SQL input

SQL input — `sqlInput`

Read rows live from a database at run time, via a reusable Connection. Because the read happens on every run, scheduled flows always process fresh data.

SQL Input

orders table — live DB

input

Filter Rows

status = shipped

clean

Group By

revenue by region

transform

File Output

daily report

output

Use cases

Run a flow against the current contents of a production table on a schedule.
Pull a query result (a join or filter computed in the database) into a flow.
Read from MongoDB by selecting a collection.

Configuration

Config key	Type	Required	Description
`connection_id`	string	Yes	The connection to read from
`mode`	string	No	`table` (default) or `query`
`table`	string	Conditional	Table name (required in `table` mode)
`schema`	string	No	Schema the table lives in
`query`	string	Conditional	Custom SQL (required in `query` mode)

Generated Python code

import os
from sqlalchemy import create_engine

_engine_1 = create_engine(f"postgresql+psycopg://reader:{os.environ['PG_PASSWORD']}@host:5432/shop")
df_orders = pd.read_sql_table('orders', _engine_1)

Each run also snapshots the input to parquet for reproducibility. MongoDB sources use collection selection (no custom query).

Tips & common mistakes

Passwords are never embedded. Exported code resolves the secret at runtime from its reference scheme — os.environ[...] for a bare name or env:NAME, keyring.get_password(...) for keyring:NAME (recommended on desktop), or a file:/path read for a mounted secret file. See Connections for the full scheme list.
Test the connection first. Use the connection's Test action to confirm credentials and reachability before wiring it into a flow.