Couchbase Architecture

Total Page:16

File Type:pdf, Size:1020Kb

Couchbase Architecture Couchbase Architecture ©2015 Couchbase Inc. 1 $whoami Laurent Doguin Couchbase Developer Advocate @ldoguin | [email protected] ©2015 Couchbase Inc. 2 2 Big Data = Operational + Analytic (NoSQL + Hadoop) Real-time, Batch-oriented interactive databases analytic databases OPERATIONAL VELOCITY ANALYTICAL VOLUME § Online § Offline, batch-oriented § Web/Mobile/IoT apps § Analytics apps § Millions of customers/ § Hundreds of business analysts ©2015 Couchbase Inc. consumers 3 Key Capabilities Combines the flexibility of JSON, the power of SQL and the scale of NoSQL •N1QL Develop with Agility Operate at Any Scale ­ Multiple data models ­ Push-button scalability ­ N1QL - SQL-Like query ­ Consistent high-performance language ­ Always on 24x7 with HA - DR ­ Multiple indexes ­ Easy Administration with Web UI, ­ Languages, ODBC / JDBC Rest API and CLI drivers and frameworks you already know ©2015 Couchbase Inc. 4 Couchbase provides a complete Data Management solution General purpose capabilities support a broad range of apps and use cases N1QL Highly available Key-value Document Embedded Sync cache store database database management ©2015 Couchbase Inc. 5 Enterprises use Couchbase to enable key objectives Profile 360 Degree Internet of Mobile Personalization Things Management Customer View Applications Content Catalog Real Time Digital Fraud Management Big Data Communication Detection ©2015 Couchbase Inc. 6 Develop with Agility ©2015 Couchbase Inc. 7 What does a JSON document look like? { “ID”: 1, “FIRST”: “Dipti”, “LAST”: “Borkar”, “ZIP”: “94040”, “CITY”: “MV”, = + “STATE”: “CA” } JSON All data in a single document ©2015 Couchbase Inc. 8 Storing and retrieving documents Clients Documents User/application data Read from / Written to Servers Data Buckets Which live on Server Nodes Based on hash partitioning That form a Couchbase Cluster Dynamically scalable ©2015 Couchbase Inc. ©2014 Couchbase, Inc. 9 Accessing Data in Couchbase § Multiple Access Paths Functional Allow for view querying, building of queries Hold on to cluster information such as Give the application developer a concurrent Manage connections to the bucket within the Allow for querying, execution of other and reasonable error handling from the cluster. topology. API for basic (k-v) or document management cluster for different services. directives such as defining indexes and View Provide a core layer where IO can be managed checking on index state. CRUD N1QL Query Query API and optimized. abucket.NewViewQueryReference Cluster Management get() API Provide a way to manage buckets. ().Limit().Stale() Query & Index Data Service abucket.NewN1QLQuery( openBucketinsert() () Services “SELECT * FROM default LIMIT 5” ) info() upsertAPI () Cluster .Consistency(disconnect() remove() insertDesignDocumentgocouchbase.RequestPlus() ); flush() listDesignDocuments() ©2015 Couchbase Inc. 10 Couchbase SDKs and Connectors ©2015 Couchbase Inc. 11 Operate at Any Scale ©2015 Couchbase Inc. 12 Couchbase Architecture – Single Node ü Data Service – builds and maintains Distributed secondary indexes Data Index Query Management REST API (MapReduce Views) Service Service Service Web UI ü Indexing Engine – builds and maintains Global Secondary Indexes Managed Cache Indexing Query Engine Engine View Engine Node / ü Query Engine – plans, coordinates, Cluster and executes queries against either Orchestration Global or Distributed indexes Managed Cache Storage Managed ü Cluster Manager – configuration, Cache heartbeat, statistics, RESTful Storage Erlang / OTP Management interface Node Manager Cluster Manager Couchbase Server Node ©2015 Couchbase Inc. 13 13 Data Service: Write Operation Single-node type means APPLICATION SERVER easier administration and scaling DOC 1 § Writes are async by default § Application gets acknowledgement when MANAGED CACHE successfully in RAM and can trade- off waiting for replication or DOC 1 persistence per-write REPLICATION/ § Replication to 1, 2 or 3 other nodes XDCR/ CONNECTORS/ VIEWS/ DISK § Replication is RAM-based so INDEXING DISK extremely fast QUEUE § Off-node replication is primary level of HA § Disk written to as fast as possible – ©2015 Couchbase Inc. no waiting 14 14 Data Service: Read Operation APPLICATION SERVER