DOCSLIB.ORG

Implementing Distributed Transactions Distributed Transaction Distributed Database Systems ACID Properties Global Atomicity Atom

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Implementing Distributed Transactions Distributed Transaction Distributed Database Systems ACID Properties Global Atomicity Atom Distributed Transaction • A distributed transaction accesses resource managers distributed across a network Implementing Distributed • When resource managers are DBMSs we refer to the Transactions system as a distributed database system Chapter 24 DBMS at Site 1 Application Program DBMS 1 at Site 2 2 Distributed Database Systems ACID Properties • Each local DBMS might export • Each local DBMS – stored procedures, or – supports ACID properties locally for each subtransaction – an SQL interface. • Just like any other transaction that executes there • In either case, operations at each site are grouped – eliminates local deadlocks together as a subtransaction and the site is referred • The additional issues are: to as a cohort of the distributed transaction – Global atomicity: all cohorts must abort or all commit – Each subtransaction is treated as a transaction at its site – Global deadlocks: there must be no deadlocks involving • Coordinator module (part of TP monitor) supports multiple sites ACID properties of distributed transaction – Global serialization: distributed transaction must be globally serializable – Transaction manager acts as coordinator 3 4 Atomic Commit Protocol Global Atomicity Transaction (3) xa_reg • All subtransactions of a distributed transaction Manager Resource must commit or all must abort (coordinator) Manager (1) tx_begin (cohort) • An atomic commit protocol, initiated by a (4) tx_commit (5) atomic coordinator (e.g., the transaction manager), commit protocol ensures this. (3) xa_reg Resource Application – Coordinator polls cohorts to determine if they are all Manager program (cohort) willing to commit (2) access • Protocol is supported in the xa interface resources (3) xa_reg between a transaction manager and a resource Resource Manager manager (cohort) 5 6 1 Cohort Abort Atomic Commit Protocol • Most commonly used atomic commit • Why might a cohort abort? protocol is the two-phase commit protocol – Deferred evaluation of integrity constraints • Implemented as an exchange of messages – Validation failure (optimistic control) between the coordinator and the cohorts – Deadlock – Crash of cohort site • Guarantees global atomicity of the – Failure prevents communication with cohort transaction even if failures should occur site while the protocol is executing 7 8 Two-Phase Commit – The Two-Phase Commit -- Phase 1 • When application invokes tx_commit, coordinator Transaction Record sends prepare message to all cohorts • During the execution of the transaction, before the • prepare message (coordinator to cohort) : two-phase commit protocol begins: – If cohort wants to abort at any time prior to or on receipt of the message, it aborts and releases locks – When the application calls tx_begin to start the – If cohort wants to commit, it moves all update records to transaction, the coordinator creates a transaction record mass store by forcing a prepare record to its log for the transaction in volatile memory • Guarantees that cohort will be able to commit (despite crashes) if – Each time a resource manager calls xa_reg to join the coordinator decides commit (since update records are durable) transaction as a cohort, the coordinator appends the • Cohort enters prepared state cohort’s identity to the transaction record – Cohort sends a vote message (“ready” or “aborting”). It • cannot change its mind • retains all locks if vote is “ready” • enters uncertain period (it cannot foretell final outcome) 9 10 Two-Phase Commit -- Phase 1 Two-Phase Commit -- Phase 2 • vote message (cohort to coordinator): Cohort indicates it • Commit or abort message (coordinator to cohort): is “ready” to commit or is “aborting” – If commit message – Coordinator records vote in transaction record • cohort commits locally by forcing a commit record to its log – If any votes are “aborting”, coordinator decides abort and deletes transaction record • cohort sends done message to coordinator – If all are “ready”, coordinator decides commit, forces commit – If abort message, it aborts record (containing transaction record) to its log (end of phase 1) – In either case, locks are released and uncertain period ends • Transaction committed when commit record is durable • done message (cohort to coordinator): • Since all cohorts are in prepared state, transaction can be – When coordinator receives a done message from each committed despite any failures cohort, it writes a complete record to its log and deletes – Coordinator sends commit or abort message to all cohorts transaction record from volatile store 11 12 2 Two-Phase Commit (commit case) Two-Phase Commit (abort case) Application Coordinator Cohort Application Coordinator Cohort tx_commit - send