Big Data Velocity in Plain English
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PS Non-Standard Database Systems Overview
PS Non-Standard Database Systems Overview Daniel Kocher March 2019 is document gives a brief overview on three categories of non-standard database systems (DBS) in the context of this class. For each category, we provide you with a motivation, a discussion about the key properties, a list of commonly used implementations (proprietary and open-source/freely-available), and recommendation(s) for your project. For your convenience, we also recap some relevant terms (e.g., ACID, OLTP, ...). Terminology OLTP Online transaction processing. OLTP systems are highly optimized to process (1) short queries (operating only on a small portion of the database) and (2) small transac- tions (inserting/updating few tuples per relation). Typically, OLTP includes insertions, updates, and deletions. e main focus of OLTP systems is on low response time and high throughput (in terms of transactions per second). Databases in OLTP systems are highly normalized [5]. OLAP Online analytical processing. OLAP systems analyze complex data (usually from a data warehouse). Typically, queries in an OLAP system may run for a long time (as opposed to queries in OLTP systems) and the number of transactions is usually low. Fur- thermore, typical OLAP queries are read-only and touch a large portion of the database (e.g., scan or join large tables). e main focus of OLAP systems is on low response time. Databases in OLAP systems are oen de-normalized intentionally [5]. ACID Traditional relational database systems guarantee ACID properties: – Atomicity All or no operation(s) of a transaction are reected in the database. – Consistency Isolated execution of a transaction preserves consistency of the database. -
Oracle Vs. Nosql Vs. Newsql Comparing Database Technology
Oracle vs. NoSQL vs. NewSQL Comparing Database Technology John Ryan Data Warehouse Solution Architect, UBS Table of Contents The World has Changed . 1 What’s Changed? . 2 What’s the Problem? . .. 3 Performance vs. Availability and Durability . 3 Consistecy vs. Availability . 4 Flexibility vs . Scalability . 5 ACID vs. Eventual Consistency . 6 The OLTP Database Reimagined . 7 Achieving the Impossible! . .. 8 NewSQL Database Technology . 9 VoltDB . 10 MemSQL . 11 Which Applications Need NewSQL Technology? . 12 Conclusion . 13 About the Author . 13 ii The World has Changed The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now billions of people are using to their smartphone or tablet 24x7 to buy just about everything, and they’re interacting with Facebook, Twitter and Instagram. The pace has been unstoppable . Expectations have also changed. If a web page doesn’t refresh within seconds we’re quickly frustrated, and go elsewhere. If a web site is down, we fear it’s the end of civilisation as we know it. If a major site is down, it makes global headlines. Instant gratification takes too long! — Ladawn Clare-Panton Aside: If you’re not a seasoned Database Architect, you may want to start with my previous articles on Scalability and Database Architecture. Oracle vs. NoSQL vs. NewSQL eBook 1 What’s Changed? The above leads to a few observations: • Scalability — With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions • High Availability — IT systems must run 24x7, and be resilient to failure. -
Performance Analysis of Blockchain Platforms
UNLV Theses, Dissertations, Professional Papers, and Capstones August 2018 Performance Analysis of Blockchain Platforms Pradip Singh Maharjan Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Computer Sciences Commons Repository Citation Maharjan, Pradip Singh, "Performance Analysis of Blockchain Platforms" (2018). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3367. http://dx.doi.org/10.34917/14139888 This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. PERFORMANCE ANALYSIS OF BLOCKCHAIN PLATFORMS By Pradip S. Maharjan Bachelor of Computer Engineering Tribhuvan University Institute of Engineering, Pulchowk Campus, Nepal 2012 A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science Department of Computer Science Howard R. Hughes College of Engineering The Graduate College University of Nevada, Las Vegas August 2018 c Pradip S. Maharjan, 2018 All Rights Reserved Thesis Approval The Graduate College The University of Nevada, Las Vegas May 4, 2018 This thesis prepared by Pradip S. -
Evaluating and Comparing Oracle Database Appliance Performance Updated for Oracle Database Appliance X8-2-HA
