BTM 382 Database Management

Chapter 2: Data modelsChapter 12.12-13: CAP and Hadoop

Chitu OkoliAssociate Professor in Business Technology ManagementJohn Molson School of Business, Concordia University, Montréal

1

Models and data models

3

What is a model?

• A model is a simplified way to describe or explain a complex reality

• A model helps people communicate and work simply yet effectively when talking about and manipulating complex real-world phenomena

4

Scientific models

Sources:

http://www.redorbit.com/education/reference_library/space_1/universe/2574692/geocentric_model/

http://hendrianusthe.wordpress.com/2012/06/21/heliocentric-vs-geocentric/

5

Conceptual models

Sources:

http://info563.malagaclasses.info/strategy-it-2/

http://fivewhys.wordpress.com/2012/05/22/business-model-innovation/

6

Importance of Data Models

Communication tool

Give an overall view of the database

Organize data for various users

Are an abstraction for the creation of good database

6

The Evolution of Data Models

8

Obsolete models:Hierarchical and network models

9

The Relational Model

• Uses key concepts from mathematical relations (tables)– “Relational” in “relational model” means “tables”

(mathematical relations), not “relationships”• Table (relations)

– Matrix consisting of row/column intersections• Relations have well defined methods (queries) for

combining their data members– Selecting (reading) and joining (combining) data is defined

based on rigorous mathematical principles• Relational data management system (RDBMS)

– Relations where originally too advanced for 1970s computing power

– As computing power increased, simplicity of the model prevailed

10

The Entity Relationship Model

• Very detailed specification of relationships and their properties

• Enhancement of the relational model– Relations (tables) become entities

• Entity relationship diagram (ERD)– Uses graphic representations to model

database components• Many variations for notation exist; we

will use the Crow’s Foot notation

11

12

The Object-Oriented Data Model (OODM)

• Addresses “impedance mismatch” problem of the ER model– The ER model’s view of data (tables) and programmers’ view of

data (objects in OOP), is completely different– This mismatch makes database programming painful, especially

for very complex data structures• OODM Uses object-oriented programming concepts to store

data– Objects represent nouns (entities or records)– Objects have attributes (properties or fields) with values (data)– Objects have methods (operations or functions)– Classes group similar objects using a hierarchy and inheritance

• In an OODBMS, the data retrieval and storage closely mirrors the data structures that programmers use, and so programming complex objects is much easier than with the ER model

• More advanced forms support the Extended Relational Data Model, Object/Relational DBMS, and XML data structures

13

OODBMS vs. RDBMS

https://youtu.be/kORTgvfHl4g

Big Data and NoSQL

15

Explaining Big Data

https://youtu.be/7D1CQ_LOizA

16

Big Data

• Volume– Huge amounts of data (terabytes and

petabytes), especially from the Internet• Velocity

– Organizations need to process the huge amounts of data rapidly, just as with smaller databases

• Variety– Wide variety of data, much of it

unstructured and even changing in structure

16

17

Big data’s solutions and RDBMS’s failure

• Scale up: use more powerful servers– RDBMS is very computing intensive– More data requires much faster, more

capable, expensive computers, and even that’s not good enough for big data

• Scale out: use many cheap distributed servers– RDBMS doesn’t work rapidly with distributed

processing– Consistency is the biggest problem:

guaranteeing consistency (which RDBMS is great at) is slow, too slow for big data

18

What is NoSQL?

https://www.youtube.com/watch?v=qUV2j3XBRHc

19

NoSQL Databases to the Big Data rescue

• “NoSQL” means:– Non-relational or non-RDBMS– Also “Not only SQL”—a few do support SQL

• It is not one model; it is many different models that are not relational

• High scalability– Support distributed database architectures

• High availability– Rapid performance for big data, including unstructured and sparse

data• Fault tolerance

– Continue to work even if some servers in the cluster fail• Geared toward performance rather than transaction consistency• Store data in key-value stores

19

20

Disadvantages of NoSQL

• Complex programming is required– “NoSQL” means you lose the ease-of-use and

structural independence of SQL– There is often no relationship support in the

database—you have to program relationships in code

• There is no transaction integrity support– The data you retrieve at any given moment might be

wrong… but it will eventually become OK– This is the price to pay for rapid performance in a

distributed database

20

21

The CAP theorem for distributed databases

• CAP stands for:– Consistency: All nodes see the same data– Availability: A request always gets a response (success or

failure)– Partition tolerance: Even if a node fails, the system can

still function• A distributed database can guarantee only two of

the three CAP characteristics, never all three at the same time– However, over time, it might be able to provide all three

• NoSQL databases are distributed, and so the CAP theorem restricts them to providing BASE, not ACID

21

22

ACID versus BASE

• A relational database guarantees the ACID properties:– Atomicity, Consistency, Isolated, Durable– In short, a set of SQL statements (called a

transaction) will either all work, or all fail—no half way success, and the result will not corrupt the database

– A price to pay: results might be somewhat slow• NoSQL database only guarantee BASE

properties:– Basically Available, Soft-state, Eventual consistency– In short, at any given moment, not everything might

be consistent, but the database will eventually get consistent

– In return, these imperfect results are delivered fast

23

Table 12.8 – Distributed Database Spectrum

23

Sacrifices availability to ensure consistency and isolation

24

Historical outline of data models

25

Which data model should you use?

• Hierarchical or network models– Obsolete—no one uses these any longer

• Entity-relationship model– Continuation or enhancement of the relational model– 90% or more of professional database situations

• Object-oriented database– When you have very complex data structures, you need

rapid performance, and it makes business sense• Source: Barry & Associates, Inc

– Data structures are so complex that organizing data as tables causes headaches in programming retrieval and storage

• NoSQL– Vast amounts of unstructured data where you need rapid

performance– Speed is more important than data consistency

26

Sources

• Most of the slides are adapted from Database Systems: Design, Implementation and Management by Carlos Coronel and Steven Morris. 11th edition (2015) published by Cengage Learning. ISBN 13: 978-1-285-19614-5

• Other sources are noted on the slides themselves

26