Objectivity/C++ Data Definition Language

Total Page:16

File Type:pdf, Size:1020Kb

Objectivity/C++ Data Definition Language Data Definition Language Objectivity/C++ Data Definition Language Release 12.0 Objectivity/C++ Data Definition Language Part Number: 12.0-DDL-0 Release 12.0, May 6, 2016 The information in this document is subject to change without notice. Objectivity, Inc. assumes no responsibility for any errors that may appear in this document. Copyright 1993–2016 by Objectivity, Inc. All rights reserved. This document may not be copied, photocopied, reproduced, translated, or converted to any electronic or machine-readable form in whole or in part without prior written approval of Objectivity, Inc. Objectivity, Objectivity/DB, and InfiniteGraph are registered trademarks of Objectivity, Inc. Active Schema, Objectivity/DB Active Schema, Assist, Objectivity/Assist, ooAssistant, Objectivity/DB ooAssistant, Objectivity/DB Fault Tolerant Option, Objectivity/FTO, Objectivity/DB Data Replication Option, Objectivity/DRO, Objectivity/DB High Availability, Objectivity/HA, Objectivity/DB Hot Failover, Objectivity/DB In-Process Lock Server, Objectivity/IPLS, Objectivity/DB Open File System, Objectivity/OFS, Objectivity/DB Parallel Query Engine, Objectivity/PQE, Objectivity/DB Persistence Designer, Objectivity/DB Secure Framework, Objectivity/Secure, Objectivity/C++, Objectivity/C++ Data Definition Language, Objectivity/DDL, Objectivity/Dashboard, Objectivity/C++ Active Schema, Objectivity/C++ Standard Template Library, Objectivity/C++ STL, Objectivity/C++ Spatial Index Framework, Objectivity/Spatial, Objectivity for Java, Objectivity/.NET, Objectivity/.NET for C#, Objectivity/Python, Objectivity/Smalltalk, Objectivity/SQL++, Objectivity/SQL++ ODBC Driver, Objectivity/ODBC, Objectivity Event Notification Services, and Persistence Designer are trademarks of Objectivity, Inc. Other trademarks and products are the property of their respective owners. ODMG information in this document is based in whole or in part on material from The Object Database Standard: ODMG 2.0, edited by R.G.G. Cattell, and is reprinted with permission of Morgan Kaufmann Publishers. Copyright 1997 by Morgan Kaufmann Publishers. The software and information contained herein are proprietary to, and comprise valuable trade secrets of, Objectivity, Inc., which intends to preserve as trade secrets such software and information. This software is furnished pursuant to a written license agreement and may be used, copied, transmitted, and stored only in accordance with the terms of such license and with the inclusion of the above copyright notice. This software and information or any other copies thereof may not be provided or otherwise made available to any other person. RESTRICTED RIGHTS NOTICE: Use, duplication or disclosure by the U.S. Government is subject to restrictions set forth in the Objectivity, Inc. license agreement and as provided in DFARS 227.7202-1(a) and 227.7202-3(a) (1998), and FAR 12.212, as applicable. Objectivity, Inc., 3099 North First Street, Suite 200, San Jose, CA 95134. Contents About This Book 9 Audience 9 Organization 9 Conventions and Abbreviations 10 Getting Help 11 Part 1 GUIDE Chapter 1 Getting Started 15 About Schema Development 15 Persistence-Capable Classes 16 Federated Database Schemas 16 Schema Development with Objectivity/DDL 17 Creating DDL Files 18 Basic DDL File Contents 18 Adapting Existing Header Files 18 Example: Adapting an Existing C++ Header File 20 Processing DDL Files 21 C++ Files Generated by the DDL Processor 22 Primary Header File 23 References Header File 24 Method Implementation File 24 Example: Including Generated Header Files 25 Treatment of Preprocessing Directives 27 Dependencies on