DOCSLIB.ORG

Testing Database Transaction Concurrency

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Testing Database Transaction Concurrency Testing Database Transaction Concurrency Yuetang Deng Phyllis Frankl Zhongqiang Chen Department of Computer and Information Science Technical Report TR -CIS-2003-03 10/20/2003 Testing Database Transaction Concurrency1 Yuetang Deng Phyllis Frankl Zhongqiang Chen Department of Computer and Information Science Polytechnic University Brooklyn, NY 11201 USA {ytdeng, phyllis, zchen}@cis.poly.edu Abstract with transactions, comprising groups of related queries. This paper focuses on concurrency problems associated Database application programs are often designed to with multiple simultaneously running instances of an be executed concurrently by many users. By grouping application program. related database queries into transactions, DBMS Many database applications are designed to support systems can guarantee that each transaction satisfies the concurrent users. For example, in an airline reservation well-known ACID properties: Atomicity, Consistency, system, many clients can simultaneously purchase tickets. Isolation, and Durability. However, if a database To avoid simultaneous updates of the same data item and application is decomposed into transactions in an other potential concurrency problems, database incorrect manner, the application may fail when executed applications employ program units called transactions, concurrently due to potential offline concurrency consisting of a group of queries that access, and possibly problems. update, data items. The DBMS schedules query This paper presents a dataflow analysis technique for executions so as to insure data integrity at transaction identifying schedules of transaction execution aimed at boundaries while trying to utilize resources efficiently. revealing concurrency faults of this nature, along with Although the DBMS employs sophisticated techniques for controlling the DBMS or the application mechanisms to assure that transactions satisfy the ACID so that execution of transaction sequences follows properties – Atomicity, Consistency, Isolation, and generated schedules. The techniques have been integrated Durability – an application can still have concurrency- into AGENDA, a tool set for testing relational database related faults if the application programmer erroneously application programs. Preliminary empirical evaluation placed queries in separate transactions when they should is presented. have been executed as a unit. The focus of this paper is on detecting faults of this nature. 1. Introduction The rest of the paper is organized as follows. Section 2 introduces the background, including the AGENDA tool Database and transaction processing systems occupy a set for database application testing. Section 3 identifies central position in our information-based society. It is some typical transaction concurrency problems. Our essential that these systems function correctly and provide approach to test for concurrency problems is presented in acceptable performance. Substantial effort has been Section 4. Section 5 discusses the problems with devoted to insuring that the algorithms and data structures transaction atomicity/durability in database applications used by Database Management Systems (DBMSs) work and our solution. Section 6 presents preliminary empirical efficiently and protect the integrity of the data. However, evaluation. Section 7 describes the related work. Section relatively little attention has been given to developing 8 concludes with our contributions and discusses future systematic techniques for assuring the correctness of the work. related database application programs. Given the critical role these systems play in modern society, there is clearly 2. Background a need for new approaches to assess the quality of the database application programs. 2.1. Database Transaction To address this need, we have developed a systematic, partially-automatable approach and a tool set based on A transaction is a means by which an application this approach to testing database applications [4,5,6] programmer can package together a sequence of database Initially, we focused on problems associated with operations so that the database system can provide a individual queries. In [8] we explore problems dealing number of guarantees, known as the ACID properties of a 1 Supported in part by NSF grant CCR-9988354 transaction [11]. Atomicity ensures that the set of updates AGENDA takes as input the database schema of the contained in a transaction must succeed or fail as a unit. database on which the application runs, the application Consistency means that a transaction will transform the source code, and “sample value files”, containing some database from one consistent state to another. The suggested values for attributes. The tester interactively isolation property guarantees that partial effects of selects test heuristics and provides information about incomplete transactions should not be visible to other expected behaviors of test cases. Using this information, transactions. Durability means that effects of a committed AGENDA populates the database, generates inputs to the transaction are permanent and must not be lost because of application, executes the application on those inputs and later failure. Therefore, transactions can be used to solve checks some aspects of correctness of the