To reduce the size of JWT tokens used in the application for improved performance and reduced bandwidth usage.
- 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.
- Audit current JWT payload.
- Identify and remove non-essential claims.
- Rename claims to shorter versions.
- Implement compression mechanisms for the JWT.
- Test the modified JWT for functionality and size reduction.
- Update documentation and client applications to align with the new token structure.
To optimize the retrieval of tenant information from the admin system by implementing Redis cache.
- 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.
- Set up a Redis server.
- Integrate Redis caching in the application.
- Modify the tenant information retrieval logic to first check the Redis cache.
- Implement logic for updating the cache when tenant information is updated.
- Test for performance improvements and data accuracy.
- Document the caching mechanism and update relevant API documentation.
To enhance scalability and maintainability by converting existing hosted and singleton services into separate microservices.
- 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.
- Identify and list down all hosted and singleton services.
- Define the boundaries and responsibilities of each potential microservice.
- Design and develop APIs for each microservice.
- Implement data migration strategies if necessary.
- Containerize each microservice using Docker.
- Test each microservice independently and then in concert with the rest of the application.
- Document the architecture, APIs, and deployment processes for each microservice.
- Train the development and operations team on the new microservices architecture.
To optimize the database design for better performance and maintainability.
- 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.
- Review the current database schema for normalization levels.
- Partition large tables by
BranchID
. - Conduct an audit of
BranchID
usage in all tables. - Create and optimize database views for data access.
To improve query performance with the effective use of indexes.
- 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.
- Identify tables and columns frequently involved in queries.
- Create and test indexes on
BranchID
. - Develop composite indexes as needed.
- Establish a routine for index maintenance and monitoring.
To optimize database operations and ensure efficient data processing.
- 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.
- Identify routine and complex database operations.
- Develop and test stored procedures.
- Implement parameterization for security and efficiency.
- Utilize batch processing techniques for large data operations.
To enhance overall database performance and application responsiveness.
- 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.
- Perform query analysis and optimization.
- Develop and integrate caching mechanisms.
- Implement asynchronous processing where applicable.
To optimize the application’s interaction with the database for performance and efficiency.
- Efficient Data Retrieval: Optimize data fetch strategies.
- ORM Tuning: Fine-tune ORM usage for efficient database interaction.
- Review and optimize data retrieval methods.
- Analyze and adjust ORM configurations and queries.