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Microsoft SQL Server

Install dlt with MS SQL​

To install the DLT library with MS SQL dependencies:

pip install dlt[mssql]

Setup guide​

Prerequisites​

Microsoft ODBC Driver for SQL Server must be installed to use this destination. This can't be included with dlt's python dependencies, so you must install it separately on your system. You can find the official installation instructions here.

Supported driver versions:

  • ODBC Driver 18 for SQL Server
  • ODBC Driver 17 for SQL Server

You can configure driver name explicitly as well.

Create a pipeline​

1. Initalize a project with a pipeline that loads to MS SQL by running

dlt init chess mssql

2. Install the necessary dependencies for MS SQL by running

pip install -r requirements.txt

or run:

pip install dlt[mssql]

This will install dlt with mssql extra which contains all the dependencies required by the SQL server client.

3. Enter your credentials into .dlt/secrets.toml.

Example, replace with your database connection info:

[destination.mssql.credentials]
database = "dlt_data"
username = "loader"
password = "<password>"
host = "loader.database.windows.net"
port = 1433
connect_timeout = 15

You can also pass a SQLAlchemy-like database connection:

# keep it at the top of your toml file! before any section starts
destination.mssql.credentials="mssql://loader:<password>@loader.database.windows.net/dlt_data?connect_timeout=15"

To pass credentials directly you can use credentials argument passed to dlt.pipeline or pipeline.run methods.

pipeline = dlt.pipeline(pipeline_name='chess', destination='postgres', dataset_name='chess_data', credentials="mssql://loader:<password>@loader.database.windows.net/dlt_data?connect_timeout=15")

Write disposition​

All write dispositions are supported

If you set the replace strategy to staging-optimized the destination tables will be dropped and recreated with an ALTER SCHEMA ... TRANSFER. The operation is atomic: mssql supports DDL transactions.

Data loading​

Data is loaded via INSERT statements by default. MSSQL has a limit of 1000 rows per INSERT and this is what we use.

Supported file formats​

Supported column hints​

mssql will create unique indexes for all columns with unique hints. This behavior may be disabled

Syncing of dlt state​

This destination fully supports dlt state sync

Data types​

MS SQL does not support JSON columns, so JSON objects are stored as strings in nvarchar column.

Additional destination options​

mssql destination does not creates UNIQUE indexes by default on columns with unique hint (ie. _dlt_id). To enable this behavior

[destination.mssql]
create_indexes=true

You can explicitly set the ODBC driver name:

[destination.mssql.credentials]
driver="ODBC Driver 18 for SQL Server"

When using a SQLAlchemy connection string, replace spaces with +:

# keep it at the top of your toml file! before any section starts
destination.mssql.credentials="mssql://loader:<password>@loader.database.windows.net/dlt_data?driver=ODBC+Driver+18+for+SQL+Server"

dbt support​

No dbt support yet

Additional Setup guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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