resulting problems such as creation of inconsistent results, database state and the application output. concurrent execution errors within transactions and lost This approach is loosely based on the Category- results due to system crash [16]. Partition method [17]: the user supplies suggested values A database application program typically consists of for attributes, optionally partitioned into groups, which SQL statements embedded in a source language, such as we call data groups. The data are provided in the sample C or Java. Ends of transactions are delimited by the value files. The tool then produces meaningful keyword COMMIT (or ROLLBACK). Once a transaction combinations of these values in order to fill database begins execution, the DBMS assures that no other tables and provide input parameters to the application transaction executes a statement that interferes with this program. Data groups are used to distinguish values that transaction (e.g. by updating data being accessed by the are expected to result in different application behaviors. transaction) until the transaction commits or rolls back. For example, in a payroll system, different categories of The system can initiate a rollback when it enters a state in employees may be treated differently. Additional which ACID properties may be violated, either due to information about data groups, such as probability for overly optimistic scheduling decisions or to external selecting a group, can also be provided via sample value events such as system failure. When a rollback occurs, the files. system is restored to the state before execution of the Using these data groups and guided by heuristics, transaction. Once a transaction commits, all of the AGENDA produces a collection of test templates changes it has made to the database state become representing abstract test cases. The tester then provides permanent. information about the expected behavior of the In this paper, we consider application programs written application on tests represented by each test template. For in a high-level language such as C, with SQL statements example, the tester might specify that the application interspersed. The SQL statements may involve host should increase the salaries of employees in the “faculty” variables, which are variables declared in the host group by 10% and should not change any other attributes. program (host variables are preceded by colons). Multiple In order to control the explosion in the number of test clients may execute an application program concurrently. templates and to force the generation of particular kinds In this case, each client has its own host variables. The of templates, the tester selects heuristics. Finally, only “variable” shared by concurrently executing clients AGENDA instantiates the templates with specific test is the database. In this paper we do not consider stored values, executes the test cases and checks that the output procedures or shared variables arising through multi- and new database state are consistent with the expected threading within an application instance. behavior indicated by the tester. The architecture of AGENDA is shown in Figure 1. 2.2. AGENDA Tool Set The first component (AGENDA Parser) extracts relevant information from the application’s database In our previous work [4,5], we have discussed issues schema, the application queries, and tester-supplied arising in testing database systems, presented an approach sample value files, and then makes this information for testing database applications, and described available to the other four components by creating an AGENDA, a set of tools based on this approach. In internal database called AGENDA DB to store the testing such applications, the states of the database before extracted information. The AGENDA DB is used and/or and after the application’s execution play an important modified by the remaining four components. AGENDA role, along with the user’s input and the system output. A Parser is a modified version of from the open-source framework for testing database applications was DBMS Postgresql parser [18]. introduced. A complete tool set based on the framework The second component (State Generator) uses the has been prototyped. The components
Load more

Recommended publications
  • SAP IQ Administration: User Management and Security Company
    ADMINISTRATION GUIDE | PUBLIC SAP IQ 16.1 SP 04 Document Version: 1.0.0 – 2019-04-05 SAP IQ Administration: User Management and Security company. All rights reserved. All rights company. affiliate THE BEST RUN 2019 SAP SE or an SAP SE or an SAP SAP 2019 © Content 1 Security Management........................................................ 5 1.1 Plan and Implement Role-Based Security............................................6 1.2 Roles......................................................................7 User-Defined Roles.........................................................8 System Roles............................................................ 30 Compatibility Roles........................................................38 Views, Procedures, and Tables That Are Owned by Roles..............................38 Display Roles Granted......................................................39 Determining the Roles and Privileges Granted to a User...............................40 1.3 Privileges..................................................................40 Privileges Versus Permissions.................................................41 System Privileges......................................................... 42 Object-Level Privileges......................................................64 System Procedure Privileges..................................................81 1.4 Passwords.................................................................85 Password and user ID Restrictions and Considerations...............................86
