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Simple Solutions for Complex Problems - Boulder Meetup

At the NATS June Meetup in Boulder, CO, Tyler Treat of Workiva gives and updated talk on how to embrace simplicity to solve complex infrastructure problems, and how shares more information on how Workiva uses NATS for microservices communication.

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Simple Solutions for Complex Problems - Boulder Meetup

  1. 1. Simple Solutions
 for Complex Problems Tyler Treat / Workiva Boulder NATS Meetup 6/7/2016
  2. 2. • Embracing the reality of complex systems • Using simplicity to your advantage • Why NATS? • How Workiva uses NATS ABOUT THIS TALK
  3. 3. • Messaging tech lead at Workiva • Platform infrastructure • Distributed systems • @tyler_treat ABOUT THE SPEAKER
  4. 4. There are a lot of parallels between real-world systems and
 distributed software systems.
  5. 5. The world is eventually consistent…
  6. 6. …and the database is just an optimization.[1] [1]
  7. 7. “There will be no further print editions [of the Merck Manual]. Publishing a printed book every five years and sending reams of paper around the world on trucks, planes, and boats is no longer the optimal way to provide medical information.” Dr. Robert S. Porter
 Editor-in-Chief, The Merck Manuals
  8. 8. Programmers find asynchrony hard to reason about, but the truth is…
  9. 9. Life is mostly asynchronous.
  10. 10. What does this mean for us as programmers?
  11. 11. time / complexity timesharing monoliths soa virtualization microservices ??? Complicated made complex…
  12. 12. Distributed!
  13. 13. Distributed computation is
 inherently asynchronous
 and the network is
 inherently unreliable[2]… [2]
  14. 14. …but the natural tendency is to build distributed systems as if they aren’t distributed at all because it’s
 easy to reason about. strong consistency - reliable messaging - predictability
  15. 15. • Complicated algorithms • Transaction managers • Coordination services • Distributed locking What’s in a guarantee?
  16. 16. • Message handed to the transport layer? • Enqueued in the recipient’s mailbox? • Recipient started processing it? • Recipient finished processing it? What’s a delivery guarantee?
  17. 17. Each of these has a very different set of conditions, constraints, and costs.
  18. 18. Guaranteed, ordered, exactly-once delivery is expensive (if not impossible[3]). [3]
  19. 19. Over-engineered
  20. 20. Complex
  21. 21. Difficult to deploy & operate
  22. 22. Fragile
  23. 23. Slow
  24. 24. At large scale, guarantees will give out.
  25. 25. 0.1% failure at scale is huge.
  26. 26. Replayable > Guaranteed
  27. 27. Replayable > Guaranteed Idempotent > Exactly-once
  28. 28. Replayable > Guaranteed Idempotent > Exactly-once Commutative > Ordered
  29. 29. But delivery != processing
  30. 30. Also, what does it even mean to “process” a message?
  31. 31. It depends on the
 business context!
  32. 32. If you need business-level guarantees, build them into
 the business layer.
  33. 33. We can always build
 stronger guarantees on top,
 but we can’t always remove
 them from below.
  34. 34. End-to-end system semantics matter much more than the semantics of an
 individual building block[4]. [4]
  35. 35. Embrace the chaos!
  36. 36. “Simplicity is the ultimate sophistication.”
  38. 38. A simple technology
 in a sea of complexity.
  39. 39. Simple doesn’t mean easy. [5]
  40. 40. “Simple can be harder than complex. You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”
  41. 41. • Wdesk: platform for enterprises to collect, manage, and report critical business data in real time • Increasing amounts of data and complexity of formats • Cloud solution:
 - Data accuracy
 - Secure
 - Highly available
 - Scalable
 - Mobile-enabled About Workiva
  42. 42. • First solution built on Google App Engine • Scaling new solutions requires service-oriented approach • Scaling new services requires a low-latency communication backplane About Workiva
  43. 43. Why ?
  44. 44. Availability
  45. 45. • Always on, always available • Protects itself at all costs—no compromises on performance • Disconnects slow consumers and lazy listeners • Clients have automatic failover and reconnect logic • Clients buffer messages while temporarily partitioned Availability over Everything
  46. 46. Simplicity as a feature.
  47. 47. • Single, lightweight binary • Embraces the “negative space”:
 - Simplicity —> high-performance
 - No complicated configuration or external dependencies
