Easy

1.How are documents organized in MongoDB?

Documents in MongoDB are organized into collections, which are equivalent to tables in a relational database.

2.How do you insert a document into a MongoDB collection using the MongoDB shell?

You can use the insertOne() or insertMany() method. For example:db.collectionName.insertOne({ key: “value” });

3.What is a primary key in MongoDB?

In MongoDB, the _id field serves as the primary key for a document. It is unique within a collection.

4.How do you create a new database in MongoDB?

MongoDB creates a new database automatically when you first insert data into it. You can also switch to a non-existing database, and it will be created when data is inserted.

5.How do you create an index in MongoDB?

You can create an index using the createIndex() method. For example:javascript db.collectionName.createIndex({ key: 1 });

6.What is the $set operator in MongoDB’s update operations?

The $set operator is used to update the value of a field in a document without affecting other fields.

7.How do you remove documents from a MongoDB collection?

You can use the deleteOne() or deleteMany() method to remove documents from a collection.

8.How do you count the number of documents in a MongoDB collection?

You can use the count() method to count the number of documents in a collection. For example:javascript db.collectionName.count();

9.What is the mongod process in MongoDB?

The mongod process is the primary MongoDB server process responsible for handling database operations.

10.How can you back up and restore data in MongoDB?

You can use the mongodump and mongorestore tools to back up and restore data in MongoDB.

11.How can you connect to a MongoDB server using the MongoDB shell?

You can use the mongo command followed by the connection string.

For example:shell mongo mongodb://localhost:27017/mydb

12.What is a URI connection string in MongoDB?

A URI connection string in MongoDB is a string that contains connection information, including the host, port, and database name, in a standardized format.

13.How do you find distinct values in a MongoDB collection?

You can use the distinct() method to find distinct values for a specific field in a collection.

For example:

db.collectionName.distinct("field");

14.What is a compound index in MongoDB?

A compound index in MongoDB is an index that involves multiple fields. It can improve query performance for queries involving those fields.

15.How do you sort query results in MongoDB?

You can use the sort() method to sort query results.

db.collectionName.find().sort({ key: 1 });

16.What is an upsert in MongoDB?

An upsert in MongoDB is an operation that updates a document if it exists or inserts it if it does not exist.

17.How can you limit the number of documents returned by a query in MongoDB?

You can use the limit() method to restrict the number of documents returned by a query.

db.collectionName.find().limit(10);

18.How do you skip a specific number of documents in a MongoDB query result?

You can use the skip() method to skip a specified number of documents in the query result.

For example:-

db.collectionName.find().skip(5);

19.What is the $match aggregation stage used for in MongoDB?

The $match stage is used to filter documents in an aggregation pipeline based on specified conditions.

20.What is the $group aggregation stage used for in MongoDB?

The $group stage is used to group documents in an aggregation pipeline by a specified field and perform aggregate operations on them.

21.Explain Vertical Scaling and Horizontal Scaling?

• Vertical Scaling: Vertical Scaling increases storage and CPU resources for expanding the capacity.
• Horizontal Scaling: Horizontal Scaling splits the datasets and circulates the data over multiple shards or servers.

22.How does MongoDB handle scaling and load balancing?

MongoDB handles scaling and load balancing through sharding. Sharding allows distributing data across multiple machines, enabling horizontal scalability. MongoDB automatically manages the routing of queries to the appropriate shards based on the shard key. This ensures an even distribution of data and workload across the shards.

23.How does MongoDB provide high availability?

MongoDB achieves high availability through replica sets. A replica set is a group of MongoDB instances that store the same data. It consists of a primary node that handles all write operations and multiple secondary nodes that replicate the data from the primary. If the primary node fails, one of the secondaries is automatically elected as the new primary, ensuring continuous availability.

24.What is the importance of profiler in MongoDB?

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.

25.What is sharding in MongoDB?

Sharding is the process of distributing data across multiple machines or servers in a MongoDB cluster. It allows horizontal scaling by partitioning data and storing it on multiple shards. Each shard contains a subset of the data, and MongoDB routes queries to the appropriate shard for efficient distributed querying.

26.How does MongoDB handle unstructured and semi-structured data?

MongoDB’s flexible document model makes it well-suited for handling unstructured and semi-structured data. It allows storing data in a free-form manner within documents, which can vary in structure from one document to another. This flexibility makes MongoDB a good choice for storing data with varying or evolving schemas.

