Execution Environment

object StreamExecutionEnvironment {
  /**
   * Creates the execution environment based on the context (local vs cluster)
   * @return StreamExecutionEnvironment instance
   */
  def getExecutionEnvironment: StreamExecutionEnvironment
  
  /**
   * Creates a local execution environment with specified parallelism
   * @param parallelism The parallelism for local execution
   * @return Local StreamExecutionEnvironment
   */
  def createLocalEnvironment(parallelism: Int = getDefaultLocalParallelism): StreamExecutionEnvironment
  
  /**
   * Creates a local environment with web UI for monitoring
   * @param config Optional configuration
   * @return Local environment with web UI
   */
  def createLocalEnvironmentWithWebUI(config: Configuration = null): StreamExecutionEnvironment
  
  /**
   * Creates a remote execution environment
   * @param host Remote JobManager host
   * @param port Remote JobManager port  
   * @param jarFiles JAR files to submit with the job
   * @return Remote StreamExecutionEnvironment
   */
  def createRemoteEnvironment(host: String, port: Int, jarFiles: String*): StreamExecutionEnvironment
  
  /**
   * Creates a remote execution environment with parallelism
   * @param host Remote JobManager host
   * @param port Remote JobManager port
   * @param parallelism Parallelism for the job
   * @param jarFiles JAR files to submit with the job
   * @return Remote StreamExecutionEnvironment
   */
  def createRemoteEnvironment(host: String, port: Int, parallelism: Int, jarFiles: String*): StreamExecutionEnvironment
}

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

// Automatic environment detection (recommended)
val env = StreamExecutionEnvironment.getExecutionEnvironment

// Local environment for testing
val localEnv = StreamExecutionEnvironment.createLocalEnvironment(4)

// Remote environment for cluster submission
val remoteEnv = StreamExecutionEnvironment.createRemoteEnvironment(
  "jobmanager-host", 8081, "my-application.jar"
)

class StreamExecutionEnvironment {
  /**
   * Sets the default parallelism for all operators
   * @param parallelism The parallelism degree
   */
  def setParallelism(parallelism: Int): Unit
  
  /**
   * Sets the maximum parallelism for all operators
   * @param maxParallelism The maximum parallelism degree
   */
  def setMaxParallelism(maxParallelism: Int): Unit
  
  /**
   * Gets the default parallelism
   * @return Current parallelism setting
   */
  def getParallelism: Int
  
  /**
   * Gets the maximum parallelism
   * @return Current maximum parallelism setting
   */
  def getMaxParallelism: Int
}

class StreamExecutionEnvironment {
  /**
   * Sets the runtime execution mode (batch vs streaming)
   * @param executionMode The runtime execution mode
   * @return This environment for chaining
   */
  def setRuntimeMode(executionMode: RuntimeExecutionMode): StreamExecutionEnvironment
  
  /**
   * Sets the buffer timeout for network buffers
   * @param timeoutMillis Timeout in milliseconds
   * @return This environment for chaining
   */
  def setBufferTimeout(timeoutMillis: Long): StreamExecutionEnvironment
  
  /**
   * Gets the buffer timeout
   * @return Current buffer timeout in milliseconds
   */
  def getBufferTimeout: Long
  
  /**
   * Disables operator chaining globally
   * @return This environment for chaining
   */
  def disableOperatorChaining(): StreamExecutionEnvironment
}

class StreamExecutionEnvironment {
  /**
   * Enables checkpointing with specified interval
   * @param interval Checkpoint interval in milliseconds
   * @return This environment for chaining
   */
  def enableCheckpointing(interval: Long): StreamExecutionEnvironment
  
  /**
   * Enables checkpointing with interval and mode
   * @param interval Checkpoint interval in milliseconds
   * @param mode Checkpointing mode (exactly-once or at-least-once)
   * @return This environment for chaining
   */
  def enableCheckpointing(interval: Long, mode: CheckpointingMode): StreamExecutionEnvironment
  
  /**
   * Gets the checkpoint configuration
   * @return CheckpointConfig for fine-tuning
   */
  def getCheckpointConfig: CheckpointConfig
  
  /**
   * Sets the state backend for checkpointing
   * @param backend The state backend implementation
   * @return This environment for chaining
   */
  def setStateBackend(backend: StateBackend): StreamExecutionEnvironment
}

import org.apache.flink.streaming.api.scala._
import org.apache.flink.core.execution.CheckpointingMode

val env = StreamExecutionEnvironment.getExecutionEnvironment

// Enable checkpointing every 5 seconds with exactly-once semantics
env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE)

// Configure checkpoint settings
val checkpointConfig = env.getCheckpointConfig
checkpointConfig.setMinPauseBetweenCheckpoints(500)
checkpointConfig.setCheckpointTimeout(60000)

class StreamExecutionEnvironment {
  /**
   * Sets the restart strategy configuration
   * @param restartStrategyConfiguration Restart strategy configuration
   */
  def setRestartStrategy(restartStrategyConfiguration: RestartStrategyConfiguration): Unit
  
