Oracle 1Z0-051
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Let's Talk About Storage & Recovery Methods for Non-Volatile Memory
Let’s Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems Joy Arulraj Andrew Pavlo Subramanya R. Dulloor [email protected] [email protected] [email protected] Carnegie Mellon University Carnegie Mellon University Intel Labs ABSTRACT of power, the DBMS must write that data to a non-volatile device, The advent of non-volatile memory (NVM) will fundamentally such as a SSD or HDD. Such devices only support slow, bulk data change the dichotomy between memory and durable storage in transfers as blocks. Contrast this with volatile DRAM, where a database management systems (DBMSs). These new NVM devices DBMS can quickly read and write a single byte from these devices, are almost as fast as DRAM, but all writes to it are potentially but all data is lost once power is lost. persistent even after power loss. Existing DBMSs are unable to take In addition, there are inherent physical limitations that prevent full advantage of this technology because their internal architectures DRAM from scaling to capacities beyond today’s levels [46]. Using are predicated on the assumption that memory is volatile. With a large amount of DRAM also consumes a lot of energy since it NVM, many of the components of legacy DBMSs are unnecessary requires periodic refreshing to preserve data even if it is not actively and will degrade the performance of data intensive applications. used. Studies have shown that DRAM consumes about 40% of the To better understand these issues, we implemented three engines overall power consumed by a server [42]. in a modular DBMS testbed that are based on different storage Although flash-based SSDs have better storage capacities and use management architectures: (1) in-place updates, (2) copy-on-write less energy than DRAM, they have other issues that make them less updates, and (3) log-structured updates. -
(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. -
Failures in DBMS
Chapter 11 Database Recovery 1 Failures in DBMS Two common kinds of failures StSystem filfailure (t)(e.g. power outage) ‒ affects all transactions currently in progress but does not physically damage the data (soft crash) Media failures (e.g. Head crash on the disk) ‒ damagg()e to the database (hard crash) ‒ need backup data Recoveryyp scheme responsible for handling failures and restoring database to consistent state 2 Recovery Recovering the database itself Recovery algorithm has two parts ‒ Actions taken during normal operation to ensure system can recover from failure (e.g., backup, log file) ‒ Actions taken after a failure to restore database to consistent state We will discuss (briefly) ‒ Transactions/Transaction recovery ‒ System Recovery 3 Transactions A database is updated by processing transactions that result in changes to one or more records. A user’s program may carry out many operations on the data retrieved from the database, but the DBMS is only concerned with data read/written from/to the database. The DBMS’s abstract view of a user program is a sequence of transactions (reads and writes). To understand database recovery, we must first understand the concept of transaction integrity. 4 Transactions A transaction is considered a logical unit of work ‒ START Statement: BEGIN TRANSACTION ‒ END Statement: COMMIT ‒ Execution errors: ROLLBACK Assume we want to transfer $100 from one bank (A) account to another (B): UPDATE Account_A SET Balance= Balance -100; UPDATE Account_B SET Balance= Balance +100; We want these two operations to appear as a single atomic action 5 Transactions We want these two operations to appear as a single atomic action ‒ To avoid inconsistent states of the database in-between the two updates ‒ And obviously we cannot allow the first UPDATE to be executed and the second not or vice versa. -
What Is Nosql? the Only Thing That All Nosql Solutions Providers Generally Agree on Is That the Term “Nosql” Isn’T Perfect, but It Is Catchy
NoSQL GREG SYSADMINBURD Greg Burd is a Developer Choosing between databases used to boil down to examining the differences Advocate for Basho between the available commercial and open source relational databases . The term Technologies, makers of Riak. “database” had become synonymous with SQL, and for a while not much else came Before Basho, Greg spent close to being a viable solution for data storage . But recently there has been a shift nearly ten years as the product manager for in the database landscape . When considering options for data storage, there is a Berkeley DB at Sleepycat Software and then new game in town: NoSQL databases . In this article I’ll introduce this new cat- at Oracle. Previously, Greg worked for NeXT egory of databases, examine where they came from and what they are good for, and Computer, Sun Microsystems, and KnowNow. help you understand whether you, too, should be considering a NoSQL solution in Greg has long been an avid supporter of open place of, or in addition to, your RDBMS database . source software. [email protected] What Is NoSQL? The only thing that all NoSQL solutions providers generally agree on is that the term “NoSQL” isn’t perfect, but it is catchy . Most agree that the “no” stands for “not only”—an admission that the goal is not to reject SQL but, rather, to compensate for the technical limitations shared by the majority of relational database implemen- tations . In fact, NoSQL is more a rejection of a particular software and hardware architecture for databases than of any single technology, language, or product . -
