Утилиты NotebookUtils для функции пользовательских данных (UDF) в Fabric

# Get functions from a UDF item in the current workspace
myFunctions = notebookutils.udf.getFunctions('UDFItemName')

# Get functions from a UDF item in another workspace
myFunctions = notebookutils.udf.getFunctions('UDFItemName', 'workspaceId')

val myFunctions = notebookutils.udf.getFunctions("UDFItemName")
val sharedFunctions = notebookutils.udf.getFunctions("UDFItemName", "workspaceId")

# Get functions from a UDF item in the current workspace
myFunctions <- notebookutils.udf.getFunctions("UDFItemName")

# Get functions from a UDF item in another workspace
myFunctions <- notebookutils.udf.getFunctions("UDFItemName", "workspaceId")

# Positional parameters
myFunctions.functionName('value1', 'value2')

# Named parameters (recommended for clarity)
myFunctions.functionName(parameter1='value1', parameter2='value2')

val res = myFunctions.functionName("value1", "value2")

result <- myFunctions$functionName("value1", "value2")

# Assume the UDF item defines a function like:
# def score_customer(customerId: str, startDate: datetime = "2025-01-01T00:00:00Z", isActive: bool = True, maxRecords: int = 100) -> dict
# The datetime defaults are specified as strings in the signature; the runtime parses them to datetime at invocation time.

# 1. Call without optional parameters — defaults are used for startDate, isActive, and maxRecords
result = myFunctions.scoreCustomer(customerId='C001')

# 2. Override one default via a named argument, keep the others at their defaults
result = myFunctions.scoreCustomer(customerId='C001', maxRecords=50)

# 3. Pass a date/time in ISO 8601 format for reliable parsing
result = myFunctions.scoreCustomer(customerId='C001', startDate='2025-12-31T23:59:59Z')

// All required parameters must be supplied; optional (default) parameters can be omitted.
// Scala does not support named arguments when calling UDF functions through notebookutils.
val result = myFunctions.scoreCustomer("C001")

# All required parameters must be supplied; optional (default) parameters can be omitted.
result <- myFunctions$scoreCustomer("C001")

display(myFunctions.itemDetails)

display(myFunctions.itemDetails)

myFunctions$itemDetails

display(myFunctions.functionDetails)

display(myFunctions.functionDetails)

myFunctions$functionDetails

import json

try:
    validators = notebookutils.udf.getFunctions('DataValidators')

    # Check if function exists before calling
    functions_info = json.loads(validators.functionDetails)
    function_names = [f['Name'] for f in functions_info]

    if 'validateSchema' in function_names:
        is_valid = validators.validateSchema(
            schema='sales_schema',
            data_path='Files/data/sales.csv'
        )
        print(f"Schema validation: {'passed' if is_valid else 'failed'}")
    else:
        print("validateSchema function not available in this UDF item")
        print(f"Available functions: {', '.join(function_names)}")

except AttributeError as e:
    print(f"Function not found: {e}")
except TypeError as e:
    print(f"Parameter type mismatch: {e}")
except Exception as e:
    print(f"Error invoking UDF: {e}")

try {
    val validators = notebookutils.udf.getFunctions("DataValidators")
    val isValid = validators.validateSchema("sales_schema", "Files/data/sales.csv")
    println(s"Schema validation: ${if (isValid) "passed" else "failed"}")
} catch {
    case e: Exception => println(s"Error invoking UDF: ${e.getMessage}")
}

validators <- notebookutils.udf.getFunctions("DataValidators")

result <- tryCatch({
    validators$validateSchema("sales_schema", "Files/data/sales.csv")
}, error = function(e) {
    print(paste("Error invoking UDF:", e$message))
    NULL
})

if (!is.null(result)) {
    print(paste("Schema validation:", ifelse(result, "passed", "failed")))
}