Single-node type means GET easier administration and DOC 1 scaling § Reads out of cache are extremely fast MANAGED CACHE § No other process/system to communicate with DOC 1 § Data connection is a TCP-binary REPLICATION/ XDCR/ protocol CONNECTORS/ VIEWS/ DISK INDEXING DISK QUEUE DOC 1 ©2015 Couchbase Inc. 15 15 Data Service: Cache Miss Single-node type means APPLICATION SERVER easier administration and GET DOC 1 scaling § Layer consolidation means 1 single interface for App to talk to and get its data back as fast as MANAGED CACHE possible § DOC 1 DOC 2 DOC 3 DOC 4 DOC 5 Separation of cache and disk allows for fastest access out of REPLICATION/ RAM while pulling data from disk XDCR/ CONNECTORS/ in parallel VIEWS/ DISK INDEXING DISK QUEUE DOC 1 DOC 2 DOC 3 DOC 4 DOC 5 ©2015 Couchbase Inc. 16 16 Couchbase Views § Local Index – Distributed indexing and scatter gather querying § Incremental Map-Reduce – Distributed simple real-time analytics – Only considers changes due to updated data ©2015 Couchbase Inc. ©2014 Couchbase, Inc. 17 Index Service ©2015 Couchbase Inc. 18 Couchbase Global Indexing Service Global Secondary Index Service Index#1 Index#2 § New to 4.0 § Indexes partitioned Index#3 Index#4 independently from data § Each index receives only its own Supervisor mutations Index maintenance & Scan coordinator § Managed Caching layer § ForestDB storage engine § B+ Trie optimized for very large data volumes Indexing Service § Optimized for SSD’s ©2015 Couchbase Inc. 19 Query Service ©2015 Couchbase Inc. 20 Query Execution Flow SELECT c_id, { c_first, "c_first": "Joe", c_last, Clients "c_id": 49165, c_max "c_last": "Montana", FROM CUSTOMER "c_max" : 50000 WHERE c_id = 49165; } 1. Submit the query over REST API 8. Query result 2. Parse, Analyze, create Plan Query 7. Evaluate: Documents to results Service 3. Scan Request; 5. Fetch Request, Index index filters doc keys Data Service Service 4. Get qualified doc keys 6. Fetch the documents ©2015 Couchbase Inc. 21 Couchbase Clustering Architecture ©2015 Couchbase Inc. 22 22 Auto sharding – Bucket and vBuckets Data buckets vB vB vB vB 1 ….. 1024 1 ….. 1024 Active Virtual buckets Replica Virtual buckets ©2015 Couchbase Inc. 23 24 vBucket1 vBucket2 vBucket3 vBucket4 Couchbase SDK Couchbase vBucket5 CLUSTER MAP CLUSTER CRC32 vBucket6 Hashing Algorithm vBucket7 ... Couchbase Cluster Couchbase vBucket1024 vBucket1 vBucket2 vBucket3 vBucket4 Couchbase SDK Couchbase vBucket5 CLUSTER MAP CLUSTER CRC32 vBucket6 Hashing Algorithm vBucket7 ... vBucket1024 Couchbase Cluster Couchbase ©2015 Couchbase Inc. Cluster Map Data Services – Sharding and Replication Application has single logical connection to cluster (client object) READ/WRITE/UPDATE § Multiple nodes added or ACTIVE ACTIVE ACTIVE ACTIVE ACTIVE removed at once SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD 5 2 9 4 7 8 1 3 6 § One-click operation SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD § Incremental movement of active and replica vbuckets and data REPLICA REPLICA REPLICA REPLICA REPLICA § Client library updated via SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD 4 1 8 6 3 2 7 9 5 cluster map SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD SHARD § Fully online operation, no downtime or loss of Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 Couchbase Server 4 Couchbase Server 5 performance ©2015 Couchbase Inc. 25 25 What is Multi-Dimensional Scaling? MDS is the architecture that enables independent scaling of data, query and indexing workloads while being managed as one cluster node1 node8 Index Service Query Service Data Service ©2015 Couchbase Inc. 26 Couchbase Cluster Modern Architecture § Independent Scalability for Best Computational Capacity per Service Heavier indexing (index more fields) : scale up index service nodes More RAM for query processing: scale up query service nodes node1 node8 node9 Query Service Index Service Data Service ©2015 Couchbase Inc. 27 Couchbase Cluster Cross Data Center Replication ©2015 Couchbase Inc. 28 Market leading memory-to-memory replication NYC Server Cluster Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 Couchbase Server 4 MEMORY DISK MEMORY DISK MEMORY DISK MEMORY DISK New York San Francisco MEMORY DISK MEMORY DISK MEMORY DISK Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 SF Server Cluster ©2015 Couchbase Inc. 29 In summary The best of both worlds •N1QL Develop with Agility Operate at Any Scale ­ Multiple data models ­ Push-button scalability ­ N1QL - SQL-Like query ­ Consistent high-performance language ­ Always on 24x7 with HA - DR ­ Multiple indexes ­ Easy Administration with Web UI, ­ Languages, ODBC / JDBC Rest API and CLI drivers and frameworks you already know ©2015 Couchbase Inc. 30 Thanks! ©2015 Couchbase Inc. 31 .
