Sources and Sinks

/**
 * Base interface for table sources
 * @tparam T Type of records produced by the source
 */
trait TableSource[T] {
  /**
   * Gets the return type of the source
   * @returns Type information for produced records
   */
  def getReturnType: TypeInformation[T]
  
  /**
   * Gets the schema of the produced table
   * @returns TableSchema describing field names and types
   */
  def getTableSchema: TableSchema
  
  /**
   * Returns a string explanation of the source
   * @returns Description of the source for debugging
   */
  def explainSource(): String
}

/**
 * Table source for batch processing
 * @tparam T Type of records produced by the source
 */
trait BatchTableSource[T] extends TableSource[T] {
  /**
   * Creates a DataSet from the source
   * @param execEnv Batch execution environment
   * @returns DataSet containing the source data
   */
  def getDataSet(execEnv: ExecutionEnvironment): DataSet[T]
}

// Custom CSV batch source
class CsvBatchSource(
  filePath: String,
  fieldNames: Array[String], 
  fieldTypes: Array[TypeInformation[_]]
) extends BatchTableSource[Row] {
  
  override def getReturnType: TypeInformation[Row] = {
    Types.ROW(fieldNames, fieldTypes)
  }
  
  override def getTableSchema: TableSchema = {
    new TableSchema(fieldNames, fieldTypes)
  }
  
  override def getDataSet(execEnv: ExecutionEnvironment): DataSet[Row] = {
    execEnv.readTextFile(filePath)
      .map(line => {
        val fields = line.split(",")
        Row.of(fields: _*)
      })
  }
  
  override def explainSource(): String = s"CsvBatchSource($filePath)"
}

// Register and use batch source
val csvSource = new CsvBatchSource(
  "/path/to/data.csv",
  Array("id", "name", "age"),
  Array(Types.LONG, Types.STRING, Types.INT)
)

tEnv.registerTableSource("CsvData", csvSource)
val table = tEnv.scan("CsvData")

/**
 * Table source for stream processing
 * @tparam T Type of records produced by the source
 */
trait StreamTableSource[T] extends TableSource[T] {
  /**
   * Creates a DataStream from the source
   * @param execEnv Stream execution environment
   * @returns DataStream containing the source data
   */
  def getDataStream(execEnv: StreamExecutionEnvironment): DataStream[T]
}

// Custom Kafka-like stream source
class KafkaStreamSource(
  topic: String,
  fieldNames: Array[String],
  fieldTypes: Array[TypeInformation[_]]
) extends StreamTableSource[Row] {
  
  override def getReturnType: TypeInformation[Row] = {
    Types.ROW(fieldNames, fieldTypes)
  }
  
  override def getTableSchema: TableSchema = {
    new TableSchema(fieldNames, fieldTypes)
  }
  
  override def getDataStream(execEnv: StreamExecutionEnvironment): DataStream[Row] = {
    // Simulate Kafka consumer
    execEnv.addSource(new SourceFunction[Row] {
      var running = true
      
      override def run(ctx: SourceContext[Row]): Unit = {
        while (running) {
          // Emit sample data
          ctx.collect(Row.of(System.currentTimeMillis(), "sample_data", 42))
          Thread.sleep(1000)
        }
      }
      
      override def cancel(): Unit = {
        running = false
      }
    })
  }
  
  override def explainSource(): String = s"KafkaStreamSource($topic)"
}

// Register and use stream source
val kafkaSource = new KafkaStreamSource(
  "events",
  Array("timestamp", "message", "value"),
  Array(Types.LONG, Types.STRING, Types.INT)
)

tEnv.registerTableSource("KafkaEvents", kafkaSource)
val eventTable = tEnv.scan("KafkaEvents")

/**
 * Source that supports projection pushdown
 */
trait ProjectableTableSource[T] extends TableSource[T] {
  /**
   * Creates a new source with projected fields
   * @param fields Array of projected field indices
   * @returns New source instance with projection applied
   */
  def projectFields(fields: Array[Int]): TableSource[T]
  
  /**
   * Checks if projection pushdown is supported
   * @returns True if projection is supported
   */
  def supportsProjection: Boolean = true
}

