Monitoring Reactive Streams - Speaker Deck

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Scale by the Bay 2018 Stefano Bonetti - @svez_faz

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Monitoring Reactive Streams Stefano Bonetti - @svez_faz

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No content

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Agenda Throughput Backpressure

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Agenda Throughput Backpressure

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Throughput Web Service req res

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Topology

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Topology

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I/O Correlation web service

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I/O Correlation Processor[T, S]

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Transparent Processors Processor[T, S] Processor[T, S] Subscriber[S] Publisher[T]

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Transparent Processors Processor[T, S] Processor[T, S] Subscriber[S] Publisher[T] c1 c2 c3 c4 Processor[T, T] Processor[S, S]

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) randomInt .via(evenOnly) .runWith(print) AKKA STREAMS - THROUGHPUT

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) randomInt .via(evenOnly) .runWith(print) AKKA STREAMS - THROUGHPUT

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) def meter[T](name: String): Flow[T, T, NotUsed] = { val msgCounter = Kamon.metrics.counter(name) Flow[T].map { x ⇒ msgCounter.increment(); x } } randomInt .via(meter("produced")) .via(evenOnly) .via(meter("processed")) .runWith(print) AKKA STREAMS - THROUGHPUT

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) def meter[T](name: String): Flow[T, T, NotUsed] = { val msgCounter = Kamon.metrics.counter(name) Flow[T].map { x ⇒ msgCounter.increment(); x } } randomInt .via(meter("produced")) .via(evenOnly) .via(meter("processed")) .runWith(print) AKKA STREAMS - THROUGHPUT

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Throughput dashboard (/s) randomInt .via(meter("produced")) .via(heavyEvenOnly) .via(meter("processed")) .runWith(print)

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Agenda Throughput Backpressure

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Agenda Throughput Backpressure

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Example Validation/ Filtering Commit Kafka offset valid invalid Store via API

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Example 100/s 25/s 75/s 75/s Validation/ Filtering Commit Kafka offset valid invalid Store via API

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Example 100/s 25/s 75/s 75/s Validation/ Filtering Commit Kafka offset valid invalid Store via API 20000/s lag

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Backpressure

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Reactive Streams 101 Processor Publisher Subscriber request request emit emit

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Reactive Streams 101 Processor Publisher Subscriber request request emit emit

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Reactive Streams 101 Processor Publisher Subscriber request request emit emit

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Reactive Streams 101 Processor Publisher Subscriber request request emit emit

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Reactive Streams 101 Processor Publisher Subscriber request request emit emit

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Backpressure Processor Publisher Subscriber request ? request ? emit emit

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Backpressure Processor Publisher Subscriber request request emit ? emit ?

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Backpressure Processor Publisher Subscriber request request emit emit request emit request request emit

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Akka Streams - Graphstage API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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final case class Map[In, Out](f: In ⇒ Out) extends GraphStage[FlowShape[In, Out]] { val in = Inlet[In]("Map.in") val out = Outlet[Out]("Map.out") override val shape = FlowShape(in, out) override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) with InHandler with OutHandler { override def onPush(): Unit = push(out, f(grab(in))) override def onPull(): Unit = pull(in) setHandlers(in, out, this) } } AKKA STREAMS - GRAPHSTAGE API

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Akka Streams Graphstage onPull onPush onPull onPull onPush

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var lastPulled: Long = System.nanoTime() var lastPushed: Long = lastPulled private val backpressure = Kamon.histogram(label + "_backpressure") override def onPush(): Unit = { push(out, grab(in)) val now = System.nanoTime() backpressure.record((lastPulled - lastPushed) * 100 / now - lastPushed) lastPushed = now } override def onPull(): Unit = { pull(in) lastPulled = System.nanoTime() } setHandlers(in, out, this) AKKA STREAMS - BACKPRESSURE

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var lastPulled: Long = System.nanoTime() var lastPushed: Long = lastPulled private val backpressure = Kamon.histogram(label + "_backpressure") override def onPush(): Unit = { push(out, grab(in)) val now = System.nanoTime() backpressure.record((lastPulled - lastPushed) * 100 / now - lastPushed) lastPushed = now } override def onPull(): Unit = { pull(in) lastPulled = System.nanoTime() } setHandlers(in, out, this) AKKA STREAMS - BACKPRESSURE

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var lastPulled: Long = System.nanoTime() var lastPushed: Long = lastPulled private val backpressure = Kamon.histogram(label + "_backpressure") override def onPush(): Unit = { push(out, grab(in)) val now = System.nanoTime() backpressure.record((lastPulled - lastPushed) * 100 / now - lastPushed) lastPushed = now } override def onPull(): Unit = { pull(in) lastPulled = System.nanoTime() } setHandlers(in, out, this) AKKA STREAMS - BACKPRESSURE

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var lastPulled: Long = System.nanoTime() var lastPushed: Long = lastPulled private val backpressure = Kamon.histogram(label + "_backpressure") override def onPush(): Unit = { push(out, grab(in)) val now = System.nanoTime() backpressure.record((lastPulled - lastPushed) * 100 / now - lastPushed) lastPushed = now } override def onPull(): Unit = { pull(in) lastPulled = System.nanoTime() } setHandlers(in, out, this) AKKA STREAMS - BACKPRESSURE

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) randomInt .via(backpressureMeter("produced")) .via(evenOnly) .via(backpressureMeter("processed")) .runWith(print) AKKA STREAMS - BACKPRESSURE

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Backpressure dashboard (%) randomInt .via(backpressureMeter("produced")) .via(heavyEvenOnly) .via(backpressureMeter("processed")) .runWith(print)

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val evenOnly = Flow[Int].filter(_ % 2 == 0) val print = Sink.foreach(println) randomInt .via(backpressureMeter("produced")) .via(evenOnly) .via(backpressureMeter("processed")) .runWith(print) AKKA STREAMS - BACKPRESSURE

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val randomInt = Source.tick(10.millis, 10.millis, NotUsed) .map(_ ⇒ Random.nextInt()) val heavyEvenOnly = Flow[Int].mapAsync(parallelism = 1) { n => after(250.millis, system.scheduler)(Future.successful(n)) }.filter(_ % 2 == 0) val print = Sink.foreach(println) randomInt .via(backpressureMeter("produced")) .via(heavyEvenOnly) .via(backpressureMeter("processed")) .runWith(print) AKKA STREAMS - BACKPRESSURE

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Bottleneck - throughput dashboard (/s) randomInt .via(meter("produced")) .via(heavyEvenOnly) .via(meter("processed")) .runWith(print)

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Bottleneck - backpressure dashboard (%) randomInt .via(backpressureMeter("produced")) .via(heavyEvenOnly) .via(backpressureMeter("processed")) .runWith(print)

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Bottlenecks https://blog.colinbreck.com/maximizing-throughput-for-akka-streams/

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Agenda Throughput Backpressure

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akka-stream-checkpoint svezfaz/akka-stream-checkpoint throughput backpressure ratio push-pull latency liveness checks failures/completion

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Stefano Bonetti Software Engineer @svezfaz @svez_faz