System requirements

Functional:

User Management:

Users should be able to create, view, and manage their profiles, including setting their availability for meetings.
Users should be able to send meeting invitations to individuals or groups, with options for time, date, and location.

Meeting Scheduling:

Users should be able to schedule meetings by selecting time slots, adding meeting details (agenda, location, etc.), and inviting participants.
The system should allow users to check the availability of meeting participants before scheduling a meeting.
The system should handle recurring meetings (daily, weekly, monthly).
The system should provide options for virtual meetings with auto-generated video conference links (e.g., Zoom, Microsoft Teams, Google Meet).

Meeting Notifications:

Users should receive notifications for meeting invitations, cancellations, reminders, and updates.
Notifications should be sent via email, SMS, and in-app (push notifications) as per user preferences.

Calendar Integration:

The system should integrate with popular calendar services (e.g., Google Calendar, Outlook) for syncing meetings.
Users should be able to import and export meetings to/from these calendars.

Meeting Management:

Users should be able to update or cancel scheduled meetings, notifying all participants of the changes.
The system should track attendance, with the ability to record who attended and at what time.

Search and Filtering:

Users should be able to search for upcoming meetings based on different parameters such as date, meeting type, participants, and keywords.

Collaboration Features:

The system should support meeting agenda creation, file attachments, and collaboration tools like comments and notes.
Users should be able to view meeting history and past agendas.

Non-Functional:

Performance:
- The system should be able to handle a large number of simultaneous users and meetings without significant delays.
- Response time for scheduling or modifying a meeting should be under 2 seconds under normal conditions.
Scalability:
- The system should be able to scale horizontally to handle increased users and meeting data.
- The backend should be able to manage millions of events, users, and calendars.
Availability:
- The system should be available 99.99% of the time, with planned maintenance windows communicated in advance.
- The system should ensure fault tolerance, especially during high traffic events (e.g., large-scale team meetings).
Security:
- The system should provide secure authentication and authorization mechanisms to ensure that only authorized users can schedule, modify, or view meetings.
- All sensitive data (e.g., user credentials, meeting details) should be encrypted at rest and during transit.
- The system should implement role-based access control (RBAC) for managing permissions (e.g., who can schedule or edit meetings).
Usability:
- The system should have an intuitive and user-friendly interface that supports easy scheduling and management of meetings.
- The system should be accessible via both desktop and mobile devices, with responsive design to cater to different screen sizes.
Interoperability:
- The system should support integration with popular calendar services (e.g., Google Calendar, Microsoft Outlook).
- The system should allow exporting and importing of meeting data in standard formats (e.g., iCal, CSV).
Data Retention:
- The system should maintain historical data for past meetings (e.g., details, attendance) for a configurable period.
- Users should be able to access historical meeting information based on retention policies.
Reliability:
- The system should have data redundancy mechanisms (e.g., backups, failover systems) to ensure that no meeting data is lost.
- It should be able to recover from failures without significant downtime.

Capacity estimation

1. User Base

Active Users (DAU): 10,000
Peak Concurrent Users: 2,000 users (20% of DAU active concurrently)

2. Meetings

Meetings per Day: 4,286 meetings
Peak Meetings per Hour: 357 meetings

3. Database Load

Database Queries per Hour: 3,570 queries (meeting updates, user actions)

4. Storage Requirements

Meeting Metadata Storage: 1.56 GB/year
Attachment Storage: 7.8 TB/year

5. Notification System

Notifications per Day: 40,000 notifications
Peak Notifications per Hour: 4,000 notifications

6. System Resource Requirements

API Request Processing Time: 50-100 ms per request (scalable horizontally)

API design

1. User Authentication APIs

POST /login
- Description: Authenticates a user by verifying their credentials (email, password) and returns a JWT token for session management.
POST /logout
- Description: Logs out the user by invalidating the JWT token, terminating the active session.
POST /register
- Description: Registers a new user by creating a new profile with details such as name, email, password, and availability preferences.

2. Meeting Management APIs

POST /meetings
- Description: Schedules a new meeting by accepting details like time, participants, agenda, and location. It checks participant availability before confirming the schedule.
GET /meetings/{id}
- Description: Retrieves the details of a specific meeting by its ID, including participants, meeting time, and agenda.
PUT /meetings/{id}
- Description: Updates an existing meeting's details (e.g., time, participants) and sends updates to affected participants.
DELETE /meetings/{id}
- Description: Cancels an existing meeting and notifies all participants.
GET /user/meetings
- Description: Retrieves all upcoming and past meetings for the authenticated user.

