Implementing Dual-Write for User/Tenant Data Separation

Introduction

Our application initially stored user-specific data directly within the Tenant model. As we've grown, the need for a more scalable and maintainable solution became apparent. To address this, we embarked on a project to separate user profiles into a dedicated table within each tenant's schema.

The Challenge

Storing user data directly in the Tenant model created several challenges:

• Scalability: As the number of users per tenant increased, the Tenant model became bloated, impacting performance.
• Data Integrity: Managing user-specific data within a shared model increased the risk of data inconsistencies.
• Flexibility: Adding new user-specific attributes required modifying the Tenant model, leading to potential conflicts.

The Solution

We implemented a dual-write pattern to migrate user profile data to a dedicated profiles table within each tenant's schema. This involved the following steps:

1. Create Profile Model: A new Profile model was created to represent user-specific data.
2. Schema Migration: Each tenant's schema was updated to include the profiles table.
3. Dual-Write Implementation: All services were updated to write user profile data to both the Tenant model and the new Profile model.
4. Dual-Read with Fallback: Views were updated to read user profile data from the Profile model, with a fallback to the Tenant model for backward compatibility during the transition period.

Key Decisions

1. Dual-Write: Using a dual-write pattern allowed us to migrate data without downtime or disruption to existing services.
2. Backward Compatibility: Implementing dual-read with fallback ensured that existing views continued to function correctly during the migration.

Results

• Improved data organization and separation of concerns.
• Enhanced scalability by reducing the size of the Tenant model.
• Increased flexibility for adding new user-specific attributes.

Lessons Learned

Implementing a dual-write pattern requires careful planning and coordination across all services. Thorough testing and monitoring are essential to ensure data consistency and prevent data loss. It's crucial to have a rollback plan in case any issues arise during the migration process.