 (e.g. ZooKeeper)
 - No fragile guarantees —> face complexity head-on, encourage async • Simple pub/sub semantics provide a versatile primitive:
 - Fan-in
 - Fan-out
 - Request/response
 - Distributed queueing • Simple text-based wire protocol Simplicity as a Feature
  48. 48. Fast as hell.
  49. 49. [6]
  50. 50. • Fast, predictable performance at scale and at tail • ~8 million messages per second • Auto-pruning of interest graph allows efficient routing • When SLAs matter, it’s hard to beat NATS Fast as Hell
  51. 51. • Low-latency service bus • Pub/Sub • RPC How We Use NATS
  52. 52. Service Service Service NATS Service Gateway Web Client Web Client Web Client
  53. 53. Service Service Service NATS Service Gateway Web Client Web Client Web Client
  54. 54. Service Service Service NATS Service Gateway Web Client Web Client Web Client
  55. 55. Service Service Service NATS Service Gateway Web Client Web Client Web Client
  56. 56. Service Service Service Service Service NATS Service Gateway Web Client Web Client Web Client
  57. 57. Web Client Web Client Web Client Service Gateway NATS Service Service Service
  58. 58. Service Service Service NATS
  59. 59. Pub/Sub
  60. 60. “Just send this thing containing these fields serialized in this way using that encoding to this topic!”
  61. 61. “Just subscribe to this topic and decode using that encoding then deserialize in
 this way and extract these fields from
 this thing!”
  62. 62. Pub/Sub is meant to decouple services but often ends up coupling the teams developing them.
  63. 63. How do we evolve services in isolation and reduce development overhead?
  64. 64. • Extension of Apache Thrift • IDL and cross-language, code-generated pub/sub APIs • Allows developers to think in terms of services and APIs rather than opaque messages and topics • Allows APIs to evolve while maintaining compatibility • Transports are pluggable (we use NATS) Frugal RPC
  65. 65. struct Event {
 1: i64 id,
 2: string message,
 3: i64 timestamp,
 } scope Events prefix {user} {
 EventCreated: Event
 EventUpdated: Event
 EventDeleted: Event
 } subscriber.SubscribeEventCreated(
 "user-1", func(e *event.Event) {
 ) . . . publisher.PublishEventCreated(
 "user-1", event.NewEvent()) generated
  66. 66. • Service instances form a queue group • Client “connects” to instance by publishing a message to the service queue group • Serving instance sets up an inbox for the client and sends it back in the response • Client sends requests to the inbox • Connecting is cheap—no service discovery and no sockets to create, just a request/response • Heartbeats used to check health of server and client • Very early prototype code: RPC over NATS
  67. 67. • Store JSON containing cluster membership in S3 • Container reads JSON on startup and creates routes w/ correct credentials • Services only talk to the NATS daemon on their VM via localhost • Don’t have to worry about encryption between services and NATS, only between NATS peers NATS per VM
  68. 68. • Only messages intended for a process on another host go over the network since NATS cluster maintains interest graph • Greatly reduces network hops (usually 0 vs. 2-3) • If local NATS daemon goes down, restart it automatically NATS per VM
  69. 69. • Doesn’t scale to large number of VMs • Fairly easy to transition to floating NATS cluster or running on a subset of machines per AZ • NATS communication abstracted from service • Send messages to services without thinking about routing or service discovery • Queue groups provide service load balancing NATS per VM
  70. 70. • We’re a SaaS company, not an infrastructure company • High availability • Operational simplicity • Performance • First-party clients:
 Go Java C C#
 Python Ruby Elixir Node.js NATS as a Messaging Backplane
  71. 71. • Handle failure at the client
 - The less state in your middleware &
 infrastructure, the easier it is to scale
 - Exponential backoffs with jitter • But never trust the client
 - Rate limits, message size limits, back pressure
 - Be strict in what you accept
 - Limit failure domain by forcing applications to
 make design decisions upfront instead of
 punting Important Corollaries
  72. 72. Assume every client is trying to DoS you (because they probably are, intentionally or not).
  73. 73. Assume every client is trying to DoS you (because they probably are, intentionally or not).
  74. 74. –Derek Landy, Skulduggery Pleasant “Every solution to every problem is simple…
 It's the distance between the two where the mystery lies.”
  75. 75. @tyler_treat Thanks!