Scaling Strategy for JMMERP

1. Reducing JWT Token Size

Objective

To reduce the size of JWT tokens used in the application for improved performance and reduced bandwidth usage.

Proposed Strategy

• Minimize Payload Data: Review and minimize the data included in the JWT payload. Only include essential claims.
• Use Efficient Claim Names: Shorten claim names to reduce overall token size.
• Compression: Implement compression algorithms like GZIP to reduce the token size during transmission.
• Token Storage: Store larger token data in server-side storage and issue a smaller reference token to the client.

Implementation Steps

1. Audit current JWT payload.
2. Identify and remove non-essential claims.
3. Rename claims to shorter versions.
4. Implement compression mechanisms for the JWT.
5. Test the modified JWT for functionality and size reduction.
6. Update documentation and client applications to align with the new token structure.

2. Implementing Redis Cache for Tenant Information

Objective

To optimize the retrieval of tenant information from the admin system by implementing Redis cache.

Proposed Strategy

• Caching Tenant Data: Use Redis to cache tenant information, reducing the need for frequent database queries.
• Cache Invalidation: Implement a strategy for cache invalidation and update to ensure data consistency.

Implementation Steps

1. Set up a Redis server.
2. Integrate Redis caching in the application.
3. Modify the tenant information retrieval logic to first check the Redis cache.
4. Implement logic for updating the cache when tenant information is updated.
5. Test for performance improvements and data accuracy.
6. Document the caching mechanism and update relevant API documentation.

3. Converting Hosted and Singleton Services to Microservices

Objective

To enhance scalability and maintainability by converting existing hosted and singleton services into separate microservices.

Proposed Strategy

• Service Identification: Identify the functionalities of the hosted and singleton services that can be modularized.
• Decoupling Services: Ensure each microservice is loosely coupled and independently deployable.
• Microservice Design: Design each microservice with its own database (if required) and API.

Implementation Steps

1. Identify and list down all hosted and singleton services.
2. Define the boundaries and responsibilities of each potential microservice.
3. Design and develop APIs for each microservice.
4. Implement data migration strategies if necessary.
5. Containerize each microservice using Docker.
6. Test each microservice independently and then in concert with the rest of the application.
7. Document the architecture, APIs, and deployment processes for each microservice.
8. Train the development and operations team on the new microservices architecture.

4. Database Optimization

Reevaluate Database Design

Objective

To optimize the database design for better performance and maintainability.

Proposed Strategy

• Normalization/Denormalization: Assess the level of normalization to balance performance and data redundancy.
• Table Partitioning: Implement partitioning by BranchID for large tables.
• Audit BranchID Usage: Evaluate the necessity of BranchID in all tables.
• Utilize Database Views: Use views for complex joins and filtering operations.

Implementation Steps

1. Review the current database schema for normalization levels.
2. Partition large tables by BranchID.
3. Conduct an audit of BranchID usage in all tables.
4. Create and optimize database views for data access.

Implementing Indexes

Objective

To improve query performance with the effective use of indexes.

Proposed Strategy

• Index on BranchID: Implement indexes on BranchID where frequently queried.
• Composite Indexes: Create composite indexes for multi-column queries.
• Index Maintenance: Regularly review and maintain indexes for optimal performance.

Implementation Steps

1. Identify tables and columns frequently involved in queries.
2. Create and test indexes on BranchID.
3. Develop composite indexes as needed.
4. Establish a routine for index maintenance and monitoring.

Stored Procedures

Objective

To optimize database operations and ensure efficient data processing.

Proposed Strategy

• Routine Operations: Use stored procedures for complex and routine database tasks.
• Parameterized Procedures: Ensure procedures are parameterized for security and performance.
• Batch Processing: Implement batch operations in stored procedures for bulk data handling.

Implementation Steps

1. Identify routine and complex database operations.
2. Develop and test stored procedures.
3. Implement parameterization for security and efficiency.
4. Utilize batch processing techniques for large data operations.

Optimization Strategies

Objective

To enhance overall database performance and application responsiveness.

Proposed Strategy

• Query Optimization: Analyze and refine slow-performing queries.
• Caching Strategy: Use caching for frequently accessed data.
• Asynchronous Processing: Implement asynchronous operations for non-critical tasks.

Implementation Steps

1. Perform query analysis and optimization.
2. Develop and integrate caching mechanisms.
3. Implement asynchronous processing where applicable.

Refactoring Data Access in Application

Objective

To optimize the application’s interaction with the database for performance and efficiency.

Proposed Strategy

• Efficient Data Retrieval: Optimize data fetch strategies.
• ORM Tuning: Fine-tune ORM usage for efficient database interaction.

Implementation Steps

1. Review and optimize data retrieval methods.
2. Analyze and adjust ORM configurations and queries.