Dml Commands with Examples
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Not ACID, Not BASE, but SALT a Transaction Processing Perspective on Blockchains
Not ACID, not BASE, but SALT A Transaction Processing Perspective on Blockchains Stefan Tai, Jacob Eberhardt and Markus Klems Information Systems Engineering, Technische Universitat¨ Berlin fst, je, [email protected] Keywords: SALT, blockchain, decentralized, ACID, BASE, transaction processing Abstract: Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. In this keynote paper, we present SALT as a model to explain blockchains and their use in application architecture. We take both, a transaction and a transaction processing systems perspective on the SALT model. From a transactions perspective, SALT is about Sequential, Agreed-on, Ledgered, and Tamper-resistant transaction processing. From a systems perspec- tive, SALT is about decentralized transaction processing systems being Symmetric, Admin-free, Ledgered and Time-consensual. We discuss the importance of these dual perspectives, both, when comparing SALT with ACID and BASE, and when engineering blockchain-based applications. We expect the next-generation of decentralized transactional applications to leverage combinations of all three transaction models. 1 INTRODUCTION against. Using the admittedly contrived acronym of SALT, we characterize blockchain-based transactions There is a common belief that blockchains have the – from a transactions perspective – as Sequential, potential to fundamentally disrupt entire industries. Agreed, Ledgered, and Tamper-resistant, and – from Whether we are talking about financial services, the a systems perspective – as Symmetric, Admin-free, sharing economy, the Internet of Things, or future en- Ledgered, and Time-consensual. -
(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. -
Data Analysis Expressions (DAX) in Powerpivot for Excel 2010
Data Analysis Expressions (DAX) In PowerPivot for Excel 2010 A. Table of Contents B. Executive Summary ............................................................................................................................... 3 C. Background ........................................................................................................................................... 4 1. PowerPivot ...............................................................................................................................................4 2. PowerPivot for Excel ................................................................................................................................5 3. Samples – Contoso Database ...................................................................................................................8 D. Data Analysis Expressions (DAX) – The Basics ...................................................................................... 9 1. DAX Goals .................................................................................................................................................9 2. DAX Calculations - Calculated Columns and Measures ...........................................................................9 3. DAX Syntax ............................................................................................................................................ 13 4. DAX uses PowerPivot data types ......................................................................................................... -
(BI) Using MS Excel Powerpivot
2018 ASCUE Proceedings Developing an Introductory Class in Business Intelligence (BI) Using MS Excel Powerpivot Dr. Sam Hijazi Trevor Curtis Texas Lutheran University 1000 West Court Street Seguin, Texas 78130 [email protected] Abstract Asking questions about your data is a constant application of all business organizations. To facilitate decision making and improve business performance, a business intelligence application must be an in- tegral part of everyday management practices. Microsoft Excel added PowerPivot and PowerPivot offi- cially to facilitate this process with minimum cost, knowing that many business people are already fa- miliar with MS Excel. This paper will design an introductory class to business intelligence (BI) using Excel PowerPivot. If an educator decides to adopt this paper for teaching an introductory BI class, students should have previ- ous familiarity with Excel’s functions and formulas. This paper will focus on four significant phases all students need to complete in a three-credit class. First, students must understand the process of achiev- ing small database normalization and how to bring these tables to Excel or develop them directly within Excel PowerPivot. This paper will walk the reader through these steps to complete the task of creating the normalization, along with the linking and bringing the tables and their relationships to excel. Sec- ond, an introduction to Data Analysis Expression (DAX) will be discussed. Introduction It is not that difficult to realize the increase in the amount of data we have generated in the recent memory of our existence as a human race. To realize that more than 90% of the world’s data has been amassed in the past two years alone (Vidas M.) is to realize the need to manage such volume. -
Rdbmss Why Use an RDBMS
RDBMSs • Relational Database Management Systems • A way of saving and accessing data on persistent (disk) storage. 51 - RDBMS CSC309 1 Why Use an RDBMS • Data Safety – data is immune to program crashes • Concurrent Access – atomic updates via transactions • Fault Tolerance – replicated dbs for instant failover on machine/disk crashes • Data Integrity – aids to keep data meaningful •Scalability – can handle small/large quantities of data in a uniform manner •Reporting – easy to write SQL programs to generate arbitrary reports 51 - RDBMS CSC309 2 1 Relational Model • First published by E.F. Codd in 1970 • A relational database consists of a collection of tables • A table consists of rows and columns • each row represents a record • each column represents an attribute of the records contained in the table 51 - RDBMS CSC309 3 RDBMS Technology • Client/Server Databases – Oracle, Sybase, MySQL, SQLServer • Personal Databases – Access • Embedded Databases –Pointbase 51 - RDBMS CSC309 4 2 Client/Server Databases client client client processes tcp/ip connections Server disk i/o server process 51 - RDBMS CSC309 5 Inside the Client Process client API application code tcp/ip db library connection to server 51 - RDBMS CSC309 6 3 Pointbase client API application code Pointbase lib. local file system 51 - RDBMS CSC309 7 Microsoft Access Access app Microsoft JET SQL DLL local file system 51 - RDBMS CSC309 8 4 APIs to RDBMSs • All are very similar • A collection of routines designed to – produce and send to the db engine an SQL statement • an original -
