Introduc/on&to&& Database&Systems&

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Introduc/on to Database Systems

CISC437/637, Lecture #1 Ben CartereAe

1 Copyright © Ben CartereAe

Copyright © Ben CartereAe 2

Physical and logical organiza/on of databases. Data retrieval languages, rela/onal database languages, security and integrity, concurrency, distributed databases.

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Database Systems

•  The overview in 5 Ws (and one H): – What is a database? What is a database management system (DBMS)?

– Why use databases? Why study them?

– Who works with databases? – How does a DBMS work? – Where and when did databases originate?


What is a Database?

•  A database is a collec/on of data – Usually large quan//es of interrelated data •  E.g. student records, faculty records, courses, classrooms, payrolls, …

•  A database management system (DBMS) is a so]ware system designed to store and manage data


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Why Use a DBMS?

“So a bunch of text files on disk can be a database. I’ll just process them with Python. Why do I need to learn about DBMS so]ware?”

•  Data too large to fit in memory; files too big for random access on disk

•  Arbitrarily complex queries that must be answered quickly •  Many users accessing data concurrently

•  Some users need different access permissions


Why Use a DBMS?

•  Data independence •  Efficient access

•  Integrity and security •  Access administra/on

•  Concurrent access •  Applica/on development /me


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Why Not Use a DBMS?

•  DBMSs are large, complex programs designed for very general data needs and workloads; not always op/mal for specialized tasks

•  Applica/on may need to manipulate data in ways not supported by DBMS

•  Security, concurrent access, crash recovery may not be cri/cal

•  Example: web search


Why Study Databases?

•  Mul/billion dollar industry, second only to opera/ng systems

•  Databases form backbone of many informa/on-‐centric applica/ons – Using computa/on to create and understand informa/on

•  Implemen/ng and understanding DBMS incorporates knowledge from every area of CS – Systems, theory, ar/ficial intelligence


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Applica/ons of Databases

•  Electronic commerce and banking – Amazon, eBay, PayPal

–  Integra/ng vast catalogs and accounts, high security

•  Social networking – Facebook, TwiAer – Analyzing flow of informa/on through large, /ghtly-‐connected networks


Applica/ons of Databases

•  Sensor networks – GPS, RFID, … – O]en supports mission-‐cri/cal applica/ons – Response to failures and trust are important

•  Bioinforma/cs, health informa/cs – Gene Ontology, PubMed, … – Requires data integra/on, paAern matching, approximate matching, ranking, automa/c inference


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Who Works With Databases?

•  DBMS programmers actually implement the DBMS so]ware

•  Database administrators design storage requirements, handle security, ensure graceful recovery, tune database performance

•  Applica;ons programmers write so]ware that interacts with a database

•  End users use the so]ware wriAen by applica/ons programmers


How Does a DBMS Work?

•  This is the focus of the course •  Today: a brief overview of the topics that will be covered

1.  Data Models 2.  Database Queries 3.  Transac/on Management 4.  DBMS Structure


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Data Models

•  A data model is a collec/on of concepts for describing data

•  A schema is a descrip/on of a par/cular collec/on of data using a given model

•  The rela;onal data model is the most commonly used – Rela;ons (tables of records) are the main concept – Every rela/on has a schema that describes the record fields/table columns


Levels of Abstrac/on

•  Physical schema describes the specific files used to store a rela/on on disk

•  Conceptual schema defines the logical structure of rela/ons

•  Views or external schema describe how users see the data


Physical Schema

Conceptual Schema

View 1 View 2 View 3

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Data Independence

•  Using an external schema does not require knowledge of conceptual schema – Logical data independence

•  Using a conceptual schema does not require knowledge of physical schema – Physical data independence

•  In other words, applica/ons are insulated from how data is structured and stored


Database Queries

•  Queries are ques/ons asked of the data •  A query language specifies how queries are posed in a specific data model –  The language consists of keywords and operators for manipula/ng rela/ons – the data manipula;on language (DML)

•  Formula/ng a query does not require knowledge of physical schema

•  Allows fast applica/on development –  Embed DML in high-‐level language like Java, C, Python


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Concurrency Control

•  Many databases are used by mul/ple users concurrently – Each user is manipula/ng rela/ons in different ways

– Simultaneous uses can result in inconsistencies •  E.g. one is looking up vacancies while another is making a reserva/on

•  DBMS ensures that these problems don’t happen


Transac/on Management

•  A transac;on is an atomic sequence of database ac/ons (reads and writes)

•  The complete execu/on of each transac/on must leave the database in a consistent state if the database is consistent when it begins – Consistency means no logical conflicts

•  User/applica/on formulates integrity constraints for the DBMS to enforce


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Scheduling Transac/ons

•  DBMS ensures that execu/on of {T1, …, Tn} is equivalent to serial execu/on T1’, …, Tn’ –  Locks: before reading or wri/ng, a transac/on requests a lock on an object, and does nothing un/l DBMS grants lock. Locks are released a]er execu/on.

– Use locks to force ordering of unordered transac/ons. – Deadlock: Ti has lock on object A and needs lock on object B. Tj has lock on object B and needs lock on object A.


Atomicity

•  “All or nothing”: an atomic transac/on is one that either completely finishes or does not happen at all

•  DBMS needs to maintain atomicity even when it crashes in the middle of transac/ons

•  Use a log to keep track of ac/ons DBMS takes to execute transac/on – Write-‐ahead log (WAL) enables this

•  Transac/on isn’t done un/l all of its ac/ons are done


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Write-‐Ahead Log

•  The log consists of the following: – For write ac/ons, the old data and the new data – A flag indica/ng whether the transac/on was commiAed or aborted

•  Transac/ons can be undone when commit not present

•  Deadlocks can be resolved by abor/ng one transac/on and allowing the other to con/nue


DBMS Structure

•  Layered architecture, each layer only aware of layer below it


Query op/miza/on & execu/on

Rela/onal operators

Files and access methods

Buffer management

Disk space management

DB

Recovery manager

Transac/on manager

Lock manager

Concurrency control

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When and Where

•  Charles Bachman designed the Integrated Data Store at General Electric in the 1960s

•  The network data model, a tree-‐based representa/on designed for explora/on rather than querying

•  First Turing Award winner in 1973


When and Where

•  Edgar Codd proposed rela/onal data model in 1970 at IBM

•  Quickly became the basis of commercial systems; strong theore/cal founda/on developed

•  Turing Award 1981


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When and Where

•  Jim Gray made fundamental contribu/ons to transac/on management in the 80s and 90s

•  Allowed DBMSs to scale to huge applica/ons with thousands or millions of users

•  Turing Award 1999 Copyright © Ben CartereAe 25

Summary

•  DBMS used to maintain and query large amounts of data

•  They allow concurrent access, recovery from failure, fast applica/on development, security

•  Levels of abstrac/on mean that one can work on one subproblem without knowing about others

•  Huge industry and huge research area in CS


Documents

Introduc/on&to&& Database&Systems&