: http://www.cs.duke.edu/~shivnath/
Email: shivnath at cs dot my_univ. Replace my_univ with duke.edu.
Office: D338 LSRC, Phone: 919-660-6579
Office hours: 4.05-5.00 PM on Monday and Wednesday (right after class), or by appointment. Let the instructor know ahead of time, either in class or via email, that you will be coming during office hours. The office hours will be held in the instructor's office: D338 LSRC

TA: Gang Luo
Email: gang at cs dot my_univ. Replace my_univ with duke.edu.
Office: N303A North Building, Phone: 919-660-6546
Office hours: 3.00-4.00 PM on Tuesday and Thursday, or by appointment. Gang's office hours will be held in LSRC room D344 for the first month.

Grading

There will be three written homework assignments. Late homeworks will not be accepted, unless there are documented excuses from a physician or dean.

There is a semester-long course project (done either individually or in groups of at most two). Details will be presented in class in the second week.

Both midterm and final exams are open-book and open-notes. Laptops and other electronic devices are not allowed.

Honor Code

Under the Duke Honor Code, you are expected to submit your own work in this course, including homeworks, projects, and exams. On many occasions when working on homeworks and projects, it is useful to ask others (the instructor or other students) for hints or debugging help, or to talk generally about the written problems or programming strategies. Such activity is both acceptable and encouraged, but you must indicate in your submission any assistance you received. Any assistance received that is not given proper citation will be considered a violation of the Honor Code. In any event, you are responsible for understanding and being able to explain on your own all written and programming solutions that you submit. The course staff will pursue aggressively all suspected cases of Honor Code violations, and they will be handled through official University channels.