Akka-CQRS（9）- gRPC，实现前端设备与平台系统的高效集成

  前面我们完成了一个CQRS模式的数据采集（录入）平台。可以预见：数据的产生是在线下各式各样的终端系统中，包括web、桌面、移动终端。那么，为了实现一个完整的系统，必须把前端设备通过某种网络连接形式与数据采集平台集成为一体。有两种方式可以实现需要的网络连接：Restful-api, gRPC。由于gRPC支持http/2通讯协议，支持持久连接方式及双向数据流。所以对于POS设备这样的前端选择gRPC作为网络连接方式来实现实时的操作控制应该是正确的选择，毕竟采用恒久连接和双向数据流效率会高很多。gRPC是google公司的标准，基于protobuffer消息：一种二进制序列化数据交换机制。gRPC的优势在这里就不再细说，读者可以参考前面有关gRPC的讨论博文。

下面是系统结构示意图：

这篇讨论焦点集中在gRPC的server,client两头的具体实现。刚才提过，gRPC是google公司的开源库，同时还提供了各种语言的客户端，有：java, C++,python,go ... 但就是没有scala的，只能找第三方的scala客户端了。现在市面可供选择的gRPC-scala-客户端有scalaPB和akka-grpc两个，akka-grpc是基于akka-stream和akka-http构建的，按理来说会更合适，但由于还是处于preview版本，以后再说吧，现在只有scalaPB可选了。scalaPB是一个比较成熟的gRPC客户端，在前面的博客里我们也进行了介绍和示范。下面我们就用scalaPB来实现上面这个例子的客户端-平台集成。

首先，gRPC是通过protobuffer进行序列化数据传输的。下面是这个例子的.proto定义文件：

syntax = "proto3";

import "google/protobuf/wrappers.proto";
import "google/protobuf/any.proto";
import "scalapb/scalapb.proto";

option (scalapb.options) = {
  // use a custom Scala package name
  // package_name: "io.ontherocks.introgrpc.demo"

  // don't append file name to package
  flat_package: true

  // generate one Scala file for all messages (services still get their own file)
  single_file: true

  // add imports to generated file
  // useful when extending traits or using custom types
  // import: "io.ontherocks.hellogrpc.RockingMessage"

  // code to put at the top of generated file
  // works only with `single_file: true`
  //preamble: "sealed trait SomeSealedTrait"
};

package com.datatech.pos.messages;

message PBVchState {      //单据状态
    string opr  = 1;    //收款员
    int64  jseq = 2;    //begin journal sequence for read-side replay
    int32  num  = 3;    //当前单号
    int32  seq  = 4;    //当前序号
    bool   void = 5;    //取消模式
    bool   refd = 6;    //退款模式
    bool   susp = 7;    //挂单
    bool   canc = 8;    //废单
    bool   due  = 9;    //当前余额
    string su   = 10;   //主管编号
    string mbr  = 11;   //会员号
    int32  mode = 12;   //当前操作流程：0=logOff, 1=LogOn, 2=Payment
}

message PBTxnItem {       //交易记录
    string txndate    = 1;   //交易日期
    string txntime    = 2;   //录入时间
    string opr        = 3;   //操作员
    int32  num        = 4;   //销售单号
    int32  seq        = 5;   //交易序号
    int32  txntype    = 6;   //交易类型
    int32  salestype  = 7;   //销售类型
    int32  qty        = 8;   //交易数量
    int32  price      = 9;   //单价（分）
    int32  amount     = 10;  //码洋（分）
    int32  disc       = 11;  //折扣率 (%)
    int32  dscamt     = 12;  //折扣额：负值  net实洋 = amount + dscamt
    string member     = 13;  //会员卡号
    string code       = 14;  //编号（商品、卡号...）
    string acct       = 15;  //账号
    string dpt        = 16;  //部类
}

message PBPOSResponse {
    int32  sts                  = 1;
    string msg                  = 2;
    PBVchState voucher          = 3;
    repeated PBTxnItem txnitems   = 4;