    [Show full text]
  • *** Check Constraints - 10G
    *** Check Constraints - 10g *** Create table and poulated with a column called FLAG that can only have a value of 1 or 2 SQL> CREATE TABLE check_const (id NUMBER, flag NUMBER CONSTRAINT check_flag CHECK (flag IN (1,2))); Table created. SQL> INSERT INTO check_const SELECT rownum, mod(rownum,2)+1 FROM dual CONNECT BY level <=10000; 10000 rows created. SQL> COMMIT; Commit complete. SQL> exec dbms_stats.gather_table_stats(ownname=>NULL, tabname=>'CHECK_CONST', estimate_percent=> NULL, method_opt=> 'FOR ALL COLUMNS SIZE 1'); PL/SQL procedure successfully completed. *** Now perform a search for a value of 3. There can be no such value as the Check constraint only permits values 1 or 2 ... *** The exection plan appears to suggest a FTS is being performed (remember, there are no indexes on the flag column) *** But the statistics clearly show no LIOs were performed, none SQL> SELECT * FROM check_const WHERE flag = 3; no rows selected Execution Plan ---------------------------------------------------------- Plan hash value: 1514750852 ---------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ---------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | 6 | 0 (0)| | |* 1 | FILTER | | | | | | |* 2 | TABLE ACCESS FULL| CHECK_CONST | 1 | 6 | 6 (0)| 00:00:01 | ---------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------
    [Show full text]
  • Chapter 10. Declarative Constraints and Database Triggers
    Chapter 10. Declarative Constraints and Database Triggers Table of contents • Objectives • Introduction • Context • Declarative constraints – The PRIMARY KEY constraint – The NOT NULL constraint – The UNIQUE constraint – The CHECK constraint ∗ Declaration of a basic CHECK constraint ∗ Complex CHECK constraints – The FOREIGN KEY constraint ∗ CASCADE ∗ SET NULL ∗ SET DEFAULT ∗ NO ACTION • Changing the definition of a table – Add a new column – Modify an existing column’s type – Modify an existing column’s constraint definition – Add a new constraint – Drop an existing constraint • Database triggers – Types of triggers ∗ Event ∗ Level ∗ Timing – Valid trigger types • Creating triggers – Statement-level trigger ∗ Option for the UPDATE event – Row-level triggers ∗ Option for the row-level triggers – Removing triggers – Using triggers to maintain referential integrity – Using triggers to maintain business rules • Additional features of Oracle – Stored procedures – Function and packages – Creating procedures – Creating functions 1 – Calling a procedure from within a function and vice versa • Discussion topics • Additional content and activities Objectives At the end of this chapter you should be able to: • Know how to capture a range of business rules and store them in a database using declarative constraints. • Describe the use of database triggers in providing an automatic response to the occurrence of specific database events. • Discuss the advantages and drawbacks of the use of database triggers in application development. • Explain how stored procedures can be used to implement processing logic at the database level. Introduction In parallel with this chapter, you should read Chapter 8 of Thomas Connolly and Carolyn Begg, “Database Systems A Practical Approach to Design, Imple- mentation, and Management”, (5th edn.).
    [Show full text]
  • 2.4. Constraints
    PostgreSQL 7.3 Documentation Prev Chapter 2. Data Definition Next 2.4. Constraints Data types are a way to limit the kind of data that can be stored in a table. For many applications, however, the constraint they provide is too coarse. For example, a column containing a product price should probably only accept positive values. But there is no data type that accepts only positive numbers. Another issue is that you might want to constrain column data with respect to other columns or rows. For example, in a table containing product information, there should only be one row for each product number. To that end, SQL allows you to define constraints on columns and tables. Constraints give you as much control over the data in your tables as you wish. If a user attempts to store data in a column that would violate a constraint, an error is raised. This applies even if the value came from the default value definition. 2.4.1. Check Constraints A check constraint is the most generic constraint type. It allows you to specify that the value in a certain column must satisfy an arbitrary expression. For instance, to require positive product prices, you could use: CREATE TABLE products ( product_no integer, name text, price numeric CHECK (price > 0) ); As you see, the constraint definition comes after the data type, just like default value definitions. Default values and constraints can be listed in any order. A check constraint consists of the key word CHECK followed by an expression in parentheses. The check constraint expression should involve the column thus constrained, otherwise the constraint would not make too much sense.
