All about MongoDB

All about MongoDB

MongoDB is an open-source, scalable, high-performance document-oriented database.A document-oriented database or NoSQL database stores data in JSON format rather than tabular format and allows you to express in the natural form in the way they meant to be.

Now to understand the NoSQL concept better we can explore and differentiate all types of databases.

• RDBMS (Relational Database Management System)
• NoSQL

Before 1970, we used to save data in a simple flat-file format and it becomes really difficult when we need to retrieve any data, there was no standard implementation.

The solution to this problem was first defined by EDGAR CODD in mid-1970 as the relational database. CODD’s View of what quantifies as RDBMS is summarized in CODD’s 12 rules and then the relational database becomes the predominant type of database.

Relational databases are the standard way of storing and querying the data. In the relational database, we store data in the form of tables E.g.

We have a class calledStudentwith the properties

Name

Roll No

Subject

The Relational database will save it in a tabular manner

Here we have to create tables one to have student master details and others to have subject information. To work with this database in our application we have to create a wrapper function for saving the content of our classes to the databases and so it will coordinate between the database and our student class.

At times it might be possible that certain attributes of the student do not match with the student class.So here our objects/class may not be uniform and mapping such objects in a tabular form at times becomes complicated.

Another disadvantage is RDBMS are not capable of handling BIG DATA because of their design and scalability. They are not horizontally scalable.

Here comes NoSQL to the rescue. They are horizontally scalable which a big drawback is in the relational database.

What is horizontal scalability?

Horizontal Scalability is the ability to add and increase the capacity by adding multiple systems so that they can work as a single unit without increasing the capacity of a single machine.

Pros and Cons of NoSQL

Pros

· Query language is used in MongoDB

· Performance is much faster

· We can store and querying with BIG DATA

More advantages of MongoDB

Cons in MongoDB or NoSQL as compared to RDBMS

· Rolling back of multiple transaction is not possible

· Joins are not supported.

· Constraints are not implemented at database level that can be implemented at document level.

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MongoDB Features

· Support Ad hoc Queries

· Indexing

· Replication

· Duplication of Data

· Load balancing

· Scalability

· Support map-reduce and aggregation tools.

· Uses JavaScript instead of procedure

Ad hoc Queries

Ad hoc Queries can be updated in real-time which will improve the overall performance, these are the queries not known while structuring the database.

Indexing

Any field in the document can be indexed. With the use of indexes performing any query in MongoDB becomes more efficient.

E.g. if we have hundreds of documents as collections in MongoDB and we query to find a certain document then MongoDB has to scan the entire collection to find the documents and if we indexed them then it will be easy for MongoDB to limit the number of documents to be searched.

E.g. of an index for a collection.

In the above example studentID and StudentCode is used to index the document so when you search with studentID the above query will return.

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Replication

It supports Master-Slave Replication that means master can perform both read and write operations whereas slave can only copy data from the masters and can only use it for reading and backup. No write operation can be performed by the slave.

Duplication of Data

MongoDB can run over multiple servers and data is duplication to keep the system running in case of any hardware failure.

Load Balancing

It has a built-in load balancing configuration because data is placed in shards in MongoDB.

Scalable

It can be scalable horizontally, new machines can be added to a running database.

Support Map reduce and aggregation tools

mapReduce is the data processing mechanism for condensing large volumes of data into useful aggregated results.MongoDBusesmapReduce command fora mapandreducesoperations.mapReduce is used for processing large data sets.

Following is the syntax of the basic mapReduce command-

db.collection.mapReduce(

function() {emit(key,value);}, //map function

function(key,values) {return reduceFunction}, { //reduce function

out: collection,

query: document,

sort: document,

limit: number

}

)

· mapis a javascript function that maps a value with a key and emits a key-value pair.

· reduceis a javascript function that reduces or groups all the documents having the same key.

· outspecifies the location of the map-reduce query result

· query

· sortspecifies the optional sort criteria

· limit;specifies the optional maximum number of documents to be returned.

Using MapReduce

Consider the following document structure.The document stores user_name of the user and the status of post.

{

"post_text": "Skillbakery is an awesome website for tutorials”

"user_name": "Bhawna”

"status":"active"

}

Now, we will use a mapReduce function on ourpostscollection to select all the active posts, group them on the basis of user_name and then count the number of posts by each user using the following code –

>db.posts.mapReduce(

function() { emit(this.user_id,1); },

function(key, values) {return Array.sum(values)}, {

query:{status:"active"},

out:"post_total"

}

)

The above mapReduce query outputs the following result –

{

"result" : "post_total",

"timeMillis" : 7,

"counts" : {

"input" : 3

"emit" : 3

"reduce" : 1,

"output" : 1

},

"ok" : 1,

; }

The result shows that a total of 3documents matched the query (status: “active"), the map function emitted 3documents with key-value pairs and finally the reduce function grouped mapped documents having the same keys into 1.

To see the result of this mapReduce query, use the find operator –

>db.posts.mapReduce(

function() { emit(this.user_id,1); },

function(key, values) {return Array.sum(values)}, {

query:{status:"active"},

out:"post_total"

}

).find()

The above query gives the following result which indicates that both usersbhawna and kiyansh has 1 post active

{ "_id" : "bhawna”,”value” : 1}

{ "_id" : "kiyansh” ,”value” :1}

In a similar manner, MapReduce queries can be used to construct large complex aggregation queries. The use of custom JavaScript functions make use of MapReduce which is very flexible and powerful.

Uses JavaScript instead of procedures

JavaScript is used in queries. These queries is transported from the server

(MongoDB in this case) to the client (the web browser) and executed on the client without an installation process.

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