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Akka in JAVA(三)

上两个部分讲了Akka的基本知识和常见的用法.接下来讲一讲Akka的远程调用以及集群的使用.因为在现在的项目中,基本上都是分布式的,单个的应用程序都快成为”熊猫”了.因此Akka的远程以及集群调用就是非常有必要的了.

Remote调用

Akka-Remoting是采用了P2P(peer-to-peer)的通信方式设计的,也就是端对端的方式.特别是Akka-Remoting不能与网络地址转换和负载均衡一起的工作.
但是,由于Akka在设计的时候就考虑了远程调用以及分布式的情况.因此,Akka-Remoting在使用上就非常的简单,几乎等于是透明的,和本地调用几乎相同.除了传递的消息需要可序列化以及创建和查找Actor的时候路径稍有不同外,没有其他的区别了.

远程调用的准备

要在项目中使用Akka-Remoting非常的简单,只需要引入Maven中的akka-remote就可以了.

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<dependency><groupId>com.typesafe.akka</groupId><artifactId>akka-remote_2.11</artifactId><version>2.4.1</version></dependency>

配置

由于Akka几乎没有特别的为Remoting提供专门的API,区别仅仅在于配置.因此,接下来就是要修改项目中的akka的配置了:

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WCMapReduceApp {akka {actor {provider = "akka.remote.RemoteActorRefProvider"}remote {enabled-transports = ["akka.remote.netty.tcp"]netty.tcp {hostname = "127.0.0.1"port = 2552       # 0表示自动选择一个可用的}}}}

我们来看看这个配置和本地的有什么不同.
这个配置文件在akka的配置项中添加了一个actor配置项,并指定provider也就是Actor提供者为akka.remote.RemoteActorRefProvider,即远程Actor提供者.
然后定义了remote远程传输方式,使用akka.remote.netty.tcp即netty的方式提供服务,服务的IP和端口分别是127.0.0.1和2552,就这么简单.而由于是端对端的通信,因此客户端的配置和服务器端的是一样的.

以上只是远程调用的最小的配置,完整的可选配置如下:

