Apache Spark Core

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.11</artifactId>
    <version>2.4.8</version>
</dependency>

libraryDependencies += "org.apache.spark" %% "spark-core" % "2.4.8"

import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.rdd.RDD
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.util.AccumulatorV2
import org.apache.spark.storage.StorageLevel

import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.SparkConf;
import org.apache.spark.broadcast.Broadcast;
import org.apache.spark.storage.StorageLevel;

import org.apache.spark.{SparkContext, SparkConf}

// Create Spark configuration
val conf = new SparkConf()
  .setAppName("MySparkApp")
  .setMaster("local[*]")

// Create Spark context
val sc = new SparkContext(conf)

// Create RDD from a collection
val data = sc.parallelize(Seq(1, 2, 3, 4, 5))

// Transform and action
val result = data
  .map(_ * 2)
  .filter(_ > 4)
  .collect()

// Clean up
sc.stop()

import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;

// Create configuration
SparkConf conf = new SparkConf()
    .setAppName("MySparkApp")
    .setMaster("local[*]");

// Create context
JavaSparkContext jsc = new JavaSparkContext(conf);

// Create RDD
JavaRDD<Integer> data = jsc.parallelize(Arrays.asList(1, 2, 3, 4, 5));

// Transform and collect
List<Integer> result = data
    .map(x -> x * 2)
    .filter(x -> x > 4)
    .collect();

// Clean up
jsc.stop();

class SparkConf() {
  def set(key: String, value: String): SparkConf
  def setMaster(master: String): SparkConf
  def setAppName(name: String): SparkConf
  def get(key: String): String
  def get(key: String, defaultValue: String): String
}

class SparkContext(config: SparkConf) {
  def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T]
  def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String]
  def stop(): Unit
  def broadcast[T: ClassTag](value: T): Broadcast[T]
  def longAccumulator(): LongAccumulator
  def doubleAccumulator(): DoubleAccumulator
}

abstract class RDD[T: ClassTag] {
  // Transformations
  def map[U: ClassTag](f: T => U): RDD[U]
  def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U]
  def filter(f: T => Boolean): RDD[T]
  def distinct(numPartitions: Int = partitions.length): RDD[T]
  def union(other: RDD[T]): RDD[T]
  def intersection(other: RDD[T]): RDD[T]
  
  // Actions
  def collect(): Array[T]
  def count(): Long
  def first(): T
  def take(num: Int): Array[T]
  def reduce(f: (T, T) => T): T
  def foreach(f: T => Unit): Unit
  
  // Persistence
  def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): this.type
  def cache(): this.type
  def unpersist(blocking: Boolean = true): this.type
}

class PairRDDFunctions[K, V](self: RDD[(K, V)]) {
  def keys: RDD[K]
  def values: RDD[V]
  def groupByKey(): RDD[(K, Iterable[V])]
  def reduceByKey(func: (V, V) => V): RDD[(K, V)]
  def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U, combOp: (U, U) => U): RDD[(K, U)]
  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]
  def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))]
  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]
  def sortByKey(ascending: Boolean = true): RDD[(K, V)]
}

public class JavaSparkContext {
    public <T> JavaRDD<T> parallelize(List<T> list);
    public JavaRDD<String> textFile(String path);
    public <T> Broadcast<T> broadcast(T value);
    public void stop();
}

public class JavaRDD<T> {
    public <R> JavaRDD<R> map(Function<T, R> f);
    public <U> JavaRDD<U> flatMap(FlatMapFunction<T, U> f);
    public JavaRDD<T> filter(Function<T, Boolean> f);
    public List<T> collect();
    public long count();
    public T first();
}

public class JavaPairRDD<K, V> {
    public JavaRDD<K> keys();
    public JavaRDD<V> values();
    public JavaPairRDD<K, Iterable<V>> groupByKey();
    public JavaPairRDD<K, V> reduceByKey(Function2<V, V, V> func);
    public <W> JavaPairRDD<K, Tuple2<V, W>> join(JavaPairRDD<K, W> other);
}

