What is an access token and refresh token? How do they work and where should we store them on the client-side?
Access tokens and refresh tokens are used to authenticate users and provide secure access to web APIs. Here Access tokens are short-lived and are used to access resources on behalf of a user. Whereas Refresh tokens are long-lived and can be used to obtain new access tokens when they expire.
Access tokens are typically generated by the authorization server when a user authenticates with a client application. They are typically used to access protected resources, such as user data or files. Access tokens are typically short-lived, expiring after a few hours or days. This helps to protect users' security, as it limits the amount of time that an attacker has to use an access token if it is compromised. Whereas Refresh tokens are typically generated by the authorization server when a user grants a client application access to their resources. They are typically longer-lived than access tokens, expiring after a few weeks or months. This allows users to access protected resources without having to re-authenticate with the client application every time their access token expires.
Access tokens should be stored on the client-side in memory or local storage. But it is important to note that storing access tokens in local storage can make them vulnerable to cross-site scripting attacks. Refresh tokens should be stored securely on the client-side using HttpOnly cookies or other secure storage mechanisms
Compare SQL and NoSQL databases?
SQL and NoSQL databases are two different types of databases that are used for storing and managing data. SQL databases are relational databases that store data in tables with fixed rows and columns. They use a structured query language to access and manipulate data. NoSQL databases, on the other hand, are non-relational databases that store data in a variety of formats such as JSON documents, key-value pairs, wide column stores, and graphs. They do not use SQL to access and manipulate data.
There are several differences between SQL and NoSQL databases. SQL databases are primarily called Relational Databases , whereas NoSQL databases are primarily called non-relational or distributed databases1. SQL databases are table-based, while NoSQL databases can be document-oriented, key-value pairs, or graph2. SQL databases feature tables while NoSQL databases are often document-based3. SQL databases contain structured data and can perform an analysis using a relational model. A NoSQL database contains unstructured data, which means it cannot be analyzed through predefined models
SQL databases are typically used for applications that require complex queries and transaction management. For example, SQL databases are often used for financial applications, customer relationship management systems, and enterprise resource planning systems.
NoSQL databases are typically used for applications that require high performance and scalability. For example, NoSQL databases are often used for web applications, mobile apps, and big data analytics.
What is express js? What is Nest JS?
Express.js is a minimal and flexible Node.js web application framework that provides a robust set of features for building web applications. It is asynchronous in nature, which allows you to execute multiple operations independently.
Whereas NestJS is another popular framework. NestJS is built on top of common Node.js frameworks like Express and Fastify. Nest.js is a TypeScript framework that builds on top of Express.js and adds additional features such as dependency injection, a modular architecture, and an intuitive CLI. It is designed to be a more robust and scalable alternative to Express.js.
What is MongoDB aggregate and how does it work?
Aggregation in MongoDB is the process of selecting data from a collection and returning computed results. It is a powerful tool that allows you to process and analyze data in MongoDB. It is a pipeline-based system, which means that you can chain together multiple operations to perform complex queries. Aggregation pipelines can be used to perform a wide variety of tasks, and they are a powerful tool for processing and analyzing data in MongoDB.