3. Calendar Integration APIs

POST /calendar/sync
- Description: Syncs the user's calendar with external services (Google Calendar, Outlook) to import and export meeting details.
GET /calendar/events
- Description: Retrieves the events from the external calendar service and integrates them into the system.

4. Notification APIs

POST /notifications/send
- Description: Sends a notification (email, SMS, or push) to a user about a meeting invitation, reminder, or update.
GET /notifications
- Description: Retrieves all notifications for the authenticated user.

5. Search and Filtering APIs

GET /meetings/search
- Description: Searches for meetings by parameters like date, time, participants, and agenda.
GET /user/availability
- Description: Retrieves the availability of the user for scheduling new meetings.

Database design

1. Users Table

Database Details:

Table Name: users
Columns:
- user_id (Primary Key, INT)
- name (VARCHAR)
- email (VARCHAR, unique)
- password (VARCHAR)
- role (VARCHAR, e.g., admin, participant)
- created_at (DATETIME)
- updated_at (DATETIME)

Purpose:

Stores details about users, such as their name, email, password, and role (admin or participant).

Technology Used:

PostgreSQL

Reason:

PostgreSQL is a robust relational database with strong ACID compliance, making it ideal for managing user credentials securely and maintaining referential integrity.

2. Meetings Table

Database Details:

Table Name: meetings
Columns:
- meeting_id (Primary Key, INT)
- title (VARCHAR)
- start_time (DATETIME)
- end_time (DATETIME)
- location (VARCHAR, nullable)
- agenda (TEXT, nullable)
- is_virtual (BOOLEAN)
- meeting_link (VARCHAR, nullable)
- created_at (DATETIME)
- updated_at (DATETIME)

Purpose:

Stores information about meetings, including their title, time, location, agenda, and whether they are virtual or in-person.

Technology Used:

PostgreSQL

Reason:

PostgreSQL provides relational features necessary to handle meeting scheduling, ensuring consistency in meeting data across users.

3. Participants Table

Database Details:

Table Name: participants
Columns:
- participant_id (Primary Key, INT)
- user_id (Foreign Key, INT, references users)
- meeting_id (Foreign Key, INT, references meetings)
- status (VARCHAR, e.g., invited, confirmed, declined)
- joined_at (DATETIME)
- left_at (DATETIME)

Purpose:

Links users to the meetings they attend, along with their status and participation timestamps.

Technology Used:

PostgreSQL

Reason:

The relationship between users and meetings is best managed using a relational database to ensure referential integrity and facilitate efficient querying of participants for each meeting.

4. Notifications Table

Database Details:

Table Name: notifications
Columns:
- notification_id (Primary Key, INT)
- user_id (Foreign Key, INT, references users)
- type (VARCHAR, e.g., invite, reminder, update)
- message (TEXT)
- is_read (BOOLEAN)
- sent_at (DATETIME)

Purpose:

Stores notifications for users related to meetings, such as invitations, updates, and reminders.

Technology Used:

PostgreSQL (or Redis for caching notifications)

Reason:

PostgreSQL is used to store notification metadata with guarantees of durability and querying, while Redis can be used for fast access to recently sent notifications.

5. Attachments Table

Database Details:

Table Name: attachments
Columns:
- attachment_id (Primary Key, INT)
- meeting_id (Foreign Key, INT, references meetings)
- file_name (VARCHAR)
- file_url (VARCHAR)
- uploaded_at (DATETIME)

Purpose:

Stores files associated with meetings (e.g., agendas, presentations) uploaded by users.

Technology Used:

AWS S3 for file storage, PostgreSQL for metadata

Reason:

AWS S3 provides scalable storage for large files, while PostgreSQL is used to store metadata (file names, URLs) that link the attachments to specific meetings.

6. Availability Table

Database Details:

Table Name: availability
Columns:
- availability_id (Primary Key, INT)
- user_id (Foreign Key, INT, references users)
- available_from (DATETIME)
- available_to (DATETIME)
- created_at (DATETIME)
- updated_at (DATETIME)

Purpose:

Stores the availability time slots for users to help schedule meetings.

Technology Used:

PostgreSQL

Reason:

PostgreSQL handles structured data efficiently and allows for complex queries (e.g., checking user availability across different time slots).

