Data Type Conversion

/**
 * Converts between Spark and Hive data representations
 * Handles complex nested types and SerDe integration
 */
trait HiveInspectors {
  // Core conversion methods implemented by the trait
}

/**
 * Convert Java type to Spark DataType
 * @param clz Java Type to convert
 * @return Corresponding Spark DataType
 */
def javaTypeToDataType(clz: Type): DataType

/**
 * Convert Hive ObjectInspector to Spark DataType
 * @param inspector Hive ObjectInspector
 * @return Corresponding Spark DataType
 */
def inspectorToDataType(inspector: ObjectInspector): DataType

/**
 * Convert Spark DataType to Hive ObjectInspector
 * @param dataType Spark DataType
 * @return Corresponding Hive ObjectInspector
 */
def toInspector(dataType: DataType): ObjectInspector

/**
 * Convert Spark Expression to Hive ObjectInspector
 * @param expr Spark Expression
 * @return Corresponding Hive ObjectInspector
 */
def toInspector(expr: Expression): ObjectInspector

import org.apache.spark.sql.types._
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector

// Convert Spark type to Hive ObjectInspector
val stringType = StringType
val stringInspector = toInspector(stringType)

// Convert Hive ObjectInspector back to Spark type
val convertedType = inspectorToDataType(stringInspector)
assert(convertedType == StringType)

// Handle complex types
val structType = StructType(Seq(
  StructField("id", IntegerType, false),
  StructField("name", StringType, true)
))
val structInspector = toInspector(structType)

/**
 * Create wrapper function for converting Spark data to Hive representation
 * @param oi Hive ObjectInspector for the target type
 * @param dataType Spark DataType of the source data
 * @return Function that converts Spark values to Hive values
 */
def wrapperFor(oi: ObjectInspector, dataType: DataType): Any => Any

/**
 * Create unwrapper function for converting Hive data to Spark representation
 * @param objectInspector Hive ObjectInspector for the source type
 * @return Function that converts Hive values to Spark values
 */
def unwrapperFor(objectInspector: ObjectInspector): Any => Any

/**
 * Create unwrapper function for struct fields
 * @param field Hive struct field definition
 * @return Function that unwraps struct field values
 */
def unwrapperFor(field: HiveStructField): (Any, InternalRow, Int) => Unit

/**
 * Wrap a value from Spark to Hive representation
 * @param a Value to wrap
 * @param oi Target Hive ObjectInspector
 * @param dataType Source Spark DataType
 * @return Wrapped value suitable for Hive
 */
def wrap(a: Any, oi: ObjectInspector, dataType: DataType): AnyRef

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.unsafe.types.UTF8String

// Create wrapper for converting Spark strings to Hive
val stringInspector = toInspector(StringType)
val wrapper = wrapperFor(stringInspector, StringType)

// Convert Spark UTF8String to Hive representation
val sparkString = UTF8String.fromString("hello")
val hiveString = wrapper(sparkString)

// Create unwrapper for the reverse conversion
val unwrapper = unwrapperFor(stringInspector)
val backToSpark = unwrapper(hiveString)

// Direct wrapping
val directlyWrapped = wrap(sparkString, stringInspector, StringType)

// Array type conversion example
val arrayType = ArrayType(IntegerType)
val arrayInspector = toInspector(arrayType)
val arrayWrapper = wrapperFor(arrayInspector, arrayType)

// Map type conversion example  
val mapType = MapType(StringType, IntegerType)
val mapInspector = toInspector(mapType)
val mapWrapper = wrapperFor(mapInspector, mapType)

// Struct type conversion example
val structType = StructType(Seq(
  StructField("id", IntegerType, false),
  StructField("name", StringType, true),
  StructField("scores", ArrayType(DoubleType), true)
))
val structInspector = toInspector(structType)
val structWrapper = wrapperFor(structInspector, structType)

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.util.ArrayData

// Convert array data
val arrayType = ArrayType(StringType)
val arrayInspector = toInspector(arrayType)
val arrayWrapper = wrapperFor(arrayInspector, arrayType)

val sparkArray = ArrayData.toArrayData(Array(
  UTF8String.fromString("a"),
  UTF8String.fromString("b")
))
val hiveArray = arrayWrapper(sparkArray)

// Convert struct data
val structType = StructType(Seq(
  StructField("id", IntegerType, false),
  StructField("name", StringType, true)
))
val structInspector = toInspector(structType)
val structWrapper = wrapperFor(structInspector, structType)

val sparkRow = InternalRow(1, UTF8String.fromString("Alice"))
val hiveStruct = structWrapper(sparkRow)

