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Akka in Production - ScalaDays 2015

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Everyone in the Scala world is using or looking into using Akka for low-latency, scalable, distributed or concurrent systems. I'd like to share my story of developing and productionizing multiple Akka apps, including low-latency ingestion and real-time processing systems, and Spark-based applications.

When does one use actors vs futures?
Can we use Akka with, or in place of, Storm?
How did we set up instrumentation and monitoring in production?
How does one use VisualVM to debug Akka apps in production?
What happens if the mailbox gets full?
What is our Akka stack like?

I will share best practices for building Akka and Scala apps, pitfalls and things we'd like to avoid, and a vision of where we would like to go for ideal Akka monitoring, instrumentation, and debugging facilities. Plus backpressure and at-least-once processing.

Published in: Engineering
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Akka in Production - ScalaDays 2015

  1. 1. Akka in Production Evan Chan Scala Days 2015 March 17, 2015
  2. 2. Who is this guy? •Principal Engineer, Socrata, Inc. •http://github.com/velvia •Author of multiple open source Akka/Scala projects - Spark Job Server, ScalaStorm, etc. •@evanfchan
  3. 3. A plug for a few projects… •http://github.com/velvia/links - my stash of interesting Scala & big data projects •http://github.com/velvia/filo - a new, extreme vector serialization library for fast analytics •Talk to me later if you are interested in fast serialization or columnar/analytics databases
  4. 4. Who is Socrata? ! We are a Seattle-based software startup. ! We make data useful to everyone. Open, Public Data Consumers Apps
  5. 5. Socrata is… The most widely adopted Open Data platform
  6. 6. Scala at Socrata •Started with old monolithic Java app •Started writing new features in Scala - 2.8 •Today - 100% backend development in Scala, 2.10 / 2.11, many micro services •custom SBT plugins, macros, more •socrata-http •rojoma-json
  7. 7. Want Reactive? event-driven, scalable, resilient and responsive
  8. 8. Agenda • How does one get started with Akka? • To be honest, Akka is what drew me into Scala • Examples of Akka use cases • Compared with other technologies • Tips on using Akka in production • Including back pressure, monitoring, VisualVM usage, etc.
  9. 9. Ingestion Architectures with Akka
  10. 10. Akka Stack • Spray - high performance HTTP • SLF4J / Logback • Yammer Metrics • spray-json • Akka 2.x • Scala 2.10
  11. 11. Ingesting 2 Billion Events / Day Nginx Raw Log Feeder Kafka Storm New Stuff Consumer watches video
  12. 12. Livelogsd - Akka/Kafka file tailer Current File Rotated File Rotated File 2 File Reader Actor File Reader Actor Kafka Feeder Coordinator Kafka
  13. 13. 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?
  14. 14. 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
  15. 15. 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
  16. 16. Simplified Ingestion Pipeline Kafka Partition 1 Kafka SimpleConsumer Converter Actor Cassandra Writer Actor Kafka Partition 2 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
  17. 17. Stackable Actor Traits
  18. 18. 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...”)! }! }
  19. 19. 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)! // or: (wrappedReceive orElse unhandled)(x)! }! }!
  20. 20. 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...")! }! }
  21. 21. 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 something! Do something after...! Despues...
  22. 22. Productionizing Akka
  23. 23. On distributed systems: “The only thing that matters is visibility”
  24. 24. 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
  25. 25. 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()! }! }! }
  26. 26. Performance Metrics (cont’d)
  27. 27. Performance Metrics (cont’d)
  28. 28. VisualVM and Akka • Bounded mailboxes = time spent enqueueing msgs
  29. 29. VisualVM and Akka • My dream: a VisualVM plugin to visualize Actor utilization across threads
  30. 30. 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?
  31. 31. Tracing Akka Message Flows
  32. 32. 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)! }! }!
