Akka in ProductionOur Story

Evan ChanPNWScala 2013

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• Staff Engineer, Compute and Data Services, Ooyala

• Building multiple web-scale real-time systems on top of C*, Kafka, Storm, etc.

• github.com/velvia

• Author of ScalaStorm, Scala DSL for Storm

• @evanfchan

Who is this guy?

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WANT REACTIVE?

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event-driven, scalable, resilient and responsive

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SCALA AND AKKAAT OOYALA

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CONFIDENTIAL—DO NOT DISTRIBUTE 5

Founded in 2007

Commercially launch in 2009

230+ employees in Silicon Valley, LA, NYC, London, Paris, Tokyo, Sydney & Guadalajara

Global footprint, 200M unique users,110+ countries, and more than 6,000 websites

Over 1 billion videos played per month and 2 billion analytic events per day

25% of U.S. online viewers watch video powered by Ooyala

COMPANY OVERVIEW

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How we started using Scala

• Ooyala was a mostly Ruby company - even MR jobs

• Lesson - don’t use Ruby for big data

• Started exploring Scala for real-time analytics and MR

• Realized a 1-2 orders of magnitude performance boost from Scala

• Today use Scala, Akka with Storm, Spark, MR, Cassandra, all new big data pipelines

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Ingesting 2 Billion Events / Day

NginxRaw Log Feeder Kafka

Storm

New Stuff

Consumer watches video

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Livelogsd - Akka/Kafka file tailer

Current File

Rotated File

Rotated File 2

File Reader Actor

File Reader Actor

Kafka Feeder

CoordinatorKafka

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Storm - with or without Akka?

Kafka Spout

Bolt

Actor

Actor

• Actors talking to each other within a bolt for locality

• Don’t really need Actors in Storm

• In production, found Storm too complex to troubleshoot

• It’s 2am - what should I restart? Supervisor? Nimbus? ZK?

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Akka Cluster-based Pipeline

Kafka Consumer

Spray endpoint

Cluster Router

Processing Actors

Kafka Consumer

Spray endpoint

Cluster Router

Processing Actors

Kafka Consumer

Spray endpoint

Cluster Router

Processing Actors

Kafka Consumer

Spray endpoint

Cluster Router

Processing Actors

Kafka Consumer

Spray endpoint

Cluster Router

Processing Actors

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Lessons Learned

• Still too complex -- would we want to get paged for this system?

• Akka cluster in 2.1 was not ready for production (newer 2.2.x version is stable)

• Mixture of actors and futures for HTTP requests became hard to grok

• Actors were much easier for most developers to understand

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Simplified Ingestion Pipeline

Kafka Partition

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Kafka SimpleConsumer

Converter Actor

Cassandra Writer Actor

Kafka Partition

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Kafka SimpleConsumer

Converter Actor

Cassandra Writer Actor

• Kafka used to partition messages

• Single process - super simple!

• No distribution of data

• Linear actor pipeline - very easy to understand

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STACKABLE ACTOR TRAITS

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Why Stackable Traits?

• Keep adding monitoring, logging, metrics, tracing code gets pretty ugly and repetitive

• We want some standard behavior around actors -- but we need to wrap the actor Receive block:

class someActor extends Actor { def wrappedReceive: Receive = { case x => blah } def receive = { case x => println(“Do something before...”) wrappedReceive(x) println(“Do something after...”) }}

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Start with a base trait...

trait ActorStack extends Actor { /** Actor classes should implement this partialFunction for standard * actor message handling */ def wrappedReceive: Receive

/** Stackable traits should override and call super.receive(x) for * stacking functionality */ def receive: Receive = { case x => if (wrappedReceive.isDefinedAt(x)) wrappedReceive(x) else unhandled(x) }}

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Instrumenting Traits...

trait Instrument1 extends ActorStack { override def receive: Receive = { case x => println("Do something before...") super.receive(x) println("Do something after...") }}

trait Instrument2 extends ActorStack { override def receive: Receive = { case x => println("Antes...") super.receive(x) println("Despues...") }}

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Now just mix the Traits in....

class DummyActor extends Actor with Instrument1 with Instrument2 { def wrappedReceive = { case "something" => println("Got something") case x => println("Got something else: " + x) }}

• Traits add instrumentation; Actors stay clean!

• Order of mixing in traits matter

Antes...Do something before...Got somethingDo something after...Despues...

