Microsoft SQL Server

Add credentials

Create a new pipeline or open an existing pipeline.
Expand the left side of your screen to view the file browser.
Scroll down and click on a file named io_config.yaml.
Enter the following keys and values under the key named default (you can have multiple profiles, add it under whichever is relevant to you)

version: 0.1.1
default:
  MSSQL_DATABASE: database
  MSSQL_SCHEMA: schema
  MSSQL_DRIVER: "ODBC Driver 18 for SQL Server"
  MSSQL_HOST: host
  MSSQL_PASSWORD: password
  MSSQL_PORT: 1433
  MSSQL_USER: SA

  # (Optional) SSH tunnel config
  MSSQL_CONNECTION_METHOD: direct
  MSSQL_SSH_HOST:
  MSSQL_SSH_PORT: 22
  MSSQL_SSH_USERNAME:
  MSSQL_SSH_PASSWORD:
  MSSQL_SSH_PKEY:

SSH tunneling

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If you want to connect to SQL Server with SSH tunnel, update the value of MSSQL_CONNECTION_METHOD to ssh_tunnel, and enter the values for keys with prefix MSSQL_SSH.

Key	Description	Sample value
`MSSQL_SSH_HOST`	The host of the intermediate bastion server.	`123.45.67.89`
`MSSQL_SSH_PORT`	The port of the intermediate bastion server. Default value: 22	`22`
`MSSQL_SSH_USERNAME`	The username used to connect to the bastion server.	`username`
`MSSQL_SSH_PASSWORD`	(Optional) The password used to connect to the bastion server. It should be set if you authenticate with the bastion server with password.	`password`
`MSSQL_SSH_PKEY`	(Optional) The path to the private key used to connect to the bastion server. It should be set if you authenticate with the bastion server with private key.	`/path/to/private/key`

When using SSH tunnel, the fast_execute option will automatically be disabled to ensure reliable connections through the tunnel.

Dependencies

To connect to the Microsoft SQL Server, you’ll need to make sure the driver is installed. By default, ODBC Driver 18 is installed in the docker image. If you want to use other ODBC Driver versions, you’ll need to build a custom docker image (use Mage image as the base image) and install the drivers. Here is the doc for installing ODBC drivers for SQL Server: https://learn.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server

Using SQL block

Create a new pipeline or open an existing pipeline.
Add a data loader, transformer, or data exporter block.
Select SQL.
Under the Data provider/Connection dropdown, select Microsoft SQL Server.
Under the Profile dropdown, select default (or the profile you added credentials underneath).
Enter the schema and optional table name of the table to write to.
Under the Write policy dropdown, select Replace or Append (please see SQL blocks guide for more information on write policies).
Enter in this test query: SELECT 1.
Run the block.

Using Python block

Create a new pipeline or open an existing pipeline.
Add a data loader, transformer, or data exporter block (the code snippet below is for a data loader).
Select Generic (no template).
Enter this code snippet (note: change the config_profile from default if you have a different profile):

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.mssql import MSSQL
from os import path
if 'data_loader' not in globals():
    from mage_ai.data_preparation.decorators import data_loader


@data_loader
def load_data_from_mssql(*args, **kwargs):
    """
    Template for loading data from a MSSQL database.
    Specify your configuration settings in 'io_config.yaml'.
    Set the following in your io_config:

    Docs: /integrations/databases/MicrosoftSQLServer
    """
    query = 'Your MSSQL query'  # Specify your SQL query here
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    with MSSQL.with_config(ConfigFileLoader(config_path, config_profile)) as loader:
        return loader.load(query)

Run the block.

Export a dataframe

Here is an example code snippet to export a dataframe to MSSQL:

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.mssql import MSSQL
from pandas import DataFrame
from os import path

if 'data_exporter' not in globals():
    from mage_ai.data_preparation.decorators import data_exporter


@data_exporter
def export_data_to_mssql(df: DataFrame, **kwargs) -> None:
    """
    Template for exporting data to a MSSQL database.
    Specify your configuration settings in 'io_config.yaml'.
    Set the following in your io_config:

    Docs: /integrations/databases/MicrosoftSQLServer
    """
    schema_name = 'dbo'  # Specify the name of the schema to export data to
    table_name = 'your_table_name'  # Specify the name of the table to export data to
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    with MSSQL.with_config(ConfigFileLoader(config_path, config_profile)) as loader:
        loader.export(
            df,
            schema_name,
            table_name,
            index=False,  # Specifies whether to include index in exported table
            if_exists='replace',  # Specify resolution policy if table name already exists
            fast_execute=True,  # Use fast_executemany option to speed up bulk inserting rows
            unique_conflict_method='UPDATE',  # Specify method to resolve row conflicts
            unique_constraints=['col'],
        )

Custom types

To overwrite a column type when running a python export block, simply specify the column name and type in the overwrite_types dict in data exporter config Here is an example code snippet:

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.mssql import MSSQL
from pandas import DataFrame
from os import path

if 'data_exporter' not in globals():
    from mage_ai.data_preparation.decorators import data_exporter


@data_exporter
def export_data_to_mssql(df: DataFrame, **kwargs) -> None:
    """
    Template for exporting data to a MSSQL database.
    Specify your configuration settings in 'io_config.yaml'.
    Set the following in your io_config:

    Docs: /integrations/databases/MicrosoftSQLServer
    """
    schema_name = 'dbo'  # Specify the name of the schema to export data to
    table_name = 'your_table_name'  # Specify the name of the table to export data to
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'
    overwrite_types = {'column_name': 'VARCHAR(255)'}

    with MSSQL.with_config(ConfigFileLoader(config_path, config_profile)) as loader:
        loader.export(
            df,
            schema_name,
            table_name,
            index=False,  # Specifies whether to include index in exported table
            if_exists='replace',  # Specify resolution policy if table name already exists
            overwrite_types=overwrite_types,
        )

Troubleshooting errors

`error: ODBC SQL type -155 is not yet supported.`

“I changed the datetime with timezone data type to a datetime and it starting working”

Windy Martin

Infrastructure

3rd party

Microsoft SQL Server

Add credentials

SSH tunneling

Dependencies

Using SQL block

Using Python block

Export a dataframe

Troubleshooting errors

`error: ODBC SQL type -155 is not yet supported.`

​Add credentials

​SSH tunneling

​Dependencies

​Using SQL block

​Using Python block

​Export a dataframe

​Troubleshooting errors

​error: ODBC SQL type -155 is not yet supported.

Add credentials

SSH tunneling

Dependencies

Using SQL block

Using Python block

Export a dataframe

Troubleshooting errors

`error: ODBC SQL type -155 is not yet supported.`