}

message PBPOSCommand {
    int64  shopid = 1;
    string commandname = 2;
    string delimitedparams = 3;   //for multiple parameters, use ; to delimit
}


service SendCommand {
    rpc SingleResponse(PBPOSCommand) returns (PBPOSResponse) {};
    rpc MultiResponse(PBPOSCommand) returns (stream PBPOSResponse) {};
}

前端通过两种模式向平台发送指令PBPOSCommand: SingleResponse代表传统的request/response交互模式，MultiResponse,又或者server-streaming,代表前端发送一个指令，服务端返回一串Response, 或response-stream。Command和PBCommand、POSResponse和PBPOSResponse之间必须具备相互转换函数： 

package com.datatech.pos.cloud
import Messages._
import com.datatech.pos.messages._


object PBConverter {
  implicit class PBConverter(pbmsg: PBPOSCommand) {
    def toPOSComand: POSMessage = pbmsg.commandname.toUpperCase match {
      case "LOGON" => POSMessage(pbmsg.shopid,LogOn(pbmsg.delimitedparams))
      case "LOGOFF" => POSMessage(pbmsg.shopid,LogOff)
      ...
    }
  }
  implicit class POSResponseConvert(resp: POSResponse) {
    def toPBPOSResponse: PBPOSResponse = new PBPOSResponse(
      sts = resp.sts,
      msg = resp.msg,
      voucher = Some(resp.voucher.toPBVchState),
      txnitems = resp.txnItems.map(_.toPBTxnItem)
    )
  }
  implicit class VchStateConvert(state: VchStates) {
    def toPBVchState: PBVchState = new PBVchState(
    opr  = state.opr,   //收款员
    jseq = state.jseq,   //begin journal sequence for read-side replay
    num  = state.num,  //当前单号
    seq  = state.seq,   //当前序号
    void = state.void,  //取消模式
    refd = state.refd, //退款模式
    susp = state.susp,   //挂单
    canc = state.canc,  //废单
    due  = state.due,   //当前余额
    su   = state.su,  //主管编号
    mbr  = state.mbr,   //会员号
    mode = state.mode //当前操作流程：0=logOff, 1=LogOn, 2=Payment
    )
  }
  implicit class TxnItemConvert(item: TxnItem) {
    def toPBTxnItem: PBTxnItem = new PBTxnItem(
    txndate    = item.txndate,   //交易日期
    txntime    = item.txntime,   //录入时间
    opr        = item.opr,   //操作员
    num        = item.num,   //销售单号
    seq        = item.seq,  //交易序号
    txntype    = item.txntype,   //交易类型
    salestype  = item.salestype,  //销售类型
    qty        = item.qty,  //交易数量
    price      = item.price,  //单价（分）
    amount     = item.amount,  //码洋（分）
    disc       = item.disc,  //折扣率 (%)
    dscamt     = item.dscamt, //折扣额：负值  net实洋 = amount + dscamt
    member     = item.member,  //会员卡号
    code       = item.code,  //编号（商品、卡号...）
    acct       = item.acct,  //账号
    dpt        = item.dpt  //部类
    )
  }
}

然后可以开始实现平台端POS接口服务了： 

package com.datatech.pos.cloud
import com.datatech.pos.messages._
import io.grpc.stub.StreamObserver
import PBConverter._
import akka.actor.ActorRef
import akka.pattern.ask
import scala.concurrent.duration._
import akka.util.Timeout
import Messages._
import scala.concurrent.{Await, Future}
import com.typesafe.config.ConfigFactory
import com.datatech.sdp
import sdp.logging._

class gRPCServices(writerRouter: ActorRef) extends SendCommandGrpc.SendCommand with LogSupport {
  import gRPCServices._
  import PBConverter._
  var posConfig: com.typesafe.config.Config = _
  var exetimeout: Int = 5
  try {
    posConfig = ConfigFactory.load("pos.conf").getConfig("pos.server")
    exetimeout = posConfig.getInt("executimeout")
  }
  catch {
    case excp : Throwable =>
      log.warn(s"gRPCServices: ${excp.getMessage}")
      exetimeout = 5
  }

