Entity Framework with Dynamic Table Mapping Based on a Parameter (PostgreSQL Partitioning)

Question

I am using Entity Framework with PostgreSQL and need a way to dynamically map a model to different tables based on a parameter.

For example, I have a Cars model, and each company should have its own separate table:

• Cars_A for Company A
• Cars_B for Company B
• Cars_C for Company C (created dynamically when a new company registers)

Each company should only see its own cars. New companies are added every week, so I cannot create separate models for each company. Instead, I need a single model that dynamically maps to the corresponding table (Masini_X) at runtime.

My questions are:

1. How can I make Entity Framework dynamically map my model to the correct table based on the company?
2. Is there a way to handle this efficiently without manually updating the DbContext for every new company?
3. Would PostgreSQL partitioning be a good approach for this use case, or is there a better alternative?

The main reason for this approach is performance and data isolation, as a single table would be too large to manage efficiently. Any suggestions or alternative solutions would be greatly appreciated!

[Edit]

I have found two potentially more efficient solutions:

1. Using Dapper – Since Dapper is a micro-ORM, it allows for direct SQL queries, making it easier to dynamically route queries to different tables without the constraints of Entity Framework.

2. Keeping a single large table and using Redis for caching – Instead of splitting tables, I could store each company’s cars in a separate Redis hash, significantly reducing database queries and improving performance.

Would these approaches be more scalable in the long run? Any insights or potential drawbacks?

Answer 1

Hi, @Florin. Welcome to Microsoft Q&A. 

1.Entity Framework cannot dynamically create Model based on Table and map it to DbContext. You could consider using SQL query instead of Entity Framework to achieve dynamic mapping.

 

2.A single table is too large to be managed effectively. PostgreSQL partitioning is a good method at this time.

PostgreSQL partitioning advantages:

Performance improvement

The query will only scan the relevant partitions instead of the entire table, which significantly reduces I/O operations.

Partitioned tables support parallel queries, which returns results faster.

Data management

Old data could be easily archived or deleted.

Isolation and security

Fine-grained permission management could be performed based on partitions, and different users could access different partitions.

 

PostgreSQL partitioning disadvantages:

Requires additional management overhead.

Complex queries may require multiple accesses to partitions.

 

Alternatives:

Horizontal Sharding

Split data horizontally across multiple database nodes to improve performance and scalability. Suitable for distributed systems that need to handle large amounts of data.

 

Archive Tables

Keep the size of the main table small and manageable by regularly migrating old data to archive tables. Access performance to historical data may be affected, but query performance on the main table will improve.

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