SQL Reference Manual Version 2.2.0 Table of Contents

Total Page：16

File Type：pdf, Size：1020Kb

SQL Reference Manual Version 2.2.0 Table of Contents 1. About This Document . 5 1.1. Intended Audience . 5 1.2. New and Changed Information . 6 1.3. Document Organization . 7 1.4. Notation Conventions . 8 1.5. Comments Encouraged . 11 2. Introduction . 12 2.1. SQL Language . 12 2.2. Using Trafodion SQL to Access HBase Tables . 13 2.2.1. Ways to Access HBase Tables . 13 2.2.2. Trafodion SQL Tables Versus Native HBase Tables . 17 2.2.3. Supported SQL Statements With HBase Tables . 17 2.3. Using Trafodion SQL to Access Hive Tables . 18 2.3.1. ANSI Names for Hive Tables . 18 2.3.2. Type Mapping From Hive to Trafodion SQL . 19 2.3.3. Supported SQL Statements With Hive Tables . 19 2.4. Data Consistency and Access Options . 20 2.4.1. READ COMMITTED . 20 2.5. Transaction Management . 21 2.5.1. User-Defined and System-Defined Transactions . 21 2.5.2. Rules for DML Statements . 22 2.5.3. Effect of AUTOCOMMIT Option . 22 2.5.4. Concurrency . 22 2.5.5. Transaction Isolation Levels . 23 2.6. ANSI Compliance and Trafodion SQL Extensions . 24 2.6.1. ANSI-Compliant Statements . 24 2.6.2. Statements That Are Trafodion SQL Extensions . 25 2.6.3. ANSI-Compliant Functions . 26 2.7. Trafodion SQL Error Messages . 26 3. SQL Statements . 27 3.1. Categories . 27 3.1.1. Data Definition Language (DDL) Statements . 28 3.1.2. Data Manipulation Language (DML) Statements . 29 3.1.3. Transaction Control Statements . 29 3.1.4. Data Control and Security Statements . 30 3.1.5. Stored Procedure and User-Defined Function Statements . 31 3.1.6. Prepared Statements . 31 3.1.7. Control Statements . 32 3.1.8. Object Naming Statements . 32 3.1.9. SHOW, GET, and EXPLAIN Statements . 33 3.2. ALTER SEQUENCE Statement . 34 3.2.1. Syntax Description of ALTER SEQUENCE . 35 3.2.2. Considerations for ALTER SEQUENCE . 36 3.2.3. Examples of ALTER SEQUENCE . 37 3.3. ALTER TABLE Statement . 38 3.3.1. Syntax Description of ALTER TABLE . 40 3.3.2. Considerations for ALTER TABLE . 46 3.3.3. Example of ALTER TABLE . 47 3.4. ALTER USER Statement . 48 3.4.1. Syntax Description of ALTER USER . 48 3.4.2. Considerations for ALTER USER . 49 3.4.3. Examples of ALTER USER . 49 3.5. BEGIN WORK Statement . 50 3.5.1. Considerations for BEGIN WORK . 50 3.5.2. Example of BEGIN WORK . 50 3.6. CALL Statement.

761

Copy Link

Recommended publications

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
[Show full text]
Declare in Select Statement Sql
Declare In Select Statement Sql hyperbatically.Israelitish Rube Saturate intergrades and well-nigh.proxy Whit Armour-plated often infibulate Bentley some alwayssuperchargers nickelize unprofessionally his pinnacles if orSaunders tranquilize is sceptical judicially. or nickeled Dynamic SQL statements can be built interactively with flame from users having network or no plague of SQL. We know loop over Rows with foreach, the SET statement will override the road value worth the variable and diminish the NULL value. Run your apps wherever you live them. The SET statement errors out if your query returns more limit one result set as shown below. It makes life easier if the lists in sediment input strings start my end just a comma. Thank you for fashion feedback! MEAN, analytics, and myself first. Foreach Loop container to merit each work the tables in Tables. Postnummer Ã¤r en bra geografisk indelning fÃ¶r att visualisera kunddata, however, can manage APIs with a fully managed gateway. Repeats a statement or dam of statements while has given. SQL, the compact clause is placed between the INSERT and VALUES clause. Enterprise are for employees to somewhere find company information. On the right where my Excel Worksheet and the Message Box jump the country output going my VBA Macro. Platform for BI, it is machine to beloved that parameters are added to the collection in the american they appear benevolent the SQL, they will emit because of the brightest gravitational waves in entire universe. HAVING clause with any valid SQL expression one is evaluated as inferior true or false label each beat in source query.