  override def singleResponse(request: PBPOSCommand): Future[PBPOSResponse] = {
    getPBResponse(writerRouter,request.toPOSComand, exetimeout)
  }
  override def multiResponse(request: PBPOSCommand, responseObserver: StreamObserver[PBPOSResponse]): Unit = ???
}

object gRPCServices {
  import scala.concurrent.ExecutionContext.Implicits.global
  def getPBResponse(ref: ActorRef, cmd: POSMessage, executimeout: Int = 5): Future[PBPOSResponse] = {
    implicit val timeout = Timeout(executimeout second)
    val futRes: Future[POSResponse] = ask(ref, cmd).mapTo[POSResponse]
    futRes.map(_.toPBPOSResponse)
  }
}

现在需要把gRPCService与POS系统集成为一体，这样前端发来的PBCommand转换成Command后经POSAgent转发给集群分片writerRouter，writeRouter再发给writer去进行具体的操作处理，完后把POSResponse转换成PBPOSResponse通过service再返回前端： 

  def getPBResponse(ref: ActorRef, cmd: POSMessage, executimeout: Int = 5): Future[PBPOSResponse] = {
    implicit val timeout = Timeout(executimeout second)
    val futRes: Future[POSResponse] = ask(ref, cmd).mapTo[POSResponse]
    futRes.map(_.toPBPOSResponse)
  }

可以看到上面使用了ask()模式来进行双向沟通。这个ref是一个中间信息交互actor （POSAgent）：

    var config = ConfigFactory.parseString("akka.remote.netty.tcp.port="" + port + """)
      .withFallback(ConfigFactory.parseString("akka.remote.netty.tcp.hostname="" + host + """))
      .withFallback(ConfigFactory.parseString("cassandra-journal.contact-points=["" + host + ""]"))
      .withFallback(ConfigFactory.parseString("cassandra-snapshot-store.contact-points=["" + host + ""]"))
    if (!seednodes.isEmpty)
      config = config.withFallback(ConfigFactory.parseString("akka.cluster.seed-nodes=[" + seednodes + "]"))

    //roles can be deployed on this node
    config = config.withFallback(ConfigFactory.parseString("akka.cluster.roles = [poswriter]"))
      .withFallback(ConfigFactory.load())

    val posSystem = ActorSystem(systemName, config)

    posSystem.actorOf(ClusterMonitor.props, "cps-cluster-monitor")

    posSystem.actorOf(ActionReader.readerProps(showSteps),"reader")
    val readerRouter = posSystem.actorOf(ReaderRouter.props(showSteps),"reader-router")

    WriterShard.deployShard(posSystem)(ReaderInfo(readerRouter,writeOnly),showSteps)
    val posHandler = ClusterSharding(posSystem).shardRegion(WriterShard.shardName)
    val posref = posSystem.actorOf(WriterRouter.props(posHandler), "writer-router")
    val passer = posSystem.actorOf(POSAgent.props(posref), "pos-agent")

    val svc = SendCommandGrpc.bindService(new gRPCServices(passer), posSystem.dispatcher)
    runServer(svc)


...


package com.datatech.pos.cloud

import akka.actor._
import com.datatech.sdp
import sdp.logging._

import Messages._
object POSAgent {
  def props(pos: ActorRef) = Props(new WriterRouter(pos))
}
class POSAgent(posHandler: ActorRef) extends Actor with LogSupport {
  var _sender: ActorRef = _
  override def receive: Receive = {
    case msg @ POSMessage(_,_) =>
      _sender = sender()
      posHandler ! msg
    case resp: POSResponse  => _sender ! resp
  }
}

...

