Intro to DB CHAPTER 3 SQL Chappqter 3: SQL
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Database Concepts 7Th Edition
Database Concepts 7th Edition David M. Kroenke • David J. Auer Online Appendix E SQL Views, SQL/PSM and Importing Data Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Appendix E — 10 9 8 7 6 5 4 3 2 1 E-2 Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E Appendix Objectives • To understand the reasons for using SQL views • To use SQL statements to create and query SQL views • To understand SQL/Persistent Stored Modules (SQL/PSM) • To create and use SQL user-defined functions • To import Microsoft Excel worksheet data into a database What is the Purpose of this Appendix? In Chapter 3, we discussed SQL in depth. We discussed two basic categories of SQL statements: data definition language (DDL) statements, which are used for creating tables, relationships, and other structures, and data manipulation language (DML) statements, which are used for querying and modifying data. In this appendix, which should be studied immediately after Chapter 3, we: • Describe and illustrate SQL views, which extend the DML capabilities of SQL. • Describe and illustrate SQL Persistent Stored Modules (SQL/PSM), and create user-defined functions. • Describe and use DBMS data import techniques to import Microsoft Excel worksheet data into a database. E-3 Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E Creating SQL Views An SQL view is a virtual table that is constructed from other tables or views. -
How to Get Data from Oracle to Postgresql and Vice Versa Who We Are
How to get data from Oracle to PostgreSQL and vice versa Who we are The Company > Founded in 2010 > More than 70 specialists > Specialized in the Middleware Infrastructure > The invisible part of IT > Customers in Switzerland and all over Europe Our Offer > Consulting > Service Level Agreements (SLA) > Trainings > License Management How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 2 About me Daniel Westermann Principal Consultant Open Infrastructure Technology Leader +41 79 927 24 46 daniel.westermann[at]dbi-services.com @westermanndanie Daniel Westermann How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 3 How to get data from Oracle to PostgreSQL and vice versa Before we start We have a PostgreSQL user group in Switzerland! > https://www.swisspug.org Consider supporting us! How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 4 How to get data from Oracle to PostgreSQL and vice versa Before we start We have a PostgreSQL meetup group in Switzerland! > https://www.meetup.com/Switzerland-PostgreSQL-User-Group/ Consider joining us! How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 5 Agenda 1.Past, present and future 2.SQL/MED 3.Foreign data wrappers 4.Demo 5.Conclusion How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 6 Disclaimer This session is not about logical replication! If you are looking for this: > Data Replicator from DBPLUS > https://blog.dbi-services.com/real-time-replication-from-oracle-to-postgresql-using-data-replicator-from-dbplus/ -
Handling Missing Values in the SQL Procedure
Handling Missing Values in the SQL Procedure Danbo Yi, Abt Associates Inc., Cambridge, MA Lei Zhang, Domain Solutions Corp., Cambridge, MA non-missing numeric values. A missing ABSTRACT character value is expressed and treated as a string of blanks. Missing character values PROC SQL as a powerful database are always same no matter whether it is management tool provides many features expressed as one blank, or more than one available in the DATA steps and the blanks. Obviously, missing character values MEANS, TRANSPOSE, PRINT and SORT are not the smallest strings. procedures. If properly used, PROC SQL often results in concise solutions to data In SAS system, the way missing Date and manipulations and queries. PROC SQL DateTime values are expressed and treated follows most of the guidelines set by the is similar to missing numeric values. American National Standards Institute (ANSI) in its implementation of SQL. This paper will cover following topics. However, it is not fully compliant with the 1. Missing Values and Expression current ANSI Standard for SQL, especially · Logic Expression for the missing values. PROC SQL uses · Arithmetic Expression SAS System convention to express and · String Expression handle the missing values, which is 2. Missing Values and Predicates significantly different from many ANSI- · IS NULL /IS MISSING compatible SQL databases such as Oracle, · [NOT] LIKE Sybase. In this paper, we summarize the · ALL, ANY, and SOME ways the PROC SQL handles the missing · [NOT] EXISTS values in a variety of situations. Topics 3. Missing Values and JOINs include missing values in the logic, · Inner Join arithmetic and string expression, missing · Left/Right Join values in the SQL predicates such as LIKE, · Full Join ANY, ALL, JOINs, missing values in the 4. -
