# PostgreSQL

This guide describes how to connect Aporia to an PostgreSQL data source in order to monitor your ML Model in production. 

We will assume that your model inputs, outputs and optionally delayed actuals can be queried with SQL. This data source may also be used to connect to your model's training set to be used as a baseline for model monitoring.

### Create a read-only user for PostgreSQL access

In order to provide access to PostgreSQL, create a read-only user for Aporia in PostgreSQL.

Please use the SQL snippet below to create the user for Aporia. Before using the snippet, you will need to populate the following:

* `<aporia_password>`: Strong password to be used by the user.
* `<your_database>`: PostgreSQL database with your ML training / inference data.
* `<your_schema>`: PostgreSQL schema with your ML training / inference data.

```sql
CREATE USER aporia WITH PASSWORD '<aporia_password>';

-- Grant access to DB and schema
GRANT CONNECT ON DATABASE database_name TO username;
GRANT USAGE ON SCHEMA <your_schema> TO username;

-- Grant access to multiple tables
GRANT SELECT ON table1 TO username;
GRANT SELECT ON table2 TO username;
GRANT SELECT ON table3 TO username;
```

### Create a PostgreSQL data source in Aporia

1. Go to [Aporia platform](https://platform.aporia.com/) and login to your account.
2. Go to **Integrations** page and click on the **Data Connectors** tab
3. Scroll to **Connect New Data Source** section
4. Click **Connect** on the PostgreSQL card and follow the instructions
   1. Note that the provided URL should be in the following format `jdbc:postgresql://<SERVER_HOSTNAME>`.

Bravo! :clap: now you can use the data source you've created across all your models in Aporia.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.aporia.com/data-sources/postgresql.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.