DDL-Generated Code 27 3 Dividing Definitions Among Multiple Files 28 Obtaining Application-Specific Definitions 28 Obtaining Generated Class Definitions 31 Modifying the Schema 36 Adding to an Existing Schema 37 Replacing a Schema in Early Development 37 Summary 38 Chapter 2 Defining Persistence-Capable Classes 39 Data Definition Language 39 Making a Class Persistence-Capable 41 Multiple Inheritance 41 Class Templates 42 Limit on Class-Name Length 43 Namespaces 43 Defining the Component Data of a Class 45 Defining Attributes 45 Overview of Association Definition 57 Prohibited Data Types 58 Method Considerations 61 Avoiding Multiple Declarations 61 Redefining Inherited new Operators 61 Special-Purpose Constructor 61 Limitation on Destructors for Persistence-Capable Classes 64 Handling Unsupported C++ Features 65 Chapter 3 Defining Associations 67 About Associations 67 Association Directionality 68 Association Cardinality 69 Object Copying and Versioning 70 Propagating Operations 72 Association Storage Properties 73 Defining an Association 79 Basic Association Syntax 79 Inline Association Syntax 82 Requesting Propagation Operations 83 4 Objectivity/C++ Data Definition Language Specifying Object Copying and Versioning Behavior 84 Combining Behavior Specifiers 86 Association Syntax Summary 87 Unidirectional Associations 87 Bidirectional Associations 87 Behavior Specifiers 88 Chapter 4 Multiple Inheritance 89 Vehicle Object Model 90 Persistence through Inheritance 91 Object Modeling Using Root Persistence 91 Composite Objects and Root Persistence 93 Object Modeling Using Leaf Persistence 95 Enhanced Leaf Persistence 97 Mixing Root and Leaf Persistence 98 Chapter 5 Schema Evolution 101 About Schema Evolution 101 Schema-Evolution Operations 102 What You Can Change 103 Impact on Objects 103 Impact on Existing Applications 105 Impact on Performance 106 Schema-Evolution History 106 Schema Distribution 107 Performing Schema-Evolution Operations 107 Supported Schema-Evolution Operations 108 Setting Up a Development and Test Environment 109 Planning Schema Changes 110 Modifying Class Definitions 111 Processing Class Definitions 111 Capturing the Modified Schema for Distribution 113 Converting Objects 114 Modifying and Rebuilding Applications 116 Evolving Class Members 117 Adding Component Data to a Class 117 Deleting Component Data From a Class 120 Renaming Component Data in a Class 122 Objectivity/C++ Data Definition Language 5 Replacing Component Data in a Class 123 Changing the Properties of Component Data 126 Changing the Properties of Associations 133 Adding or Removing a Virtual Method 136 Evolving Classes 137 Adding a Class 137 Renaming a Class 137 Deleting a Class 138 Changing the Inheritance of a Class 142 Adding Persistence 153 Removing Persistence 154 Restructuring Classes 154 Distributing Schema Changes 158 Preparing for Distribution 159 Reproducing a Schema Operation 160 Deploying Updated Applications 160 Chapter 6 Class Versioning 163 About Class Versions 163 Class Versioning and Schema Evolution 164 Creating a New Version of a Class 164 Providing a Nickname for the Original Class 165 Creating the New Version 166 Using the Old and New Versions 168 Versioning Interrelated Classes 168 Preparing a Suitable DDL File 168 Nicknaming Multiple Classes in a DDL File 169 Versioning Multiple Classes in a DDL File 170 Chapter 7 Partitioning an Object Model 173 About Multiple Schemas 173 Adding Definitions to a Named Schema 175 Switching Between Multiple Schemas 176 6 Objectivity/C++ Data Definition Language Chapter 8 Schema Tuning 179 Tuning for Federated Database Size 179 Use Inline Associations 179 Store Data Efficiently 179 Tuning for Application Speed 182 Use Inline Associations 182 Part 2 REFERENCE Chapter 9 Tools 