/**
 * Implicit class providing convenient type conversion methods
 * @param dt Spark DataType
 */
implicit class typeInfoConversions(dt: DataType) {
  /**
   * Convert Spark DataType to Hive TypeInfo
   * @return Corresponding Hive TypeInfo
   */
  def toTypeInfo: TypeInfo
}

import org.apache.spark.sql.types._
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo

// Use implicit conversion
val sparkType: DataType = StructType(Seq(
  StructField("id", IntegerType, false),
  StructField("name", StringType, true)
))

// Implicit conversion to TypeInfo
val typeInfo: TypeInfo = sparkType.toTypeInfo

// Can also be used inline
def processTypeInfo(ti: TypeInfo): Unit = {
  println(s"Processing type: ${ti.getTypeName}")
}

processTypeInfo(IntegerType.toTypeInfo)
processTypeInfo(StringType.toTypeInfo)

// Spark Type -> Hive TypeInfo mappings
BooleanType        // -> BOOLEAN
ByteType           // -> TINYINT  
ShortType          // -> SMALLINT
IntegerType        // -> INT
LongType           // -> BIGINT
FloatType          // -> FLOAT
DoubleType         // -> DOUBLE
StringType         // -> STRING
BinaryType         // -> BINARY
DateType           // -> DATE
TimestampType      // -> TIMESTAMP
DecimalType        // -> DECIMAL(precision, scale)

// Check type compatibility
val supportedTypes = Seq(
  BooleanType, ByteType, ShortType, IntegerType, 
  LongType, FloatType, DoubleType, StringType,
  BinaryType, DateType, TimestampType
)

supportedTypes.foreach { sparkType =>
  val inspector = toInspector(sparkType)
  val backToSpark = inspectorToDataType(inspector)
  assert(sparkType == backToSpark, s"Round-trip failed for $sparkType")
}

// Example of working with SerDe through inspectors
def processSerDeData(
  data: Any, 
  serDe: AbstractSerDe, 
  inspector: ObjectInspector
): InternalRow = {
  
  // Get deserializer inspector
  val deInspector = serDe.getObjectInspector
  
  // Create unwrapper for converting Hive data to Spark
  val unwrapper = unwrapperFor(deInspector)
  
  // Convert Hive data to Spark representation
  val sparkData = unwrapper(data)
  
  // Convert to InternalRow if needed
  sparkData.asInstanceOf[InternalRow]
}

// Type conversion may throw exceptions for unsupported types
try {
  val unsupportedType = UserDefinedType.sqlType(new CustomUDT)
  val inspector = toInspector(unsupportedType)
} catch {
  case _: UnsupportedOperationException =>
    println("Type not supported for Hive conversion")
  case _: IllegalArgumentException =>
    println("Invalid type configuration")
}

// Cache wrappers and unwrappers for repeated use
class CachedConverter(schema: StructType) {
  private val inspector = toInspector(schema)
  private val wrapper = wrapperFor(inspector, schema)
  private val unwrapper = unwrapperFor(inspector)
  
  def toHive(row: InternalRow): Any = wrapper(row)
  def fromHive(hiveData: Any): Any = unwrapper(hiveData)
}

// Use for batch operations
val converter = new CachedConverter(schema)
val convertedRows = sparkRows.map(converter.toHive)

// Example from HiveSimpleUDF implementation
def evaluateUDF(
  udf: GenericUDF,
  inputs: Seq[Any],
  inputTypes: Seq[DataType],
  outputType: DataType
): Any = {
  
  // Convert inputs to Hive representation
  val inputInspectors = inputTypes.map(toInspector)
  val wrappers = inputInspectors.zip(inputTypes).map { 
    case (inspector, dataType) => wrapperFor(inspector, dataType)
  }
  
  val hiveInputs = inputs.zip(wrappers).map {
    case (input, wrapper) => wrapper(input)
  }
  
  // Execute UDF
  val hiveResult = udf.evaluate(hiveInputs.toArray)
  
  // Convert result back to Spark
  val outputInspector = udf.getObjectInspector(inputInspectors.toArray)
  val unwrapper = unwrapperFor(outputInspector)
  unwrapper(hiveResult)
}

import org.apache.hadoop.hive.ql.udf.generic.GenericUDF
import org.apache.spark.sql.types._

// Set up UDF evaluation with proper type conversion
val inputTypes = Seq(StringType, IntegerType)
val inputs = Seq(UTF8String.fromString("test"), 42)

val result = evaluateUDF(myGenericUDF, inputs, inputTypes, StringType)
println(s"UDF result: $result")