27.Explain what is an “ObjectId” in MongoDB and how it is generated.

An “ObjectId” is a 12-byte identifier in MongoDB. It consists of a timestamp, machine identifier, process identifier, and a counter. It is generated automatically when a document is inserted and serves as a unique identifier.

28.What is the purpose of the $lookup aggregation stage in MongoDB?

The $lookup stage performs a left outer join between documents from two collections. It combines documents from different collections based on a specified field.

29.Explain the differences between pushandaddToSet operators in MongoDB.

$push adds an element to an array field, even if it already exists.

$addToSet adds an element to an array field only if it doesn’t already exist.

30.How can you implement field-level access control in MongoDB?

Field-level access control can be implemented using document-level validation rules, which allow you to specify validation conditions for individual fields in a document.

31.What is a wildcard index in MongoDB, and when might you use it?

A wildcard index is an index that includes all fields of a document. It can be useful for queries where you don’t know the specific fields in advance, but it may not be as efficient as targeted indexes.

32.Explain the differences between “wiredTiger” and “MMAPv1” storage engines in MongoDB.

wiredTiger” is the default storage engine in MongoDB, known for its performance and concurrency control.”MMAPv1″ is an older storage engine with simpler configuration but may not perform as well in certain scenarios.

33.What is the purpose of the dbStats command in MongoDB, and how can it be useful for monitoring?

The dbStats command provides statistics about a database, including data size, index size, and storage utilization. It can be useful for monitoring database health and resource usage.

34.What is the use of the BulkWrite API in MongoDB, and how does it differ from individual write operations?

The BulkWrite API allows you to perform multiple write operations in a single request, reducing the overhead of individual write operations and improving performance.

36.Explain the concept of “zone sharding” in MongoDB.

Zone sharding is a technique where you can assign specific ranges of shard keys to specific shards. It’s useful for ensuring data locality and optimizing query performance.

37.What is the role of the mongotop tool in MongoDB, and how is it used for monitoring?

The mongotop tool provides real-time information about the usage of individual databases and collections in a MongoDB instance, helping monitor resource utilization.

38.How do you enable authentication and authorization in MongoDB?

Authentication and authorization can be enabled in MongoDB by configuring user accounts, roles, and access control settings in the MongoDB server.

39.Explain how to implement a unique index in MongoDB and its use case.

A unique index can be created using the createIndex() method with the unique option set to true. It ensures that no two documents have the same value for the indexed field, making it useful for enforcing data integrity constraints.

40.What is the $facet aggregation stage in MongoDB, and how can it be used?

The $facet stage allows you to perform multiple aggregations in a single pipeline. It’s useful for executing multiple independent aggregations and combining their results.

INTERMEDIATE

41.What is the $redact aggregation stage used for in MongoDB?

The $redact stage is used for data filtering and can be used to selectively include or exclude data based on specified conditions.

42.How can you implement field-level encryption in MongoDB to secure sensitive data?

Field-level encryption in MongoDB involves encrypting specific fields in documents. It can be implemented using the MongoDB Encryption library to protect sensitive data.

43.Explain the use of the $merge aggregation stage in MongoDB.

The $merge stage allows you to merge the output of an aggregation pipeline into an existing collection or create a new collection with the aggregated results.

44.Explain the significance of the covered query?

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.

45.What is MongoDB, and how does it differ from traditional relational databases?

MongoDB is a NoSQL database that stores data in a document-based format. Unlike traditional relational databases, MongoDB uses a flexible schema, making it suitable for handling unstructured or semi-structured data.

46.Explain the concept of NoSQL databases and where MongoDB fits into this category.

NoSQL databases are non-relational databases designed to handle large volumes of unstructured or semi-structured data. MongoDB is a type of NoSQL database that uses a document-based model to store data.

47.How does MongoDB store data? Describe its document-based data model.

MongoDB stores data in a format called BSON (Binary JSON). BSON is a binary-encoded serialization of JSON-like documents. It includes data types such as strings, numbers, arrays, and nested documents.

48.What is BSON, and why is it used in MongoDB?

BSON stands for Binary JSON. It is used in MongoDB to represent data in a binary-encoded format, which allows for efficient storage and retrieval of data.

49.What are the key features of MongoDB that make it suitable for handling large volumes of data?

Key features of MongoDB include horizontal scalability through sharding, support for complex queries and aggregations, flexible schema design, high availability through replica sets, and support for geospatial data.

