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SMART: Making DB2 (More) Autonomic
SMART: Making DB2 (More) Autonomic Guy M. Lohman Sam S. Lightstone IBM Almaden Research Center IBM Toronto Software Lab K55/B1, 650 Harry Rd. 8200 Warden Ave. San Jose, CA 95120-6099 Markham, L6G 1C7 Ontario U.S.A. Canada [email protected] [email protected] Abstract The database community has already made many significant contributions toward autonomic systems. IBM’s SMART (Self-Managing And Resource Separating the logical schema from the physical schema, Tuning) project aims to make DB2 self- permitting different views of the same data by different managing, i.e. autonomic, to decrease the total applications, and the entire relational model of data, all cost of ownership and penetrate new markets. simplified the task of building new database applications. Over several releases, increasingly sophisticated Declarative query languages such as SQL, and the query SMART features will ease administrative tasks optimizers that made them possible, further aided such as initial deployment, database design, developers. But with the exception of early research in system maintenance, problem determination, and the late 1970s and early 1980s on database design ensuring system availability and recovery. algorithms, little has been done to help the beleaguered database administrator (DBA) until quite recently, with 1. Motivation for Autonomic Databases the founding of the AutoAdmin project at Microsoft [http://www.research.microsoft.com/dmx/autoadmin/] and While Moore’s Law and competition decrease the per-unit the SMART project at IBM, described herein. cost of hardware and software, the shortage of skilled professionals that can comprehend the growing complexity of information technology (IT) systems 2. -
Minutes of the January 25, 2010, Meeting of the Board of Regents
MINUTES OF THE JANUARY 25, 2010, MEETING OF THE BOARD OF REGENTS ATTENDANCE This scheduled meeting of the Board of Regents was held on Monday, January 25, 2010, in the Regents’ Room of the Smithsonian Institution Castle. The meeting included morning, afternoon, and executive sessions. Board Chair Patricia Q. Stonesifer called the meeting to order at 8:31 a.m. Also present were: The Chief Justice 1 Sam Johnson 4 John W. McCarter Jr. Christopher J. Dodd Shirley Ann Jackson David M. Rubenstein France Córdova 2 Robert P. Kogod Roger W. Sant Phillip Frost 3 Doris Matsui Alan G. Spoon 1 Paul Neely, Smithsonian National Board Chair David Silfen, Regents’ Investment Committee Chair 2 Vice President Joseph R. Biden, Senators Thad Cochran and Patrick J. Leahy, and Representative Xavier Becerra were unable to attend the meeting. Also present were: G. Wayne Clough, Secretary John Yahner, Speechwriter to the Secretary Patricia L. Bartlett, Chief of Staff to the Jeffrey P. Minear, Counselor to the Chief Justice Secretary T.A. Hawks, Assistant to Senator Cochran Amy Chen, Chief Investment Officer Colin McGinnis, Assistant to Senator Dodd Virginia B. Clark, Director of External Affairs Kevin McDonald, Assistant to Senator Leahy Barbara Feininger, Senior Writer‐Editor for the Melody Gonzales, Assistant to Congressman Office of the Regents Becerra Grace L. Jaeger, Program Officer for the Office David Heil, Assistant to Congressman Johnson of the Regents Julie Eddy, Assistant to Congresswoman Matsui Richard Kurin, Under Secretary for History, Francisco Dallmeier, Head of the National Art, and Culture Zoological Park’s Center for Conservation John K. -
Relational Database Design Chapter 7
Chapter 7: Relational Database Design Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition Boyce-Codd Normal Form Third Normal Form Multivalued Dependencies and Fourth Normal Form Overall Database Design Process Database System Concepts 7.2 ©Silberschatz, Korth and Sudarshan 1 First Normal Form Domain is atomic if its elements are considered to be indivisible units + Examples of non-atomic domains: Set of names, composite attributes Identification numbers like CS101 that can be broken up into parts A relational schema R is in first normal form if the domains of all attributes of R are atomic Non-atomic values complicate storage and encourage redundant (repeated) storage of data + E.g. Set of accounts stored with each customer, and set of owners stored with each account + We assume all relations are in first normal form (revisit this in Chapter 9 on Object Relational Databases) Database System Concepts 7.3 ©Silberschatz, Korth and Sudarshan First Normal Form (Contd.) Atomicity is actually a property of how the elements of the domain are used. + E.g. Strings would normally be considered indivisible + Suppose that students are given roll numbers which are strings of the form CS0012 or EE1127 + If the first two characters are extracted to find the department, the domain of roll numbers is not atomic. + Doing so is a bad idea: leads to encoding of information in application program rather than in the database. Database System Concepts 7.4 ©Silberschatz, Korth and Sudarshan 2 Pitfalls in Relational Database Design Relational database design requires that we find a “good” collection of relation schemas. -