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akka {actor {# 序列化方式serializers {proto = "akka.serialization.ProtobufSerializer"}# 发布远程Akkadeployment {default {# 手动指定Actor的远程地址# if this is set to a valid remote address, the named actor will be deployed# at that node e.g. "akka://sys@host:port"remote = ""# 目标target {# A list of hostnames and ports for instantiating the children of a# non-direct router#   The format should be on "akka://sys@host:port", where:#    - sys is the remote actor system name#    - hostname can be either hostname or IP address the remote actor#      should connect to#    - port should be the port for the remote server on the other node# The number of actor instances to be spawned is still taken from the# nr-of-instances setting as for local routers; the instances will be# distributed round-robin among the given nodes.nodes = []}}}}remote {# 传输方式# Which implementation of akka.remote.RemoteTransport to use# default is a TCP-based remote transport based on Nettytransport = "akka.remote.netty.NettyRemoteTransport"# 受信模式# Enable untrusted mode for full security of server managed actors, allows# untrusted clients to connect.untrusted-mode = off# 集群检测超时时间# Timeout for ACK of cluster operations, like checking actor out etc.remote-daemon-ack-timeout = 30s# 是否记录接收的消息# If this is "on", Akka will log all inbound messages at DEBUG level, if off then they are not loggedlog-received-messages = off# 是否记录发送的消息# If this is "on", Akka will log all outbound messages at DEBUG level, if off then they are not loggedlog-sent-messages = off# Each property is annotated with (I) or (O) or (I&O), where I stands for “inbound” and O for “outbound” connections.# The NettyRemoteTransport always starts the server role to allow inbound connections, and it starts# active client connections whenever sending to a destination which is not yet connected; if configured# it reuses inbound connections for replies, which is called a passive client connection (i.e. from server# to client).netty {# 延迟阻塞超时时间# (O) In case of increased latency / overflow how long should we wait (blocking the sender)# until we deem the send to be cancelled?# 0 means "never backoff", any positive number will indicate time to block at most.backoff-timeout = 0ms# 加密cookie# (I&O) Generate your own with '$AKKA_HOME/scripts/generate_config_with_secure_cookie.sh'# or using 'akka.util.Crypt.generateSecureCookie'secure-cookie = ""# 是否需要cookie# (I) Should the remote server require that its peers share the same secure-cookie# (defined in the 'remote' section)?require-cookie = off# 使用被动连接# (I) Reuse inbound connections for outbound messagesuse-passive-connections = on# 域名,连接地址# (I) The hostname or ip to bind the remoting to,# InetAddress.getLocalHost.getHostAddress is used if emptyhostname = ""# 端口# (I) The default remote server port clients should connect to.# Default is 2552 (AKKA), use 0 if you want a random available portport = 2552# # (O) The address of a local network interface (IP Address) to bind to when creating# outbound connections. Set to "" or "auto" for automatic selection of local address.outbound-local-address = "auto"# 消息帧大小# (I&O) Increase this if you want to be able to send messages with large payloadsmessage-frame-size = 1 MiB# 超时时间# (O) Timeout durationconnection-timeout = 120s#连接备份日志大小# (I) Sets the size of the connection backlogbacklog = 4096# 执行线程池存活时间# (I) Length in akka.time-unit how long core threads will be kept alive if idlingexecution-pool-keepalive = 60s# 执行线程池大小# (I) Size of the core pool of the remote execution unitexecution-pool-size = 4#最大通道内存大小# (I) Maximum channel size, 0 for offmax-channel-memory-size = 0b#总计通道内存大小# (I) Maximum total size of all channels, 0 for offmax-total-memory-size = 0b#重试时间间隔# (O) Time between reconnect attempts for active clientsreconnect-delay = 5s# 读取超时时间# (O) Read inactivity period (lowest resolution is seconds)# after which active client connection is shutdown;# will be re-established in case of new communication requests.# A value of 0 will turn this feature offread-timeout = 0s# 写入超时时间# (O) Write inactivity period (lowest resolution is seconds)# after which a heartbeat is sent across the wire.# A value of 0 will turn this feature offwrite-timeout = 10s# 所有超时时间# (O) Inactivity period of both reads and writes (lowest resolution is seconds)# after which active client connection is shutdown;# will be re-established in case of new communication requests# A value of 0 will turn this feature offall-timeout = 0s# 重连窗口时间# (O) Maximum time window that a client should try to reconnect forreconnection-time-window = 600s}# The dispatcher used for the system actor "network-event-sender"network-event-sender-dispatcher {executor = thread-pool-executortype = PinnedDispatcher}}
}

创建远程Actor

通过上面的配置后,在程序里面创建远程的Actor就非常的简单了,基本上感觉不到是创建的远程Actor.

只需要在创建ActorSystem的时候使用上面所说的配置文件即可,接下来的就和本地的Actor没有任何的区别:

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ActorSystem system = ActorSystem.create("WCMapReduceApp", ConfigFactory.load("application").getConfig("WCMapReduceApp"));

通过这个actorSystem创建出来的Actor的路径会是这样的:akka.tcp://WCMapReduceApp@127.0.0.1:2552/user/remoteActor,即是一个远程的Actor.

当然,除了直接在服务器端创建服务外,还能在客户端远程的要求服务器端创建一个Actor,并保持引用.

要实现这样的功能,同样需要修改配置文件:

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akka {actor {deployment {/sampleActor {remote = "akka.tcp://sampleActorSystem@127.0.0.1:2553"}}}
}

这个配置文件告诉akka,在创建一个/sampleActor的时候,即system.actorOf().指定的actor并不会在本地创建,而是会请求远程的actorSystem创建这个actor.

查找远程Actor

当客户端发布了一个远程的Actor后,客户端就需要调用它.而向它发送消息的先决条件就是要找到这个Actor.

