You can download the latest version of MongoDB here: https://www.mongodb.com/download-center#community. Follow the setup instructions to install it.

Once MongoDB is installed on your Windows PC, you have to create the following directory:

Data directory C:\data\db  

Once you have successfully installed MongoDB, you will be able to see the following executable:

We have to start the mongod instances to begin working with MongoDB. To start the mongod instance, execute it from the command prompt, as shown in the following screenshot:

Once mongod has started, we have to connect this instance using the mongo client with the mongo executable:

Once we are connected to the database, we can start working on the database operations.

Documents in MongoDB are JSON-like objects. JSON is a simple representation of data. It supports the following data types:

• null: The null data type is used to represent the null value as well as a value that does not exist:

• boolean: The boolean type is used to represent true and false values:

• number: In MongoDB, the shell default supports 64-bit floating-point numbers. To process long and integer numbers, MongoDB provides NumberLong and NumberInt, which represent 4 bytes and 8 bytes, respectively.
• string: The string data type represents the collection of characters. The MongoDB default supports UTF-* character encoding:

• date: MongoDB stores dates in milliseconds since the epoch. The time zone information is not saved:

• After inserting a date using the preceding way in the document, when we query using find it returns a document with...

The MongoDB collection does not enforce structure on the document. This allows the document to map to objects or entities easily. Each document can match a data field of the entity. In practice, documents in collections share the same structure.

When deciding on data modeling, we have to consider the requirements of the application, the performance characteristics of the database's design, and data retrieval patterns. When designing data models, we have to focus on the application's usage of data and the inherent structure of the data.

While deciding the data model, we have to consider the structure of the document and how documents relate to each other. There are two key data models that show these relationships:

• The reference document data model
• The embedded data model

Indexes allow efficient execution of MongoDB queries. If we don't have indexes, MongoDB has to scan all the documents in the collection to select those documents that match the criteria. If proper indexing is used, MongoDB can limit the scanning of documents and select documents efficiently. Indexes are a special data structure that store some field values of documents in an easy-to-traverse way.

Indexes store the values of specific fields or sets of fields, ordered by the values of fields. The ordering of field values allows us to apply effective algorithms of traversing, such as the mid-search algorithm, and also supports range-based operations effectively. In addition, MongoDB can return sorted results easily.

Indexes in MongoDB are the same as indexes in other database systems. MongoDB defines indexes at the collection level and supports...

A replica set is a group of MongoDB instances that store the same set of data. Replicas are basically used in production to ensure a high availability of data.

Redundancy and data availability: because of replication, we have redundant data across the MongoDB instances. We are using replication to provide a high availability of data to the application. If one instance of MongoDB is unavailable, we can serve data from another instance. Replication also increases the read capacity of applications as reading operations can be sent to different servers and retrieve data faster. By maintaining data on different servers, we can increase the locality of data and increase the availability of data for distributed applications. We can use the replica copy for backup, reporting, as well as disaster recovery.

Sharding is a methodology to distribute data across multiple machines. Sharding is basically used for deployment with a large dataset and high throughput operations. The single database cannot handle a database with large datasets as it requires larger storage, and bulk query operations can use most of the CPU cycles, which slows down processing. For such scenarios, we need more powerful systems.

One approach is to add more capacity to a single server, such as adding more memory and processing units or adding more RAM on the single server, this is also called vertical scaling. Another approach is to divide a large dataset across multiple systems and serve a data application to query data from multiple servers. This approach is called horizontal scaling. MongoDB handles horizontal scaling through sharding.