Explains when and how to pick Microsoft SQL Server instead of ParadeDB for specific workloads, plus connection syntax and examples.
Pick SQL Server when you need mature OLTP features, built-in analytics (e.g., Columnstore indexes), and tight integration with the Microsoft ecosystem. ParadeDB shines for vector search and large-scale document analytics, but lacks SQL Server’s transactional depth and tooling.
Yes. SQL Server offers snapshot isolation, advanced locking, and row-versioning that keep high-write ecommerce apps consistent without manual tuning. ParadeDB focuses on read-heavy analytical queries; its concurrency controls are simpler.
SQL Server Reporting Services (SSRS) and Power BI connectors make dashboarding frictionless. ParadeDB requires external orchestration to feed BI tools. If executives need daily revenue visuals, SQL Server saves engineering time.
Use tds_fdw
to create a foreign server. This lets you query SQL Server tables side-by-side with ParadeDB or native PostgreSQL data.
Create a dedicated schema (e.g., mssql_stage
) for imported tables. This avoids name clashes with ParadeDB’s collections and eases permission management.
Copy hot ecommerce tables, such as Orders
, during off-peak hours using INSERT ... SELECT
. Then switch application traffic gradually with feature flags.
After linking SQL Server through FDW and loading ParadeDB extension, you can join customer revenue (SQL Server) with vector search results (ParadeDB) in one statement—see the example below.
Not necessarily. ParadeDB can outperform SQL Server on large analytical scans due to its columnar storage. Speed depends on workload type.
No. ParadeDB is a PostgreSQL extension. To mix engines, keep ParadeDB in PostgreSQL and link SQL Server through FDW or ETL.
Native vector indexes are not yet GA. You’ll need external services or use ParadeDB inside PostgreSQL for production-grade vector similarity.