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# Stateful Operations
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Advanced operations for maintaining state across streaming batches, including updateStateByKey and mapWithState for building stateful streaming applications.
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## UpdateStateByKey Operations
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### Basic UpdateStateByKey
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Maintain state across batches using update function:
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```scala { .api }
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def updateStateByKey[S: ClassTag](
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updateFunc: (Seq[V], Option[S]) => Option[S]
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): DStream[(K, S)] // On DStream[(K, V)]
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```
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With custom partitioning:
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```scala { .api }
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def updateStateByKey[S: ClassTag](
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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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def updateStateByKey[S: ClassTag](
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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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Example word count with state:
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```scala
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val lines = ssc.socketTextStream("localhost", 9999)
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val words = lines.flatMap(_.split("\\s+")).map((_, 1))
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// Running word count across all batches
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val runningCounts = words.updateStateByKey[Int] { (values, state) =>
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val currentCount = values.sum
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val newCount = state.getOrElse(0) + currentCount
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Some(newCount)
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}
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runningCounts.print()
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```
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### Advanced UpdateStateByKey Examples
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User session tracking:
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```scala
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case class SessionInfo(loginTime: Long, lastActivity: Long, pageViews: Int)
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val userEvents = ssc.socketTextStream("localhost", 9999)
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.map(parseUserEvent) // Returns (userId, event)
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val userSessions = userEvents.updateStateByKey[SessionInfo] { (events, sessionOpt) =>
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val currentTime = System.currentTimeMillis()
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val session = sessionOpt.getOrElse(SessionInfo(currentTime, currentTime, 0))
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val updatedSession = events.foldLeft(session) { (sess, event) =>
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event match {
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case "login" => sess.copy(loginTime = currentTime, lastActivity = currentTime)
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case "pageview" => sess.copy(lastActivity = currentTime, pageViews = sess.pageViews + 1)
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case "logout" => return None // Remove session
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case _ => sess.copy(lastActivity = currentTime)
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}
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}
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// Expire sessions after 30 minutes of inactivity
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if (currentTime - updatedSession.lastActivity > 30 * 60 * 1000) {
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None
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} else {
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Some(updatedSession)
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}
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}
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```
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Real-time analytics with state:
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```scala
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case class Analytics(count: Long, sum: Double, min: Double, max: Double) {
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def avg: Double = if (count > 0) sum / count else 0.0
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}
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val metrics = ssc.socketTextStream("localhost", 9999)
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.map(line => (line.split(",")(0), line.split(",")(1).toDouble)) // (metric_name, value)
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val runningAnalytics = metrics.updateStateByKey[Analytics] { (values, stateOpt) =>
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val current = stateOpt.getOrElse(Analytics(0, 0.0, Double.MaxValue, Double.MinValue))
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val updated = values.foldLeft(current) { (analytics, value) =>
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Analytics(
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count = analytics.count + 1,
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sum = analytics.sum + value,
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min = math.min(analytics.min, value),
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max = math.max(analytics.max, value)
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)
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}
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Some(updated)
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}
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```
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## MapWithState Operations
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### StateSpec Configuration
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Create StateSpec with mapping function:
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```scala { .api }
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object StateSpec {
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def function[K, V, S, T](
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mappingFunction: (Time, K, Option[V], State[S]) => Option[T]
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): StateSpec[K, V, S, T]
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def function[K, V, S, T](
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mappingFunction: (K, Option[V], State[S]) => T
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): StateSpec[K, V, S, T]
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}
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```
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StateSpec configuration methods:
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```scala { .api }
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abstract class StateSpec[K, V, S, T] {
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def initialState(rdd: RDD[(K, S)]): this.type
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def initialState(javaPairRDD: JavaPairRDD[K, S]): this.type
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def numPartitions(numPartitions: Int): this.type
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def partitioner(partitioner: Partitioner): this.type
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def timeout(idleDuration: Duration): this.type
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}
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```
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### MapWithState Operation
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Apply stateful mapping:
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```scala { .api }
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def mapWithState[S: ClassTag, T: ClassTag](
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spec: StateSpec[K, V, S, T]
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): MapWithStateDStream[K, V, S, T] // On DStream[(K, V)]
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```
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### State Management Interface