Recommended publications
  • LIST of NOSQL DATABASES [Currently 150]
    Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features.
    [Show full text]
  • High Performance with Distributed Caching
    High Performance with Distributed Caching Key Requirements For Choosing The Right Solution High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution Table of Contents Executive summary 3 Companies are choosing Couchbase for their caching layer, and much more 3 Memory-first 4 Persistence 4 Elastic scalability 4 Replication 5 More than caching 5 About this guide 5 Memcached and Oracle Coherence – two popular caching solutions 6 Oracle Coherence 6 Memcached 6 Why cache? Better performance, lower costs 6 Common caching use cases 7 Key requirements for an effective distributed caching solution 8 Problems with Oracle Coherence: cost, complexity, capabilities 8 Memcached: A simple, powerful open source cache 10 Lack of enterprise support, built-in management, and advanced features 10 Couchbase Server as a high-performance distributed cache 10 General-purpose NoSQL database with Memcached roots 10 Meets key requirements for distributed caching 11 Develop with agility 11 Perform at any scale 11 Manage with ease 12 Benchmarks: Couchbase performance under caching workloads 12 Simple migration from Oracle Coherence or Memcached to Couchbase 13 Drop-in replacement for Memcached: No code changes required 14 Migrating from Oracle Coherence to Couchbase Server 14 Beyond caching: Simplify IT infrastructure, reduce costs with Couchbase 14 About Couchbase 14 Caching has become Executive Summary a de facto technology to boost application For many web, mobile, and Internet of Things (IoT) applications that run in clustered performance as well or cloud environments, distributed caching is a key requirement, for reasons of both as reduce costs. performance and cost. By caching frequently accessed data in memory – rather than making round trips to the backend database – applications can deliver highly responsive experiences that today’s users expect.
    [Show full text]
  • Database Software Market: Billy Fitzsimmons +1 312 364 5112
    Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions
    [Show full text]
  • Couchbase Vs Mongodb Architectural Differences and Their Impact
    MongoDB vs. Couchbase Server: Architectural Differences and Their Impact This 45-page paper compares two popular NoSQL offerings, diving into their architecture, clustering, replication, and caching. By Vladimir Starostenkov, R&D Engineer at Altoros Q3 2015 Table of Contents 1. OVERVIEW ......................................................................................................................... 3 2. INTRODUCTION ................................................................................................................. 3 3. ARCHITECTURE................................................................................................................. 4 4. CLUSTERING ..................................................................................................................... 4 4.1 MongoDB ............................................................................................................................................. 4 4.2 Couchbase Server .............................................................................................................................. 5 4.3 Partitioning .......................................................................................................................................... 6 4.4 MongoDB ............................................................................................................................................. 7 4.5 Couchbase Server .............................................................................................................................