查询远程的Actor也非常的简单.每一个远程的Actor都会有一个它自己的Path.其格式是:akka://<actorsystemname>@<hostname>:<port>/<actor path>,比如上面所说的akka.tcp://WCMapReduceApp@127.0.0.1:2552/user/remoteActor.那么获取这个Actor的ActorRef,就是通过actorFor或actorSelection方法传入这个ActorPath即可.

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final ActorRef remoteActor = system.actorFor("akka.tcp://WCMapReduceApp@127.0.0.1:2552/user/WCMapReduceActor");

接下来的操作就和本地的Actor一模一样了.

序列化

既然是远程调用,那么就涉及到消息的序列化.Akka内置了集中序列化的方式,也提供了序列化的扩展,你可以使用内置的序列化方式,也可以自己实现一个.

要选择使用何种序列化,需要修改配置文件.在akka一节中配置serializers选项,指定序列化的实现类:

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akka {actor {serializers {java = "akka.serialization.JavaSerializer" #本地调用的默认方式proto = "akka.remote.serialization.ProtobufSerializer" #远程调用的默认方式myown = "cn.sunxiang0918.akka.demo5.CustomSerializer"}}
}

配置好这个后,还可以绑定数据类型与序列化方式之间的映射:

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akka {actor {serialization-bindings {"java.lang.String" = java"akka.docs.serialization.Customer" = java"com.google.protobuf.Message" = proto"akka.docs.serialization.MyOwnSerializable" = myown"java.lang.Boolean" = myown}}}

上面这段代码就指定了各种数据类型分别采用不同的序列化方式.

如果觉得Akka内置的序列化方式不满足你的要求,可以自定义一个序列化类(通常并不需要).
这个也比较的简单,需要自定义的序列化类继承自JSerializer类.然后实现其中的方法:

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import akka.actor.*;
import akka.serialization.*;
import com.typesafe.config.*;public static class MyOwnSerializer extends JSerializer {// This is whether "fromBinary" requires a "clazz" or not@Override public boolean includeManifest() {return false;}// Pick a unique identifier for your Serializer,// you've got a couple of billions to choose from,// 0 - 16 is reserved by Akka itself@Override public int identifier() {return 1234567;}// "toBinary" serializes the given object to an Array of Bytes@Override public byte[] toBinary(Object obj) {//序列化方法}// "fromBinary" deserializes the given array,// using the type hint (if any, see "includeManifest" above)@Override public Object fromBinaryJava(byte[] bytes,Class<?> clazz) {//反序列化方法}}

远程Actor的路由

由于Akka-remoting是基于点对点的.因此,并不能很好的使用网络提供的负载均衡等功能.其实,要解决这个问题,我们可以使用Akka的路由功能.即在配置远程Actor的时候,增加router的参数.

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akka.actor.deployment {/parent/remotePool {router = round-robin-poolnr-of-instances = 10target.nodes = ["akka.tcp://app@10.0.0.2:2552", "akka://app@10.0.0.3:2552"]}
}

那么当向这个/parent/remotePool发送消息的时候,会轮询的把消息分发到不同的远程服务上,从而实现了高可用和负载均衡.

远程事件

同Akka的本地Actor的生命周期Hook相同,Akka为远程的Actor申明了很多的事件.我们可以监听这些远程调用中发生的事件,也可以订阅这些事件.只需要在ActorSystem.eventStream中为下面的事件增加注册监听器即可. 需要注意的是如果要订阅任意的远程事件,是订阅RemotingLifecycleEvent,如果只订阅涉及链接的生命周期,需要订阅akka.remote.AssociationEvent.