package com.datatech.pos.cloud

import akka.actor._
import com.datatech.sdp
import sdp.logging._

import Messages._
object WriterRouter {
  def props(pos: ActorRef) = Props(new WriterRouter(pos))
}
class WriterRouter(posHandler: ActorRef) extends Actor with LogSupport {
  var _sender: ActorRef = _
  override def receive: Receive = {
    case msg @ POSMessage(_,_) =>
      _sender = sender()
      posHandler ! msg
    case resp: POSResponse  => _sender ! resp
//      log.info(s"*********response from server: $resp *********")
  }
}

前端是gRPC的客户端。我们构建一个来测试后台控制逻辑： 

package poc.client

import scala.concurrent.Future
import com.datatech.pos.messages._
import com.datatech.pos.messages.SendCommandGrpc
import io.grpc.netty.{NegotiationType, NettyChannelBuilder}

object POCClient {

  def main(args: Array[String]): Unit = {

    val channel = NettyChannelBuilder
      .forAddress("192.168.11.189",50051)
        .negotiationType(NegotiationType.PLAINTEXT)
        .build()

 /*
    //build connection channel
    val channel = io.grpc.ManagedChannelBuilder
      .forAddress("192.168.11.189",50051)
      .usePlaintext(true)
      .build()

    val pbCommand = PBPOSCommand(1022,"LogOn","888")
    //async call
   val asyncStub = SendCommandGrpc.blockingStub(channel)
    val futResponse: Future[PBPOSResponse] = asyncStub.singleResponse(pbCommand)

    import scala.concurrent.ExecutionContext.Implicits.global
    futResponse.foreach(result => println(result)) */


    val pbCommand = PBPOSCommand(1022,"LogOn","888")
    val syncStub1: SendCommandGrpc.SendCommandBlockingClient = SendCommandGrpc.blockingStub(channel)
    val response1: PBPOSResponse = syncStub1.singleResponse(pbCommand)

    println(s"${response1.msg}")


    val pbCommand2 = PBPOSCommand(1022,"LogOff","")
    //sync call
    val syncStub: SendCommandGrpc.SendCommandBlockingClient = SendCommandGrpc.blockingStub(channel)
    val response: PBPOSResponse = syncStub.singleResponse(pbCommand2)

    println(s"${response.msg}")

    scala.io.StdIn.readLine()
    channel.shutdown()

  }

}

这里有几点必须注意：

1、protobuffer对象的强名称必须一致。在客户端用了同一个posmessages.proto定义文件：

syntax = "proto3";

import "google/protobuf/wrappers.proto";
import "google/protobuf/any.proto";
import "scalapb/scalapb.proto";

option (scalapb.options) = {
  // use a custom Scala package name
  // package_name: "io.ontherocks.introgrpc.demo"

  // don't append file name to package
  flat_package: true

  // generate one Scala file for all messages (services still get their own file)
  single_file: true

  // add imports to generated file
  // useful when extending traits or using custom types
  // import: "io.ontherocks.hellogrpc.RockingMessage"

  // code to put at the top of generated file
  // works only with `single_file: true`
  //preamble: "sealed trait SomeSealedTrait"
};

package com.datatech.pos.messages;

message PBVchState {      //单据状态
    string opr  = 1;    //收款员
    int64  jseq = 2;    //begin journal sequence for read-side replay
    int32  num  = 3;    //当前单号
    int32  seq  = 4;    //当前序号
    bool   void = 5;    //取消模式
    bool   refd = 6;    //退款模式
    bool   susp = 7;    //挂单
    bool   canc = 8;    //废单
    bool   due  = 9;    //当前余额
    string su   = 10;   //主管编号
    string mbr  = 11;   //会员号
    int32  mode = 12;   //当前操作流程：0=logOff, 1=LogOn, 2=Payment
}