7. Meeting Type Table

Database Details:

Table Name: meeting_types
Columns:
- meeting_type_id (Primary Key, INT)
- type_name (VARCHAR, e.g., "virtual", "in-person")
- created_at (DATETIME)
- updated_at (DATETIME)

Purpose:

Stores different types of meetings (virtual or in-person) for classification.

Technology Used:

PostgreSQL

Reason:

PostgreSQL provides relational integrity and supports structured data, ideal for managing and categorizing meeting types.

8. Recurring Meetings Table

Database Details:

Table Name: recurring_meetings
Columns:
- recurring_id (Primary Key, INT)
- meeting_id (Foreign Key, INT, references meetings)
- recurrence_pattern (VARCHAR, e.g., "weekly", "monthly")
- next_occurrence (DATETIME)
- created_at (DATETIME)
- updated_at (DATETIME)

Purpose:

Stores information about recurring meetings, such as recurrence pattern and the next scheduled occurrence.

Technology Used:

PostgreSQL

Reason:

PostgreSQL’s relational capabilities are well-suited to manage recurring meeting schedules, allowing for easy querying and modification of recurrence patterns.

erDiagram
    USER }|..|{ MEETING : schedules
    USER ||--o{ PARTICIPANT : joins
    MEETING ||--o{ PARTICIPANT : has
    MEETING ||--o{ ATTACHMENT : contains
    USER ||--o{ NOTIFICATION : receives
    PARTICIPANT ||--o{ NOTIFICATION : receives
    USER ||--o{ AVAILABILITY : has
    MEETING ||--|{ MEETING_TYPE : is
    MEETING ||--|{ RECURRING_MEETING : is

High-level design

1. Client (Web/Mobile)

The Client is the user-facing interface of the Meeting Calendar System. This component consists of both web and mobile applications that allow users to interact with the system. Users can log in, manage their profiles, schedule and join meetings, view notifications, and interact with meeting content (e.g., attachments, agenda). The client communicates with the backend services through the API Gateway.

Responsibilities:
- Display user interface for meeting scheduling, viewing, and management.
- Handle real-time updates and notifications.
- Send requests (e.g., meeting scheduling, user authentication) to backend services.

2. Load Balancer

The Load Balancer distributes incoming traffic evenly across multiple instances of the API Gateway. This ensures that the system can handle a large number of concurrent users without overwhelming any single backend server. It improves fault tolerance and ensures high availability by routing traffic to healthy service instances.

Responsibilities:
- Distribute incoming requests to API Gateway instances.
- Ensure scalability and fault tolerance.

3. Application Programming Interface (API) Gateway

The API Gateway serves as the entry point for all client requests. It routes incoming requests to the appropriate backend services (e.g., Authentication Service, Meeting Service, Notification Service). It also handles security, such as authentication, rate limiting, and logging.

Responsibilities:
- Route client requests to backend services.
- Handle authentication, authorization, and input validation.
- Aggregate responses from multiple services and send them back to the client.

4. Authentication Service

The Authentication Service is responsible for managing user login, registration, and session management. It validates user credentials (email/password) and generates a JWT (JSON Web Token) for maintaining secure sessions. It also handles token expiration and user logout.

Responsibilities:
- Authenticate users and issue JWT tokens.
- Handle session management and user login/logout.
- Manage user roles and permissions.

5. Meeting Service

The Meeting Service manages the creation, scheduling, and management of meetings. It allows users to set meeting details, such as time, participants, location, and agenda. It checks participant availability and provides an interface for meeting modifications or cancellations. It integrates with other services like notifications and attachments.

Responsibilities:
- Schedule new meetings, including recurring ones.
- Manage participants and meeting updates.
- Handle meeting cancellation and updates.

6. Notification Service

The Notification Service handles sending notifications to users regarding meeting updates, invitations, reminders, and changes. It uses various channels such as email, SMS, and push notifications to notify users. Notifications are cached in Redis to ensure quick delivery.

Responsibilities:
- Send meeting invitations, reminders, and status updates to users.
- Cache notifications for low-latency delivery.
- Handle multi-channel notification delivery (email, SMS, push).

7. Search Service

The Search Service enables users to search for meetings, participants, and other relevant content within the system. It uses Elasticsearch to index meeting data and provide efficient search functionality. Users can filter meetings based on criteria such as date, time, participants, or keywords.

Responsibilities:
- Index meeting data and make it searchable.
- Provide efficient and fast search capabilities for users.
- Support complex query capabilities for meeting and participant searches.

