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MongoDB

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MongoDB
Developer(s)MongoDB Inc.
Initial release2009 (2009)
Stable release
3.2.1[1] / 12 January 2016 (2016-01-12)
Repository
Written inC++, JavaScript, C
Operating systemCross-platform
Available inEnglish
TypeDocument-oriented database
LicenseDual licensed (GNU AGPL (drivers: Apache license) + permissive commercial license exception)
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. MongoDB is developed by MongoDB Inc. and is published as free and open-source software under a combination of the GNU Affero General Public License and the Apache License. As of July 2015, MongoDB is the fourth most popular type of database management system, and the most popular for document stores.[2]

History

See also: MongoDB Inc. § History

The software company 10gen began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[3]

Main features

Some of the features include:[4]

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.[5]

Ad hoc queries

MongoDB supports 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 – including within arrays and embedded documents (indices in MongoDB are conceptually similar to those in RDBMSes). Primary and secondary indices are available.

Replication

MongoDB provides high availability with replica sets.[6] 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 optionally perform read operations, but that data is eventually consistent by default.

Load balancing

MongoDB scales horizontally using sharding.[7] 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.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.

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. MongoDB 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,[8] is included with MongoDB drivers and available for many 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[9] and lighttpd.[10] 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.[11]

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. Aggregation operators can be strung together to form a pipeline – analogous to Unix pipes. The aggregation framework includes the $lookup operator which can join documents from multiple documents.

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

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.[12]

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.[13] Since version 3.0,[14] pluggable storage engines were introduced, and each storage engine may implement concurrency control differently.[15] With MongoDB 3.0 concurrency control is implemented at the collection level for the MMAPv1 storage engine,[16] and at the document level with the WiredTiger storage engine.[17] With versions prior to 3.0, one approach to increase concurrency is to use sharding.[18] 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.[19]

Another criticism is related to the limitations of MongoDB when used on 32-bit systems.[20] In some cases, this was due to inherent memory limitations.[21][self-published source] MongoDB recommends 64-bit systems and that users provide sufficient RAM for their working set.

Additionally, MongoDB does not support collation-based sorting and is limited to byte-wise comparison via memcmp,[22] which will not provide correct ordering for many non-English languages[23] when used with a Unicode encoding.

Architecture

Language support

MongoDB has official drivers for a variety of popular programming languages and development environments.[24] There are also a large number of unofficial or community-supported drivers for other programming languages and frameworks.[25]

Management and graphical front-ends

Record insertion in MongoDB with Robomongo 0.8.5.

Most administration is done from command line tools such as the mongo shell because MongoDB does not include a GUI-style administrative interface. There are products and third-party projects that offer user interfaces for administration and data viewing.[26]

Licensing and support

MongoDB is available for free under the GNU Affero General Public License.[27] The language drivers are available under an Apache License. In addition, MongoDB Inc. offers proprietary licenses for MongoDB.

Performance

United Software Associates published a benchmark using Yahoo's Cloud Serving Benchmark 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.[28]

Another benchmark for top NoSQL databases utilizing Amazon's Elastic Compute Cloud that was done by End Point arrived at opposite results, placing MongoDB last among the tested databases.[29]

Production deployments

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

  • Adobe: Adobe Experience Manager is intended 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.[30]
  • Amadeus IT Group uses MongoDB for its back-end software.[31]
  • The Compact Muon Solenoid at CERN uses MongoDB as the primary back-end for the Data Aggregation System for the Large Hadron Collider.[32]
  • 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.[33]
  • eBay uses MongoDB in the search suggestion and the internal Cloud Manager State Hub.[34]
  • FIFA (video game series): EA's Spearhead development studio uses MongoDB[35] to store user data and game state. Auto-sharding allows scaling MongoDB across EA's 250+ servers as user demand grows.
  • Foursquare deploys MongoDB on Amazon AWS to store venues and user check-ins into venues.[36]
  • LinkedIn uses MongoDB as its backend DB.[37]
  • 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]
  • MetLife uses MongoDB for “The Wall", a customer service application providing a "360-degree view" of MetLife customers.[39]
  • SAP uses MongoDB in the SAP PaaS.[40]
  • Shutterfly uses MongoDB for its photo platform. As of 2013, the photo platform stores 18 billion photos uploaded by Shutterfly's 7 million users.[41]
  • Tuenti uses MongoDB as its backend DB.[42]
  • Yandex: The largest search engine in Russia uses MongoDB to manage all user and metadata for its file sharing service. MongoDB has scaled[43] to support tens of billions of objects and TBs of data, growing at 10 million new file uploads per day.

