Indexes support the efficient resolution of queries. Without indexes, MongoDB must scan every document of a collection to select those documents that match the query statement. This scan is highly inefficient and require MongoDB to process a large volume of data.

Indexes are special data structures, that store a small portion of the data set in an easy-to-traverse form. The index stores the value of a specific field or set of fields, ordered by the value of the field as specified in the index.

The createIndex() Method

To create an index, you need to use createIndex() method of MongoDB.

Syntax

The basic syntax of createIndex() method is as follows().

>db.COLLECTION_NAME.createIndex({KEY:1})

Here key is the name of the field on which you want to create index and 1 is for ascending order. To create index in descending order you need to use -1.

Example

>db.mycol.createIndex({"title":1})
{
	"createdCollectionAutomatically" : false,
	"numIndexesBefore" : 1,
	"numIndexesAfter" : 2,
	"ok" : 1
}
>

In createIndex() method you can pass multiple fields, to create index on multiple fields.

>db.mycol.createIndex({"title":1,"description":-1})
>

This method also accepts list of options (which are optional). Following is the list −

The dropIndex() method

You can drop a particular index using the dropIndex() method of MongoDB.

Syntax

The basic syntax of DropIndex() method is as follows().

>db.COLLECTION_NAME.dropIndex({KEY:1})

Here, “key” is the name of the file on which you want to remove an existing index. Instead of the index specification document (above syntax), you can also specify the name of the index directly as:

dropIndex("name_of_the_index")

Example

> db.mycol.dropIndex({"title":1})
{
	"ok" : 0,
	"errmsg" : "can''t find index with key: { title: 1.0 }",
	"code" : 27,
	"codeName" : "IndexNotFound"
}

The dropIndexes() method

This method deletes multiple (specified) indexes on a collection.

Syntax

The basic syntax of DropIndexes() method is as follows() −

>db.COLLECTION_NAME.dropIndexes()

Example

Assume we have created 2 indexes in the named mycol collection as shown below −

> db.mycol.createIndex({"title":1,"description":-1})

Following example removes the above created indexes of mycol −

>db.mycol.dropIndexes({"title":1,"description":-1})
{ "nIndexesWas" : 2, "ok" : 1 }
>

The getIndexes() method

This method returns the description of all the indexes int the collection.

Syntax

Following is the basic syntax od the getIndexes() method −

db.COLLECTION_NAME.getIndexes()

Example

Assume we have created 2 indexes in the named mycol collection as shown below −

> db.mycol.createIndex({"title":1,"description":-1})

Following example retrieves all the indexes in the collection mycol −

> db.mycol.getIndexes()
[
	{
		"v" : 2,
		"key" : {
			"_id" : 1
		},
		"name" : "_id_",
		"ns" : "test.mycol"
	},
	{
		"v" : 2,
		"key" : {
			"title" : 1,
			"description" : -1
		},
		"name" : "title_1_description_-1",
		"ns" : "test.mycol"
	}
]
>

Sharding is the process of storing data records across multiple machines and it is MongoDB”s approach to meeting the demands of data growth. As the size of the data increases, a single machine may not be sufficient to store the data nor provide an acceptable read and write throughput. Sharding solves the problem with horizontal scaling. With sharding, you add more machines to support data growth and the demands of read and write operations.

Why Sharding?

• In replication, all writes go to master node

• Latency sensitive queries still go to master

• Single replica set has limitation of 12 nodes

• Memory can”t be large enough when active dataset is big

• Local disk is not big enough

• Vertical scaling is too expensive

Sharding in MongoDB

The following diagram shows the Sharding in MongoDB using sharded cluster.

In the following diagram, there are three main components −

• Shards − Shards are used to store data. They provide high availability and data consistency. In production environment, each shard is a separate replica set.

• Config Servers − Config servers store the cluster”s metadata. This data contains a mapping of the cluster”s data set to the shards. The query router uses this metadata to target operations to specific shards. In production environment, sharded clusters have exactly 3 config servers.

• Query Routers − Query routers are basically mongo instances, interface with client applications and direct operations to the appropriate shard. The query router processes and targets the operations to shards and then returns results to the clients. A sharded cluster can contain more than one query router to divide the client request load. A client sends requests to one query router. Generally, a sharded cluster have many query routers.

When you are preparing a MongoDB deployment, you should try to understand how your application is going to hold up in production. It’s a good idea to develop a consistent, repeatable approach to managing your deployment environment so that you can minimize any surprises once you’re in production.