Object-Oriented Design of Database Stored Procedures (PDF)
Object-Oriented Design of Database Stored Procedures Michael Blaha, [email protected] Bill Huth, [email protected] Peter Cheung, [email protected] Abstract base I/O. One nice feature of SPs is their support for op- tional input parameters. An SP may have optional inputs Object-oriented (OO) software engineering techniques are if: (1) the definition lists all outputs before inputs and (2) often used with programming languages, but they also ap- optional inputs have default values. ply to relational databases. OO techniques are not only helpful for determining database structure, but also for de- 3. Financial Application Example signing stored procedure code. In fact, we were surprised by the magnitude of the stored procedure benefits. OO The examples in this article are based on an application for techniques boost development productivity as well as the managing syndicated loans. The loans can be huge, involv- quality, clarity, and maintainability of the resulting code. ing billions of dollars, and can arise from lending to gov- ernments and multinational corporations. Participation in a 1. Introduction loan must be spread across multiple lenders because the risk is too large for a single lender. Object-oriented (OO) techniques are versatile. They are not Figure 1 shows an excerpt of a UML class model for only helpful for developing programs and structuring data- the application. An Asset is something of value and can be bases, but they are also effective for designing stored proce- a Currency (such as US Dollars, Euros, and Yen), a Loan- dures. A stored procedure is programming code that runs in Instrument, or an OtherAsset (such as bonds and stock). -
10 Weeks to 0 Vulnerabilities Program
10 WEEKS TO 0 CRITICAL VULNERABILITIES INJECTION ATTACKS Sherif Koussa @skoussa 1 HOUSEKEEPING • Why we’re all here • Recording for internal training purposes • Slides will be provided after the session • Your mic will be muted, please use “Q&A” for any questions 2 ABOUT ME 2006 2008 2010 Joined SANS Mentor & Founded OWASP GIAC Consultant Software Secured 1999 2007 2009 2019 Software Founded Wells Fargo Founded Development OWASP Chapter Security Engineer Reshift Security Certifications: GSSP-Java, GSSP-NET, GWAPT 3 Reshift integrates with your modern software development pipeline to help your team find and fix vulnerabilities. Penetration Testing as a Service company based out of Ottawa, Canada. 4 10 WEEK SCHEDULE 1. April 10th: Injection 2. April 17th : Broken Authentication 3. April 24th: Sensitive data Exposure 4. May 1st : External Entity Injection 5. May 8th : Broken Access Control 6. May 15th: Security Misconfiguration 7. May 22nd: Cross-site Scripting 8. May 29th: Insecure Deserialization 9. June 5th: Using Components with Known Vulnerabilities 10. June 12th: Insufficient Logging and Monitoring 5 SESSION 1: AGENDA 1.What are Injection Attacks and their impacts 2.Injection Theory 3.Types of Injection Attacks: • SQL Injection (Exercise) • JavaScript Server Side Injection • NoSQL Injection 5.Injection Attacks Mitigation 6. Tools and Resources 6 WHAT ARE INJECTION ATTACKS Injection attacks denote a wide range of attacks targeting the server, where the attacker supplies untrusted input to software. This gets processed by an interpreter -
Polymorphic Stored Procedure? Venkat Subramaniam [email protected]
Polymorphic Stored Procedure? Venkat Subramaniam [email protected] http://www.durasoftcorp.com/download Abstract A typical user of JDBC or ASP.NET issues a SQL query to the underlying database, grabs the fields returned by the record set/result set (or dataset) and then populates an object with the data fetched. Not considering the use of Entity Beans and JDO in Java, what does one do if the object being fetched is one of several types derived from a common base type? This article addresses one way this can be solved in an extensible manner. A Simple Data Access Scenario For some one who has spent significant time developing OO applications that used object-oriented databases and those that did not use any database at all, it is painful to use a relational database with objects. I am sure you have heard people with OODBMS experience talk about data access impedance mismatch when it comes to storing an object in a relational database. You hear less of this now than you did in the early nineties. While there is some market for ODBMS still, I have come to agree that relational databases are here to stay. After developing a couple of applications using relational databases, I was pondering about arriving at a solution to the problem of mapping the inheritance hierarchy in an extensible way. Is this really a good approach to follow, is for you to decide. I simply present here my thoughts on what I tried recently. Your comments are most welcome. Let’s consider a database with four tables: Customer, Account, CheckingAccount, SavingsAccount. -
Sql Server to Aurora Postgresql Migration Playbook
Microsoft SQL Server To Amazon Aurora with Post- greSQL Compatibility Migration Playbook 1.0 Preliminary September 2018 © 2018 Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offer- ings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without war- ranty of any kind, whether express or implied. This document does not create any warranties, rep- resentations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agree- ments, and this document is not part of, nor does it modify, any agreement between AWS and its cus- tomers. - 2 - Table of Contents Introduction 9 Tables of Feature Compatibility 12 AWS Schema and Data Migration Tools 20 AWS Schema Conversion Tool (SCT) 21 Overview 21 Migrating a Database 21 SCT Action Code Index 31 Creating Tables 32 Data Types 32 Collations 33 PIVOT and UNPIVOT 33 TOP and FETCH 34 Cursors 34 Flow Control 35 Transaction Isolation 35 Stored Procedures 36 Triggers 36 MERGE 37 Query hints and plan guides 37 Full Text Search 38 Indexes 38 Partitioning 39 Backup 40 SQL Server Mail 40 SQL Server Agent 41 Service Broker 41 XML 42 Constraints -
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. -
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 -
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.