prepare msg to tx_commit - send prepare msg to cohorts in trans. rec. - force prepare cohorts in trans. rec. - force prepare phase 1 rec. to cohort log phase 1 rec. to cohort log - record vote in trans. rec. - send vote msg u - record vote in trans.rec. p - send vote msg n u e p c n r e e c - if all vote ready, force i - if any vote abort, r r e o t i r a d o t i a commit rec. to coord. log delete transaction rec. d n i - send commit msg -force commit - send abort msg - local abort n rec. to cohort log resume - return status - release locks phase 2 - release locks - when all done msgs rec’d, - send done msg xa interface write complete rec. to log - delete trans. rec. xa interface resume - return status 13 14 Distributing the Coordinator Coordinator/Cohort Tree Domain A • A transaction manager controls resource TMA managers in its domain Applic. • When a cohort in domain A invokes a resource RM1 RM2 manager, RMB, in domain B, the local transaction manager, TMA, and remote Domain C transaction manager, TMB, are notified Domain B – TMB is a cohort of TMA and a coordinator of TMB TMC RMB • A coordinator/cohort tree results RM3 RM4 RM5 invocations 15 protocol msgs 16 Distributing the Coordinator Failures and Two-Phase Commit • The two-phase commit protocol progresses • A participant recognizes two failure situations. down and up the tree in each phase – Timeout : No response to a message. Execute a timeout protocol – When TMB gets a prepare msg from TMA it sends a prepare msg to each child and waits – Crash : On recovery, execute a restart protocol – If each child votes ready, TMB sends a ready • If a cohort cannot complete the protocol until msg to TMA some failure is repaired, it is said to be blocked • if not it sends an abort msg – Blocking can impact performance at the cohort site since locks cannot be released 17 18 3 Timeout Protocol Timeout Protocol • Cohort (in prepared state) times out waiting for • Cohort times out waiting for prepare message commit/abort message – Cohort is blocked since it does not know coordinator’s – Abort the subtransaction decision • Since the (distributed) transaction cannot commit unless • Coordinator might have decided commit or abort cohort votes to commit, atomicity is preserved • Cohort cannot unilaterally decide since its decision • Coordinator times out waiting for vote message might be contrary to coordinator’s decision, violating atomicity – Abort the transaction • Locks cannot be released • Since coordinator controls decision, it can force all – Cohort requests status from coordinator; remains blocked cohorts to abort, preserving atomicity • Coordinator times out waiting for done message – Requests done message from delinquent cohort 19 20 Restart Protocol - Cohort Restart Protocol - Coordinator • On restart: • On restart cohort finds in its log – Search log and restore to volatile memory the transaction – begin_transaction record, but no prepare record: record of each transaction for which there is a commit record, • Abort (transaction cannot have committed because cohort has not but no complete record voted) • Commit record contains transaction record – prepare record, but no commit record (cohort crashed in its uncertain period) • On receiving a request from a cohort for transaction • Does not know if transaction committed or aborted status: • Locks items mentioned in update records before restarting system – If transaction record exists in volatile memory, reply based • Requests status from coordinator and blocks until it receives an answer on information in transaction record – commit record – If no transaction record exists in volatile memory, reply abort • Recover transaction to committed state using log • Referred to as presumed abort property 21 22 Presumed Abort Property Presumed Abort Property • If, when a cohort asks for the status of a – The coordinator had crashed and restarted and found a transaction, there is no transaction record in complete record for the transaction in its log coordinator’s volatile storage, either – The coordinator had committed the transaction, – The coordinator had aborted the transaction and deleted received done messages from all cohorts and hence the transaction record deleted the transaction record from volatile memory – The coordinator had crashed and restarted and did not • The last two possibilities cannot occur find the commit record in its log because • It was in Phase 1 of the protocol and had not yet made a – In both cases, the cohort has sent a done message and decision, or hence would not request status • It had previously aborted the transaction • Therefore, coordinator can respond abort – or … 23 24 4 Heuristic Commit Variants/Optimizations • What does a cohort do when in the blocked state and the coordinator does not respond to a request • Read/only subtransactions need not for status? participate
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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 .
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]