Diagnostics.Trace

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The M Code Behind the Power Query M function Diagnostics.Trace

In this article, we will take a closer look at the M code behind the Power Query M function Diagnostics.Trace. We will explore how this function works, and how it can help you to diagnose and solve complex data processing problems.

Understanding Diagnostics.Trace

The Diagnostics.Trace function is a built-in M function that allows you to log messages, warnings, and errors during the data processing pipeline. This function is especially useful when working with large and complex data sets, where it can be difficult to track down issues and errors.

The basic syntax for the Diagnostics.Trace function is:


Diagnostics.Trace([Message], [Severity])


The [Message] parameter is the message that you want to log, and the [Severity] parameter is the severity level of the message. There are three severity levels that you can use:

- Error

- Warning

- Information

By default, Diagnostics.Trace logs messages with the Information severity level. However, you can specify a different severity level by providing a value from the Severity enumeration:

- Severity.Error

- Severity.Warning

- Severity.Information

How Diagnostics.Trace Works

The Diagnostics.Trace function works by logging messages to the Power Query trace log. When you execute a query in Power Query, the trace log records all of the M functions that are called, along with their input and output values.

By using the Diagnostics.Trace function, you can add custom log messages to the trace log. These messages can provide valuable insight into the data processing pipeline, and help you to identify and resolve issues more quickly.

To view the trace log in Power Query, you can use the View -> Advanced Editor option. This opens the Advanced Editor window, which displays the M code for your query. The trace log is located at the bottom of the window, and shows all of the log messages that were generated during the query execution.

Using Diagnostics.Trace for Debugging

The Diagnostics.Trace function is a powerful debugging tool that can help you to diagnose and solve data processing issues. By using this function, you can log messages at key points in the data processing pipeline, and gain valuable insight into how your query is working.

For example, you might use Diagnostics.Trace to log messages when:

- Data is loaded from a source

- A filter is applied to the data

- A join is performed between two tables

- A custom column is added to a table

By logging messages at these key points, you can quickly identify issues and errors in your data processing pipeline. This can save you valuable time and effort, and help you to deliver accurate and reliable data to your stakeholders.

Best Practices for Using Diagnostics.Trace

To get the most out of the Diagnostics.Trace function, it is important to follow some best practices for logging messages. Here are a few tips to keep in mind:

- Be specific: When logging messages, be as specific as possible. Include details about the data being processed, the function being called, and any relevant context.

- Be consistent: Use a consistent message format throughout your query. This makes it easier to search for and filter log messages.

- Use severity levels appropriately: Choose the appropriate severity level for each message. Use the Error severity level for critical issues that require immediate attention, and the Warning severity level for less critical issues that can be addressed later.

- Keep it simple: Avoid logging too many messages, as this can clutter the trace log and make it difficult to find important information.

The Diagnostics.Trace function is a powerful tool for debugging and troubleshooting data processing issues in Power Query. By using this function, you can log custom messages at key points in the data processing pipeline, and gain valuable insight into how your query is working.

In this article, we have explored the M code behind the Diagnostics.Trace function, and discussed how it can be used to diagnose and solve complex data processing problems. By following some best practices for logging messages, you can get the most out of this powerful debugging tool, and deliver accurate and reliable data to your stakeholders.

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