lambda-redesign

A clone of AWS Lambda that executes Python Script and offers FaaS. The service is distributed with a master slave architeture, and built on consensus algorithm between slaves.

System Architecture

Design Designing an entire system with all its complexities is a detailed process that involves considering various aspects like architecture, components, communication protocols, data flow, and more. Below is a high-level design of the system you described, considering its complexities:

System Architecture: [kafka to be added]

Components:

1. User Interface (UI):

   • A web-based dashboard for users to interact with the system.
   • A command-line interface (CLI) for programmatic interaction.

2. API Gateway:

   • Handles incoming requests and routes them to the appropriate services.

3. Task Scheduler:

   • Receives code submissions and schedules tasks based on user-defined cron expressions or intervals.

4. Execution Manager:

   • Manages the execution of Python code in isolated environments (containers).
   • Monitors resource usage and enforces resource limits.

5. Replication Manager:

   • Manages primary and replica nodes for fault tolerance.
   • Replicates user code, configurations, and results to ensure availability.

6. Consensus Module:

   • Implements a consensus algorithm (e.g., Raft or Paxos) to maintain consistency among primary and replica nodes.

7. Result Storage:

   • Stores the results of code executions in a distributed and fault-tolerant manner.

8. Logging and Monitoring:

   • Captures logs generated during code execution.
   • Provides real-time and historical performance metrics.

9. User Management:

   • Handles user authentication and authorization.
   • Manages user roles and permissions.

10. Dependency Management:

    • Manages Python runtime versions and dependencies for code execution.

11. Event Trigger System:

    • Allows integration with external event sources for triggering code execution.

12. Notification Service:

    • Sends notifications to users based on task status (success, failure).

13. Cost Management System:

    • Tracks resource usage for billing purposes.
    • Provides cost estimates to users.

Communication Protocols:

• gRPC: For communication between various services.
• REST API: For user interactions and external integrations.
• Message Queues (e.g., RabbitMQ): For handling asynchronous communication between components.

Data Flow:

1. User Submits Code:

   • Users submit Python code through the UI or CLI.

2. Task Scheduling:

   • Task Scheduler receives code submissions and schedules tasks based on user-defined schedules.

3. Code Execution:

   • Execution Manager executes the code in isolated environments, managing resources and dependencies.

4. Logging and Monitoring:

   • Execution Manager logs execution details and metrics.

5. Result Storage:

   • Results of code execution are stored in a distributed and fault-tolerant storage system.

6. Notification:

   • Users receive notifications based on task status.

7. Event Trigger (Optional):

   • Event Trigger System may initiate code execution based on external events.

8. Consensus and Replication:

   • Consensus Module ensures consistency among primary and replica nodes.
   • Replication Manager replicates user code, configurations, and results for fault tolerance.

9. Cost Tracking:

   • Cost Management System tracks resource usage for billing.

Security Considerations:

• Isolation: Ensure secure isolation between code executions.
• Authentication and Authorization: Implement robust user authentication and authorization mechanisms.
• Code Analysis: Scan user-submitted code for security vulnerabilities.
• Secure Communication: Use secure communication channels between services.

Scalability Considerations:

• Horizontal Scaling: Allow for adding more nodes to handle increased load.
• Load Balancing: Distribute tasks evenly across execution environments.
• Auto-scaling: Automatically adjust resources based on demand.

Other Considerations:

• Documentation: Provide comprehensive documentation for users and developers.
• Testing: Implement unit tests, integration tests, and performance tests.
• Backup and Recovery: Regularly backup user data and configurations, and have a disaster recovery plan.

Technologies:

• Programming Language: Golang for its concurrency support and performance.
• gRPC: For efficient communication between services.
• Docker: For containerization.
• Kubernetes: For orchestration and scaling.
• etcd or Consul: For distributed configuration management.
• Raft or Paxos Library: For consensus and fault tolerance.

Deployment:

• Deploy the system on a cloud infrastructure like AWS, GCP, or Azure.
• Utilize container orchestration tools like Kubernetes for managing the deployment.

Maintenance:

• Implement automated updates and health checks.
• Regularly review and update security measures.
• Provide a mechanism for users to report issues.

Data Flow with Kafka: User Submits Code:

Users submit Python code through the UI or CLI. Task Scheduling:

Task Scheduler publishes scheduling events to a Kafka topic. Code Execution:

Execution Manager subscribes to the code execution topic, retrieves tasks, and executes code. Publishes execution results to a Kafka topic. Logging and Monitoring:

Optionally, publish logs and metrics events to Kafka for centralized monitoring. Result Storage:

Listens for execution result events on Kafka topics for storage. Notification:

Users receive notifications based on Kafka events. Consensus and Replication:

Utilize Kafka for communication and coordination among primary and replica nodes. Event Trigger (Optional):

Event Trigger System may initiate code execution based on external events and publish to Kafka.