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Dr. Roland Kuhn: Reactive Design Patterns

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At the 3rd Reactive Systems Hamburg Meetup we had the great pleasure to have Dr. Roland Kuhn (Akka Tech Lead @Lightbend) presenting some highlights from his new book "Reactive Design Patterns".
First he introduced reactive traits: coming from the motivating factors elasticity and resilience he pointed out resulting values responsiveness, decoupling and extensibility.
Afterwards he explored some selected patterns like the single component pattern, the circuit breaker pattern, multi-master replication patterns and the saga pattern. These patterns are language-agnostic and also independent of the abundant choice of reactive programming frameworks and libraries.
And notice the conclusion: none of this is dead easy: *thinking is required!*

Blogpost with recording: https://inoio.de/blog/2016/02/25/reactive-systems-reactive-design-patterns/

Veröffentlicht in: Technologie

Dr. Roland Kuhn: Reactive Design Patterns

  1. 1. Reactive Design Patterns Dr. Roland Kuhn @rolandkuhn — Akka Tech Lead
  2. 2. Reactive Design Patterns • currently in MEAP • all chapters in review now • use code 39kuhn (39% off) 2
  3. 3. Reactive?
  4. 4. Elasticity: Performance at Scale 4
  5. 5. Resilience: Don’t put all eggs in one basket! 5
  6. 6. Result: Responsiveness • elastic components that scale with their load • responses in the presence of partial failures 6
  7. 7. Result: Decoupling • containment of • failures • implementation details • responsibility • shared-nothing architecture, clear boundaries 7
  8. 8. Result: Maintainability & Fexibility • decoupled responsibility—decoupled teams • develop pieces at their own pace • continuous delivery • Microservices: Single Responsibility Principle 8
  9. 9. Implementation: Message-Driven • focus on communication between components • model message flows and protocols • common transports: async HTTP, *MQ, Actors 9
  10. 10. Reactive Traits 10 elastic resilient responsive maintainable extensible message-­‐driven Value Means Form
  11. 11. Architecture Patterns
  12. 12. Simple Component Pattern 12 «A component shall do only one thing, but do it in full.»
  13. 13. Simple Component Pattern • SingleResponsibilityPrinciple formulated by DeMarco in «Structured analysis and system specification» (Yourdon, New York, 1979) • “maximize cohesion and minimize coupling” • “a class should have only one reason to change”
 (UncleBobMartin’sformulationforOOD) 13
  14. 14. Example: the Batch Job Service • users submit jobs • planning and validation rules • execution on elastic compute cluster • users query job status and results 14
  15. 15. Example: the Batch Job Service 15
  16. 16. Example: the Batch Job Service 16
  17. 17. Example: the Batch Job Service 17
  18. 18. Let-It-Crash Pattern 18 «Prefer a full component restart to complex internal failure handling.»
  19. 19. Let-It-Crash Pattern • Candea & Fox: “Crash-Only Software”
 (USENIX HotOS IX, 2003) • transient and rare failures are hard to detect and fix • write component such that full restart is always o.k. • simplified failure model leads to more reliability 19
  20. 20. Let-It-Crash Pattern • Erlang philosophy from day one • popularized by Netflix Chaos Monkey • make sure that system is resilient by arbitrarily performing recovery restarts • exercise failure recovery code paths for real • failure will happen, fault-avoidance is doomed 20
  21. 21. Implementation Patterns
  22. 22. Circuit Breaker Pattern 22 «Protect services by breaking the connection during failure periods.»
  23. 23. Circuit Breaker Pattern • well-known, inspired by electrical engineering • first published by M. Nygard in «Release It!» • protects both ways: • allows client to avoid long failure timeouts • gives service some breathing room to recover 23
  24. 24. Circuit Breaker Example 24 private object StorageFailed extends RuntimeException private def sendToStorage(job: Job): Future[StorageStatus] = { // make an asynchronous request to the storage subsystem val f: Future[StorageStatus] = ??? // map storage failures to Future failures to alert the breaker f.map { case StorageStatus.Failed => throw StorageFailed case other => other } } private val breaker = CircuitBreaker( system.scheduler, // used for scheduling timeouts 5, // number of failures in a row when it trips 300.millis, // timeout for each service call 30.seconds) // time before trying to close after tripping def persist(job: Job): Future[StorageStatus] = breaker .withCircuitBreaker(sendToStorage(job)) .recover { case StorageFailed => StorageStatus.Failed case _: TimeoutException => StorageStatus.Unknown case _: CircuitBreakerOpenException => StorageStatus.Failed }
  25. 25. Multiple-Master Replication Patterns 25 «Keep multiple distributed copies,
 accept updates everywhere,
 disseminate updates among replicas.»
  26. 26. Multiple-Master Replication Patterns • this is a tough problem with no perfect solution • requires a trade-off to be made between consistency and availability • consensus-based focuses on consistency • conflict-free focuses on availability • conflictresolution gives up a bit of both • each requires a different programming model and can express different transactional behavior 26