message PBTxnItem {       //交易记录
    string txndate    = 1;   //交易日期
    string txntime    = 2;   //录入时间
    string opr        = 3;   //操作员
    int32  num        = 4;   //销售单号
    int32  seq        = 5;   //交易序号
    int32  txntype    = 6;   //交易类型
    int32  salestype  = 7;   //销售类型
    int32  qty        = 8;   //交易数量
    int32  price      = 9;   //单价（分）
    int32  amount     = 10;  //码洋（分）
    int32  disc       = 11;  //折扣率 (%)
    int32  dscamt     = 12;  //折扣额：负值  net实洋 = amount + dscamt
    string member     = 13;  //会员卡号
    string code       = 14;  //编号（商品、卡号...）
    string acct       = 15;  //账号
    string dpt        = 16;  //部类
}

message PBPOSResponse {
    int32  sts                  = 1;
    string msg                  = 2;
    PBVchState voucher          = 3;
    repeated PBTxnItem txnitems   = 4;

}

message PBPOSCommand {
    int64  shopid = 1;
    string commandname = 2;
    string delimitedparams = 3;
}


service SendCommand {
    rpc SingleResponse(PBPOSCommand) returns (PBPOSResponse) {};
    rpc MultiResponse(PBPOSCommand) returns (stream PBPOSResponse) {};
}

注意package com.datatech.pos.messages, 这项在服务端和客户端必须一致。

2、另外就是客户端的channelbuilder：在scalaPB例子里使用的是ManagedChannelBuilder，这是一个实验阶段的东东：

    //build connection channel
    val channel = io.grpc.ManagedChannelBuilder
      .forAddress("132.232.229.60",50051)
      .usePlaintext(true)
      .build()

要用gRPC中正式的channelbuilder： 

    val channel = NettyChannelBuilder
      .forAddress("192.168.11.189",50051)
        .negotiationType(NegotiationType.PLAINTEXT)
        .build()

上面这个NettyChannelBuilder的设置与那个io.grpc.ManagedChannelBuilder功能相等。但NettyChannelBuilder还具备更多的设置参数，如ssl/tls设置。

3、还有：因为客户端是按照顺序来发送操作指令的，每发一个指令，等待返回结果后才能再发下一个指令。所以必须使用同步客户端调用函数blockingStub。

下面是本次示范的一些配置文档：

project/plugins.sbt

addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.9")
addSbtPlugin("net.virtual-void" % "sbt-dependency-graph" % "0.9.2")
addSbtPlugin("com.typesafe.sbt" % "sbt-native-packager" % "1.3.15")
addSbtPlugin("com.thesamet" % "sbt-protoc" % "0.99.21")
libraryDependencies += "com.thesamet.scalapb" %% "compilerplugin" % "0.9.0-M6"

build.sbt

name := "pos-on-cloud"

version := "0.1"

scalaVersion := "2.12.8"

scalacOptions += "-Ypartial-unification"

val akkaVersion = "2.5.23"

libraryDependencies := Seq(
  "com.typesafe.akka" %% "akka-cluster-metrics" % akkaVersion,
  "com.typesafe.akka" %% "akka-cluster-sharding" % akkaVersion,
  "com.typesafe.akka" %% "akka-persistence" % akkaVersion,
  "com.lightbend.akka" %% "akka-stream-alpakka-cassandra" % "1.0.1",
  "org.mongodb.scala" %% "mongo-scala-driver" % "2.6.0",
  "com.lightbend.akka" %% "akka-stream-alpakka-mongodb" % "1.0.1",
  "com.typesafe.akka" %% "akka-persistence-query" % akkaVersion,
  "com.typesafe.akka" %% "akka-persistence-cassandra" % "0.97",
  "com.datastax.cassandra" % "cassandra-driver-core" % "3.6.0",
  "com.datastax.cassandra" % "cassandra-driver-extras" % "3.6.0",
  "ch.qos.logback"  %  "logback-classic"   % "1.2.3",
  "io.monix" %% "monix" % "3.0.0-RC2",
  "org.typelevel" %% "cats-core" % "2.0.0-M1",
  "io.grpc" % "grpc-netty" % scalapb.compiler.Version.grpcJavaVersion,
  "com.thesamet.scalapb" %% "scalapb-runtime" % scalapb.compiler.Version.scalapbVersion % "protobuf",
  "com.thesamet.scalapb" %% "scalapb-runtime-grpc" % scalapb.compiler.Version.scalapbVersion

