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Add credentials

  1. Create a new pipeline or open an existing pipeline.
  2. Expand the left side of your screen to view the file browser.
  3. Scroll down and click on a file named io_config.yaml.
  4. 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

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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

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Using SQL block

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

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Using Python block

  1. Create a new pipeline or open an existing pipeline.
  2. Add a data loader, transformer, or data exporter block (the code snippet below is for a data loader).
  3. Select Generic (no template).
  4. 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)
  1. Run the block.

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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'],
        )

  1. 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,
        )

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Troubleshooting errors

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

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