Chapter 11 Querying
Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Blind folio: 273 CHAPTER 11 Querying 273 11-ch11.indd 273 9/5/11 4:23:56 PM Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 274 Oracle Database 11g & MySQL 5.6 Developer Handbook Chapter 11: Querying 275 he SQL SELECT statement lets you query data from the database. In many of the previous chapters, you’ve seen examples of queries. Queries support several different types of subqueries, such as nested queries that run independently or T correlated nested queries. Correlated nested queries run with a dependency on the outer or containing query. This chapter shows you how to work with column returns from queries and how to join tables into multiple table result sets. Result sets are like tables because they’re two-dimensional data sets. The data sets can be a subset of one table or a set of values from two or more tables. The SELECT list determines what’s returned from a query into a result set. The SELECT list is the set of columns and expressions returned by a SELECT statement. The SELECT list defines the record structure of the result set, which is the result set’s first dimension. The number of rows returned from the query defines the elements of a record structure list, which is the result set’s second dimension. You filter single tables to get subsets of a table, and you join tables into a larger result set to get a superset of any one table by returning a result set of the join between two or more tables. -
Firebird 3 Windowing Functions
Firebird 3 Windowing Functions Firebird 3 Windowing Functions Author: Philippe Makowski IBPhoenix Email: pmakowski@ibphoenix Licence: Public Documentation License Date: 2011-11-22 Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions What are Windowing Functions? • Similar to classical aggregates but does more! • Provides access to set of rows from the current row • Introduced SQL:2003 and more detail in SQL:2008 • Supported by PostgreSQL, Oracle, SQL Server, Sybase and DB2 • Used in OLAP mainly but also useful in OLTP • Analysis and reporting by rankings, cumulative aggregates Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions Windowed Table Functions • Windowed table function • operates on a window of a table • returns a value for every row in that window • the value is calculated by taking into consideration values from the set of rows in that window • 8 new windowed table functions • In addition, old aggregate functions can also be used as windowed table functions • Allows calculation of moving and cumulative aggregate values. Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions A Window • Represents set of rows that is used to compute additionnal attributes • Based on three main concepts • partition • specified by PARTITION BY clause in OVER() • Allows to subdivide the table, much like GROUP BY clause • Without a PARTITION BY clause, the whole table is in a single partition • order • defines an order with a partition • may contain multiple order items • Each item includes -
Using the Set Operators Questions
UUSSIINNGG TTHHEE SSEETT OOPPEERRAATTOORRSS QQUUEESSTTIIOONNSS http://www.tutorialspoint.com/sql_certificate/using_the_set_operators_questions.htm Copyright © tutorialspoint.com 1.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: A. Set operators are used to combine the results of two ormore SELECT statements.Valid set operators in Oracle 11g are UNION, UNION ALL, INTERSECT, and MINUS. When used with two SELECT statements, the UNION set operator returns the results of both queries.However,if there are any duplicates, they are removed, and the duplicated record is listed only once.To include duplicates in the results,use the UNION ALL set operator.INTERSECT lists only records that are returned by both queries; the MINUS set operator removes the second query's results from the output if they are also found in the first query's results. INTERSECT and MINUS set operations produce unduplicated results. 2.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: B. UNION ALL Returns the combined rows from two queries without sorting or removing duplicates. sql_certificate 3.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: C. INTERSECT Returns only the rows that occur in both queries' result sets, sorting them and removing duplicates. 4.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: D. MINUS Returns only the rows in the first result set that do not appear in the second result set, sorting them and removing duplicates. -
SQL Version Analysis
