SQL Execution

/**
 * SQL statement execution operation using Spark SQL engine
 * Handles query execution, result fetching, and cancellation
 */
class SparkExecuteStatementOperation extends ExecuteStatementOperation {
  /**
   * Fetch the next set of result rows
   * @param order Fetch orientation (FETCH_NEXT, FETCH_PRIOR, etc.)
   * @param maxRowsL Maximum number of rows to fetch
   * @return TRowSet containing the fetched rows
   */
  def getNextRowSet(order: FetchOrientation, maxRowsL: Long): TRowSet
  
  /**
   * Get the schema of the result set
   * @return TTableSchema describing the result columns and types
   */
  def getResultSetSchema: TTableSchema
  
  /**
   * Internal method that executes the SQL statement
   * Called by the operation framework
   */
  def runInternal(): Unit
  
  /**
   * Cancel the currently executing statement
   */
  def cancel(): Unit
  
  /**
   * Cancel the statement due to timeout
   */
  def timeoutCancel(): Unit
}

import org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation
import org.apache.hive.service.cli.{FetchOrientation, OperationHandle}

// Execute a SQL statement (typically done through CLI service)
val statement = "SELECT name, age, department FROM employees WHERE age > 25"
val confOverlay = Map("spark.sql.adaptive.enabled" -> "true").asJava

// Get operation handle from session manager
val operationHandle = sessionManager.executeStatement(sessionHandle, statement, confOverlay)

// Fetch results
val resultSet = operation.getNextRowSet(FetchOrientation.FETCH_NEXT, 1000)
val schema = operation.getResultSetSchema()

// Process results
val columns = schema.getColumns().asScala
println(s"Result has ${columns.size} columns:")
columns.foreach { col =>
  println(s"  ${col.getColumnName()}: ${col.getTypeDesc()}")
}

/**
 * SQL driver providing Hive Driver compatibility with Spark SQL execution
 * @param context SQL context to use for query execution (defaults to SparkSQLEnv.sqlContext)
 */
class SparkSQLDriver(val context: SQLContext = SparkSQLEnv.sqlContext) extends Driver {
  /**
   * Execute a SQL command
   * @param command SQL statement to execute
   * @return CommandProcessorResponse indicating success/failure and details
   */
  def run(command: String): CommandProcessorResponse
  
  /**
   * Get results from the last executed command
   * @param res List to populate with result strings
   * @return true if more results are available, false otherwise
   */
  def getResults(res: JList[_]): Boolean
  
  /**
   * Get the schema of the last query result
   * @return Schema object describing result structure
   */
  def getSchema: Schema
  
  /**
   * Initialize the driver
   */
  def init(): Unit
  
  /**
   * Close the driver and release resources
   * @return 0 on success
   */
  def close(): Int
  
  /**
   * Destroy the driver and clean up all resources
   */
  def destroy(): Unit
}

import org.apache.spark.sql.hive.thriftserver.SparkSQLDriver
import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse

// Create and initialize driver
val driver = new SparkSQLDriver()
driver.init()

try {
  // Execute SQL command
  val response = driver.run("SELECT COUNT(*) FROM sales WHERE year = 2023")
  
  if (response.getResponseCode == 0) {
    // Success - get results
    val results = new java.util.ArrayList[String]()
    while (driver.getResults(results)) {
      results.asScala.foreach(println)
      results.clear()
    }
    
    // Get schema information
    val schema = driver.getSchema
    if (schema != null && schema.getFieldSchemas != null) {
      schema.getFieldSchemas.asScala.foreach { field =>
        println(s"Column: ${field.getName}, Type: ${field.getType}")
      }
    }
  } else {
    // Error occurred
    println(s"Query failed: ${response.getErrorMessage}")
  }
  
