Md Talib

Hi. My name is, and I'm from Colombia, Arkansas, India. Currently, I'm in Hyderabad. I completed my graduation in 2021. And after that, I did a full stack web development certification from a boot camp. So, in the boot camp, I joined after completing my career. In the boot camp, I took a training on full stack development. And the tech stack I used was React, Node Express, MongoDB, and also Spring Boot and Java for the backend part. For the development of the website, I used React, Node Express, MongoDB, and MySQL. And, after that, I worked in Chehal Academy. After completing the boot camp, I joined the company, which is Chehal Academy. In Chehal Academy, I worked as a full stack web developer using React, Node Express, MongoDB. There, I worked on an e-learning website where students can get enrolled, purchase a course, give a test, and pay through EMI. I worked on both the user side and admin side. In both sides, I worked on that project. After that, we integrated 3rd party APIs, such as Razorpay and Gateway, and I worked on RESTful APIs there. I worked there for one year. Then, I joined a ritual company. In the ritual, I worked on the digital side. I worked as a senior full stack developer, leading a team of 4 to 5 members. I used technologies such as React, Node Express, MongoDB, and others. For deployment, I used AWS and Apollo. After completing the project, the company closed, and then I joined x u technology. In x u technology, I worked as a backend developer. I used technologies such as Node Express, MongoDB, and for deployment, I used AWS. We deployed many sites there. In x u technology, I worked on an e-commerce website where I managed a team as a backend developer. We worked on an e-commerce website like goldheart.com, goldbox.com, and goldcenter.com. There, users can purchase gold and silver as per their requirement. This is all. Including all, I have 3 years of experience in the training and boot camp industry. Apart from that, I have 3 years of experience in companies. So, including all, it is 3 years. Mostly, I use technologies such as Node Express, Meteor, Apollo, and Socket.IO for live prices and everything. So, that is all the details about me. This is the background and everything about me. That's all. Thank you.

So discussing about discussing how to scale a socket.io based on the best messaging service in a Node.js to handle sudden spiking users. So for this, I'd like to discuss how to scale socket.io. So for this, it will handle spikes in users, requiring the convenience of ensuring the system remains responsive, reliable, and scalable. So for that, we'll scale by horizontally, like multiple Node.js instances. Socket.io is based on web sockets and maintains long-lived connections between servers and clients. It handles a large number of simultaneous users, and this requires distributing connections across multiple Node.js instances. So for that, in Node.js, one thing is cluster mode. Using the Node.js cluster module to run multiple instances of the socket.io servers on a single machine, and it leverages multiple CPU cores. And after that, for the multiple servers, we will deploy several socket.io instances on multiple servers. Each server will handle a portion of that traffic, but the challenge is ensuring that all instances remain in sync with user events or messages. So and after that, we will like to use sticky sessions for that, since web sockets maintain persistent connections when using load balancers. So at that time, we need to ensure that each client is consistently routed to the same server instance. And for that, we enable sticky sessions at the load balancer level, through NGINX. And after that, it will ensure that connections from the same user are sent to the same backend instance. So using and also, we use Redis for pub/sub and state synchronization. And so when we'll scale up multiple instances, so it's possible that a server may have users connected to it that need to extend messages in real time. However, since WebSocket connections are tied to individual servers, and we need to make a mechanism to broadcast events across all server instances. So for that, Redis pops up as it will be used. Redis is to enable message broadcasting between socket.io servers. Redis acts as a central messaging broker and it will ensure that messages from one server are appropriate to all servers. So like this, it will be done, and we have to install the Redis adapter for socket.io. Like, so it will configure the server port, we have to configure socket.io to use Redis. And with these setups, when a user on the server sends a message, it will be broadcasted to the users and to all the socket.io instances through Redis.

