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MongoDB
Developer(s)MongoDB Inc.
Initial release2009 (2009)
Stable release
3.0.5 / 28 July 2015 (2015-07-28)
Written inC++, JavaScript, C
Operating systemCross-platform
Available inEnglish
TypeDocument-oriented database
LicenseGNU AGPL v3.0 (drivers: Apache license)
Websitewww.mongodb.org

MongoDB (from humongous) is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software.

First developed by the software company MongoDB Inc. in October 2007 as a component of a planned platform as a service product, the company shifted to an open source development model in 2009, with MongoDB offering commercial support and other services.[1] Since then, MongoDB has been adopted as backend software by a number of major websites and services, including Craigslist, eBay, Foursquare, SourceForge, Viacom, and The New York Times among others. MongoDB was the most popular NoSQL database system.[2]

History of MongoDB [3]

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MongoDB was created by the founders of DoubleClick. Since leaving DoubleClick, they founded a number of startups and kept running into the same scaling problems over and over. They decided to try to create an application stack that would scale out easily, as companies everywhere seemed to be running into the same issues.

In Fall 2007, they founded 10gen and started working on an application platform for the cloud, similar to Google App Engine. The 10gen engine’s main language was server-side JavaScript, so the scalable database they were designing for it (proto-MongoDB) was also JavaScript-y.

The 10gen appengine was called ed (for Eliot and Dwight) and the database was called p (for platform). In the summer of 2008, someone decided that they needed real names, so they came up with Babble for the app engine and MongoDB for the database. The name “Mongo” was, originally, from Blazing Saddles (it was back-named to humongous).

Dwight commented below about the original of the name “Mongo:”

"Actually the name choice really does come from the word humongous. A couple years earlier a naming consultant showed me a list of 50 names for potential companies and consumer web products, and it was in that deck, and the point made was that it connoted “big”. But as you say some folks joked about the movie reference when we gave that name to the db, and I didn’t elaborate on the logic behind the naming at the time I would guess. I certainly didn’t in my mind make a negative association about the name at the time; my last encounter with it before that point was probably in Shrek 2. I knew it was campy but it was just a piece of the tech stack at first, not then a big standalone product and technology as it is today. Of course I now know that in some parts of the world it’s an odd choice — apologies about that to those of you in those locales."

The problem was, no one cared about Google App Engine and certainly no one cared about 10gen’s app engine. Developers would say, “well, the database is cool, but blech, app engine.”

After a year of work and practically no users, they ripped the database out of the app engine and open sourced them. Immediately, MongoDB started getting users.

Later on in the year 2009, MongoDB was introduced in the market as an open source database server that was maintained and supported by the MongoDB Inc. Many large and medium scale organizations such as SourceForge, Foursquare, craigslist and eBay are now using MongoDB in development of their database applications.[4]

Main features

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Mongo DB is one of newest database introduced by 10gen. Mongo DB is an open source, on the whole a document oriented Database system and is a part of NoSQL family of database. Despite the fact that it’s not a relational database it has some of the imperative features of RDBMS and has got implausible speed.

Instead of storing data in tables here rows are replaced by Documents (basic unit of Data in Mongo DB just like a ROW in RDMS) and Collections (collection is a group of documents.) which allow representing complex relationships. It can manage huge amount of data and can load data across a cluster. Mongo DB can perform some features which relational database cannot do[5].

Overview of the features
  • Mongo DB supports Map reduce and Aggregation Tools
  • Java Scripts are used instead of Procedures
  • Mongo DB is a schema less Database
  • Most Importantly Mongo DB supports secondary indexes and geospatial indexes.
  • Simple to Administer the Mongo DB in cases of failures
  • Mongo DB designed to provide High Performance
  • MongoDB stores files of any size without complicating your stack.

Some of the other features include:[6]

Document-oriented

Instead of taking a business subject and breaking it up into multiple relational structures, MongoDB can store the business subject in the minimal number of documents. For example, instead of storing title and author information in two distinct relational structures, title, author, and other title-related information can all be stored in a single document called Book.[7]

Ad hoc queries

MongoDB supports search by field, range queries, regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions.

Indexing

Any field in a MongoDB document can be indexed (indices in MongoDB are conceptually similar to those in RDBMSes). Secondary indices are also available.

Replication

MongoDB provides high availability with replica sets.[8] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. The primary replica performs all writes and reads by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can also perform read operations, but the data is eventually consistent by default.