    [Show full text]
  • Reasoning Over Large Semantic Datasets
    R O M A TRE UNIVERSITA` DEGLI STUDI DI ROMA TRE Dipartimento di Informatica e Automazione DIA Via della Vasca Navale, 79 – 00146 Roma, Italy Reasoning over Large Semantic Datasets 1 2 2 1 ROBERTO DE VIRGILIO ,GIORGIO ORSI ,LETIZIA TANCA , RICCARDO TORLONE RT-DIA-149 May 2009 1Dipartimento di Informatica e Automazione Universit`adi Roma Tre {devirgilio,torlone}@dia.uniroma3.it 2Dipartimento di Elettronica e Informazione Politecnico di Milano {orsi,tanca}@elet.polimi.it ABSTRACT This paper presents NYAYA, a flexible system for the management of Semantic-Web data which couples an efficient storage mechanism with advanced and up-to-date ontology reasoning capa- bilities. NYAYA is capable of processing large Semantic-Web datasets, expressed in a variety of formalisms, by transforming them into a collection of Semantic Data Kiosks that expose the native meta-data in a uniform fashion using Datalog±, a very general rule-based language. The kiosks form a Semantic Data Market where the data in each kiosk can be uniformly accessed using conjunctive queries and where users can specify user-defined constraints over the data. NYAYA is easily extensible and robust to updates of both data and meta-data in the kiosk. In particular, a new kiosk of the semantic data market can be easily built from a fresh Semantic- Web source expressed in whatsoever format by extracting its constraints and importing its data. In this way, the new content is promptly available to the users of the system. The approach has been experimented using well known benchmarks with very promising results. 2 1 Introduction Ever since Tim Berners Lee presented, in 2006, the design principles for Linked Open Data1, the public availability of Semantic-Web data has grown rapidly.
    [Show full text]
  • CSC 443 – Database Management Systems Data and Its Structure
    CSC 443 – Database Management Systems Lecture 3 –The Relational Data Model Data and Its Structure • Data is actually stored as bits, but it is difficult to work with data at this level. • It is convenient to view data at different levels of abstraction . • Schema : Description of data at some abstraction level. Each level has its own schema. • We will be concerned with three schemas: physical , conceptual , and external . 1 Physical Data Level • Physical schema describes details of how data is stored: tracks, cylinders, indices etc. • Early applications worked at this level – explicitly dealt with details. • Problem: Routines were hard-coded to deal with physical representation. – Changes to data structure difficult to make. – Application code becomes complex since it must deal with details. – Rapid implementation of new features impossible. Conceptual Data Level • Hides details. – In the relational model, the conceptual schema presents data as a set of tables. • DBMS maps from conceptual to physical schema automatically. • Physical schema can be changed without changing application: – DBMS would change mapping from conceptual to physical transparently – This property is referred to as physical data independence 2 Conceptual Data Level (con’t) External Data Level • In the relational model, the external schema also presents data as a set of relations. • An external schema specifies a view of the data in terms of the conceptual level. It is tailored to the needs of a particular category of users. – Portions of stored data should not be seen by some users. • Students should not see their files in full. • Faculty should not see billing data. – Information that can be derived from stored data might be viewed as if it were stored.
    [Show full text]
  • Data Base Lab the Microsoft SQL Server Management Studio Part-3
    Data Base Lab Islamic University – Gaza Engineering Faculty Computer Department Lab -5- The Microsoft SQL Server Management Studio Part-3- By :Eng.Alaa I.Haniy. SQL Constraints Constraints are used to limit the type of data that can go into a table. Constraints can be specified when a table is created (with the CREATE TABLE statement) or after the table is created (with the ALTER TABLE statement). We will focus on the following constraints: NOT NULL UNIQUE PRIMARY KEY FOREIGN KEY CHECK DEFAULT Now we will describe each constraint in details. SQL NOT NULL Constraint By default, a table column can hold NULL values. The NOT NULL constraint enforces a column to NOT accept NULL values. The NOT NULL constraint enforces a field to always contain a value. This means that you cannot insert a new record, or update a record without adding a value to this field. The following SQL enforces the "PI_Id" column and the "LName" column to not accept NULL values CREATE TABLE Person ( PI_Id int NOT NULL, LName varchar(50) NOT NULL, FName varchar(50), Address varchar(50), City varchar(50) ) 2 SQL UNIQUE Constraint The UNIQUE constraint uniquely identifies each record in a database table. The UNIQUE and PRIMARY KEY constraints both provide a guarantee for uniqueness or a column or set of columns. A PRIMARY KEY constraint automatically has a UNIQUE constraint defined on it. Note that you can have many UNIQUE constraints per table, but only one PRIMARY KEY constraint per table. SQL UNIQUE Constraint on CREATE TABLE The following SQL creates a UNIQUE
