TYPED SERVICES USING FINCHTom Adams, @tomjadams

LambdaJam 2016

1SERVICES

SERVICE• We care about things like

• HTTP primitives• Request/response

encode/decode• Transport protocols• Talking to downstream

services• Local data storage

• But not these• Asset packaging

• View rendering• JavaScript, SASS,

LESS, etc.

LANDSCAPE

• Go: gokit, Kite

• Elixir: Phoenix

• Javascript: Node.js (TypeScript)

• Clojure: Caribou, Liberator, Rook

• Ruby: Rails, Sinatra, Grape, Lotus

• Erlang: Leptus, Yaws

• Haskell: Snap, rest, servant, Hapstack, Yesod

• Java: Play, Spring, Jersey, Spark, RESTEasy, (Dropwizard)

• Swift: Swifton, Vapor

WHAT’S IN A HTTP FRAMEWORK?

WE NEED

1. Routing

• path/headers/methods/etc. to a function

2. Req → Resp

SERVICE PRIMITIVES

• Routing is a function

• r :: URI → a

• An “action” is a function

• a :: Req → Resp

• A “controller” is (scoped) a collection of actions

• c :: List a

• A “service” is a collection of controllers

• s :: List c

BUT WAIT, THERE’S MORE

• HTTP primitives

• Datastore• Metrics• Logging• JSON codec• Databinding• Configuration• Environments

• HTTP clients• Failure

isolation• Async

primitives• Monitoring• Service

discovery

• Debugging/tracing

• Caching• Messaging• Deployment• Testing• Live code

reload• …

2SCALALet’s talk about Scala

https://github.com/OlegIlyenko/scala-icon

WHY SCALA?• JVM support - “Better Java”

• Fast, scalable

• Deployment & runtime behaviour well understood

• Library & tool support (distributed heap, debugging, etc.)

• Decent (not great) static type system

• Succinct - closures, type classes, type aliases, type inference, no semi-colons

• Features - immutability, equational reasoning, functions, case classes, implicits, packages, mixins, currying/partial application, etc.

• Standard library - option, either, future, etc.

• Cool stuff! scalaz, actors, higher-kinds, etc.

WELL USED• Twitter, Pinterest, Airbnb, SoundCloud, Uber, Strava,

Gilt, LinkedIn, Amazon, Tumblr, Foursquare, Box, Gigya, Simple, Localytics, LivingSocial, eHarmony, Yammer, Firebase, Disqus, Asana, Hootsuite, PagerDuty, Rdio

• Apple, Novell, The Guardian, Sony, BSkyB, AOL, Xerox, Siemens, VMware

• REA, Seek, Skedulo, CBA, Atlassian, Fairfax, RedBalloon, Canva*, Oomph*

Source: Quora, AngelList, scala-lang.org, reddit, LinkedIn, Finagle Adopters

WHY FP?

• (Static) Types, and

• Immutability, and

• Composition, gives rise to

• Equational reasoning, and

• Certainty, and

• Reliability

FRAMEWORK OPTIONS

• Karyon (Netflix)

• Play (Typesafe/Lightbend)

• Unfiltered (OSS)

• Dropwizard (Yammer)

• Spray (Typesafe/Lightbend)

• Finagle (Twitter) / Finatra (OSS) / Finch (OSS)

• Akka, Lagom (Typesafe/Lightbend)

• Colossus (Tumblr)

• Chaos (Mesosphere)

PERFORMANCE

3FINCH

FINCH

Finch is a thin layer of purely functional basic blocks on top of Finagle for building HTTP APIs.

It provides developers with simple and robust HTTP primitives, while being as close as possible to the bare metal Finagle API.

HELLO, WORLD

val service = new Service[Request, Response] { def apply(req: Request) { request.path match { case "/hello" => val resp: Response = Response() resp.content = Buf.Utf8("Hello, World!") Future.value(resp) case _ => Future.value(Response()) }}

Http.server.serve(":8080", service)

HELLO, WORLD

import io.finch._import com.twitter.finagle.Http

val api: Endpoint[String] = get("hello") { Ok("Hello, World!") }

Http.server.serve(“:8080", api.toService)

HELLO, WORLD

import io.finch._import com.twitter.finagle.Http

val api: Endpoint[String] = get("hello") { Ok("Hello, World!") }

Http.server.serve(“:8080", api.toService)

Finch

Finagle

FINCH FEATURES

• High level abstraction on top of Finagle (don’t need to drop down to Finagle*)

• Small footprint

• Flexible use (what you make of it)

• Referentially transparent & compositional

• Request / response decoding / encoding

• Explicit async modelling

FINAGLE

A fault tolerant, protocol-agnostic, extensible RPC system for the JVM, used to construct high-concurrency servers.