/**
 * Source that supports filter pushdown  
 */
trait FilterableTableSource[T] extends TableSource[T] {
  /**
   * Creates a new source with pushed-down filters
   * @param predicates Array of filter expressions
   * @returns New source instance with filters applied
   */
  def applyPredicate(predicates: java.util.List[Expression]): TableSource[T]
  
  /**
   * Checks if filter pushdown is supported
   * @returns True if filtering is supported
   */
  def supportsFiltering: Boolean = true
}

/**
 * Source with custom field mapping
 */
trait DefinedFieldMapping extends TableSource[_] {
  /**
   * Defines mapping from physical to logical fields
   * @returns Map from logical field name to physical field name
   */
  def getFieldMapping: java.util.Map[String, String]
}

/**
 * Source that defines rowtime attributes for event time
 */
trait DefinedRowtimeAttributes extends TableSource[_] {
  /**
   * Gets rowtime attribute descriptors
   * @returns List of rowtime attribute descriptors
   */
  def getRowtimeAttributeDescriptors: java.util.List[RowtimeAttributeDescriptor]
}

/**
 * Source that defines processing time attribute
 */
trait DefinedProctimeAttribute extends TableSource[_] {
  /**
   * Gets the processing time attribute name
   * @returns Processing time attribute name, null if none
   */
  def getProctimeAttribute: String
}

// Advanced source with projection and filtering
class OptimizedCsvSource(
  filePath: String,
  fieldNames: Array[String],
  fieldTypes: Array[TypeInformation[_]]
) extends BatchTableSource[Row] 
  with ProjectableTableSource[Row] 
  with FilterableTableSource[Row] {
  
  private var projectedFields: Option[Array[Int]] = None
  private var filters: List[Expression] = List.empty
  
  override def projectFields(fields: Array[Int]): TableSource[Row] = {
    val newSource = new OptimizedCsvSource(filePath, fieldNames, fieldTypes)
    newSource.projectedFields = Some(fields)
    newSource.filters = this.filters
    newSource
  }
  
  override def applyPredicate(predicates: java.util.List[Expression]): TableSource[Row] = {
    val newSource = new OptimizedCsvSource(filePath, fieldNames, fieldTypes) 
    newSource.projectedFields = this.projectedFields
    newSource.filters = predicates.asScala.toList
    newSource
  }
  
  override def getDataSet(execEnv: ExecutionEnvironment): DataSet[Row] = {
    var dataSet = execEnv.readTextFile(filePath)
      .map(line => {
        val fields = line.split(",")
        Row.of(fields: _*)
      })
    
    // Apply filters if present
    filters.foreach { filter =>
      // Apply filter logic (simplified)
      dataSet = dataSet.filter(row => evaluateFilter(row, filter))
    }
    
    // Apply projection if present
    projectedFields match {
      case Some(fields) => dataSet.map(row => projectRow(row, fields))
      case None => dataSet
    }
  }
  
  private def evaluateFilter(row: Row, filter: Expression): Boolean = {
    // Simplified filter evaluation
    true
  }
  
  private def projectRow(row: Row, fields: Array[Int]): Row = {
    Row.of(fields.map(row.getField): _*)
  }
  
  // Other required methods...
  override def getReturnType: TypeInformation[Row] = Types.ROW(fieldNames, fieldTypes)
  override def getTableSchema: TableSchema = new TableSchema(fieldNames, fieldTypes)
  override def explainSource(): String = s"OptimizedCsvSource($filePath)"
}

/**
 * Base interface for table sinks
 * @tparam T Type of records consumed by the sink
 */
trait TableSink[T] {
  /**
   * Gets the expected input type
   * @returns Type information for consumed records
   */
  def getOutputType: TypeInformation[T]
  
  /**
   * Configures the sink with field information
   * @param fieldNames Array of field names
   * @param fieldTypes Array of field types
   * @returns Configured sink instance
   */
  def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[T]
}

/**
 * Table sink for batch processing
 * @tparam T Type of records consumed by the sink
 */
trait BatchTableSink[T] extends TableSink[T] {
  /**
   * Emits the DataSet to the sink
   * @param dataSet DataSet to write
   * @param execEnv Batch execution environment
   */
  def emitDataSet(dataSet: DataSet[T], execEnv: ExecutionEnvironment): Unit
}

