Apache Spark SQL

import org.apache.spark.sql.{SparkSession, DataFrame, Dataset, Column, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._

import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import static org.apache.spark.sql.functions.*;

import org.apache.spark.sql.{SparkSession, DataFrame}
import org.apache.spark.sql.functions._

// Create SparkSession
val spark = SparkSession.builder()
  .appName("Spark SQL Example")
  .master("local[*]")
  .getOrCreate()

// Load data
val df = spark.read
  .format("json")
  .option("multiline", "true")
  .load("path/to/data.json")

// Transform data
val result = df
  .select(col("name"), col("age").cast("int"))
  .filter(col("age") > 18)
  .groupBy(col("name"))
  .agg(avg(col("age")).alias("avg_age"))

// Execute and show results
result.show()

// SQL queries
df.createOrReplaceTempView("people")
val sqlResult = spark.sql("SELECT name, AVG(age) as avg_age FROM people WHERE age > 18 GROUP BY name")
sqlResult.show()

spark.stop()

object SparkSession {
  def builder(): SparkSession.Builder
}

class SparkSession {
  def sql(sqlText: String): DataFrame
  def read: DataFrameReader
  def readStream: DataStreamReader  
  def catalog: Catalog
  def conf: RuntimeConfig
  def table(tableName: String): DataFrame
  def stop(): Unit
}

class Dataset[T] {
  def select(cols: Column*): DataFrame
  def filter(condition: Column): Dataset[T]
  def groupBy(cols: Column*): RelationalGroupedDataset
  def join(right: Dataset[_]): DataFrame
  def union(other: Dataset[T]): Dataset[T]
  def count(): Long
  def collect(): Array[T]
  def show(): Unit
}

type DataFrame = Dataset[Row]

class DataFrameReader {
  def format(source: String): DataFrameReader
  def schema(schema: StructType): DataFrameReader
  def option(key: String, value: String): DataFrameReader
  def load(): DataFrame
  def json(path: String): DataFrame
  def parquet(path: String): DataFrame
  def jdbc(url: String, table: String, properties: Properties): DataFrame
}

class DataFrameWriter[T] {
  def mode(saveMode: SaveMode): DataFrameWriter[T]
  def format(source: String): DataFrameWriter[T]
  def option(key: String, value: String): DataFrameWriter[T]
  def save(): Unit
  def saveAsTable(tableName: String): Unit
}

// Column operations
class Column {
  def +(other: Any): Column
  def ===(other: Any): Column
  def isNull: Column
  def cast(to: DataType): Column
  def alias(alias: String): Column
}

// Built-in functions (from functions object)
def col(colName: String): Column
def lit(literal: Any): Column
def when(condition: Column, value: Any): Column
def sum(e: Column): Column
def avg(e: Column): Column
def count(e: Column): Column
def max(e: Column): Column
def min(e: Column): Column

class RelationalGroupedDataset {
  def agg(expr: Column, exprs: Column*): DataFrame
  def count(): DataFrame
  def sum(colNames: String*): DataFrame
  def avg(colNames: String*): DataFrame
  def pivot(pivotColumn: String): RelationalGroupedDataset
}

class KeyValueGroupedDataset[K, V] {
  def agg[U1](column: TypedColumn[V, U1]): Dataset[(K, U1)]
  def count(): Dataset[(K, Long)]
  def mapGroups[U](f: (K, Iterator[V]) => U): Dataset[U]
  def reduceGroups(f: (V, V) => V): Dataset[(K, V)]
}

class DataStreamWriter[T] {
  def outputMode(outputMode: OutputMode): DataStreamWriter[T]
  def trigger(trigger: Trigger): DataStreamWriter[T]
  def start(): StreamingQuery
  def foreach(writer: ForeachWriter[T]): DataStreamWriter[T]
}

class StreamingQuery {
  def isActive: Boolean
  def awaitTermination(): Unit
  def stop(): Unit
  def status: StreamingQueryStatus
}

class Catalog {
  def currentDatabase: String
  def listDatabases(): Dataset[Database]
  def listTables(): Dataset[Table]
  def listColumns(tableName: String): Dataset[Column]
  def listFunctions(): Dataset[Function]
  def cacheTable(tableName: String): Unit
  def isCached(tableName: String): Boolean
}

// Core data types
abstract class DataType
case object StringType extends DataType
case object IntegerType extends DataType
case object LongType extends DataType
case object DoubleType extends DataType
case object BooleanType extends DataType
case object DateType extends DataType
case object TimestampType extends DataType

// Complex types
case class StructType(fields: Array[StructField]) extends DataType
case class StructField(name: String, dataType: DataType, nullable: Boolean)
case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType
case class MapType(keyType: DataType, valueType: DataType, valueContainsNull: Boolean) extends DataType

// Row representation
class Row {
  def get(i: Int): Any
  def getString(i: Int): String
  def getInt(i: Int): Int
  def getLong(i: Int): Long
  def getDouble(i: Int): Double
  def getBoolean(i: Int): Boolean
}

// Save modes
object SaveMode extends Enumeration {
  val Overwrite, Append, ErrorIfExists, Ignore = Value
}