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The once and future layer 5: Resilient, Twitter-style microservices

Oliver Gould
September 22, 2016

The once and future layer 5: Resilient, Twitter-style microservices

What is required to operate microservices at scale? Beyond containers, schedulers, and frameworks, what is actually required to turn hundreds of services, tens of thousands of machines, and millions of requests per second into a unified, performant application? Oliver Gould explores the evolution of Twitter’s stack from monolith to highly distributed microservices and the surprising glue that held it all together: layer 5 in the OSI model, the oft-overlooked session layer.

Oliver offers an overview of Finagle, the high-scale RPC library developed at Twitter and adopted by Pinterest, SoundCloud, ING Bank, and other companies, tracing Finagle’s evolution from a simple library into something much more: a unified, global mechanism for operability and control over a highly disaggregated application architecture. Oliver explains how this mechanism provides Twitter with higher-level, service-based semantics around scalability, reliability, and fault tolerance and how the control over layer 5 afforded by Finagle allowed Twitter to solve some of the most surprising and difficult problems with its highly distributed architecture—when the software architecture diagram and the org chart intersected.

Oliver concludes by introducing linkerd, an open source proxy form of Finagle, which extends Finagle’s operational model to non-JVM or polyglot microservices, and demonstrates how linkerd can be used to “wrap” multiservice applications, independent of application language(s) or infrastructure, to obtain many of the benefits that Finagle provides for Twitter.

Oliver Gould

September 22, 2016
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  1. The Once and Future Layer 5 Resilient, Twitter-style microservices oliver

    gould
 cto, buoyant Velocity NYC, September 22 2016 from
  2. oliver gould • founding cto @ buoyant
 open-source microservice infrastructure

    • previously, tech lead @ twitter: • observability • traffic • core contributor: finagle • creator: linkerd • likes: dogs • dislikes: being woken up for computers @olix0r
 [email protected]
  3. overview • 2010: Riding the Whale • “Microservices” • The

    Once and Future Layer 5 • Introducing linkerd • Demotime!
  4. Twitter, 2010 107 users 107 tweets/day 102 engineers 101 services

    101 deploys/week 102 hosts 10-1 datacenters 101 user-facing outages/week https://blog.twitter.com/2010/measuring-tweets
  5. Resilience is an imperative: our software runs on the truly

    dismal computers we call datacenters. Besides being heinously
 complex… they are unreliable and prone to
 operator error. Marius Eriksen @marius
 RPC Redux
  6. resilience in microservices software you didn’t write hardware you can’t

    touch network you can’t configure break in new and surprising ways and your customers shouldn’t notice
  7. datacenter [1] physical [2] link [3] network [4] transport kubernetes

    
 canal, weave, … aws, azure, digitalocean, gce, … business languages, libraries [7] application rpc [5] session [6] presentation json, protobuf, thrift, … http/2, mux, …
  8. programming finagle val users = Thrift.newIface[UserSvc](“/s/users”)
 val timelines = Thrift.newIface[TimelineSvc](“/s/timeline”)

    Http.serve(“:8080”, Service.mk[Request, Response] { req => for { user <- users.get(userReq(req)) timeline <- timelines.get(user) } yield renderHTML(user, timeline) })
  9. your server is a function trait Service[Req, Rsp] { def

    apply(req: Req): Future[Rsp] def close(deadline: Time): Future[Unit] }
  10. your server is a function trait ServiceFactory[Req, Rsp] { def

    apply(conn: ClientConnection): Future[Service[Req, Rsp]] def close(deadline: Time): Future[Unit] }
  11. your server is a function trait Filter[InReq, OutRsp, OutReq, InRsp]

    { def apply(req: InReq, service: Service[OutReq, InRsp]): Future[OutRsp] def andThen[A, B](f: Filter[OutReq, InRsp, A, B]): Filter[OutReq, InRsp, A, B] def andThen[A, B](s: Service[A, B]): Service[OutReq, InRsp] def andThen[A, B](sf: ServiceFactory[A, B]): ServiceFactory[OutReq, InRsp] }
  12. your server is a function val service: Service[http.Request, http.Response] =

    recordHandletime andThen traceRequest andThen logRequest andThen timeouts andThen myService val server: ListeningServer = Http.serve(“:8080”, service) val client: ServiceFactory[http.Request, http.Response] = retries andThen Http.newClient(“127.1:8080”)
  13. operating finagle transport security service discovery circuit breaking backpressure deadlines

    retries tracing monitoring keep-alive multiplexing load balancing per-request routing service-level objectives Observe Session timeout Retries Request draining Load balancer Monitor Observe Trace Failure accrual Request timeout Pool Fail fast Expiration Dispatcher
  14. layer 5 naming applications refer to logical names
 requests are

    bound to concrete names
 delegations express routing /s/users /#/io.l5d.zk/prod/users/http /s => /#/io.l5d.zk/prod/http
  15. “It’s slow”
 is the hardest problem you’ll ever debug. Jeff

    Hodges @jmhodges
 Notes on Distributed Systems for Young Bloods
  16. lb algorithms: • round-robin • fewest connections • queue depth

    • exponentially-weighted moving average (ewma) • aperture load balancing at layer 5
  17. timeouts & retries timelines users web db timeout=400ms retries=3 timeout=400ms

    retries=2 timeout=200ms retries=3 timelines users web db
  18. timeouts & retries timelines users web db timeout=400ms retries=3 timeout=400ms

    retries=2 timeout=200ms retries=3 timelines users web db 800ms! 600ms!
  19. magic ops sprinkles transport security service discovery circuit breaking backpressure

    deadlines retries tracing metrics keep-alive multiplexing load balancing per-request routing service-level objectives Observe Session timeout Retries Request draining Load balancer Monitor Observe Trace Failure accrual Request timeout Pool Fail fast Expiration Dispatcher
  20. github.com/buoyantio/linkerd microservice rpc proxy layer-5 router aka l5d built on

    finagle & netty pluggable http, thrift, … consul, etcd, k8s, marathon, zk, … …
  21. magic operability sprinkles transport security service discovery circuit breaking backpressure

    deadlines retries tracing metrics keep-alive multiplexing load balancing per-request routing service-level objectives Service B instance linkerd Service C instance linkerd Service A instance linkerd
  22. namerd service discovery service delegates logical names to service discovery

    centralized routing policy pluggable consul, etcd, k8s, zk, …
  23. host app: a app: b app: a host app: b

    app: a app: b service-a
  24. linkerd roadmap • HTTP/2+gRPC linkerd#174 • Deadline Enforcement (in progress)

    • Dark Traffic • Improved namerd API • All configurable everything