Evaluating and Comparing Oracle Database Appliance Performance Updated for Oracle Database Appliance X8-2-HA ORACLE WHITE PAPER | JANUARY 2020 DISCLAIMER The performance results in this paper are intended to give an estimate of the overall performance of the Oracle Database Appliance X8-2-HA system. The results are not a benchmark, and cannot be used to characterize the relative performance of Oracle Database Appliance systems from one generation to another, as the workload characteristics and other environmental factors including firmware, OS, and database patches, which includes Spectre/Meltdown fixes, will vary over time. For an accurate comparison to another platform, you should run the same tests, with the same OS (if applicable) and database versions, patch levels, etc. Do not rely on older tests or benchmark runs, as changes made to the underlying platform in the interim may substantially impact results. EVALUATING AND COMPARING ORACLE DATABASE APPLIANCE PERFORMANCE Table of Contents Introduction and Executive Summary 1 Audience 2 Objective 2 Oracle Database Appliance Deployment Architecture 3 Oracle Database Appliance Configuration Templates 3 What is Swingbench? 4 Swingbench Download and Setup 4 Configuring Oracle Database Appliance for Testing 5 Configuring Oracle Database Appliance System for Testing 5 Benchmark Setup 5 Database Setup 6 Schema Setup 6 Workload Setup and Execution 10 Benchmark Results 11 Workload Performance 11 Database and Operating System Statistics 12 Average CPU Busy 12 REDO Writes 13 Transaction Rate 13 Important Considerations for Performing the Benchmark 14 Conclusion 15 Appendix A Swingbench configuration files 16 Appendix B The loadgen.pl file 19 Appendix C The generate_awr.sh script 24 References 25 EVALUATING AND COMPARING ORACLE DATABASE APPLIANCE PERFORMANCE Introduction and Executive Summary Oracle Database Appliance X8-2-HA is a highly available Oracle database system. -
Voltdb Achieves 3M Ops/Second Scaling Linearly on Telecom Benchmark
BENCHMARKS VoltDB Achieves 3M ops/second Scaling Linearly on Telecom Benchmark Executive Overview VoltDB is trusted globally by telecommunications software solution providers as the in-memory database of choice to power their mission-critical applications deployed at over 100 operators around the world. VoltDB was chosen for its performance and features to solve not only the current challenges, but also to support the rapid evolution of systems in the industry. Here is our benchmark that shows how the performance of VoltDB meets or exceeds the requirements of telecom systems. We showcase VoltDB’s high performance, low latency and linear scaling that are necessary to power revolutions in the industry such as 5G. In this benchmark, we tested VoltDB v8.3 in the cloud and observed the performance scale linearly with the number of servers and achieve over 3 million operations/sec- ond with latency consistently in single digits. VoltDB and the 5G Landscape The advent of 5G brings several key challenges to telecom software solution providers. In addition to the new hardware standards, this network evolution necessitates a transformation of the supporting IT functions of OSS and BSS as well. The opportunity to create additional revenues through new service creation generates pressure on the OSS and BSS functions to be agile and scalable to enable new use cases under ever-increasing load. This pressure on the systems, elevates the role of the supporting database from simply storing data to serving as an active real-time decisioning system. Simply put, VoltDB is a Telco-grade database that exactly meets the requirements of an active real-time decision- ing system such as one that might be needed to support 5G. -
60-539: Emerging Non-Traditional Database Systems (Data Warehousing and Mining)
60-539: Emerging non-traditional database systems (Data Warehousing and Mining): sdb1 warehouse sdb2 sdb3 Dr. C.I. Ezeife School of Computer Science, University of Windsor, Canada. Email: [email protected] 60-539 Dr. C.I. Ezeife @c 2021 1 PART I (DATABASE MANAGEMENT SYSTEM OVERVIEW) DBMS (PART 1) OVERVIEW Components of a DBMS DBMS Data model Data Definition and Manipulation Language File Organization Techniques Query Optimization and Evaluation Facility Database Design and Tuning Transaction Processing Concurrency Control Database Security and Integrity Issues 60-539 Dr. C.I. Ezeife © 2021 2 DBMS OVERVIEW(What are?) What is a database? : It is a collection of data, typically describing the activities of one or more related organizations, e.g., a University, an airline reservation or a banking database. What is a DBMS?: A DBMS is a set of software for creating, querying, managing and keeping databases. Examples of DBMS’s are DB2, Informix, Sybase, Oracle, MYSQL, Microsoft Access (relational). Alternative to Databases: Storing all data for university, airline and banking information in separate files and writing separate program for each data file. What is a Data Warehouse?:A subject-oriented, historical, non- volatile database integrating a number of data sources. What is Data Mining?:Is data analysis for finding interesting trends or patterns in large dataset to guide decisions about future activities. 60-539 Dr. C.I. Ezeife © 2021 3 DBMS OVERVIEW(Evolution of Information Technology) 1960s and earlier: Primitive file processing: data are collected in files and manipulated with programs in Cobol and other languages. Disadv.: any change in storage structure of data requires changing the program (i.e., no logical or physical data independence). -