    [Show full text]
  • Couchbase Server Manual 2.1.0 Couchbase Server Manual 2.1.0
    Couchbase Server Manual 2.1.0 Couchbase Server Manual 2.1.0 Abstract This manual documents the Couchbase Server 2.1.0 series, including installation, monitoring, and administration interface and associ- ated tools. For the corresponding Moxi product, please use the Moxi 1.8 series. See Moxi 1.8 Manual. External Community Resources. Download Couchbase Server 2.1 Couchbase Developer Guide 2.1 Client Libraries Couchbase Server Forum Last document update: 05 Sep 2013 23:46; Document built: 05 Sep 2013 23:46. Documentation Availability and Formats. This documentation is available online: HTML Online . For other documentation from Couchbase, see Couchbase Documentation Library Contact: [email protected] or couchbase.com Copyright © 2010-2013 Couchbase, Inc. Contact [email protected]. For documentation license information, see Section F.1, “Documentation License”. For all license information, see Appendix F, Licenses. Table of Contents Preface ................................................................................................................................................... xiii 1. Best Practice Guides ..................................................................................................................... xiii 1. Introduction to Couchbase Server .............................................................................................................. 1 1.1. Couchbase Server and NoSQL ........................................................................................................ 1 1.2. Architecture
    [Show full text]
  • Document Store Database Example
    Document Store Database Example Roderich is Barmecide: she predefine originally and juicing her currants. Prototypal Eli still utilises: purgatorial and associate Ron dimes quite eerily but drift her equalisers abidingly. Very and tantalizing Rikki luminesces, but Davis leftwardly rightens her docks. Returns an idea of database document model needs with each also accepts parameters such as strings Break when out nor the JSON and have it light an explicit issue in a hybrid store. Running a document database on Sql Server. Documents are stored in Collections and glide their dedicated CRUD operation set. How they use SQL Server as a Document Store Octopus Deploy. Examples of RDBMS and NoSQL databases Rackspace. MySQL JSON Document Store dasininet Diary create a MySQL. Tinydb PyPI. A database document store represents a collection of documents imported into. As we knew also see play are four tables in the worldx database but db. 3Pillar blog post by Girish Kumar and Rahul Checker exploring the different types of NoSQL databases that you cancel consider for each enterprise needs. How will data here a document database also possess as an own database stored? Document databases make it easier for developers to display and hence data in most database. Why You mother Never Use MongoDB Sarah Mei. For utility if you now looking at video surveillance data sensor. NoSQL Tutorial Types of NoSQL Databases What is & Example. To connect an obedience of the DocumentStore you need to abduct a best of URL addresses that compassion to RavenDB server nodes new DocumentStoreurls database. Xml document store lists of document stores do the database store and console gamer, specially if available.
    [Show full text]
  • APPLICATION of BLOCKCHAIN NETWORK for the USE of INFORMATION SHARING by Linir Zamir
    APPLICATION OF BLOCKCHAIN NETWORK FOR THE USE OF INFORMATION SHARING by Linir Zamir A Thesis Submitted to the Faculty of The College of Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, FL August 2019 Copyright 2019 by Linir Zamir ii ACKNOWLEDGEMENTS I would like to thank my committee members: Dr. Feng-Hao Liu, my commit- tee chair; Dr. Mehrdad Nojoumian and Dr. Elias Bou-Harb for their encouragement, insightful comments and hard questions. I would also like to thank my mother, who supported me with love and understanding. Without you, I could never have made it this far in my academical studies and generally in life. iv ABSTRACT Author: Linir Zamir Title: Application of Blockchain networkfor the use of Information Sharing Institution: Florida Atlantic University Thesis Advisor: Dr. Feng-Hao Liu Degree: Master of Science Year: 2019 The Blockchain concept was originally developed to provide security in the Bitcoin cryptocurrency network, where trust is achieved through the provision of an agreed-upon and immutable record of transactions between parties. The use of a Blockchain as a secure, publicly distributed ledger is applicable to fields beyond finance, and is an emerging area of research across many other fields in the industry. This thesis considers the feasibility of using a Blockchain to facilitate secured infor- mation sharing between parties, where a lack of trust and absence of central control are common characteristics. Implementation of a Blockchain Information Sharing system will be designed on an existing Blockchain network with as a communicative party members sharing secured information.