8. Calendar Sync Service

The Calendar Sync Service integrates the system with external calendar applications such as Google Calendar or Microsoft Outlook. This allows users to import/export meetings and sync them with their personal or team calendars.

Responsibilities:
- Sync meetings with external calendar services (e.g., Google Calendar, Outlook).
- Allow users to import and export meeting events.

9. User Management Service

The User Management Service is responsible for managing user profiles, preferences, and availability. It tracks individual user settings, such as their availability for meetings, and ensures that the system can schedule meetings efficiently based on these preferences.

Responsibilities:
- Manage user profile details and preferences.
- Track user availability and assist in scheduling meetings.
- Handle user session data caching through Redis for fast access.

10. Analytics Service

The Analytics Service tracks user behavior and meeting data for generating real-time and historical insights. It provides metrics such as meeting attendance, engagement levels, and system performance. The service uses Apache Kafka for real-time data streaming and Apache Spark for processing large datasets. It stores real-time metrics in DynamoDB and historical data in PostgreSQL.

Responsibilities:
- Collect and process real-time and historical meeting data.
- Provide insights into meeting performance and user engagement.
- Generate reports on system usage, performance, and analytics.

11. PostgreSQL: Meetings, Participants, Users, and Historical Metrics

PostgreSQL is used to store structured data such as user information, meeting details, participant records, and historical metrics. It ensures ACID compliance, relational integrity, and efficient querying for the data.

Responsibilities:
- Store meeting metadata (time, location, participants).
- Store user details (name, email, role, etc.).
- Track participants in each meeting and their attendance.

12. Redis: Notification Cache and User Sessions

Redis is used for caching frequently accessed data like notifications and user session information. This helps improve the system’s performance by reducing the load on databases and providing low-latency access to critical data.

Responsibilities:
- Cache notifications for fast retrieval.
- Cache user session data for quick access and reduced database load.

13. Elasticsearch: Search Index

Elasticsearch is used to index and search meeting data efficiently. It allows for fast, full-text search across various fields (e.g., meeting title, agenda, and participants), enabling users to quickly find relevant meetings.

Responsibilities:
- Index meeting data for efficient and fast search.
- Provide scalable search capabilities across large datasets.

14. AWS S3 and CloudFront CDN

AWS S3 is used for storing meeting attachments (e.g., documents, agendas). CloudFront CDN is used to deliver media files and attachments to users with low latency, ensuring fast content delivery globally.

Responsibilities:
- Store large files such as meeting attachments.
- Deliver media content globally with low-latency through CloudFront CDN.

flowchart TD
    A[Client Web/Mobile] --> LB[Load Balancer]
    LB --> API_GATEWAY[Application Programming Interface API Gateway]
    API_GATEWAY --> AuthService[Authentication Service]
    API_GATEWAY --> MeetingService[Meeting Service]
    API_GATEWAY --> NotificationService[Notification Service]
    API_GATEWAY --> SearchService[Search Service]
    API_GATEWAY --> CalendarSyncService[Calendar Sync Service]
    API_GATEWAY --> UserService[User Management Service]
    API_GATEWAY --> AnalyticsService[Analytics Service]
    MeetingService --> PostgreSQL_Meetings[PostgreSQL Meetings]
    MeetingService --> PostgreSQL_Participants[PostgreSQL Participants]
    UserService --> PostgreSQL_Users[PostgreSQL Users]
    NotificationService --> PostgreSQL_Notifications[PostgreSQL Notifications]
    NotificationService --> Redis_NotificationCache[Redis Notification Cache]
    SearchService --> Elasticsearch_SearchIndex[Elasticsearch Search Index]
    AnalyticsService --> DynamoDB_RealTimeMetrics[DynamoDB Real-Time Metrics]
    AnalyticsService --> PostgreSQL_HistoricalMetrics[PostgreSQL Historical Metrics]
    MeetingService --> S3_AWS_Attachments[AWS S3 Attachments]
    MeetingService --> CloudFront_CDN[CloudFront CDN Media Delivery]
    UserService --> Redis_UserSessions[Redis User Sessions]

Request flows

1. User Login Request

Client (A) sends a login request to the API Gateway (B) with email and password.
API Gateway (B) forwards the login request to the Authentication Service (C) for credential validation.
Authentication Service (C) queries the PostgreSQL User Database (J) to verify the email and password.
The PostgreSQL User Database (J) returns the user data if the credentials are correct.
Authentication Service (C) generates a JWT token for the authenticated user and sends it back to the API Gateway (B).
The API Gateway (B) sends the JWT token back to the Client (A), allowing the user to authenticate further requests without re-entering credentials.