See also

References

  1. ^ "Release Notes for MongoDB 3.2". MongoDB.
  2. ^ "Popularity ranking of database management systems". db-engines.com. Solid IT. Retrieved 2015-07-04.
  3. ^ "10gen embraces what it created, becomes MongoDB Inc". Gigaom. Retrieved 29 January 2016.
  4. ^ MongoDB. "MongoDB Developer Manual". MongoDB.
  5. ^ Data Modeling for MongoDB
  6. ^ MongoDB. "Introduction to Replication". MongoDB.
  7. ^ MongoDB. "Introduction to Sharding". MongoDB.
  8. ^ MongoDB. "GridFS article on MongoDB Developer's Manual". MongoDB.
  9. ^ "NGINX plugin for MongoDB source code". GitHub.
  10. ^ "lighttpd plugin for MongoDB source code". Bitbucket.
  11. ^ Malick Md. "MongoDB overview". Expertstown.
  12. ^ Kyle Kingsbury (2015-04-20). "Call me maybe: MongoDB stale reads". Retrieved 2015-07-04.
  13. ^ "MongoDB Jira Ticket 4328". jira.mongodb.org.
  14. ^ Eliot Horowitz (2015-01-22). "Renaming Our Upcoming Release to MongoDB 3.0". MongoDB. Retrieved 2015-02-23.
  15. ^ "MongoDB 2.8 release". MongoDB.
  16. ^ MongoDB. "MMAPv1 Concurrency Improvement". MongoDB.
  17. ^ MongoDB. "WiredTiger Concurrency and Compression". MongoDB.
  18. ^ MongoDB. "FAQ Concurrency - How Does Sharding Affect Concurrency". MongoDB.
  19. ^ MongoDB. "FAQ Concurrency - Do Operations Ever Yield the Lock". MongoDB.
  20. ^ MongoDB (8 July 2009). "32-bit Limitations". MongoDB.
  21. ^ David Mytton (25 September 2012). "Does Everybody Hate MongoDB". Server Density.
  22. ^ "memcmp". cppreference.com. 31 May 2013. Retrieved 26 April 2014.
  23. ^ "MongoDB Jira ticket 1920". jira.mongodb.org.
  24. ^ MongoDB. "MongoDB Drivers and Client Libraries". MongoDB. Retrieved 2013-07-08.
  25. ^ MongoDB. "Community Supported Drivers". MongoDB. Retrieved 2014-07-09.
  26. ^ MongoDB. "Admin UIs". Retrieved 15 September 2015.
  27. ^ MongoDB. "The AGPL". The MongoDB NoSQL Database Blog. MongoDB.
  28. ^ United Software Associates. "High Performance Benchmarking: MongoDB and NoSQL Systems" (PDF).
  29. ^ End Point (13 April 2015). "Benchmarking Top NoSQL Databases; Apache Cassandra, Couchbase, HBase, and MongoDB" (PDF).
  30. ^ MongoDB. "Adobe Experience Manager". MongoDB.
  31. ^ "Presentation by Amadeus 11/2014". MongoDB.
  32. ^ "Holy Large Hadron Collider, Batman!". MongoDB.
  33. ^ MongoDB. "Craigslist". MongoDB.
  34. ^ "MongoDB at eBay". Slideshare.
  35. ^ "MongoDB based FIFA Online". MongoDB.
  36. ^ "Experiences Deploying MongoDB on AWS". MongoDB.
  37. ^ "Presentation by LinkedIn". MongoDB.
  38. ^ MongoDB. "McAfee is Improving Global Cybersecurity with MongoDB". MongoDB.
  39. ^ Doug Henschen (13 May 2013). "Metlife uses nosql for customer service". Information Week. Retrieved 8 November 2014.
  40. ^ Richard Hirsch (30 September 2011). "The Quest to Understand the Use of MongoDB in the SAP PaaS".
  41. ^ Guy Harrison (28 January 2011). "Real World NoSQL: MongoDB at Shutterfly". Gigaom.
  42. ^ "We host the MongoDB user group meetup at our office".
  43. ^ "Yandex: MongoDB". Yandex.

Bibliography

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