Transactions Lab I
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ACS-3902 Ron Mcfadyen Slides Are Based on Chapter 5 (7Th Edition)
ACS-3902 Ron McFadyen Slides are based on chapter 5 (7th edition) (chapter 3 in 6th edition) ACS-3902 1 The Relational Data Model and Relational Database Constraints • Relational model – Ted Codd (IBM) 1970 – First commercial implementations available in early 1980s – Widely used ACS-3902 2 Relational Model Concepts • Database is a collection of relations • Implementation of relation: table comprising rows and columns • In practice a table/relation represents an entity type or relationship type (entity-relationship model … later) • At intersection of a row and column in a table there is a simple value • Row • Represents a collection of related data values • Formally called a tuple • Column names • Columns may be referred to as fields, or, formally as attributes • Values in a column are drawn from a domain of values associated with the column/field/attribute ACS-3902 3 Relational Model Concepts 7th edition Figure 5.1 ACS-3902 4 Domains • Domain – Atomic • A domain is a collection of values where each value is indivisible • Not meaningful to decompose further – Specifying a domain • Name, data type, rules – Examples • domain of department codes for UW is a list: {“ACS”, “MATH”, “ENGL”, “HIST”, etc} • domain of gender values for UW is the list (“male”, “female”) – Cardinality: number of values in a domain – Database implementation & support vary ACS-3902 5 Domain example - PostgreSQL CREATE DOMAIN posint AS integer CHECK (VALUE > 0); CREATE TABLE mytable (id posint); INSERT INTO mytable VALUES(1); -- works INSERT INTO mytable VALUES(-1); -- fails https://www.postgresql.org/docs/current/domains.html ACS-3902 6 Domain example - PostgreSQL CREATE DOMAIN domain_code_type AS character varying NOT NULL CONSTRAINT domain_code_type_check CHECK (VALUE IN ('ApprovedByAdmin', 'Unapproved', 'ApprovedByEmail')); CREATE TABLE codes__domain ( code_id integer NOT NULL, code_type domain_code_type NOT NULL, CONSTRAINT codes_domain_pk PRIMARY KEY (code_id) ) ACS-3902 7 Relation • Relation schema R – Name R and a list of attributes: • Denoted by R (A1, A2, ...,An) • E.g. -
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. -
Not ACID, Not BASE, but SALT a Transaction Processing Perspective on Blockchains
Not ACID, not BASE, but SALT A Transaction Processing Perspective on Blockchains Stefan Tai, Jacob Eberhardt and Markus Klems Information Systems Engineering, Technische Universitat¨ Berlin fst, je, [email protected] Keywords: SALT, blockchain, decentralized, ACID, BASE, transaction processing Abstract: Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. In this keynote paper, we present SALT as a model to explain blockchains and their use in application architecture. We take both, a transaction and a transaction processing systems perspective on the SALT model. From a transactions perspective, SALT is about Sequential, Agreed-on, Ledgered, and Tamper-resistant transaction processing. From a systems perspec- tive, SALT is about decentralized transaction processing systems being Symmetric, Admin-free, Ledgered and Time-consensual. We discuss the importance of these dual perspectives, both, when comparing SALT with ACID and BASE, and when engineering blockchain-based applications. We expect the next-generation of decentralized transactional applications to leverage combinations of all three transaction models. 1 INTRODUCTION against. Using the admittedly contrived acronym of SALT, we characterize blockchain-based transactions There is a common belief that blockchains have the – from a transactions perspective – as Sequential, potential to fundamentally disrupt entire industries. Agreed, Ledgered, and Tamper-resistant, and – from Whether we are talking about financial services, the a systems perspective – as Symmetric, Admin-free, sharing economy, the Internet of Things, or future en- Ledgered, and Time-consensual. -