    [Show full text]
  • Declarative Constraints
    THREE Declarative Constraints 3.1 PRIMARY KEY 3.1.1 Creating the Constraint 3.1.2 Naming the Constraint 3.1.3 The Primary Key Index 3.1.4 Sequences 3.1.5 Sequences in Code 3.1.6 Concatenated Primary Key 3.1.7 Extra Indexes with Pseudo Keys 3.1.8 Enable, Disable, and Drop 3.1.9 Deferrable Option 3.1.10 NOVALIDATE 3.1.11 Error Handling in PL/SQL 3.2 UNIQUE 3.2.1 Combining NOT NULL, CHECK with UNIQUE Constraints 3.2.2 Students Table Example 3.2.3 Deferrable and NOVALIDATE Options 3.2.4 Error Handling in PL/SQL 3.3 FOREIGN KEY 3.3.1 Four Types of Errors 3.3.2 Delete Cascade 3.3.3 Mandatory Foreign Key Columns 3.3.4 Referencing the Parent Syntax 3.3.5 Referential Integrity across Schemas and Databases 3.3.6 Multiple Parents and DDL Migration 3.3.7 Many-to-Many Relationships 3.3.8 Self-Referential Integrity 3.3.9 PL/SQL Error Handling with Parent/Child Tables 3.3.10 The Deferrable Option 69 70 Chapter 3 • Declarative Constraints 3.4 CHECK 3.4.1 Multicolumn Constraint 3.4.2 Supplementing Unique Constraints 3.4.3 Students Table Example 3.4.4 Lookup Tables versus Check Constraints 3.4.5 Cardinality 3.4.6 Designing for Check Constraints 3.5 NOT NULL CONSTRAINTS 3.6 DEFAULT VALUES 3.7 MODIFYING CONSTRAINTS 3.8 EXCEPTION HANDLING 3.9 DATA LOADS This is the first of several chapters that cover enforcing business rules with con- straints.
    [Show full text]
  • On Enforcing Relational Constraints in Matbase
    Scan to know paper details and author's profile On enforcing relational constraints in MatBase Christian Mancas,Valentina Dorobantu ABSTRACT MatBase is a very powerful database management system based not only on the relational data model, but also on the elementary mathematical, entity-relationship, and Datalog logic ones. This paper briefly introduces MatBase and the elementary mathematical data model, after which is focused on the five relational constraint types and their enforcement in relational database management systems, as well as in MatBase. Keywords: relational data model, domain constraint, not null constraint, unique key constraint, referential integrity constraint, tuple constraint, elementary mathematical data model, MatBase. Classification: H.2.6 H.2 Language: English LJP Copyright ID: 206059 ISBN 10: 1537631683 London ISBN 13: 978-1537631684 LJP Journals Press London Journal of Research in Computer Science and Technology 16UK Volume 17 | Issue 1 | Compilation 1.0 © 2017. Christian Mancas,Valentina Dorobantu. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 Unported License http://creativecommons.org/licenses/by-nc/4.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Powered by TCPDF (www.tcpdf.org) On Enforcing Relational Constraints in MatBase ​ α σ Valentina Dorobantu ​ & Christian Mancas ​ ​ ​ ______________________________________________ I. ABSTRACT 2.1 MatBase MatBase is a very powerful database manage- MatBase [1-5] is a powerful prototype multi- ment system based not only on the relational model, multi-user, and multi-language knowledge data model, but also on the elementary and data (KD) base management system mathematical, entity-relationship, and Datalog (KDBMS) that currently has two versions: one in MS Access 2015 and the other in MS SQL Server logic ones.
    [Show full text]
  • 8 the Effectiveness of Test Coverage Criteria for Relational Database Schema Integrity Constraints
    The Effectiveness of Test Coverage Criteria for Relational Database Schema Integrity Constraints PHIL MCMINN and CHRIS J. WRIGHT, University of Sheffield GREGORY M. KAPFHAMMER, Allegheny College Despite industry advice to the contrary, there has been little work that has sought to test that a relational database’s schema has correctly specified integrity constraints. These critically important constraints ensure the coherence of data in a database, defending it from manipulations that could violate requirements such as “usernames must be unique” or “the host name cannot be missing or unknown.” This article is the first to propose coverage criteria, derived from logic coverage criteria, that establish different levels of testing for the formulation of integrity constraints in a database schema. These range from simple criteria that mandate the testing of successful and unsuccessful INSERT statements into tables to more advanced criteria that test the formulation of complex integrity constraints such as multi-column PRIMARY KEYs and arbitrary CHECK constraints. Due to different vendor interpretations of the structured query language (SQL) specification with regard to how integrity constraints should actually function in practice, our criteria crucially account for the underlying semantics of the database management system (DBMS). After formally defining these coverage criteria and relating them in a subsumption hierarchy, we present two approaches for automatically generating tests that satisfy the criteria. We then describe the results of an empirical study that uses mutation analysis to investigate the fault-finding capability of data generated when our coverage criteria are applied to a wide variety of relational schemas hosted by three well-known and representative DBMSs— HyperSQL, PostgreSQL, and SQLite.
