Shows when and how to pick MySQL instead of ParadeDB, plus FDW-based integration steps from PostgreSQL.
MySQL’s InnoDB engine excels at high-volume OLTP, low-latency reads, and straightforward replication. If your workload relies on short, indexed transactions and you need proven tools like Group Replication or Aurora MySQL, selecting MySQL keeps throughput predictable and operational cost low.
ParadeDB targets vector search and hybrid similarity queries. When your application is purely row-oriented and you can forgo ANN search in favor of simpler scaling, MySQL is the better choice.
Use the mysql_fdw
extension so PostgreSQL can query MySQL tables without ETL. Create a foreign server, map credentials, then import the needed tables.
1) CREATE EXTENSION mysql_fdw;
2) CREATE SERVER
3) CREATE USER MAPPING
4) IMPORT FOREIGN SCHEMA
5) Query the foreign tables. Each step is idempotent and scriptable for CI pipelines.
SELECT id, name, email
FROM mysql_customers
WHERE created_at > NOW() - INTERVAL '30 days';
Align time zones, collations, and character sets to avoid data mismatches. Store timestamps as UTC in both systems and monitor replica lag so cross-database JOINs stay consistent.
Choose ParadeDB when you need ANN vector search, hybrid filtering, or tight PostgreSQL type integration. It offers millisecond cosine similarity searches that MySQL cannot match today.
Do not assume MySQL’s FULLTEXT equals ParadeDB vectors—they solve different problems. Always benchmark with production-sized data before migrating and verify data types when using FDWs.
Yes. Use mysql_fdw
for MySQL and local ParadeDB tables. PostgreSQL performs the JOIN, so minimize transferred columns to cut latency.
Not natively. You would need external plugins or services; ParadeDB provides ANN indexes out of the box.
Row-oriented data moves easily via pg_dump
or FDWs; vector columns require serialization to JSON or binary before import.