Rory McGann SQL Version Analysis Structured Query Language, or SQL, is a powerful tool for interacting with and utilizing databases through the use of relational algebra and calculus, allowing for efficient and effective manipulation and analysis of data within databases. There have been many revisions of SQL, some minor and others major, since its standardization by ANSI in 1986, and in this paper I will discuss several of the changes that led to improved usefulness of the language. In 1970, Dr. E. F. Codd published a paper in the Association of Computer Machinery titled A Relational Model of Data for Large shared Data Banks, which detailed a model for Relational database Management systems (RDBMS) [1]. In order to make use of this model, a language was needed to manage the data stored in these RDBMSs. In the early 1970’s SQL was developed by Donald Chamberlin and Raymond Boyce at IBM, accomplishing this goal. In 1986 SQL was standardized by the American National Standards Institute as SQL-86 and also by The International Organization for Standardization in 1987. The structure of SQL-86 was largely similar to SQL as we know it today with functionality being implemented though Data Manipulation Language (DML), which defines verbs such as select, insert into, update, and delete that are used to query or change the contents of a database. SQL-86 defined two ways to process a DML, direct processing where actual SQL commands are used, and embedded SQL where SQL statements are embedded within programs written in other languages. SQL-86 supported Cobol, Fortran, Pascal and PL/1. -
“A Relational Model of Data for Large Shared Data Banks”
“A RELATIONAL MODEL OF DATA FOR LARGE SHARED DATA BANKS” Through the internet, I find more information about Edgar F. Codd. He is a mathematician and computer scientist who laid the theoretical foundation for relational databases--the standard method by which information is organized in and retrieved from computers. In 1981, he received the A. M. Turing Award, the highest honor in the computer science field for his fundamental and continuing contributions to the theory and practice of database management systems. This paper is concerned with the application of elementary relation theory to systems which provide shared access to large banks of formatted data. It is divided into two sections. In section 1, a relational model of data is proposed as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data bank and changes in traffic. A normal form for the time-varying collection of relationships is introduced. In Section 2, certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model. Relational model provides a means of describing data with its natural structure only--that is, without superimposing any additional structure for machine representation purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and machine representation and organization of data on the other. A further advantage of the relational view is that it forms a sound basis for treating derivability, redundancy, and consistency of relations. -
Relational Algebra and SQL Relational Query Languages
Relational Algebra and SQL Chapter 5 1 Relational Query Languages • Languages for describing queries on a relational database • Structured Query Language (SQL) – Predominant application-level query language – Declarative • Relational Algebra – Intermediate language used within DBMS – Procedural 2 1 What is an Algebra? · A language based on operators and a domain of values · Operators map values taken from the domain into other domain values · Hence, an expression involving operators and arguments produces a value in the domain · When the domain is a set of all relations (and the operators are as described later), we get the relational algebra · We refer to the expression as a query and the value produced as the query result 3 Relational Algebra · Domain: set of relations · Basic operators: select, project, union, set difference, Cartesian product · Derived operators: set intersection, division, join · Procedural: Relational expression specifies query by describing an algorithm (the sequence in which operators are applied) for determining the result of an expression 4 2 The Role of Relational Algebra in a DBMS 5 Select Operator • Produce table containing subset of rows of argument table satisfying condition σ condition (relation) • Example: σ Person Hobby=‘stamps’(Person) Id Name Address Hobby Id Name Address Hobby 1123 John 123 Main stamps 1123 John 123 Main stamps 1123 John 123 Main coins 9876 Bart 5 Pine St stamps 5556 Mary 7 Lake Dr hiking 9876 Bart 5 Pine St stamps 6 3 Selection Condition • Operators: <, ≤, ≥, >, =, ≠ • Simple selection -
Best Practices Managing XML Data