185 Chapter 10 DDL Pragmas 193 Part 3 APPENDIXES Appendix A Objectivity/C++ Include Files 205 Appendix B Schema Class Descriptions 207 About Schema Class Descriptions 207 Content of a Schema Class Description 207 Schema Class Names 208 Namespace and Package Qualifiers 208 Objectivity/DB Primitive Types 210 Mapping Objectivity/C++ Primitive Types 211 Mapping C++ Primitive Types 212 Objectivity/DB Primitive Types in Earlier Schemas 214 Appendix C Schema-Evolution Quick Reference 215 Index 219 Objectivity/C++ Data Definition Language 7 8 Objectivity/C++ Data Definition Language About This Book This book describes how to use the Objectivity/C++ Data Definition Language (DDL) and the DDL processing tool to develop a schema within an Objectivity/DB federated database. A schema defines the types of persistent objects you can store in a database. Audience This book assumes that you are familiar with the C++ programming language and with the Objectivity/C++ programming interface. Organization ■ Chapter 1 provides basic terminology, a simple example, and the steps for creating a schema from the class definitions in one or more DDL files. ■ Chapter 2 and Chapter 3 describe how to use the DDL to define persistence-capable classes and associations between them; Chapter 4 provides design guidelines when using multiple inheritance. ■ Chapter 5 and Chapter 6 describe two alternative ways of modifying an existing schema: schema evolution and versioning. ■ Chapter 7 and Chapter 8 describe mechanisms for organizing the definitions in a schema and techniques for tuning class definitions to promote efficient storage or faster access. ■ Chapter 9 and Chapter 10 contain reference information about the DDL processor and the pragma directives it accepts. ■ Appendix A is an overview of Objectivity/C++ header files. ■ Appendix B describes the Objectivity/DB primitive types that are specified in the schema for the corresponding Objectivity/C++ primitive types. ■ Appendix C is a quick reference of schema-evolution operations. 9 About This Book Conventions and Abbreviations Navigation In the online version of this book, table of contents entries, index entries, cross-references,
Recommended publications
  • (DDL) Reference Manual
    Data Definition Language (DDL) Reference Manual Abstract This publication describes the DDL language syntax and the DDL dictionary database. The audience includes application programmers and database administrators. Product Version DDL D40 DDL H01 Supported Release Version Updates (RVUs) This publication supports J06.03 and all subsequent J-series RVUs, H06.03 and all subsequent H-series RVUs, and G06.26 and all subsequent G-series RVUs, until otherwise indicated by its replacement publications. Part Number Published 529431-003 May 2010 Document History Part Number Product Version Published 529431-002 DDL D40, DDL H01 July 2005 529431-003 DDL D40, DDL H01 May 2010 Legal Notices Copyright 2010 Hewlett-Packard Development Company L.P. Confidential computer software. Valid license from HP required for possession, use or copying. Consistent with FAR 12.211 and 12.212, Commercial Computer Software, Computer Software Documentation, and Technical Data for Commercial Items are licensed to the U.S. Government under vendor's standard commercial license. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein. Export of the information contained in this publication may require authorization from the U.S. Department of Commerce. Microsoft, Windows, and Windows NT are U.S. registered trademarks of Microsoft Corporation. Intel, Itanium, Pentium, and Celeron are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.
    [Show full text]
  • Exploring the Visualization of Schemas for Aggregate-Oriented Nosql Databases?
