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SD-SQL Server: Scalable Distributed Database System
The International Arab Journal of Information Technology, Vol. 4, No. 2, April 2007 103 SD -SQL Server: Scalable Distributed Database System Soror Sahri CERIA, Université Paris Dauphine, France Abstract: We present SD -SQL Server, a prototype scalable distributed database system. It let a relational table to grow over new storage nodes invis ibly to the application. The evolution uses splits dynamically generating a distributed range partitioning of the table. The splits avoid the reorganization of a growing database, necessary for the current DBMSs and a headache for the administrators. We il lustrate the architecture of our system, its capabilities and performance. The experiments with the well -known SkyServer database show that the overhead of the scalable distributed table management is typically minimal. To our best knowledge, SD -SQL Server is the only DBMS with the discussed capabilities at present. Keywords: Scalable distributed DBS, scalable table, distributed partitioned view, SDDS, performance . Received June 4 , 2005; accepted June 27 , 2006 1. Introduction or k -d based with respect to the partitioning key(s). The application sees a scal able table through a specific The explosive growth of the v olume of data to store in type of updateable distributed view termed (client) databases makes many of them huge and permanently scalable view. Such a view hides the partitioning and growing. Large tables have to be hash ed or partitioned dynamically adjusts itself to the partitioning evolution. over several storage sites. Current DBMSs, e. g., SQL The adjustment is lazy, in the sense it occurs only Server, Oracle or DB2 to name only a few, provide when a query to the scalable table comes in and the static partitioning only. -
Socrates: the New SQL Server in the Cloud
Socrates: The New SQL Server in the Cloud Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, Vikram Wakade Microsoft Azure & Microsoft Research ABSTRACT 1 INTRODUCTION The database-as-a-service paradigm in the cloud (DBaaS) The cloud is here to stay. Most start-ups are cloud-native. is becoming increasingly popular. Organizations adopt this Furthermore, many large enterprises are moving their data paradigm because they expect higher security, higher avail- and workloads into the cloud. The main reasons to move ability, and lower and more flexible cost with high perfor- into the cloud are security, time-to-market, and a more flexi- mance. It has become clear, however, that these expectations ble “pay-as-you-go” cost model which avoids overpaying for cannot be met in the cloud with the traditional, monolithic under-utilized machines. While all these reasons are com- database architecture. This paper presents a novel DBaaS pelling, the expectation is that a database runs in the cloud architecture, called Socrates. Socrates has been implemented at least as well as (if not better) than on premise. Specifically, in Microsoft SQL Server and is available in Azure as SQL DB customers expect a “database-as-a-service” to be highly avail- Hyperscale. This paper describes the key ideas and features able (e.g., 99.999% availability), support large databases (e.g., of Socrates, and it compares the performance of Socrates a 100TB OLTP database), and be highly performant. -
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. -
An Overview of Distributed Databases
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 2 (2014), pp. 207-214 © International Research Publications House http://www. irphouse.com /ijict.htm An Overview of Distributed Databases Parul Tomar1 and Megha2 1Department of Computer Engineering, YMCA University of Science & Technology, Faridabad, INDIA. 2Student Department of Computer Science, YMCA University of Science and Technology, Faridabad, INDIA. Abstract A Database is a collection of data describing the activities of one or more related organizations with a specific well defined structure and purpose. A Database is controlled by Database Management System(DBMS) by maintaining and utilizing large collections of data. A Distributed System is the one in which hardware and software components at networked computers communicate and coordinate their activity only by passing messages. In short a Distributed database is a collection of databases that can be stored at different computer network sites. This paper presents an overview of Distributed Database System along with their advantages and disadvantages. This paper also provides various aspects like replication, fragmentation and various problems that can be faced in distributed database systems. Keywords: Database, Deadlock, Distributed Database Management System, Fragmentation, Replication. 1. Introduction A Database is systematically organized or structuredrepository of indexed information that allows easy retrieval, updating, analysis, and output of data. Each database may involve different database management systems and different architectures that distribute the execution of transactions [1]. A distributed database is a database in which storage devices are not all attached to a common processing unit such as the CPU. It may be stored in multiple computers, located in the same physical location; or may be dispersed over a network of interconnected computers. -
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. -
ACID Compliant Distributed Key-Value Store