  • DisassociatedEvent : 链接结束事件,这个事件包含了链接方向以及参与方的地址.
  • AssociatedEvent : 链接成功建立事件,这个事件包含了链接方向以及参与方的地址.
  • AssociationErrorEvent : 链接相关错误事件,这个事件包含了链接方向以及参与方的地址以及错误的原因.
  • RemotingListenEvent : 远程子系统准备好接受链接时的事件,这个事件包含了链接方向以及参与方的地址.
  • RemotingShutdownEvent : 远程子系统被关闭的事件
  • RemotingErrorEvent : 远程相关的所有错误

Demo

这里举一个稍微复杂点的例子——单词计数.这个是Hadoop的入门的例子,我们使用Akka配合远程调用来实现一次. 功能是这样的,服务端提供了map/reduce方式的单词数量的计算功能,而服务端提供了文本内容的.
它大概的运行流程是这样的:

  1. 首先客户端的FileReadActor从文本文件中读取文件.
  2. 然后通过ClientActor发送给远端的服务端.
  3. 服务端通过WCMapReduceActor接受客户端发送的消息,并发消息放入优先级MailBox
  4. WCMapReduceActor把接收到的文本内容分发给MapActor做Map计算
  5. Map计算把结果都发送给ReduceActor,做汇总reduce计算.
  6. 最后AggregateActor把计算的结果显示出来.

我们先来看客户端:

FileReadActor.java

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public class FileReadActor extends UntypedActor {@Overridepublic void onReceive(Object message) throws Exception {if (message instanceof String) {/*如果消息是String类型的*/String fileName = (String) message;try {BufferedReader reader = new BufferedReader(new InputStreamReader(Thread.currentThread().getContextClassLoader().getResource(fileName).openStream()));String line;while ((line = reader.readLine()) != null) {/*遍历,一行一个消息反馈给消息发送方*/getSender().tell(line,null);}System.out.println("All lines send !");/*发送一个结束标识*/getSender().tell(String.valueOf("EOF"),null);} catch (IOException x) {System.err.format("IOException: %s%n", x);}} else {throw new IllegalArgumentException("Unknown message [" + message + "]");}}
}

通过InputStreamReader把文本内容一行一个的发送给Sender.

ClientActor.java

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public class ClientActor extends UntypedActor {private ActorRef remoteServer = null;private long start;/*** @param inRemoteServer*/public ClientActor(ActorRef inRemoteServer) {remoteServer = inRemoteServer;}@Overridepublic void onReceive(Object message) throws Exception {/*如果接收到的任务是String的,那么就直接发送给remoteServer这个Actor*/if (message instanceof String) {String msg = (String) message;if (message.equals("EOF")){//这个的Sender设置为自己是为了接收聚合完成的消息remoteServer.tell(msg, getSelf());}else{remoteServer.tell(msg, null);}}else if (message instanceof Boolean) {System.out.println("聚合完成");//聚合完成后发送显示结果的消息remoteServer.tell("DISPLAY_LIST",null);//执行完毕,关机getContext().stop(self());}}@Overridepublic void preStart() {/*记录开始时间*/start = System.currentTimeMillis();}@Overridepublic void postStop() {/*计算用时*/// tell the world that the calculation is completelong timeSpent = (System.currentTimeMillis() - start);System.out.println(String.format("\n\tClientActor estimate: \t\t\n\tCalculation time: \t%s MS",timeSpent));}
}

这个Actor重载了preStart()和postStop()方法用以记录性能.

ClientMain.java

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public class ClientMain {public static void main(String[] args) throws Exception {//文件名final String fileName = "Othello.txt";/*根据配置,找到System*/ActorSystem system = ActorSystem.create("ClientApplication", ConfigFactory.load("client").getConfig("WCMapReduceClientApp"));/*实例化远程Actor*/final ActorRef remoteActor = system.actorFor("akka.tcp://WCMapReduceApp@127.0.0.1:2552/user/WCMapReduceActor");/*实例化Actor的管道*/final ActorRef fileReadActor = system.actorOf(Props.create(FileReadActor.class));/*实例化Client的Actor管道*/final ActorRef clientActor = system.actorOf(Props.create(ClientActor.class,remoteActor));/*发送文件名给fileReadActor.设置sender或者说回调的Actor为clientActor*/fileReadActor.tell(fileName,clientActor);}
}

