MongoDB has gained much popularity since its inception because of its ability to handle large volumes of data. Its adoption is on the rise, and the market is expected to grow further. If you are aiming for a MongoDB career, this is the right time. To help you in your path, we have curated a list of frequently asked MongoDB interview questions and answer.
MongoDB is a document database that stores the data in JSON documents. It works over the documents and collections concept. MongoDB can store multiple databases and provides higher performance besides scalability and redundancy. This MongoDB interview question is mainly designed to provide you with basic ideas about the kind of interview questions you might face.
Normally, in interviews, recruiters start with basic questions, and slowly they will increase the difficulty level. So, in this MongoDB Interview Questions blog also, first, we will cover the basic questions, and then we will move to complex questions. Through these hand-picked MongoDB interview questions, you can prepare for your MongoDB job interview.
We have categorized MongoDB Interview Questions - 2024 (Updated) into 4 levels they are:
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MongoDB is a cross-platform document-based database. Categorized as a NoSQL database, MongoDB avoids the conventional table-oriented relational database structure in support of the JSON-like documents with the dynamic schemas, making the data integration in specific kinds of applications quicker and simpler.
MongoDB was developed by a software company “10gen”, in October 2007 as an element of the planned platform as the service product. After that, the company was shifted to a freeware deployment model in 2009, providing sales assistance and other services.
Following are the important features of MongoDB:
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MongoDB is a document-oriented database. It stores the data in the form of the BSON structure-oriented databases. We store these documents in a collection.
A namespace is the series of the collection name and database name.
Despite MySQL and MongoDB being freeware and open source databases, there are several differences between them in terms of a data relationship, transaction, performance speed, querying data, schema design, normalization, etc. The comparison between MongoDB and MySQL is similar to the comparison between Non-relational and Relational databases.
Check out the Related Article MongoDB Vs MySQL |
In MongoDB, we use Indexes for executing the queries efficiently; without using Indexes, MongoDB should carry out a collection scan, i.e., scan all the documents of a collection, for selecting the documents which match the query statement. If a suitable index is available for a query, MongoDB will use an index for restricting the number of documents it should examine.
MongoDB is the best NoSQL database due to the following features:
A covered query makes the query implementation quicker as we store the indexes in the RAM or consecutively located on the disk. It makes query execution quicker. The covered query covers all the fields in the index, MongoDB matches the query condition along with returning the result fields.
We can specify the replica as a set of the mongo instances which host a similar data set. In the replica set, one node will be primary, and another one will be secondary. We replicate all the data from the primary to the secondary nodes.
MongoDB | Cassandra |
It is a cross-platform document-oriented database system | It is a high-performance distributed database system. |
It is developed in C++ | It is developed in Java |
It is simple to administer in the failure case | It offers high availability |
11) Explain the primary and secondary replica set?
In MongoDB, primary nodes are the nodes that accept writing. Primary nodes are also called master nodes. Replication in MongoDB is a single master. Therefore, only one node will accept the write operations at once.
At Present, MongoDB offers driver support to C++, Java, PHP, Perl, Python, Go, Scala, and Ruby.
Check out Cassandra vs MongoDB |
Storage encryption encodes all the MongoDB data over the storage or over the operating systems for assuring that only authenticated processes will access the safeguarded data.
Primary Replica Set receives all the write operations from the clients. Secondary replica sets replicate the primary replica sets and implement the operations for their datasets so that secondary datasets affect the primary datasets.
MongoDB does not use traditional locking with the reduction because it is high-speed, knowable, and light in the presentation. We can consider it as the MyISAM, MySQL auto entrust script. Through the simpler business sustain, we can enhance the performance, specifically in the structure with various servers.
We save the write operations in the memory while journaling is taking place. The on-disk journal files are dependable for the reason that journal writers are usual. In the DB path, MongoDB designs a journal subdirectory.
MongoDB utilizes the reader-writer locks, enabling concurrent readers to access any supply such as collection or database though it provides private access to individual writers.
MongoDB contains the database profiler that shows the performance characteristics of every operation against the database. Through the profiler, we can identify the queries that are slower than they should be and use this data to determine when we require an index.
The collection is a set of MongoDB documents.
The aggregation Pipeline acts as a framework to perform aggregation tasks. We use this pipeline for transforming the documents into aggregated results.
MapReduce is a standard multi-phase data aggregation modality that we use to process the data quantities.
Splitting is the background process that we use to store chunks from increasing too large.
We use the save() method for replacing the existing documents with new documents.
We use the “DB” command to get the name of the presently selected database.
When we run a 32-bit version of MongoDB, the total storage size of the server, containing indexes and data, is 2GB. Due to this reason, we will not deploy MongoDB to the production on the 32-bit machines. If we deploy a 64-bit version of MongoDB, there is no virtual restriction to the storage size. For the creation deployments, we strongly recommend 64-bit operating systems and builds.