(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. -
Keys Are, As Their Name Suggests, a Key Part of a Relational Database
The key is defined as the column or attribute of the database table. For example if a table has id, name and address as the column names then each one is known as the key for that table. We can also say that the table has 3 keys as id, name and address. The keys are also used to identify each record in the database table . Primary Key:- • Every database table should have one or more columns designated as the primary key . The value this key holds should be unique for each record in the database. For example, assume we have a table called Employees (SSN- social security No) that contains personnel information for every employee in our firm. We’ need to select an appropriate primary key that would uniquely identify each employee. Primary Key • The primary key must contain unique values, must never be null and uniquely identify each record in the table. • As an example, a student id might be a primary key in a student table, a department code in a table of all departments in an organisation. Unique Key • The UNIQUE constraint uniquely identifies each record in a database table. • Allows Null value. But only one Null value. • A table can have more than one UNIQUE Key Column[s] • A table can have multiple unique keys Differences between Primary Key and Unique Key: • Primary Key 1. A primary key cannot allow null (a primary key cannot be defined on columns that allow nulls). 2. Each table can have only one primary key. • Unique Key 1. A unique key can allow null (a unique key can be defined on columns that allow nulls.) 2. -
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 -
Data Definition Language
1 Structured Query Language SQL, or Structured Query Language is the most popular declarative language used to work with Relational Databases. Originally developed at IBM, it has been subsequently standard- ized by various standards bodies (ANSI, ISO), and extended by various corporations adding their own features (T-SQL, PL/SQL, etc.). There are two primary parts to SQL: The DDL and DML (& DCL). 2 DDL - Data Definition Language DDL is a standard subset of SQL that is used to define tables (database structure), and other metadata related things. The few basic commands include: CREATE DATABASE, CREATE TABLE, DROP TABLE, and ALTER TABLE. There are many other statements, but those are the ones most commonly used. 2.1 CREATE DATABASE Many database servers allow for the presence of many databases1. In order to create a database, a relatively standard command ‘CREATE DATABASE’ is used. The general format of the command is: CREATE DATABASE <database-name> ; The name can be pretty much anything; usually it shouldn’t have spaces (or those spaces have to be properly escaped). Some databases allow hyphens, and/or underscores in the name. The name is usually limited in size (some databases limit the name to 8 characters, others to 32—in other words, it depends on what database you use). 2.2 DROP DATABASE Just like there is a ‘create database’ there is also a ‘drop database’, which simply removes the database. Note that it doesn’t ask you for confirmation, and once you remove a database, it is gone forever2. DROP DATABASE <database-name> ; 2.3 CREATE TABLE Probably the most common DDL statement is ‘CREATE TABLE’. -
SQL Vs Nosql: a Performance Comparison