Onsite Program
2015 ASAP Global Alliance Summit Collaboration at the Core: Forging the Future of Partnering Onsite Program March 2 – 5, 2015 Orlando, Florida Hyatt Regency Orlando www.strategic-alliances.org/summit +1-774-256-1401 Platinum Sponsor Gold Sponsor Silver Sponsors Table of Contents Welcome to the 2015 ASAP Global Alliance Summit . .3 ASAP Executive Committee & Management Board & ASAP Program Committee . .4 Social Media Outlets & ASAP Global Staff . .5 Conference Agenda . .6 ASAP Global Members & Corporate Members . .11 Summit Sponsor Recognition . .12 ASAP Executive, Management & Advisory Board of Directors . .13 Overview of Sessions . .14 Alliance Excellence Awards Finalists . .16 Pre-Conference Professional Development Workshops . .18 Monday Session Descriptions . .21 Tuesday Session Descriptions . .22 Wednesday Session Descriptions . .27 Alliance Management Resource Center . .34 Conference Speakers . .36 2015 ASAP BioPharma Conference Call For Topics . .51 Floor Plan . .53 2 Stay up to date at www.strategic-alliances.org I March 2 – 5, 2015 I Orlando, Florida USA Welcome to the 2015 ASAP Global Alliance Summit On behalf of the Board of Directors and staff of the Association of Strategic Alliance Professionals, welcome to our 2015 Global Alliance Summit. We invite you to take the next three days to learn, engage, and experience the best the profession has to offer! We invite you to fully participate in this highly interactive experience where the learning comes as much from those assembled as from the many top-flight speakers and discussion leaders. Connect with your peers, partners, and industry executives to learn how others are confronting the challenges of our ever-changing business environment and forging the future of partnering. -
The X-Internet Invigorates Supply Chain Collaboration
AnalystAnalyst OutlookOutlook COLLABORATION5 http://radjou.ASCET.com Navi Radjou The X-Internet Invigorates Forrester Research Supply Chain Collaboration The X-Internet – an executable and extended Internet built from Rapid responses to unforeseen events. Web- post-Web technologies – will transform online collaboration based B2B apps like e-procurement rely on a between supply chain partners. rule-based workflow engine to automate rou- tine transactions between partners. But they’re not designed to let firms actively find Global firms pin big hopes on the Internet’s the world has changed: Empowered customers unplanned events like delayed parts or a capabilities to connect them with customers are more fickle and competitors challenge suc- looming equipment failure in a contractor’s and suppliers. For example, 84 percent of busi- cessful new products more quickly. Firms have plant, let alone resolve them in real time. To nesses surveyed by NAPM/Forrester in come to rely more on partners to improve their provide preemptive customer support and October 2001 view the Internet as a critical ele- supply net performance and customer service. continually optimize asset usage, firms need ment of their purchasing strategy. Many firms To meet the new realities of B2B collaboration, to sense and respond to unforeseen events are stepping up investments in business appli- business apps need to support: without human intervention. cations (apps) that promise to move their Rich, real-time collaboration. Web- offline supply chain transactions to the Web. anchored supply chain apps like those from The X-Internet Reshapes But so far, these Web-centric B2B apps have Manugistics Group can’t rapidly reflect Supply Chain Collaboration failed to deliver the promised benefits because changes in partners’ production schedules Since its birth 30 years ago, the Internet has they are undependable and sluggish. -
Comparing Approaches: Analytic Workspaces and Materialized Views
Comparing Materialized Views and Analytic Workspaces in Oracle Database 11g An Oracle White Paper March 2008 Comparing Materialized Views and Analytic Workspaces in Oracle Database 11g Introduction ....................................................................................................... 3 Fast Reporting – Comparing The Approaches............................................. 3 Overview of Materialized Views................................................................. 3 Overview of Analytic Workspaces............................................................. 4 Working Together in the Oracle Database ............................................... 6 Explanation of Data Used for this White Paper........................................... 6 Methodology for Designing and Building the Materialized Views........ 7 Manually Creating the Materialized View ............................................. 7 Generating Recommendations from the SQL Access Advisor ........ 9 Methodology for Defining the Analytic Workspace ............................. 10 Tools For Querying ........................................................................................ 15 Refreshing the Data ........................................................................................ 16 Refreshing Materialized Views.................................................................. 16 Refreshing Analytic Workspace Cubes.................................................... 17 Other Issues To Consider............................................................................. -
