Db2 Event Store: a Purpose-Built Iot Database Engine
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Index Selection for In-Memory Databases
International Journal of Computer Sciences &&& Engineering Open Access Research Paper Volume-3, Issue-7 E-ISSN: 2347-2693 Index Selection for in-Memory Databases Pratham L. Bajaj 1* and Archana Ghotkar 2 1*,2 Dept. of Computer Engineering, Pune University, India, www.ijcseonline.org Received: Jun/09/2015 Revised: Jun/28/2015 Accepted: July/18/2015 Published: July/30/ 2015 Abstract —Index recommendation is an active research area in query performance tuning and optimization. Designing efficient indexes is paramount to achieving good database and application performance. In association with database engine, index recommendation technique need to adopt for optimal results. Different searching methods used for Index Selection Problem (ISP) on various databases and resemble knapsack problem and traveling salesman. The query optimizer reliably chooses the most effective indexes in the vast majority of cases. Loss function calculated for every column and probability is assign to column. Experimental results presented to evidence of our contributions. Keywords— Query Performance, NPH Analysis, Index Selection Problem provides rules for index selections, data structures and I. INTRODUCTION materialized views. Physical ordering of tuples may vary from index table. In cluster indexing, actual physical Relational Database System is very complex and it is design get change. If particular column is hits frequently continuously evolving from last thirty years. To fetch data then clustering index drastically improves performance. from millions of dataset is time consuming job and new In in-memory databases, memory is divided among searching technologies need to develop. Query databases and indexes. Different data structures are used performance tuning and optimization was an area of for indexes like hash, tree, bitmap, etc. -
Amazon Aurora Mysql Database Administrator's Handbook
Amazon Aurora MySQL Database Administrator’s Handbook Connection Management March 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 DNS Endpoints .................................................................................................................... 2 Connection Handling in Aurora MySQL and MySQL ......................................................... 3 Common Misconceptions .................................................................................................... 5 Best Practices ...................................................................................................................... 6 Using Smart Drivers ........................................................................................................ -
Innodb 1.1 for Mysql 5.5 User's Guide Innodb 1.1 for Mysql 5.5 User's Guide
InnoDB 1.1 for MySQL 5.5 User's Guide InnoDB 1.1 for MySQL 5.5 User's Guide Abstract This is the User's Guide for the InnoDB storage engine 1.1 for MySQL 5.5. Beginning with MySQL version 5.1, it is possible to swap out one version of the InnoDB storage engine and use another (the “plugin”). This manual documents the latest InnoDB plugin, version 1.1, which works with MySQL 5.5 and features cutting-edge improvements in performance and scalability. This User's Guide documents the procedures and features that are specific to the InnoDB storage engine 1.1 for MySQL 5.5. It supplements the general InnoDB information in the MySQL Reference Manual. Because InnoDB 1.1 is integrated with MySQL 5.5, it is generally available (GA) and production-ready. WARNING: Because the InnoDB storage engine 1.0 and above introduces a new file format, restrictions apply to the use of a database created with the InnoDB storage engine 1.0 and above, with earlier versions of InnoDB, when using mysqldump or MySQL replication and if you use the older InnoDB Hot Backup product rather than the newer MySQL Enterprise Backup product. See Section 1.4, “Compatibility Considerations for Downgrade and Backup”. For legal information, see the Legal Notices. Document generated on: 2014-01-30 (revision: 37565) Table of Contents Preface and Legal Notices .................................................................................................................. v 1 Introduction to InnoDB 1.1 ............................................................................................................... 1 1.1 Features of the InnoDB Storage Engine ................................................................................ 1 1.2 Obtaining and Installing the InnoDB Storage Engine ............................................................... 3 1.3 Viewing the InnoDB Storage Engine Version Number ............................................................ -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo........................................................................................................................................................... -