50.What is a collection in MongoDB, and how is it different from a table in a relational database?

A collection in MongoDB is a group of MongoDB documents. It is similar to a table in a relational database but does not require a fixed schema, allowing documents within a collection to have different structures.

51.Explain what a document is in the context of MongoDB.

In MongoDB, a document is a JSON-like data structure used to store data. It consists of field-value pairs and can have nested documents and arrays. Documents are the primary unit of data storage in MongoDB.

52.How does MongoDB ensure data consistency and reliability?

MongoDB ensures data consistency and reliability through the use of replica sets, which provide automatic failover and data redundancy. Writes are acknowledged when a majority of nodes have confirmed receipt of the data.

53.What is a replica set in MongoDB, and why is it used?

A replica set in MongoDB is a group of MongoDB servers that maintain the same data. It provides data redundancy and high availability by automatically electing a primary node and allowing for failover in case of node failures.

54.How does sharding work in MongoDB, and why is it essential for scaling?

Sharding in MongoDB is a technique for distributing data across multiple servers or shards. It allows MongoDB to horizontally scale and handle large datasets by partitioning data and distributing it among different nodes.

55.What is an index in MongoDB, and why is it crucial for query optimization?

An index in MongoDB is a data structure that improves the speed of data retrieval operations on a collection. MongoDB supports various types of indexes, including single-field, compound, text, and geospatial indexes.

56.Describe the syntax for inserting a document into a MongoDB collection.

insertMany() method. Here’s an example using insertOne():

db.collectionName.insertOne({ field1: ""value1"", field2: ""value2"" });

57.How do you query data in MongoDB? Provide an example of a basic query.

Javascript

db.collectionName.find({ field1: "value1" });

58.What is aggregation in MongoDB, and why is it useful?

Aggregation in MongoDB is the process of performing data transformations and computations on documents in a collection. It allows you to group, filter, and manipulate data to extract meaningful insights.

59.Explain the concept of a cursor in MongoDB, and how it differs from a result set in SQL.

A cursor in MongoDB is an iterator that allows you to retrieve and process query results in batches. It helps manage memory efficiently when dealing with large result sets.

60.What is the purpose of the _id field in MongoDB documents?

The _id field in MongoDB documents is a unique identifier for each document within a collection. It is automatically generated if not provided explicitly when inserting a document.

61.How can you perform updates on documents in a MongoDB collection?

You can perform updates on documents in MongoDB using methods like updateOne(), updateMany(), or replaceOne(). These methods allow you to modify existing documents based on specific criteria.

62.What is a TTL (Time-To-Live) index, and when might you use it in MongoDB?

A TTL (Time-To-Live) index in MongoDB is used to automatically remove documents from a collection after a specified period. It can be used for data that has an expiration date.

63.How do you perform text search in MongoDB?

Text search in MongoDB can be performed using the $text operator and the text index. It allows you to perform full-text search queries on text fields in documents.

64What is a covered query in MongoDB, and why is it beneficial for performance?

A covered query in MongoDB is a query in which all the fields returned are part of an index. It can significantly improve query performance because MongoDB can fulfill the query using the index alone, without accessing the actual documents.

66.How does MongoDB handle transactions, and when should you use them?

MongoDB supports multi-document transactions starting from version 4.0. Transactions are used to ensure data integrity when multiple operations need to be atomic (all succeed or all fail).

67Explain how to create an index in MongoDB, and discuss the types of indexes available.

Javascript

db.collectionName.createIndex({ field1: 1 });

68.What is the significance of the explain method in MongoDB queries?

The explain method in MongoDB is used to analyze query execution plans. It provides information about which indexes are used and other details about the query’s performance.

69.How can you perform a backup and restore operation in MongoDB?

MongoDB provides tools like mongodump and mongorestore to perform backup and restore operations. These tools allow you to create backups of your data and restore it when needed.

70.Describe the concept of geospatial indexing in MongoDB and when it might be used.

Geospatial indexing in MongoDB allows you to store and query geospatial data, such as points, polygons, and lines. It enables spatial queries like finding nearby locations.

ADVANCED

71.What are GridFS and how do they relate to MongoDB?

GridFS is a specification used in MongoDB for storing and retrieving large files, such as images, audio, and video. It allows you to split large files into smaller chunks for efficient storage.

72.How can you secure a MongoDB database, and what are some best practices for doing so?

