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An Introduction to Relational Database Management System

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An Introduction to Relational Database Management System An introduction to relational database management system Fabrice Rossi CEREMADE Université Paris Dauphine 2021 Database Management Systems Database (DB) An organized collection of data Database Management System (DBMS) A software used to manage databases, namely I creating a database I maintaining a database I interacting with a database 2 Advantages of DBMS I meta data management I integrity constraints I security (access control) I concurrency control I etc. DBMS versus Files File-Based data management I data in CSV files (or other format) I management with e.g. Python + Pandas (or R + tidyverse) 3 DBMS versus Files File-Based data management I data in CSV files (or other format) I management with e.g. Python + Pandas (or R + tidyverse) Advantages of DBMS I meta data management I integrity constraints I security (access control) I concurrency control I etc. 3 DBMS components DBMS architecture I storage manager I handles saving data and meta data on the storage system I manages part of the concurrency aspects I query processor I core part of the DBMS I handles computation on the database (e.g. search, insertion, deletion, modification, aggregation) I optimize queries for efficiency I high level tools I meta data management I connection and security manager 4 DBMS components DBMS languages I data definition language (DDL) I used to specify a data model I data types I links between data I meta data management I data manipulation language (DML) I used to query a database I specifies computation on a database 5 Types of DBMS Data model categorization I old approaches: hierarchical DBMS and network DBMS I current standard: relational DBMS I based on a relational data model I typical based on SQL (Structured Query Language, sequel) I niche approach: object-oriented DBMS I large scale or unstructured data: NoSQL I not-only SQL (or more specifically not relational) I document stores I graph oriented databases I column oriented databases 6 What’s missing? I A lot! I DBMS are a very rich and complex subject I numerous theoretical aspects I getting under the hood is yet another subject I practical aspects are also very complex In this course An introduction to Relational DBMS 7 In this course An introduction to Relational DBMS What’s missing? I A lot! I DBMS are a very rich and complex subject I numerous theoretical aspects I getting under the hood is yet another subject I practical aspects are also very complex 7 Outline Introduction Conceptual Data Modeling The Relational Model SQL 8 Outline Introduction Conceptual Data Modeling The Relational Model SQL 9 Data Management Specifying the data I ideally the first step of a data oriented project I important questions I what are the objects under study? I how are they described? I how are they related? Two steps process 1. conceptual/formal model 2. implementation in a meta data oriented language 10 Entity Relationship Model Proposed by Peter Chen in 1976 Concepts I Entity: uniquely identified object under study (e.g. a person) I Relationship: a way to relate entities (e.g. a has access to b) I Attribute: a property of an entity or of a relationship. An attribute has a domain (the set of values it can take) I an ER model describes types, e.g. entity type, not values 11 Example Loan application data set I https://relational.fit.cvut.cz/dataset/Financial I 8 tables including I client table I account table I credit card table I loan table I etc. 12 key attribute Example (part of the) Client table ER model client_id gender birth_date I entity type: client 1 F 1970-12-13 2 M 1945-02-04 I attributes 3 F 1940-10-09 4 M 1956-12-01 I gender with domain F and 5 F 1960-07-03 M I birth_date with a date domain I client_id 13 key attribute Example (part of the) Client table ER model client_id gender birth_date I entity type: client 1 F 1970-12-13 2 M 1945-02-04 I attributes 3 F 1940-10-09 4 M 1956-12-01 I gender with domain F and 5 F 1960-07-03 M I birth_date with a date domain I client_id Client gender birth_date client_id 13 Example (part of the) Client table ER model client_id gender birth_date I entity type: client 1 F 1970-12-13 2 M 1945-02-04 I attributes 3 F 1940-10-09 4 M 1956-12-01 I gender with domain F and 5 F 1960-07-03 M I birth_date with a date domain I client_id key attribute Client gender birth_date client_id 13 Graphical representation Entity type Entity types are represented by rectangles link to attributes Attribute type I an ellipse per type I derived