Mysql Table Doesn T Exist in Engine
Mysql Table Doesn T Exist In Engine expensive:resumedIs Chris explicable majestically she outnumber or unlaboriousand prematurely, glowingly when and she short-lists pokes retread her some her gunmetals. ruffian Hirohito denationalise snools pleadingly? finically. Varicose Stanwood Sloane is Some basic functions to tinker with lowercase table name of the uploaded file size of lines in table mysql doesn exits If school has faced such a meadow before, please help herself with develop you fixed it. Hi actually am facing the trial error MySQL error 1932 Table '' doesn't exist in engine All of him sudden when cold try with access my module. This website uses cookies to insist you get the aggregate experience after our website. Incorrect file in my database corruption can not. File or work on your help others failed database is randomly generated unique identifiers, so i may require it work and different database and reload the. MDY file over a external network. Cookies usually has not contain information that allow your track broke down. You want to aws on how can a data is no, and easy to. Can be used for anybody managing a missing. Please double its capital letter promote your MySQL table broke and. Discuss all view Extensions that their available for download. Xampp-mysql Table doesn't exist the engine 1932 I have faced same high and sorted using below would Go to MySQL config file my file at Cxamppmysql. Mysqlfetchassoc supplied argument is nice a valid MySQL result. Config table in the. Column count of table in my problem persists. -
Real-Time Data Analytics with Apache Druid Correa Bosco Hilary Department of Information Technology, (Msc
IJARSCT ISSN (Online) 2581-9429 International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) Volume 5, Issue 2, May 2021 Impact Factor: 4.819 Real-Time Data Analytics with Apache Druid Correa Bosco Hilary Department of Information Technology, (MSc. IT Part 1) Sir Sitaram and Lady Shantabai Patkar College of Arts and Science, Mumbai, India Abstract: The shift towards real-time data flow has a major impact on the way applications are designed and on the work of data engineers. Dealing with real-time data ingestion brings a paradigm shift and an added layer of challenges compared to traditional integration and processing methods. There are real benefits to leveraging real-time data, but it requires specialized considerations in setting up the ingestion, processing, storing, and serving of that data. It brings about specific operational needs and a change in the way data engineers work. These should be considered while embarking on a real- time journey. In this paper we are going to see real time data analytics with apache druid. Apache Druid (incubating) performant analytics data store for event-driven data .Druid’s core design combines ideas from OLAP/analytic databases, time series databases, and search systems to create a unified system for operational analytics. Keywords: Distributed, Real-time, Fault-tolerant, Highly Available, Open Source, Analytics, Column- oriented, Olap, Apache Druid I. INTRODUCTION Streaming data integration is the foundation for streaming analytics. Specific use cases such as IoT devices log, contextual marketing triggers, Dynamic pricing all rely on using a data feed or real-time data. If you cannot source the data in real-time, there is very little value to be gained in attempting to tackle these use cases. -
Migrating Borland® Database Engine Applications to Dbexpress
® Borland® dbExpress–a new vision Migrating Borland Past attempts to create a common API for multiple databases have all had one or more problems. Some approaches have been large, Database Engine slow, and difficult to deploy because they tried to do too much. Others have offered a least common denominator approach that denied developers access to the specialized features of some Applications to databases. Others have suffered from the complexity of writing drivers, making them either limited in functionality, slow, or buggy. dbExpress Borland® dbExpress overcomes these problems by combining a new approach to providing a common API for many databases New approach uses with the proven provide/resolve architecture from Borland for provide/resolve architecture managing the data editing and update process. This paper examines the architecture of dbExpress and the provide/resolve mechanism, by Bill Todd, President, The Database Group, Inc. demonstrates how to create a database