    Exploring the Visualization of Schemas for Aggregate-Oriented NoSQL Databases? Alberto Hernández Chillón, Severino Feliciano Morales, Diego Sevilla Ruiz, and Jesús García Molina Faculty of Computer Science, University of Murcia Campus Espinardo, Murcia, Spain {alberto.hernandez1,severino.feliciano,dsevilla,jmolina}@um.es Abstract. The lack of an explicit data schema (schemaless) is one of the most attractive NoSQL database features for developers. Being schema- less, these databases provide a greater flexibility, as data with different structure can be stored for the same entity type, which in turn eases data evolution. This flexibility, however, should not be obtained at the expense of losing the benefits provided by having schemas: When writ- ing code that deals with NoSQL databases, developers need to keep in mind at any moment some kind of schema. Also, database tools usu- ally require the knowledge of a schema to implement their functionality. Several approaches to infer an implicit schema from NoSQL data have been proposed recently, and some utilities that take advantage of inferred schemas are emerging. In this article we focus on the requisites for the vi- sualization of schemas for aggregate-oriented NoSQL Databases. Schema diagrams have proven useful in designing and understanding databases. Plenty of tools are available to visualize relational schemas, but the vi- sualization of NoSQL schemas (and the variation that they allow) is still in an immature state, and a great R&D effort is required to achieve tools with the desired capabilities. Here, we study the main challenges to be addressed, and propose some visual representations. Moreover, we outline the desired features to be supported by visualization tools.
    [Show full text]
  • SQL: Triggers, Views, Indexes Introduction to Databases Compsci 316 Fall 2014 2 Announcements (Tue., Sep
    SQL: Triggers, Views, Indexes Introduction to Databases CompSci 316 Fall 2014 2 Announcements (Tue., Sep. 23) • Homework #1 sample solution posted on Sakai • Homework #2 due next Thursday • Midterm on the following Thursday • Project mixer this Thursday • See my email about format • Email me your “elevator pitch” by Wednesday midnight • Project Milestone #1 due Thursday, Oct. 16 • See project description on what to accomplish by then 3 Announcements (Tue., Sep. 30) • Homework #2 due date extended to Oct. 7 • Midterm in class next Thursday (Oct. 9) • Open-book, open-notes • Same format as sample midterm (from last year) • Already posted on Sakai • Solution to be posted later this week 4 “Active” data • Constraint enforcement: When an operation violates a constraint, abort the operation or try to “fix” data • Example: enforcing referential integrity constraints • Generalize to arbitrary constraints? • Data monitoring: When something happens to the data, automatically execute some action • Example: When price rises above $20 per share, sell • Example: When enrollment is at the limit and more students try to register, email the instructor 5 Triggers • A trigger is an event-condition-action (ECA ) rule • When event occurs, test condition ; if condition is satisfied, execute action • Example: • Event : some user’s popularity is updated • Condition : the user is a member of “Jessica’s Circle,” and pop drops below 0.5 • Action : kick that user out of Jessica’s Circle http://pt.simpsons.wikia.com/wiki/Arquivo:Jessica_lovejoy.jpg 6 Trigger example
    [Show full text]
  • Oracle Nosql Database EE Data Sheet
    Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure.
    [Show full text]
  • KB SQL Database Administrator Guide a Guide for the Database Administrator of KB SQL
    KB_SQL Database Administrator Guide A Guide for the Database Administrator of KB_SQL © 1988-2019 by Knowledge Based Systems, Inc. All rights reserved. Printed in the United States of America. No part of this manual may be reproduced in any form or by any means (including electronic storage and retrieval or translation into a foreign language) without prior agreement and written consent from KB Systems, Inc., as governed by United States and international copyright laws. The information contained in this document is subject to change without notice. KB Systems, Inc., does not warrant that this document is free of errors. If you find any problems in the documentation, please report them to us in writing. Knowledge Based Systems, Inc. 43053 Midvale Court Ashburn, Virginia 20147 KB_SQL is a registered trademark of Knowledge Based Systems, Inc. MUMPS is a registered trademark of the Massachusetts General Hospital. All other trademarks or registered trademarks are properties of their respective companies. Table of Contents Preface ................................................. vii Purpose ............................................. vii Audience ............................................ vii Conventions Used in this Manual ...................................................................... viii The Organization of this Manual ......................... ... x Additional Documentation .............................. xii Chapter 1: An Overview of the KB_SQL User Groups and Menus ............................................................................................................
