UDF and Function Support

import org.apache.spark.sql.hive.HiveFunctionRegistry
import org.apache.spark.sql.catalyst.FunctionIdentifier
import org.apache.spark.sql.catalyst.expressions.Expression
import org.apache.hadoop.hive.ql.exec.FunctionInfo
import org.apache.hadoop.hive.ql.udf.generic._

class HiveFunctionRegistry(
  underlying: FunctionRegistry, 
  executionHive: ClientWrapper
) extends FunctionRegistry with HiveInspectors {
  
  def getFunctionInfo(name: String): FunctionInfo
  def lookupFunction(name: String, children: Seq[Expression]): Expression
  def registerFunction(name: FunctionIdentifier, builder: Seq[Expression] => Expression): Unit
}

val hiveContext = new HiveContext(sc)
val registry = hiveContext.sessionState.functionRegistry

// Get function information
val funcInfo = registry.getFunctionInfo("concat")
println(s"Function class: ${funcInfo.getFunctionClass}")

// Look up function with arguments
val concatExpr = registry.lookupFunction("concat", Seq(stringLit1, stringLit2))

// Register custom function
registry.registerFunction(
  FunctionIdentifier("my_custom_func"),
  (args: Seq[Expression]) => new MyCustomFunction(args)
)

// Use built-in functions in SQL
val result = hiveContext.sql("""
  SELECT 
    concat(first_name, ' ', last_name) as full_name,
    year(birth_date) as birth_year,
    round(salary * 1.1, 2) as adjusted_salary
  FROM employees
  WHERE length(first_name) > 3
""")

// Use built-in functions in DataFrame API
import org.apache.spark.sql.functions._
val df = hiveContext.table("employees")
  .select(
    concat(col("first_name"), lit(" "), col("last_name")).as("full_name"),
    year(col("birth_date")).as("birth_year")
  )

// UDF base classes supported
abstract class UDF extends org.apache.hadoop.hive.ql.exec.UDF
abstract class GenericUDF extends org.apache.hadoop.hive.ql.udf.generic.GenericUDF
abstract class GenericUDTF extends org.apache.hadoop.hive.ql.udf.generic.GenericUDTF
abstract class GenericUDAF extends org.apache.hadoop.hive.ql.udf.generic.GenericUDAF

// Evaluator for UDAFs
abstract class GenericUDAFEvaluator 
  extends org.apache.hadoop.hive.ql.udf.generic.GenericUDAF.GenericUDAFEvaluator

// Register Hive UDF class
hiveContext.sql("ADD JAR /path/to/my-udfs.jar")
hiveContext.sql("""
  CREATE TEMPORARY FUNCTION my_string_func 
  AS 'com.company.hive.MyStringUDF'
""")

// Use registered UDF
val result = hiveContext.sql("""
  SELECT customer_id, my_string_func(customer_name) as processed_name
  FROM customers
""")

// Register UDAF
hiveContext.sql("""
  CREATE TEMPORARY FUNCTION my_aggregate_func
  AS 'com.company.hive.MyAggregateUDAF'
""")

val aggregateResult = hiveContext.sql("""
  SELECT 
    department,
    my_aggregate_func(salary) as custom_aggregate
  FROM employees
  GROUP BY department
""")

case class HiveGenericUDF(
  name: String,
  funcWrapper: HiveFunctionWrapper,
  children: Seq[Expression]
) extends Expression with HiveInspectors with CodegenFallback

// This is typically handled internally by the query planner
// when Hive UDFs are encountered in SQL queries

case class HiveGenericUDTF(
  name: String,
  funcWrapper: HiveFunctionWrapper,
  children: Seq[Expression]
) extends Generator with HiveInspectors with CodegenFallback

// UDTF usage with LATERAL VIEW
val result = hiveContext.sql("""
  SELECT 
    customer_id,
    tag
  FROM customers
  LATERAL VIEW explode(tags) exploded_table AS tag
""")

// Custom UDTF
hiveContext.sql("""
  CREATE TEMPORARY FUNCTION my_explode_func
  AS 'com.company.hive.MyExplodeUDTF'
""")

val customResult = hiveContext.sql("""
  SELECT 
    id,
    value
  FROM input_table
  LATERAL VIEW my_explode_func(data_column) t AS value
""")

// Add JAR containing UDFs
def addJar(path: String): Unit

// Create temporary function
def createFunction(
  name: String,
  className: String,
  resources: Seq[String] = Seq.empty
): Unit

// Drop temporary function
def dropFunction(name: String): Unit

// Add UDF JAR to classpath
hiveContext.sql("ADD JAR hdfs://namenode:port/path/to/udf.jar")

// Create temporary function
hiveContext.sql("""
  CREATE TEMPORARY FUNCTION advanced_analytics
  AS 'com.analytics.AdvancedAnalyticsUDF'
  USING JAR 'hdfs://namenode:port/path/to/analytics.jar'
""")

// Use in queries
val insights = hiveContext.sql("""
  SELECT 
    customer_segment,
    advanced_analytics(purchase_history, demographic_data) as insights
  FROM customer_data
  GROUP BY customer_segment
""")

// Clean up function
hiveContext.sql("DROP TEMPORARY FUNCTION advanced_analytics")

// List available functions
def listFunctions(): Array[String]
def listFunctions(pattern: String): Array[String]

// Describe function
def describeFunction(name: String): String
def describeFunction(name: String, extended: Boolean): String

// List all functions
val allFunctions = hiveContext.sql("SHOW FUNCTIONS").collect()

// List functions matching pattern
val stringFunctions = hiveContext.sql("SHOW FUNCTIONS 'str*'").collect()

// Get function description
val description = hiveContext.sql("DESCRIBE FUNCTION concat").collect()
val extendedDesc = hiveContext.sql("DESCRIBE FUNCTION EXTENDED concat").collect()

// Solution: Add JAR before creating function
hiveContext.sql("ADD JAR /path/to/udf.jar")

// Check function signature and argument types
// Verify UDF implementation matches expected signature

// Ensure UDF classes implement Serializable if needed
// Use transient fields for non-serializable components