Securing a MongoDB database involves practices like enabling authentication, using role-based access control, and configuring network security. Best practices include regular updates, monitoring, and limiting access.

73.What is the role of the MongoDB Atlas service, and how does it differ from self-hosted MongoDB deployments?

MongoDB Atlas is a fully managed database service provided by MongoDB, Inc. It offers a cloud-based deployment option with features like automated backups, scaling, and monitoring. It differs from self-hosted MongoDB in that it simplifies database management tasks.

74.Explain the differences between the find() and findOne() methods in MongoDB.

The find() method returns a cursor that can be used to iterate over multiple documents, while the findOne() method returns a single document that matches the query criteria.

75.How can you optimize MongoDB performance for read-heavy and write-heavy workloads?

To optimize MongoDB performance for read-heavy workloads, you can use read preferences to route queries to secondary nodes. For write-heavy workloads, consider sharding and proper indexing to distribute the load.

76.Can you explain how the MongoDB query optimizer impacts the overall performance of a database system?

The MongoDB query optimizer is crucial for database performance. It analyzes queries, selects efficient indexes, and generates execution plans. This impacts performance by reducing query execution time, optimizing resource use, improving scalability, ensuring data consistency, and enhancing response times. A well-optimized database leads to a smoother user experience, reduced server load, and efficient index maintenance. Understanding and using the query optimizer effectively is essential for MongoDB administrators and developers.

77.How do “mongodump” and “mongorestore” differ from other backup and restore methods in MongoDB?

The “mongodump” command is used to create a binary dump of a MongoDB database or collection. It creates a backup by capturing the data and metadata in BSON format. The “mongorestore” command is used to restore the data from a MongoDB dump created by “mongodump”. It imports the BSON data back into the database.

78.What write concern configurations are available in MongoDB for write operations, and how do they affect data durability?

1.Unacknowledged (w: 0): Fast but least durable.

2.Acknowledged (w: 1): Moderately durable.

3.Journaled (w: “”majority””, j: true): Good durability.

4.Replica Acknowledged (w: “”majority””): Strong durability.

5.Custom Write Concerns: Tailored durability control.”

79.How does MongoDB enforce the uniqueness of a primary key and unique key? What issues can arise from this?

MongoDB enforces the uniqueness of primary keys and unique keys using indexes. It checks these indexes to prevent duplicate values. Issues can arise from potential race conditions in highly concurrent environments, increased index size, performance overhead, and the need for regular index maintenance. Careful planning and monitoring are essential to mitigate these issues.

80.Explain how MongoDB resolves conflicts when multiple clients are accessing the same data in a distributed environment.

MongoDB uses optimistic concurrency control to handle conflicts in a distributed environment. It allows multiple clients to read and modify data concurrently, and conflicts are resolved during the write operation. If conflicting writes occur, MongoDB detects the conflicts and applies the write that occurs last, discarding the earlier conflicting writes.

81.What methods are available in MongoDB to enforce document validation rules? Can you give an example?

MongoDB supports various authentication mechanisms, including SCRAM, x.509, Kerberos, LDAP proxy, MongoDB-CR (deprecated), AWS IAM, and client certificate authentication. These mechanisms differ in their security levels and use cases, so the choice depends on your organization’s needs and infrastructure.

82.Differentiate between the “find()” and “findOne()” methods in MongoDB. Can you give an example?

The “find()” method in MongoDB returns a cursor that can be iterated over to retrieve multiple documents matching the query criteria. It can return multiple documents or an empty cursor. On the other hand, the “findOne()” method returns a single document that matches the query criteria. If multiple documents match the query, “findOne()” returns the first document found, or null if no matching document is found.

83.Describe the structure and use-cases of a MongoDB aggregation pipeline.

The MongoDB aggregation pipeline is a framework for performing data processing and transformation tasks on documents in a collection. It allows you to combine multiple stages such as filtering, grouping, projecting, sorting, and aggregating data in a declarative and efficient manner. The aggregation pipeline is particularly useful for complex data manipulations and analytics.

84.What authentication mechanisms are supported in MongoDB, and how do they differ?

• SCRAM: Default for username/password.
• x.509: Certificates for authentication.
• Kerberos: Centralized authentication.
• LDAP: Integration with directory services.
• AWS IAM: For MongoDB Atlas.
• MONGODB-CR: Deprecated.
• Field Level Encryption: Data encryption.
• Custom Mechanisms: Tailored solutions.

85.How does MongoDB ensure data consistency?

