improve error messages in func when complex column passed

[shcheklein]

Makes it a bit easier to understand what is going on in certain user scenarios.

Summary by Sourcery

Disallow complex object columns in SQL functions by raising descriptive errors and add fallback resolution for complex column types.

Enhancements:

• Guard against passing Pydantic model columns to SQL functions (min, collect, greatest) by raising a DataChainParamsError with guidance on using leaf fields or UDFs
• Add fallback logic in get_db_col_type to resolve complex object column types via subtree lookup when initial name resolution fails

Tests:

• Add functional tests in aggregate and conditional modules to assert DataChainParamsError is raised for collect, min, and greatest with complex object columns
• Add unit tests for func.get_column to verify errors on complex object columns

[sourcery-ai]

Reviewer's Guide

This PR introduces strict guards in SQL function handling to disallow Pydantic-based complex object columns with clear error messages, adds a fallback resolution path for unresolved columns, and includes targeted tests to ensure these safeguards in aggregate, conditional, and unit contexts.

Sequence diagram for error handling when complex column is passed to SQL function

sequenceDiagram
    participant User
    participant Func
    participant SignalSchema
    participant ModelStore
    participant Error
    User->>Func: Call SQL function with column
    Func->>SignalSchema: get_column_type(arg, with_subtree=True)
    SignalSchema-->>Func: Return column type
    Func->>ModelStore: is_pydantic(column_type)
    ModelStore-->>Func: Return True (if complex)
    Func->>Error: Raise DataChainParamsError with message
    Error-->>User: Error message: "Function X doesn't support complex object columns..."

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Class diagram for updated error handling in func.py

classDiagram
    class Func {
        +get_column(signals_schema, label, table)
        +_db_cols
        +name
    }
    class SignalSchema {
        +get_column_type(name, with_subtree)
    }
    class ModelStore {
        +is_pydantic(type)
    }
    class DataChainParamsError
    Func --> SignalSchema : uses
    Func --> ModelStore : uses
    Func --> DataChainParamsError : raises

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Class diagram for updated get_db_col_type fallback logic

classDiagram
    class Func {
        +get_db_col_type(signals_schema, col)
    }
    class SignalSchema {
        +get_column_type(name)
        +get_column_type(name, with_subtree)
    }
    class SignalResolvingError
    Func --> SignalSchema : calls
    Func --> SignalResolvingError : handles

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File-Level Changes

Change	Details	Files
Prevent usage of complex Pydantic object columns in SQL functions and improve type resolution fallbacks	Added a guard in get_column that detects Pydantic types and raises DataChainParamsError with usage guidance Wrapped get_column_type in get_db_col_type with try/catch on SignalResolvingError to fallback to subtree resolution	src/datachain/func/func.py
Add tests for disallowed complex object columns in aggregate, conditional, and unit functions	Inserted pytest cases in test_aggregate for collect and min on complex objects Added test in test_conditional to assert error for func.greatest with complex objects Created unit test in test_func to validate get_column raises when passed complex types	tests/func/functions/test_aggregate.py tests/func/functions/test_conditional.py tests/unit/test_func.py

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[sourcery-ai]

Hey there - I've reviewed your changes - here's some feedback:

• Consider extracting the complex‐object guard logic into a shared helper so you can reuse it in both get_column and get_db_col_type and keep the error messaging consistent.
• The SignalResolvingError import inside get_db_col_type could be moved to the module level, and you may want to narrow or log the fallback catch so it doesn’t silently mask other schema resolution issues.
• It might help to centralize the error message text (e.g. in a constant) to ensure that all SQL functions report the same actionable guidance when a complex column is passed.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider extracting the complex‐object guard logic into a shared helper so you can reuse it in both `get_column` and `get_db_col_type` and keep the error messaging consistent.
- The `SignalResolvingError` import inside `get_db_col_type` could be moved to the module level, and you may want to narrow or log the fallback catch so it doesn’t silently mask other schema resolution issues.
- It might help to centralize the error message text (e.g. in a constant) to ensure that all SQL functions report the same actionable guidance when a complex column is passed.

---

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View logs

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.91%. Comparing base (cdf8ee9) to head (9ee76ff).
⚠️ Report is 3 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1441   +/-   ##
=======================================
  Coverage   87.90%   87.91%           
=======================================
  Files         160      160           
  Lines       15300    15309    +9     
  Branches     2206     2210    +4     
=======================================
+ Hits        13450    13459    +9     
  Misses       1336     1336           
  Partials      514      514           

Flag	Coverage Δ
datachain	87.86% <100.00%> (+<0.01%)	⬆️

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Files with missing lines	Coverage Δ
src/datachain/func/func.py	85.10% <100.00%> (+0.59%)	⬆️

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[dreadatour]

Looks good to me! 👍