84 lines
2.1 KiB
Markdown
84 lines
2.1 KiB
Markdown
# SQL Query Troubleshooting
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## Common Issues
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### Issue: `invalid value "09" for "AM"`
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**Problem:** PostgreSQL's `HH12` format doesn't accept leading zeros like "09 AM". It expects "9 AM".
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**Solution:** The SQL query has been updated to handle this by:
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1. Normalizing times like "09 AM" to "9:00 AM" before parsing
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2. Adding regex replacements to remove leading zeros
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3. Handling edge cases for times without colons
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**If you still see this error:**
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- Check your data format in `OptionsFlow_monthly.CreatedTime`
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- The query now handles: "09 AM", "9 AM", "09:00 AM", "9:00 AM", etc.
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- If you have other formats, you may need to add additional normalization
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### Issue: Python Service Unavailable
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**This is normal if:**
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- Python service is not running
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- Python service is on a different port
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- Network/firewall issues
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**The system will automatically:**
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- Fall back to SQL query
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- Continue working normally
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- Log the fallback for monitoring
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**To fix:**
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1. Start Python service: `cd backend/python_service && uvicorn main:app --port 8000`
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2. Check `PYTHON_SERVICE_URL` in `.env` matches the service URL
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3. Verify network connectivity
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### Issue: Date Parsing Errors
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**Common causes:**
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- Invalid date formats in database
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- Missing date/time values
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- Timezone issues
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**Solutions:**
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- The query handles multiple date formats (YYYY-MM-DD, MM/DD/YYYY)
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- Handles multiple time formats (12-hour with AM/PM, 24-hour)
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- Returns NULL for invalid dates (won't crash)
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## Data Validation
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To check your data format:
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```sql
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SELECT
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"CreatedDate",
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"CreatedTime",
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COUNT(*) as count
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FROM "OptionsFlow_monthly"
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GROUP BY "CreatedDate", "CreatedTime"
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ORDER BY count DESC
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LIMIT 20;
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```
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This will show you the most common date/time formats in your data.
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## Testing SQL Query
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Test the query directly in PostgreSQL:
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```sql
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-- Test with a small date range
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SELECT * FROM (
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-- Your full query here with date parameters
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) subquery
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LIMIT 10;
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```
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## Performance
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If the SQL query is slow:
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1. Check indexes on `OptionsFlow_monthly`
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2. Verify date range is reasonable
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3. Consider using Python service (faster for complex processing)
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