    [Show full text]
  • Declarative Constraints
    5685ch03.qxd_kp 11/6/03 1:36 PM Page 69 THREE Declarative Constraints 3.1 PRIMARY KEY 3.1.1 Creating the Constraint 3.1.2 Naming the Constraint 3.1.3 The Primary Key Index 3.1.4 Sequences 3.1.5 Sequences in Code 3.1.6 Concatenated Primary Key 3.1.7 Extra Indexes with Pseudo Keys 3.1.8 Enable, Disable, and Drop 3.1.9 Deferrable Option 3.1.10 NOVALIDATE 3.1.11 Error Handling in PL/SQL 3.2 UNIQUE 3.2.1 Combining NOT NULL, CHECK with UNIQUE Constraints 3.2.2 Students Table Example 3.2.3 Deferrable and NOVALIDATE Options 3.2.4 Error Handling in PL/SQL 3.3 FOREIGN KEY 3.3.1 Four Types of Errors 3.3.2 Delete Cascade 3.3.3 Mandatory Foreign Key Columns 3.3.4 Referencing the Parent Syntax 3.3.5 Referential Integrity across Schemas and Databases 3.3.6 Multiple Parents and DDL Migration 3.3.7 Many-to-Many Relationships 3.3.8 Self-Referential Integrity 3.3.9 PL/SQL Error Handling with Parent/Child Tables 3.3.10 The Deferrable Option 69 5685ch03.qxd_kp 11/6/03 1:36 PM Page 70 70 Chapter 3 • Declarative Constraints 3.4 CHECK 3.4.1 Multicolumn Constraint 3.4.2 Supplementing Unique Constraints 3.4.3 Students Table Example 3.4.4 Lookup Tables versus Check Constraints 3.4.5 Cardinality 3.4.6 Designing for Check Constraints 3.5 NOT NULL CONSTRAINTS 3.6 DEFAULT VALUES 3.7 MODIFYING CONSTRAINTS 3.8 EXCEPTION HANDLING 3.9 DATA LOADS This is the first of several chapters that cover enforcing business rules with con- straints.
    [Show full text]
  • Relational Data Model and SQL Data Definition Language
    The Relational Data Model and SQL Data Definition Language Paul Fodor CSE316: Fundamentals of Software Development Stony Brook University http://www.cs.stonybrook.edu/~cse316 1 The Relational Data Model A particular way of structuring data (using relations) proposed in 1970 by E. F. Codd Mathematically based Expressions ( queries) can be analyzed by DBMS and transformed to equivalent expressions automatically (query optimization) Optimizers have limits (=> the programmer still needs to know how queries are evaluated and optimized) 2 (c) Pearson Education Inc. and Paul Fodor (CS Stony Brook) Relational Databases In Relational DBs, the data is stored in tables Attributes (column Relation name: Student headers) Id Name Address Status 1111 John 123 Main fresh Tuples 2222 Mary 321 Oak (rows) 1234 Bob 444 Pine 9999 Joan 777 Grand senior 3 (c) Pearson Education Inc. and Paul Fodor (CS Stony Brook) Table Set of rows (no duplicates) Each row/tuple describes a different entity Each column/attribute states a particular fact about each entity Each column has an associated domain Id Name Address Status 1111 John 123 Main fresh 2222 Mary 321 Oak 1234 Bob 444 Pine 9999 Joan 777 Grand senior Domain of Id: integers Domain of Name: strings 4 Domain of(c) PearsonStatus Education= Inc.{fresh, and Paul Fodor soph, (CS Stony junior,Brook) senior} Relation A relation is the mathematical entity corresponding to a table: A relation R can be thought of as predicate R A tuple (x,y,z) is in the relation R iff R(x,y,z) is true Why Relations/Tables? Very simple model.
    [Show full text]