It relies on our objectives. Normalization provides an updated effective data representation. Denormalisation makes data reading effective. Generally, we utilize embedded data models when:
Generally, we use normalized data models:
For finding any data in MongoDB, we use the find() method. The discovery () method returns the collection’s documents over which we invoked this method. We can use the “Where” clause in the MongoDB query in order to restrict the output by using MongoDB projection. Anytime we execute the find() method, MongoDB returns all the documents associated with a particular collection.
db.<collection_name>.find({ }, {<key_Name>:<Flag to display>})
We have to find the best method for structuring the data in MongoDB for simulating what would be the simple subquery or join in SQL. For example, we have users and posts, with the users in one collection and posts in another collection. We have to find all the posts by the users whose city is “Hyderabad”.
An operational log (oplog) is a special kind of limited collection that stores a rolling record of all the operations which change the data we store in our databases. Primarily, it applies all the database operations over the primary and, after that, records these operations on the oplog of the primary. After that, the secondary members replicate and apply the operations in the asynchronous process.
When I want to create a database in MongoDB, I faced the following error:
:~$mongo
MongoDB shell version:1.65
Connecting to: test
Error: Could not connect to the server
Exception: connect failed
The solution to the above error:
skip() method syntax is:
db.COLLECTION_NAME.find().limit(NUMBER).skip(NUMBER)
By using the following code, we can delete everything from the MongoDB database:
use [database];
db.dropDatabase();
Ruby code should be pretty similiar.
Also, from the command line:
mongo [Database] -eval "db.dropDatabase();"
use
[databaseName]
db.Drop+databasename();
drop colllection
use databaseName
db.collectionName.drop();
We use the mongodump command for creating the database backup.
We use mongorestore for restoring the backup.
In MongoDB, we use dot notation for accessing the array elements and the fields of an embedded document.
Syntax of the limit() method is:
>db.COLLECTION_NAME.find().limit(NUMBER)
Syntax of the sort() method is:
>db.COLLECTION_NAME.find().sort({KEY:1})
NoSQL refers to “Not Only SQL”. NoSQL is a kind of database that handles and sorts all kinds of structured, massive, and difficult data. It is a new method to think about databases. Kinds of NoSQL databases:
We use the “DB.drop database” command for dropping a database.
In MongoDB, we use Projection for selecting only the required data. It will not select the complete data of a document.
We use the pretty() method for displaying the results in a formatted way.
By using the remove() method, we remove a document from the collection.
We must consider the following points while creating a schema:
ObjectId contains the following:
For instance, if we have to select all the attributes and groups by name throughout the records. For example:
{Name: George, x: 5, y: 3}
{Name: George, z: 9}
{Name: Rob, x: 12, y: 2}
We can do MongoDB aggregation as follows:
db.example.aggregate(
{
$group:{
_id:'$name',
x: {$addToSet: "$x" },
y: {$addToSet: "$y" },
z: {$addToSet: "$z" },
}
}
)
Following are the elements of the Sharded Cluster:
Following are the substitutes to MongoDB:
In the course of general shard balancing operations, we make the old files as backups, and we can delete them when those operations are completed.
Storage Engine is a component of the database that is accountable to manage how we store on the disk. For instance, one storage engine may provide better performance for the read-heavy workloads, and another one may support a great throughput for the write operations.
No, MongoDB does not require plenty of RAM. It can run on a small amount of memory. MongoDB dynamically assigns and unassigns RAM according to the needs of other processes.
MongoDB | CouchDB |
MongoDB is quicker than CouchDB | CouchDB is more secure than MongoDB |
Triggers do not exist in MongoDB. | Triggers exist in CouchDB |
MongoDB serializes the JSON Data to the BSON | CouchDB does not store the data in JSON format |
In MongoDB, the Capped collection is a special kind of collection. This indicates that in this collection, we can restrict the collection size. Syntax of Capped Collection is as follows:
db.createCollection(<collection_name>, {capped: Boolean, autoIndexId: Boolean, size: Number, max : Number})
In the Capped Collection syntax, we have the following fields:
From MongoDB3.2, we can perform the Join operation. The new $lookup operator included with the aggregation pipeline is the same as the left outer join. Example:
{
$lookup:
{
from: <collection to join>,
localField: <field from the input documents>,
foreignField: <field from the documents of the "from" collection>,
as: <output array field>
}
}
WiredTiger and MMAPv1 are the two storage engines used by MongoDB.
In MongoDB, we cannot configure the cache. MongoDB utilizes the free spaces over the system automatically by using memory-mapped files.
We can control the MongoDB Performance by:
Following are the aggregate functions of MongoDB:
Following are the CRUD operations of MongoDB:
Create-db.collection.insert();
Read-db.collection.find();
Update-db.collection.update();
Delete-db.collection.remove();
Following are the datatypes of MongoDB:
No, it is not required to invoke “get last error”. The server acts as if it has been invoked. “get last error” enables us to acquire confirmation that a write operation is committed. You will get the confirmation, yet the durability and safety of the writer are independent.