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PRODUCTIONIZING AKKA

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Our Akka Stack

• Spray - high performance HTTP

• SLF4J / Logback

• Yammer Metrics

• spray-json

• Akka 2.x

• Scala 2.9 / 2.10

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On distributed systems:“The only thing that matters is

Visibility”

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Using Logback with Akka

• Pretty easy setup

• Include the Logback jar

• In your application.conf:event-handlers = ["akka.event.slf4j.Slf4jEventHandler"]

• Use a custom logging trait, not ActorLogging

• ActorLogging does not allow adjustable logging levels

• Want the Actor path in your messages?• org.slf4j.MDC.put(“actorPath”, self.path.toString)

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Using Logback with Akka

trait Slf4jLogging extends Actor with ActorStack { val logger = LoggerFactory.getLogger(getClass) private[this] val myPath = self.path.toString

logger.info("Starting actor " + getClass.getName)

override def receive: Receive = { case x => org.slf4j.MDC.put("akkaSource", myPath) super.receive(x) }}

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Akka Performance Metrics

• We define a trait that adds two metrics for every actor:

• frequency of messages handled (1min, 5min, 15min moving averages)

• time spent in receive block

• All metrics exposed via a Spray route /metricz

• Daemon polls /metricz and sends to metrics service

• Would like: mailbox size, but this is hard

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Akka Performance Metrics

trait ActorMetrics extends ActorStack { // Timer includes a histogram of wrappedReceive() duration as well as moving avg of rate of invocation val metricReceiveTimer = Metrics.newTimer(getClass, "message-handler", TimeUnit.MILLISECONDS, TimeUnit.SECONDS)

override def receive: Receive = { case x => val context = metricReceiveTimer.time() try { super.receive(x) } finally { context.stop() } }}

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Performance Metrics (cont’d)

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Performance Metrics (cont’d)

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Flow control

• By default, actor mailboxes are unbounded

• Using bounded mailboxes

• When mailbox is full, messages go to DeadLetters

• mailbox-push-timeout-time: how long to wait when mailbox is full

• Doesn’t work for distributed Akka systems!

• Real flow control: pull, push with acks, etc.

• Works anywhere, but more work

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Flow control (Cont’d)

• A working flow control system causes the rate of all actor components to be in sync.

• Witness this message flow rate graph of the start of event processing:

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VisualVM and Akka• Bounded mailboxes = time spent enqueueing msgs

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VisualVM and Akka

• My dream: a VisualVM plugin to visualize Actor utilization across threads

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Tracing Akka Message Flows

• Stack trace is very useful for traditional apps, but for Akka apps, you get this:

at akka.dispatch.Future$$anon$3.liftedTree1$1(Future.scala:195) ~[akka-actor-2.0.5.jar:2.0.5]

at akka.dispatch.Future$$anon$3.run(Future.scala:194) ~[akka-actor-2.0.5.jar:2.0.5]

at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:94) [akka-actor-2.0.5.jar:2.0.5]

at akka.jsr166y.ForkJoinTask$AdaptedRunnableAction.exec(ForkJoinTask.java:1381) [akka-actor-2.0.5.jar:2.0.5]

at akka.jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:259) [akka-actor-2.0.5.jar:2.0.5]

at akka.jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:975) [akka-actor-2.0.5.jar:2.0.5]

at akka.jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1479) [akka-actor-2.0.5.jar:2.0.5]

at akka.jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) [akka-actor-2.0.5.jar:2.0.5]

--> trAKKAr message trace <-- akka://Ingest/user/Super --> akka://Ingest/user/K1: Initialize akka://Ingest/user/K1 --> akka://Ingest/user/Converter: Data

• What if you could get an Akka message trace?

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Tracing Akka Message Flows

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Tracing Akka Message Flows

• Trait sends an Edge(source, dest, messageInfo) to a local Collector actor

• Aggregate edges across nodes, graph and profit!

trait TrakkarExtractor extends TrakkarBase with ActorStack { import TrakkarUtils._

val messageIdExtractor: MessageIdExtractor = randomExtractor

override def receive: Receive = { case x => lastMsgId = (messageIdExtractor orElse randomExtractor)(x) Collector.sendEdge(sender, self, lastMsgId, x) super.receive(x) }}

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Good Akka development practices

• Don't put things that can fail into Actor constructor

• Default supervision strategy stops an Actor which cannot initialize itself

• Instead use an Initialize message

• Put your messages in the Actor’s companion object

• Namespacing is nice

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PUTTING IT ALL TOGETHER

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Akka Visibility, Minimal Footprint

trait InstrumentedActor extends Slf4jLogging with ActorMetrics with TrakkarExtractor

object MyWorkerActor { case object Initialize case class DoSomeWork(desc: String)}

class MyWorkerActor extends InstrumentedActor { def wrappedReceive = { case Initialize => case DoSomeWork(desc) => }}

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Next Steps

• Name?

• Open source?

• Talk to me if you’re interested in contributing

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THANK YOUAnd YES, We’re HIRING!!

ooyala.com/careers

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