The best approach incorporates prototyping your set up, conducting load testing, monitoring key metrics, and using that information to scale your set up. The key part of the approach is to proactively monitor your entire system – this will help you understand how your production system will hold up before deploying, and determine where you will need to add capacity. Having insight into potential spikes in your memory usage, for example, could help put out a write-lock fire before it starts.

To monitor your deployment, MongoDB provides some of the following commands −

mongostat

This command checks the status of all running mongod instances and return counters of database operations. These counters include inserts, queries, updates, deletes, and cursors. Command also shows when you’re hitting page faults, and showcase your lock percentage. This means that you”re running low on memory, hitting write capacity or have some performance issue.

To run the command, start your mongod instance. In another command prompt, go to bin directory of your mongodb installation and type mongostat.

D:set upmongodbbin>mongostat

Following is the output of the command −

mongotop

This command tracks and reports the read and write activity of MongoDB instance on a collection basis. By default, mongotop returns information in each second, which you can change it accordingly. You should check that this read and write activity matches your application intention, and you’re not firing too many writes to the database at a time, reading too frequently from a disk, or are exceeding your working set size.

To run the command, start your mongod instance. In another command prompt, go to bin directory of your mongodb installation and type mongotop.

D:set upmongodbbin>mongotop

Following is the output of the command −

To change mongotop command to return information less frequently, specify a specific number after the mongotop command.

D:set upmongodbbin>mongotop 30

The above example will return values every 30 seconds.

Apart from the MongoDB tools, 10gen provides a free, hosted monitoring service, MongoDB Management Service (MMS), that provides a dashboard and gives you a view of the metrics from your entire cluster.

In this chapter, we will see how to create a backup in MongoDB.

Dump MongoDB Data

To create backup of database in MongoDB, you should use mongodump command. This command will dump the entire data of your server into the dump directory. There are many options available by which you can limit the amount of data or create backup of your remote server.

Syntax

The basic syntax of mongodump command is as follows −

>mongodump

Example

Start your mongod server. Assuming that your mongod server is running on the localhost and port 27017, open a command prompt and go to the bin directory of your mongodb instance and type the command mongodump

Consider the mycol collection has the following data.

>mongodump

The command will connect to the server running at 127.0.0.1 and port 27017 and back all data of the server to directory /bin/dump/. Following is the output of the command −

Following is a list of available options that can be used with the mongodump command.

Restore data

To restore backup data MongoDB”s mongorestore command is used. This command restores all of the data from the backup directory.

Syntax

The basic syntax of mongorestore command is −

>mongorestore

Following is the output of the command −

In this chapter, we will see how to create a collection using MongoDB.

The createCollection() Method

MongoDB db.createCollection(name, options) is used to create collection.

Syntax

Basic syntax of createCollection() command is as follows −

db.createCollection(name, options)

In the command, name is name of collection to be created. Options is a document and is used to specify configuration of collection.

Options parameter is optional, so you need to specify only the name of the collection. Following is the list of options you can use −

While inserting the document, MongoDB first checks size field of capped collection, then it checks max field.

Examples

Basic syntax of createCollection() method without options is as follows −

>use test
switched to db test
>db.createCollection("mycollection")
{ "ok" : 1 }
>

You can check the created collection by using the command show collections.

>show collections
mycollection
system.indexes

The following example shows the syntax of createCollection() method with few important options −

> db.createCollection("mycol", { capped : true, autoIndexID : true, size : 6142800, max : 10000 } ){
"ok" : 0,
"errmsg" : "BSON field ''create.autoIndexID'' is an unknown field.",
"code" : 40415,
"codeName" : "Location40415"
}
>

In MongoDB, you don”t need to create collection. MongoDB creates collection automatically, when you insert some document.

>db.tutorialspoint.insert({"name" : "tutorialspoint"}),
WriteResult({ "nInserted" : 1 })
>show collections
mycol
mycollection
system.indexes
tutorialspoint
>

In this chapter, we will see how to create a database in MongoDB.

The use Command

MongoDB use DATABASE_NAME is used to create database. The command will create a new database if it doesn”t exist, otherwise it will return the existing database.

Syntax

Basic syntax of use DATABASE statement is as follows −

use DATABASE_NAME

Example

If you want to use a database with name <mydb>, then use DATABASE statement would be as follows −

>use mydb
switched to db mydb

To check your currently selected database, use the command db

>db
mydb

If you want to check your databases list, use the command show dbs.