Load balancing

MongoDB scales horizontally using sharding.[9] The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.) MongoDB can run over multiple servers, balancing the load and/or duplicating data to keep the system up and running in case of hardware failure. Automatic configuration is easy to deploy, and new machines can be added to a running database.

File storage

MongoDB can be used as a file system, taking advantage of load balancing and data replication features over multiple machines for storing files.

This function, called Grid File System,[10] is included with MongoDB drivers and available for development languages (see "Language Support" for a list of supported languages). MongoDB exposes functions for file manipulation and content to developers. GridFS is used, for example, in plugins for NGINX[11] and lighttpd.[12] Instead of storing a file in a single document, GridFS divides a file into parts, or chunks, and stores each of those chunks as a separate document.[13]

In a multi-machine MongoDB system, files can be distributed and copied multiple times between machines transparently, thus effectively creating a load-balanced and fault-tolerant system.

Aggregation

MapReduce can be used for batch processing of data and aggregation operations. The aggregation framework enables users to obtain the kind of results for which the SQL GROUP BY clause is used.

Server-side JavaScript execution

JavaScript can be used in queries, aggregation functions (such as MapReduce), and sent directly to the database to be executed.

Capped collections

MongoDB supports fixed-size collections called capped collections. This type of collection maintains insertion order and, once the specified size has been reached, behaves like a circular queue.

Criticisms

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In some failure scenarios where an application can access two distinct MongoDB processes, but these processes cannot access each other, it is possible for MongoDB to return stale reads. In this scenario it is also possible for MongoDB to acknowledge writes that will be rolled back.[14]

Before version 2.2, concurrency control was implemented on a per-mongod basis. With version 2.2, concurrency control was implemented at the database level.[15] Since version 3.0,[16] pluggable storage engines were introduced, and each storage engine may implement concurrency control differently.[17] With MongoDB 3.0 concurrency control is implemented at the collection level for the MMAPv1 storage engine,[18] and at the document level with the WiredTiger storage engine.[19] With versions prior to 3.0, one approach to increase concurrency is to use sharding.[20] In some situations, reads and writes will yield their locks. If MongoDB predicts a page is unlikely to be in memory, operations will yield their lock while the pages load. The use of lock yielding expanded greatly in 2.2.[21]

Another criticism is related to the limitations of MongoDB when used on 32-bit systems.[22] In some cases, this was due to inherent memory limitations.[23] MongoDB recommends 64-bit systems and that users provide sufficient RAM for their working set. Some users experience increased latency when their working set exceeds available RAM and the system encounters page faults. Compose, a provider of managed MongoDB infrastructure, recommends a scaling checklist for large systems.[24]

Additionally, MongoDB does not support collation-based sorting and is limited to byte-wise comparison via memcmp,[25] which will not provide correct ordering for many non-English languages[26] when used with a Unicode encoding.

Language support

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MongoDB has official drivers for a variety of popular programming languages and development environments.[27] There are also a large number of unofficial or community-supported drivers for other programming languages and frameworks.[28]

Management and graphical front-ends

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Third-Party GUI tools[29]

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Record insertion in MongoDB with Robomongo 0.8.5.

There is an active and growing community of developers building third-party rich GUI tools for the MongoDB. Some relevant examples (listed in alphabetical order):

  • 3T MongoChef, cross-platform MongoDB GUI
  • Database Master, web-based client software, supports RDMS
  • Fang of Mongo, web-based UI, built with Django and jQuery
  • HumongouS.io, Web based GUI for MongoDB
  • mms, Mongo Management Studio, cross-platfrorm and web-based GUI
  • MongoHub, a native OS-X-application for MongoDB management
  • NoSQL Manager for MongoDB, a MS Windows GUI application for MongoDB management with Shell
  • Opricot, browser-based MongoDB-Shell, implemented in PHP
  • Robomongo, Shell-centric cross-platform MongoDB management tool
  • UMongo (JMongoBrowser), cross-platform Management-GUI, implemented in Java

Performance

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United Software Associates[30] published a benchmark using YCSB as a basis of all the tests. MongoDB provides greater performance than Couchbase Server or Cassandra in all the tests they ran, in some cases by as much as 25x.[31]

Another benchmark for top NoSQL databases was done by End Point,[32] which compares MongoDB with other NoSQL database solutions.[33]

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There are many examples of MongoDB users who are pushing the limits to scalability. Here are a few, organized around each scaling dimension.