2. Schedule Meeting Request

Client (A) sends a meeting schedule request to the API Gateway (B), including details like meeting time, participants, location, and agenda.
API Gateway (B) forwards the meeting scheduling request to the Meeting Service (D).
The Meeting Service (D) first checks the availability of the user and participants for the meeting.
Meeting Service (D) stores the meeting information in the PostgreSQL Meetings Database (K).
The PostgreSQL Meetings Database (K) returns a confirmation of the meeting creation.
The Meeting Service (D) adds participants to the meeting, storing their data in the PostgreSQL Participants Database (L).
PostgreSQL Participants Database (L) returns confirmation of participant addition.
Meeting Service (D) then sends meeting invitations to the participants through the Notification Service (E).
Notification Service (E) caches the notifications in Redis (M) for fast retrieval and sends the notifications to users (e.g., email, SMS, push).
The API Gateway (B) confirms the meeting schedule to the Client (A), including meeting details.

3. Get Meeting Details Request

Client (A) sends a request to API Gateway (B) to retrieve meeting details (e.g., agenda, time, participants).
API Gateway (B) forwards the request to the Meeting Service (D).
Meeting Service (D) queries the PostgreSQL Meetings Database (K) to retrieve meeting details.
PostgreSQL Meetings Database (K) returns the meeting details.
API Gateway (B) sends the meeting details back to Client (A), which then displays them to the user.

4. Search for Meetings Request

Client (A) sends a search request to API Gateway (B) to search for meetings based on criteria (e.g., date, participants, keywords).
API Gateway (B) forwards the request to the Search Service (G).
Search Service (G) queries the Elasticsearch Search Index (N) to find matching meeting records.
Elasticsearch Search Index (N) returns the search results to Search Service (G).
Search Service (G) sends the search results back to the API Gateway (B).
API Gateway (B) sends the search results to Client (A), where they are displayed.

5. Calendar Sync Request

Client (A) sends a calendar sync request to the API Gateway (B) to sync external calendars (e.g., Google Calendar, Outlook).
API Gateway (B) forwards the request to the Calendar Sync Service (H).
Calendar Sync Service (H) checks if there is any cached data related to the calendar sync in Redis (M).
If no cache exists, Calendar Sync Service (H) retrieves and stores calendar data in the PostgreSQL User Database (J).
The sync status is sent back from Calendar Sync Service (H) to API Gateway (B).
API Gateway (B) confirms the calendar sync status to Client (A).

6. Request Analytics Data

Client (A) sends a request for analytics data (e.g., meeting engagement, attendance) to API Gateway (B).
API Gateway (B) forwards the request to the Analytics Service (I).
Analytics Service (I) queries PostgreSQL Participants Database (L) for real-time engagement metrics (e.g., user participation in meetings).
PostgreSQL Participants Database (L) returns the real-time engagement data.
Analytics Service (I) queries the PostgreSQL Historical Metrics Database (K) for long-term analytics data (e.g., past meeting trends).
PostgreSQL Historical Metrics Database (K) returns the historical metrics.
The combined analytics data is sent back to API Gateway (B).
API Gateway (B) sends the analytics data to Client (A) for display.

7. User Logout Request

Client (A) sends a logout request to API Gateway (B) to log the user out.
API Gateway (B) forwards the request to Authentication Service (C).
Authentication Service (C) invalidates the JWT token associated with the user session.
Authentication Service (C) sends a confirmation of token invalidation to API Gateway (B).
API Gateway (B) confirms the logout status to Client (A), logging the user out.

sequenceDiagram
    participant A as Client (Web/Mobile)
    participant B as API Gateway
    participant C as Authentication Service
    participant D as Meeting Service
    participant E as Notification Service
    participant F as User Management Service
    participant G as Search Service
    participant H as Calendar Sync Service
    participant I as Analytics Service
    participant J as PostgreSQL (User Data)
    participant K as PostgreSQL (Meeting Data)
    participant L as PostgreSQL (Participant Data)
    participant M as Redis (Cache)
    participant N as Elasticsearch (Search Index)
    participant O as AWS S3 (Attachments)
    participant P as CloudFront CDN