Finagle implements uniform client and server APIs for several protocols, and is designed for high performance and concurrency.

FINAGLE FEATURES• Connection pools (w/

throttling)• Failure detection• Failover strategies• Load-balancers• Back-pressure• Statistics, logs, and

exception reports• Distributed tracing

(Zipkin)

• Service discovery (ZooKeeper)

• Sharding strategies• Config

TWITTERSERVER

• Lightweight server template • Command line args• HTTP admin server• Logging• Tracing• Metrics• System stats

WHAT DOES THAT MEAN FOR

• Performance & scalability out of the box

• Maturity of a battle tested framework

• Fast ramp up

• Won’t bottom out as you scale

• Known deployment, monitoring, runtime, etc.

4CORE FINCH CONCEPTS

TRIUMVIRATE

• Endpoint

• Filters

• Futures

• (Services)

ENDPOINT

• A function that takes a request & returns a value

• Automatically handles Future/async

• Provides routing behaviour

• Extracts/matches values from the request

• Values are serialised to the HTTP response

• Composable (applicative)

EXAMPLE

val divOrFail: Endpoint[Int] = post("div" :: int :: int) { (a: Int, b: Int) => if (b == 0) BadRequest(new ArithmeticException("...")) else Ok(a / b) }

FILTER (FINAGLE)

• Many common behaviours are service agnostic

• Cross cutting concerns

• Timeouts, logging, retries, stats, authentication, etc.

• Filters are composed over services

• Alter the behaviour of a service without caring what it is

FILTER EXAMPLE

val timeout: Filter[...]val auth: Filter[...]val service: Service[Req, Resp]

val composed = timeout andThen auth andThen service

FILTERS ARE FUNCTIONS

type Filter[...] = (ReqIn, Service[ReqOut, RespIn]) => Future[RespOut]

FILTERS ARE TYPESAFE

// A service that requires an authenticated requestval service: Service[AuthReq, Resp]

// Bridge with a filterval auth: Filter[HttpReq, HttpResp, AuthHttpReq, HttpResp]

// Authenticate, and serveval authService: Service[HttpReq, HttpResp] = auth andThen service

FUTURE

• A placeholder for a value that may not yet exist

• Long computations, network calls, disk reads, etc.

• The value is supplied concurrently (executed on thread pool)

• Like callbacks, but not shit

• Oh, and composable (monadic)

CALLBACK FUTURES

val f: Future[String]

f onSuccess { s => log.info(s)} onFailure { ex => log.error(ex)}

STATES OF A FUTURE

• 3 states; empty, complete or failed

• “Taints” the types of calling code

• Easy to program against & make async explicit

• Forces handling of async behaviour

• Can also be blocked (if required)

FUTURE IN PRACTICE

val dbUser = facebook.authenticate(token).flatMap { fbUser => val query = findByEmail(fbUser.email).result database.run(query).flatMap(_.headOption)}dbUser.transform { case Return(user) => success(user) case Throw(e) => handleError(e)}

SERVICE (FINAGLE)

• System boundaries are represented by asynchronous functions called services

• Symmetric and uniform API represents both clients and servers

• You never (usually) write a Finagle service, Finch does that for you

• Services are monadic (you’ll see this a lot…)

SERVICES ARE FUNCTIONS

type Service[Req, Resp] = Req => Future[Resp]

SERVICES IN FINCH

object LiegeApi extends ErrorOps with ResponseEncoders { private def api = usersApi() :+: ridesApi()

def apiService: Service[Request, Response] = { val service = api.handle(errorHandler).toService RequestLoggingFilter.andThen(service) }}

5OTHER GOOD BITS

DATABINDINGGiven a model

val ts: RequestReader[Token] = (param("t") :: param("a")).as[Token]val ts: RequestReader[Token] = RequestReader.derive[Token].fromParams

case class Token(token: String, algorithm: String)

Create a reader to parse the querystring

val getToken: Endpoint[Token] = get("tokens" ? ts) { (t: Token) => ... }

Automatically parse the querystring in an endpoint

DATABINDING

Given a model

case class Token(token: String, algorithm: String)

