Features of MongoDB | Database

In this video, we are discussing a very important topic that is the features of MongoDB

So there are multiple different features for which this MongoDB has become so popular

So, MongoDB has some different and unique features and they are like this

So, that is the ad hoc queries. We're having this aggregation, schema-less database, gridFS, document-oriented, sharding, indexing, high performance and replications

So, here we are having the nine different properties which will be discussing one after another

So, let us start with this ad hoc queries. So, at first, we are starting with this ad hoc queries

So now ad hoc queries are those type of queries which are not known while the structuring

the database. So sometimes it may come, the situation may come that whenever we are designing our database, we

may not be knowing the future query types. So, if those queries come, so those queries will be known as the ad hoc queries and those

ad hoc queries can easily be handled by this MongoDB. So MongoDB supports this type of queries and these queries can also be updated in real time The next point is our aggregation very important point So MongoDB has aggregation framework and you can batch process the data and get single

result after doing some sequence of tasks on the group of data

So, that means here the batch processing will take place and some sequence of stocks will

be carried out on the data and afterwards output will be produced

Just consider this one. We are having one input and then we are doing some match operations

We are doing some matching, we are doing some searching operations and then we are doing

some grouping operations depending upon the value of some attribute we are doing some grouping

then we are doing the shorting and then the output will be obtained

So this is a batch operations where we are having multiple tasks going to get executed sequentially

one after another and that is known as the aggregation framework. one we are having this schema less database. So, in this case, the different document can have

different fields and the size, content type may vary from one field to another field in different

documents So there is a flexibility in dealing with the data In case of ad we are having the schema which is totally static That means all the records all the tuples must have the same same kind of data and the data type will be there

But in case of MongoDB, we're having the flexibility, different documents may have different

data types and different number of data. So, next one is the grid fs

So grid fs is a framework to store and access large set of data

So here to store our images, to store our videos, we require this rest of the system

respective grid fs and this grid fs is a framework to store and access large set of data and it divides the data in the chunks and stored them in the different documents

So this data will be divided into multiple different chunks and piece of data and they will be stored in the documents

You can find that we are having a huge file here and this grid fs will divide that file into multiple different portions, also known as chunk of data and they will get saved in different documents. documents

Next one is MongoDB is document oriented. We know that MongoDB is cross-platform and document-oriented

MongoDB is having collections and collections will have the documents and MongoDB is having high accessibility availability and it is having high performance and so on So this is a document oriented So MongoDB is document oriented database and there are different documents to store different

types of data. And each document has unique system generated key or ID

Next one is a sharding. So for the large set of data, we need the sorting mechanism and it helps to distribute large

problematic data into some MongoDB instances. So here the data will be divided and it will be stored onto the multiple machines

So sharding is a type of database partitioning that separates very large databases into smaller

faster, more easily manageable parts and they are known as the data shirts

So when the database size will get enlarged, then the data bills will be divided into multiple

different shirts and they will get stored on. the multiple different machines. So let us consider the following diagram. Here you can

find we are having this application running, then we are having the driver, then the query

router and the database has got divided into multiple different shirts are there. So shared

one, two, three and n. And here we are having multiple nodes. So this note is known as the