)

PB.targets in Compile := Seq(
  scalapb.gen() -> (sourceManaged in Compile).value
)

enablePlugins(JavaAppPackaging)

 resouces/application.conf

akka.actor.warn-about-java-serializer-usage = off
akka.log-dead-letters-during-shutdown = off
akka.log-dead-letters = off
akka.remote.use-passive-connections=off

akka {
  loglevel = INFO
  actor {
    provider = "cluster"
  }

  remote {
    log-remote-lifecycle-events = on
    netty.tcp {
      hostname = "127.0.0.1"
      # port set to 0 for netty to randomly choose from
      port = 0
    }
  }

  cluster {
    seed-nodes = [
      "akka.tcp://cloud-pos-server@172.27.0.8:2551"
      ,"akka.tcp://cloud-pos-server@172.27.0.7:2551"
    ]

    log-info = off
    sharding {
      role = "poswriter"
      passivate-idle-entity-after = 30 m
    }
  }

  persistence {
    journal.plugin = "cassandra-journal"
    snapshot-store.plugin = "cassandra-snapshot-store"
  }

}

cassandra-journal {
  contact-points = [
    "172.27.0.8",
    "172.27.0.7",
    "172.27.0.15"
  ]
}

cassandra-snapshot-store {
  contact-points = [
    "172.27.0.8",
    "172.27.0.7",
    "172.27.0.15"
  ]
}

# Enable metrics extension in akka-cluster-metrics.
akka.extensions=["akka.cluster.metrics.ClusterMetricsExtension"]

akka.actor.deployment {
  /reader-router/readerRouter = {
    # Router type provided by metrics extension.
    router = cluster-metrics-adaptive-group
    # Router parameter specific for metrics extension.
    # metrics-selector = heap
    # metrics-selector = load
    # metrics-selector = cpu
    metrics-selector = mix
    #
    routees.paths = ["/user/reader"]
    cluster {
      max-nr-of-instances-per-node = 10
      max-total-nr-of-instances = 1000
      enabled = on
      #set to on when there is a instance of routee created
      #on the same node as the router
      #very important to set this off, could cause missing msg in local cluster
      allow-local-routees = on
    }
  }
}

dbwork-dispatcher {
  # Dispatcher is the name of the event-based dispatcher
  type = Dispatcher
  # What kind of ExecutionService to use
  executor = "fork-join-executor"
  # Configuration for the fork join pool
  fork-join-executor {
    # Min number of threads to cap factor-based parallelism number to
    parallelism-min = 2
    # Parallelism (threads) ... ceil(available processors * factor)
    parallelism-factor = 2.0
    # Max number of threads to cap factor-based parallelism number to
    parallelism-max = 10
  }
  # Throughput defines the maximum number of messages to be
  # processed per actor before the thread jumps to the next actor.
  # Set to 1 for as fair as possible.
  throughput = 100
}

resources/logback.xml

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
        <encoder>
            <Pattern>
                %d{yyyy-MM-dd HH:mm:ss} [%thread] %-5level %logger{36} - %msg%n
            </Pattern>
        </encoder>
    </appender>

    <logger name="com.datatech" level="info"
            additivity="false">
        <appender-ref ref="STDOUT" />
    </logger>

    <logger name="com.datatech.sdp" level="info"
            additivity="false">
        <appender-ref ref="STDOUT" />
    </logger>

    <root level="warn">
        <appender-ref ref="STDOUT" />
    </root>
</configuration>

 resources/pos.conf

pos {
  server {
    debug = false
    cqlport = 9042
    readinterval = 1000
    executimeout = 5
  }
}