MongoDB (from "humongous") is an open source document-oriented database system developed and supported by 10gen (founded by Dwight Merriman). First publicly released in 2009, and since then it have been a rising star in the NoSQL world. MongoDB stores structured data as JSON-like documents with dynamic schemas (technically data is stored in a binary form of JSON known as BSON), making the integration of data in certain types of applications easier and faster.
Installation
- Download the latest mongoDB version from here.
- Extract the archive to your preferred location (in my case C:\mongodb). MongoDB is self-contained and does not have any other system dependencies. You can run MongoDB from any folder you choose. You may install MongoDB in any directory.
- MongoDB requires a data folder to store its files (default is C:\data\db ). You may specify a different path with the dbpath setting when lunching mongod.exe.
Starting the Server
To start MongoDB, open the command prompt window, and run mongod.exe from the bin directory (specify the data path if needed)
The waiting for connections message in the console output indicates that the mongod.exe process is running successfully and waiting for connections on port 27017Connecting to the Server
To connect to the server, open another command prompt window and run mongo.exe from the bin directory.
Run MongoDB as a Windows Service
- Create a log file for MongoDB (in my case c:\mongodb\log\mongo.log ).
- Create a data directory for MongoDB (in my case c:\mongodb\data ).
- Open the command prompt window as an administrator.
- Run the following command C:\mongodb\bin>mongod.exe –install –rest –master --logpath "c:\mongodb\log\mongo.log"
- Run regedit from start menu.
- Go to HKEY_LOCAL_MACHINE >> SYSTEM >> CurrentControlSet >> services
- Find the MongoDB directory & edit the ImagePath key.
- Set value as c:\mongodb\bin\mongod --service --rest --master --logpath=C:\mongodb\log\mongo.log --dbpath=C:\mongodb\data
- Save and exit registry editor
- Open ComponentServices from Start menu >> Run
- Locate the Mongo DB service, and reigh click >> Properties.
- Set the Startup Type to Automatic. Then start the service.
- To run the MongoDB service from command window, use net start MongoDB
- Check at http://localhost:28017/ to see , MongoDB should return stats.
Data Model
Data in MongoDB has a flexible schema.- Database consists on a set of collections.
- Collections consists of a set of documents.
- Documents in the same collection do not need to have the same set of fields or structure.
- Common fields in a collection’s documents can hold different types of data.
CRUD operations
- When you start the mongo shell, it connect to test database by default. To create a new database or switch to another database, use use newdb
- To show a list of databases, use show dbs (databases are created when you insert the first values in it. This means that any database have been created but no values inserted in it, it really doesn’t exist).
- To confirm the current session database, use db
- To create a collection, just insert an initial record to it. Since Mongo is schema-less, there is no need to define anything up front. The following code creates/inserts a towns collection:
name: "New York",
population: 22200000,
last_census: ISODate("2009-07-31"),
famous_for: [ "statue of liberty", "food" ],
mayor : {
name : "Michael Bloomberg",
party : "I"
}
})
- brackets like {...} denote an object with key-value pairs.
- brackets like [...] denote an array.
- You can nest these values to any depth.
- To show a list of collections, use show collections.
- To list the contents of a collection, use db.towns.find()
- You will see a system generated field _id ( composed of a timestamp, client machine ID, client process ID, and a 3-byte incremented counter) (you can override this system generated
- MongoDB commands are JavaScript functions.
- db is a JavaScript object that contains information about the current database. Try typeof db
- db.x is a JavaScript object that represent a collection named x within the current database. Try typeof db.towns
- db.x.help() will list available functions related to the given object. Try typeof db.towns.insert
- If you want to inspect the source code of a function, call it without parameter or parentheses.
- Functions You can create JavaScript functions and call them on the mongo shell like:
function insertCity( name, population, last_census, famous_for, mayor_info) {
db.towns.insert({
name:name,
population:population,
last_census: ISODate(last_census),
famous_for:famous_for,
mayor : mayor_info
});
}
insertCity("Punxsutawney", 6200, '2008-31-01', ["phil the groundhog"], { name : "Jim Wehrle" } )
insertCity("Portland", 582000, '2007-20-09', ["beer", "food"], { name : "Sam Adams", party : "D" } )
Now have three towns in our collection
- To get a specific document, we only need _id passed to find() function in type ObjectId(findOne() retrieves only one matching document). String can be converted to ObjectId using ObjectId(str) function.
- The find() function also accepts an optional second parameter: a fields object we can use to filter which fields are retrieved. If we want only the town name (along with _id), pass in name with a value resolving to 1 (or true).
- To retrieve all fields except name, set name to 0 (or false or null).
- You can retrieve documents based on criteria other than _id. You can use regular expressions or any operator.
We said before that the query language is JavaScript, which means we can construct operations as we would construct objects. In the following query, we build a criteria where the population must be between 10.000 and 1 million. Ranges work also on dates.
> var population_range = {}
> population_range['$lt'] = 100000
100000
> population_range['$gt'] = 10000
10000
> population_range['$lt'] = 1000000
1000000
> population_range['$gt'] = 10000
10000
> db.towns.find( {name : /^P/, population : population_range }, {name: 1})
{ "_id" : ObjectId("51df08e72476b99608460870"), "name" : "Portland" }
- You can also query based on values in nested arrays, either matching exact values or matching partial values or all matching values or the lack of matching values.
