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User Management:
Meeting Scheduling:
Joining Meetings:
Audio/Video Communication:
Content Sharing:
Chat and Messaging:
Recording and Playback:
Breakout Rooms:
Meeting Controls:
Notifications and Reminders:
Analytics and Reporting:
Integration:
Performance:
Reliability:
Scalability:
Security:
Usability:
Compatibility:
Data Privacy:
Fault Tolerance:
Maintainability:
Cost Efficiency:
user_id (UUID, Primary Key): Unique identifier for each user.name (VARCHAR): Full name of the user.email (VARCHAR, Unique): User’s email address.password_hash (VARCHAR): Encrypted password.profile_picture_url (TEXT): URL for the user’s profile picture.created_at (TIMESTAMP): Account creation timestamp.meeting_id (UUID, Primary Key): Unique identifier for each meeting.title (VARCHAR): Meeting title or agenda.start_time (TIMESTAMP): Scheduled start time.end_time (TIMESTAMP): Scheduled end time.meeting_link (VARCHAR): Unique meeting link.host_id (UUID, Foreign Key): ID of the host user.is_recorded (BOOLEAN): Indicates whether the meeting is recorded.participation_id (UUID, Primary Key): Unique participation record.user_id (UUID, Foreign Key): User participating in the meeting.meeting_id (UUID, Foreign Key): Meeting being attended.role (ENUM): Role of the participant (e.g., host, co-host, participant).message_id (UUID, Primary Key): Unique identifier for each message.meeting_id (UUID, Foreign Key): Meeting in which the message was sent.sender_id (UUID, Foreign Key): User who sent the message.content (TEXT): The actual chat message.sent_at (TIMESTAMP): Time the message was sent.recording_id (UUID, Primary Key): Unique identifier for each recording.meeting_id (UUID, Foreign Key): Meeting associated with the recording.storage_url (TEXT): URL to the recording file in cloud storage.created_at (TIMESTAMP): Recording creation timestamp.format (ENUM): Recording format (e.g., MP4, MP3).notification_id (UUID, Primary Key): Unique identifier for each notification.user_id (UUID, Foreign Key): User receiving the notification.meeting_id (UUID, Foreign Key): Associated meeting (if applicable).message (TEXT): Notification content.sent_at (TIMESTAMP): Timestamp when the notification was sent.is_read (BOOLEAN): Indicates if the notification has been read.metric_id (UUID, Primary Key): Unique identifier for each metric.meeting_id (UUID, Foreign Key): Meeting associated with the metric.metric_type (ENUM): Type of metric (e.g., duration, engagement).value (FLOAT): Metric value.timestamp (TIMESTAMP): Time when the metric was recorded.metric_id (UUID, Primary Key): Unique identifier for each metric.user_id (UUID, Foreign Key): User associated with the metric.metric_type (ENUM): Type of metric (e.g., time spent, meetings attended).value (FLOAT): Metric value.timestamp (TIMESTAMP): Time when the metric was recorded.User Login:
Meeting Creation:
Joining a Meeting:
Chat Messaging:
Screen Sharing:
Notifications:
Search:
Analytics:
Logout:
The Authentication Service plays a critical role in ensuring that only authorized users can access the video conferencing system. Upon receiving the login request with credentials (email and password), the service validates the data against the User Database (PostgreSQL). Once verified, it generates a JSON Web Token (JWT), which contains user-specific information (such as user ID, role, and permissions) that will be used in all subsequent requests to validate user identity and manage access. This JWT token is encrypted and has a set expiration time to increase security, ensuring that the session remains valid for a limited duration. The service also integrates with Redis, which stores active session data to allow fast lookups and token validation without querying the database repeatedly, improving performance and scalability.
For handling edge cases, the Authentication Service manages scenarios like expired or invalid tokens, ensuring that no unauthorized users can access restricted resources. When a token expires, the service forces the user to log in again to obtain a fresh token. If an invalid or tampered token is detected, the service immediately denies access, preventing unauthorized actions. Additionally, the service deals with multiple concurrent logins, ensuring secure session management across devices. If a user logs in from multiple devices, the service can manage separate sessions for each device or limit the number of concurrent logins, based on security policies. By ensuring token expiration and managing concurrent sessions, this service plays a pivotal role in maintaining system integrity and security.
The Meeting Service is at the core of the system, responsible for scheduling, managing, and controlling meetings. When a user schedules a meeting, they provide essential information such as the title, start time, participants, and meeting settings (e.g., password protection, waiting rooms). This information is then stored in PostgreSQL, which provides a relational structure to manage the meeting's metadata efficiently. The service generates a unique meeting ID, which is used to create a secure link for participants to join. During the meeting lifecycle, the Meeting Service ensures smooth operation by handling features such as enabling/disabling participant audio or video, managing breakout rooms, and tracking participant activity. It also integrates with the Authentication Service to validate participant credentials before allowing entry into the meeting.
Edge cases are common in a dynamic meeting environment. One key issue is the potential for overlapping meetings, where two users attempt to schedule meetings at the same time, possibly leading to conflicts in room allocation or scheduling errors. The service ensures that meetings are scheduled correctly by checking for availability and suggesting alternative times if conflicts are detected. Additionally, there are instances where a meeting might be canceled or missed by participants. In such cases, the Meeting Service manages the meeting's status, updating participants and triggering automatic archiving or deletion of the meeting details. This ensures that the system remains organized and efficient, with canceled or no-show meetings cleaned up appropriately to save resources and storage.