A Survey of Ledger Technology-Based Databases
future internet Article A Survey of Ledger Technology-Based Databases Dénes László Fekete 1 and Attila Kiss 1,2,* 1 Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary; [email protected] 2 Department of Informatics, J. Selye University, 945 01 Komárno, Slovakia * Correspondence: [email protected] Abstract: The spread of crypto-currencies globally has led to blockchain technology receiving greater attention in recent times. This paper focuses more broadly on the uses of ledger databases as a traditional database manager. Ledger databases will be examined within the parameters of two categories. The first of these are Centralized Ledger Databases (CLD)-based Centralised Ledger Technology (CLT), of which LedgerDB will be discussed. The second of these are Permissioned Blockchain Technology-based Decentralised Ledger Technology (DLT) where Hyperledger Fabric, FalconDB, BlockchainDB, ChainifyDB, BigchainDB, and Blockchain Relational Database will be examined. The strengths and weaknesses of the reviewed technologies will be discussed, alongside a comparison of the mentioned technologies. Keywords: ledger technology; permissioned blockchain; centralised ledger database 1. Introduction Citation: Fekete, D.L.; Kiss, A. A In recent years, we have been witness to the rise of multiple new ledger technologies. Survey of Ledger Technology-Based Since Satosi Nakamoto wrote the Bitcoin white paper [1] in 2009, many different systems Databases. Future Internet 2021, 13, that endeavour to provide decentralization and trustless collaboration have been developed 197. https://doi.org/10.3390/ based on blockchain technology. Of late, a great amount of research and academia has been fi13080197 focused on the further development and reformation of the basis of blockchain in an attempt to solve the issues that continue to arise [2–4]. -
Technical Overview
Technical Overview This white paper is for technical readers, and explains: { The reasons why traditional databases are difficult and expensive to scale { The “scale-out” VoltDB architecture and what makes it different { VoltDB application design considerations High Performance RDBMS for Fast Data Applications Requiring Smart Streaming with Transactions Overview Traditional databases (DBMS) have been around since the 70ies, they offer proven standard SQL, ad hoc query and reporting, along with a rich ecosystem of standards-based tooling. However, these legacy DBMSs are hard to scale and offer a rigid general purpose architecture that is not suitable for modern apps. The NoSQL category of tools was born from the need to solve the scaling problems associated with legacy SQL. Along with scale, NoSQL also offers greater availability and flexibility. However, NoSQL sacrifices: data consistency, transactional (ACID) guarantees, and ad-hoc querying capabilities, while significantly increasing app complexity. VoltDB is a leader in the NewSQL category of databases that were engineered to offer the best of both worlds. VoltDB combines ACID transactions, SQL capabilities, HA clusters and DR of traditional DBMSs with linear scale-out, virtualization and cloud nativeness of NoSQL. Moreover it allows for capabilities that weren’t present in either, such as in-database Machine Learning driven smart decisions on an large event stream for Smart Streaming. This allows VoltDB to be the system-of-record for data-intensive applications, while offering an integrated -
Release Notes
Release Notes Product VoltDB Version 11.1 VoltDB Operator 1.4.0 VoltDB Helm Chart 1.4.0 Release Date September 14, 2021 This document provides information about known issues and limitations to the current release of VoltDB. If you encounter any problems not listed below, please be sure to report them to [email protected]. Thank you. Upgrading From Older Versions The process for upgrading from the recent versions of VoltDB is as follows: 1. Shutdown the database, creating a final snapshot (using voltadmin shutdown --save). 2. Upgrade the VoltDB software. 3. Restart the database (using voltdb start). For Kubernetes, see the section on "Upgrading the VoltDB Software and Helm Charts" in the VoltDB Kubernetes Administrator's Guide. For DR clusters, see the section on "Upgrading VoltDB Software" in the VoltDB Administra- tor's Guide for special considerations related to DR upgrades. If you are upgrading from versions before V6.8, see the section on "Upgrading Older Versions of VoltDB Manually" in the same manual. Finally, for all customers upgrading from earlier versions of VoltDB, please be sure to read the upgrade notes for your current and subsequent releases, including V6, V7, V8, and V10. Changes Since the Last Release Users of previous versions of VoltDB should take note of the following changes that might impact their existing applications. 1. Release V11.1 (September 14, 2021) 1.1. New Java client API, Client2 (BETA) VoltDB V11.1 includes the Beta release of a new Java client library. Client2 provides a modern, robust and extensible interface to VoltDB for client applications written in Java. -