} finally {
  driver.close()
  driver.destroy()
}

// Query execution stages
class QueryExecution {
  def analyzed: LogicalPlan        // Analyzed logical plan
  def optimizedPlan: LogicalPlan   // Optimized logical plan  
  def sparkPlan: SparkPlan         // Physical execution plan
  def executedPlan: SparkPlan      // Executed physical plan
}

// Execution context and job management
object SQLExecution {
  /**
   * Execute code with a new execution ID for tracking
   * @param queryExecution Query execution context
   * @param name Optional name for the execution
   * @param body Code block to execute
   * @return Result of the code block
   */
  def withNewExecutionId[T](queryExecution: QueryExecution, name: Option[String])(body: => T): T
}

/**
 * Base class for result sets
 */
abstract class RowSet {
  /**
   * Add a row to the result set
   * @param row Array of objects representing the row data
   */
  def addRow(row: Array[Object]): Unit
  
  /**
   * Convert to Thrift row set format
   * @return TRowSet for network transfer
   */
  def toTRowSet(): TRowSet
  
  /**
   * Get the number of rows in the result set
   * @return Row count
   */
  def numRows(): Int
}

/**
 * Row-based result set implementation
 */
class RowBasedSet extends RowSet {
  /**
   * Create row-based result set with schema
   * @param schema Table schema defining columns and types
   */
  def this(schema: TableSchema)
}

/**
 * Column-based result set implementation (more efficient for large results)
 */
class ColumnBasedSet extends RowSet {
  /**
   * Create column-based result set with schema
   * @param schema Table schema defining columns and types
   */
  def this(schema: TableSchema)
}

import org.apache.spark.sql.hive.thriftserver.RowSetUtils
import org.apache.hive.service.cli.{RowSet, TableSchema, ColumnDescriptor}

// Create schema for results
val schema = new TableSchema()
schema.addColumn(new ColumnDescriptor("name", "string", "Employee name"))
schema.addColumn(new ColumnDescriptor("age", "int", "Employee age"))
schema.addColumn(new ColumnDescriptor("salary", "decimal(10,2)", "Employee salary"))

// Create result set (row-based for small results)
val rowSet = RowSetFactory.create(schema, ProtocolVersion.HIVE_CLI_SERVICE_PROTOCOL_V10, false)

// Add data rows
rowSet.addRow(Array("Alice", Integer.valueOf(30), new java.math.BigDecimal("75000.00")))
rowSet.addRow(Array("Bob", Integer.valueOf(25), new java.math.BigDecimal("65000.00")))

// Convert to Thrift format for network transfer
val tRowSet = rowSet.toTRowSet()

/**
 * Base trait for result fetching iterators
 */
sealed trait FetchIterator[A] extends Iterator[A] {
  /**
   * Get the fetch type for this iterator
   * @return FetchType indicating the data source
   */
  def getFetchType(): FetchType
}

/**
 * Iterator for query output results
 */
class ArrayFetchIterator[A](iter: Iterator[A]) extends FetchIterator[A] {
  def hasNext: Boolean = iter.hasNext
  def next(): A = iter.next()
  def getFetchType(): FetchType = FetchType.QUERY_OUTPUT
}

/**
 * Iterator for operation logs
 */
class IterableFetchIterator[A](iterable: Iterable[A]) extends FetchIterator[A] {
  def getFetchType(): FetchType = FetchType.LOG
}

/**
 * Possible states for operations
 */
enum OperationState {
  INITIALIZED,  // Operation created but not started
  RUNNING,      // Operation currently executing
  FINISHED,     // Operation completed successfully
  CANCELED,     // Operation was canceled
  CLOSED,       // Operation closed and resources cleaned up
  ERROR,        // Operation failed with error
  UNKNOWN       // State cannot be determined
}

/**
 * Operation status information
 */
class OperationStatus {
  /**
   * Get the current state of the operation
   * @return OperationState indicating current status
   */
  public OperationState getState()
  
  /**
   * Get error information if operation failed
   * @return HiveSQLException with error details, or null if no error
   */
  public HiveSQLException getOperationException()
  