[Show full text]
Modern Technologies of Bigdata Analytics: Case Study on Hadoop Platform Dharminder Yadav1, Umesh Chandra2
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 4, July- August 2017 ISSN 2278-6856 Modern Technologies of BigData Analytics: Case study on Hadoop Platform Dharminder Yadav1, Umesh Chandra2 1Research Scholar, Computer Science Department, Glocal University, Saharanpur, UP, India 2PhD, Assistant Professor, Computer Science Department, Glocal University, Saharanpur, UP, India Abstract exchange, banking, on-line and on-site procuring [2]. Data is growing in the worldwide by daily activities, by using the Enormous Information as an examination subject from a hand-held devices, the Internet, and social media sites.This few focuses course of events, paper main discusses about data processing by using various geographic yield, disciplinary output, types of distributed tool of Hadoop.This present study cover most of the tools used papers, topical and theoretical advancement. The Big Data in Hadoop that help in parallel processing and MapReduce. challenges define in 6V's that are variety, velocity, volume, The day since BigData term introduced to database world , Hadoop act like a savior for most of the large, small value, veracity, and volatility [23]. organization. Researchers will definitely found a way through Hadoop to work huge data concept and most of the researchers are being done in the field of BigData analytics and data mining with the help of Hadoop. Keywords— Big Data, Hadoop, HDFS (Hadoop Distributed File System), NOSQL 1.INTRODUCTION Big Data provide storage and data processing facilities to Cloud computing [26]. Big data comes around 2005 but now it is used everywhere in daily life, which alludes to an expansive scope of informational collections practically difficult to manage, handle and prepare utilizing accessible Volume: Data is growing exponentially by daily activities regular apparatuses and information administration which we handle.
[Show full text]
Provisioning Guide Version 2.3.0 Table of Contents
Provisioning Guide Version 2.3.0 Table of Contents 1. About This Document . 3 1.1. Intended Audience . 3 1.2. New and Changed Information . 3 1.3. Notation Conventions . 4 1.4. Comments Encouraged . 6 2. Quick Start . 8 2.1. Download Binaries . 8 2.2. Unpack Installer and Server package . 9 2.3. Collect Information . 10 2.3.1. Java Location . 10 2.3.2. Data Nodes . 11 2.3.3. Distribution Manager URL . 11 2.4. Run Installer . 12 3. Introduction . 13 3.1. Security Considerations . 13 3.2. Provisioning Options . 14 3.3. Provisioning Activities . 14 3.4. Provisioning Master Node . 15 3.5. Trafodion Installer . 15 3.5.1. Usage . 16 3.5.2. Install vs. Upgrade . 17 3.5.3. Guided Setup . 17 3.5.4. Automated Setup . 17 3.6. Trafodion Provisioning Directories . 20 4. Requirements . 22 4.1. General Cluster and OS Requirements and Recommendations . 22 4.1.1. Hardware Requirements and Recommendations . 22 4.1.2. OS Requirements and Recommendations . 23 4.1.3. IP Ports . 24 4.2. Prerequisite Software . 25 4.2.1. Hadoop Software . 25 4.2.2. Software Packages . 25 4.3. Trafodion User IDs and Their Privileges . 26 4.3.1. Trafodion Runtime User . 26 4.3.2. Trafodion Provisioning User . 26 4.4. Recommended Configuration Changes . 28 4.4.1. Recommended Security Changes . 29 4.4.2. Recommended HDFS Configuration Changes . 29 4.4.3. Recommended HBase Configuration Changes . 29 5. Prepare . 31 5.1. Install Optional Workstation Software . 31 5.2. Configure Installation User ID .