这个类里面就使用了远程Actor的查找方法,通过system.actorFor("akka.tcp://WCMapReduceApp@127.0.0.1:2552/user/WCMapReduceActor");获取到了远程的Actor

client.conf

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WCMapReduceClientApp {include "common"akka {actor {provider = "akka.remote.RemoteActorRefProvider"}remote {enabled-transports = ["akka.remote.netty.tcp"]netty.tcp {hostname = "127.0.0.1"port = 2553   //0表示自动选择一个可用的}}}
}

接下来我们来看看服务器端:

MyPriorityMailBox.java

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public class MyPriorityMailBox extends UnboundedPriorityMailbox {/*** 创建一个自定义优先级的无边界的邮箱. 用来规定命令的优先级. 这个就保证了DISPLAY_LIST 这个事件是最后再来处理.*/public MyPriorityMailBox(ActorSystem.Settings settings, Config config) {// Creating a new PriorityGenerator,super(new PriorityGenerator() {@Overridepublic int gen(Object message) {if (message.equals("DISPLAY_LIST"))return 2; // 'DisplayList messages should be treated// last if possibleelse if (message.equals(PoisonPill.getInstance()))return 3; // PoisonPill when no other leftelsereturn 0; // By default they go with high priority}});}}

通过这个类,自定了一个无边界的优先级邮箱,这样做的目的是保证DISPLAY_LIST命令最后的响应.否则会出现文本内容还没有发送完成的情况下,就进行了结果的统计显示了.要使用这个自定义的优先级邮箱,需要在配置文件中进行配置:
server.conf

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WCMapReduceApp {include "common"akka {actor {provider = "akka.remote.RemoteActorRefProvider"}remote {enabled-transports = ["akka.remote.netty.tcp"]netty.tcp {hostname = "127.0.0.1"port = 2552}}}priorityMailBox-dispatcher {mailbox-type = "cn.sunxiang0918.akka.demo2.server.MyPriorityMailBox"}
}

