Principles of Data Management Syllabus & Intro

1. Principles of Data ManagementSyllabus & Intro

2. Welcome! • Course website: • http://cis.temple.edu/~edragut/CIS5516-fall2014/teaching.htm • Important Pre-Req • Text Book(s) • Workload • Intended Schedule • Projects • Grading • Papers

3. What Is a DBMS? • A very large, integrated collection of data. • Models real-world enterprise. • Entities (e.g., students, courses) • Relationships (e.g., Madonna is taking CS564) • A Database Management System (DBMS)is a software package designed to store and manage data.

4. Files vs. DBMS • Application must stage large datasets between main memory and secondary storage (e.g., buffering, page-oriented access, 32-bit addressing, etc.) • Special code for different queries • Must protect data from inconsistency due to multiple concurrent users • Crash recovery • Security and access control

5. Why Use a DBMS? • Data independence and efficient access. • Reduced application development time. • Data integrity and security. • Uniform data administration. • Concurrent access, recovery from crashes.

6. ? Why Study Databases?? • Shift from computation to information • at the “low end”: scramble to webspace (a mess!) • at the “high end”: scientific applications • Datasets increasing in diversity and volume. • Digital libraries, interactive video, Human Genome project, EOS project • ... need for DBMS exploding • DBMS encompasses most of CS • OS, languages, theory, AI, multimedia, logic

7. A Brief DB History • Early 1970s • Many database systems • Incompatible, exposing many implementation details • Then Ted Codd came along • Relational model • Structured Query Language (SQL) • Implementation differences became irrelevant • A few major DB systems dominated the market

8. Then Web 2.0 & 3.0, Big Data Happen • What do you think happen? • Semi-structured data happen. • A lot of it and in many forms…

9. Some Facts about Web x.0 and Big Data • Twitter: 255 million monthly active users and 500 million Tweets are sent per day, • Facebook: over 1 billion monthly users and faces 3 million message per 20 minute • Instagram: 200 Million Monthly Active Users and 1.6 Billion Likes and 60 Million Photos shared every day

10. Database Systems Landscape Nowadays

11. Somebody, Please, Bring Some Order to This Madness – Cont’d

12. NoSQL Databases Somebody, Please, Bring Some Order to This Madness – Cont’d

13. Different Interfaces Different hardware support Different application support Lack of Uniformity Somebody, Please, Bring Some Order to This Madness Source: http://www.infoq.com/articles/State-of-NoSQL

14. Database Evolution Timeline

15. Additional Resources • Tutorial by C. Mohan, An In-Depth Look at Modern Database Systems • https://docs.google.com/file/d/0B7lNUaak0bK1encwYnBVUWZSWjA/edit

16. Tables or Relations Relational Data

17. Relational Database: Schemas

18. Relational Database: Query Language • SQL - Structured Query Language • a declarative language designed for managing data held in a relational database management system • Tell what you want and from where • Do not tell: how to get the data

19. Key-Value Store • Implemented as an associative array, map, symbol table, or dictionary abstract data type composed of a collection of (key, value) pairs such that each possible key appears at most once in the collection. • A simple put/get interface • Great properties: scalability, availability, reliability

20. Key-Value Store Usage Scenarios • Increasingly popular within data centers and in P2P amazon.com LinkedIn Facebook Vuze uTorrent P2P Data center Voldemort Dynamo Cassandra Vuze DHT uTorrent DHT

21. Row Store and Column Store • In row store data are stored in the disk tuple by tuple. • Where in column store data are stored in the disk column by column. • Column-stores are more I/O efficient for read-only queries as they read, only those attributes which are accessed by a query. Source: Column-Oriented Database Systems, VLDB 2009. Tutorial; S. Harizopoulos, D. Abadi, P. Boncz

22. So column stores are suitable for read-mostly, read-intensive, large data repositories Row Store and Column Store

23. Graph Databases Ecological Network Social Network Biological Network Chemical Network Web Graph Program Flow

24. Graph Databases: Query • Find all the restaurants my friends (in Facebook) like

25. So, What Does CS Curricula Cover? • Undergrad Database Courses • Introduction to Relational Databases • Grad Database Courses • AdvancedRelational Databases in IS&T • Principles of Data Management in CS • Still relational DBMS. • How about the rest of database system types? • Good question… • Where is most of the research activity going on nowadays? • Good question again…

26. So, Why Study Relational DBs? • Jack Clark, The Register, 30 August 2013: “The tech world is turning back toward SQL, bringing to a close a possibly misspent half-decade in which startups courted developers with promises of infinite scalability and the finest imitation-Google tools available, and companies found themselves exposed to unstable data and poor guarantees.” • Google Spanner paper, October 2012: “We believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rather than always coding around the lack of transactions.” • Sean Doherty in Wired, September 2013: “But don’t become unnecessarily distracted by the shiny, new-fangled, NoSQL red buttons just yet. Relational databases may not be hot or sexy but for your important data there is no substitute.”

