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# State Management
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Stateful processing capabilities for maintaining state across batches, including updateStateByKey and mapWithState operations for building stateful streaming applications.
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## Capabilities
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### State Class
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Abstract class for managing state in mapWithState operations, providing methods to check, get, update, and remove state.
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```scala { .api }
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/**
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 * Abstract class for managing state in mapWithState operations
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 * @tparam S - Type of the state
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 */
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abstract class State[S] {
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  /** Check if state exists for the key */
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  def exists(): Boolean
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  /** Get the state value (throws exception if not exists) */
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  def get(): S
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  /** Get state as Option */
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  def getOption(): Option[S]
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  /** Update the state with new value */
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  def update(newState: S): Unit
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  /** Remove the state for this key */
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  def remove(): Unit
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  /** Check if this state is timing out in current batch */
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  def isTimingOut(): Boolean
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}
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```
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### StateSpec
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Specification class for configuring mapWithState operations, including mapping functions, initial state, partitioning, and timeout settings.
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```scala { .api }
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/**
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 * Specification for mapWithState operation configuration
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 * @tparam KeyType - Type of keys in the DStream
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 * @tparam ValueType - Type of values in the DStream  
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 * @tparam StateType - Type of the state being maintained
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 * @tparam MappedType - Type of mapped output
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 */
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abstract class StateSpec[KeyType, ValueType, StateType, MappedType] {
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  /** Set initial state RDD */
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  def initialState(rdd: RDD[(KeyType, StateType)]): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Set initial state from JavaPairRDD */
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  def initialState(rdd: JavaPairRDD[KeyType, StateType]): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Set number of partitions for state */
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  def numPartitions(numPartitions: Int): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Set custom partitioner for state */
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  def partitioner(partitioner: Partitioner): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Set timeout duration for inactive keys */
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  def timeout(timeout: Duration): StateSpec[KeyType, ValueType, StateType, MappedType]
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}
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/**
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 * Factory object for creating StateSpec instances
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 */
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object StateSpec {
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  /** Create StateSpec with Scala function */
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  def function[KeyType, ValueType, StateType, MappedType](
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    mappingFunction: (KeyType, Option[ValueType], State[StateType]) => MappedType
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  ): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Create StateSpec with Java Function3 */
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  def function[KeyType, ValueType, StateType, MappedType](
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    mappingFunction: Function3[KeyType, Optional[ValueType], State[StateType], MappedType]
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  ): StateSpec[KeyType, ValueType, StateType, MappedType]
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  /** Create StateSpec with Java Function4 (includes timeout) */
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  def function[KeyType, ValueType, StateType, MappedType](
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    mappingFunction: Function4[Time, KeyType, Optional[ValueType], State[StateType], Optional[MappedType]]
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  ): StateSpec[KeyType, ValueType, StateType, MappedType]
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}
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```
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**Usage Examples:**
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```scala
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// Define state mapping function
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def updateFunction(key: String, value: Option[Int], state: State[Int]): Int = {
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  val currentState = state.getOption().getOrElse(0)
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  val newState = currentState + value.getOrElse(0)
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  state.update(newState)
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  newState
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}
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// Create StateSpec
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val stateSpec = StateSpec.function(updateFunction _)
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  .initialState(initialStateRDD)
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  .numPartitions(10)
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  .timeout(Minutes(10))
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// Apply to DStream
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val stateDStream = pairDStream.mapWithState(stateSpec)
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```
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### MapWithState Operations
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Advanced stateful operations using StateSpec for efficient state management with configurable timeouts and partitioning.
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```scala { .api }
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/**
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 * Available on DStream[(K, V)] through implicit conversion
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 */
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class PairDStreamFunctions[K, V](self: DStream[(K, V)]) {
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  /** 
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   * Map with state operation using StateSpec
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   * @param spec - StateSpec configuration
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   * @return MapWithStateDStream for further operations
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   */
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  def mapWithState[StateType, MappedType](
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    spec: StateSpec[K, V, StateType, MappedType]
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  ): MapWithStateDStream[K, V, StateType, MappedType]
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}
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/**
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 * DStream returned by mapWithState operations
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 */
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abstract class MapWithStateDStream[KeyType, ValueType, StateType, MappedType] 
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  extends DStream[MappedType] {
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  /** Get current state snapshots as DStream */
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  def stateSnapshots(): DStream[(KeyType, StateType)]
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}
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```
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**Usage Examples:**
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```scala
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// Running word count with state
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val lines = ssc.socketTextStream("localhost", 9999)
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val words = lines.flatMap(_.split(" ")).map((_, 1))
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// Define state update function  
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def updateWordCount(word: String, count: Option[Int], state: State[Int]): Int = {
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  val currentCount = state.getOption().getOrElse(0)
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  val newCount = currentCount + count.getOrElse(0)
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  state.update(newCount)
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  newCount
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}
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// Configure and apply mapWithState
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val wordCountsState = words.mapWithState(
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  StateSpec.function(updateWordCount _)
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    .timeout(Minutes(5))
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)
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// Get state snapshots
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val currentCounts = wordCountsState.stateSnapshots()
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currentCounts.print()
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```
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### UpdateStateByKey Operations
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Legacy stateful operations for maintaining state across batches using update functions.