// Custom CSV batch sink
class CsvBatchSink(outputPath: String) extends BatchTableSink[Row] {
  private var fieldNames: Array[String] = _
  private var fieldTypes: Array[TypeInformation[_]] = _
  
  override def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[Row] = {
    val newSink = new CsvBatchSink(outputPath)
    newSink.fieldNames = fieldNames
    newSink.fieldTypes = fieldTypes
    newSink
  }
  
  override def getOutputType: TypeInformation[Row] = {
    Types.ROW(fieldNames, fieldTypes)
  }
  
  override def emitDataSet(dataSet: DataSet[Row], execEnv: ExecutionEnvironment): Unit = {
    dataSet
      .map(row => (0 until row.getArity).map(row.getField).mkString(","))
      .writeAsText(outputPath)
  }
}

// Register and use batch sink
val csvSink = new CsvBatchSink("/path/to/output.csv")
tEnv.registerTableSink("CsvOutput", fieldNames, fieldTypes, csvSink)
table.insertInto("CsvOutput")

/**
 * Append-only stream sink for insert-only tables
 * @tparam T Type of records consumed by the sink
 */
trait AppendStreamTableSink[T] extends TableSink[T] {
  /**
   * Emits the DataStream to the sink
   * @param dataStream DataStream to write
   * @param execEnv Stream execution environment
   */
  def emitDataStream(dataStream: DataStream[T], execEnv: StreamExecutionEnvironment): Unit
}

/**
 * Retract stream sink for tables with updates and deletes
 * @tparam T Type of records consumed by the sink
 */
trait RetractStreamTableSink[T] extends TableSink[T] {
  /**
   * Emits the retract DataStream to the sink
   * @param dataStream DataStream of (Boolean, T) where Boolean indicates add/retract
   * @param execEnv Stream execution environment
   */
  def emitDataStream(dataStream: DataStream[(Boolean, T)], execEnv: StreamExecutionEnvironment): Unit
}

/**
 * Upsert stream sink for tables with primary keys
 * @tparam T Type of records consumed by the sink  
 */
trait UpsertStreamTableSink[T] extends TableSink[T] {
  /**
   * Gets the primary key fields for upsert operations
   * @returns Array of primary key field names
   */
  def getKeys: Array[String]
  
  /**
   * Indicates if the sink expects upsert or retract stream
   * @returns True for upsert stream, false for retract stream
   */
  def isUpsertMode: Boolean
  
  /**
   * Emits the upsert DataStream to the sink
   * @param dataStream DataStream of (Boolean, T) for upsert/delete operations
   * @param execEnv Stream execution environment
   */
  def emitDataStream(dataStream: DataStream[(Boolean, T)], execEnv: StreamExecutionEnvironment): Unit
}

// Append-only stream sink
class PrintAppendSink extends AppendStreamTableSink[Row] {
  private var fieldNames: Array[String] = _
  private var fieldTypes: Array[TypeInformation[_]] = _
  
  override def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[Row] = {
    val newSink = new PrintAppendSink()
    newSink.fieldNames = fieldNames
    newSink.fieldTypes = fieldTypes
    newSink
  }
  
  override def getOutputType: TypeInformation[Row] = Types.ROW(fieldNames, fieldTypes)
  
  override def emitDataStream(dataStream: DataStream[Row], execEnv: StreamExecutionEnvironment): Unit = {
    dataStream.print()
  }
}

// Retract stream sink for aggregated results
class DatabaseRetractSink(jdbcUrl: String) extends RetractStreamTableSink[Row] {
  private var fieldNames: Array[String] = _
  private var fieldTypes: Array[TypeInformation[_]] = _
  
  override def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[Row] = {
    val newSink = new DatabaseRetractSink(jdbcUrl)
    newSink.fieldNames = fieldNames  
    newSink.fieldTypes = fieldTypes
    newSink
  }
  
  override def getOutputType: TypeInformation[Row] = Types.ROW(fieldNames, fieldTypes)
  