MongoDB ensures data consistency within a replica set by using a primary-secondary replication model. All write operations are performed on the primary node, and the changes are replicated to the secondary nodes. MongoDB uses the “Oplog” (operations log) to record and synchronize write operations across the replica set, ensuring data consistency.

86.What are some common data modeling techniques used in MongoDB?

Some common data modeling techniques in MongoDB include:

1.Embedded Data Model: Embedding related data within a single document.

2.Normalized Data Model: Storing related data in separate collections and establishing relationships using references.

3.Denormalized Data Model: Duplication of data across documents to optimize query performance and avoid frequent joins.

4.Hybrid Data Model: A combination of embedded and normalized data models based on the application’s requirements.

87.What is the difference between a capped collection and a regular collection in MongoDB?

In MongoDB, a capped collection is a fixed-size collection that has a maximum limit on the number of documents it can store or the total size of the collection. When the limit is reached, new documents overwrite the oldest documents in the collection. Regular collections, on the other hand, do not have such size constraints and store documents without overwriting old ones.

88.What are the advantages and disadvantages of using subdocuments in MongoDB?

Advantages:

1.Subdocuments can represent hierarchical data.

2.They allow embedding related data within a single document.

Read operations can be faster when data is embedded.

Disadvantages:

1.Updates to subdocuments require rewriting the entire document.

2.Complex queries may become challenging.”

89.How can you perform a case-insensitive search in MongoDB?

You can use the $regex operator with the “”i”” option to perform a case-insensitive search. For example:

db.collectionName.find({ field: { $regex: /pattern/i } });

90.Explain the benefits and limitations of MongoDB’s automatic sharding feature.

Benefits: Automatic sharding allows horizontal scaling of MongoDB, distributing data across multiple shards for improved performance and capacity.

Limitations: It may require careful planning and maintenance, and certain operations (e.g., range queries) can be less efficient.

91.Differentiate MongoDB and Cassandra?

MongoDB

• It is a cross-platform document-oriented database system
• It is developed in C++ It is developed in Java
• It is simple to administer in the failure case

Cassandra

• It is a high-performance distributed database system.
• It is developed in Java
• It offers high availability

90.Explain Storage Encryption?

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.

91.Explain Primary and Secondary Replica Sets?

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.

92.How to do Journaling in MongoDB?

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.

93.What are the different types of MongoDB deployments?

MongoDB supports several deployment options:

1.Standalone: A single MongoDB instance running on a single machine.

2.Replica Set: A group of MongoDB instances that provide high availability by replicating data across multiple nodes.

3.Sharded Cluster: A deployment where data is distributed across multiple shards to achieve horizontal scalability.

4.Config Server Cluster: A cluster that stores metadata and configuration information for sharded clusters.

94.When should we normalize the data in MongoDB?

It relies on our objectives. Normalization provides an updated effective data representation. Denormalisation makes data reading effective. Generally, we utilize embedded data models when:

• When we have “contains” relationships between the entities.
• When we have one-to-many relationships between the entities. In the relationships, “many” or the child documents display in the context of the parent documents.

Generally, we use normalized data models:

• When embedding results in duplication of the data yet they will not give enough read performance advantages to prevail the duplication implications.
• For representing more difficult many-to-many relationships.
• For modeling the big hierarchical data sets.

95.How can MongoDB simulate subquery or join?

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”.

96.Define oplog(operational log)?

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.

97.How do we delete everything from the MongoDB database?

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();

98.What are the points we should consider while creating a schema in MongoDB?

We must consider the following points while creating a schema:

• Designing the Scheme based on the user requirements.
• Combining the objects into one document, if we have to use them jointly, or else, separate them.
• Perform joins while on write, and not while it is reading.
• For most general application scenarios, maximize the schema.
• Perform complex aggregations in the schema.

99.How do we use the select * group by MongoDB aggregation?

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:Javascript

db.example.aggregate(
  {
    $group:{
      _id:'$name',
x: {$addToSet: "$x"    },
y: {$addToSet: "$y"    },
z: {$addToSet: "$z"    },
 }
}
)

100.How does horizontal scaling (scale-out) occur in MongoDB?

MongoDB achieves horizontal scaling by distributing data across multiple servers or nodes in a cluster. It uses a sharding technique where data is partitioned into chunks and distributed across different shards (servers). This allows MongoDB to handle larger data sets and provide high availability and performance by leveraging the computing power of multiple machines.