Utilities

implicit class DataSetUtils[T](dataSet: DataSet[T]) {
  /**
   * Adds consecutive indices to each element starting from 0
   * @return DataSet of (index, element) tuples
   */
  def zipWithIndex: DataSet[(Long, T)]
  
  /**
   * Adds unique identifiers to each element  
   * @return DataSet of (uniqueId, element) tuples
   */
  def zipWithUniqueId: DataSet[(Long, T)]
  
  /**
   * Counts the number of elements in each partition
   * @return DataSet of (partitionIndex, elementCount) tuples
   */
  def countElementsPerPartition: DataSet[(Int, Long)]
  
  /**
   * Computes checksum and total count of the DataSet
   * @return ChecksumHashCode with checksum and count
   */
  def checksumHashCode(): ChecksumHashCode
}

import org.apache.flink.api.scala._
import org.apache.flink.api.scala.utils._

val env = ExecutionEnvironment.getExecutionEnvironment
val data = env.fromElements("apple", "banana", "cherry", "date")

// Add consecutive indices
val indexed = data.zipWithIndex
// Result: [(0, "apple"), (1, "banana"), (2, "cherry"), (3, "date")]

// Add unique IDs (useful in distributed environment)
val withIds = data.zipWithUniqueId
// Result: [(uniqueId1, "apple"), (uniqueId2, "banana"), ...]

// Count elements per partition
val partitionCounts = data.countElementsPerPartition
// Result: [(0, 2), (1, 2)] for 2 partitions with 2 elements each

// Get checksum and count
val checksum = data.checksumHashCode()
println(s"Checksum: ${checksum.getChecksum}, Count: ${checksum.getCount}")

implicit class DataSetUtils[T](dataSet: DataSet[T]) {
  /**
   * Samples elements randomly by fraction
   * @param withReplacement Whether to sample with replacement
   * @param fraction Fraction of elements to sample (0.0 to 1.0)
   * @param seed Random seed for reproducibility
   * @return DataSet with sampled elements
   */
  def sample(withReplacement: Boolean, fraction: Double, seed: Long = Random.nextLong()): DataSet[T]
  
  /**
   * Samples a specific number of elements
   * @param withReplacement Whether to sample with replacement
   * @param numSamples Number of elements to sample
   * @param seed Random seed for reproducibility
   * @return DataSet with sampled elements
   */
  def sampleWithSize(withReplacement: Boolean, numSamples: Int, seed: Long = Random.nextLong()): DataSet[T]
}

val largeDataset = env.generateSequence(1, 1000000)

// Sample 10% of elements without replacement
val sample10Percent = largeDataset.sample(withReplacement = false, fraction = 0.1)

// Sample exactly 1000 elements with replacement
val exactSample = largeDataset.sampleWithSize(withReplacement = true, numSamples = 1000)

// Reproducible sampling with seed
val reproducibleSample = largeDataset.sample(
  withReplacement = false, 
  fraction = 0.05, 
  seed = 12345L
)

implicit class DataSetUtils[T](dataSet: DataSet[T]) {
  /**
   * Partitions by range using custom data distribution
   * @param distribution Custom data distribution
   * @param fields Field positions for partitioning
   * @return DataSet with custom range partitioning
   */
  def partitionByRange(distribution: DataDistribution, fields: Int*): DataSet[T]
  
  /**
   * Partitions by range using custom data distribution and field names
   * @param distribution Custom data distribution
   * @param firstField First field name
   * @param otherFields Additional field names
   * @return DataSet with custom range partitioning
   */
  def partitionByRange(
    distribution: DataDistribution, 
    firstField: String, 
    otherFields: String*
  ): DataSet[T]
  
  /**
   * Partitions by range using custom data distribution and key selector
   * @param distribution Custom data distribution
   * @param fun Key selector function
   * @return DataSet with custom range partitioning
   */
  def partitionByRange[K: TypeInformation](
    distribution: DataDistribution, 
    fun: T => K
  ): DataSet[T]
}