  override def emitDataStream(dataStream: DataStream[(Boolean, Row)], execEnv: StreamExecutionEnvironment): Unit = {
    dataStream.addSink(new SinkFunction[(Boolean, Row)] {
      override def invoke(value: (Boolean, Row)): Unit = {
        val (isAdd, row) = value
        if (isAdd) {
          // Insert or update row in database
          insertOrUpdate(row)
        } else {
          // Delete row from database  
          delete(row)
        }
      }
    })
  }
  
  private def insertOrUpdate(row: Row): Unit = {
    // Database insertion/update logic
  }
  
  private def delete(row: Row): Unit = {
    // Database deletion logic
  }
}

// Upsert stream sink with primary key
class KafkaUpsertSink(topic: String, keyFields: Array[String]) extends UpsertStreamTableSink[Row] {
  private var fieldNames: Array[String] = _
  private var fieldTypes: Array[TypeInformation[_]] = _
  
  override def getKeys: Array[String] = keyFields
  override def isUpsertMode: Boolean = true
  
  override def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[Row] = {
    val newSink = new KafkaUpsertSink(topic, keyFields)
    newSink.fieldNames = fieldNames
    newSink.fieldTypes = fieldTypes
    newSink
  }
  
  override def getOutputType: TypeInformation[Row] = Types.ROW(fieldNames, fieldTypes)
  
  override def emitDataStream(dataStream: DataStream[(Boolean, Row)], execEnv: StreamExecutionEnvironment): Unit = {
    dataStream.addSink(new SinkFunction[(Boolean, Row)] {
      override def invoke(value: (Boolean, Row)): Unit = {
        val (isUpsert, row) = value
        val key = extractKey(row)
        val message = if (isUpsert) serializeRow(row) else null // null for delete
        sendToKafka(topic, key, message)
      }
    })
  }
  
  private def extractKey(row: Row): String = {
    // Extract key fields from row
    keyFields.map(field => row.getField(fieldNames.indexOf(field))).mkString("|")
  }
  
  private def serializeRow(row: Row): String = {
    // Serialize row to JSON or other format
    (0 until row.getArity).map(row.getField).mkString(",")
  }
  
  private def sendToKafka(topic: String, key: String, message: String): Unit = {
    // Send to Kafka
  }
}

// TableEnvironment methods for source/sink registration
def registerTableSource(name: String, tableSource: TableSource[_]): Unit
def registerTableSink(name: String, fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]], tableSink: TableSink[_]): Unit

// Creating tables from sources
def fromTableSource(source: TableSource[_]): Table

// Register multiple sources and sinks
val csvSource = new CsvBatchSource("/input/data.csv", Array("id", "name"), Array(Types.LONG, Types.STRING))
val kafkaSource = new KafkaStreamSource("events", Array("timestamp", "message"), Array(Types.LONG, Types.STRING))
val printSink = new PrintAppendSink()

tEnv.registerTableSource("CsvInput", csvSource)
tEnv.registerTableSource("KafkaInput", kafkaSource)
tEnv.registerTableSink("PrintOutput", Array("result"), Array(Types.STRING), printSink)

// Use registered sources and sinks
val csvTable = tEnv.scan("CsvInput")
val kafkaTable = tEnv.scan("KafkaInput")
val result = csvTable.union(kafkaTable.select('message.cast(Types.STRING)))
result.insertInto("PrintOutput")

trait TableSource[T]
trait BatchTableSource[T] extends TableSource[T]
trait StreamTableSource[T] extends TableSource[T]
trait ProjectableTableSource[T] extends TableSource[T]
trait FilterableTableSource[T] extends TableSource[T]
trait DefinedFieldMapping extends TableSource[_]
trait DefinedRowtimeAttributes extends TableSource[_]
trait DefinedProctimeAttribute extends TableSource[_]

trait TableSink[T]
trait BatchTableSink[T] extends TableSink[T]
trait AppendStreamTableSink[T] extends TableSink[T]
trait RetractStreamTableSink[T] extends TableSink[T]  
trait UpsertStreamTableSink[T] extends TableSink[T]

case class RowtimeAttributeDescriptor(attributeName: String, timestampExtractor: TimestampExtractor, watermarkStrategy: WatermarkStrategy)