    [Show full text]
  • Couchbase Analytics: Noetl for Scalable Nosql Data Analysis
    Couchbase Analytics: NoETL for Scalable NoSQL Data Analysis Murtadha Al Hubail1 Ali Alsuliman1 Michael Blow1 Michael Carey12 Dmitry Lychagin1 Ian Maxon12 Till Westmann1 1Couchbase, Inc. 2University of California, Irvine [email protected] ABSTRACT Couchbase Server is a highly scalable document-oriented database management system. With a shared-nothing architecture, it exposes a fast key-value store with a managed cache for sub-millisecond Full-Text data operations, indexing for fast queries, and a powerful query Eventing Query Data Analytics Indexing engine for executing declarative SQL-like queries. Its Query Ser- Search vice debuted several years ago and supports high volumes of low- latency queries and updates for JSON documents. Its recently intro- Figure 1: Couchbase Server Overview duced Analytics Service complements the Query Service. Couch- base Analytics, the focus of this paper, supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections however, since the early days of mainframes and their attached ter- of JSON documents. This paper describes the Analytics Service minals. from the outside in, including its user model, its SQL++ based Today’s mission-critical applications demand support for mil- query language, and its MPP-based storage and query processing lions of interactions with end-users via the Web and mobile devices. architecture. It also briefly touches on the relationship of Couch- In contrast, traditional database systems were built for thousands of base Analytics to Apache AsterixDB, the open source Big Data users. Designed for strict consistency and data control, they tend to management system at the core of Couchbase Analytics. lack agility, flexibility, and scalability.
    [Show full text]
  • How Travel and Hospitality Companies Are Transforming Their
    How Travel and Hospitality Companies are Transforming their Customer Experience in the Age of Kayak, Airbnb and Uber EXECUTIVE SUMMARY Flight availability, booking, In the competitive hospitality industry customers are demanding a better, more and pricing analytics personalized experience. Despite increased consumer spending, operational expenses are putting brands under pressure leading them to search for new efficiency. Mergers, new partnerships, personalization, and digitization are causing the volumes of data to grow. Older technologies aren’t keeping up with the change despite rising maintenance costs. Inventory and pricing Travel companies must contend with a complex set of ever-changing products and recommendations services. Hospitality companies require the ability to provide premium services at a lower cost. Meanwhile, airlines are forced to compete with low-cost alternatives. Everyone in this sector must personalize the travel experience and cultivate customer loyalty, and make each experience one that delights customers and brings them back. Information is at the heart of all of these issues. Personalization and digital Real-time crew transformation require data. The only way to meet all of the data needs is to implement management, scheduling, a database that supports these efforts efficiently even when things change. and resources 2 INTRODUCTION Over the last 30 years, According to the Deloitte 2019 Travel and Hospitality Outlook, the past 10 years have seen Ryanair has experienced a huge increase in travel. From 2009 to 2018, US hotel bookings increased by $69 billion. exponential growth. Since Airline revenue surged by $67 billion. More consumers started traveling again, benefitting we launched our new the cruise, ground transportation, and restaurant industries as well.
    [Show full text]
  • Web Scalability with Nosql
    Web Scalability with NoSQL How to Support Millions of Users with Couchbase Modern Web Scalability – How Big is Big? Web applications are driving the need for more easily scalable database technology. Not long ago, 1,000 daily users of an application was a lot and 10,000 was an extreme case. Modern web applications often need to deliver a great interactive experience to millions of users per day, and support wildly-fluctuating numbers of concurrent users. A great user experience means a responsive application, which means fast, predictable access to data. Supporting large user base is important, but it’s just as important to handle rapidly growing (or shrinking) numbers of concurrent users: • A newly launched app can go viral, growing from zero to a million users overnight – literally. • Seasonal swings like those around Black Friday, the holiday season or Valentine’s Day can create massive spikes for short periods. With relational technologies, many application developers find it difficult, or even impossible, to get the dynamic scalability and level of scale they need while also maintaining the performance users demand. Many are turning to NoSQL for help. Massive Scale – Couchbase Style What does large-scale mean to Couchbase? The world’s largest enterprises rely on Couchbase for their mission- critical web and mobile applications. Couchbase Server has proven scale to a billion user profiles in a single application (adding 500k per day!), and we have customers supporting more than 6M concurrent users with systems capable of more than a million operations per second. When a Zynga game called Draw Something exploded from 0-50M users in 50 days, they were able to scale seamlessly as demand increased.