  /**
   * Gets the current restart strategy
   * @return Current restart strategy configuration
   */
  def getRestartStrategy: RestartStrategyConfiguration
}

class StreamExecutionEnvironment {
  /**
   * Creates a DataStream from a sequence of elements
   * @param data Elements to include in the stream
   * @return DataStream containing the elements
   */
  def fromElements[T: TypeInformation](data: T*): DataStream[T]
  
  /**
   * Creates a DataStream from a collection
   * @param data Collection to convert to stream
   * @return DataStream containing collection elements
   */
  def fromCollection[T: TypeInformation](data: Seq[T]): DataStream[T]
  
  /**
   * Creates a DataStream from an iterator
   * @param data Iterator to convert to stream
   * @return DataStream containing iterator elements
   */
  def fromCollection[T: TypeInformation](data: Iterator[T]): DataStream[T]
  
  /**
   * Creates a DataStream from a number sequence
   * @param from Starting number (inclusive)
   * @param to Ending number (inclusive)
   * @return DataStream of Long numbers
   */
  def fromSequence(from: Long, to: Long): DataStream[Long]
  
  /**
   * Reads a text file as a DataStream
   * @param filePath Path to the text file
   * @return DataStream of file lines
   */
  def readTextFile(filePath: String): DataStream[String]
  
  /**
   * Creates a socket text stream
   * @param hostname Host to connect to
   * @param port Port to connect to
   * @param delimiter Line delimiter character
   * @param maxRetry Maximum retry attempts
   * @return DataStream of socket text lines
   */
  def socketTextStream(hostname: String, port: Int, delimiter: Char = '\n', maxRetry: Long = 0): DataStream[String]
  
  /**
   * Adds a custom source function
   * @param function Source function implementation
   * @return DataStream from the source
   */
  def addSource[T: TypeInformation](function: SourceFunction[T]): DataStream[T]
  
  /**
   * Creates a DataStream from a new Source interface
   * @param source Source implementation
   * @param watermarkStrategy Watermark strategy for event time
   * @param sourceName Name for the source operator
   * @return DataStream from the source
   */
  def fromSource[T: TypeInformation](
    source: Source[T, _ <: SourceSplit, _], 
    watermarkStrategy: WatermarkStrategy[T], 
    sourceName: String
  ): DataStream[T]
}

class StreamExecutionEnvironment {
  /**
   * Executes the streaming job
   * @return JobExecutionResult with execution details
   */
  def execute(): JobExecutionResult
  
  /**
   * Executes the streaming job with a name
   * @param jobName Name for the job  
   * @return JobExecutionResult with execution details
   */
  def execute(jobName: String): JobExecutionResult
  
  /**
   * Executes the job asynchronously
   * @return JobClient for monitoring the job
   */
  def executeAsync(): JobClient
  
  /**
   * Executes the job asynchronously with a name
   * @param jobName Name for the job
   * @return JobClient for monitoring the job
   */
  def executeAsync(jobName: String): JobClient
  
  /**
   * Gets the execution plan as a JSON string
   * @return Execution plan representation
   */
  def getExecutionPlan: String
  
  /**
   * Gets the StreamGraph representation
   * @return StreamGraph for the defined transformations
   */
  def getStreamGraph: StreamGraph
}

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

val env = StreamExecutionEnvironment.getExecutionEnvironment

// Define your streaming pipeline
val stream = env.fromElements(1, 2, 3, 4, 5)
  .map(_ * 2)
  .print()

// Execute synchronously
val result = env.execute("My Streaming Job")
println(s"Job completed in ${result.getJobExecutionTime} ms")

// Or execute asynchronously  
val jobClient = env.executeAsync("My Async Job")
// Monitor job status with jobClient

// Runtime execution modes
sealed trait RuntimeExecutionMode
object RuntimeExecutionMode {
  case object STREAMING extends RuntimeExecutionMode
  case object BATCH extends RuntimeExecutionMode  
  case object AUTOMATIC extends RuntimeExecutionMode
}

// Checkpointing modes
sealed trait CheckpointingMode
object CheckpointingMode {
  case object EXACTLY_ONCE extends CheckpointingMode
  case object AT_LEAST_ONCE extends CheckpointingMode
}

// Job execution result
trait JobExecutionResult {
  def getJobExecutionTime: Long
  def getAccumulatorResult[V](accumulatorName: String): V
  def getAllAccumulatorResults: java.util.Map[String, AnyRef]
}

// Job client for async execution
trait JobClient {
  def getJobID: JobID
  def getJobStatus: CompletableFuture[JobStatus]
  def cancel(): CompletableFuture[Void]
  def stopWithSavepoint(advanceToEndOfEventTime: Boolean, savepointDirectory: String): CompletableFuture[String]
}