Evaluation of SQL Benchmark for Distributed In-Memory Database Management Systems
IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 59 Evaluation of SQL benchmark for distributed in-memory Database Management Systems Oleg Borisenko† and David Badalyan†† Ivannikov Institute for System Programming of the RAS, Moscow, Russia Summary Requirements for modern DBMS in terms of speed are growing every day. As an alternative to traditional relational DBMS 2. Overview distributed, in-memory DBMS are proposed. In this paper, we investigate capabilities of Apache Ignite and VoltDB from the This chapter discusses the features of Apache Ignite and point of view of relational operations and compare them to VoltDB. Both DBMS support data replication in distributed PostgreSQL using our implementation of TPC-H like workload. mode. However, the paper will consider only the case of Key words: data distribution without replication. Apache Ignite, VoltDB, PostgreSQL, In-memory Computing, distributed systems. 2.1 Apache Ignite 1. Introduction Apache Ignite is a distributed in-memory DBMS [3] [4] written in the Java programming language. It is a caching Today, most applications have to handle large amounts of and data processing platform designed for managing large data. Architects of complex systems face a problem of amounts of data using large number of compute nodes. choosing a DBMS corresponding to reliability, scalability Despite original key-value nature of the system, the and usability requirements. Traditionally used RDBMS developers declare ACID compliance and full support for have several advantages, such as integrity control, data the SQL:1999 standard. consistency, matureness. However, the centralized data H2 Database is used as a subsystem to process SQL queries. -
CSE 344 Final Examination Name
CSE 344 Final Examination Monday, December 10, 2018, 2:30-4:20 Name: Question Points Score 1 30 2 20 3 20 4 30 5 20 6 35 7 45 Total: 200 • This exam is CLOSED book and CLOSED devices. • You are allowed TWO, HAND-WRITTEN letter-size sheets with notes (both sides). • You have 110 minutes; • Answer the easy questions before you spend too much time on the more difficult ones. • Good luck! 1 CSE 344 Final Dec 10, 2018, 2:30-4:20 1 Relational Data Model 1. (30 points) You are now a Climate Scientist, and run simulations to predict future changes in the climate. Your job is to improve the simulator in order to improve the climate predictions. Every time you have a new version of the simulator, you run it, and it will generate a dataset where, for each year, it records the predicted temperature at each location of your simulated area. You analyze the results, improve the simulator, and repeat. As a good scientist, you keep the results of all simulations in a relation with the following schema: Sim(version, year, loc, temp) The data types of Version and year are integers; temp is a real number; and loc is a text. (a) (5 points) Write a SQL CREATE TABLE statement to create the Sim relation. Identify the key in Sim, and create the key in your SQL statement. Choose float and for the real number type and text for the text type. Page 2 CSE 344 Final Dec 10, 2018, 2:30-4:20 Sim(version, year, loc, temp) (b) (5 points) Simulation version 13 is wrong, due to a bug in the simulator. -
Using Voltdb
Using VoltDB Abstract This book explains how to use VoltDB to design, build, and run high performance appli- cations. V4.3 Using VoltDB V4.3 Copyright © 2008-2014 VoltDB, Inc. The text and illustrations in this document are licensed under the terms of the GNU Affero General Public License Version 3 as published by the Free Software Foundation. See the GNU Affero General Public License (http://www.gnu.org/licenses/) for more details. Many of the core VoltDB database features described herein are part of the VoltDB Community Edition, which is licensed under the GNU Affero Public License 3 as published by the Free Software Foundation. Other features are specific to the VoltDB Enterprise Edition, which is distributed by VoltDB, Inc. under a commercial license. Your rights to access and use VoltDB features described herein are defined by the license you received when you acquired the software. This document was generated on May 11, 2014. Table of Contents Preface ............................................................................................................................ xi 1. Overview ....................................................................................................................... 1 1.1. What is VoltDB? .................................................................................................. 1 1.2. Who Should Use VoltDB ...................................................................................... 1 1.3. How VoltDB Works ............................................................................................