Execution Environment

object ExecutionEnvironment {
  // Get environment based on execution context (local or cluster)
  def getExecutionEnvironment: ExecutionEnvironment
  
  // Create local execution environment
  def createLocalEnvironment(parallelism: Int = Runtime.getRuntime.availableProcessors()): ExecutionEnvironment
  
  // Create environment for testing with collections
  def createCollectionsEnvironment: ExecutionEnvironment
  
  // Create remote execution environment  
  def createRemoteEnvironment(host: String, port: Int, jarFiles: String*): ExecutionEnvironment
  def createRemoteEnvironment(host: String, port: Int, parallelism: Int, jarFiles: String*): ExecutionEnvironment
}

class ExecutionEnvironment {
  // Get the underlying Java execution environment
  def getJavaEnv: JavaEnv
  
  // Get execution configuration  
  def getConfig: ExecutionConfig
  
  // Set default parallelism for all operations
  def setParallelism(parallelism: Int): Unit
  def getParallelism: Int
  
  // Configure restart strategy
  def setRestartStrategy(restartStrategyConfiguration: RestartStrategyConfiguration): Unit  
  def getRestartStrategy: RestartStrategyConfiguration
  
  // Set number of execution retries (deprecated)
  def setNumberOfExecutionRetries(numRetries: Int): Unit
  def getNumberOfExecutionRetries: Int
  
  // Kryo serialization registration
  def registerTypeWithKryoSerializer[T <: Serializer[_] with Serializable](
    clazz: Class[_], 
    serializer: T
  ): Unit
  def registerTypeWithKryoSerializer(
    clazz: Class[_], 
    serializer: Class[_ <: Serializer[_]]
  ): Unit
  def registerType(typeClass: Class[_]): Unit
  
  // Cached file registration
  def registerCachedFile(filePath: String, name: String, executable: Boolean = false): Unit
  
  // Default parallelism for local execution
  def setDefaultLocalParallelism(parallelism: Int): Unit
}

class ExecutionEnvironment {
  // Get unique job identifier
  def getId: JobID
  def getIdString: String
  
  // Session management
  def startNewSession(): Unit
  def setSessionTimeout(timeout: Long): Unit  
  def getSessionTimeout: Long
  
  // Execution results
  def getLastJobExecutionResult: JobExecutionResult
  
  // Default parallelism for local execution
  def setDefaultLocalParallelism(parallelism: Int): Unit
  
  // Buffer timeout configuration 
  def setBufferTimeout(timeoutMillis: Long): ExecutionEnvironment
  def getBufferTimeout: Long
}

class ExecutionEnvironment {
  // Execute the job and return results
  def execute(): JobExecutionResult
  def execute(jobName: String): JobExecutionResult
  
  // Get execution plan as string
  def getExecutionPlan(): String
}

class ExecutionEnvironment {
  // Create DataSet from collections
  def fromCollection[T: ClassTag : TypeInformation](data: Iterable[T]): DataSet[T]
  def fromCollection[T: ClassTag : TypeInformation](data: Iterator[T]): DataSet[T]
  def fromElements[T: ClassTag : TypeInformation](data: T*): DataSet[T]
  def fromParallelCollection[T: ClassTag : TypeInformation](data: SplittableIterator[T]): DataSet[T]
  
  // Generate sequence of numbers
  def generateSequence(from: Long, to: Long): DataSet[Long]
  
  // Read from files
  def readTextFile(filePath: String, charsetName: String = "UTF-8"): DataSet[String]
  def readTextFileWithValue(filePath: String, charsetName: String = "UTF-8"): DataSet[StringValue]
  def readCsvFile[T: ClassTag : TypeInformation](filePath: String): CsvReader[T]
  def readFileOfPrimitives[T: ClassTag : TypeInformation](filePath: String, delimiter: String): DataSet[T]
  def readFileOfPrimitives[T: ClassTag : TypeInformation](filePath: String, typeClass: Class[T]): DataSet[T]
  
  // Custom input formats
  def createInput[T: ClassTag : TypeInformation](inputFormat: InputFormat[T, _]): DataSet[T]
  
