"Walk in a distributed systems park with Orleans" Евгений Бобров
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Долгое время разработка производительных, масштабируемых, надежных и экономически эффективных распределенных систем, была прерогативой узкого круга специалистов. Переезд в «облако», сам по себе, проблему не решил. Обещанная провайдерами дешевая линейная масштабируемость, по-прежнему, недостижимая мечта для всех, сидящих «на игле» реляционных баз данных и монолитных архитектур. С выходом Microsoft Orleans, разработчики, наконец-то, получили максимально простую и удобную платформу для создания масштабируемых и отказоустойчивых распределенных систем, предназначенных для запуска в «облаке» или в приватном дата-центре. В докладе будут рассмотрены основные концепции и прецеденты использования платформы, такие как: Internet Of Things (IoT), распределенная обработка потоков данных, масштабирование РСУБД и любых других ограниченных ресурсов, отказоустойчивая координация длительно выполняющихся бизнес-процессов.
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"Walk in a distributed systems park with Orleans" Евгений Бобров
- 1. Walk in a distributed systems park with Orleans Yevhen Bobrov yevhen YouScan.io@
- 2. Orleans is … … runtime and programming model for building distributed systems based on actor model
- 3. Built by eXtreme Computing Group at Microsoft Research Has been used internally (during last few years) Open-sourced in 2015 (available on GitHub and Nuget)
- 6. Why do we need it?
- 28. Stateless Services (a.k.a 3-tier) scales very badly
- 29. In a Cloud latency will kill you
- 44. Stateful Services shared state concurrency, locks, and all that jazz
- 45. Stateful Services shared state concurrency, locks, and all that jazz
- 46. Actor Model Single-threaded execution (no need to use locks) Data locality and consistency (due to encapsulation) Objects on steroids
- 47. Stateful Services Actors make it simple
- 49. Akka / Erlang 10.0.0.1 10.0.0.2 var game = activate(“game”, “tcp://10.0.0.1”) game.invoke(“foo()”) var user = activate(“user”, “tcp://10.0.0.2”) user.invoke(“bar()”)
- 53. Routing Problems Cluster Membership Workload Distribution
- 54. Routing Problems Cluster Membership Workload Distribution Static
- 55. Routing Problems Cluster Membership Workload Distribution Static Dynamic
- 56. Routing Problems Cluster Membership Workload Distribution Static Dynamic Distributed Consensus
- 57. Routing Problems Cluster Membership Workload Distribution Static Dynamic Distributed Consensus Timeouts
- 58. Routing Problems Cluster Membership Workload Distribution Static Dynamic Distributed Consensus Timeouts Lifecycle
- 59. Routing Problems Cluster Membership Workload Distribution Static Dynamic Distributed Consensus Timeouts Lifecycle
- 60. Orleans is … … runtime and programming model for building distributed systems based on actor model
- 62. Virtual Actors
- 63. Location Transparency var game = activate(“game”, “tcp://10.0.0.1”) game.invoke(“foo()”) Akka / Erlang Orleans var game = activate(“game”) “tcp://10.0.0.1”) game.invoke(“foo()”)
- 64. Perpetual Existence var game = activate(“game”, “tcp://10.0.0.1”) game.invoke(“foo()”) Akka / Erlang Orleans var game = activate(“game”, “tcp://10.0.0.1”) game.invoke(“foo()”)
- 65. inversion of control • Placement • Lifecycle Runtime takes care of
- 68. Threads ~= Cores Core 1 Core 2 Core N Mechanical sympathy Scheduler (turns) Thread 1 Thread N
- 69. Non-blocking IO Promises (TPL, Task, Combinators) Multiplexed TCP Millions of actors with low OS overhead Automatic backpressure by design Synchronous, request-response messaging
- 70. Tour De Orleans
- 71. Grain (a.k.a. Actor) Silo (a.k.a. Node) Cluster (a.k.a. Cluster) Major Concepts
- 72. Define interface: public interface IInventoryItemGrain : IGrain { Task Increment(int qty); Task Decrement(int qty); Task<int> Total(); }
- 73. Define implementation: class InventoryItemGrain : Grain, IInventoryItemGrain { int total; public Task Increment(int qty) { total++; return TaskDone.Done; } public Task Decrement(int qty) { total--; return TaskDone.Done; } public Task<int> Total() => Task.FromResult(total); }
- 74. Invoke: var item = GrainFactory .GetGrain<IInventoryItemGrain>("iPhone"); await item.Increment(100); await item.Decrement(100); Console.WriteLine(await item.Total());
- 75. More Orleans
- 76. Placement hints • Random (default) • Prefer-local (clustering actors) • Distribute evenly (# of activations)
- 77. Automatic deactivation (garbage collection) • Global timeout (idle time) • Per-actor type • Imperative deactivation
- 78. Timers/Reminders • Async timers (single-threaded guarantee) • Durable recurrent reminders retries, recurrent tasks
- 79. Streams • Direct TCP / Azure Queues • Durable Pub-Sub (WATS/SQL) • Declarative subscriptions • Cluster-wide
- 80. When to use?
- 81. Scaling 3-tier architecture (stateless services)
- 82. IoT 1. GPS Tracker (RFID, inventory, cars) 2. Counters (running total) 3. Sensor data
- 83. Data stream processing 1. Fraud detection 2. Aggregation
- 84. Ultra-scalable & reliable web crawling
- 85. END