Lecture 1: Introduction

Focus on: Relational database, storage, execution, concurrency control, recovery, distributed databases and potpourri.

1. For getting data from a data form using Python, several Data Integrity problems:
   • Different names of one artist
   • Overwrite the year (integer) with an invalid string
   • one album contains several artists
   • Delete artist

Several Implementation problems:

• Particular record
• Create new application uses same database.
• Concurrency problem
• Delete artist

Several Durability problems:

• Machine crash while uploading new records
• Replicate the database on multiple machines for high availability.

DBMS: to allow definition, creation, querying, update, and administration of databases

1. Data Model: collection of concepts for describing the data in a database.

   such as: Relational, K/V, Graph, Document, Column-family, Array/Matrix(Machine Learning), Hierarchical, Network, Multi-value.

   Schema: Description of a particular collection of data , using a given data model.

2. Relational Model

Database abstraction to avoid maintenance:

• Store database in simple data structures.
• Access data through high-level language, DBMS figures out best strategy.
• Physical storage left up to the DBMS implementation.

Structure: Definition of the database’s relations and their contents.

Integrity: Ensure database’s contents satisfy constraints.

Manipulation: Programming interface for accessing and modifying a database’s contents.

e.g. Artist(name, year, country) NULL is a member of every domain.

n-ary Relation = Table with n columns

Primary key identifies a single tuple. (Some DBMSs create an internal primary key if a table does not define one. e.g. SEQUENCE AUTO_INCREMENT)

Foreign Key specifies that an attribute from on relation has to map to a tuple in another relation.

1. Data Manipulation Languages()