Skip to content

Technology Stack

Jump to bottom
Nurmuhammad edited this page Sep 21, 2024 · 1 revision

1. Backend

Overview
The backend of the platform will manage job data collection, processing, and API services, ensuring that job listings are aggregated, standardized, and delivered efficiently to the frontend.

Key Technologies

  1. Node.js: The main framework for the backend server, chosen for its non-blocking I/O model and ability to handle large-scale asynchronous tasks like job scraping and real-time notifications.
  2. Express: A lightweight framework built on Node.js that will handle API routing, middleware, and HTTP request management.
  3. Python: Used for developing AI models and machine learning algorithms to analyze and standardize job listings.
  4. MySQL: A relational database to store job listings, user profiles, and employer data with scalability and reliability.
  5. TDLib: Telegram Database Library for seamless integration with Telegram API, enabling efficient parsing and extraction of job posts from channels and groups.

Examples of Use

  • Node.js handles incoming job scraping requests and processes them asynchronously to avoid performance bottlenecks.
  • Express is used to build a REST API that communicates between the frontend and backend, enabling users to search for jobs or submit applications.
  • Python machine learning scripts are triggered to clean and structure raw job data before storing it in the MySQL database.

2. Frontend

Overview
The frontend will serve as the interface for job seekers and employers to interact with the platform. It must be highly responsive, intuitive, and support real-time updates.

Key Technologies

  1. Next.js: A React-based framework used for building the platform’s user interface. Chosen for its server-side rendering, which improves performance and SEO.
  2. React: A JavaScript library for building dynamic and interactive UI components, ensuring a smooth user experience across devices.
  3. HTML5/CSS3: Used to ensure responsive and accessible design across different platforms, including desktop, tablet, and mobile devices.

Examples of Use

  • Next.js is used to dynamically generate job listings and handle server-side rendering for faster load times and SEO optimization.
  • React components manage user interactions, such as applying for jobs, viewing profiles, and using the search filters.
  • HTML5/CSS3 is used to create a responsive and mobile-friendly layout that ensures users on all devices have an optimal experience.

3. Database

Overview
The platform requires a robust and scalable database system to manage large volumes of job listings, user data, and employer information.

Key Technologies

  1. MySQL: A relational database that stores structured job data, user profiles, job applications, and employer information. Its indexing capabilities ensure fast search queries and data retrieval.
  2. Sequelize: An ORM (Object-Relational Mapping) tool used to manage interactions between Node.js and the MySQL database, allowing for easier query management and database migration.

Examples of Use

  • MySQL stores all structured job listings after they are processed by the AI module, allowing for fast retrieval during user searches.
  • Sequelize is used to abstract complex SQL queries into manageable code, simplifying database operations for developers.
  • The database handles high volumes of job data with optimized indexing to keep response times low, even during peak usage.

4. AI and Machine Learning

Overview
AI plays a crucial role in the platform by standardizing job listings, detecting duplicates, and making the job search more intuitive.

Key Technologies

  1. Python: The primary programming language for building machine learning models. Python is widely used in the AI/ML community and offers powerful libraries for data analysis.
  2. Scikit-learn: A Python library used for building machine learning models, including classification, clustering, and natural language processing.
  3. Natural Language Processing (NLP): Used to analyze job descriptions, extract key details (e.g., job title, location, salary), and normalize the data into a consistent structure.

Examples of Use

  • Python scripts are used to process raw job data scraped from Telegram or job websites, standardizing it into a uniform format.
  • Scikit-learn is used to train machine learning models that classify job listings and detect duplicates.
  • NLP models analyze job descriptions to extract essential details such as job requirements, salary, and company names.

5. Integrations

Overview
The platform needs to interact with several external systems for data collection, notifications, and user authentication.

Key Technologies

  1. TDLib: The Telegram Database Library used to interact with Telegram API for scraping job listings from channels and groups.
  2. Telegram Bot API: Used to send real-time notifications and updates to users about relevant job postings.
  3. Twilio or other SMS APIs: Used for phone number verification via SMS during user registration and login.
  4. Payment Gateways (Stripe, PayPal, etc.): For handling transactions related to job postings, such as fees for promoted listings.

Examples of Use

  • TDLib interacts with the Telegram API to gather job posts in real-time from channels and stores them in the database.
  • Telegram Bot API is used to send personalized job notifications and alerts to users based on their preferences.
  • Twilio handles user authentication by sending SMS verification codes during account creation and login.
  • Payment Gateway integration allows employers to pay for job promotions or listings, ensuring a smooth and secure transaction process.

6. DevOps and Deployment

Overview
The platform will need a stable and scalable infrastructure to handle continuous integration and deployment (CI/CD), as well as cloud-based hosting.

Key Technologies

  1. Docker: Used to containerize the application, ensuring consistency across development, staging, and production environments.
  2. Jenkins or GitHub Actions: Tools used for continuous integration (CI) and continuous deployment (CD), automating the testing and deployment of new features.
  3. AWS or Google Cloud Platform (GCP): Cloud platforms for hosting the platform, providing scalable compute, storage, and networking resources.
  4. Nginx: Used as a reverse proxy and load balancer to manage incoming traffic and distribute it across multiple backend services.

Examples of Use

  • Docker ensures that the application runs in the same environment across all stages, reducing errors caused by differences in dependencies.
  • Jenkins or GitHub Actions automate the testing and deployment pipelines, allowing for rapid updates and bug fixes.
  • AWS or GCP host the platform in the cloud, providing on-demand scaling to handle fluctuations in traffic.
  • Nginx serves as a load balancer, distributing traffic to backend services based on load, ensuring high availability.

Next: Integrations

Clone this wiki locally