So in ExpressJet's applications, a good pattern for secure and structured error handling is the centralized error handling pattern. Often combined with custom error classes and middleware, ensure all errors are properly handled, logged, and communicated securely to the client. For custom error classes, we will create custom error classes to represent different error types. Example: client errors, server errors, and authentication errors. This adds clarity and structure when dealing with errors. So this will be, for error handling middleware, centralized error handling middleware. This middleware checks all errors, including those thrown asynchronously, and ensures consistent error responses. It also distinguishes between operational errors, like request errors, and programming errors, like undefined variables, for better logging and security. After that, we'll use try-catch for asynchronous code. We'll use async/await for asynchronous code and re-throw errors in catch functions or higher-order functions that pass any errors to the centralized error handler. And then we'll look for the global error handler for uncaught sessions and rejections. So we will see all that application handles uncaught exceptions and handle promise rejections, which are programming errors and should be logged and safely shut down the application. After that, we will look for safe error responses for productions. In productions, we'll avoid leaking sensitive addresses or internal error details to clients. We only show a generic error message for critical errors while logging in detail error for debugging purposes. And after that, for the 404 not found, let's create a middle layer to catch routes that are not found and return a 404 error. And for logging, we will implement logging for all errors using a logging service like Winston, Banyan, or a logging service like Sendry to ensure errors are monitored and tracked correctly. And through all these, we ensure errors are handled consistently securely. And in this way, we can make some debugging easier. So this all things defining custom error classes, and we'll use dry-run blocks and async/await to handle errors at the web app level. And we will use centralized error handling using error handling middleware that sends structured error responses, and we'll ensure uncaught exceptions and promise rejections are handled globally. And for log errors, security, especially in production environments, and we'll hide sensitive error details to avoid exposing internal application logic. So through all this, we can handle error handling in Express applications.

So what steps would you take to verify the integrity of data transmitted over WebSocket connections in a real-time app? To ensure the integrity of data transmitted over WebSocket connections in a real-time app, I can implement a range of security measures to protect the data from tempering during transmission. So for that, we'll use WebSocket over TLS, which is wss, and this will ensure that all WebSocket connections are established over a secure transport layer by using wss or WebSocket over TLS and SSL. This provides encryption, preventing man-in-the-middle attacks and ensuring the confidentiality and integrity of transmitted data. In the steps, we'll use wss instead of ws when connected to WebSocket, and we will obtain and configure an SSL and TLS certificate on the server. So after that, we'll do message authentication codes, or MACs. We'll use message authentication codes, or MACs, to verify that the data has not been tampered with. And I can append a cryptographic hash to each message, which is calculated using a shared secret between the client and the server. Upon receiving a message, the server recalculates the hash and compares it to ensure data integrity. And for that, we'll create a hash of the message and we'll use HMAC with a secret key and share the message with the hash. And after that, we'll verify the hash on the server before processing the message. And after that, for data validation and anti-scammers, we'll structure and format incoming data that is correct by implementing data validations on the server side, and we'll use libraries like Zoe or Yep to enforce scammers for incoming messages and prevent malicious data from being processed. And we define a schema for expected message formats, and we'll validate each message received against the schema. And after that, we use JSON Web Tokens for message authentication, for like, for additional security and authentication. I can sign each message using a JWT. This ensures that the sender is authenticated and the message was not tampered with. And then, for that, we take the steps like the client signs the message with the secret key and sends it to the server, and the server verifies the JWT before processing the message. Through all this, we can verify the integrity of data transmitted over WebSocket connections in a real-time app, and also for sequence numbers or time stamps for replay attack prevention. To prevent replay attacks, where attackers resend already sent messages, including sequence numbers or time stamps with each message, the server should track the sequence numbers or validate that the time stamp is within an acceptable range. So through all this, we can transmit our WebSocket connections securely, and we can also implement rate limiting and throttling, and also sequence numbers through time stamps for replay attack prevention. And after that, we can check some hash variables for data integrity. And so by using the benefits of WSS and edge access, we can deliver data securely, with distance and data time stamps directly meeting and checking some details. All through all this, we can do to transmit data to our WebSocket connections in a real-time.

