Auto-Completion

/**
 * Auto-completion functionality for the REPL using JLine
 * @param intp SparkIMain interpreter instance to provide completion context
 */
@DeveloperApi
class SparkJLineCompletion(val intp: SparkIMain) {
  /**
   * Completion verbosity level controlling detail of completion output
   * 0 = minimal, higher values = more verbose
   */
  var verbosity: Int
  
  /**
   * Reset verbosity level to zero (minimal output)
   */
  def resetVerbosity(): Unit
  
  /**
   * Create a JLineTabCompletion instance for performing completions
   * @return Configured JLineTabCompletion instance
   */
  def completer(): JLineTabCompletion
}

import org.apache.spark.repl.{SparkIMain, SparkJLineCompletion}

// Create interpreter and completion system
val interpreter = new SparkIMain()
interpreter.initializeSynchronous()

val completion = new SparkJLineCompletion(interpreter)

// Configure verbosity
completion.verbosity = 1  // More detailed completion output
completion.resetVerbosity()  // Reset to minimal output

// Get completer instance
val completer = completion.completer()

/**
 * JLineTabCompletion provides the actual completion logic
 * Extends ScalaCompleter from scala.tools.nsc.interpreter
 */
class JLineTabCompletion extends ScalaCompleter {
  /**
   * Perform completion on input buffer
   * @param buffer Current input buffer
   * @param cursor Cursor position in buffer
   * @return Candidates object with completions and cursor position
   */
  def complete(buffer: String, cursor: Int): Candidates
}

/**
 * Result of completion operation from scala.tools.nsc.interpreter
 */
case class Candidates(
  cursor: Int,                   // Position where completion applies
  candidates: List[String]       // Possible completions
)

import org.apache.spark.repl.{SparkIMain, SparkJLineCompletion}

val interpreter = new SparkIMain()
interpreter.initializeSynchronous()

// Define some variables for completion context
interpreter.interpret("val myList = List(1, 2, 3)")
interpreter.interpret("val myString = \"hello world\"")
interpreter.interpret("import scala.util.Random")

// Create completion system
val completion = new SparkJLineCompletion(interpreter)
val completer = completion.completer()

// Perform completion (simulated - in real REPL this is automatic)
val buffer = "myLi"
val cursor = buffer.length
val result = completer.complete(buffer, cursor)

println(s"Completions for '$buffer': ${result.candidates.mkString(", ")}")

/**
 * Helper object for accessing Scala compiler settings
 */
@DeveloperApi
object SparkHelper {
  /**
   * Get explicit parent class loader from compiler settings
   * @param settings Scala compiler settings
   * @return Optional parent ClassLoader
   */
  def explicitParentLoader(settings: Settings): Option[ClassLoader]
}

import org.apache.spark.repl.SparkHelper
import scala.tools.nsc.Settings

val settings = new Settings()
val parentLoader = SparkHelper.explicitParentLoader(settings)
parentLoader.foreach(loader => 
  println(s"Parent classloader: ${loader.getClass.getName}")
)

// After typing "myList."
// Completer suggests: head, tail, map, filter, foreach, etc.

// After typing "import scala."
// Completer suggests: util, collection, concurrent, math, etc.

// After typing "val x: "
// Completer suggests available types: Int, String, List, etc.

import org.apache.spark.repl.{SparkIMain, SparkJLineCompletion}
import scala.tools.nsc.Settings

// Create interpreter with custom settings
val settings = new Settings()
val interpreter = new SparkIMain(settings)
interpreter.initializeSynchronous()

// Set up completion system
val completion = new SparkJLineCompletion(interpreter)
completion.verbosity = 2  // Verbose completion output

// Get completer for integration with input system
val completer = completion.completer()

// In a real REPL, this would be integrated with JLine ConsoleReader
// Here's a simplified example of how completion might be used:
def performCompletion(input: String, position: Int): List[String] = {
  val result = completer.complete(input, position)
  result.candidates
}

// Example usage
val input = "List(1,2,3).ma"
val position = input.length
val suggestions = performCompletion(input, position)
println(s"Suggestions: ${suggestions.mkString(", ")}")

import org.apache.spark.repl.{SparkIMain, SparkJLineCompletion}

val interpreter = new SparkIMain()
interpreter.initializeSynchronous()

// Add custom context for completion
interpreter.interpret("""
  case class Person(name: String, age: Int, email: String)
  val people = List(
    Person("Alice", 30, "alice@example.com"),
    Person("Bob", 25, "bob@example.com")
  )
  import java.time.LocalDateTime
  import scala.util.{Random, Try, Success, Failure}
""")

// Create completion system with enriched context
val completion = new SparkJLineCompletion(interpreter)
val completer = completion.completer()

// Now completion will include:
// - Person class and its methods
// - people variable and List methods
// - LocalDateTime methods
// - Random, Try, Success, Failure from imports

// Test completion on defined context
val testCases = List(
  ("people.hea", "head"),
  ("Person(", "constructor parameters"),
  ("LocalDateTime.", "LocalDateTime methods"),
  ("Random.", "Random methods")
)

testCases.foreach { case (input, description) =>
  val result = completer.complete(input, input.length)
  println(s"$description: ${result.candidates.take(5).mkString(", ")}")
}

import org.apache.spark.repl.{SparkIMain, SparkJLineCompletion}
import scala.tools.nsc.Settings

// Create interpreter with advanced settings
val settings = new Settings()
settings.usejavacp.value = true  // Use Java classpath
settings.embeddedDefaults[SparkIMain]  // Apply REPL-specific defaults

val interpreter = new SparkIMain(settings)
interpreter.initializeSynchronous()

// Add external JARs for completion context
import java.net.URL
val sparkSqlJar = new URL("file:///path/to/spark-sql.jar")
interpreter.addUrlsToClassPath(sparkSqlJar)

// Import external libraries
interpreter.interpret("import org.apache.spark.sql.{SparkSession, DataFrame}")
interpreter.interpret("import org.apache.spark.sql.functions._")

// Set up completion with enhanced context
val completion = new SparkJLineCompletion(interpreter)
completion.verbosity = 1

val completer = completion.completer()

// Now completion includes Spark SQL classes and functions
val sparkCompletions = completer.complete("SparkSession.", 13)
println(s"SparkSession methods: ${sparkCompletions.candidates.take(10).mkString(", ")}")

val functionsCompletions = completer.complete("col(", 4)
println(s"Column functions: ${functionsCompletions.candidates.take(10).mkString(", ")}")