The Chat Service enables real-time communication between meeting participants. It supports both group chat and private messaging, ensuring that users can communicate with each other efficiently during a meeting. When a user sends a message, the service immediately stores it in MongoDB, a NoSQL database designed to handle high-throughput, unstructured data like chat logs. MongoDB allows the service to scale horizontally as the number of messages increases, while maintaining fast read and write operations. To optimize performance, the service integrates with Redis, a fast in-memory caching system, to store frequently accessed messages and reduce the load on the database, providing low-latency message retrieval for active participants. Redis also ensures that chat history is cached and can be quickly fetched, especially for ongoing meetings where chat activity is high.
In terms of edge cases, one challenge is message duplication, which can occur due to network failures or accidental user actions, where the same message is sent multiple times. The Chat Service addresses this by implementing deduplication logic to detect and discard identical messages before storing them in the database. Another issue is large message sizes or attachments, which might exceed the system’s limits for a single document in MongoDB. To handle this, the service uses AWS S3 to store media files and GridFS for storing large messages, ensuring that even messages with media or extensive text are properly handled without overwhelming the database. These strategies ensure that the chat functionality remains fast, reliable, and scalable.
The Media Service is responsible for managing all media-related operations, including real-time audio/video streaming, screen sharing, and meeting recordings. The service handles video streams from participants using WebRTC for low-latency, peer-to-peer communication. It ensures that audio and video data is transmitted securely and efficiently to all participants in the meeting. Additionally, the Media Service handles the recording of meetings, storing the media in AWS S3. Media files such as video recordings, screen shares, or audio files are uploaded using multipart uploads, which splits the media into smaller chunks, allowing for more reliable uploads, especially for large files. These media files are then cached in CloudFront CDN for faster retrieval by users across different geographical locations, improving the overall user experience.
Handling large media files can lead to edge cases such as network instability and large file uploads. In cases of network instability, where a user’s connection is intermittent or drops, WebRTC ensures that the video and audio streams are adaptive, adjusting the bitrate based on network conditions to maintain a stable connection. Additionally, large video recordings can pose challenges when trying to upload or stream them, especially with limited bandwidth. To address this, the Media Service compresses videos before storage and uses AWS S3’s multipart upload feature, which breaks the file into smaller parts, allowing for more efficient and reliable uploads. By employing these techniques, the service ensures that media content is delivered reliably, even in challenging conditions.
The Notification Service is responsible for keeping users informed by sending them timely updates regarding meetings, messages, and system events. It sends notifications for scheduled meetings, reminders, new messages, and other important updates. It integrates with Firebase Cloud Messaging (FCM) for push notifications to mobile and web clients and uses AWS SNS to send SMS and email notifications. The service caches notifications in Redis, allowing quick access to frequently viewed notifications. This caching ensures that notifications can be sent without querying the database for every message or alert. The service is designed to send notifications in real-time, as well as deliver periodic updates like daily summaries or meeting reminders.
Handling edge cases in notifications involves scenarios like missed notifications (when users are offline or disconnected) and overwhelming notification volume (which can flood users with excessive alerts). To address missed notifications, the system queues notifications and sends them once the user is back online, ensuring they don’t miss important updates. For overwhelming volumes, the Notification Service employs throttling mechanisms to limit the number of notifications sent per user in a given time period, prioritizing high-urgency alerts (e.g., meeting invitations) over non-urgent ones. Additionally, caching in Redis helps prevent unnecessary duplicate notifications and ensures that only unique, relevant notifications are delivered.
The Analytics Service is responsible for gathering and processing real-time and historical data about user behavior, meeting activity, and system performance. It tracks key metrics such as meeting duration, participant engagement, and feature usage. This data is essential for providing insights into how the system is being used and identifying areas for improvement. The service uses Apache Kafka for event streaming, ensuring that events like meeting starts, participant joins, and messages sent are captured in real-time. These events are processed by Apache Spark, which aggregates data for reporting purposes. The results are stored in DynamoDB for fast, real-time access and in PostgreSQL for long-term storage of historical data.
Edge cases for analytics include scenarios like delayed data processing, where high volumes of real-time data might be temporarily delayed before being aggregated, and data skew, where some meetings or users generate disproportionate amounts of data. To address these issues, the service uses streaming data pipelines to handle high-throughput data and batch processing for periodic reporting. The system also implements sharding and partitioning to distribute data efficiently across multiple servers or clusters, ensuring that data ingestion remains fast and scalable. These techniques ensure that the Analytics Service can handle high data volumes without impacting performance, providing valuable insights in real time.
These trade-offs were made to balance between performance, scalability, and complexity, ensuring the system could efficiently handle the expected load while maintaining flexibility and reliability.
Database Overload:
Network Latency:
Real-Time Data Processing Delays:
Notification Delivery Failures:
Large Media Files:
Scaling Chat Service:
Single Points of Failure:
These improvements would enhance system scalability, reduce latency, and ensure better fault tolerance.