The setup is slightly more involved than Foreign Data Wrappers, and is documented below. As data in your database changes, it is streamed into PostgresML in milliseconds, which is very similar to how Postgres streaming replication works as well. Logical replication is designed as a pub/sub system, where your production database is the publisher and PostgresML is the subscriber. This is suitable for medium to large PostgreSQL deployments (e.g. It allows to copy entire tables and schemas from any database into PostgresML and keeping them up-to-date in real time fairly cheaply as the data in production changes. Logical replication is a replication mechanism that's been available since PostgreSQL 10. train on the last two weeks of data, or something similar, FDWs will do its best to fetch only the last two weeks of data in an efficient manner, leaving the rest behind on the primary. PostgresML snapshots the data before training on it, so every time you run ain with a relation_name argument, the data will be fetched from the foreign data wrapper and imported into PostgresML.įDWs are reasonably good at fetching only the data specified by the VIEW, so if you place sufficient limits on your dataset in the CREATE VIEW statement, e.g. The tables are now available for querying in PostgresML. This will import all tables from your production DB public schema into the public schema in PostgresML. IMPORT FOREIGN SCHEMA public FROM SERVER your_production_db INTO public Connect to your PostgresML database as a superuser and run: PostgreSQL comes with postgres_fdw already available, but the extension needs to be explicitly installed into the database. Official PostgreSQL docs explain FDWs with more detail we'll document a basic example below. Setting up FDWs is a bit more involved than pg_dump but they provide real time access to your production data and are good for small to medium size datasets (e.g. Foreign Data Wrappers (10GB - 100GB) ¶įoreign Data Wrappers, or FDWs for short, are another good tool for reading or importing data from another PostgreSQL database into PostgresML. You can also use pg_dump to copy the pgml schema to other servers which will make the trained models available in a distributed fashion. PostgresML tables and functions are located in the pgml schema, so you can safely import your data into PostgresML without conflicts. PostgresML is 8-40x faster than Python HTTP microservices.Backwards Compatible or Bust: Python Inside Rust Inside Postgres.PostgresML is Moving to Rust for our 2.0 Release.Distributed Training Distributed Training Table of contents.
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