CMSC724: Transactions and ACID Properties
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A Fast Implementation of Parallel Snapshot Isolation
A FAST IMPLEMENTATION OF PARALLEL SNAPSHOT ISOLATION UNA IMPLEMENTACIÓN RÁPIDA DE PARALLEL SNAPSHOT ISOLATION Borja Arnau de Régil Basáñez Trabajo de Fin de Grado del Grado en Ingeniería Informática Facultad de Informática, Universidad Complutense de Madrid Junio 2020 Director: Maria Victoria López López Co-director: Alexey Gotsman Contents Acknowledgements iv Abstract v Resumen vi 1. Introduction1 1.1. Motivation . .1 1.2. Goals . .2 1.3. Work Plan . .3 1.4. Document Structure . .3 1.5. Sources and Repositories . .4 1.6. Related Program Courses . .4 2. Preliminaries5 2.1. Notation . .5 2.1.1. Objects and Replication . .5 2.1.2. Transactions . .5 2.1.3. Histories . .6 2.2. Consistency Models . .6 2.2.1. Read Committed (RC) . .7 2.2.2. Serialisability (SER) . .8 2.2.3. Snapshot Isolation (SI) . .9 2.2.4. Parallel Snapshot Isolation (PSI) . 10 2.2.5. Non-Monotonic Snapshot Isolation (NMSI) . 11 2.2.6. Anomaly Comparison . 11 3. The fastPSI protocol 12 3.1. Consistency Guarantees . 12 3.2. Overview and System Model . 13 3.3. Server data structures . 14 3.4. Protocol Description . 16 3.4.1. Transaction Execution . 16 3.4.2. Transaction Termination . 20 3.5. Consistency Tradeoffs and Read Aborts . 23 ii 4. Implementation and Evaluation 26 4.1. Implementation . 26 4.2. Evaluation . 27 4.2.1. Performance & Scalability Limits . 28 4.2.2. Abort Ratio . 31 5. Related Work 35 6. Conclusions and Future Work 37 6.1. Conclusions . 37 6.2. Future Work . 37 A. Serialisable and Read Committed Protocols 39 A.1. -
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. -
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. -
One-Copy Serializability with Snapshot Isolation Under the Hood
One-Copy Serializability with Snapshot Isolation under the Hood Mihaela A. Bornea 1, Orion Hodson 2, Sameh Elnikety 2, Alan Fekete 3 1Athens U. of Econ and Business, 2Microsoft Research, 3University of Sydney Abstract—This paper presents a method that allows a repli- With careful design and engineering, SI engines can be cated database system to provide a global isolation level stronger replicated to get even higher performance with a replicated than the isolation level provided on each individual database form of SI such as Generalized Snapshot Isolation (GSI) [13], replica. We propose a new multi-version concurrency control al- gorithm called, serializable generalized snapshot isolation (SGSI), which is also not serializable. The objective of this paper is that targets middleware replicated database systems. Each replica to provide a concurrency control algorithm for replicated SI runs snapshot isolation locally and the replication middleware databases that achieves almost the same performance as GSI guarantees global one-copy serializability. We introduce novel while providing global one-copy-serializability (1SR). techniques to provide a stronger global isolation level, namely To guarantee serializability, concurrency control algorithms readset extraction and enhanced certification that prevents read- write and write-write conflicts in a replicated setting. We prove typically require access to data read and written in update the correctness of the proposed algorithm, and build a proto- transactions. Accessing this data is especially difficult in a type replicated database system to evaluate SGSI performance replicated database system since built-in facilities inside a experimentally. Extensive experiments with an 8 replica database centralized DBMS, like the lock manager, are not available system under the TPC-W workload mixes demonstrate the at a global level. -
Making Snapshot Isolation Serializable
Making Snapshot Isolation Serializable ALAN FEKETE University of Sydney DIMITRIOS LIAROKAPIS, ELIZABETH O’NEIL, and PATRICK O’NEIL University of Massachusetts and DENNIS SHASHA Courant Institute Snapshot Isolation (SI) is a multiversion concurrency control algorithm, first described in Berenson et al. [1995]. SI is attractive because it provides an isolation level that avoids many of the com- mon concurrency anomalies, and has been implemented by Oracle and Microsoft SQL Server (with certain minor variations). SI does not guarantee serializability in all cases, but the TPC-C benchmark application [TPC-C], for example, executes under SI without serialization anoma- lies. All major database system