  /**
   * Get the operation start time
   * @return Start time in milliseconds since epoch
   */
  public long getOperationStarted()
  
  /**
   * Get the operation completion time
   * @return Completion time in milliseconds since epoch, or 0 if not completed
   */
  public long getOperationCompleted()
}

import org.apache.hive.service.cli.{OperationState, OperationStatus}

// Check operation status
val operationStatus = cliService.getOperationStatus(operationHandle)

operationStatus.getState match {
  case OperationState.RUNNING =>
    println("Query is still executing...")
  case OperationState.FINISHED =>
    println("Query completed successfully")
    val duration = operationStatus.getOperationCompleted - operationStatus.getOperationStarted
    println(s"Execution time: ${duration}ms")
  case OperationState.ERROR =>
    val exception = operationStatus.getOperationException
    println(s"Query failed: ${exception.getMessage}")
  case OperationState.CANCELED =>
    println("Query was canceled")
  case _ =>
    println(s"Query state: ${operationStatus.getState}")
}

/**
 * Spark-specific throwable with SQL state information
 */
trait SparkThrowable extends Throwable {
  def getSqlState: String
  def getErrorClass: String
}

/**
 * Command processor response containing execution results and errors
 */
class CommandProcessorResponse {
  /**
   * Create response indicating success
   * @param responseCode 0 for success, non-zero for failure
   */
  def this(responseCode: Int)
  
  /**
   * Create response with error information
   * @param responseCode Non-zero error code
   * @param errorMessage Error message
   * @param sqlState SQL state code
   * @param exception Underlying exception
   */
  def this(responseCode: Int, errorMessage: String, sqlState: String, exception: Throwable)
  
  def getResponseCode(): Int
  def getErrorMessage(): String
  def getSQLState(): String
  def getException(): Throwable
}

import org.apache.spark.SparkThrowable
import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse

try {
  val response = driver.run("SELECT * FROM non_existent_table")
  
  if (response.getResponseCode != 0) {
    val exception = response.getException
    exception match {
      case st: SparkThrowable =>
        println(s"Spark SQL Error: ${st.getMessage}")
        println(s"Error Class: ${st.getErrorClass}")
        println(s"SQL State: ${st.getSqlState}")
      case _ =>
        println(s"General Error: ${exception.getMessage}")
        println(s"SQL State: ${response.getSQLState}")
    }
  }
} catch {
  case e: Exception =>
    println(s"Unexpected error during query execution: ${e.getMessage}")
}

// Cancel operation through CLI service
cliService.cancelOperation(operationHandle)

// Cancel through operation object
operation.cancel()

// Timeout-based cancellation
operation.timeoutCancel()

// Check if operation supports cancellation
val operation = operationManager.getOperation(operationHandle)
if (operation.shouldRunAsync()) {
  // Async operations support cancellation
  operation.cancel()
}

import java.util.concurrent.{Executors, TimeUnit}

// Start a long-running query
val operationHandle = cliService.executeStatementAsync(
  sessionHandle, 
  "SELECT * FROM large_table ORDER BY column1", 
  Map.empty.asJava
)

// Set up cancellation after timeout
val executor = Executors.newSingleThreadScheduledExecutor()
executor.schedule(new Runnable {
  def run(): Unit = {
    try {
      cliService.cancelOperation(operationHandle)
      println("Query canceled due to timeout")
    } catch {
      case e: Exception => println(s"Error canceling query: ${e.getMessage}")
    }
  }
}, 30, TimeUnit.SECONDS)

// Monitor query progress
while (true) {
  val status = cliService.getOperationStatus(operationHandle)
  status.getState match {
    case OperationState.FINISHED =>
      println("Query completed")
      break
    case OperationState.CANCELED =>
      println("Query was canceled")
      break
    case OperationState.ERROR =>
      println("Query failed")
      break
    case _ =>
      Thread.sleep(1000) // Poll every second
  }
}

executor.shutdown()