    A->>+B: Login Request (email, password)
    B->>+C: Authenticate User
    C->>J: Verify Credentials (email, password)
    J-->>C: Return User Data
    C-->>B: Return JWT Token
    B-->>A: Send JWT Token

    A->>+B: Schedule Meeting Request (meeting details)
    B->>+D: Validate User Availability and Schedule Meeting
    D->>K: Store Meeting Data
    K-->>D: Return Meeting Confirmation
    D->>L: Add Participants to Meeting
    L-->>D: Return Participant Confirmation
    D->>E: Send Meeting Invitation
    E->>+M: Cache Notification
    M-->>E: Return Cache Status
    E-->>B: Send Notification Status
    B-->>A: Return Meeting Scheduled Confirmation

    A->>+B: Get Meeting Details Request (meeting ID)
    B->>+D: Retrieve Meeting Data
    D->>K: Fetch Meeting Data (meeting ID)
    K-->>D: Return Meeting Data
    D-->>B: Return Meeting Details
    B-->>A: Display Meeting Details

    A->>+B: Search for Meetings (criteria)
    B->>+G: Process Search Request
    G->>N: Query Elasticsearch for Meeting Index
    N-->>G: Return Search Results
    G-->>B: Return Search Results
    B-->>A: Display Search Results

    A->>+B: Sync Calendar (external service)
    B->>+H: Sync with External Calendar
    H->>M: Check Existing Data in Cache
    M-->>H: Return Cached Data (if available)
    H->>+J: Retrieve/Store Data in PostgreSQL
    J-->>H: Return Synced Calendar Data
    H-->>B: Return Calendar Sync Confirmation
    B-->>A: Return Calendar Sync Status

    A->>+B: Request Analytics Data (meeting engagement)
    B->>+I: Retrieve Real-Time Analytics
    I->>+L: Query Participant Data (real-time)
    L-->>I: Return Engagement Data
    I->>+K: Query Historical Meeting Data
    K-->>I: Return Historical Metrics
    I-->>B: Return Analytics Data
    B-->>A: Display Analytics Data

    A->>+B: Logout Request
    B->>+C: Invalidate JWT Token
    C-->>B: Token Invalidated
    B-->>A: Logout Confirmation

Detailed component design

1. Authentication Service

The Authentication Service is the backbone for managing user access and ensuring security across the system. It handles the process of validating user credentials (email and password), managing sessions, and issuing JSON Web Tokens (JWT) for subsequent requests. When a user attempts to log in, the service checks the provided credentials against the data stored in the PostgreSQL User Database. If the credentials are correct, a JWT is generated containing encrypted claims such as user ID, roles, and permissions. This token is then used for all future requests, providing a secure, stateless way to authenticate users. The Authentication Service also facilitates user registration, ensuring that each user has a unique email and securely storing passwords using hashing techniques.

Edge Case Handling:

Invalid Credentials: When a user provides incorrect login credentials, the service prevents further access and returns an error message indicating the failure.
- Mitigation: The service incorporates rate limiting to prevent brute-force attacks, automatically locking the user account temporarily after multiple failed login attempts. Additionally, the service uses account lockout mechanisms to add another layer of protection.
Session Expiration: JWT tokens have a limited lifespan and may expire during the user’s session. This necessitates the handling of token expiration or tampering.
- Mitigation: The service checks the token's expiration time for each request and prompts the user to log in again if the token is expired. Additionally, refresh tokens can be implemented to allow users to obtain new JWT tokens without needing to log in again.

Scaling:

Horizontal Scaling: The Authentication Service can scale horizontally to handle a large number of users. Multiple instances of the service can be deployed behind a Load Balancer to distribute traffic evenly. This ensures the system can handle high traffic without downtime or performance degradation.
Data Scaling: User data is stored in PostgreSQL, which can be scaled vertically by optimizing the database schema and adding read replicas to handle high read traffic. For high availability, PostgreSQL can be horizontally scaled using sharding techniques, where user data is partitioned across multiple database instances.

2. Meeting Service

The Meeting Service is responsible for scheduling, managing, and organizing meetings. It facilitates the creation of meetings by accepting details such as time, agenda, location, and participant lists. Once the data is validated, the service stores this information in the PostgreSQL Meetings Database. The service ensures that all participants are available before confirming the meeting. Additionally, the Meeting Service manages recurring meetings (e.g., weekly or monthly) and sends invitations to participants through the Notification Service. The service can also handle modifications to meeting schedules and cancellations, notifying all affected participants accordingly.