  • FIFA (video game series): EA Sports FIFA is the world's best-selling sports video game franchise. To serve millions of players, EA's Spearhead development studio selected MongoDB[34] to store user data and game state. Auto-sharding makes it simple to scale MongoDB across EA's 250+ servers with no limits to growth as EA FIFA wins more fans.
  • Yandex: The largest search engine in Russia uses MongoDB to manage all user and metadata for its file sharing service. MongoDB has scaled[35] to support tens of billions of objects and TBs of data, growing at 10 million new file uploads per day.
  • eBay: The world's largest online auction site uses MongoDB[36] to store all media metadata for the site. This includes references to images of every item for sale on eBay. The MongoDB cluster is deployed across multiple data centers and delivers 99.999% availability.
  • Foursquare: Foursquare is used by over 50 million people worldwide, who have checked in over 6 billion times, with millions more added every day. MongoDB is Foursquare's main database,[37] supporting hundreds of thousands of operations per second and storing all check-ins and history, user, and venue data along with reviews.
  • McAfee: MongoDB powers McAfee Global Threat Intelligence (GTI), a cloud-based intelligence service that correlates data from millions of sensors around the globe. Billions of documents are stored and analyzed in MongoDB to deliver real-time threat intelligence to other McAfee end-client products.[38]
  • Adobe: Many of the world's most recognizable brands use Adobe Experience Manager to accelerate development of digital experiences that increase customer loyalty, engagement and demand. Adobe uses MongoDB to store petabytes of data in the large-scale content repositories underpinning the Experience Manager.[39]
  • Craigslist: With 80 million classified ads posted every month, Craigslist needs to archive billions of records in multiple formats, and must be able to query and report on these archives at runtime. Craigslist migrated from MySQL to MongoDB to support its active archive, with continuous availability mandated for regulatory compliance across 700 sites in 70 different countries.[40]

Applications Using MongoDB[41]

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  • Bussion Application for creating dashboards with drilldown and collaboration capabilities.
  • c5t Content-management using TurboGears and Mongo.
  • Calipso Content management system built using NodeJS and MongoDB.
  • Cube Cube is an open-source system for visualizing time series data, built on MongoDB, Node and D3.
  • ErrorApp ErrorApp tracks errors from your apps. It reports them to you and gathers all information and make reports available to you.
  • Forward A full-featured, developer centric open source e-commerce platform that makes custom code easy, with powerful templates & expressive syntax.
  • Graylog2 Graylog2 is an open source syslog server implementation that stores logs in MongoDB and provides a Rails frontend.
  • HarmonyApp Harmony is a powerful web-based platform for creating and managing websites. It helps connect developers with content editors, for unprecedented flexibility and simplicity. For more information, view Steve Smith’s presentation on Harmony at MongoSF (April 2010).
  • Hummingbird Hummingbird is a real-time web traffic visualization tool developed by Gilt Groupe.
  • KeystoneJS KeystoneJS is a Node.js content management system and web application platform built on Express.JS and MongoDB.
  • Locomotive Locomotive is an open source CMS for Rails. It’s flexible and integrates with Heroku and Amazon S3.
  • Mogade Mogade offers a free and simple to use leaderboard and achievement services for mobile game developers.
  • MongoLantern MongoLantern is an open source full text search server using MongoDB as index storage, which allows MongoLantern to migrate any changes very easily into account using MongoDB API. *It’s written originally written in PHP can be migrated to any desired language as required using it’s future APIs.
  • Mongoop Monitor and locate long running operations on MongoDB and automatically trigger specific actions (email, sentry, nsca...) for alerting and performance analysis.
  • MongoPress A flexible CMS that uses MongoDB and PHP.
  • Mongs A simple, web-based data browser for MongoDB.
  • Mongeez Mongeez is an opensource solution allowing you to manage your mongo document changes in a manner that is easy to synchronize with your code changes. Check out mongeez.org.
  • NewsBlur NewsBlur is an open source visual feed reader that powers http://newsblur.com. NewsBlur is built with Django, MongoDB, Postgres and RabbitMQ.
  • Pics.io Pics.io is a digital asset management system that helps to centralize, manage and share images, videos, audio, PDFs and other valuable files across organization.
  • Quantum GIS Plugin for Quantum GIS that lets you plot geographical data stored in MongoDB.
  • Rubedo Rubedo is a full featured open source Enterprise Content Management System, built on MongoDB and Elastic Search with Zend Framework, Sencha Ext JS and Boostrap. It offers a complete set of back-office tools to easily manage galaxies of responsive, flexible and performant applications or websites.
  • Scribe Open source image transcription tool.
  • Shapado Free and open source Q&A software, open source stackoverflow style app written in Ruby, Rails, MongoMapper and MongoDB.
  • Strider Strider: Open Source Continuous Integration & Deployment Server.
  • Thundergrid Thundergrid is a simple framework written in PHP that allows you to store large files in your Mongo database in seconds.
  • Websko Websko is a content management system designed for individual Web developers and cooperative teams.

