Data Management 09 Transaction Processing
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Rainblock: Faster Transaction Processing in Public Blockchains
RainBlock: Faster Transaction Processing in Public Blockchains Soujanya Ponnapalli1, Aashaka Shah1, Souvik Banerjee1, Dahlia Malkhi2, Amy Tai3, Vijay Chidambaram1,3, and Michael Wei3 1University of Texas at Austin, 2Diem Association and Novi Financial, 3VMware Research Abstract Metric No state State: 10M Ratio We present RAINBLOCK, a public blockchain that achieves Time taken to mine txs (s) 1047 6340 6× " high transaction throughput without modifying the proof-of- # Txs per block 2150 833 2.5× # work consensus. The chief insight behind RAINBLOCK is that Tx throughput (txs/s) 28.6 4.7 6× # while consensus controls the rate at which new blocks are added to the blockchain, the number of transactions in each Table 1: Impact of system state on blockchain throughput. block is limited by I/O bottlenecks. Public blockchains like This table shows the throughput of Ethereum with proof-of- Ethereum keep the number of transactions in each block low work consensus when 30K txs are mined using three miners, so that all participating servers (miners) have enough time to in two scenarios: first, there are no accounts on the blockchain, process a block before the next block is created. By removing and in the second, 10M accounts have been added. Despite the I/O bottlenecks in transaction processing, RAINBLOCK al- no other difference, tx throughput is 6× lower in the second lows miners to process more transactions in the same amount scenario; we trace this to the I/O involved in processing txs. of time. RAINBLOCK makes two novel contributions: the RAIN- BLOCK architecture that removes I/O from the critical path of processing transactions (txs), and the distributed, multi- Bitcoin [1] and Ethereum, process only tens of transactions versioned DSM-TREE data structure that stores the system per second, limiting their applications [33, 65]. -
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. -
SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations
SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations Johes Bater Yongjoo Park Xi He Northwestern University University of Illinois (UIUC) University of Waterloo [email protected] [email protected] [email protected] Xiao Wang Jennie Rogers Northwestern University Northwestern University [email protected] [email protected] ABSTRACT 1. INTRODUCTION A private data federation enables clients to query the union of data Querying the union of multiple private data stores is challeng- from multiple data providers without revealing any extra private ing due to the need to compute over the combined datasets without information to the client or any other data providers. Unfortu- data providers disclosing their secret query inputs to anyone. Here, nately, this strong end-to-end privacy guarantee requires crypto- a client issues a query against the union of these private records graphic protocols that incur a significant performance overhead as and he or she receives the output of their query over this shared high as 1,000× compared to executing the same query in the clear. data. Presently, systems of this kind use a trusted third party to se- As a result, private data federations are impractical for common curely query the union of multiple private datastores. For example, database workloads. This gap reveals the following key challenge some large hospitals in the Chicago area offer services for a cer- in a private data federation: offering significantly fast and accurate tain percentage of the residents; if we can query the union of these query answers without compromising strong end-to-end privacy. databases, it may serve as invaluable resources for accurate diag- To address this challenge, we propose SAQE, the Secure Ap- nosis, informed immunization, timely epidemic control, and so on. -
What Is a Database Management System? What Is a Transaction
What is a Database? Collection of data central to some enterprise Overview of Databases and Essential to operation of enterprise Transaction Processing Contains the only record of enterprise activity An asset in its own right Chapter 1 Historical data can guide enterprise strategy Of interest to other enterprises State of database mirrors state of enterprise Database is persistent 2 What is a Database Management What is a Transaction? System? When an event in the real world changes the A Database Management System (DBMS) state of the enterprise, a transaction is is a program that manages a database: executed to cause the corresponding change in the database state Supports a high-level access language (e.g. With an on-line database, the event causes the SQL). transaction to be executed in real time Application describes database accesses using A transaction is an application program that language. with special properties - discussed later - to DBMS interprets statements of language to guarantee it maintains database correctness perform requested database access. 3 4 What is a Transaction Processing Transaction Processing System System? Transaction execution is controlled by a TP monitor s DBMS database n o i Creates the abstraction of a transaction, t c a analogous to the way an operating system s n a r creates the abstraction of a process t DBMS database TP monitor and DBMS together guarantee the special properties of transactions TP Monitor A Transaction Processing System consists of TP monitor, databases, and transactions 5 6 1 System -
SQL Server Protection Whitepaper
