interloper-sql
A SQL database destination for interloper: SQLDestination, a
DatabaseDestination over SQLAlchemy Core, and the SQLConnection that holds
the database URL.
One backend serves every SQLAlchemy dialect whose driver is installed.
PostgreSQL works out of the box (the package depends on psycopg[binary]);
MySQL, SQLite, Redshift, SQL Server and the rest need their own driver.
Setup
The connection is a single SQLAlchemy URL:
| Database | URL | Driver to install |
|---|---|---|
| PostgreSQL | postgresql+psycopg://user:password@host:5432/db |
none, ships with the package |
| MySQL / MariaDB | mysql+pymysql://user:password@host:3306/db |
pymysql |
| SQL Server | mssql+pyodbc://user:password@host/db?driver=ODBC+Driver+18+for+SQL+Server |
pyodbc (and the ODBC driver) |
| Redshift | redshift+redshift_connector://user:password@host:5439/db |
sqlalchemy-redshift, redshift_connector |
| SQLite | sqlite:///path/to/file.db |
none, in the standard library |
Install the driver next to interloper (uv pip install pymysql, or a custom
image built from the slim variant). Special characters in the password must be
URL-encoded.
The URL carries the password, so it is a secret field: encrypted at rest in a
deployed instance and masked in the UI. It also loads from the environment
(SQL_URL), so SQLConnection() works with no arguments.
The connection's check runs SELECT 1.
Usage
import interloper as il
from interloper_sql import SQLConnection, SQLDestination
warehouse = SQLDestination(
connection=SQLConnection(url="postgresql+psycopg://user:password@host:5432/analytics"),
default_dataset="marketing",
)
In a deployed instance you configure this through the UI instead: add a SQL database connection, then a SQL database destination using it.
Datasets are schemas
An asset's dataset is the SQL schema its table lives in. Without one, the
destination's default_dataset applies; without either, the table goes in
the connection's default schema (public on PostgreSQL, the database itself on
MySQL). A named schema is created on first write when the database supports
schemas and it does not exist yet.
Tables
A table is created on first write from the asset's effective schema (declared,
or inferred during conform): integers are BIGINT, floats DOUBLE, decimals
NUMERIC(38, 9), datetimes timezone-aware timestamps, strings TEXT, and
nested or repeated fields JSON. An existing table is never altered: columns
it lacks are dropped from the write with a warning, and NaN or infinite floats
are written as NULL.
Partitions
Writes replace, inside one transaction:
- unpartitioned: every row is deleted, then the data inserted;
- a time partition: the rows inside its half-open bounds
(
column >= start AND column < end) are deleted, then the data inserted; - any other partition: the rows equal to its id are deleted;
- a window: each partition's rows are deleted, then the whole batch is inserted once.
A failed insert rolls the delete back, so a partition is never left empty.
Inserts go in batches of 1000 rows. Reads return rows as list[dict]; reading
a table that was never written raises DataNotFoundError.
Metadata
Release files for interloper-sql 0.104.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| interloper_sql-0.104.0-py3-none-any.whl | Python 3 | none | any | Details |
Release files / interloper_sql-0.104.0-py3-none-any.whl
| Download URL | interloper_sql-0.104.0-py3-none-any.whl |
|---|---|
| Size | 9.4 kB |
| Tags | Python 3 |
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