Operating System and Runtime Environment

Oct 13, 2025 by pynkocean.com 145 views

Introduction

In this article, we will discuss a common issue encountered while building an extractor from a MySQL data source to a DuckDB destination using the Data Lineage Toolkit (DLT). The problem arises when all the data in a particular column of the MySQL source table is NULL, causing the column to be skipped during the extraction process. This issue is particularly problematic when working with bit-type columns, where a value of '0' is also treated as NULL. In this article, we will explore the root cause of this issue and provide a solution to ensure that all columns are extracted, regardless of their data values.

Problem Description

When using the sql_table function to extract data from a MySQL source table, the DLT pipeline skips columns that contain only NULL values or '0' values for bit-type columns. This behavior is not desirable, as it can lead to incomplete data extraction and potential data loss.

To illustrate this issue, let's consider an example. Suppose we have a MySQL source table with a column named "name" that contains only NULL values. When we run the pipeline using the sql_table function, the "name" column is skipped, and the data is not extracted to the DuckDB destination.

Expected Behavior

The expected behavior is that the DLT pipeline should not skip columns based on the presence of NULL values or '0' values for bit-type columns. Instead, the pipeline should extract all columns, including those with NULL or '0' values.

Steps to Reproduce

To reproduce this issue, follow these steps:

Initialize a new DLT project using the dlt init command.
Create a source table in the MySQL database with a column that contains only NULL values.
Use the sql_table function to extract data from the MySQL source table.
Configure the pipeline to use the DuckDB destination.
Run the pipeline to extract the data.

Operating System and Runtime Environment

This issue has been observed on both Linux and macOS operating systems. The runtime environment is local, and the Python version is 3.11.

DLT Data Source and Destination

The DLT data source is MySQL, and the destination is DuckDB.

Solution

To resolve this issue, we need to modify the DLT pipeline to extract all columns, including those with NULL or '0' values. One possible solution is to use the sql_query function instead of sql_table to extract the data. The sql_query function allows us to specify a custom SQL query to extract the data, which can be used to include columns with NULL or '0' values.

Here is an example of how to modify the pipeline to use the sql_query function:

SELECT * FROM my_table

This SQL query will extract all columns from the my_table table, including those with NULL or '0' values.

Modified Pipeline

To modify the pipeline to use the sql_query function, follow these steps:

Update the pipeline configuration to use the sql_query function instead of sql_table.
Specify the custom SQL query to extract the data.
Run the pipeline to extract the data.

Conclusion

In this article, we discussed a common issue encountered while building an extractor from a MySQL data source to a DuckDB destination using the DLT. The issue arises when all the data in a particular column of the MySQL source table is NULL, causing the column to be skipped during the extraction process. We provided a solution to modify the pipeline to use the sql_query function, which allows us to extract all columns, including those with NULL or '0' values. By following the steps outlined in this article, you can resolve this issue and ensure that all columns are extracted during the data extraction process.

Additional Information

DLT version: 1.5.0
Operating system: Linux, macOS
Runtime environment: Local
Python version: 3.11
DLT data source: MySQL
DLT destination: DuckDB
Column Extraction Getting Skipped if All Column Data is Having NULL Values [Mysql to Duck db] - Q&A =====================================================================================

Introduction

In our previous article, we discussed a common issue encountered while building an extractor from a MySQL data source to a DuckDB destination using the Data Lineage Toolkit (DLT). The issue arises when all the data in a particular column of the MySQL source table is NULL, causing the column to be skipped during the extraction process. In this Q&A article, we will address some frequently asked questions related to this issue and provide additional information to help you resolve it.

Q: What is the root cause of this issue?

A: The root cause of this issue is the way the DLT pipeline handles NULL values in the MySQL source table. When all the data in a particular column is NULL, the DLT pipeline skips that column during the extraction process.

Q: Why is this issue specific to bit-type columns?

A: This issue is specific to bit-type columns because a value of '0' is treated as NULL in these columns. As a result, if all the values in a bit-type column are '0', the DLT pipeline will skip that column during the extraction process.

Q: Can I modify the pipeline to extract all columns, including those with NULL or '0' values?

A: Yes, you can modify the pipeline to extract all columns, including those with NULL or '0' values. One possible solution is to use the sql_query function instead of sql_table to extract the data. The sql_query function allows you to specify a custom SQL query to extract the data, which can be used to include columns with NULL or '0' values.

Q: How do I modify the pipeline to use the `sql_query` function?

A: To modify the pipeline to use the sql_query function, follow these steps:

Update the pipeline configuration to use the sql_query function instead of sql_table.
Specify the custom SQL query to extract the data.
Run the pipeline to extract the data.

Q: What is the SQL query to extract all columns, including those with NULL or '0' values?

A: The SQL query to extract all columns, including those with NULL or '0' values, is:

SELECT * FROM my_table

This SQL query will extract all columns from the my_table table, including those with NULL or '0' values.

Q: Can I use the `sql_query` function with other DLT data sources?

A: Yes, you can use the sql_query function with other DLT data sources, not just MySQL. The sql_query function is a generic function that can be used with any DLT data source that supports SQL queries.

Q: What are the benefits of using the `sql_query` function?

A: The benefits of using the sql_query function include:

Ability to extract all columns, including those with NULL or '0' values.
Flexibility to use custom SQL queries to extract data.
Compatibility with other DLT data sources.

Conclusion

In this Q&A article, we addressed some frequently asked questions related to the issue of column extraction getting skipped if all column data is having NULL values when using the DLT pipeline. We provided additional information and solutions to help you resolve this issue and extract all columns, including those with NULL or '0' values. By following the steps outlined in this article, you can modify the pipeline to use the sql_query function and extract all columns, including those with NULL or '0' values.

Additional Information

DLT version: 1.5.0
Operating system: Linux, macOS
Runtime environment: Local
Python version: 3.11
DLT data source: MySQL
DLT destination: DuckDB