Function-specific issues

Common issues and solutions when working with specific function types in ClickHouse and Tinybird.

Overview

This section covers troubleshooting for specific function categories that commonly cause issues in ClickHouse and Tinybird.

Function categories

Date and time functions

Common issues with date and time operations:

  • ILLEGAL_TYPE_OF_ARGUMENT - Wrong data types passed to date functions
  • CANNOT_PARSE_DATETIME - Invalid date formats
  • Timezone handling - Unexpected timezone conversions
  • Date arithmetic - Performance issues in Pipes

View date and time troubleshooting →

String manipulation

Common issues with string operations:

  • ILLEGAL_TYPE_OF_ARGUMENT - Non-string data passed to string functions
  • BAD_ARGUMENTS - String index out of bounds
  • UTF-8 encoding - Special character handling
  • String concatenation - Type conversion issues

View string manipulation troubleshooting →

Array operations

Common issues with array functions:

  • SIZES_OF_ARRAYS_DONT_MATCH - Arrays of different lengths
  • ZERO_ARRAY_OR_TUPLE_INDEX - 0-based indexing (should be 1-based)
  • Nested arrays - Complex nested structure handling
  • Array type mismatches - Mixed element types

View array operations troubleshooting →

Join keys and aliases

Common issues with joins and aliases:

  • INVALID_JOIN_ON_EXPRESSION - Incorrect join conditions
  • AMBIGUOUS_COLUMN_NAME - Same column name in multiple tables
  • Type mismatches - Different data types in join keys
  • Nullable join keys - Handling null values in joins

View join keys and aliases troubleshooting →

Common patterns

Type conversion issues

Many function errors stem from type conversion problems:

  1. Check data types - Use toTypeName() to verify types
  2. Use safe conversions - Use toTypeOrNull() functions
  3. Handle nulls explicitly - Check for null values before operations
  4. Validate inputs - Ensure data meets function requirements

Performance considerations

Function usage can impact performance:

  1. Pre-calculate in data sources - Do heavy operations in data sources
  2. Use intermediate columns - Break down complex operations
  3. Avoid chaining - Limit function chaining in queries
  4. Monitor resource usage - Watch for memory and CPU impact

Best practices

  1. Always validate inputs - Check data types and values before function calls
  2. Use safe functions - Prefer functions that handle errors gracefully
  3. Test with sample data - Verify function behavior with real data
  4. Document function usage - Keep track of function patterns and solutions
  5. Monitor performance - Watch for function-related performance issues