Edge Case Handling:

Overlapping Meeting Times: Scheduling conflicts can arise when multiple meetings are scheduled at the same time, especially for users who are invited to more than one meeting.
- Mitigation: The system checks the availability of participants before confirming the meeting. If a conflict is detected, the system suggests alternative times or sends a notification to the user indicating the conflict.
Meeting Cancellations: Cancellations are a common scenario, especially if the meeting organizer changes their mind or if participants can’t attend.
- Mitigation: The service provides an API for cancelling meetings, which triggers automatic updates to the meeting status in the database and sends notifications to participants informing them of the cancellation.

Scaling:

Horizontal Scaling: The Meeting Service can be scaled horizontally by deploying multiple instances behind a Load Balancer. This allows the system to handle a large number of simultaneous meeting requests, especially during peak scheduling times.
Data Scaling: The PostgreSQL Meetings Database can be vertically scaled by increasing its resources (CPU, memory), and horizontally scaled using partitioning and sharding to distribute data across multiple nodes. Additionally, PostgreSQL replicas can be used to handle read-heavy operations, improving system performance during high-demand periods.

3. Notification Service

The Notification Service ensures that users receive notifications related to meetings, such as invitations, reminders, and updates. The service can send notifications through various channels, including email, SMS, and push notifications. These notifications are cached in Redis to ensure low-latency access and to reduce the load on the backend services. The system allows users to choose their preferred notification channels (email, SMS, in-app) and customizes notifications accordingly. The service also provides a mechanism for reminders, sending timely alerts before meetings start.

Edge Case Handling:

Undelivered Notifications: Network failures or temporary service disruptions might cause notifications to fail.
- Mitigation: The service uses a retry mechanism to resend failed notifications a few times. If notifications still cannot be delivered, they are placed in a queue for future delivery, or the user is notified of the failure via alternative channels.
Notification Overload: In some cases, a user may receive excessive notifications, which can cause frustration.
- Mitigation: The Notification Service implements rate limiting and priority queues to ensure that high-priority notifications (e.g., meeting invitations) are delivered immediately, while less urgent notifications (e.g., reminders) are delivered at appropriate intervals.

Scaling:

Horizontal Scaling: The Notification Service can be scaled horizontally to handle a high volume of notifications, especially when there are simultaneous meeting updates or large numbers of users. Multiple instances of the service can be deployed behind a Load Balancer to distribute the load.
Data Scaling: Redis is used to cache notifications for faster delivery, and it can be horizontally scaled by adding more nodes to the Redis cluster. PostgreSQL handles long-term notification storage and can be horizontally scaled using replication and sharding to distribute data and reduce load on individual database instances.

4. Search Service

The Search Service allows users to search for meetings, participants, and other related data in the system. It integrates with Elasticsearch, which indexes and stores the searchable data. When a user initiates a search query, the Search Service queries the Elasticsearch Index and returns the matching results. The service provides support for full-text search, filtering, and sorting based on various parameters, such as date, time, and participant details.

Edge Case Handling:

Slow or Inaccurate Search Results: As the dataset grows, there might be cases where search queries become slower or return inaccurate results if the search index is out of sync.
- Mitigation: Elasticsearch supports real-time indexing, ensuring that the search index is continuously updated with new or modified data. The system can implement re-indexing and batch indexing to keep the search index up-to-date and accurate.
Data Skew: Some search terms or meeting types might dominate the search results, leading to slower response times or overload on specific indices.
- Mitigation: Elasticsearch supports sharding, where data is distributed across multiple nodes, ensuring that no single node is overloaded. Query optimization strategies can be used to ensure faster and more efficient search results.

Scaling:

Horizontal Scaling: Elasticsearch is designed to scale horizontally. By adding more nodes to the Elasticsearch cluster, the system can handle larger datasets and more search queries concurrently without compromising performance.
Data Scaling: Elasticsearch supports sharding and replication to distribute data across multiple nodes. As the amount of data grows, additional shards can be added, improving the search capability and ensuring that queries remain fast even as the dataset scales.

5. Analytics Service

The Analytics Service tracks and processes data related to meetings, user interactions, and system performance. It collects metrics such as meeting duration, participant engagement, and system performance. The service uses Apache Kafka for real-time data streaming and Apache Spark for batch processing and data aggregation. Real-time metrics are stored in DynamoDB, while historical data is stored in PostgreSQL for long-term analysis.