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State management in mapping functions:
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```scala { .api }
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abstract class State[S] {
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def exists(): Boolean
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def get(): S
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def update(newState: S): Unit
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def remove(): Unit
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def isTimingOut(): Boolean
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def getOption(): Option[S]
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}
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```
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### Basic MapWithState Example
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Simple counter with mapWithState:
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```scala
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val words = ssc.socketTextStream("localhost", 9999)
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.flatMap(_.split("\\s+"))
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.map((_, 1))
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val mappingFunction = (word: String, one: Option[Int], state: State[Int]) => {
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val sum = one.getOrElse(0) + state.getOption().getOrElse(0)
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val output = (word, sum)
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state.update(sum)
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output
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}
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val stateDStream = words.mapWithState(StateSpec.function(mappingFunction))
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stateDStream.print()
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```
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### Advanced MapWithState Examples
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User behavior analysis:
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```scala
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case class UserBehavior(
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totalSessions: Int,
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totalPageViews: Int,
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lastActivity: Long,
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avgSessionDuration: Double
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)
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val userActions = ssc.socketTextStream("localhost", 9999)
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.map(parseUserAction) // Returns (userId, action, timestamp)
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val behaviorSpec = StateSpec.function(
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(userId: String, action: Option[(String, Long)], state: State[UserBehavior]) => {
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val currentTime = System.currentTimeMillis()
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val behavior = state.getOption().getOrElse(
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UserBehavior(0, 0, currentTime, 0.0)
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)
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action match {
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case Some(("session_start", timestamp)) =>
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val newBehavior = behavior.copy(
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totalSessions = behavior.totalSessions + 1,
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lastActivity = timestamp
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)
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state.update(newBehavior)
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Some((userId, "session_started", newBehavior))
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case Some(("page_view", timestamp)) =>
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val newBehavior = behavior.copy(
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totalPageViews = behavior.totalPageViews + 1,
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lastActivity = timestamp
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)
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state.update(newBehavior)
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Some((userId, "page_viewed", newBehavior))
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case None if state.isTimingOut() =>
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// State is timing out, emit final statistics
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Some((userId, "user_summary", behavior))
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case _ =>
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None
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}
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}
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).timeout(Minutes(30)) // Timeout inactive users after 30 minutes
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val userBehaviorStream = userActions.mapWithState(behaviorSpec)
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```
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Real-time anomaly detection:
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```scala
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case class MetricState(
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values: Queue[Double],
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sum: Double,
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count: Int,
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windowSize: Int = 100
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) {
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def mean: Double = if (count > 0) sum / count else 0.0
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def stdDev: Double = {
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if (count < 2) return 0.0
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val meanVal = mean
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val variance = values.map(v => math.pow(v - meanVal, 2)).sum / count
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math.sqrt(variance)
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}
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}
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val metrics = ssc.socketTextStream("localhost", 9999)
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.map(line => (line.split(",")(0), line.split(",")(1).toDouble))
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val anomalySpec = StateSpec.function(
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(metric: String, value: Option[Double], state: State[MetricState]) => {
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value match {
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case Some(v) =>
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val currentState = state.getOption().getOrElse(
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MetricState(Queue.empty, 0.0, 0)
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)
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val newValues = if (currentState.values.size >= currentState.windowSize) {
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val (removed, remaining) = currentState.values.dequeue
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remaining.enqueue(v)
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} else {
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currentState.values.enqueue(v)
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}
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val newState = MetricState(
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values = newValues,
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sum = currentState.sum - (if (currentState.values.size >= currentState.windowSize)
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currentState.values.head else 0.0) + v,
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count = math.min(currentState.count + 1, currentState.windowSize)
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)
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state.update(newState)
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// Detect anomaly (value is more than 3 standard deviations from mean)
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if (newState.count > 10) {
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val zScore = math.abs(v - newState.mean) / newState.stdDev
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if (zScore > 3.0) {
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Some((metric, s"ANOMALY: $v (z-score: $zScore)"))