{ "name" : "New York", "famous_for" : [ "statue of liberty", "food" ] }
{ "name" : "Portland", "famous_for" : [ "beer", "food" ] }
> db.towns.find( { famous_for : /statue/ }, { _id : 0, name : 1, famous_for : 1 } )
{ "name" : "New York", "famous_for" : [ "statue of liberty", "food" ] }
> db.towns.find( { famous_for : { $all : ['food', 'beer'] } }, { _id : 0, name:1, famous_for:1 } )
{ "name" : "Portland", "famous_for" : [ "beer", "food" ] }
> db.towns.find( { famous_for : { $nin : ['food', 'beer'] } }, { _id : 0, name : 1, famous_for : 1 } )
{ "name" : "Punxsutawney", "famous_for" : [ "phil the groundhog" ] }
- You can query a sub-document by giving the field name as a string separating nested layers with a dot.
{ "name" : "New York", "mayor" : { "name" : "Michael Bloomberg", "party" : "I" } }
- To query the nonexistence of a field value
{ "name" : "Punxsutawney", "mayor" : { "name" : "jim Wehrle" } }
-
elemMatch
> db.countries.insert({ _id : "us",
... name : "United States",
... exports : {
... foods : [
... { name : "bacon", tasty : true },
... { name : "burgers" }
... ]
... }
... })
> db.countries.insert({ _id : "ca",
... name : "Canada",
... exports : {
... foods : [
... { name : "bacon", tasty : false },
... { name : "syrup", tasty : true }
... ]
... }
... })
> db.countries.insert({ _id : "mx",
... name : "Mexico",
... exports : {
... foods : [
... { name : "salsa", tasty : true, condiment : true }
... ]
... }
... })
> print( db.countries.count() )
3
Now if we need to select countries that export tasty bacon, we should $elemMatch in a query like:
> db.countries.find(
... {
... 'exports.foods' : {
... $elemMatch : {
... name : "bacon",
... tasty : true
... }
... }
... },
... { _id : 0, name : 1 }
... )
{ "name" : "United States" }
If we didn’t used $elemMatch and wrote a query like the following, it will return countries that export bacon or tasty food, not tasty bacon
> db.countries.find(
... { 'exports.foods.name' : 'bacon' , 'exports.foods.tasty' : true },
... { _id : 0, name : 1 }
... )
{ "name" : "United States" }
{ "name" : "Canada" }
-
Boolean Operators
> db.countries.find({
... $or : [ { _id : "mx" } , { name : "United States" } ] },
... {_id : 1} )
{ "_id" : "us" }
{ "_id" : "mx" }
Update
The find() function toke two parameters, criteria and list of fields to return. The update() function works the same way. The first parameter is a criteria (the same way you will use to retreive the document through find()). The second parameter is an object whose fields will replace the matched document(s) or a modifier operation ($set to set a field value, $unset to delete a field, $inc to increment a field value by a number).The following query will set the field state with the string OR for the matching document.
db.towns.update( { _id : ObjectId("4d0ada87bb30773266f39fe5") }, { $set : { "state" : "OR" } } )
but the following query will replace the matching document with a new document { state : “OR”}
db.towns.update( { _id : ObjectId("4d0ada87bb30773266f39fe5") }, { state : "OR" } )
References
Although MongoDB is schema-less but you can make one document reference another document using a construct like { $ref : “collection_name”, $id : “reference_id” }. In the following query we linking New York town with the country US. Notice the display of the new country field in the New York town document.> db.towns.update(
... { _id : ObjectId("51def56c1cf66f4c40bb7f4a") },
... { $set : { country : { $ref : "countries", $id : "us" } } }
... )
> db.towns.find( { _id : ObjectId("51def56c1cf66f4c40bb7f4a") } )
{ "_id" : ObjectId("51def56c1cf66f4c40bb7f4a"), "country" : DBRef("countries", "us"), "famous_for" : [ "statue of liberty", "food" ], "last_census" : ISODate("2009-07-31T00:00:00Z"), "mayor" : { "na
me" : "Michael Bloomberg", "party" : "I" }, "name" : "New York", "population" : 22200000 }Now we can retrieve New york from towns collection, then use it to retrieve its country
> var NY = db.towns.findOne( { _id : ObjectId("51def56c1cf66f4c40bb7f4a") } )
> db.countries.findOne( { _id : NY.country.$id })
{
"_id" : "us",
"name" : "United States",
"exports" : {
"foods" : [
{
"name" : "bacon",
"tasty" : true
},
{
"name" : "burgers"
}
]
}
}
or in a different way > db[ NY.country.$ref ].findOne( { _id : NY.country.$id} )
Delete
Removing documents from a collections is simple, use your criteria with a call to remove() function and all matching documents will be removed. It’s a recommended practice to build your criteria in an object, use that criteria to ensure that matching documents are the expected ones, pass this criteria to remove() function.> var bad_bacon = { 'exports.foods' : {
... $elemMatch : { name : 'bacon', tasty : false }
... } }
> db.countries.find ( bad_bacon)
{ "_id" : "ca", "name" : "Canada", "exports" : { "foods" : [ { "name" : "bacon", "tasty" : false }, { "name" : "syrup", "tasty" : true } ] } }
> db.countries.remove( bad_bacon)
> db.countries.count()
2
Reading with Code
You could ask MongoDb to run a decision function across documents.db.towns.find( function() {or in a short-hand format db.towns.find("this.population > 6000 && this.population < 600000")
return this.population > 6000 && this.population < 600000;
} )
or combine custom code with other criteria using $where
db.towns.find( {Custom code should be your last resort due to the following:
$where : "this.population > 6000 && this.population < 600000",
famous_for : /groundhog/
} )
- Custom code queries run slower than regular queries.
- Custom code queries can’t be indexed.
- MongoDb can’t optimize custom code queries.
- If your custom code assume the existence of a particular field and this field is missing in a single, the entire query will fail.