  33. 33. Akka Service Discovery • Akka remote - need to know remote nodes • Akka cluster - need to know seed nodes • Use Zookeeper or /etcd • http://blog.eigengo.com/2014/12/13/akka-cluster- inventory/ - Akka cluster inventory extension • Be careful - Akka is very picky about IP addresses. Beware of AWS, Docker, etc. etc. Test, test, test.
  34. 34. Akka Instrumentation Libraries • http://kamon.io • Uses AspectJ to “weave” in instrumentation. Metrics, logging, tracing. • Instruments Akka, Spray, Play • Provides statsD / graphite and other backends • https://github.com/levkhomich/akka-tracing • Zipkin distributed tracing for Akka
  35. 35. Backpressure and Reliability
  36. 36. Intro to Backpressure • Backpressure - ability to tell senders to slow down/stop • Must look at entire system. • Individual components (eg TCP) having flow control does not mean entire system behaves well
  37. 37. Why not bounded mailboxes? • 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
  38. 38. Backpressure in Action • A working back pressure system causes the rate of all actor components to be in sync. • Witness this message flow rate graph of the start of event processing:
  39. 39. Akka Streams • Very conservative (“pull based”) • Consumer must first give permission to Publisher to send data • How does it work for fan-in scenarios?
  40. 40. Backpressure for fan-in • Multiple input streams go to a single resource (DB?) • May come and go • Pressure comes from each stream and from # streams Stream 1 Stream 2 Stream 3 Stream 4 Writer Actor DB
  41. 41. Backpressure for fan-in • Same simple model, can control number of clients • High overhead: lots of streams to notify “Ready” Stream 1 Stream 2 Writer Actor Register Ready for data Data
  42. 42. At Least Once Delivery What if you can’t drop messages on the floor?
  43. 43. At Least Once Delivery • Let every message have a unique ID. • Ack returns with unique ID to confirm message send. • What happens if you don’t get an ack? Actor A Actor B Msg 100 Msg 101 Msg 102 Ack 100 Ack 101?
  44. 44. At Least Once Delivery • Resend unacked messages until confirmed == “at least once” Actor A Actor B Msg 100 Msg 101 Msg 102 Ack 100 Ack 101? Resend 101 Ack timeout
  45. 45. At Least Once Delivery & Akka • Resending messages requires keeping message history around • Unless your source of messages is Kafka - then just replay from the last successful offset + 1 • Use Akka Persistence - has at-least-once semantics + persistence of messages for better durability • Exactly Once = at least once + deduplication • Akka Persistence has this too!
  46. 46. Backpressure and at-least-once • How about a system that works for fan-in, and handles back pressure and at-least-once too? • Let the client have an upper limit of unacked messages • Server can reject new messages Stream 1 Stream 2 Writer Actor Msg 100 Ack 100 Msg 101 Msg 200 Reject!
  47. 47. Backpressure and Futures • Use an actor to limit # of outstanding futures class CommandThrottlingActor(mapper: CommandThrottlingActor.Mapper, maxFutures: Int) extends BaseActor { import CommandThrottlingActor._ import context.dispatcher // for future callbacks ! val mapperWithDefault = mapper orElse ({ case x: Any => Future { NoSuchCommand } }: Mapper) var outstandingFutures = 0 ! def receive: Receive = { case FutureCompleted => if (outstandingFutures > 0) outstandingFutures -= 1 case c: Command => if (outstandingFutures >= maxFutures) { sender ! TooManyOutstandingFutures } else { outstandingFutures += 1 val originator = sender // sender is a function, don't call in the callback mapperWithDefault(c).onSuccess { case response: Response => self ! FutureCompleted originator ! response } } } }
  48. 48. 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
  49. 49. Couple more random hints • Learn Akka Testkit. • Master it! The most useful tool for testing Akka actors. • Many examples in spark-jobserver repo • gracefulStop() • TestKit.shutdownActorSystem(system)
  50. 50. Thank you!! • Queues don’t fix overload • Stackable actor traits - see ActorStack in spark- jobserver repo
  51. 51. Extra slides
  52. 52. Putting it all together
  53. 53. 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) =>! }! }
  54. 54. 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)
  55. 55. 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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