27. And, The Key Reason of All • Gartner estimates RDBMS market at $26B with about 9% annual growth, whereas Market Research Media Ltd expects NoSQL market to be at $3.5B by 2018. • Source: C Mohan’s tutorial

28. Data Models • A data modelis a collection of concepts for describing data. • Aschemais a description of a particular collection of data, using the a given data model. • The relational model of datais the most widely used model today. • Main concept: relation, basically a table with rows and columns. • Every relation has a schema, which describes the columns, or fields.

29. Levels of Abstraction View 1 View 2 View 3 • Many views, single conceptual (logical) schemaand physical schema. • Views describe how users see the data. • Conceptual schema defines logical structure • Physical schema describes the files and indexes used. Conceptual Schema Physical Schema • Schemas are defined using DDL; data is modified/queried using DML.

30. Example: University Database • Conceptual schema: • Students(sid: string, name: string, login: string, age: integer, gpa:real) • Courses(cid: string, cname:string, credits:integer) • Enrolled(sid:string, cid:string, grade:string) • Physical schema: • Relations stored as unordered files. • Index on first column of Students. • External Schema (View): • Course_info(cid:string,enrollment:integer)

31. Data Independence * • Applications insulated from how data is structured and stored. • Logical data independence: Protection from changes in logical structure of data. • Physical data independence: Protection from changes in physical structure of data. • One of the most important benefits of using a DBMS!

32. Concurrency Control • Concurrent execution of user programs is essential for good DBMS performance. • Because disk accesses are frequent, and relatively slow, it is important to keep the cpu humming by working on several user programs concurrently. • Interleaving actions of different user programs can lead to inconsistency: e.g., check is cleared while account balance is being computed. • DBMS ensures such problems don’t arise: users can pretend they are using a single-user system.

33. Transaction: An Execution of a DB Program • Key concept is transaction, which is an atomicsequence of database actions (reads/writes). • Each transaction, executed completely, must leave the DB in a consistent stateif DB is consistent when the transaction begins. • Users can specify some simple integrity constraintson the data, and the DBMS will enforce these constraints. • Beyond this, the DBMS does not really understand the semantics of the data. (e.g., it does not understand how the interest on a bank account is computed). • Thus, ensuring that a transaction (run alone) preserves consistency is ultimately the user’s responsibility!

34. Scheduling Concurrent Transactions • DBMS ensures that execution of {T1, ... , Tn} is equivalent to some serial execution T1’ ... Tn’. • Before reading/writing an object, a transaction requests a lock on the object, and waits till the DBMS gives it the lock. All locks are released at the end of the transaction. (Strict 2PL locking protocol.) • Idea: If an action of Ti (say, writing X) affects Tj (which perhaps reads X), one of them, say Ti, will obtain the lock on X first and Tj is forced to wait until Ti completes; this effectively orders the transactions. • What if Tj already has a lock on Y and Ti later requests a lock on Y? (Deadlock!) Ti or Tj is abortedand restarted!

35. Ensuring Atomicity • DBMS ensures atomicity(all-or-nothing property) even if system crashes in the middle of a Xact. • Idea: Keep a log(history) of all actions carried out by the DBMS while executing a set of Xacts: • Before a change is made to the database, the corresponding log entry is forced to a safe location. (WAL protocol; OS support for this is often inadequate.) • After a crash, the effects of partially executed transactions are undone using the log. (Thanks to WAL, if log entry wasn’t saved before the crash, corresponding change was not applied to database!)

36. The Log • The following actions are recorded in the log: • Ti writes an object: The old value and the new value. • Log record must go to disk beforethe changed page! • Ti commits/aborts: A log record indicating this action. • Log records chained together by Xact id, so it’s easy to undo a specific Xact (e.g., to resolve a deadlock). • Log is often duplexed and archived on “stable” storage. • All log related activities (and in fact, all CC related activities such as lock/unlock, dealing with deadlocks etc.) are handled transparently by the DBMS.

37. Databases make these folks happy ... • End users and DBMS vendors • DB application programmers • E.g., smart webmasters • Database administrator (DBA) • Designs logical /physical schemas • Handles security and authorization • Data availability, crash recovery • Database tuning as needs evolve Must understand how a DBMS works!

38. Query Optimization and Execution Relational Operators Files and Access Methods Buffer Management Disk Space Management DB Structure of a DBMS These layers must consider concurrency control and recovery • A typical DBMS has a layered architecture. • The figure does not show the concurrency control and recovery components. • This is one of several possible architectures; each system has its own variations.

39. Summary • DBMS used to maintain, query large datasets. • Benefits include recovery from system crashes, concurrent access, quick application development, data integrity and security. • Levels of abstraction give data independence. • A DBMS typically has a layered architecture. • DBAs hold responsible jobs and are well-paid!  • DBMS R&D is one of the broadest, most exciting areas in CS.