Debugging Failed Runs
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When running dbt™ in production, it's crucial to quickly identify and resolve failed runs. This guide will walk you through the process of debugging failed dbt™ runs using Bolt's comprehensive logging and debugging tools.
Estimated completion time: 15 minutes
Access to the Bolt application in Paradime
At least one configured Bolt schedule
Basic understanding of dbt™ commands and SQL
In this guide, you'll learn how to:
There are two main ways to identify failed runs:
Method 1: Notifications
Set up Slack or email notifications to receive immediate alerts when a run fails. This is the recommended approach for production environments.
See Setting Up Notifications for details on configuring alerts.
Method 2: Bolt UI
Navigate to the Bolt home screen and check the "Status" column in the Bolt Schedule List. Failed runs are marked with a "Error" status indicator.
See Viewing Run History and Analytics for more details on using the Bolt UI.
Once you've identified a failed run, access the logs through the Bolt Schedule Detail Views:
Click on the failed Bolt Schedule (one marked with a "Error" status indicator)
Navigate to the Run History section
Select the failed run (one marked with a "Error" status indicator)
Scroll to the Logs and Artifacts section
Click on the executed command that failed (ex. dbt run)
Bolt Provides three types of logs:
Summary Logs
Quick assessment of issues
Console Logs
Detailed execution record
Finding specific errors and compiled SQL
Debug Logs
System-level operations
Deep technical troubleshooting
Console logs are typically the most useful for debugging as they show errors, warnings, and the compiled SQL code that failed in production.
Follow these steps to debug a failed run:
Review Summary Logs
Check the AI-generated overview
Note any suggested fixes
Review Console Logs
Locate error messages and warnings using the "jump to" feature
Click on the link for compiled SQL code
Review the execution flow and timing
Test and Fix
Copy the compiled SQL code from console logs
Test the SQL:
Directly in your data warehouse, or
In the Code IDE scratchpad
Fix common issues:
Invalid column names
Missing model references
SQL syntax errors
Data type mismatches
After testing the compiled SQL code against your data warehouse, you'll be better equipped to resolve common issues with failed scheduled runs.
You've learned how to identify failed dbt™ runs, access and interpret different types of logs, and systematically debug and resolve issues. This knowledge will help you maintain reliable data pipelines and quickly resolve any failures that occur.
Next, consider exploring the related documentation to handle more complex failure scenarios.
overview of failures