So ensuring that API implants developed with Express are strongly typed when using TypeScript. So for this, ensuring that API endpoints developed with Express are strongly typed when using TypeScript, I need to integrate TypeScript, type checking, and enforce that for request and response middleware and route handlers. So for this, we'll use a TypeScript project where it will be set up with TypeScript by installing necessary dependencies and configuring case config dot JSONs. And after that, we'll store a module and pass the strict and other required things. And after that, we'll define a strong type for request and response. We'll use the filtering type for types and Express for best and response. However, for custom data, for example, body query params, route params, we can clear the interfaces to strongly type those objects, and we'll use define custom types for request parameters, body, and query. And after that, for the middleware, we'll check for that at middleware in Express. It can also be strongly typed, especially when I need to ensure that types are additional properties, like it might add to the request object. And after that, we'll check what type custom handlers for error handling, custom error classes. And ensure that error handlers are strongly typed to handle various types of errors. And we use also type checking with route handlers, which you define routes and routes, and it will ensure that handlers are properly typed particularly when dealing with our route parameters and query parameters or request bodies. So and after that, we'll ensure type safety for API responses. Like, we'll ensure that API responses are consistently structured when we'll clear the types for the response payloads, and install the types when sending responses. And after that, we use type script utility type script for generic cases. So we'll use type script, type script provides capabilities like partial, speak, and omit, which can help enforce more flexible or partial typing when needed. And after that, we'll build it using leverage like Jod or Joy with type script. For validating the request body parameters or query strings, we can integrate friendly validations library like Jod or Joy. And so through all this, request response typing and middleware typing and error handling and response typing and utility type script and for validations. So by following these steps, I can assure our Express apps are strongly typed, which leads to more predictable and maintainable code. So through all this, we can achieve.

So suggesting a strategy for implementing role-based access control in a Node JS API using Passport. Implementing role-based access control in a Node JS API using Passport. Implementing role-based access control in a Node JS API using Passport involves several steps. First, we'll set up authentication with Passport.js and assign roles to users. For example, admin, editor, or user. After that, we'll configure Passport.js for role-based access control. We'll protect routes based on the user's role. For example, only users with the admin role can access the admin dashboard. We'll use Passport.js with the JWT strategy to ensure user roles are included in the JWT payload. We'll apply the downloaded token generation to generate JWT tokens. To test access control, we'll create routes that require specific roles. For example, only users with the admin or editor role can access the create content route. We'll use Passport.js to authenticate users and include their roles in the JWT payload. We'll create a middleware to check roles and apply it to the routes. This approach allows for secure, scalable, and flexible role-based access control across our API. We'll use Passport.js with the JWT strategy to authenticate users and include their roles in the JWT payload. We'll create a middleware to check roles and apply it to the routes. This approach allows for secure, scalable, and flexible role-based access control across our API. We'll use Passport.js with the JWT strategy to authenticate users and include their roles in the JWT payload. We'll create a middleware to check roles and apply it to the routes. This approach allows for secure, scalable, and flexible role-based access control across our API. Implementing role-based access control in a Node JS API using Passport involves several steps. First, we'll set up authentication with Passport.js and assign roles to users. For example, admin, editor, or user. After that, we'll configure Passport.js for role-based access control. We'll protect routes based on the user's role. For example, only users with the admin role can access the admin dashboard. We'll use Passport.js with the JWT strategy to ensure user roles are included in the JWT payload. We'll apply the downloaded token generation to generate JWT tokens. To test access control, we'll create routes that require specific roles. For example, only users with the admin or editor role can access the create content route. We'll use Passport.js to authenticate users and include their roles in the JWT payload. We'll create a middleware to check roles and apply it to the routes. This approach allows for secure, scalable, and flexible role-based access control across our API.

So in this given support IO board and for handling real-time, but there are frequent performance issues and optimizations that can be applied for better scale scalability and efficiency. So for that, if you have an inefficient event listeners setup, so that was important. So for that, we'll use optimizations by limiting event emissions. We'll make sure the server emits events selectively, emitting only necessary events, especially to all connected clients can cause performance issues. So for this, we'll limit event emission. And after that, for handling large amounts of data and efficiency, so, for that, we'll optimize through compressed data and use binary formats. We'll use compression on our binary formats like messages back instead of JSON to reduce the size of the data being transmitted. And for potential memory leaks, for that, we'll ensure proper resource cleanup. We'll ensure that all resources like database connections, timers, or event listeners are cleaned up on disconnect. We'll make sure nothing persists in memory after the client disconnects. And for scaling, it's used in single-server limitations. So for that, we'll use a socket IO with a Redis adapter. So for that, we'll use socket IO with the Redis adapter for scaling across multiple instances, and this will allow socket IO to work across a cluster of Node JS processors, even multiple servers. And for rate limiting or throttling, so for that, we'll limit the number of events a client can emit in a given period to prevent abuse. So, I can use a library like Express Rate Limit or implement my own rate limit logic. And for no namespace, room usage, so we'll use socket IO that provides namespace and rooms for better organization and efficiency, and by splitting clients into groups, like rooms and namespaces. I can reduce unnecessary event broadcasts, and there will be namespace best practices, and that can be used for logically separating different types of clients. And rooms can be used for grouping clients, for like, for chat rooms or game rooms. After that, for error handling and logging, we'll implement error handling and logging. We'll use proper error handling and log recorded events for monitoring and debugging. So, we can also check with the emit less frequency or fetch data. Instead of emitting data similarly, we'll batch events and only emit changes that are significant. So through all these strategies, we'll help ensure that our socket IO build real-time algorithm performs well under a heavy load. So through all this, we can optimize.