When the Shard is slow, the query returns an error until partial query options are fixed. When the shard is reacting slowly, MongoDB waits for it.
“_id field” is reticent for a primary key in MongoDB. And it is a distinct value. If we do not set anything to the “_id”, it will systematically fill it with the “MongoDB Id Object”. Yet, we can store any distinct information in that field.
For seeing the connections utilized by MongoDB, we use db_adminCommand(”connPoolStats”).
No, it is not required to clean up the partly moved docs because chunk moves are deterministic and consistent. The move will try again, and when finished, data will be on the latest Shard.
We have to follow the below steps for starting the MongoDB Server:
Basis for Comparison | MongoDB | RDBMS |
Definition | It is a non-relational database | It is a relational database management system |
Working | It works over relationships among the tables, which use columns and rows | It is a document-oriented database system through fields and documents |
Scalability | It is horizontally and vertically scalable | It is vertically scalable |
Performance | Performance enhances with the rise in the processors | Performance enhances with the rise in the RAM capacity |
Hierarchical Data Storage | It has a built-in provision to store the hierarchical data | It is hard to store the hierarchical data |
Support to Joins | It does not support difficulty Joins | It supports complex joins |
Query Language | It uses BSON for database querying | It uses SQL to query the database |
Javascript Support | It provides support to javascript-based clients for querying the database | It does not provide support to the javascript-based clients to query the database |
Applications access the real-time data modifications through the Change streams that serve as the subscriber for every collection operation like delete, insert, and update.
Following are the different kinds of Indexes in MongoDB:
Binary JSON or BSON is a binary-encoded format of the JSON. BSON extends the JSON and offers various data fields and types.
As it is a document-based database, MongoDB stores the documents in Binary Javascript Object Notation or BSON, which is a binary-encoded format of JSON.
Yes, MongoDB supports ACID Transaction. ACID refers to Atomicity, Consistency, Isolation, and Durability. Transaction manager assures that we handle these attributes.
In MongoDB, ObjectID is associated with the “_id” field, and MongoDB uses it as the default value of the “_id” in the documents. For generating “ObjectID”, we use the following Syntax:
ObjectId([SomeHexaDecimalValue])
Example:
ObjectId() = newObjectId
ObjectID has the following methods:
For example, if we have the below documents:
{ _id: 1, numbers: [1000, -1000]]
{ _id: 2, numbers: [500]]
When we execute the following command:
db.example.find( { numbers: { $elemMatch: { $gt: -10, $lt: 10 } } } );
By executing the following code, we calculate x-y.
db.eval(function() {
return db.scratch.find().toArray().sort(function(doc1, doc2) {
return doc1.a – doc2.a
})
});
Versus the equivalent client-side sort:
db.scratch.find().toArray().sort(function(doc1, doc2) {
return doc1.a – doc2.b
});
By using the aggregation pipeline and “$orderby” operator, it is possible to sort.
MongoDB shred stands on the collection. Therefore, we store all the substances in a mass or a lump. When we have an additional time slot, then we will have few slice data achievement options, yet when we have multiple lumps, data will be extended to numerous slices.
In the MongoDB terminal, we can run “Show DBS” to retrieve the existing databases. To get the MongoDB databases programmatically, we execute the following code:
use admin
dbs = db.runCommand({listDatabases: 1})
dbNames = []
for (var i in dbs.databases) { dbNames.push(dbs.databases[i].name) }
Hopefully this will help someone else.
The below will create an array of the names of the database:
var connection = new Mongo();
var dbNames = connection.getDBNames();
By executing the following code, we update the object:
Skip code block
{
“_id” : ObjectId(“4faaba123412d654fe83hg876”),
“user_id” : 123456,
“total” : 100,
“items” : [
{
“item_name” : “my_item_one”,
“price” : 20
},
{
“item_name” : “my_item_two”,
“price” : 50
},
{
“item_name” : “my_item_three”,
“price” : 30
}
]
}
I have a collection that has an embedded document known as notes.
Skip code block
{
“_id” : ObjectId(“4f7ee46e08403d063ab0b4f9”),
“name” : “MongoDB”,
“notes” : [
{
“title” : “Hello MongoDB”,
“content” : “Hello MongoDB”
},
{
“title” : “ReplicaSet MongoDB”,
“content” : “ReplicaSet MongoDB”
}
]
}
To query the nested join, we use “tested”. For example:
{“_id” : ObjectId( “abcd” ),
“className” : “com.myUser”,
“reg” : 12345,
“test” : [
{ “className” : “com.abc”,
“testid” : “pqrs” } ] }
Yes, we can run multiple javascript operations in one MongoDB instance.
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