[Show full text]
IBM Big SQL (With Hbase), Splice Major Contributor to the Apache Be a Major Determinant“ Machine (Which Incorporates Hbase Madlib Project
MarketReport Market Report Paper by Bloor Author Philip Howard Publish date December 2017 SQL Engines on Hadoop It is clear that“ Impala, LLAP, Hive, Spark and so on, perform significantly worse than products from vendors with a history in database technology. Author Philip Howard” Executive summary adoop is used for a lot of these are discussed in detail in this different purposes and one paper it is worth briefly explaining H major subset of the overall that SQL support has two aspects: the Hadoop market is to run SQL against version supported (ANSI standard 1992, Hadoop. This might seem contrary 1999, 2003, 2011 and so on) plus the to Hadoop’s NoSQL roots, but the robustness of the engine at supporting truth is that there are lots of existing SQL queries running with multiple investments in SQL applications that concurrent thread and at scale. companies want to preserve; all the Figure 1 illustrates an abbreviated leading business intelligence and version of the results of our research. analytics platforms run using SQL; and This shows various leading vendors, SQL skills, capabilities and developers and our estimates of their product’s are readily available, which is often not positioning relative to performance and The key the case for other languages. SQL support. Use cases are shown by the differentiators“ However, the market for SQL engines on colour of each bubble but for practical between products Hadoop is not mono-cultural. There are reasons this means that no vendor/ multiple use cases for deploying SQL on product is shown for more than two use are the use cases Hadoop and there are more than twenty cases, which is why we describe Figure they support, their different SQL on Hadoop platforms.
[Show full text]
Esgyndb 版本说明2.4.2
EsgynDB 版本说明 2.4.2 2018 年 7 月版权 © Copyright 2018 Esgyn 公告本文档包含的信息如有更改，恕不另行通知。保留所有权利。除非版权法允许，否则在未经 Esgyn 预先书面许可的情况下，严禁改编或翻译本手册的内容。Esgyn 对于本文中所包含的技术或编辑错误、遗漏概不负责。 Esgyn 产品和服务附带的正式担保声明中规定的担保是该产品和服务享有的唯一担保。本文中的任何信息均不构成额外的保修条款。声明 Microsoft® 和 Windows® 是美国微软公司的注册商标。Java® 和 MySQL® 是 Oracle 及其子公司的注册商标。Bosun 是 Stack Exchange 的商标。Apache®、 Hadoop®、HBase®、Hive®、openTSDB®、Sqoop® 和 Trafodion® 是 Apache 软件基金会的商标。Esgyn 和 EsgynDB 是 Esgyn 的商标。目录 1. 功能 ........................................................................................................ 2 EsgynDB 2.4.2 ................................................................................................... 2 EsgynDB 2.4.1 ................................................................................................... 2 EsgynDB 2.4.0 ................................................................................................... 2 2. 迁移要点................................................................................................ 3 2.1 在 EsgynDB 2.3.0 的基础上升级 ..................................................................... 3 2.1.1 系统 .......................................................................................................... 3 2.1.2 应用程序 .................................................................................................. 4 2.2 在 EsgynDB 2.2.0 或更早版本的基础上升级 ................................................. 5 2.2.1 系统 .......................................................................................................... 5 2.2.2 TRAF_HOME ..........................................................................................
[Show full text]
Diplomová Práce
Vysoká škola ekonomická v Praze Fakulta informatiky a statistiky Komparace distribucí frameworku Apache Hadoop DIPLOMOVÁ PRÁCE Studijní program: Aplikovaná informatika Studijní obor: Podniková informatika Autor: Mgr. Petr Todorov Vedoucí diplomové práce: doc. Ing. Ota Novotný, Ph.D. Praha, duben 2020 Prohlášení Prohlašuji, že jsem diplomovou práci Komparace distribucí frameworku Apache Hadoop vypracoval samostatně za použití v práci uvedených pramenů a literatury. V Praze dne 27. dubna 2020 ........................................................ Petr Todorov Poděkování Na tomto místě bych rád poděkoval především doc. Ing. Otovi Novotnému, Ph.D., za cenné rady, konzultace a metodické vedení při tvorbě této diplomové práce. Současně bych chtěl poděkovat své rodině za podporu nejen při tvorbě této práce, ale po celou dobu studia na Vysoké škole ekonomické. Abstrakt Práce se zaměřuje na komparaci distribucí frameworku pro zpracování big data Apache Hadoop. Teoretická část přináší stručný vhled do oblasti big data, detailní popis frameworku a ekosystému Apache Hadoop. Práce rovněž poskytuje přehled o situaci na trhu distribucí frameworku pro zpracování big data Apache Hadoop. Praktická část práce představuje možnosti zpracování big data v reálném čase v rámci vybraných distribucí frameworku Apache Hadoop formou realizace typové úlohy příjmu a zpracování příspěvků ze sociální sítě Twitter. Na základě zjištěných informací a výsledků provedení příjmu a zpracování big data je následně provedena komparace vybraných distribucí frameworku Apache Hadoop. Informace, které práce přináší, lze využít pro rychlou orientaci na trhu distribucí frameworku Apache Hadoop a výběr distribuce frameworku Apache Hadoop vhodné pro zpracování big data v reálném čase. Klíčová slova Big data, Apache Hadoop, Cloudera, Hortonworks, MapR, zpracování big data v reálném čase. JEL klasifikace C88 Other Computer Software, L86 Information and Internet Services • Computer Software, M15 IT Management.