Oracle Database 10G Materialized Views & Query Rewrite
Oracle Materialized Views & Query Rewrite An Oracle White Paper May 2005 Oracle Materialized Views & Query Rewrite Executive Overview.......................................................................................... 3 Introduction ....................................................................................................... 3 Why use Summary Management..................................................................... 4 Components of Summary Management ........................................................ 5 Schema Requirements.................................................................................. 5 Dimensions ........................................................................................................ 5 Hints on Defining Dimensions .................................................................. 7 Materialized Views ............................................................................................ 8 Creating a Materialized View....................................................................... 8 Using your own pre-built materialized views............................................ 9 Index selection for Materialized Views...................................................... 9 What can this Materialized View Do?...................................................... 10 Tuning a Materialized View....................................................................... 11 Materialized View Invalidation ................................................................. 12 Security Implications -
Apache Hive Overview Date of Publish: 2018-07-12
Data Access 3 Apache Hive overview Date of Publish: 2018-07-12 http://docs.hortonworks.com Contents What's new in this release: Apache Hive...............................................................3 Apache Hive 3 architectural overview................................................................... 4 Apache Hive 3 upgrade process..............................................................................6 Changes after upgrading to Apache Hive 3.........................................................................................................7 Convert Hive CLI scripts to Beeline................................................................................................................... 9 Hive Semantic and Syntax Changes.................................................................................................................. 10 Creating a table.......................................................................................................................................10 Escaping db.table references.................................................................................................................. 11 Casting timestamps................................................................................................................................. 11 Renaming tables......................................................................................................................................12 Checking compatibility of column changes...........................................................................................12 -
Short Type Questions and Answers on DBMS
BIJU PATNAIK UNIVERSITY OF TECHNOLOGY, ODISHA Short Type Questions and Answers on DBMS Prepared by, Dr. Subhendu Kumar Rath, BPUT, Odisha. DABASE MANAGEMENT SYSTEM SHORT QUESTIONS AND ANSWERS Prepared by Dr.Subhendu Kumar Rath, Dy. Registrar, BPUT. 1. What is database? A database is a logically coherent collection of data with some inherent meaning, representing some aspect of real world and which is designed, built and populated with data for a specific purpose. 2. What is DBMS? It is a collection of programs that enables user to create and maintain a database. In other words it is general-purpose software that provides the users with the processes of defining, constructing and manipulating the database for various applications. 3. What is a Database system? The database and DBMS software together is called as Database system. 4. What are the advantages of DBMS? 1. Redundancy is controlled. 2. Unauthorised access is restricted. 3. Providing multiple user interfaces. 4. Enforcing integrity constraints. 5. Providing backup and recovery. 5. What are the disadvantage in File Processing System? 1. Data redundancy and inconsistency. 2. Difficult in accessing data. 3. Data isolation. 4. Data integrity. 5. Concurrent access is not possible. 6. Security Problems. 6. Describe the three levels of data abstraction? The are three levels of abstraction: 1. Physical level: The lowest level of abstraction describes how data are stored. 2. Logical level: The next higher level of abstraction, describes what data are stored in database and what relationship among those data. 3. View level: The highest level of abstraction describes only part of entire database. -