Licensing and support

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MongoDB is available for free under the GNU Affero General Public License.[42] The language drivers are available under an Apache License. In addition, MongoDB Inc. offers proprietary licenses for MongoDB.[1]

Popularity

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As of July 2015, MongoDB is the fourth most popular type of database management system, and the most popular for document stores.[2]

Production deployments

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Large-scale deployments of MongoDB are tracked by MongoDB Inc. Notable users of MongoDB include:

See also

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References

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  1. ^ a b "10gen embraces what it created, becomes MongoDB Inc". Gigaom. Retrieved 27 August 2013.
  2. ^ a b "Popularity ranking of database management systems". db-engines.com. Solid IT. Retrieved 2015-07-04.
  3. ^ History of MongoDB
  4. ^ {http://blog.bytescout.com/2014/09/mongodb-history-and-advantages.html}
  5. ^ Features of MongoDB
  6. ^ MongoDB Developer Manual
  7. ^ Data Modeling for MongoDB
  8. ^ Introduction to Replication
  9. ^ Introduction to Sharding
  10. ^ GridFS article on MongoDB Developer's Manual
  11. ^ NGINX plugin for MongoDB source code
  12. ^ lighttpd plugin for MongoDB source code
  13. ^ Expertstown - MongoDB overview
  14. ^ Kyle Kingsbury (2015-04-20). "Call me maybe: MongoDB stale reads". Retrieved 2015-07-04.
  15. ^ MongoDB Jira Ticket 4328
  16. ^ Eliot Horowitz (2015-01-22). "Renaming Our Upcoming Release to MongoDB 3.0". Retrieved 2015-02-23.
  17. ^ [1] - MongoDB 2.8 release
  18. ^ MMAPv1 Concurrency Improvement
  19. ^ WiredTiger Concurrency and Compression
  20. ^ FAQ Concurrency - How Does Sharding Affect Concurrency
  21. ^ FAQ Concurrency - Do Operations Ever Yield the Lock
  22. ^ 32-bit Limitations
  23. ^ Does Everybody Hate MongoDB
  24. ^ [2]
  25. ^ "memcmp". cppreference.com. 31 May 2013. Retrieved 26 April 2014.
  26. ^ MongoDB Jira ticket 1920
  27. ^ "MongoDB Drivers and Client Libraries". Mongodb.org. Retrieved 2013-07-08.
  28. ^ "Community Supported Drivers". Mongodb.org. Retrieved 2014-07-09.
  29. ^ MongoDB: Admin UIs
  30. ^ United Software Associates
  31. ^ info-mongodb-com.s3.amazonaws.com/High%2BPerformance%2BBenchmark%2BWhite%2BPaper_final.pdf
  32. ^ EndPoint.com
  33. ^ NoSQL Benchmarks: YCSB
  34. ^ MongoDB based FIFA Online
  35. ^ Yandrex: MongoDB
  36. ^ Ebay's Media Metadata MongoDB
  37. ^ FourSquare
  38. ^ www.mongodb.com/customers/mcafee
  39. ^ Adobe Experience Manager
  40. ^ Craigslist
  41. ^ MongoDB: Applications
  42. ^ The MongoDB NoSQL Database Blog, The AGPL
  43. ^ Presentation by Amadeus 11/2014
  44. ^ Holy Large Hadron Collider, Batman!
  45. ^ MongoDB at eBay
  46. ^ Experiences Deploying MongoDB on AWS
  47. ^ Presentation by LinkedIn
  48. ^ "Metlife uses nosql for customer service". Information Week. Retrieved 8 November 2014.
  49. ^ The Quest to Understand the Use of MongoDB in the SAP PaaS
  50. ^ Real World NoSQL: MongoDB at Shutterfly
  51. ^ Here's How We Think Of Shutterfly's Stock Value
  52. ^ Scaling SourceForge with MongoDB

Bibliography

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Category:Free database management systems Category:Document-oriented databases Category:Distributed computing architecture Category:Structured storage Category:NoSQL Category:Database-related software for Linux Category:2009 software

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