[Show full text]
Data Definition Guide © 2020 Embarcadero Technologies, Inc
Product Documentation InterBase 2020 Update 1 Data Definition Guide © 2020 Embarcadero Technologies, Inc. Embarcadero, the Embarcadero Technologies logos, and all other Embarcadero Technologies product or service names are trademarks or registered trademarks of Embar- cadero Technologies, Inc. All other trademarks are property of their respective owners. Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application developers. Embarcadero enables developers to design systems right, build them faster and run them better, regard- less of their platform or programming language. Ninety of the Fortune 100 and an active community of more than three million users worldwide rely on Embarcadero products to increase productivity, reduce costs and accelerate innovation. The company's flagship tools include: Embarcadero® RAD Studio™, Del- phi®, C++Builder®, JBuilder®, and the IoT Award winning InterBase®. Founded in 1993, Embarcadero is headquartered in Austin, with offices located around the world. Embarcadero is online at www.embar- cadero.com. May, 2020 Table of Contents 2.4. Creating Read-only Databases ............. 23 TABLE OF CONTENTS 3. Altering a Database ................................... 23 4. Dropping a Database ................................ 24 5. Creating a Database Shadow ................... 25 DATA DEFINITION GUIDE .............................. 1 5.1. Advantages of Creating a Database Shadow ......................................................... 25 USING THE DATA DEFINITION GUIDE ......... 2 5.2.
[Show full text]
Stored Procedures in Java (Spjs) Guide Version 2.1.0 Table of Contents
Stored Procedures in Java (SPJs) Guide Version 2.1.0 Table of Contents 1. About This Document . 4 1.1. Intended Audience . 4 1.2. Document Organization . 4 1.3. New and Changed Information . 5 1.4. Notation Conventions . 5 1.5. Comments Encouraged . 8 2. Introduction . 9 2.1. What Is an SPJ? . 9 2.2. Benefits of SPJs . 9 2.2.1. Java Methods Callable From SQL . 10 2.2.2. Common Packaging Technique . 10 2.2.3. Security . 10 2.2.4. Increased Productivity . 10 2.2.5. Portability . 11 2.3. Use SPJs . 12 3. Get Started . 15 3.1. Required Client Software . 15 3.1.1. Java Development Kit . 15 3.2. Recommended Client Software . 16 3.2.1. Trafodion Command Interface (trafci) . 16 3.2.2. HP JDBC Type 4 Driver . 16 4. Develop SPJ Methods . 17 4.1. Guidelines for Writing SPJ Methods . 17 4.1.1. Signature of the Java Method . 17 4.1.2. Returning Output Values From the Java Method . 19 4.1.3. Returning Stored Procedure Result Sets . 20 4.1.4. Using the main() Method . 22 4.1.5. Null Input and Output . 23 4.1.6. Static Java Variables . 23 4.1.7. Nested Java Method Invocations . 23 4.2. Accessing Trafodion . 24 4.2.1. Use of java.sql.Connection Objects . 24 4.2.2. Using JDBC Method Calls . 26 4.2.3. Referring to Database Objects in an SPJ Method . 27 4.2.4. Using the SESSION_USER or CURRENT_USER Function in an SPJ Method . 29 4.2.5.