  27. 27. Consensus-Based Replication • strong coupling between replicas to ensure that all are “on the same page” • unavailable during network outages or certain machine failures • programming model “just like a single thread” • Postgres, Zookeeper, etc. 27
  28. 28. Replication with Conflict Resolution • requires conflict detection • resolution without user intervention will have to discard some updates • detection/resolution unavailable during partitions • programming model “like single thread” with caveat • popular RDBMS in default configuration offer this 28
  29. 29. Conflict-Free Replication • express updates such that they can be merged • cannot express “non-local” constraints • all expressible updates can be performed under any conditions without losses or inconsistencies • replicas may temporarily be out of sync • different programming model, explicitly distributed • Riak 2.0, Akka Distributed Data 29
  30. 30. Multiple-Master Replication Patterns • no one size fits all • you will have to think and decide! 30
  31. 31. Saga Pattern 31 «Divide long-lived distributed transactions into quick local ones with compensating actions for recovery.»
  32. 32. Saga Pattern: Background • Microservice Architecture means distribution of knowledge, no more central database instance • Pat Helland: • “Life Beyond Distributed Transactions”, CIDR 2007 • “Memories, Guesses, and Apologies”, MSDN blog 2007 • What about transactions that affect multiple microservices? 32
  33. 33. Saga Pattern • Garcia-Molina & Salem: “SAGAS”, ACM, 1987 • Bank transfer avoiding lock of both accounts: • T₁: transfer money from X to local working account • T₂: transfer money from local working account to Y • C₁: compensate failure by transferring money back to X • Compensating transactions are executed during Saga rollback • concurrent Sagas can see intermediate state 33
  34. 34. Saga Pattern • backward recovery:
 T₁ T₂ T₃ C₃ C₂ C₁ • forward recovery with save-points:
 T₁ (sp) T₂ (sp) T₃ (sp) T₄ • in practice Sagas need to be persistent to recover after hardware failures, meaning backward recovery will also use save-points 34
  35. 35. Example: Bank Transfer 35 trait Account { def withdraw(amount: BigDecimal, id: Long): Future[Unit] def deposit(amount: BigDecimal, id: Long): Future[Unit] } case class Transfer(amount: BigDecimal, x: Account, y: Account) sealed trait Event case class TransferStarted(amount: BigDecimal, x: Account, y: Account) extends Event case object MoneyWithdrawn extends Event case object MoneyDeposited extends Event case object RolledBack extends Event
  36. 36. Example: Bank Transfer 36 class TransferSaga(id: Long) extends PersistentActor { import context.dispatcher override val persistenceId: String = s"transaction-$id" override def receiveCommand: PartialFunction[Any, Unit] = { case Transfer(amount, x, y) => persist(TransferStarted(amount, x, y))(withdrawMoney) } def withdrawMoney(t: TransferStarted): Unit = { t.x.withdraw(t.amount, id).map(_ => MoneyWithdrawn).pipeTo(self) context.become(awaitMoneyWithdrawn(t.amount, t.x, t.y)) } def awaitMoneyWithdrawn(amount: BigDecimal, x: Account, y: Account): Receive = { case m @ MoneyWithdrawn => persist(m)(_ => depositMoney(amount, x, y)) } ... }
  37. 37. Example: Bank Transfer 37 def depositMoney(amount: BigDecimal, x: Account, y: Account): Unit = { y.deposit(amount, id) map (_ => MoneyDeposited) pipeTo self context.become(awaitMoneyDeposited(amount, x)) } def awaitMoneyDeposited(amount: BigDecimal, x: Account): Receive = { case Status.Failure(ex) => x.deposit(amount, id) map (_ => RolledBack) pipeTo self context.become(awaitRollback) case MoneyDeposited => persist(MoneyDeposited)(_ => context.stop(self)) } def awaitRollback: Receive = { case RolledBack => persist(RolledBack)(_ => context.stop(self)) }
  38. 38. Example: Bank Transfer 38 override def receiveRecover: PartialFunction[Any, Unit] = { var start: TransferStarted = null var last: Event = null { case t: TransferStarted => { start = t; last = t } case e: Event => last = e case RecoveryCompleted => last match { case null => // wait for initialization case t: TransferStarted => withdrawMoney(t) case MoneyWithdrawn => depositMoney(start.amount, start.x, start.y) case MoneyDeposited => context.stop(self) case RolledBack => context.stop(self) } } }
  39. 39. Saga Pattern: Reactive Full Circle • Garcia-Molina & Salem note: • “search for natural divisions of the work being performed” • “it is the database itself that is naturally partitioned into relatively independent components” • “the database and the saga should be designed so that data passed from one sub-transaction to the next via local storage is minimized” • fully aligned with Simple Components and isolation 39
  40. 40. Conclusion
  41. 41. Conclusion • reactive systems are distributed • this requires new (old) architecture patterns • … helped by new (old) code patterns & abstractions • none of this is dead easy: thinking is required! 41
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