® IBM® DB2® for Linux®, UNIX®, and Windows® Best Practices Managing XML Data Matthias Nicola IBM Silicon Valley Lab Susanne Englert IBM Silicon Valley Lab Last updated: January 2011 Managing XML Data Page 2 Executive summary ............................................................................................. 4 Why XML .............................................................................................................. 5 Pros and cons of XML and relational data ................................................. 5 XML solutions to relational data model problems.................................... 6 Benefits of DB2 pureXML over alternative storage options .................... 8 Best practices for DB2 pureXML: Overview .................................................. 10 Sample scenario: derivative trades in FpML format............................... 11 Sample data and tables................................................................................ 11 Choosing the right storage options for XML data......................................... 16 Selecting table space type and page size for XML data.......................... 16 Different table spaces and page size for XML and relational data ....... 16 Inlining and compression of XML data .................................................... 17 Guidelines for adding XML data to a DB2 database .................................... 20 Inserting XML documents with high performance ................................ 20 Splitting large XML documents into smaller pieces .............................. -
Chapter 2 – Object-Relational Views and Composite Types Outline
Prof. Dr.-Ing. Stefan Deßloch AG Heterogene Informationssysteme Geb. 36, Raum 329 Tel. 0631/205 3275 [email protected] Chapter 2 – Object-Relational Views and Composite Types Recent Developments for Data Models Outline Overview I. Object-Relational Database Concepts 1. User-defined Data Types and Typed Tables 2. Object-relational Views and Composite Types 3. User-defined Routines and Object Behavior 4. Application Programs and Object-relational Capabilities 5. Object-relational SQL and Java II. Online Analytic Processing 6. Data Analysis in SQL 7. Windows and Query Functions in SQL III. XML 8. XML and Databases 9. SQL/XML 10. XQuery IV. More Developments (if there is time left) temporal data models, data streams, databases and uncertainty, … 2 © Prof.Dr.-Ing. Stefan Deßloch Recent Developments for Data Models 1 The "Big Picture" Client DB Server Server-side dynamic SQL Logic SQL99/2003 JDBC 2.0 SQL92 static SQL SQL OLB stored procedures ANSI SQLJ Part 1 user-defined functions SQL Routines ISO advanced datatypes PSM structured types External Routines subtyping methods SQLJ Part 2 3 © Prof.Dr.-Ing. Stefan Deßloch Recent Developments for Data Models Objects Meet Databases (Atkinson et. al.) Object-oriented features to be supported by an (OO)DBMS ; Extensibility user-defined types (structure and operations) as first class citizens strengthens some capabilities defined above (encapsulation, types) ; Object identity object exists independent of its value (i.e., identical ≠ equal) ; Types and classes "abstract data types", static type checking class as an "object factory", extension (i.e., set of "instances") ? Type or class and view hierarchies inheritance, specialization ? Complex objects type constructors: tuple, set, list, array, … Encapsulation separate specification (interface) from implementation Overloading, overriding, late binding same name for different operations or implementations Computational completeness use DML to express any computable function (-> method implementation) 4 © Prof.Dr.-Ing. -
On the Logic of SQL Nulls
On the Logic of SQL Nulls Enrico Franconi and Sergio Tessaris Free University of Bozen-Bolzano, Italy lastname @inf.unibz.it Abstract The logic of nulls in databases has been subject of invest- igation since their introduction in Codd's Relational Model, which is the foundation of the SQL standard. In the logic based approaches to modelling relational databases proposed so far, nulls are considered as representing unknown values. Such existential semantics fails to capture the behaviour of the SQL standard. We show that, according to Codd's Relational Model, a SQL null value represents a non-existing value; as a consequence no indeterminacy is introduced by SQL null values. In this paper we introduce an extension of first-order logic accounting for predicates with missing arguments. We show that the domain inde- pendent fragment of this logic is equivalent to Codd's relational algebra with SQL nulls. Moreover, we prove a faithful encoding of the logic into standard first-order logic, so that we can employ classical deduction ma- chinery. 1 Relational Databases and SQL Null Values Consider a database instance with null values over the relational schema fR=2g, and a SQL query asking for the tuples in R being equal to themselves: 1 2 1 | 2 SELECT * FROM R ---+--- R : a b WHERE R.1 = R.1 AND R.2 = R.2 ; ) a | b b N (1 row) Figure 1. In SQL, the query above returns the table R if and only if the table R does not have any null value, otherwise it returns just the tuples not containing a null value, i.e., in this case only the first tuple ha; bi.