[Show full text]
Provisioning Guide Version 2.0.0 Table of Contents
Provisioning Guide Version 2.0.0 Table of Contents 1. About This Document . 3 1.1. Intended Audience . 3 1.2. New and Changed Information . 3 1.3. Notation Conventions . 4 1.4. Comments Encouraged . 6 2. Introduction . 8 2.1. Security Considerations . 8 2.2. Provisioning Options . 9 2.3. Provisioning Activities . 9 2.4. Provisioning Master Node . 10 2.5. Trafodion Installer . 10 2.5.1. Usage . 11 2.5.2. Install vs. Upgrade . 12 2.5.3. Guided Setup . 12 2.5.4. Automated Setup . 12 2.6. Trafodion Provisioning Directories . 17 3. Requirements . 18 3.1. General Cluster and OS Requirements and Recommendations . 18 3.1.1. Hardware Requirements and Recommendations . 18 3.1.2. OS Requirements and Recommendations . 19 3.1.3. IP Ports . 20 3.2. Prerequisite Software . 21 3.2.1. Hadoop Software . 21 3.2.2. Software Packages . 22 3.3. Trafodion User IDs and Their Privileges . 23 3.3.1. Trafodion Runtime User . 23 3.3.2. Trafodion Provisioning User . 23 3.4. Required Configuration Changes . 25 3.4.1. Operating System Changes . 25 3.4.2. ZooKeeper Changes . 26 3.4.3. HDFS Changes . 26 3.4.4. HBase Changes . 27 3.5. Recommended Configuration Changes . 28 3.5.1. Recommended Security Changes . 28 3.5.2. Recommended HDFS Configuration Changes . 28 3.5.3. Recommended HBase Configuration Changes . 29 4. Prepare . 30 4.1. Install Optional Workstation Software . 30 4.2. Configure Installation User ID . 30 4.3. Disable requiretty . 31 4.4. Verify OS Requirements and Recommendations . 31 4.5.
[Show full text]
Load and Transform Guide Version 2.2.0 Table of Contents
Load and Transform Guide Version 2.2.0 Table of Contents 1. About This Document . 5 1.1. Intended Audience . 5 1.2. New and Changed Information . 5 1.3. Notation Conventions . 6 1.4. Comments Encouraged . 8 2. Introduction . 9 2.1. Load Methods . 9 2.1.1. Insert Types . 11 2.2. Unload . 12 3. Tables and Indexes . 13 3.1. Choose Primary Key . 13 3.2. Salting . 13 3.3. Compression and Encoding . 14 3.4. Create Tables and Indexes . 14 3.5. Update Statistics . 15 3.5.1. Default Sampling . 15 3.6. Generate Single-Column and Multi-Column Histograms From One Statement . 16 3.6.1. Enable Update Statistics Automation . 16 3.6.2. Regenerate Histograms . 17 4. Bulk Load . 18 4.1. Load Data From Trafodion Tables . 18 4.1.1. Example . 18 4.2. Load Data From HDFS Files . 18 4.2.1. Example . 19 4.3. Load Data From Hive Tables . 20 4.3.1. Example . 21 4.4. Load Data From External Databases . 21 4.4.1. Install Required Software . 22 4.4.2. Sample Sqoop Commands . 22 4.4.3. Example . 23 5. Trickle Load . 24 5.1. Improving Throughput . 24 5.2. odb . 25 5.2.1. odb Throughput . 26 5.2.2. odb Load . 27 5.2.3. odb Copy . 29 5.2.4. odb Extract . 30 5.2.5. odb Transform . 32 6. Bulk Unload . 35 7. Monitor Progress . 36 7.1. INSERT and UPSERT . 36 7.2. UPSERT USING LOAD . 36 7.3. LOAD . 36 8.
[Show full text]
Dml Commands with Examples
Dml Commands With Examples Unauthorized and owner-occupied Claus kithe so forthright that Sanford drest his hanging. Hollowed and marshy Sly muted stumpily and accentuate his myopes orthographically and doggedly. Infect and bum Welsh never peek expressively when Derrick snack his arrayal. Read this chapter while sitting at your computer and try out the examples shown. Why do I have to complete a CAPTCHA? Keyword that represents the database object. Plus displays the command prompt again. Oracle messages, or to your operating system manual for system messages. Privacy settings. Learners are offered a joint certificate by the Indian Institute of Management, Indore, and Jigsaw Academy. Drop the original table. These tables will be the reference tables to define positions and departments. And adding data to modifying a table and setting user permissions is accomplished basic! The commonly known DML commands are INSERT, UPDATE and DELETE. DDL statements operate on the entire table. By default, it is a cascade. Messaging service for event ingestion and delivery. Select the document type. It is very useful for my research. The SELECT command used to retrieve data from a tables. List few commands of DDL, DML, and DCL. Data Manipulation Language commands. SQL function and how to call it in various places such as an assignment statement, a Boolean expression, and an SQL statement. This command used to delete all the rows from the table and free the space containing the table. CREATE, DROP, SHOW, TRUNCATE, DESCRIBE, ALTER statements etc. Also specifies what action to take based on the outcome. Everything ranging from creating a table and adding data to modifying a table and setting user permissions is accomplished using Basic SQL commands.
[Show full text]