{ "token": "CAAX...kfR", "algorithm": "sha1"}

post("sign-in" ? body.as[Token]) { (t: Token) => ... }

And incoming JSON from a POST request

We can bind as

“CONTROLLER”object RidesApi extends HttpOps with Logging { def ridesApi() = list :+: details

def list: Endpoint[List[Attendance]] = get("rides" ? authorise) { u: AuthenticatedUser => ... }

def details: Endpoint[Attendance] = get("rides" / string("type") / string("id") ? authorise) { (backend: String, rideId: Id, u: AuthenticatedUser) => ... }}

class ItemsApiController extends Controller { val itemsService = ... val itemReader = body.as[Item]

def findItemById(itemId: Long): Action = securedAction { reqContext => itemsService.findItemById(itemId) }

def userItems: Action = securedAction(pageReader) { page => implicit reqContext =>

itemsService.userItems(user.id.get, PageRequest(page)) } override def routes: Endpoint[HttpRequest, HttpResponse] = { (Get / "api" / "items" /> userItems) | (Get / "api" / "items" / long /> findItemById) | (Post / "api" / "items" /> newItem) | }}

import io.finch._import ru.arkoit.finchrich.controller._

object MyAwesomeController extends Controller { val healthcheck = get("healthcheck") { Ok() }

val greeter = get("greet" / param("name")) { n: String => Ok(s"Hello, $n!") }}

val ep = controllerToEndpoint(MyAwesomeController)

Source: https://github.com/akozhemiakin/finchrich

METRICS

val stats = Stats(statsReceiver)val server = Http.server.configured(stats).serve(":8081", api)

val rides: Counter = statsReceiver.counter("rides")rides.incr()

val ridesLatency: Stat = statsReceiver.stat("rides_latency")Stat.time(ridesLatency) { rides(u).map(rs => Ok(rs.map(r => Attendance(u, r)))) }

HTTP CLIENTS

val client = Http.client.newService("twitter.com:8081,twitter.com:8082")

val f: Future[HttpRep] = client(HttpReq("/"))

val result: Future[String] = f.map { resp => handleResponse(resp) }

TESTING

service(HttpReq("/")) map { resp => doStuff(resp) }

6GETTING STARTED

WHEN SHOULD I USE IT?

• Complex long / lived system / many developers

• Scale or performance requirements

• Integration with downstream services

• Need to run on the JVM

FINCH

• Watch the Finch videos

• https://skillsmatter.com/skillscasts/6876-finch-your-rest-api-as-a-monad

• Examples

• https://github.com/finagle/finch/tree/master/examples/src/main/scala/io/finch

• Best practices

• https://github.com/finagle/finch/blob/master/docs/best-practices.md

READ UP ON FINAGLE

• Finagle Users Guide

• Your function as a server (original Finagle paper)

• The anatomy of a twitter microservice

• Fault tolerant clients with Finagle

54

QUESTIONS?

7YOW WEST SLIDES

DDL

final case class User(id: Option[Int] = None, name: String, email: String, location: Option[String], avatarUrl: String)

final class UserOps(tag: Tag) extends Table[User](tag, "users") { def id = column[Int]("id", O.PrimaryKey, O.AutoInc) def name = column[String]("name") def email = column[String]("email") def location = column[String]("location")

def * = (id.?, name, email, location.?) <>(User.tupled, User.unapply)

def nameIdx = index("name_idx", name, unique = true)}

DB ACCESS

object UserOps extends TableQuery(new UserOps(_)) { val findByName = this.findBy(_.name) val findByEmail = this.findBy(_.email)

def insert(u: User) = UserOps += u

def userForToken(token: UserAccessToken): Future[Option[AuthenticatedUser]] = database.run(find(token).result).map(_.headOption.flatMap(asAuthenticatedUser))

def deauthenticateUser(token: AuthToken): Future[Unit] = { val q = for {u <- UserOps if u.authToken === token.asSessionId} yield u.authToken database.run(q.update(null)).flatMap(_ => Future.Done) }}

MIGRATIONS

object Database { lazy val migrationDatabase = new MigrationDatabase { def migrate(): Unit = { val flyway = new Flyway() flyway.setDataSource(env.dbUrl, env.dbUsername, env.dbPassword) flyway.migrate() } }}

FUTURE COMBINATORS

• Composed via map, flatMap, handle, transform and rescue

• Exception handling via

• onSuccess(f: A => Unit)

• onFailure(ex: Throwable => Unit)

IDEAS

• More on request reader stuff, auth, etc.

• Using circle’s auto-derivation

• Abstracting/mapping Twitter Futures from/between Scala Futures from Scalaz Tasks

IDEAS

• Issues, these may be ok for you

• Twitter stack

• Everything is async, “sensitive to blocking code”, “reactive” bandwagon

• Stuck to netty3

• Documentation not exhaustive, need to rely on Gitter

• Finagle hard to use other metrics

PROBLEMS WITH FUTURES

• Futures are harder to compose than they need to be

• Try clouds the issue

• respond vs transform

• respond for purely side-effecting callbacks.

• map & flatMap for dealing strictly with successful computations (implemented using transform)

• handle and rescue for dealing strictly with exceptional computations.

IDEAS

• Other data layers

• Quill, finagle-mysql