To integrate rate limiting in a Node.js application, using middleware to restrict the number of requests a client can make within a specific time window. So, a common package for this is express-rate-limit to it. This will help protect our API from abuse such as denial of service, DoS attacks, or brute force attacks. So, for this, we'll implement rate limiting. I will install the express-rate-limit, and then we'll configure the rate limiting middleware, and we'll apply the middleware to a specific route or globally. And after that, we'll configure rate limiting. Like, for the rate limiting time window in milliseconds, and we'll match the number of requests a lot within that window. And after that, for the message, we'll use a custom error message to return when the limit is exceeded. And in the headers, we'll set to true if including x-rate-limiting, x-rate-limit-remaining, and x-rate-limit-reset headers in the response to inform the client about the limits. And we apply middleware. We can apply the middleware either globally or to a specific route. And after that, applying rate limiting globally or to a specific route. Then we'll just implement the response for that. And we can also implement rate limiting for advanced users, like, for rate limiting by role or users, we can stick the rate limit or logging doubts, like, for the different limits based on the roles, user type, or for a specific criteria. We can use the stricter rate limit on login dots. For logging rate limit violations, like, for security and monitoring purposes, we can apply rate limit violations. So, implementing express-rate-limit easily integrates rate limiting and configures rate limiting to specify the maximum number of requests per time window. And if we apply the rate limiter globally or on a specific route. And after that, we customize the response, and then we'll add logging to create violations. So, by applying rate limiting in our Express applications, I can prevent abuse and protect our servers from overload and ensure better security for critical routes like logging. So, through all these, rate limiting is not just a reason for using express-rate-limit to prevent abuse of the APIs.

So migrating a complex database schema without downtime using Type ORM and Node JS requires careful planning and execution. So for that, we'll process this process is often referred to as a zero-downtime migration, where I can apply changes to our database while ensuring the application continues running smoothly. And for that, we'll use backward-compatible migrations. So the core principle of 0-downtime migration is to make backward-compatible changes to the new schema that work with both the old and the new versions of our code. The steps for avoiding breaking changes, like dropping columns or changing column tabs, until the application is fully migrated. And also, we can do new columns or tables visually, and we can migrate the time for this. And we'll plan for phase migrations, like instead of applying all changes at once, we'll beat the migrations into phases. And this allows us to migrate the schema and update the application in a step-by-step manner. So for the migration phase, we'll add a new column or table, and we can update the application score. Here, we'll write both all the new schema versions. And after that, we'll backfill data from the old column to the new column, and we'll switch to the new schema. And after that, we'll remove all columns that are no longer in use. And after that, we'll handle the schema migration with Type 1. For Type 1 provides a migration system to handle the schema changes in a structured way. So for that, we can generate and apply migrations. Type 1 allows us to create migrations using CLI commands. So for that, we generate a migrations file with our schema. And like, with that schema, we'll create a migration file under the migration folder. And in the migration file, I can define the schema, just like adding new columns or modifying existing ones. And after that, we'll apply the migration. Once the migration is created, we'll apply it using the migration run command. And after that, we'll make sure backward compatibility is in place. So in the completion in the application board, please show that new column is only used after verifying that the migration has completed, and the column exists, and for data backfill and for the data migrations. If I'm introducing new columns or tables that depend on the existing data, I must backfill the data. This should be done after new schema changes are in place. But before switching to the new schema, I can add a new column to the user table, and we'll create a migration to skip that populates the new column with the data from the old column. So and then we'll run this migration separately to avoid any performance impact on the application. And after that, we can also switch the application to use the new schema, and we can remove all columns for tables. And after that, we can do testing and staging moments. And after that, we'll monitor the migrations. So through all these, we ensure minimal disruptions to help migrate our database system without downtime. So this is all.