    [Show full text]
  • SUGI 23: an Investigation of the Efficiency of SQL DML Operations Performed on an Oracle DBMS Using SAS/Accessr Software
    Posters An Investigation of the Efficiency of SQL DML Operations Performed on an ORACLE DBMS using SAS/ACCESS Software Annie Guo, Ischemia Research and Education Foundation, San Francisco, California Abstract Table 1.1: Before Modification Of Final Records Id Entry MedCode Period1 Period2 Indication AG1001 Entry1 AN312 Postop Day1 Routine AG1001 Entry2 AN312 Postop Day1 Routine In an international epidemiological study of 2000 cardiac AG1001 Final AN312 Postop Day1 Non-routine ← To be updated surgery patients, the data of 7000 variables are entered AG1001 Final HC527 Intraop PostCPB Maintenance ← To be deleted AG1002 Entry1 PV946 Intraop PreCPB Non-routine ← To be inserted through a Visual Basic data entry system and stored in 57 AG1002 Entry2 PV946 Intraop PreCPB Non-routine as ‘Final’ large ORACLE tables. A SAS application is developed to Table 1.2: After Modification Of Final Records automatically convert the ORACLE tables to SAS data sets, Id Entry MedCode Period1 Period2 Indication AG1001 Entry1 AN312 Postop Day1 Routine perform a series of intensive data processing, and based AG1001 Entry2 AN312 Postop Day1 Routine AG1001 Final AN312 Postop Day1 Routine ← Updated upon the result of the data processing, dynamically pass AG1002 Entry1 PV946 Intraop PreCPB Non-routine AG1002 Entry2 PV946 Intraop PreCPB Non-routine ORACLE SQL Data Manipulation Language (DML) AG1002 Final PV946 Intraop PreCPB Non-routine ← Inserted commands such as UPDATE, DELETE and INSERT to ORACLE database and modify the data in the 57 tables. A Visual Basic module making use of ORACLE Views, Functions and PL/SQL Procedures has been developed to The modification of ORACLE data using SAS software can access the ORACLE data through ODBC driver, compare be resource-intensive, especially in dealing with large tables the 7000 variables between the 2 entries, and modify the and involving sorting in ORACLE data.
    [Show full text]
  • 3 Data Definition Language (DDL)
    Database Foundations 6-3 Data Definition Language (DDL) Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Roadmap You are here Data Transaction Introduction to Structured Data Definition Manipulation Control Oracle Query Language Language Language (TCL) Application Language (DDL) (DML) Express (SQL) Restricting Sorting Data Joining Tables Retrieving Data Using Using ORDER Using JOIN Data Using WHERE BY SELECT DFo 6-3 Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 3 Data Definition Language (DDL) Objectives This lesson covers the following objectives: • Identify the steps needed to create database tables • Describe the purpose of the data definition language (DDL) • List the DDL operations needed to build and maintain a database's tables DFo 6-3 Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 4 Data Definition Language (DDL) Database Objects Object Description Table Is the basic unit of storage; consists of rows View Logically represents subsets of data from one or more tables Sequence Generates numeric values Index Improves the performance of some queries Synonym Gives an alternative name to an object DFo 6-3 Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 5 Data Definition Language (DDL) Naming Rules for Tables and Columns Table names and column names must: • Begin with a letter • Be 1–30 characters long • Contain only A–Z, a–z, 0–9, _, $, and # • Not duplicate the name of another object owned by the same user • Not be an Oracle server–reserved word DFo 6-3 Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 6 Data Definition Language (DDL) CREATE TABLE Statement • To issue a CREATE TABLE statement, you must have: – The CREATE TABLE privilege – A storage area CREATE TABLE [schema.]table (column datatype [DEFAULT expr][, ...]); • Specify in the statement: – Table name – Column name, column data type, column size – Integrity constraints (optional) – Default values (optional) DFo 6-3 Copyright © 2015, Oracle and/or its affiliates.