MapActor.java

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public class MapActor extends UntypedActor {//停用词String[] STOP_WORDS = {"a", "about", "above", "above", "across", "after","afterwards", "again", "against", "all", "almost", "alone","along", "already", "also", "although", "always", "am", "among","amongst", "amoungst", "amount", "an", "and", "another", "any","anyhow", "anyone", "anything", "anyway", "anywhere", "are","around", "as", "at", "back", "be", "became", "because", "become","becomes", "becoming", "been", "before", "beforehand", "behind","being", "below", "beside", "besides", "between", "beyond", "bill","both", "bottom", "but", "by", "call", "can", "cannot", "cant","co", "con", "could", "couldnt", "cry", "de", "describe", "detail","do", "done", "down", "due", "during", "each", "eg", "eight","either", "eleven", "else", "elsewhere", "empty", "enough", "etc","even", "ever", "every", "everyone", "everything", "everywhere","except", "few", "fifteen", "fify", "fill", "find", "fire","first", "five", "for", "former", "formerly", "forty", "found","four", "from", "front", "full", "further", "get", "give", "go","had", "has", "hasnt", "have", "he", "hence", "her", "here","hereafter", "hereby", "herein", "hereupon", "hers", "herself","him", "himself", "his", "how", "however", "hundred", "ie", "if","in", "inc", "indeed", "interest", "into", "is", "it", "its","itself", "keep", "last", "latter", "latterly", "least", "less","ltd", "made", "many", "may", "me", "meanwhile", "might", "mill","mine", "more", "moreover", "most", "mostly", "move", "much","must", "my", "myself", "name", "namely", "neither", "never","nevertheless", "next", "nine", "no", "nobody", "none", "noone","nor", "not", "nothing", "now", "nowhere", "of", "off", "often","on", "once", "one", "only", "onto", "or", "other", "others","otherwise", "our", "ours", "ourselves", "out", "over", "own","part", "per", "perhaps", "please", "put", "rather", "re", "same","see", "seem", "seemed", "seeming", "seems", "serious", "several","she", "should", "show", "side", "since", "sincere", "six","sixty", "so", "some", "somehow", "someone", "something","sometime", "sometimes", "somewhere", "still", "such", "system","take", "ten", "than", "that", "the", "their", "them","themselves", "then", "thence", "there", "thereafter", "thereby","therefore", "therein", "thereupon", "these", "they", "thickv","thin", "third", "this", "those", "though", "three", "through","throughout", "thru", "thus", "to", "together", "too", "top","toward", "towards", "twelve", "twenty", "two", "un", "under","until", "up", "upon", "us", "very", "via", "was", "we", "well","were", "what", "whatever", "when", "whence", "whenever", "where","whereafter", "whereas", "whereby", "wherein", "whereupon","wherever", "whether", "which", "while", "whither", "who","whoever", "whole", "whom", "whose", "why", "will", "with","within", "without", "would", "yet", "you", "your", "yours","yourself", "yourselves", "the"};List<String> STOP_WORDS_LIST = Arrays.asList(STOP_WORDS);/*reduce聚合的Actor*/private ActorRef actor = null;public MapActor(ActorRef inReduceActor) {actor = inReduceActor;}@Overridepublic void preStart() throws Exception {System.out.println("启动MapActor:"+Thread.currentThread().getName());}/*** 用于分词 计算单词的数量的* @param line* @return*/private List<Result> evaluateExpression(String line) {List<Result> list = new ArrayList<>();/*字符串分词器*/StringTokenizer parser = new StringTokenizer(line);while (parser.hasMoreTokens()) {/*如果是,那么就判断是否是字母.然后把结果记录下来*/String word = parser.nextToken().toLowerCase();if (isAlpha(word) && !STOP_WORDS_LIST.contains(word)) {list.add(new Result(word, 1));}}return list;}/*** 判断是否是字母* @param s* @return*/private boolean isAlpha(String s) {s = s.toUpperCase();for (int i = 0; i < s.length(); i++) {int c = (int) s.charAt(i);if (c < 65 || c > 90)return false;}return true;}@Overridepublic void onReceive(Object message) throws Exception {if (message instanceof String) {String work = (String) message;if (work.equals("EOF")){/*表示已经结束了*/actor.tell(true,null);return;}// 计算这一行的单词情况List<Result> list = evaluateExpression(work);// 把这一行的单词情况发送给汇总的ReduceActoractor.tell(list, null);} elsethrow new IllegalArgumentException("Unknown message [" + message + "]");}
}

当这个actor接收到消息后,判断是否是结束标识,如果是就发送消息给reduceActor表示已经结束了.否则就计算这一行中的单词的个数,并把这个个数发送给reduceActor.

ReduceActor.java

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public class ReduceActor extends UntypedActor {/*管道Actor*/private ActorRef actor = null;public ReduceActor(ActorRef inAggregateActor) {actor = inAggregateActor;}@Overridepublic void preStart() throws Exception {System.out.println("启动ReduceActor:"+Thread.currentThread().getName());}@Overridepublic void onReceive(Object message) throws Exception {if (message instanceof List) {/*强制转换结果*/List<Result> work = (List<Result>) message;// 第一次汇总单词表结果.NavigableMap<String, Integer> reducedList = reduce(work);// 把这次汇总的结果发送给最终的结果聚合Actoractor.tell(reducedList, null);}else if (message instanceof Boolean) {//表示已经计算结束了// 把这次汇总的结果发送给最终的结果聚合Actoractor.tell(message, null);} elsethrow new IllegalArgumentException("Unknown message [" + message + "]");}/*** 聚合计算本次结果中各个单词的出现次数* @param list* @return*/private NavigableMap<String, Integer> reduce(List<Result> list) {NavigableMap<String, Integer> reducedMap = new ConcurrentSkipListMap<>();for (Result result : list) {/*遍历结果,如果在这个小的结果中已经存在相同的单词了,那么数量+1,否则新建*/if (reducedMap.containsKey(result.getWord())) {Integer value = reducedMap.get(result.getWord());value++;reducedMap.put(result.getWord(), value);} else {reducedMap.put(result.getWord(), 1);}}return reducedMap;}
}

这个Actor接收到消息后,判断是什么消息,如果是结果消息,那么就对结果进行整理,得出某个单词出现的次数,否则就是结束标记,告诉管道Actor统计已经结束.