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```scala { .api }
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/**
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 * Available on DStream[(K, V)] through implicit conversion
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 */
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class PairDStreamFunctions[K, V](self: DStream[(K, V)]) {
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  /**
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   * Update state by key using update function
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   * @param updateFunc - Function to update state given current values and previous state
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   * @return DStream of (key, state) pairs
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   */
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  def updateStateByKey[S](
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    updateFunc: (Seq[V], Option[S]) => Option[S]
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  ): DStream[(K, S)]
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  /**
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   * Update state by key with custom partitioner
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   */
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  def updateStateByKey[S](
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    updateFunc: (Seq[V], Option[S]) => Option[S],
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    partitioner: Partitioner
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  ): DStream[(K, S)]
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  /**
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   * Update state by key with specified number of partitions
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   */
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  def updateStateByKey[S](
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    updateFunc: (Seq[V], Option[S]) => Option[S],
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    numPartitions: Int
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  ): DStream[(K, S)]
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  /**
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   * Update state by key with initial state RDD
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   */
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  def updateStateByKey[S](
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    updateFunc: (Seq[V], Option[S]) => Option[S],
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    partitioner: Partitioner,
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    initialRDD: RDD[(K, S)]
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  ): DStream[(K, S)]
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}
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```
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**Usage Examples:**
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```scala
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// Simple running count
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val lines = ssc.socketTextStream("localhost", 9999)
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val words = lines.flatMap(_.split(" ")).map((_, 1))
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// Update function for word counting
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def updateWordCount(values: Seq[Int], state: Option[Int]): Option[Int] = {
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  val currentCount = state.getOrElse(0)
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  val newCount = currentCount + values.sum
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  Some(newCount)
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}
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// Apply updateStateByKey
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val wordCounts = words.updateStateByKey(updateWordCount)
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wordCounts.print()
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// With custom partitioner
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val wordCountsPartitioned = words.updateStateByKey(
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  updateWordCount,
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  new HashPartitioner(4)
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)
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// With initial state
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val initialCounts: RDD[(String, Int)] = ssc.sparkContext.parallelize(
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  List(("hello", 10), ("world", 5))
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)
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val wordCountsWithInitial = words.updateStateByKey(
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  updateWordCount,
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  new HashPartitioner(4),
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  initialCounts
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)
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```
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### Checkpointing for State
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Checkpointing configuration required for stateful operations to enable fault tolerance.
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```scala { .api }
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/**
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 * StreamingContext methods for checkpoint configuration
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 */
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class StreamingContext {
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  /** Set checkpoint directory for fault tolerance */
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  def checkpoint(directory: String): Unit
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  /** Get checkpoint directory if set */  
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  def checkpointDir: Option[String]
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}
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/**
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 * Factory method for creating StreamingContext from checkpoint
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 */
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object StreamingContext {
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  /** Create StreamingContext from checkpoint data */
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  def getOrCreate(
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    checkpointDirectory: String,
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    creatingFunc: () => StreamingContext
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  ): StreamingContext
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}
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```
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**Usage Examples:**
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```scala
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// Enable checkpointing for stateful operations
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ssc.checkpoint("hdfs://namenode:9000/checkpoints")
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// Or create fault-tolerant streaming context
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def createStreamingContext(): StreamingContext = {
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  val ssc = new StreamingContext(conf, Seconds(2))
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  ssc.checkpoint("hdfs://namenode:9000/checkpoints")
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  // Define streaming computation with stateful operations
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  val lines = ssc.socketTextStream("localhost", 9999)
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  val words = lines.flatMap(_.split(" ")).map((_, 1))
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  val wordCounts = words.updateStateByKey(updateWordCount)
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  wordCounts.print()
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  ssc
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}
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// Get or create from checkpoint
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val ssc = StreamingContext.getOrCreate(
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  "hdfs://namenode:9000/checkpoints",
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  createStreamingContext _
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)
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```
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## State Management Best Practices
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### When to Use MapWithState vs UpdateStateByKey
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**Use mapWithState when:**
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- Need efficient state management with timeouts
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- Working with large state that needs partitioning control
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- Require better performance for state operations
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- Need access to timing information
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**Use updateStateByKey when:**
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- Simple state updates without timeouts
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- Legacy code compatibility
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- Straightforward aggregation scenarios
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### Memory Management
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```scala
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// Configure state timeout to prevent memory leaks
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val stateSpec = StateSpec.function(updateFunc _)
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  .timeout(Minutes(30))  // Remove inactive state after 30 minutes
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// Control partitioning for memory distribution
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val stateSpec = StateSpec.function(updateFunc _)
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  .numPartitions(100)    // Distribute state across 100 partitions
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```
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### Performance Optimization
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```scala
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// Use custom partitioner for better locality
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import org.apache.spark.HashPartitioner
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val stateSpec = StateSpec.function(updateFunc _)
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  .partitioner(new HashPartitioner(50))
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// Provide initial state to avoid cold start
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val initialState: RDD[(String, Int)] = loadInitialState()
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val stateSpec = StateSpec.function(updateFunc _)
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  .initialState(initialState)
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```