Automated Selection of Materialized Views and Indexes for SQL Databases
Automated Selection of Materialized Views and Indexes for SQL Databases Sanjay Agrawal Surajit Chaudhuri Vivek Narasayya Microsoft Research Microsoft Research Microsoft Research [email protected] [email protected] [email protected] Abstract large number of recent papers in this area, most of the Automatically selecting an appropriate set of prior work considers the problems of index selection and materialized views and indexes for SQL materialized view selection in isolation. databases is a non-trivial task. A judicious choice Although indexes and materialized views are similar, must be cost-driven and influenced by the a materialized view is much richer in structure than an workload experienced by the system. Although index since a materialized view may be defined over there has been work in materialized view multiple tables, and can have selections and GROUP BY selection in the context of multidimensional over multiple columns. In fact, an index can logically be (OLAP) databases, no past work has looked at considered as a special case of a single-table, projection the problem of building an industry-strength tool only materialized view. This richness of structure of for automated selection of materialized views materialized views makes the problem of selecting and indexes for SQL workloads. In this paper, materialized views significantly more complex than that we present an end-to-end solution to the problem of index selection. We therefore need innovative of selecting materialized views and indexes. We techniques for dealing with the large space of potentially describe results of extensive experimental interesting materialized views that are possible for a given evaluation that demonstrate the effectiveness of set of SQL queries and updates over a large schema. -
Materialized Views
MATERIALIZED VIEWS - WISSENSWERTES UND NEUERUNGEN IN 12C AUTHOR: JONAS GASSENMEYER www.syntegris.de © SYNTEGRIS INFORMATION SOLUTIONS GMBH Mein Haus, mein Auto,… [email protected] @gassenmj Stand 42 © SYNTEGRIS INFORMATION SOLUTIONS GMBH AGENDA . Vorstellung (2 Minuten) . Use Case Pharma Konzern (3 Minuten) •Wissenswertes (vor 12c) .Was ist eine MV, (SQL Hinterlegen und Index drauf) (2 Minuten) .Query Rewrite (1 Minuten) .Atomic_refresh (5 Minuten) .Tablespace (1 Minute) .Fast refresh (incrementell) (5 Minuten) .„es kommt drauf an“ (1 Minuten) • Out of place refresh . Prebuilt table (3 Minuten) . Demo (3 Minuten) • On Statement MVs . Erklärung (3 Minuten) . Demo (3 Minuten) • Real time MVs . Erklärung (3 Minuten) . Demo (3 Minuten) Refresh Statistiken .Erklärung (3 Minuten) .Demo (3 Minuten) .Bug? Fazit (3 Minuten) © SYNTEGRIS INFORMATION SOLUTIONS GMBH Use Case - Aggregation Table Table Table complex Tables MView fast select Report aggregation runs when user runs at night requests the report Materialized View pre-computes result sets © SYNTEGRIS INFORMATION SOLUTIONS GMBH Use Case - Replication › Use Case - Replication Database Local DB MView Link Table Remote DB Data Materialized View copies remote data to local DB © SYNTEGRIS INFORMATION SOLUTIONS GMBH Real World Example? © SYNTEGRIS INFORMATION SOLUTIONS GMBH Chemical Substances › Ordering Vials/Tubes/Plates Differ on each Site → Aggregation From ca. 50 Mio aggregated to ca. 6.5 Mio › Metadata about Substances Same on each Site → Replication Ca. 18.5 Mio © SYNTEGRIS INFORMATION -
User Defined Functions and Materialized Views in Cassandra
UDF/UDA & Materialized Views in Depth DuyHai DOAN, Apache Cassandra™ Evangelist Trademark Policy From now on … Cassandra ⩵ Apache Cassandra™ 2 @doanduyhai Materialized Views (MV) @doanduyhai Why Materialized Views ? • Relieve the pain of manual denormalization CREATE TABLE user( id int PRIMARY KEY, country text, … ); CREATE TABLE user_by_country( country text, id int, …, PRIMARY KEY(country, id) ); 4 @doanduyhai Materialzed View In Action CREATE MATERIALIZED VIEW user_by_country AS SELECT country, id, firstname, lastname FROM user WHERE country IS NOT NULL AND id IS NOT NULL PRIMARY KEY(country, id) CREATE TABLE user_by_country ( country text, id int, firstname text, lastname text, PRIMARY KEY(country, id)); 5 @doanduyhai Materialzed View Syntax CREATE MATERIALIZED VIEW [IF NOT EXISTS] keyspace_name.view_name Must select all primary key columns of base table AS SELECT column , column , ... 1 2 • IS NOT NULL condition for now • more complex conditions in future FROM keyspace_name.table_name WHERE column1 IS NOT NULL AND column2 IS NOT NULL ... • at least all primary key columns of base table PRIMARY KEY(column1, column2, ...) (ordering can be different) • maximum 1 column NOT pk from base table 6 @doanduyhai Materialized Views Demo 7 Materialized View Impl C* C* ① C* C* • send mutation to all replicas • waiting for ack(s) with CL UPDATE user SET country=‘FR’ C* C* WHERE id=1 C* C* 8 @doanduyhai Materialized View Impl C* C* ② * * C C Acquire local lock on base table partition UPDATE user SET country=‘FR’ C* C* WHERE id=1 C* C* 9 @doanduyhai