ACID Compliant Distributed Key-Value Store #1 #2 #3 Lakshmi Narasimhan Seshan , Rajesh Jalisatgi , Vijaeendra Simha G A # 1l [email protected] # 2r [email protected] #3v [email protected] Abstract thought that would be easier to implement. All Built a fault-tolerant and strongly-consistent key/value storage components run http and grpc endpoints. Http endpoints service using the existing RAFT implementation. Add ACID are for debugging/configuration. Grpc Endpoints are transaction capability to the service using a 2PC variant. Build used for communication between components. on the raftexample[1] (available as part of etcd) to add atomic Replica Manager (RM): RM is the service discovery transactions. This supports sharding of keys and concurrent transactions on sharded KV Store. By Implementing a part of the system. RM is initiated with each of the transactional distributed KV store, we gain working knowledge Replica Servers available in the system. Users have to of different protocols and complexities to make it work together. instantiate the RM with the number of shards that the user wants the key to be split into. Currently we cannot Keywords— ACID, Key-Value, 2PC, sharding, 2PL Transaction dynamically change while RM is running. New Replica Servers cannot be added or removed from once RM is I. INTRODUCTION up and running. However Replica Servers can go down Distributed KV stores have become a norm with the and come back and RM can detect this. Each Shard advent of microservices architecture and the NoSql Leader updates the information to RM, while the DTM revolution. Initially KV Store discarded ACID properties leader updates its information to the RM. -
Lecture 15: March 28 15.1 Overview 15.2 Transactions
CMPSCI 677 Distributed and Operating Systems Spring 2019 Lecture 15: March 28 Lecturer: Prashant Shenoy 15.1 Overview This section covers the following topics: Distributed Transactions: ACID Properties, Concurrency Control, Serializability, Two-Phase Locking 15.2 Transactions Transactions are used widely in databases as they provide atomicity (all or nothing property). Atomicity is the property in which operations inside a transaction all execute successfully or none of the operations execute. Thus, if a process crashes during a transaction, all operations that happened during that transaction must be undone. Transactions are usually implemented using locks. Without locks there would be interleaving of operations from two transactions, causing overwrites and inconsistencies. It has to look like all instructions/operations of one transaction are executed at once, while ensuring other external instructions/operations are not executing at the same time. This is especially important in real-world applications such as banking applications. 15.2.1 ACID: Most transaction systems will provide the following properties • Atomicity: All or nothing (all instructions execute or none; cannot have some instructions execute and some do not) • Consistency: Transaction takes system from one consistent (valid) state to another • Isolated: Transaction executes as if it is the only transaction in the system. Ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed sequentially (serializability). -
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 . -
Blockchain Database for a Cyber Security Learning System
Session ETD 475 Blockchain Database for a Cyber Security Learning System Sophia Armstrong Department of Computer Science, College of Engineering and Technology East Carolina University Te-Shun Chou Department of Technology Systems, College of Engineering and Technology East Carolina University John Jones College of Engineering and Technology East Carolina University Abstract Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students’ scores accumulated by successful attacks or defends from the other students’ implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data. 1. Introduction New buzz words are constantly surfacing in the ever evolving field of computer science, so it is critical to distinguish the difference between temporary fads and new evolutionary technology. Blockchain is one of the newest and most developmental technologies currently drawing interest. -
Guide to Design, Implementation and Management of Distributed Databases
NATL INST OF STAND 4 TECH H.I C NIST Special Publication 500-185 A111D3 MTfiDfi2 Computer Systems Guide to Design, Technology Implementation and Management U.S. DEPARTMENT OF COMMERCE National Institute of of Distributed Databases Standards and Technology Elizabeth N. Fong Nisr Charles L. Sheppard Kathryn A. Harvill NIST I PUBLICATIONS I 100 .U57 500-185 1991 C.2 NIST Special Publication 500-185 ^/c 5oo-n Guide to Design, Implementation and Management of Distributed Databases Elizabeth N. Fong Charles L. Sheppard Kathryn A. Harvill Computer Systems Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899 February 1991 U.S. DEPARTMENT OF COMMERCE Robert A. Mosbacher, Secretary NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY John W. Lyons, Director Reports on Computer Systems Technology The National institute of Standards and Technology (NIST) has a unique responsibility for computer systems technology within the Federal government, NIST's Computer Systems Laboratory (CSL) devel- ops standards and guidelines, provides technical assistance, and conducts research for computers and related telecommunications systems to achieve more effective utilization of Federal Information technol- ogy resources. CSL's responsibilities include development of technical, management, physical, and ad- ministrative standards and guidelines for the cost-effective security and privacy of sensitive unclassified information processed in Federal computers. CSL assists agencies in developing security plans and in improving computer security awareness training. This Special Publication 500 series reports CSL re- search and guidelines to Federal agencies as well as to organizations in industry, government, and academia. National Institute of Standards and Technology Special Publication 500-185 Natl. Inst. Stand. Technol. -
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 -
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.