Introduction to Databases Data Organisation Definition Data modelling SQL DBMS functions

Basics of data Organisation: DATA HIERARCHY (four categories) Fields = represent a single data item Records = made up of a related set of fields describing one instance of an entity File / Table = a set of related records - as many as instances (occurrence) in the set Database = a collection of related files

Example of data structure Fields Name First name Telephone Zidane Zinedine 45 25 65 65 Feller Joe 25 58 96 63 Clinton Bill 12 25 28 89 Henry Thierry 25 78 85 85 Records + Other files =>complete data Structure = DB File / Table

Database: Definition. "A collection of interrelated data stored together with controlled redundancy, to serve one or more applications in an optimal fashion; the data is stored so that it is independent of the application programs which use it; a common and controlled approach is used in adding new data and in modifying existing data within the database."

Definition - closer look A collection of interrelated data stored together with controlled redundancy to serve one or more applications in an optimal fashion the data is stored so that it is independent of the application programs which use it a common and controlled approach is used in adding new data and in modifying existing data within the database.

Advantages of Databases: data are independent from applications - stored centrally data repository accessible to any new program data are not duplicated in different locations programmers do not have to write extensive descriptions of the files Physical and logical protection is centralised

Disadvantages of DBs: Centralisation can be a weakness Large DBs require expensive hardware and software specialised / scarce personnel is required to develop and maintain large DBs Standardisation of data on a central repository has implications for the format in which it is stored

Characteristics of DBs… High concurrency (high performance under load) Multi-user (read does not interfere with write) Data consistency – changes to data don’t affect running queries + no phantom data changes High degree of recoverability (pull the plug test)

ACID test Atomicity Consistency Isolation Durability All or nothing Preserve consistency of database Transactions are independent Once committed data is preserved

DataBase Management System (DBMS): program that makes it possible to: create Use (insert / update / delete data) maintain a database It provides an interface / translation mechanism between the logical organisation of the data stored in the DB and the physical organisation of the data

Using a database: Two main functions of the DBMS : Query language – searching answers in data (SQL) Data manipulation language - for programmers who want to modify tha data model in which the data is stored + Host Language - the language used by programmers to develop the rest of the application - eg: Oracle developer 2000

Relational DBs: Data items stored in tables Specific fields in tables related to other field in other tables (joint) infinite number of possible viewpoints on the data (queries) Highly flexible DB but overly slow for complex searches Oracle, SyBase, Ingres, Access, Paradox for Windows...

Describing relationships Attempt at modelling the business elements (entities) and their relationships (links) Can be based on users’ descriptions of the business processes Specifies dependencies between the data items Coded in an Entity-Relationship Diagram (ERD)

Types of Relationships one-to-one: one instance of one data item corresponds to one instance of another one-to-many: one instance to many instances many-to-many: many instance correspond to many instances Also some relationships may be: compulsory optional

Example Student registering system What are the entities? What type of relationship do they have? Draw the diagram

Entity Relationship Diagram

Example 2 – Sales Order Processing Entities Relationships Use a business object based approach?

Next step - creating the data structure Few rules - a lot of experience Can get quite complex (paramount for the speed of the DB) Tables must be normalised - ie redundancy is limited to the strict minimum by an algorithm In practice, normalisation is not always the best

Data Structure Diagrams Describe the underlying structure of the DB: the complete logical structure Data items are stored in tables linked by pointers attribute pointers: data fields in one table that will link it to another (common information) logical pointers: specific links that exist between tables Tables have a key Is it an attribute or an entity?

* compulsory attributes 0 optional attributes ORDER order number Item description Item Price Quantity ordered Customer number Item number Customer Customer number Customer name Customer address Customer balance Customer special rate 1 2 3 4 Item Item number Item description Item cost Quantity on hand * compulsory attributes 0 optional attributes

Normalisation Process of simplifying the relationships amongst data items as much as possible (see example provided - handout) Through an iterative process, structure of data is refined to 1NF, 2NF, 3NF etc. Reasons for normalisation: to simplify retrieval (speed of response) to simplify maintenance (updates, deletion, insertions) to reduce the need to restructure the data for each new application

First Normal Form design record structure so that each record looks the same (same length, no repeating groups) repetition within a record means one relation was missed = create new relation elements of repeating groups are stored as a separate entity, in a separate table normalised records have a fixed length and expanded primary key