attribute type I key attribute type I can be computed from I a unique identifier of the another one (e.g. age corresponding entity from birth date) I underlined in the I dashed border representation I multi-valued attribute type I composite attribute type I several values are I can be decomposed into authorized sub-attributes I double border I linked ellipses 14 Example Last name First name email name Person gender birth_date age client_id 15 Typical domains Numerical Textual I integers I words I decimal numbers I codes (such as post codes) I possible constraints: positive I structured strings (e.g. numbers, number of emails) significant digits, etc. Others Temporal I truth values (boolean) I dates I binary content (such as I times images) 16 Relationship Principle I a relationship represents an association between at least two entities I it can have attributes I it is characterized by cardinalities I minimum and maximum number of relationships to which a given entity can participate I asymmetric I a relationship type is graphically represented by a rhombus 17 Example Loan application data set I client entities and account entities I relationships I a client can be the owner of an account I a client can be allowed to use an account I cardinalities I owner: I each account has exactly one owner I each client can own at most one account (in this database) I user: I each account may have some users I each client can be the user of some accounts 18 Example Owns 0-1 1-1 client_id Client Account account_id 0-n 0-m Uses 19 Outline Introduction Conceptual Data Modeling The Relational Model SQL 20 Relational Model History I invented by Edgar F. Codd in 1969-1970 I based on a mathematical model, the relational algebra I associated relational calculus Main concepts I a domain: a set Qn I a relation: a (finite) subset of i=1(Di [ fNULLg) where each Di is a domain and NULL a special value (for missing values) I a tuple: an element of a relation I a database: a collection of relations 21 Examples Clients Accounts I Domains: I Domains: + + I N : positive integers I N : positive integers I G = fF; Mg: genders I D: dates I D: dates I An account relation: + + I A client relation: I a subset of N × N × D + I a subset of N × G × D I a set of tuples such as I a set of tuples such as (1; 8; 1995−03−24) (1; F; 1970−12−13) 22 Relation type/variable Specifying relations I somewhat imprecise vocabulary Qn I a relation (value) r 2 i=1(Di [ fNULLg) I a relation type/variable (relvar) I a named specification of the relation I domains and names: column types Notation I a relation type/variable is denoted R(A1;:::; An): I R: name of the relation type I Ak : column type names I e.g. client(client_id, gender, birth_date) I and account(account_id, district_id, date) 23 ER and relational model Correspondence I Entity type: relation type/variable, relvar I Entity: tuple I Attribute type: column type (frequently called attribute type) I Attribute: value in a tuple More about ER ! relational later 24 Keys Superkeys and keys I a relation is a set ) each tuple is unique I a superkey I any subset of the attribute types such that no tuples of the relation share exactly the same values for these attribute types I default superkey: all the attribute types! I a key: a minimal superkey (removing attribute types removes it superkey status) France data set I departement(region_id,departement_id,name) I superkeys I (region_id,departement_id,name) I (departement_id,name) I (departement_id): a key! I (name): another key! 25 Primary and foreign keys Primary key I several keys: candidate keys I one of the is the primary key I other are alternative keys Foreign keys I a set of attribute types FK in a relation type R1 is a foreign key of R1 if 1. a candidate key in a relation R2 has exactly the same domains as the ones of the attribute types in FK 2. values on FK in a tuple in r1 are either NULL or occur in a tuple in r2 I in simple terms: a foreign key links R1 to R2 26 Example Relvar I client(client_id, gender, birth_date) I account(account_id, district_id, date) I disp(disp_id, client_id, account_id, type) with type2 fOWNER; DISPONENT g Many keys I bold and underlined: primary keys I underlined: foreign keys I district_id is also a foreign key but for another relation I in this database, the relation between a client and an account is unique and thus (client_id, account_id) is candidate key 27 Integrity Constraints Relational constraints When implemented the relational model must enforce some constraints I Domain integrity: the values in a tuple are unique values from the associated domain or NULL (if allowed, i.e.
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