products are delivered with default nonserializable isolation levels, often ones that encounter serialization anomalies more commonly than SI, and we sus- pect that numerous isolation errors occur each day at many large sites because of this, lead- ing to corrupt data sometimes noted in data warehouse applications. The classical justification for lower isolation levels is that applications can be run under such levels to improve efficiency when they can be shown not to result in serious errors, but little or no guidance has been of- fered to application programmers and DBAs by vendors as to how to avoid such errors. This article develops a theory that characterizes when nonserializable executions of applications can occur under SI. Near the end of the article, we apply this theory to demonstrate that the TPC-C benchmark application has no serialization anomalies under SI, and then discuss how this demon- stration can be generalized to other applications. We also present a discussion on how to modify the program logic of applications that are nonserializable under SI so that serializability will be guaranteed. -
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 . -
Firebird 3.0 Developer's Guide
Firebird 3.0 Developer’s Guide Denis Simonov Version 1.1, 27 June 2020 Preface Author of the written material and creator of the sample project on five development platforms, originally as a series of magazine articles: Denis Simonov Translation of original Russian text to English: Dmitry Borodin (MegaTranslations Ltd) Editor of the translated text: Helen Borrie Copyright © 2017-2020 Firebird Project and all contributing authors, under the Public Documentation License Version 1.0. Please refer to the License Notice in the Appendix This volume consists of chapters that walk through the development of a simple application for several language platforms, notably Delphi, Microsoft Entity Framework and MVC.NET (“Model-View-Controller”) for web applications, PHP and Java with the Spring framework. It is hoped that the work will grow in time, with contributions from authors using other stacks with Firebird. 1 Table of Contents Table of Contents 1. About the Firebird Developer’s Guide: for Firebird 3.0 . 6 1.1. About the Author . 6 1.1.1. Translation… . 6 1.1.2. … and More Translation . 6 1.2. Acknowledgments . 6 2. The examples.fdb Database . 8 2.1. Database Creation Script. 8 2.1.1. Database Aliases . 9 2.2. Creating the Database Objects. 10 2.2.1. Domains . 10 2.2.2. Primary Tables. 11 2.2.3. Secondary Tables . 13 2.2.4. Stored Procedures. 17 2.2.5. Roles and Privileges for Users . 25 2.3. Saving and Running the Script . 26 2.4. Loading Test Data . 27 3. Developing Firebird Applications in Delphi . 28 3.1. -
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 -
Ensuring Consistency Under the Snapshot Isolation
World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:8, No:7, 2014 Ensuring Consistency under the Snapshot Isolation Carlos Roberto Valencio,ˆ Fabio´ Renato de Almeida, Thatiane Kawabata, Leandro Alves Neves, Julio Cesar Momente, Mario Luiz Tronco, Angelo Cesar Colombini Abstract—By running transactions under the SNAPSHOT isolation both transactions write the same data item, giving rise to we can achieve a good level of concurrency, specially in databases a write-write conflict. In turn, a temporal overlap occurs with high-intensive read workloads. However, SNAPSHOT is not < when Ti and Tj run concurrently, that is tsi tcj and immune to all the problems that arise from competing transactions < and therefore no serialization warranty exists. We propose in this tsj tci [2]. Thus, a transaction Ti under the SNAPSHOT paper a technique to obtain data consistency with SNAPSHOT by using isolation is allowed to commit only if no other transaction with δ <δ< some special triggers that we named DAEMON TRIGGERS. Besides commit-timestamp in the running time of Ti (tsi tci ) keeping the benefits of the SNAPSHOT isolation, the technique is wrote a data item also written by Ti [4]. specially useful for those database systems that do not have an Implementations of the SNAPSHOT isolation have the isolation level that ensures serializability, like Firebird and Oracle. We describe all the anomalies that might arise when using the SNAPSHOT advantage that writes of a transaction do not block the isolation and show how to preclude them with DAEMON TRIGGERS. reads of others. -
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.