Reactive Design Patterns — J on the Beach
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Transcript of Reactive Design Patterns — J on the Beach
Reactive Design Patterns
• currently in MEAP
• all chapters done,in pre-production
• use code 39kuhn (39% off),see http://rolandkuhn.com
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Result: Responsiveness
• elastic components that scale with their load
• responses in the presence of partial failures
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Result: Decoupling
• containment of • failures
• implementation details
• responsibility
• shared-nothing architecture, clear boundaries
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Result: Maintainability & Fexibility
• decoupled responsibility—decoupled teams
• develop pieces at their own pace
• continuous delivery
• Microservices: Single Responsibility Principle
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Implementation: Message-Driven
• focus on communication between components
• model message flows and protocols
• common transports: async HTTP, *MQ, Actors
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Reactive Traits
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elastic resilient
responsive maintainable extensible
message-‐driven
Value
Means
Form
Basically: Microservices Best Practices
• Simple Component Pattern • DeMarco in «Structured analysis and system specification»
(Yourdon, New York, 1979)
• “maximize cohesion and minimize coupling”
• Let-It-Crash Pattern • Candea & Fox: “Crash-Only Software” (USENIX HotOS IX,
2003)
• Error Kernel Pattern • Erlang (late 1980’s)
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Request–Response Pattern
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«Include a return address in the message
in order to receive a response.»
Request–Response Pattern
• return address is often implicit: • HTTP response over same TCP connection
• automatic sender reference capture in Akka
• explicit return address is needed otherwise • *MQ
• Akka Typed
• correlation ID needed for long-lived participants
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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
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Circuit Breaker Example
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private object StorageFailed extends RuntimeExceptionprivate 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 }
Multiple-Master Replication Patterns
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«Keep multiple distributed copies,
accept updates everywhere,
disseminate updates among replicas.»
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
• conflict resolution gives up a bit of both
• each requires a different programming model and can express different transactional behavior
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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.
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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
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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
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Saga Pattern
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«Divide long-lived distributed
transactions into quick local ones with
compensating actions for recovery.»
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?
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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
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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
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Example: Bank Transfer
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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 Eventcase class TransferStarted(amount: BigDecimal, x: Account, y: Account) extends Eventcase object MoneyWithdrawn extends Eventcase object MoneyDeposited extends Eventcase object RolledBack extends Event
Example: Bank Transfer
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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)) }
...}
Example: Bank Transfer
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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))}
Example: Bank Transfer
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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) } }}
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
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