application using the for Borland Software Coporation dbExpress components and describes the process of converting a September 2002 database application that uses the Borland® Database Engine to dbExpress. Contents The dbExpress architecture Borland® dbExpress—a new vision 1 dbExpress (formerly dbExpress) was designed to meet the The dbExpress architecture 1 following six goals. How provide/resolve architecture works 2 Building a dbExpress application 3 • Minimize size and system resource use. Borland® Database Engine versus dbExpress 12 • Maximize speed. Migrating a SQL Links application • Provide cross-platform support. for Borland Database Engine to dbExpress 12 • Provide easier deployment. Migrating a desktop database application to dbExpress 15 • Make driver development easier. Summary 17 • Give the developer more control over memory usage About the author 17 and network traffic. -
A Methodology for Evaluating Relational and Nosql Databases for Small-Scale Storage and Retrieval
Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 9-1-2018 A Methodology for Evaluating Relational and NoSQL Databases for Small-Scale Storage and Retrieval Ryan D. Engle Follow this and additional works at: https://scholar.afit.edu/etd Part of the Databases and Information Systems Commons Recommended Citation Engle, Ryan D., "A Methodology for Evaluating Relational and NoSQL Databases for Small-Scale Storage and Retrieval" (2018). Theses and Dissertations. 1947. https://scholar.afit.edu/etd/1947 This Dissertation is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. A METHODOLOGY FOR EVALUATING RELATIONAL AND NOSQL DATABASES FOR SMALL-SCALE STORAGE AND RETRIEVAL DISSERTATION Ryan D. L. Engle, Major, USAF AFIT-ENV-DS-18-S-047 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. AFIT-ENV-DS-18-S-047 The views expressed in this paper are those of the author and do not reflect official policy or position of the United States Air Force, Department of Defense, or the U.S. Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. i AFIT-ENV-DS-18-S-047 A METHODOLOGY FOR EVALUATING RELATIONAL AND NOSQL DATABASES FOR SMALL-SCALE STORAGE AND RETRIEVAL DISSERTATION Presented to the Faculty Department of Systems and Engineering Management Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Ryan D. -
Digital Forensic Innodb Database Engine for Employee Performance Appraisal Application
125 E3S W eb of C onferences , 25002 (2019) https://doi.org/10.1051/e3sconf/201912525002 ICENIS 2019 Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application Daniel Yeri Kristiyanto1,*, Bambang Suhartono2 and Agus Wibowo1 1Department of Computer Systems, School of Electronics and Computer Science, Semarang - Indonesia 2 Department of Electrical Engineering, School of Electronics and Computer Science, Semarang – Indonesia Abstract. Data is something that can be manipulated by irresponsible people and causing fraud. The use of log files, data theft, unauthorized person, and infiltration are things that should be prevented before the problem occurs. The authenticity of application data that evaluates the performance of company employees is very crucial. This paper will explain how to maintain the company’s big data as a valid standard service to assess employee performance database, based on employee performance of MariaDB or MySQL 5.0.11 with InnoDB storage engine. The authenticity of data is required for decent digital evidence when sabotage occurs. Digital forensic analysis carried out serves to reveal past activities, record time and be able to recover deleted data in the InnoDB storage engine table. A comprehensive examination is carried out by looking at the internal and external aspects of the Relational Database Management System (RDBMS). The result of this research is in the form of forensic tables in the InnoDB engine before and after sabotage occurs. Keywords: Data log; data analytics; artefact digital; sabotage; InnoDB; penetration test. 1 Introduction research uses NTFS (New Technology File System). NTFS is very good for digital forensic analysis with At present almost all companies have a database system extensive application support [5, 6]. -
Release Notes Date Published: 2020-10-13 Date Modified