    [Show full text]
  • Today SQL Views CREATE VIEW Command
    Today IT420: Database Management and SQL Views Organization SQL Views (Chapter 7) 1 Kroenke, Database Processing 2 SQL Views CREATE VIEW Command SQL view is a virtual table that is constructed from other CREATE VIEW command: tables or views CREATE VIEW view_name It has no data of its own, but obtains data from tables or AS other views select_statement It only has a definition Use the view: SELECT statements are used to define views In SELECT statements A view definition may not include an ORDER BY clause Sometimes in INSERT statements Views can be used as regular tables in SELECT Sometimes in UPDATE statements statements Sometimes in DELETE statements Kroenke, Database Processing 3 Kroenke, Database Processing 4 1 CREATE VIEW Command Uses for SQL Views CREATE VIEW Security: hide columns and rows command: CREATE VIEW CustomerNameView Display results of computations AS Hide complicated SQL syntax SELECT CustName AS CustomerName Provide a level of isolation between actual FROM CUSTOMER; data and the user’s view of data To use the view: three-tier architecture SELECT * Assign different processing permissions to FROM CustomerNameView ORDER BY CustomerName; different views on same table Kroenke, Database Processing 5 Kroenke, Database Processing 6 Security: hide columns and rows Display results of computations MIDS database, Midshipmen table Faculty (EmpID , LName, FName, Department, AreaCode, LocalPhone) View for faculty – all mids with IT major Create a view to display 2 columns: View for students – all mids, no
    [Show full text]
  • Database Views
    DATABASE VIEWS CHAPTER 5 (6/E) CHAPTER 8 (5/E) 1 LECTURE OUTLINE . Database views provide convenient usage • Virtual view realized when query uses that view . Materialized views allow efficient re-use • Must be recalculated or updated when base tables change 2 VIEWS FOR CUSTOMIZATION . Consider database(s) describing university’s activities • Academic institution • Students, professors, classes • Grades, transcripts • Admissions, convocations • Alumni • Corporate institution • Finances, human resources • Board of Governors • Capital assets • Charitable institution • Donors, fundraising activities • Research institution • Granting agencies, industrial/non-profits/academic partners • Grants and contracts, intellectual property, licensing . Each user group provided appropriate “subset” of the data • e.g., some financial/scheduling info relevant to most groups; other info confidential • Underlying data shared, not silo’d. Updates must be seen by all affected users. 3 VIEWS (VIRTUAL TABLES) . Consider again the query SELECT title, year, genre FROM Film WHERE director = 'Steven Spielberg' AND year > 1990; • Returns all matching films currently in the database • If re-run after updates, will give revised table of matches . A view is an unexecuted query that can be run on demand. • Single table derived from other table(s) • A virtual table 4 USING VIEWS IN SQL . CREATE VIEW command • View name and a query to specify the contents of the view CREATE VIEW Big_Earners AS SELECT E.Ssn AS Ssn, E.Lname AS Name, E.Salary AS Salary, M.Lname AS Manager FROM EMPLOYEE E, EMPLOYEE M WHERE E.Super_ssn = M.Ssn and E.Salary > M.Salary; . Queries can use view as if it were a base table. SELECT * FROM Big_Earners WHERE Salary < 100000; .