// Custom data distribution for range partitioning
case class SalesRecord(region: String, amount: Double, date: String)

val salesData = env.fromElements(
  SalesRecord("North", 1000.0, "2023-01-01"),
  SalesRecord("South", 1500.0, "2023-01-02"),
  SalesRecord("East", 800.0, "2023-01-03")
)

// Create custom distribution (implementation depends on requirements)
val customDistribution = new DataDistribution {
  // Implementation for determining partition boundaries
}

val partitionedSales = salesData.partitionByRange(customDistribution, _.amount)

// Import for partial function support
import org.apache.flink.api.scala.extensions._

implicit class OnDataSet[T](dataSet: DataSet[T]) {
  /**
   * Maps using partial function with pattern matching
   * @param fun Partial function for transformation
   * @return DataSet with partial function mapping
   */
  def mapWith[R: TypeInformation: ClassTag](fun: T => R): DataSet[R]
  
  /**
   * FlatMaps using partial function
   * @param fun Partial function returning traversable
   * @return DataSet with partial function flatMap
   */
  def flatMapWith[R: TypeInformation: ClassTag](fun: T => TraversableOnce[R]): DataSet[R]
  
  /**
   * Filters using partial function
   * @param fun Partial function for filtering
   * @return DataSet with partial function filtering
   */
  def filterWith(fun: T => Boolean): DataSet[T]
  
  /**
   * Reduces using partial function
   * @param fun Partial function for reduction
   * @return DataSet with partial function reduction
   */
  def reduceWith(fun: (T, T) => T): DataSet[T]
  
  /**
   * Reduces groups using partial function
   * @param fun Partial function for group reduction
   * @return DataSet with partial function group reduction
   */
  def reduceGroupWith[R: TypeInformation: ClassTag](fun: Stream[T] => R): DataSet[R]
  
  /**
   * Groups by using partial function
   * @param fun Partial function for key extraction
   * @return GroupedDataSet with partial function grouping
   */
  def groupingBy[K: TypeInformation](fun: T => K): GroupedDataSet[T]
  
  /**
   * MapPartition using partial function on streams
   * @param fun Partial function for partition transformation
   * @return DataSet with partial function mapPartition
   */
  def mapPartitionWith[R: TypeInformation: ClassTag](fun: Stream[T] => R): DataSet[R]
}

import org.apache.flink.api.scala._
import org.apache.flink.api.scala.extensions._

sealed trait Event
case class UserEvent(userId: String, action: String) extends Event
case class SystemEvent(level: String, message: String) extends Event
case class ErrorEvent(error: String, stackTrace: String) extends Event

val events: DataSet[Event] = env.fromElements(
  UserEvent("user1", "login"),
  SystemEvent("INFO", "System started"),
  ErrorEvent("NPE", "NullPointerException at line 42")
)

// Pattern matching with partial functions
val userActions = events.mapWith {
  case UserEvent(userId, action) => s"$userId performed $action"
  case _ => "Not a user event"
}

val criticalEvents = events.filterWith {
  case ErrorEvent(_, _) => true
  case SystemEvent("ERROR", _) => true
  case _ => false
}

// Group by event type using pattern matching
val groupedByType = events.groupingBy {
  case _: UserEvent => "user"
  case _: SystemEvent => "system"
  case _: ErrorEvent => "error"
}

implicit class OnGroupedDataSet[T](groupedDataSet: GroupedDataSet[T]) {
  /**
   * Reduces groups using partial function
   * @param fun Partial function for group reduction
   * @return DataSet with partial function group reduction
   */
  def reduceWith(fun: (T, T) => T): DataSet[T]
  