Edge Case Handling:

Data Inconsistency: Real-time data might experience delays or inconsistencies if the system cannot process data in real-time.
- Mitigation: Kafka guarantees the order of events and ensures that data is processed sequentially. Apache Spark performs periodic batch processing to smooth out spikes in data ingestion and avoid inconsistencies.
High Throughput Failures: If the system experiences a surge in data throughput, the processing infrastructure may become overwhelmed.
- Mitigation: The system uses partitioned Kafka topics and scales Apache Spark to distribute processing across multiple nodes. Additionally, DynamoDB automatically scales horizontally to handle high write throughput.

Scaling:

Horizontal Scaling: Both Kafka and Apache Spark are horizontally scalable. Additional Kafka brokers and Spark workers can be added to handle increasing data volumes and processing loads.
Data Scaling: DynamoDB scales horizontally, supporting automatic partitioning to accommodate high read and write throughput. PostgreSQL can be horizontally scaled using read replicas and sharding to support complex queries and data aggregation.

Trade offs/Tech choices

PostgreSQL vs MongoDB:

Trade-off: Used PostgreSQL for structured data (users, meetings) to leverage relational integrity and ACID compliance, and MongoDB for unstructured data (e.g., chat messages) for flexibility and scalability.
Reason: PostgreSQL ensures consistency and supports complex queries, while MongoDB scales better for rapidly changing data like chat logs.

Redis vs Database:

Trade-off: Used Redis for caching notifications and user session data instead of querying the database every time.
Reason: Redis offers low-latency, high-performance access, reducing database load and improving response times.

WebRTC vs Traditional Streaming:

Trade-off: Chose WebRTC for real-time, peer-to-peer video and audio communication instead of traditional server-based streaming.
Reason: WebRTC reduces server load and latency, providing a more scalable solution for video conferencing.

Elasticsearch vs Relational Database:

Trade-off: Used Elasticsearch for search functionality over relational databases.
Reason: Elasticsearch provides faster, scalable full-text search, which relational databases struggle with for large datasets.

Kafka vs Traditional Queueing:

Trade-off: Used Kafka for event streaming instead of traditional queueing systems.
Reason: Kafka handles high-throughput, real-time data streams, ensuring scalability and resilience, especially for analytics and notifications.

Failure scenarios/bottlenecks

Database Overload:

Scenario: High traffic could overwhelm PostgreSQL or MongoDB, causing slow queries.
Mitigation: Implement sharding, replicas, and caching (e.g., Redis) to distribute load.

Network Latency:

Scenario: Poor network conditions affect WebRTC video/audio quality.
Mitigation: Use adaptive bitrate streaming and multi-path streaming to adjust based on network quality.

Notification Failures:

Scenario: Notifications might fail due to service disruptions.
Mitigation: Implement retry mechanisms and store undelivered notifications in a queue for later delivery.

Search Performance:

Scenario: Large datasets can slow down Elasticsearch queries.
Mitigation: Sharding and index optimization in Elasticsearch improve query performance.

High Throughput Failures:

Scenario: Kafka or Apache Spark might struggle with massive real-time data streams.
Mitigation: Use partitioning in Kafka and horizontal scaling for Spark to handle large volumes.

Session Management Issues:

Scenario: Excessive concurrent sessions may overwhelm the Authentication Service.
Mitigation: Auto-scaling for authentication servers and Redis for caching sessions.

Future improvements

Improve Database Scalability:

Future Improvement: Use multi-region replication for databases to enhance performance and fault tolerance.
Mitigation: Implement read replicas and sharding to distribute load across multiple nodes.

Enhance Media Streaming:

Future Improvement: Integrate AI-powered video optimization for better quality under varying network conditions.
Mitigation: Implement multi-path streaming and network monitoring to adapt dynamically to network issues.

Optimize Notification System:

Future Improvement: Implement priority queues to handle urgent notifications better.
Mitigation: Use geographically distributed notification servers to reduce latency and ensure delivery.

Improve Search Efficiency:

Future Improvement: Implement advanced indexing strategies in Elasticsearch for faster search results.
Mitigation: Add sharding and replication in Elasticsearch to distribute the search load.

Optimize Data Processing:

Future Improvement: Adopt serverless data processing for more efficient handling of spikes in traffic.
Mitigation: Scale Kafka and Apache Spark horizontally to manage real-time data streams.

Solutions

iridescent_luminous693 - 01/01/2025

0

utopia4715 - 07/23/2024

2

redzrdi - 01/05/2025

1