Debug SQL in Ascend
Keep your Ascend pipelines running smoothly with these essential SQL debugging techniques.
Explain errors with Otto
Stuck on an error that's blocking your progress? Otto can help.
When you encounter an issue:
- Click the error message in the build info panel
- View the error details in their dedicated tab
- Click the sparkle icon in the top right to start an Otto chat in the sidebar
- Get Otto's assistance to resolve the issue
Testing
Validate your SQL queries before they run in production pipelines to identify issues early and ensure reliable data transformation.
Test queries on sample data
Run your queries against representative sample data to verify results:
-- Test a transformation on a smaller dataset
SELECT * FROM your_table
WHERE date_column > '2023-01-01'
LIMIT 100;
Validate expected outputs
Check that your queries produce the expected output structure and values:
-- Verify column types and data patterns
SELECT
TYPEOF(column_a) as type_a,
MIN(column_b) as min_b,
MAX(column_b) as max_b,
COUNT(*) as row_count
FROM your_transformed_table;
Common debugging strategies
Incremental query building
Build complex queries step by step, validating each part:
Data validation
Include validation checks in your queries to identify potential issues:
-- Check for unexpected NULL values
SELECT
COUNT(*) as total_rows,
SUM(CASE WHEN critical_column IS NULL THEN 1 ELSE 0 END) as null_count,
SUM(CASE WHEN critical_column IS NULL THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as null_percentage
FROM your_table;
-- Verify data ranges
SELECT
MIN(date_column) as earliest_date,
MAX(date_column) as latest_date
FROM your_table;
By applying these strategies, you can efficiently identify and resolve SQL issues in your Ascend data pipelines.