Cloudera Runtime 7.1.4 Release Notes Date published: 2020-10-13 Date modified: https://docs.cloudera.com/ Legal Notice © Cloudera Inc. 2021. All rights reserved. The documentation is and contains Cloudera proprietary information protected by copyright and other intellectual property rights. No license under copyright or any other intellectual property right is granted herein. Copyright information for Cloudera software may be found within the documentation accompanying each component in a particular release. Cloudera software includes software from various open source or other third party projects, and may be released under the Apache Software License 2.0 (“ASLv2”), the Affero General Public License version 3 (AGPLv3), or other license terms. Other software included may be released under the terms of alternative open source licenses. Please review the license and notice files accompanying the software for additional licensing information. Please visit the Cloudera software product page for more information on Cloudera software. For more information on Cloudera support services, please visit either the Support or Sales page. Feel free to contact us directly to discuss your specific needs. Cloudera reserves the right to change any products at any time, and without notice. Cloudera assumes no responsibility nor liability arising from the use of products, except as expressly agreed to in writing by Cloudera. Cloudera, Cloudera Altus, HUE, Impala, Cloudera Impala, and other Cloudera marks are registered or unregistered trademarks in the United States and other countries. All other trademarks are the property of their respective owners. Disclaimer: EXCEPT AS EXPRESSLY PROVIDED IN A WRITTEN AGREEMENT WITH CLOUDERA, CLOUDERA DOES NOT MAKE NOR GIVE ANY REPRESENTATION, WARRANTY, NOR COVENANT OF ANY KIND, WHETHER EXPRESS OR IMPLIED, IN CONNECTION WITH CLOUDERA TECHNOLOGY OR RELATED SUPPORT PROVIDED IN CONNECTION THEREWITH. -
General Introduction
PEOPLE'S DEMOCRATIC REPUBLIC OF ALGERIA MINISTRY OF HIGHER EDUCATION AND SCIENTIFIC RESEARCH UNIVERSITY MOHAMED BOUDIAF - M'SILA FACULTY: Mathematics and DOMAIN: Mathematics and Computer Science Computer Science DEPARTMENT: Computer Science BRANCH: Computer Science N°: ………………………………… OPTION: SI-GL Dissertation submitted to obtain Master degree By: Mr. FODIL Youssouf Islam & Mr. MOKRAN Abdelrrahim SUBJECT Real-time data Analytics Apache Druid Supported before the jury composed of: ……………………………………. University of M'sila President Mr. Hichem Debbi University of M'sila Supervisor ……………………………………. University of M'sila Examiner ……………………………………. University of M'sila Examiner Academic year: 2019/2020 ACKNOWLEDGMENT First and foremost, heartfelt gratitude and praises go to the Almighty Allah who guided me through and through. This work could not have reached fruition without the unflagging assistance and participation of so many people whom I would never thank enough for the huge contribution that made this work what it is now. I would like to thank profoundly Mr Hichem Debbi for his scientific guidance and corrections, suggestions and advice, pertinent criticism and pragmatism, and particularly for his hard work and patience. I am very grateful to him, for without his help an important part of this work would have been missed. I would also like to thank all the Jury Members, who have agreed to review this work. I thank all the teachers who guided us throughout our journey. Thanks to all who helped me Thanks DEDICATION This dissertation is dedicated to My parents and for their encouragement, prayers, motivations and being there. , all of my Family members near or far. All of my friends and colleagues. -
Introduction to Databases Presented by Yun Shen ([email protected]) Research Computing
Research Computing Introduction to Databases Presented by Yun Shen ([email protected]) Research Computing Introduction • What is Database • Key Concepts • Typical Applications and Demo • Lastest Trends Research Computing What is Database • Three levels to view: ▫ Level 1: literal meaning – the place where data is stored Database = Data + Base, the actual storage of all the information that are interested ▫ Level 2: Database Management System (DBMS) The software tool package that helps gatekeeper and manage data storage, access and maintenances. It can be either in personal usage scope (MS Access, SQLite) or enterprise level scope (Oracle, MySQL, MS SQL, etc). ▫ Level 3: Database Application All the possible applications built upon the data stored in databases (web site, BI application, ERP etc). Research Computing Examples at each level • Level 1: data collection text files in certain format: such as many bioinformatic databases the actual data files of databases that stored through certain DBMS, i.e. MySQL, SQL server, Oracle, Postgresql, etc. • Level 2: Database Management (DBMS) SQL Server, Oracle, MySQL, SQLite, MS Access, etc. • Level 3: Database Application Web/Mobile/Desktop standalone application - e-commerce, online banking, online registration, etc. Research Computing Examples at each level • Level 1: data collection text files in certain format: such as many bioinformatic databases the actual data files of databases that stored through certain DBMS, i.e. MySQL, SQL server, Oracle, Postgresql, etc. • Level 2: Database