---
title: CSV type issues troubleshooting
meta:
  description: Common issues and solutions for CSV type problems in ClickHouse and Tinybird.
---

# CSV type issues troubleshooting

Common issues and solutions for CSV type problems in ClickHouse and Tinybird.

## Common errors

### CANNOT_PARSE_TEXT

**Error**: `CANNOT_PARSE_TEXT: Cannot parse CSV`

**Cause**: Malformed CSV data or wrong delimiter

**Solution**: Check CSV format and delimiter
```csv
-- Valid CSV
id,user_id,value,timestamp
123,456,10.5,2023-01-01 12:00:00

-- Invalid CSV (wrong delimiter)
id;user_id;value;timestamp
123;456;10.5;2023-01-01 12:00:00
```

### TYPE_MISMATCH

**Error**: `TYPE_MISMATCH: Cannot convert String to UInt32`

**Cause**: CSV column contains non-numeric data in numeric column

**Solution**: Use safe conversion functions
```sql
-- Use safe conversion
SELECT toUInt32OrNull(numeric_column) as safe_number
FROM events
```

## CSV format issues

### Missing headers

**Issue**: CSV file without column headers

**Solution**: Specify column names in schema
```sql
-- Specify column names
SCHEMA >
  id UInt32,
  user_id String,
  value Float64,
  timestamp DateTime
```

### Inconsistent delimiters

**Issue**: Mixed delimiters in CSV file

**Solution**: Use consistent delimiter or handle in schema
```sql
-- Handle different delimiters
-- Use tab-separated values
FORMAT TabSeparated
```

### Quote handling

**Issue**: CSV fields with quotes causing parsing issues

**Solution**: Handle quotes properly
```sql
-- Use proper quote handling
FORMAT CSVWithNames
SETTINGS format_csv_allow_single_quotes = 1
```

## Data type problems

### Mixed data types in columns

**Issue**: Same column has different data types

**Solution**: Use string type and convert later
```sql
-- Use string for mixed types
CREATE TABLE events (
  id UInt32,
  mixed_column String,  -- Handle mixed types as string
  value Float64
)
```

### Date format issues

**Issue**: CSV dates in unexpected format

**Solution**: Parse dates explicitly
```sql
-- Parse dates with specific format
SELECT toDateTime(date_string, 'YYYY-MM-DD HH:MM:SS') as parsed_date
FROM events
```

## Best practices

1. **Validate CSV format** - Check delimiter and quote handling
2. **Use consistent types** - Keep column types consistent
3. **Handle mixed data** - Use string type for mixed data
4. **Parse dates explicitly** - Don't rely on automatic date parsing
5. **Use schema hints** - Specify expected types in schema

## Related documentation

- [Data types](/sql-reference/data-types)
- [Data source configuration](/forward/get-data-in/data-sources)
- [Type conversion functions](/sql-reference/functions/type-conversion-functions)