    [Show full text]
  • 2.15 Couchbase Sync Gateway
    VISVESVARAYA TECHNOLOGICAL UNIVERSITY “Jnana Sangama”, Belgaum – 590 014 A PROJECT REPORT ON “SOYA ASSIST” Submitted in partial fulfillment for the award of the degree of BACHELOR OF ENGINEERING IN COMPUTER SCIENCE AND ENGINEERING BY PRUTHVI DIWAKAR HEGDE (1NH11CS079) RASEENA A (1NH11CS090) SAMYUKTHA SUBRAMANIAN (1NH11CS100) SHRUTHI S (1NH11CS109) Under the guidance of Mr. Puneet S Palagi (Assistant Professor, Dept. of CSE, NHCE) DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING NEW HORIZON COLLEGE OF ENGINEERING (ISO-9001:2000 certified, Accredited by NBA, Permanently affiliated to VTU) Outer Ring Road,Panathur Post, Near Marathalli, Bangalore – 560103 NEW HORIZON COLLEGE OF ENGINEERING (ISO-9001:2000 certified, Accredited by NBA, Permanently affiliated to VTU) Outer Ring Road, Panathur Post, Near Marathalli, Bangalore-560 103 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING CERTIFICATE Certified that the project work entitled “SOYA ASSIST” carried out by PRUTHVI DIWAKAR HEGDE (1NH11CS079), RASEENA A (1NH11CS090), SAMYUKTHA SUBRAMANIAN (1NH11CS100), SHRUTHI S (1NH11CS109) and a bonafide student of NEW HORIZON COLLEGE OF ENGINEERING in partial fulfillment for the award of Bachelor Of Engineering/Bachelor Of Technology in Computer Science and Engineering of the Visvesvaraya Technological University, Belgaum during the year 2014 - 2015. It is certified that all corrections/suggestions indicated for Internal Assessment have been incorporated in the report deposited in the department library. The project report has been approved as it satisfies the academic requirements in respect of Project work prescribed for the said Degree. Name & Signature of Guide Name Signature of HOD Signature of Principal (Mr. Puneet .S .Palagi ) (Dr. Prashanth C.S.R.) (Dr. Manjunatha) External Viva Name of Examiner Signature with date 1.
    [Show full text]
  • Nosql - Notonly Sql
    International Journal of Enterprise Computing and Business Systems ISSN (Online) : 2230-8849 Volume 2 Issue 2 July 2013 International Manuscript ID : ISSN22308849-V2I2M3-072013 NOSQL - NOTONLY SQL Dr. S. George University of New Zealand Abstract A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are often highly optimized key–value stores intended for simple retrieval and appending operations, with the goal being significant performance benefits in terms of latency and throughput. NoSQL databases are finding significant and growing industry use in big data and real-time web applications. NoSQL systems are also referred to as "Not only SQL" to emphasize that they do in fact allow SQL-like query languages to be used. ACID vs BASE NoSQL cannot necessarily give full ACID guarantees. Usually eventual consistency is guaranteed or transactions limited to single data items. This means that given a sufficiently long period of time over which no changes are sent, all updates can be expected to propagate eventually through the system. [citation needed ]. Contents History Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface. Strozzi suggests that, as the International Journal of Enterprise Computing and Business Systems ISSN (Online) : 2230-8849 Volume 2 Issue 2 July 2013 International Manuscript ID : ISSN22308849-V2I2M3-072013 current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.
    [Show full text]