Overview of Database Systems CS3860 - Jay Urbain, PhD Introduction - - PowerPoint PPT Presentation

Overview of Database Systems

CS3860 - Jay Urbain, PhD Introduction to Database Systems

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UFR – Uniform Financial Report (I think)

What is a database?

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What is a database?

Collection of data – Files, notes, to do list, patient records, play lists, etc.

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What is a Database Management System?

DBMS?

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My family room

Database Management System

DBMS: A Database Management System (DBMS) is a software package designed to store and manage databases.

• Typically large, integrated collection of data
• Models real-world enterprise

– Entities (e.g., students, courses, professors) – Relationships (e.g., Bono is enrolled in CS3860; urbain is teaching CS3860)

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Files vs. DBMS

Why not just use files?

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Why use a DBMS?

• Data independence
• Efficient access
• Reduced application development time
• Data integrity
• Data security
• Uniform data administration
• Concurrent access, recovery from crashes
• Distributability, scalability
• Aggregate functions

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Why Study Databases?

• Shift from computation to information

– At the low-end: scramble to web-space, big data – At the high-end: scientific applications, data analytics, collective intelligence, eScience, machine learning

• Shift to functional computing on large datasets.
• Datasets increasing in diversity & volume
• Digital libraries, interactive video, Human Genome, the

web…

• Integration of structured and unstructured data
• Polyglot persistance

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Why Study Databases?

• DBMS encompass most of CS

– OS, languages, theory, computational complexity, data structures, algorithms, AI, multimedia, logic

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Big science is data driven. IceCube Neutrino Observatory.

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Data analysis in the fight against human trafficking. All of society is online.

New York DA use MEMEX Data for all trafficking investigations this year.

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Increasingly many companies see themselves as data driven.

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https://www.youtube.com/watch?v=OvfU1NpCJQQ https://www.youtube.com/watch?v=3xGoBlI_fdg https://www.youtube.com/watch?v=OpDIEJrog3s

EVEN MORE “TRADITIONAL” COMPANIES…

THE WORLD IS INCREASINGLY DRIVEN BY DATA…

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This class teaches the basics of how to use & manage data.

Big Data Landscape… Infrastructure is Changing

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http://www.bigdatalandscape.com/

New tech. Same Principles.

Why should you study databases?

• Mercenary- make more $$$:

– Startups need DB talent right away = low employee # – Massive industry…

• Intellectual:

– Science: data poor to data rich

• No idea how to handle the data!

– Fundamental ideas to/from all of CS:

• Systems, theory, AI, logic, stats, analysis….

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Many great computer systems ideas started in DB.

Data Models

• A data model is a collection of concepts for describing data.
• The relational data model is one of most popular models.
• Main concept: relation, basically a table with rows (records) and

columns (attributes).

• Every relation has a schema which describes the relation name, the

name of the columns (or fields), and the field types.

• A schema is a description of a particular collection of data, using the

given data model.

• A semantic data model is a more abstract high level representation

that makes it easier to come up with an initial description.

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Levels of Abstraction

• Many views (external schema)

– Describe how users see the data

• Single conceptual (logical)

schema

– Defines logical structure

• Single physical schema

– Describes the files (pages, blocks) and indexes used.

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Example: University Database

• Conceptual schema:

– Students(sid: string, name: string, login: string, age: integer, gpa: real); – Courses(cid: string, cname: string, credit: integer); – Enroll(sid: string, cid: string, grade: string);

• Physical schema:

– Relations stored as ordered/unordered files – Index on first column of Students

• External schema (View):

– Course_Info(cid: string, enrollment: integer);

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Data Independence

Applications insulated from how data is structured and stored.

• Logical data independence: Protection from changes in

the logical structure (conceptual schema) of data.

• Physical data independence: Protection from changes in

physical structure of data.

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Concurrency Control

• Concurrent execution (threads) of user programs is

required for good DBMS performance.

• Since disk access is frequent and 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 cleared while account balance is still being computed.

• DBMS ensures such problems do not happen: users can

pretend they are using a single-user system.

• Alternatively, other database models allow eventual

consistency.

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Transaction

• A transaction is an execution of a DB program.
• Key concept of transaction: an atomic sequence of

database actions (reads/writes).

• Each transaction, executed completely, must leave the

DB in a consistent state (provided DB was consistent when the transaction begins).

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Transactions

• Users can specify some simple integrity constraints on

the data and the DBMS will enforce these constraints.

• The DBMS does not really understand the semantics

(meaning) of the data. – E.g., how interest is calculated on student overdue accounts.

• So, ensuring that a transaction (runs alone) is ultimately

the user’s responsibility!

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Polyglot Persistence

• Using different data storage technologies to handle

varying data storage needs.

• An application that talks to different databases using

each for what they are best at to achieve an end goal,

• http://www.dummies.com/programming/big-data/engineering/big-

data-and-polyglot-persistence/

• http://www.informit.com/articles/article.aspx?p=1930511
• https://martinfowler.com/bliki/PolyglotPersistence.html

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A Note on DMBS’s: there are many

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