SQL Server Protection Whitepaper Contents 1. Introduction ..................................................................................................................................... 2 Documentation .................................................................................................................................................................. 2 Licensing ............................................................................................................................................................................... 2 The benefits of using the SQL Server Add-on ....................................................................................................... 2 Requirements ...................................................................................................................................................................... 2 2. SQL Protection overview ................................................................................................................ 3 User databases ................................................................................................................................................................... 3 System databases .............................................................................................................................................................. 4 Transaction logs ................................................................................................................................................................ -
How to Conduct Transaction Log Analysis for Web Searching And
Search Log Analysis: What is it; what’s been done; how to do it Bernard J. Jansen School of Information Sciences and Technology The Pennsylvania State University 329F IST Building University Park, Pennsylvania 16802 Email: [email protected] Abstract The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript. Introduction Researchers have used transaction logs for analyzing a variety of Web systems (Croft, Cook, & Wilder, 1995; Jansen, Spink, & Saracevic, 2000; Jones, Cunningham, & McNab, 1998; Wang, 1 of 42 Berry, & Yang, 2003). Web search engine companies use transaction logs (also referred to as search logs) to research searching trends and effects of system improvements (c.f., Google at http://www.google.com/press/zeitgeist.html or Yahoo! at http://buzz.yahoo.com/buzz_log/?fr=fp- buzz-morebuzz). -
Transaction Processing Monitors
Chapter 24: Advanced Transaction Processing ! Transaction-Processing Monitors ! Transactional Workflows ! High-Performance Transaction Systems ! Main memory databases ! Real-Time Transaction Systems ! Long-Duration Transactions ! Transaction management in multidatabase systems 1 Database System Concepts 24.1 ©Silberschatz, Korth and Sudarshan Transaction Processing Monitors ! TP monitors initially developed as multithreaded servers to support large numbers of terminals from a single process. ! Provide infrastructure for building and administering complex transaction processing systems with a large number of clients and multiple servers. ! Provide services such as: ! Presentation facilities to simplify creating user interfaces ! Persistent queuing of client requests and server responses ! Routing of client messages to servers ! Coordination of two-phase commit when transactions access multiple servers. ! Some commercial TP monitors: CICS from IBM, Pathway from Tandem, Top End from NCR, and Encina from Transarc 2 Database System Concepts 24.2 ©Silberschatz, Korth and Sudarshan 1 TP Monitor Architectures 3 Database System Concepts 24.3 ©Silberschatz, Korth and Sudarshan TP Monitor Architectures (Cont.) ! Process per client model - instead of individual login session per terminal, server process communicates with the terminal, handles authentication, and executes actions. ! Memory requirements are high ! Multitasking- high CPU overhead for context switching between processes ! Single process model - all remote terminals connect to a single server process. ! Used in client-server environments ! Server process is multi-threaded; low cost for thread switching ! No protection between applications ! Not suited for parallel or distributed databases 4 Database System Concepts 24.4 ©Silberschatz, Korth and Sudarshan 2 TP Monitor Architectures (Cont.) ! Many-server single-router model - multiple application server processes access a common database; clients communicate with the application through a single communication process that routes requests. -
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. -
WIN: an Efficient Data Placement Strategy for Parallel XML Databases
WIN : An Efficient Data Placement Strategy for Parallel XML Databases Nan Tang† Guoren Wang‡ Jeffrey Xu Yu† Kam-Fai Wong† Ge Yu‡ † Department of SE & EM, The Chinese University of Hong Kong, Hong Kong, China ‡ College of Information Science and Engineering, Northeastern University, Shenyang, China {ntang, yu, kfwong}se.cuhk.edu.hk {wanggr, yuge}@mail.neu.edu.cn Abstract graph-based. Distinct from them, XML employs a tree struc- The basic idea behind parallel database systems is to ture. Conventional data placement strategies in perform operations in parallel to reduce the response RDBMSs and OODBMSs cannot serve XML data well. time and improve the system throughput. Data place- Semistructured data involve nested structures; there ment is a key factor on the overall performance of are much intricacy and redundancy when represent- parallel systems. XML is semistructured data, tradi- ing XML as relations. Data placement strategies in tional data placement strategies cannot serve it well. RDBMSs cannot, therefore, well solve the data place- In this paper, we present the concept of intermediary ment problem of XML data. Although graph partition node INode, and propose a novel workload-aware data algorithms in OODBMSs can be applied to tree parti- placement WIN to effectively decluster XML data, to tion problems, the key relationships between tree nodes obtain high intra query parallelism. The extensive ex- such as ancestor-descendant or sibling cannot be clearly periments show that the speedup and scaleup perfor- denoted on graph. Hence data placement strategies mance of WIN outperforms the previous strategies. for OODBMSs cannot well adapt to XML data either. -