abstract class Broadcast[T] {
  def value: T
  def unpersist(): Unit
  def unpersist(blocking: Boolean): Unit
  def destroy(): Unit
  def id: Long
}

abstract class AccumulatorV2[IN, OUT] {
  def isZero: Boolean
  def copy(): AccumulatorV2[IN, OUT]
  def reset(): Unit
  def add(v: IN): Unit
  def merge(other: AccumulatorV2[IN, OUT]): Unit
  def value: OUT
}

class LongAccumulator extends AccumulatorV2[java.lang.Long, java.lang.Long] {
  def add(v: Long): Unit
  def add(v: java.lang.Long): Unit
  def sum: Long
  def count: Long
  def avg: Double
}

object StorageLevel {
  val NONE: StorageLevel
  val DISK_ONLY: StorageLevel
  val DISK_ONLY_2: StorageLevel
  val MEMORY_ONLY: StorageLevel
  val MEMORY_ONLY_2: StorageLevel
  val MEMORY_ONLY_SER: StorageLevel
  val MEMORY_ONLY_SER_2: StorageLevel
  val MEMORY_AND_DISK: StorageLevel
  val MEMORY_AND_DISK_2: StorageLevel
  val MEMORY_AND_DISK_SER: StorageLevel
  val MEMORY_AND_DISK_SER_2: StorageLevel
  val OFF_HEAP: StorageLevel
}

class StorageLevel {
  def useDisk: Boolean
  def useMemory: Boolean
  def useOffHeap: Boolean
  def deserialized: Boolean
  def replication: Int
}

abstract class TaskContext {
  def isCompleted(): Boolean
  def isInterrupted(): Boolean
  def stageId(): Int
  def partitionId(): Int
  def attemptNumber(): Int
  def taskAttemptId(): Long
  def addTaskCompletionListener(listener: TaskCompletionListener): TaskContext
  def addTaskFailureListener(listener: TaskFailureListener): TaskContext
  def getLocalProperty(key: String): String
}

object TaskContext {
  def get(): TaskContext
  def getPartitionId(): Int
}

class SparkStatusTracker {
  def getJobIdsForGroup(jobGroup: String): Array[Int]
  def getActiveStageIds(): Array[Int]
  def getActiveJobIds(): Array[Int]
  def getJobInfo(jobId: Int): Option[SparkJobInfo]
  def getStageInfo(stageId: Int): Option[SparkStageInfo]
  def getExecutorInfos: Array[SparkExecutorInfo]
}

class SparkJobInfo {
  def jobId(): Int
  def stageIds(): Array[Int]
  def status(): JobExecutionStatus
}

class SparkStageInfo {
  def stageId(): Int
  def name(): String
  def numTasks(): Int
  def numActiveTasks(): Int
  def numCompleteTasks(): Int
  def numFailedTasks(): Int
}

// Core type constraints
type ClassTag[T] = scala.reflect.ClassTag[T]

// Partitioning
abstract class Partitioner {
  def numPartitions: Int
  def getPartition(key: Any): Int
}

class HashPartitioner(partitions: Int) extends Partitioner
class RangePartitioner[K: Ordering: ClassTag, V](
    partitions: Int,
    rdd: RDD[_ <: Product2[K, V]]
) extends Partitioner

// Function types for Java API
@FunctionalInterface
trait Function[T1, R] extends Serializable {
  def call(v1: T1): R
}

@FunctionalInterface  
trait Function2[T1, T2, R] extends Serializable {
  def call(v1: T1, v2: T2): R
}

@FunctionalInterface
trait VoidFunction[T] extends Serializable {
  def call(t: T): Unit
}

@FunctionalInterface
trait FlatMapFunction[T, R] extends Serializable {
  def call(t: T): java.util.Iterator[R]
}

@FunctionalInterface
trait PairFunction[T, K, V] extends Serializable {
  def call(t: T): Tuple2[K, V]
}

// Exception types
class SparkException(message: String, cause: Throwable) extends Exception
class TaskKilledException(reason: String) extends RuntimeException
class TaskNotSerializableException(className: String) extends SparkException