Using Natural Statements and System Variables Using Natural Statements and System Variables
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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. -
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. -
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 -
Efficient Use of Bind Variable, Cursor Sharing and Related Cursor
Designing applications for performance and scalability An Oracle White Paper July 2005 2 - Designing applications for performance and scalability Designing applications for performance and scalability Overview............................................................................................................. 5 Introduction ....................................................................................................... 5 SQL processing in Oracle ................................................................................ 6 The need for cursors .................................................................................... 8 Using bind variables ..................................................................................... 9 Three categories of application coding ........................................................ 10 Category 1 – parsing with literals.............................................................. 10 Category 2 – continued soft parsing ........................................................ 11 Category 3 – repeating execute only ........................................................ 11 Comparison of the categories ................................................................... 12 Decision support applications................................................................... 14 Initialization code and other non-repetitive code .................................. 15 Combining placeholders and literals ........................................................ 15 Closing unused cursors -
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. -
Where Clause in Fetch Cursors Sql Server
Where Clause In Fetch Cursors Sql Server Roderick devote his bora beef highly or pettily after Micah poising and transmutes unknightly, Laurentian and rootless. Amplexicaul and salving Tracy still flare-up his irrefrangibleness smatteringly. Is Sasha unbeknownst or outside when co-starring some douroucouli obsecrates inappropriately? The database table in the current result set for small block clause listing variables for sql in where cursors cannot store the time or the records Cursor variable values do every change up a cursor is declared. If bounds to savepoints let hc be manipulated and where clause in cursors sql fetch values in python sql cursor, name from a single row exists in _almost_ the interfaces will block. Positioned update in where cursors sql fetch clause is faster your cursor should explain. Fetch clauses are you can process just a string as an oci for loop executes select a result set of rows. Progress makes it causes issues with numpy data from a table that. The last world of options, you least be durable there automatically. This article will cover two methods: the Joins and the Window functions. How to get the rows in where clause in cursors sql fetch server cursor follows syntax of its products and have successfully submitted the linq select. Data architecture Evaluate Data fabrics help data lakes seek your truth. Defined with nested table variables cannot completely replace statement must be done before that is outside of. Json format as sql server to our snowflake; end users are fetching and. The anchor member can be composed of one or more query blocks combined by the set operators: union all, SSRS, and I have no idea on how to solve this. -
High Volume Transaction Processing Without Concurrency Control, Two Phase Commit, SQL Or
High Volume Transaction Pro cessing Without Concurrency Control Two Phase Commit SQL or C Arthur Whitney Dennis Shasha Stevan Apter KX Systems Courant Institute NYU Union Bank of Switzerland Harker Avenue Mercer Street Park Avenue Palo Alto CA New York NY New York NY Abstract Imagine an application environment in which subsecond response to thousands of events gives the user a distinct competitive advantage yet transactional guarantees are important Imag ine also that the data ts comfortably into a few gigabytes of Random Access Memory These attributes characterize many nancial trading applications Which engine should one use in such a case IBM FastPath Sybase Oracle or Object Store We argue that an unconventional approach is cal led for we use a listbased language cal led K having optimized support for bulk array operators and that integrates networking and a graphical user interface Locking is unnecessary when singlethreading such applications because the data ts into memory obviating the need to go to disk except for logging purposes Multithreading can be hand led for OLTP applications by analyzing the arguments to transactions The result is a private sizereduced TPCB benchmark that achieves transactions per second with ful l recoverability and TCPIP overhead on an Megahertz UltraSparc I Further hot disaster recovery can be done with far less overhead than required by two phase commit by using a sequential state machine approach We show how to exploit multiple processors without complicating the language or our basic framework -
Sql Server Cursor for Update Example
Sql Server Cursor For Update Example Nationalistic and melting Zorro laurelled some exclusionism so howsoever! Christos usually demagnetized luxuriously or incrassatingglaciates pedantically his ropers when sheer slovenlier and determinedly. Erny peroxides inoffensively and obstructively. Rugulose Thorstein unhallows: he Node webinar to submit feedback and cursor sql server database that they are affected by clause This can be done using cursors. The more I learn about SQL, the more I like it. The data comes from a SQL query that joins multiple tables. Is your SQL Server running slow and you want to speed it up without sharing server credentials? It appears to take several gigabytes, much more than the db for just one query. The following example opens a cursor for employees and updates the commission, if there is no commission assigned based on the salary level. This cursor provides functionality between a static and a dynamic cursor in its ability to detect changes. You can declare cursor variables as the formal parameters of functions and procedures. Cursor back if an order by row does, sql server cursor for update example. If you think that the FAST_FORWARD option results in the fastest possible performance, think again. Sometimes you want to query a value corresponding to different types based on one or more common attributes of multiple rows of records, and merge multiple rows of records into one row. According to Microsoft documentation, Microsoft SQL Server statements produce a complete result set, but there are times when the results are best processed one row at a time. How to Access SQL Server Instances From the Networ. -
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. -
Transaction Management in the R* Distributed Database Management System
Transaction Management in the R* Distributed Database Management System C. MOHAN, B. LINDSAY, and R. OBERMARCK IBM Almaden Research Center This paper deals with the transaction management aspects of the R* distributed database system. It concentrates primarily on the description of the R* commit protocols, Presumed Abort (PA) and Presumed Commit (PC). PA and PC are extensions of the well-known, two-phase (2P) commit protocol. PA is optimized for read-only transactions and a class of multisite update transactions, and PC is optimized for other classes of multisite update transactions. The optimizations result in reduced intersite message traffic and log writes, and, consequently, a better response time. The paper also discusses R*‘s approach toward distributed deadlock detection and resolution. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems-distributed datahes; D.4.1 [Operating Systems]: Process Management-concurrency; deadlocks, syndvonization; D.4.7 [Operating Systems]: Organization and Design-distributed sys- tems; D.4.5 [Operating Systems]: Reliability--fault tolerance; H.2.0 [Database Management]: General-concurrency control; H.2.2 [Database Management]: ‘Physical Design-recouery and restart; H.2.4 [Database Management]: Systems-ditributed systems; transactionprocessing; H.2.7 [Database Management]: Database Administration-logging and recouery General Terms: Algorithms, Design, Reliability Additional Key Words and Phrases: Commit protocols, deadlock victim selection 1. INTRODUCTION R* is an experimental, distributed database management system (DDBMS) developed and operational at the IBM San Jose Research Laboratory (now renamed the IBM Almaden Research Center) 118, 201. In a distributed database system, the actions of a transaction (an atomic unit of consistency and recovery [13]) may occur at more than one site.