ODBC interface

ODBC [1] is short for Open Database Connectivity. ODBC is an API specification for connecting software programming language to a database. ODBC is developed by Microsoft.

In a nutshell it works like this:

Any database of relevance has an ODBC driver. The ODBC driver translates calls from the ODBC API into a format the database can understand. It returns the results of the query in a format the ODBC API can understand.

If you want to connect getML or any other software to a database using ODBC, you have to first install the ODBC driver provided by your database vendor.

In theory, ODBC drivers should support translating queries from the SQL 99 standard into the SQL dialect. In practice, this requirement is often ignored. Also, not all ODBC drivers support all ODBC calls.

At getML, we try to use native APIs for connecting to relational databases whereever possible. Whenever we cannot do that due to licensing or other restrictions, we use ODBC.

In particular, we use ODBC for connecting to proprietary databases like Oracle, Microsoft SQL Server or IBM DB2.

ODBC is pre-installed in modern Windows operating systems. For Linux and macOS, two open-source implementations exist, namely unixODBC and iODBC. getML uses unixODBC for Linux and macOS.

An example: Microsoft SQL Server

In this example, we will show you how to connect to Microsoft SQL Server using ODBC. If you do not have an Microsoft SQL Server instance at your disposal, you can actually download a trial or development version for free.

The first step is to download the ODBC driver for SQL Server.

The second step is to configure the ODBC driver. Many ODBC drivers provide customized scripts for this, so you might not have to do it by hand.

On Linux and macOS, you create a file named .odbc.ini in your home directory (if no such file already exists). In that file, leave an entry like this:

Driver = /opt/microsoft/msodbcsql17/lib64/libmsodbcsql-17.5.so.2.1
Server =
Port = 1433
Language = us_english
NeedODBCTypesOnly = 1

On Docker you can make appropriate changes to the Dockerfile and then rerun ./setup.sh or bash setup.sh.

You will need to set the following parameters:

  • The first line is the server name or data source name. You can use this name to tell getML that this is the server you want to connect to.

  • The Driver is the location of the ODBC driver you have just downloaded. The location or file name might be different on your system.

  • The Server is the IP address of the server. If the server is on the same machine as getML, just write “localhost”.

  • The Port is the port on which to connect the server. The default port for SQL Server is 1433.

  • User and Password are the user name and password that allow to access the server.

  • The Database is the database inside the server you want to connect to.

You can now connect getML to the database:


Important: Always pass escape_chars

Earlier we mentioned that ODBC drivers are supposed to translate standard SQL queries into the specific SQL dialects, but that this requirement is often ignored.

A typical example of this issue are escape characters. Escape characters are needed when the names of your schemas, tables or columns are SQL keywords. For instance, the loans dataset contains a table named ORDER, which is a registered SQL keyword.

To avoid this problem, you can envelop the schema, table and column names in escape characters, like this:


getML always uses escape characters for its automatically generated queries.

The SQL standard requires that the quotation mark (”) be used as the escape character. However, many SQL dialects to not follow this requirement. For instance, SQL Server uses “[]”:


MySQL and MariaDB work like this:


To save yourself some frustration, please figure your server’s escape characters and then explicitly pass them to connect_odbc().

Import data using ODBC

By selecting an existing table from your database in the from_db() class method, you can create a new DataFrame containing all its data. Alternatively you can use the read_db() and read_query() methods to replace the content of the current DataFrame instance or append further rows based on either a table or a specific query.

Export data using ODBC

You can also write your results back into the PostgreSQL database. When you provide a name for the destination table in getml.pipeline.Pipeline.transform(), the features generated from your raw data will be written back. Passing it into getml.pipeline.Pipeline.predict() generates predictions of the target variables to new, unseen data and stores the result into the corresponding table.