  // Hadoop integration
  def readHadoopFile[K: ClassTag : TypeInformation, V: ClassTag : TypeInformation](
    inputFormat: MapredInputFormat[K, V], 
    keyClass: Class[K], 
    valueClass: Class[V], 
    inputPath: String
  ): DataSet[(K, V)]
  
  def readHadoopFile[K: ClassTag : TypeInformation, V: ClassTag : TypeInformation](
    inputFormat: MapreduceInputFormat[K, V], 
    keyClass: Class[K], 
    valueClass: Class[V], 
    inputPath: String
  ): DataSet[(K, V)]
}

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

// Get execution environment (local or cluster based on context)
val env = ExecutionEnvironment.getExecutionEnvironment

// Set parallelism
env.setParallelism(4)

// Create data and execute
val data = env.fromElements(1, 2, 3, 4, 5)
data.print()

env.execute("My Flink Job")

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

// Create local environment with specific parallelism
val localEnv = ExecutionEnvironment.createLocalEnvironment(2)

val data = localEnv.fromCollection(List("hello", "world", "flink"))
val result = data.map(_.toUpperCase)
result.print()

localEnv.execute()

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

// Connect to remote Flink cluster
val remoteEnv = ExecutionEnvironment.createRemoteEnvironment(
  "flink-jobmanager", 
  6123, 
  "/path/to/my-job.jar"
)

val data = remoteEnv.readTextFile("hdfs://data/input.txt")
val processed = data.flatMap(_.split(" ")).groupBy(identity).sum(1)
processed.writeAsText("hdfs://data/output")

remoteEnv.execute("Word Count Job")

class ExecutionEnvironment {
  // Register accumulators 
  def addDefaultKryoSerializer[T](clazz: Class[T], serializer: Serializer[T]): Unit
  def addDefaultKryoSerializer[T](clazz: Class[T], serializerClass: Class[_ <: Serializer[T]]): Unit
  def registerKryoType(clazz: Class[_]): Unit
}

// Basic accumulator types
trait Accumulator[V, R] {
  def add(value: V): Unit
  def getLocalValue: R
  def resetLocal(): Unit
  def merge(other: Accumulator[V, R]): Unit
  def clone(): Accumulator[V, R]
}

// Built-in accumulator implementations
class IntCounter extends Accumulator[Int, Int]
class LongCounter extends Accumulator[Long, Long] 
class DoubleCounter extends Accumulator[Double, Double]
class Histogram extends Accumulator[Int, java.util.Map[Int, Int]]

// List accumulator for collecting values
class ListAccumulator[T] extends Accumulator[T, java.util.ArrayList[T]]

// Maximum/Minimum accumulators
class IntMaximum extends Accumulator[Int, Int]
class IntMinimum extends Accumulator[Int, Int]
class DoubleMaximum extends Accumulator[Double, Double] 
class DoubleMinimum extends Accumulator[Double, Double]

import org.apache.flink.api.scala._
import org.apache.flink.api.common.accumulators.{IntCounter, ListAccumulator}
import org.apache.flink.api.common.functions.RichMapFunction
import org.apache.flink.configuration.Configuration

val env = ExecutionEnvironment.getExecutionEnvironment

// Example using accumulator to count processed records
class CountingMapFunction extends RichMapFunction[String, String] {
  var counter: IntCounter = _
  var errorList: ListAccumulator[String] = _
  
  override def open(config: Configuration): Unit = {
    counter = new IntCounter()
    errorList = new ListAccumulator[String]()
    
    // Register accumulators with runtime context
    getRuntimeContext.addAccumulator("processed-count", counter)
    getRuntimeContext.addAccumulator("errors", errorList)
  }
  
  override def map(value: String): String = {
    counter.add(1)
    
    if (value.contains("error")) {
      errorList.add(value)
      return "ERROR_PROCESSED"
    }
    
    value.toUpperCase
  }
}

val data = env.fromElements("hello", "error1", "world", "error2", "flink")
val result = data.map(new CountingMapFunction())

// Execute and get accumulator results
val jobResult = env.execute("Accumulator Example")
val processedCount = jobResult.getAccumulatorResult[Int]("processed-count")
val errors = jobResult.getAccumulatorResult[java.util.List[String]]("errors")

println(s"Processed $processedCount records")
println(s"Found errors: ${errors.size()}")