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(19) TZZ¥ ¥¥Z_T (11) EP 3 267 330 A1 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 10.01.2018 Bulletin 2018/02 G06F 17/30 (2006.01) (21) Application number: 16187202.3 (22) Date of filing: 05.09.2016 (84) Designated Contracting States: (72) Inventors: AL AT BE BG CH CY CZ DE DK EE ES FI FR GB • Abolhassani, Neda GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO Athens, GA Georgia 30605 (US) PL PT RO RS SE SI SK SM TR • Sheausan Tung, Teresa Designated Extension States: San Jose, CA California 95120 (US) BA ME • Gomadam, Karthik Designated Validation States: San Jose, CA California 95112 (US) MA MD (74) Representative: Müller-Boré & Partner (30) Priority: 07.07.2016 US 201662359547 P Patentanwälte PartG mbB 03.08.2016 US 201615227605 Friedenheimer Brücke 21 80639 München (DE) (71) Applicant: Accenture Global Solutions Limited Dublin 4 (IE) (54) QUERY REWRITING IN A RELATIONAL DATA HARMONIZATION FRAMEWORK (57) A query rewriting processor (processor) analyz- an SQL query). The processor may then pass the rela- es database semantic models (e.g., RDF knowledge tional database query to another system or process (e.g., graphs) that capture the interconnections (e.g., foreign a data virtualization layer) for execution against the indi- and primary key links to other tables) present in a rela- vidual relational databases. In this manner, the processor tional database. The processor generates an enriched automatically translates queries for information about the model query given an initial model query (e.g., a SPARQL relational database structure to a corresponding or query) against the semantic model. -
A Transaction Processing Method for Distributed Database
Advances in Computer Science Research, volume 87 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019) A Transaction Processing Method for Distributed Database Zhian Lin a, Chi Zhang b School of Computer and Cyberspace Security, Communication University of China, Beijing, China [email protected], [email protected] Abstract. This paper introduces the distributed transaction processing model and two-phase commit protocol, and analyses the shortcomings of the two-phase commit protocol. And then we proposed a new distributed transaction processing method which adds heartbeat mechanism into the two- phase commit protocol. Using the method can improve reliability and reduce blocking in distributed transaction processing. Keywords: distributed transaction, two-phase commit protocol, heartbeat mechanism. 1. Introduction Most database services of application systems will be distributed on several servers, especially in some large-scale systems. Distributed transaction processing will be involved in the execution of business logic. At present, two-phase commit protocol is one of the methods to distributed transaction processing in distributed database systems. The two-phase commit protocol includes coordinator (transaction manager) and several participants (databases). In the process of communication between the coordinator and the participants, if the participants without reply for fail, the coordinator can only wait all the time, which can easily cause system blocking. In this paper, heartbeat mechanism is introduced to monitor participants, which avoid the risk of blocking of two-phase commit protocol, and improve the reliability and efficiency of distributed database system. 2. Distributed Transactions 2.1 Distributed Transaction Processing Model In a distributed system, each node is physically independent and they communicates and coordinates each other through the network. -
Destiny® System Backups
Destiny® system backups Establishing a backup and restore plan for Destiny Overview It is important to establish a backup and restore plan for your Destiny installation. The plan must be validated and monitored to ensure that your data is sufficiently backed up and can be recovered in the event of hardware failure or other disaster. IMPORTANT Follett recommends deploying a comprehensive backup solution and testing and monitoring all backup processes. There are tradeoff decisions to be made in the backup strategy that will be shaped by your organization’s risk tolerance, technology constraints, and ability to absorb data loss or downtime. This document provides an overview of standard backup and restore for the Destiny system. IMPORTANT This content does not cover high-availability configurations such as clustered servers or log shipping. Please contact Follett School Solutions, Inc. Technical Support should you have any questions about backing up your Destiny installation. For details, see Destiny® system backups. Backing up Destiny: an overview SQL database The heart of the Destiny data resides in the SQL database. These are the main components of SQL data to be backed up: The Destiny SQL database. This database contains the main Destiny data. Proper SQL backup configuration of this database is essential. NOTE If your installation is a consortium, you will have to ensure a proper backup of multiple SQL databases (one per member). The Destiny SQL transaction log. The Master SQL database. This system database is useful when restoring to a replacement server. It is not necessary to back up the tempdb database. This is a SQL Server work area and is recreated automatically.