| Developer(s) | 10gen |
|---|---|
| Initial release | 2009 |
| Stable release | 2.0.2 / December 14, 2011 |
| Development status | Active |
| Written in | C++ |
| Operating system | Cross-platform |
| Available in | English |
| Type | Document-oriented database |
| License | GNU AGPL v3.0 (drivers: Apache license) |
| Website | www.mongodb.org |
MongoDB (from "humongous") is an open source, high-performance, schema-free, document-oriented NoSQL database system written in the C++ programming language.[1] It manages collections of BSON documents that can be nested in complex hierarchies and still be easy to query and index, which allows many applications to store data in a natural way that matches their native data types and structures.
Development of MongoDB began in October 2007 by 10gen. The first public release was in February 2009.[2]
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Among the features are:
More features:
In MongoDB, any field can be queried at any time. MongoDB supports range queries, regular expression searches, and other special types of queries in addition to exactly matching fields. Queries can also include user-defined JavaScript functions (if the function returns true, the document matches).
Queries can return specific fields of documents (instead of the entire document), as well as sorting, skipping, and limiting results.
Queries can "reach into" embedded objects and arrays. If the following object is inserted into the users collection:
{ "username" : "bob", "address" : { "street" : "123 Main Street", "city" : "Springfield", "state" : "NY" } }
We can query for this document (and all documents with an address in New York) with:
> db.users.find({"address.state" : "NY"})
Array elements can also be queried:
> db.food.insert({"fruit" : ["peach", "plum", "pear"]}) > db.food.find({"fruit" : "pear"})
The software supports secondary indexes, including single-key, compound, unique, non-unique, and geospatial[3] indexes. Nested fields (as described above in the ad hoc query section) can also be indexed and indexing an array type will index each element of the array.
MongoDB's query optimizer will try a number of different query plans when a query is run and select the fastest, periodically resampling. Developers can see the index being used with the `explain` function and choose a different index with the `hint` function.
Indexes can be created or removed at any time.
In addition to ad hoc queries, the database supports a couple of tools for aggregation, including MapReduce[4] and a group function similar to SQL's GROUP BY.
The software implements a protocol called GridFS[5] that is used to store and retrieve files from the database. This file storage mechanism has been used in plugins for NGINX[6] and lighttpd.[7]
JavaScript is the lingua franca of MongoDB and can be used in queries, aggregation functions (such as MapReduce), and sent directly to the database to be executed.
Example of JavaScript in a query:
> db.foo.find({$where : function() { return this.x == this.y; }})
Example of code sent to the database to be executed:
> db.eval(function(name) { return "Hello, "+name; }, ["Joe"])
This returns "Hello, Joe".
JavaScript variables can also be stored in the database and used by any other JavaScript as a global variable. Any legal JavaScript type, including functions and objects, can be stored in MongoDB so that JavaScript can be used to write "stored procedures."
MongoDB supports fixed-size collections called capped collections.[8] A capped collection is created with a set size and, optionally, number of elements. Capped collections are the only type of collection that maintains insertion order: once the specified size has been reached, a capped collection behaves like a circular queue.
A special type of cursor, called a tailable cursor,[9] can be used with capped collections. This cursor was named after the `tail -f` command, and does not close when it finishes returning results but continues to wait for more to be returned, returning new results as they are inserted into the capped collection.
MongoDB can be built and installed from source, but it is more commonly installed from a binary package. Many Linux package management systems now include a MongoDB package, including CentOS and Fedora,[10] Debian and Ubuntu,[11] Gentoo[12] and Arch Linux.[13] Also OS X Homebrew package manager includes MongoDB[14]. It can also be acquired through the official website.[15]
MongoDB uses memory-mapped files, limiting data size to 2GB on 32-bit machines (64-bit systems have a much larger data size).[16] The MongoDB server can only be used on little-endian systems, although most of the drivers work on both little-endian and big-endian systems.
MongoDB has official drivers for:
There are also a large number of unofficial drivers, for C# and .NET,[19] ColdFusion,[30] Delphi,[31] Erlang,[32][33] Factor,[34] Fantom,[35] Go,[36] JVM languages (Clojure, Groovy,[37] Scala, etc.),[38] Lua,[39] node.js,[40] HTTP REST,[41] Ruby,[42] Racket,[43] and Smalltalk.[44]
MongoDB supports master-slave replication. A master can perform reads and writes. A slave copies data from the master and can only be used for reads or backup (not writes).
MongoDB allows developers to guarantee that an operation has been replicated to at least N servers on a per-operation basis.
As operations are performed on the master, the slave will replicate any changes to the data.
Example: starting a master/slave pair locally:
$ mkdir -p ~/dbs/master ~/dbs/slave $ ./mongod --master --port 10000 --dbpath ~/dbs/master $ ./mongod --slave --port 10001 --dbpath ~/dbs/slave --source localhost:10000
Replica sets are similar to master-slave, but they incorporate the ability for the slaves to elect a new master if the current one goes down.
MongoDB scales horizontally using a system called sharding[45] which is very similar to the BigTable and PNUTS scaling model. The developer 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.)
The developer's application must know that it is talking to a sharded cluster when performing some operations. For example, a "findAndModify" query must contain the shard key if the queried collection is sharded.[46] The application talks to a special routing process called `mongos` that looks identical to a single MongoDB server. This `mongos` process knows what data is on each shard and routes the client's requests appropriately. All requests flow through this process: it not only forwards requests and responses but also performs any necessary final data merges or sorts. Any number of `mongos` processes can be run: usually one per application server is recommended.
The most powerful and useful management tool is the database shell, mongo. The shell lets developers view, insert, remove, and update data in their databases, as well as get replication information, setting up sharding, shut down servers, execute JavaScript, and more. mongo is built on SpiderMonkey, so it is a full JavaScript shell as well as being able to connect to MongoDB servers.
Administrative information can also be accessed through the admin interface: a simple html webpage that serves information about the current server status. By default, this interface is 1000 ports above the database port (http://localhost:28017) and it can be turned off with the --norest option.
mongostat is a command-line tool that displays a simple list of stats about the last second: how many inserts, updates, removes, queries, and commands were performed, as well as what percentage of the time the database was locked and how much memory it is using.
mongosniff sniffs network traffic going to and from MongoDB.
There are monitoring plugins available for MongoDB:
Several GUIs have been created by MongoDB's developer community to help visualize their data. Some popular ones are:
MongoDB is available for free under the GNU Affero General Public License. The language drivers are available under an Apache License.[57]
Objects in MongoDB are assigned an ObjectID, which incorporates a 32 bit representation of time in seconds since epoch (which in computers is typically seconds since the start of 1970), and another 64 bits containing a 24 bit machine id, 16 bit process id, and a 24 bit counter. As with all fixed size representations of time, this is susceptible to rollover, specifically the Year 2038 problem. Applications built upon mongo that make use of the embedded time representation contained within the ObjectID would misinterpret dates even though MongoDB itself would continue to function.
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