    [Show full text]
  • Granada: Relational Database Navigation and Scaling
    Granada: Relational Database Navigation and Scaling Jens K¨ottersand Peter W. Eklundy yCentre for Cyber Security Research and Innovation Deakin University, Geelong, Australia. Abstract. In this paper an application is presented that allows the con- struction of conjunctive queries (represented as labeled graphs), the pre- sentation of the results of the queries (as tables), and the selection of options for specialization or generalization of the graph queries. The graph labels correspond to the query vocabulary, which is obtained by conceptual scaling of the database. Each scale provides the specialization and generalization options for a particular kind of graph node. At any time, the user is shown the current query, the result table, and a set of options to modify the query. Keywords: conjunctive queries, pattern concepts, relational databases, navigation, conceptual scaling 1 Introduction The Granada tool (hosted at https://github.com/koetters/dbnav) allows a user to query a relational data source without prior knowledge of the schema. This is achieved through point-and-click extension of graph-based queries, which makes the tool suitable for data exploration as well. Data analysis is currently not supported by the tool, but some functionality could be integrated later. The graph-based queries provide an abstraction from the data source, but we assume a MySQL database in the following (other backends have not yet been imple- mented). No assumptions regarding the structure or contents of the database have to be made. Navigation with the tool is described in Section 2. Section 3 describes the tool in the context of Formal Concept Analysis (FCA) and our own work within FCA.
    [Show full text]
  • Sql Server Management Studio View Table Schema
    Sql Server Management Studio View Table Schema Uli is subastral and gilt tangibly while unhusked Mario remint and witch. Wrongful Smith geologising disposingly, he dighting his Crookes very complexly. Disbelieving and uncaught Ulric superscribe wondrously and set-ups his inhospitableness adagio and cloudlessly. Sql server table but if the main highlander script file in the past the database server sql server Il consenso fornito sarà utilizzato solo per il trattamento dei dati provenienti da questo sito web. Notify me of new posts via email. Include details about your collection, demonstration, along with the tech tips and tricks. Is your SQL Server running slow and you want to speed it up without sharing server credentials? Use the sample database to answer these questions. What is Remote Desktop for Windwos VPS? Please enter a valid email address. Wait, create text and mathematical results and set distinct values, etc. Returns a DDL statement that can be used to recreate the specified object. If you are a SQL Server database administrator or developer, company or government agency. Sign up to get the latest news and insights. Click next at the welcome screen. Most users, use or disclosure of Personal Information, and the control will select the best matching node for you. This has to do with the way objects are organized on the server. Gets schema information of the database, or everyone. Why is This a Problem? In contrast, I would recommend a regular FULL backup, a comprender cómo los visitantes interactúan con los sitios mediante la recolección y reporte de información anónima.
    [Show full text]
  • View-Centric Performance Optimization for Database-Backed Web Applications
    View-Centric Performance Optimization for Database-Backed Web Applications Junwen Yang1, Cong Yan2, Chengcheng Wan1, Shan Lu1, Alvin Cheung2 1University of Chicago, junwen, cwan, [email protected] 2University of Washington, congy, [email protected] Abstract—Web developers face the stringent task of designing … informative web pages while keeping the page-load time low. <p> … This task has become increasingly challenging as most web <%= @active_projects … > contents are now generated by processing ever-growing amount … </p> of user data stored in back-end databases. It is difficult for developers to understand the cost of generating every web-page … element, not to mention explore and pick the web design with @active_projects = user.projects.active the best trade-off between performance and functionality. In this … paper, we present Panorama, a view-centric and database-aware development environment for web developers. Using database- aware program analysis and novel IDE design, Panorama pro- Fig. 1: Performance understanding challenge vides developers with intuitive information about the cost and the performance-enhancing opportunities behind every HTML file sidebar.html.erb, which produces this sidebar, renders element, as well as suggesting various global code refactorings these projects based on a Ruby variable @active projects that enable developers to easily explore a wide spectrum of embedded in the HTML file; this variable is computed performance and functionality trade-offs. in a controller Ruby file todos controller.rb through a seemingly straightforward assignment @active projects = I. INTRODUCTION user.projects.active. It turns out that this code actually A. Motivation retrieves objects stored in a database, and is translated into High-quality web applications need to provide both good a SQL query by the Rails framework at run time.
    [Show full text]