>show dbs
local     0.78125GB
test      0.23012GB

Your created database (mydb) is not present in list. To display database, you need to insert at least one document into it.

>db.movie.insert({"name":"tutorials point"})
>show dbs
local      0.78125GB
mydb       0.23012GB
test       0.23012GB

In MongoDB default database is test. If you didn”t create any database, then collections will be stored in test database.

In this chapter, we will see how to drop a collection using MongoDB.

The drop() Method

MongoDB”s db.collection.drop() is used to drop a collection from the database.

Syntax

Basic syntax of drop() command is as follows −

db.COLLECTION_NAME.drop()

Example

First, check the available collections into your database mydb.

>use mydb
switched to db mydb
>show collections
mycol
mycollection
system.indexes
tutorialspoint
>

Now drop the collection with the name mycollection.

>db.mycollection.drop()
true
>

Again check the list of collections into database.

>show collections
mycol
system.indexes
tutorialspoint
>

drop() method will return true, if the selected collection is dropped successfully, otherwise it will return false.

Data in MongoDB has a flexible schema.documents in the same collection. They do not need to have the same set of fields or structure Common fields in a collection’s documents may hold different types of data.

Data Model Design

MongoDB provides two types of data models: — Embedded data model and Normalized data model. Based on the requirement, you can use either of the models while preparing your document.

Embedded Data Model

In this model, you can have (embed) all the related data in a single document, it is also known as de-normalized data model.

For example, assume we are getting the details of employees in three different documents namely, Personal_details, Contact and, Address, you can embed all the three documents in a single one as shown below −

{
	_id: ,
	Emp_ID: "10025AE336"
	Personal_details:{
		First_Name: "Radhika",
		Last_Name: "Sharma",
		Date_Of_Birth: "1995-09-26"
	},
	Contact: {
		e-mail: "radhika_sharma.123@gmail.com",
		phone: "9848022338"
	},
	Address: {
		city: "Hyderabad",
		Area: "Madapur",
		State: "Telangana"
	}
}

Normalized Data Model

In this model, you can refer the sub documents in the original document, using references. For example, you can re-write the above document in the normalized model as:

Employee:

{
	_id: <ObjectId101>,
	Emp_ID: "10025AE336"
}

Personal_details:

{
	_id: <ObjectId102>,
	empDocID: " ObjectId101",
	First_Name: "Radhika",
	Last_Name: "Sharma",
	Date_Of_Birth: "1995-09-26"
}

Contact:

{
	_id: <ObjectId103>,
	empDocID: " ObjectId101",
	e-mail: "radhika_sharma.123@gmail.com",
	phone: "9848022338"
}

Address:

{
	_id: <ObjectId104>,
	empDocID: " ObjectId101",
	city: "Hyderabad",
	Area: "Madapur",
	State: "Telangana"
}

Considerations while designing Schema in MongoDB

• Design your schema according to user requirements.

• Combine objects into one document if you will use them together. Otherwise separate them (but make sure there should not be need of joins).

• Duplicate the data (but limited) because disk space is cheap as compare to compute time.

• Do joins while write, not on read.

• Optimize your schema for most frequent use cases.

• Do complex aggregation in the schema.

Example

Suppose a client needs a database design for his blog/website and see the differences between RDBMS and MongoDB schema design. Website has the following requirements.

• Every post has the unique title, description and url.

• Every post can have one or more tags.

• Every post has the name of its publisher and total number of likes.

• Every post has comments given by users along with their name, message, data-time and likes.

• On each post, there can be zero or more comments.

In RDBMS schema, design for above requirements will have minimum three tables.

While in MongoDB schema, design will have one collection post and the following structure −

{
   _id: POST_ID
   title: TITLE_OF_POST,
   description: POST_DESCRIPTION,
   by: POST_BY,
   url: URL_OF_POST,
   tags: [TAG1, TAG2, TAG3],
   likes: TOTAL_LIKES,
   comments: [
      {
         user:''COMMENT_BY'',
         message: TEXT,
         dateCreated: DATE_TIME,
         like: LIKES
      },
      {
         user:''COMMENT_BY'',
         message: TEXT,
         dateCreated: DATE_TIME,
         like: LIKES
      }
   ]
}

So while showing the data, in RDBMS you need to join three tables and in MongoDB, data will be shown from one collection only.

Let us now see how to install MongoDB on Windows.

Install MongoDB On Windows

To install MongoDB on Windows, first download the latest release of MongoDB from .