  /**
   * Reduces groups using partial function on streams
   * @param fun Partial function processing group as stream
   * @return DataSet with stream-based group reduction
   */
  def reduceGroupWith[R: TypeInformation: ClassTag](fun: Stream[T] => R): DataSet[R]
}

implicit class OnJoinFunctionAssigner[L, R](joiner: JoinFunctionAssigner[L, R]) {
  /**
   * Applies join function using partial functions
   * @param fun Partial function for joining elements
   * @return DataSet with partial function join
   */
  def applyWith[O: TypeInformation: ClassTag](fun: (L, R) => O): DataSet[O]
}

implicit class OnCrossDataSet[L, R](crossDataSet: CrossDataSet[L, R]) {
  /**
   * Applies cross function using partial functions
   * @param fun Partial function for crossing elements
   * @return DataSet with partial function cross
   */
  def applyWith[O: TypeInformation: ClassTag](fun: (L, R) => O): DataSet[O]
}

implicit class OnCoGroupDataSet[L, R](coGroupDataSet: CoGroupDataSet[L, R]) {
  /**
   * Applies coGroup function using partial functions on streams
   * @param fun Partial function for coGrouping streams
   * @return DataSet with partial function coGroup
   */
  def applyWith[O: TypeInformation: ClassTag](fun: (Stream[L], Stream[R]) => O): DataSet[O]
}

case class Order(customerId: Int, productId: Int, amount: Double)
case class Customer(id: Int, name: String, segment: String)

val orders = env.fromElements(
  Order(1, 101, 99.99),
  Order(2, 102, 149.99)
)

val customers = env.fromElements(
  Customer(1, "Alice", "Premium"),
  Customer(2, "Bob", "Standard")
)

// Join with partial functions
val customerOrders = customers
  .join(orders)
  .where(_.id)
  .equalTo(_.customerId)
  .applyWith { (customer, order) =>
    s"${customer.name} (${customer.segment}) ordered product ${order.productId} for $${order.amount}"
  }

class ScalaGauge[T](getValue: () => T) extends Gauge[T] {
  /**
   * Gets the current gauge value
   * @return Current value
   */
  override def getValue: T = getValue()
}

import org.apache.flink.api.scala.metrics.ScalaGauge
import org.apache.flink.api.common.functions.RichMapFunction
import org.apache.flink.metrics.MetricGroup

class MonitoredMapFunction extends RichMapFunction[String, String] {
  @volatile private var processedCount = 0L
  
  override def open(parameters: Configuration): Unit = {
    val metricGroup: MetricGroup = getRuntimeContext.getMetricGroup
    
    // Register Scala gauge
    metricGroup.gauge("processedCount", new ScalaGauge(() => processedCount))
  }
  
  override def map(value: String): String = {
    processedCount += 1
    value.toUpperCase
  }
}

package object utils {
  /**
   * Gets call location name for debugging
   * @param depth Stack depth to examine
   * @return Call location name
   */
  def getCallLocationName(depth: Int = 3): String
}

class ChecksumHashCode {
  /**
   * Gets the computed checksum
   * @return Checksum value
   */
  def getChecksum: Long
  
  /**
   * Gets the element count
   * @return Number of elements
   */
  def getCount: Long
}

trait DataDistribution {
  /**
   * Gets bucket boundary for range partitioning
   * @param bucketNum Bucket number
   * @param totalNumBuckets Total number of buckets
   * @return Bucket boundary object
   */
  def getBucketBoundary(bucketNum: Int, totalNumBuckets: Int): AnyRef
}

// Wrapper classes for partial function support
class OnDataSet[T](dataSet: DataSet[T]) {
  // Enhanced DataSet with partial function capabilities
}

class OnGroupedDataSet[T](groupedDataSet: GroupedDataSet[T]) {
  // Enhanced GroupedDataSet with partial function capabilities
}

class OnJoinFunctionAssigner[L, R](joiner: JoinFunctionAssigner[L, R]) {
  // Enhanced join operations with partial function capabilities
}

class OnCrossDataSet[L, R](crossDataSet: CrossDataSet[L, R]) {
  // Enhanced cross operations with partial function capabilities
}

class OnCoGroupDataSet[L, R](coGroupDataSet: CoGroupDataSet[L, R]) {
  // Enhanced coGroup operations with partial function capabilities
}

class ScalaGauge[T](getValue: () => T) extends Gauge[T] {
  // Scala-friendly gauge metric implementation
}

// Stream type for partial function support
type Stream[T] = scala.collection.immutable.Stream[T]