Oracle Nosql Database
An Oracle White Paper November 2012 Oracle NoSQL Database Oracle NoSQL Database Table of Contents Introduction ........................................................................................ 2 Technical Overview ............................................................................ 4 Data Model ..................................................................................... 4 API ................................................................................................. 5 Create, Remove, Update, and Delete..................................................... 5 Iteration ................................................................................................... 6 Bulk Operation API ................................................................................. 7 Administration .................................................................................... 7 Architecture ........................................................................................ 8 Implementation ................................................................................... 9 Storage Nodes ............................................................................... 9 Client Driver ................................................................................. 10 Performance ..................................................................................... 11 Conclusion ....................................................................................... 12 1 Oracle NoSQL Database Introduction NoSQL databases -
The Integration of Database Systems
Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1993 The Integration of Database Systems Tony Schaller Omran A. Bukhres Ahmed K. Elmagarmid Purdue University, [email protected] Xiangning Liu Report Number: 93-046 Schaller, Tony; Bukhres, Omran A.; Elmagarmid, Ahmed K.; and Liu, Xiangning, "The Integration of Database Systems" (1993). Department of Computer Science Technical Reports. Paper 1061. https://docs.lib.purdue.edu/cstech/1061 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. The Integration of Database Systems Tony Schaller, Omran A. Bukhres, Ahmed K. Elmagarmid and Xiangning Liu CSD-TR-93-046 July 1993 I I The Integration of Database Systems Tony Schaller Molecular Design Ltd. 2132 Farallon Drive San Leandro, CA 94577 Omran A. Bukhres, Ahmed K. Elmagarmid and Xiangning Liu DeparLment of Computer Sciences Purdue University West Lafayette, IN 47907 eJIlail: {bukhres,ake,xl} .es.purdue.edu 1 Introduction A database system is composed of two elements: a software program, called a database management system, and a set of data, called a database. The data in a database is organized according to some data model, such as the relational model used in a DB2 database [DW88] or the hierarchical model found with IMS databases [Dat77] . Users access the data through an interface (the query language) provided by the database management system. A schema describes the actual data structures and organization within the system. During the decade ofthe nineteen-seventies, centralized databases were predominant, but recent innovations in communications and database technologies have engendered a revolution in data processing, giving rlse to a new generation of decentralized database systems. -
High-Performance Transaction Processing in SAP HANA
High-Performance Transaction Processing in SAP HANA Juchang Lee1, Michael Muehle1, Norman May1, Franz Faerber1, Vishal Sikka1, Hasso Plattner2, Jens Krueger2, Martin Grund3 1SAP AG 2Hasso Plattner Insitute, Potsdam, Germany, 3eXascale Infolab, University of Fribourg, Switzerland Abstract Modern enterprise applications are currently undergoing a complete paradigm shift away from tradi- tional transactional processing to combined analytical and transactional processing. This challenge of combining two opposing query types in a single database management system results in additional re- quirements for transaction management as well. In this paper, we discuss our approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries. We present our approach to distributed snapshot isolation and optimized two-phase commit protocols. 1 Introduction An efficient and holistic data management infrastructure is one of the key requirements for making the right deci- sions at an operational, tactical, and strategic level and is core to support all kinds of enterprise applications[12]. In contrast to traditional architectures of database systems, the SAP HANA database takes a different approach to provide support for a wide range of data management tasks. The system is organized in a main-memory centric fashion to reflect the shift within the memory hierarchy[2] and to consistently provide high perfor- mance without prohibitively slow disk interactions. Completely transparent for the application, data is orga- nized along its life cycle either in column or row format, providing the best performance for different workload characteristics[11, 1]. Transactional workloads with a high update rate and point queries can be routed against a row store; analytical workloads with range scans over large datasets are supported by column oriented data struc- tures. -
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. -
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 ............................................................................................................ -
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.