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} else {
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Some((metric, s"NORMAL: $v"))
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}
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} else {
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Some((metric, s"LEARNING: $v"))
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}
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case None => None
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}
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}
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)
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val anomalies = metrics.mapWithState(anomalySpec)
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anomalies.print()
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```
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### MapWithStateDStream Operations
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Access state snapshots:
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```scala { .api }
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class MapWithStateDStream[K, V, S, T] extends DStream[T] {
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def stateSnapshots(): DStream[(K, S)]
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}
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```
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Example state snapshots:
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```scala
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val stateDStream = words.mapWithState(StateSpec.function(mappingFunction))
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// Get periodic snapshots of all state
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val snapshots = stateDStream.stateSnapshots()
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snapshots.foreachRDD { rdd =>
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println(s"Current state count: ${rdd.count()}")
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rdd.take(10).foreach { case (key, state) =>
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println(s"$key -> $state")
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}
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}
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```
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## State Management Best Practices
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### Initial State Setup
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Provide initial state from external source:
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```scala
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// Load initial state from database or file
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val initialState = ssc.sparkContext.parallelize(loadInitialStateFromDB())
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val stateSpec = StateSpec.function(mappingFunction)
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.initialState(initialState)
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.numPartitions(10)
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.timeout(Minutes(60))
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```
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### Memory Management
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Control state memory usage:
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```scala
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val memoryEfficientSpec = StateSpec.function(
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(key: String, value: Option[String], state: State[Map[String, Int]]) => {
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val currentMap = state.getOption().getOrElse(Map.empty)
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value match {
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case Some(v) =>
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val updated = currentMap + (v -> (currentMap.getOrElse(v, 0) + 1))
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// Limit map size to prevent memory issues
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val trimmed = if (updated.size > 1000) {
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updated.toSeq.sortBy(_._2).takeRight(800).toMap
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} else {
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updated
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}
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state.update(trimmed)
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Some((key, trimmed.size))
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case None if state.isTimingOut() =>
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// Clean up before timeout
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Some((key, -1)) // Indicate removal
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case _ => None
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}
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}
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).timeout(Hours(1))
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```
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### Checkpointing Requirements
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Enable checkpointing for stateful operations:
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```scala
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val ssc = new StreamingContext(conf, Seconds(5))
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ssc.checkpoint("hdfs://namenode:9000/checkpoint")
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// Stateful operations require checkpointing
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val statefulStream = inputStream.updateStateByKey(updateFunction)
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```
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### Performance Considerations
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Optimize stateful operations:
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```scala
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// Use appropriate partitioning
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val optimizedState = keyValueStream.updateStateByKey(
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updateFunction,
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new HashPartitioner(20) // Match your cluster size
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)
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// Consider using mapWithState for better performance
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val efficientState = keyValueStream.mapWithState(
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StateSpec.function(mappingFunction)
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.numPartitions(20)
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.timeout(Minutes(30))
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)
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```
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### Error Handling in Stateful Operations
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Handle errors in state updates:
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```scala
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val robustStateSpec = StateSpec.function(
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(key: String, value: Option[String], state: State[Int]) => {
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try {
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val current = state.getOption().getOrElse(0)
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val newValue = current + value.map(_.toInt).getOrElse(0)
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state.update(newValue)
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Some((key, newValue))
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} catch {
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case e: NumberFormatException =>
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// Log error and maintain previous state
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logError(s"Invalid number for key $key: ${value.getOrElse("None")}")
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Some((key, state.getOption().getOrElse(0)))
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case e: Exception =>
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// Handle other errors
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logError(s"Error updating state for $key", e)
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None
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}
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}
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)
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```