That's a to recommend for implementing custom variations loading in tag warm that are not supported out of the box. So when I was using tag warm, it's not just when I use it. So it might encounter situations where the out-of-the-box variations feature, like column extensions, are not sufficient for our needs. To implement custom validation logic in tag one, I can take one of the pros, like, we will use class-built data sets. So we can use the class validator package in combination with the type-safe entity classes to define the custom validation logic. And this will allow us to keep our validation logic separated from our database schema, and it supports both building and custom validation integrators. And we can install the class-related and class transformation. So after installing both, it will make it work with the type-safe entities, and we can define custom validation logic. So we can define a custom validation generator extending the validator constants class from class validator. So for applying this, I want to validate a user's password with certain security criteria. And, yeah, so through all this, we can apply it. And after that, we'll use the custom validity in our entity. So we can use the custom validate in our app or entity classes. And after that, we can build it before persisting data. So when I need to, like, I need to explicitly build our entity before saving it to the database. And after that, we use life cycles also for validations. Type 1 provides entity listeners like before insert and before update. Here, I can perform custom validation logic directly within our entities, and this method compels the validations with the persistence lifestyle but can be effective for schema-specific relations. So through all these, we want to ensure that the time will fit for user entities uniquely. And after using middleware or service, it's just for unit logic gradations. And after that, we'll use database constraints for critical validations. And then we use class-built data for defining custom validations, logic, other decoders in our entities. And for life cycle rules, like before insert and after before update, we got life cycle bound validations. And for the series-based build systems, we'll use complex multi-digital business rules. And for the date, the database constraints or critical data integrity that must always be enforced. So through combining these methods, I can implement robust custom validation logic that meets all our application needs while maintaining clean and maintainable code. So that is all.

So managing SMS processing in Node.js when you do multiple third-party APIs is crucial for optimizing performance and saving both user experience. So first, I'll understand similar Node.js. So Node.js operates on a single-threaded event loop, making it ideal for input/output-bound operations, like API calls, and understanding how to leverage similar programming, such as callbacks, promises, and async events. It's key to optimizing API integrations, and we use promises and async events. So instead of callbacks, we'll use promises and async events for better readability and error handling. This makes our code easier to maintain and understand. And after that, for the concrete request, I'll use Promise.all when making multiple independent API calls, so we can optimize performance by using it correctly, which reduces overall waiting times as all requests are sent simultaneously. And after that, we'll implement rate limiting. So when integrating multiple APIs, it may incorporate limits. So implementing rate limits to avoid exceeding the limits set by APIs, I can use libraries like p-limit to control concurrency. And after that, for error handling and retries, we'll integrate a robust error handling and retrying mechanism for failed requests. We use libraries like exos-retry to automatically retry failed requests. And after that, we use caching for frequently accessed data to significantly improve performance by reducing the number of API calls for data that doesn't change often. So for that, we use in-memory caching, like Redis or local caching strategies. And after that, we'll use API request batching whenever possible. So some APIs support batch requests, allowing us to send multiple requests in a single API call. This reduces the number of network round trips and can enhance performance. And we'll monitor and log API performance, error rates, and response time. This can help to identify bottlenecks and optimize our integration further. And after that, we use a similar approach for heavy processing operations. For operations that involve heavy processing after fetching data, consider using a job queue, such as Celery or RabbitMQ, to handle these operations simultaneously, outside of the main request-response cycle. So through all these, using async for clarity and error handling, we can also leverage Promise.all for concrete requests, and we'll implement rate limiting with the help of libraries like p-limit. And we can also add retry logic for failed requests with the help of libraries like exos-retry. And we can use batching to minimize API calls and for the best request when supported by the APIs. And we'll monitor the logs for performance and errors, and we consider using job queues for heavy post-processing tasks. So following these strategies, I can easily manage SMS processing in Node.js. Just, optimize the performance of our business and ensure seamless integrations with multiple third-party APIs. So that is all. Thank you.