Second Normal Form Record must be in first normal form first each item in the record must be fully dependent on the key for identification Functional dependency means a data item’s value is uniquely associated with another’s only on-to-one relationship between elements in the same file otherwise split into more tables

Third normal form to remove transitive dependencies when one item is dependent on an item which is dependent from the key in the file relationship is split to avoid data being lost inadvertently this will give greater flexibility for the design of the application + eliminate deletion problems in practice, 3 NF not used all the time - speed of retrieval can be affected

Beyond data modeling Model must be normalised Optimised model “no surprise” model resilience Outcome is a set of tables = logical design Then, design can be warped until it meets the realistic constraints of the system Eg: what business problem are we trying to solve? – see handout [riccardi p. 113, 127] Update anomalies Each item should appear only once + you ask many good questions

Realistic constraints Users cannot cope with too many tables Too much development required in hiding complex data structure Too much administration Optimisation is impossible with too many tables Actually: RDBs can be quite slow!

Key practical questions What are the most important tasks that the DB MUST accomplish efficiently? How must the DB be rigged physically to address these? What coding practices will keep the coding clean and simple? What additional demands arise from the need for resilience and security?

Analysis - Three Levels of Schema External Schema 1 External Schema 2 External Schema … Tables Logical Schema Disk Array Internal Schema

4 way trade-off Security Performance Ease of use Clarity of code

Key decisions Oracle offers many different ways to do things Indexes Backups… Good analysis is not only about knowing these => understanding whether they are appropriate Failure to think it through => unworkable model Particularly, predicting performance must be done properly Ok on the technical side, tricky on the business side

Design optimisation Sources of problems: Network traffic Excess CPU usage But physical I/O is greatest threat (different from logical I/O) Disks still the slowest in the loop Solution: minimise or re-schedule access Also try to minimise the impact of Q4 (e.g. mirroring, internal consistency checks…)

Using scenarios for analysis Define standard situation for DB use Analyse their specific requirements Understand the implications for DB design Compare and contrast new problems with old ones

Categories of critical operations Manual transaction processing = complex DE by small number of operators Automatic transaction processing: large number of concurrent users performing simple DE High batch throughput: automatic batch input into DB of very large number of complex transactions Data warehousing: large volumes of new data thrown on top every day at fixed intervals + intensive querying

Manual transaction processing Insurance telemarketing broker Data entry Retrieving reference info Calculations On-line human-computer interaction!! Instant validation (field by field) Drop-down lists (DE accelerators) Quick response time Critical issue = user-friendly front end, but minimise traffic between interface and back end!

Automatic transaction processing Large number of user performing simple tasks Real-time credit card system (e.g. authorisation) or check out (EPOS) Human interaction at its most simple – eg typing a code or swiping a card Minimum validation, no complex feed back… Large numbers mean potential problems are: Connection opening / closing rate Contention between concurrent users SQL engine pbs + data consistency costs Design with multiple servers

Automatic transaction processing Another eg: on-line shopping What specific problems would arise from shopping cart type applications? How do you handle lost customers?

High batch throughput Eg mobile phone network operator Real time + huge volume of simultaneous complex transactions Number checks Account info Price info Pattern checks Large processing capacity required + need to tackle all transactions together in batches DB query may not be only solution (or quickest) Move customer account to cache Copy updated figures for accounts to a log and updated accounts in slack periods (2.5GB an hour!) Indexing or partitioning for quicker access

“Data warehouse” Huge store of data Large volume added every day 99% new data, 1% corrections to existing data Substantial analysis required prior to development: What to include How to aggregate and organise it Where data comes from Real Oracle territory because schedule is lax – ie not a real time application Key issues: Getting partitioning right Deciding how many summary levels Deciding what to hold and what to recalulate

Partitioning Oldest trick in the book to speed up retrieval (eg?) Smaller bunch of data Well labeled so it can be easily found Smaller index Data manipulation – maintenance, copy and protection far easier Break down big problem (eg table) into small ones

Internet Databases In between types 1 and 2 Many concurrent sessions Reduced interaction front end back end Internet = Extra response time (2 secs!) In practice, many sites are quite slow Key issues “thin client” Reduced dialogue Management of sessions (eg coockies) to avoid multiple restarts

Conclusion: Key issues At one end: very large numbers of small transactions Threat of network or process contention At other end: small number of processes with complex data crunching and time constraints Design of DB and application must reflect these constraints