SQL vs NoSQL: A Performance Comparison Ruihan Wang Zongyan Yang University of Rochester University of Rochester [email protected] [email protected] Abstract 2. ACID Properties and CAP Theorem We always hear some statements like ‘SQL is outdated’, 2.1. ACID Properties ‘This is the world of NoSQL’, ‘SQL is still used a lot by We need to refer the ACID properties[12]: most of companies.’ Which one is accurate? Has NoSQL completely replace SQL? Or is NoSQL just a hype? SQL Atomicity (Structured Query Language) is a standard query language A transaction is an atomic unit of processing; it should for relational database management system. The most popu- either be performed in its entirety or not performed at lar types of RDBMS(Relational Database Management Sys- all. tems) like Oracle, MySQL, SQL Server, uses SQL as their Consistency preservation standard database query language.[3] NoSQL means Not A transaction should be consistency preserving, meaning Only SQL, which is a collection of non-relational data stor- that if it is completely executed from beginning to end age systems. The important character of NoSQL is that it re- without interference from other transactions, it should laxes one or more of the ACID properties for a better perfor- take the database from one consistent state to another. mance in desired fields. Some of the NOSQL databases most Isolation companies using are Cassandra, CouchDB, Hadoop Hbase, A transaction should appear as though it is being exe- MongoDB. In this paper, we’ll outline the general differences cuted in iso- lation from other transactions, even though between the SQL and NoSQL, discuss if Relational Database many transactions are execut- ing concurrently. -
Oracle Database Advanced Application Developer's Guide, 11G Release 2 (11.2) E17125-03
Oracle® Database Advanced Application Developer's Guide 11g Release 2 (11.2) E17125-03 August 2010 Oracle Database Advanced Application Developer's Guide, 11g Release 2 (11.2) E17125-03 Copyright © 1996, 2010, Oracle and/or its affiliates. All rights reserved. Primary Author: Sheila Moore Contributing Authors: D. Adams, L. Ashdown, M. Cowan, J. Melnick, R. Moran, E. Paapanen, J. Russell, R. Strohm, R. Ward Contributors: D. Alpern, G. Arora, C. Barclay, D. Bronnikov, T. Chang, L. Chen, B. Cheng, M. Davidson, R. Day, R. Decker, G. Doherty, D. Elson, A. Ganesh, M. Hartstein, Y. Hu, J. Huang, C. Iyer, N. Jain, R. Jenkins Jr., S. Kotsovolos, V. Krishnaswamy, S. Kumar, C. Lei, B. Llewellyn, D. Lorentz, V. Moore, K. Muthukkaruppan, V. Moore, J. Muller, R. Murthy, R. Pang, B. Sinha, S. Vemuri, W. Wang, D. Wong, A. Yalamanchi, Q. Yu This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. -
APPENDIX G Acid Dissociation Constants
harxxxxx_App-G.qxd 3/8/10 1:34 PM Page AP11 APPENDIX G Acid Dissociation Constants § ϭ 0.1 M 0 ؍ (Ionic strength ( † ‡ † Name Structure* pKa Ka pKa ϫ Ϫ5 Acetic acid CH3CO2H 4.756 1.75 10 4.56 (ethanoic acid) N ϩ H3 ϫ Ϫ3 Alanine CHCH3 2.344 (CO2H) 4.53 10 2.33 ϫ Ϫ10 9.868 (NH3) 1.36 10 9.71 CO2H ϩ Ϫ5 Aminobenzene NH3 4.601 2.51 ϫ 10 4.64 (aniline) ϪO SNϩ Ϫ4 4-Aminobenzenesulfonic acid 3 H3 3.232 5.86 ϫ 10 3.01 (sulfanilic acid) ϩ NH3 ϫ Ϫ3 2-Aminobenzoic acid 2.08 (CO2H) 8.3 10 2.01 ϫ Ϫ5 (anthranilic acid) 4.96 (NH3) 1.10 10 4.78 CO2H ϩ 2-Aminoethanethiol HSCH2CH2NH3 —— 8.21 (SH) (2-mercaptoethylamine) —— 10.73 (NH3) ϩ ϫ Ϫ10 2-Aminoethanol HOCH2CH2NH3 9.498 3.18 10 9.52 (ethanolamine) O H ϫ Ϫ5 4.70 (NH3) (20°) 2.0 10 4.74 2-Aminophenol Ϫ 9.97 (OH) (20°) 1.05 ϫ 10 10 9.87 ϩ NH3 ϩ ϫ Ϫ10 Ammonia NH4 9.245 5.69 10 9.26 N ϩ H3 N ϩ H2 ϫ Ϫ2 1.823 (CO2H) 1.50 10 2.03 CHCH CH CH NHC ϫ Ϫ9 Arginine 2 2 2 8.991 (NH3) 1.02 10 9.00 NH —— (NH2) —— (12.1) CO2H 2 O Ϫ 2.24 5.8 ϫ 10 3 2.15 Ϫ Arsenic acid HO As OH 6.96 1.10 ϫ 10 7 6.65 Ϫ (hydrogen arsenate) (11.50) 3.2 ϫ 10 12 (11.18) OH ϫ Ϫ10 Arsenious acid As(OH)3 9.29 5.1 10 9.14 (hydrogen arsenite) N ϩ O H3 Asparagine CHCH2CNH2 —— —— 2.16 (CO2H) —— —— 8.73 (NH3) CO2H *Each acid is written in its protonated form.