Enter the required details, select the Server tab, in it you can choose the version of MongoDB, operating system and, packaging as:

Now install the downloaded file, by default, it will be installed in the folder C:Program Files.

MongoDB requires a data folder to store its files. The default location for the MongoDB data directory is c:datadb. So you need to create this folder using the Command Prompt. Execute the following command sequence.

C:>md data
C:md datadb

Then you need to specify set the dbpath to the created directory in mongod.exe. For the same, issue the following commands.

In the command prompt, navigate to the bin directory current in the MongoDB installation folder. Suppose my installation folder is C:Program FilesMongoDB

C:UsersXYZ>d:cd C:Program FilesMongoDBServer4.2bin
C:Program FilesMongoDBServer4.2bin>mongod.exe --dbpath "C:data"

This will show waiting for connections message on the console output, which indicates that the mongod.exe process is running successfully.

Now to run the MongoDB, you need to open another command prompt and issue the following command.

C:Program FilesMongoDBServer4.2bin>mongo.exe
MongoDB shell version v4.2.1
connecting to: mongodb://127.0.0.1:27017/?compressors=disabled&gssapiServiceName=mongodb
Implicit session: session { "id" : UUID("4260beda-f662-4cbe-9bc7-5c1f2242663c") }
MongoDB server version: 4.2.1
>

This will show that MongoDB is installed and run successfully. Next time when you run MongoDB, you need to issue only commands.

C:Program FilesMongoDBServer4.2bin>mongod.exe --dbpath "C:data"
C:Program FilesMongoDBServer4.2bin>mongo.exe

Install MongoDB on Ubuntu

Run the following command to import the MongoDB public GPG key −

sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 7F0CEB10

Create a /etc/apt/sources.list.d/mongodb.list file using the following command.

echo ''deb http://downloads-distro.mongodb.org/repo/ubuntu-upstart dist 10gen''
   | sudo tee /etc/apt/sources.list.d/mongodb.list

Now issue the following command to update the repository −

sudo apt-get update

Next install the MongoDB by using the following command −

apt-get install mongodb-10gen = 4.2

In the above installation, 2.2.3 is currently released MongoDB version. Make sure to install the latest version always. Now MongoDB is installed successfully.

Start MongoDB

sudo service mongodb start

Stop MongoDB

sudo service mongodb stop

Restart MongoDB

sudo service mongodb restart

To use MongoDB run the following command.

mongo

This will connect you to running MongoDB instance.

MongoDB Help

To get a list of commands, type db.help() in MongoDB client. This will give you a list of commands as shown in the following screenshot.

MongoDB Statistics

To get stats about MongoDB server, type the command db.stats() in MongoDB client. This will show the database name, number of collection and documents in the database. Output of the command is shown in the following screenshot.

MongoDB is a cross-platform, document oriented database that provides, high performance, high availability, and easy scalability. MongoDB works on concept of collection and document.

Database

Database is a physical container for collections. Each database gets its own set of files on the file system. A single MongoDB server typically has multiple databases.

Collection

Collection is a group of MongoDB documents. It is the equivalent of an RDBMS table. A collection exists within a single database. Collections do not enforce a schema. Documents within a collection can have different fields. Typically, all documents in a collection are of similar or related purpose.

Document

A document is a set of key-value pairs. Documents have dynamic schema. Dynamic schema means that documents in the same collection do not need to have the same set of fields or structure, and common fields in a collection”s documents may hold different types of data.

The following table shows the relationship of RDBMS terminology with MongoDB.

Sample Document

Following example shows the document structure of a blog site, which is simply a comma separated key value pair.

{
   _id: ObjectId(7df78ad8902c)
   title: ''MongoDB Overview'',
   description: ''MongoDB is no sql database'',
   by: ''tutorials point'',
   url: ''http://www.tutorialspoint.com'',
   tags: [''mongodb'', ''database'', ''NoSQL''],
   likes: 100,
   comments: [
      {
         user:''user1'',
         message: ''My first comment'',
         dateCreated: new Date(2011,1,20,2,15),
         like: 0
      },
      {
         user:''user2'',
         message: ''My second comments'',
         dateCreated: new Date(2011,1,25,7,45),
         like: 5
      }
   ]
}

_id is a 12 bytes hexadecimal number which assures the uniqueness of every document. You can provide _id while inserting the document. If you don’t provide then MongoDB provides a unique id for every document. These 12 bytes first 4 bytes for the current timestamp, next 3 bytes for machine id, next 2 bytes for process id of MongoDB server and remaining 3 bytes are simple incremental VALUE.