AggregateActor.java

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public class AggregateActor extends UntypedActor {/*最终的结果*/private Map<String, Integer> finalReducedMap = new HashMap<>();@Overridepublic void preStart() throws Exception {System.out.println("启动AggregateActor:"+Thread.currentThread().getName());}@Overridepublic void onReceive(Object message) throws Exception {/*如果是Map,那么就进行reduce操作*/if (message instanceof Map) {Map<String, Integer> reducedList = (Map<String, Integer>) message;aggregateInMemoryReduce(reducedList);} else if (message instanceof String) {/*如果是String,那么就是打印结果*/if (((String) message).compareTo("DISPLAY_LIST") == 0) {//getSender().tell(finalReducedMap.toString());System.out.println(finalReducedMap.toString());}}else if (message instanceof Boolean) {/*向客户端发送已经reduce完成的信息*/getSender().tell(true,null);}}private void aggregateInMemoryReduce(Map<String, Integer> reducedList) {for (String key : reducedList.keySet()) {/*最终的数量的累加*/if (finalReducedMap.containsKey(key)) {Integer count = reducedList.get(key) + finalReducedMap.get(key);finalReducedMap.put(key, count);} else {finalReducedMap.put(key, reducedList.get(key));}}}}

这个actor接收到消息后,判断消息的类型,如果是DISPLAY_LIST标识,那么就打印结果.如果是Boolean就表示统计完成了,那么就发送消息给客户端.如果是Map,那么这个就是某一个map/reduce的结果,那么就把这个结果聚合到最终的结果中去.

WCMapReduceActor.java

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public class WCMapReduceActor extends UntypedActor{private ActorRef mapRouter;private ActorRef aggregateActor;@Overridepublic void preStart() throws Exception {System.out.println("启动WCMapReduceActor:"+Thread.currentThread().getName());}@Overridepublic void onReceive(Object message) throws Exception {if (message instanceof String) {/*如果接收到的是显示结果的请求,那么就调用reduce的Actor*/if (((String) message).compareTo("DISPLAY_LIST") == 0) {System.out.println("Got Display Message");aggregateActor.tell(message, getSender());}if (message.equals("EOF")){//表示发送完毕aggregateActor.tell(true, getSender());}else {/*否则给map的Actor进行计算*/mapRouter.tell(message,null);}}}public WCMapReduceActor(ActorRef inAggregateActor, ActorRef inMapRouter) {mapRouter = inMapRouter;aggregateActor = inAggregateActor;}
}

消息中转和统管的Actor,它统管了其他的几个Actor,是消息的入口.

WCMapReduceServer.java

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public class WCMapReduceServer{private ActorRef mapRouter;private ActorRef reduceRouter;private ActorRef aggregateActor;private ActorRef wcMapReduceActor;public WCMapReduceServer(int no_of_reduce_workers, int no_of_map_workers) {/*创建了Actor系统*/ActorSystem system = ActorSystem.create("WCMapReduceApp", ConfigFactory.load("application").getConfig("WCMapReduceApp"));// 创建聚合ActoraggregateActor = system.actorOf(Props.create(AggregateActor.class));// 创建多个聚合的ActorreduceRouter = system.actorOf(Props.create(ReduceActor.class,aggregateActor).withRouter(new RoundRobinPool(no_of_reduce_workers)));// 创建多个Map的ActormapRouter = system.actorOf(Props.create(MapActor.class,reduceRouter).withRouter(new RoundRobinPool(no_of_map_workers)));// create the overall WCMapReduce Actor that acts as the remote actor// for clientsProps props = Props.create(WCMapReduceActor.class,aggregateActor,mapRouter).withDispatcher("priorityMailBox-dispatcher");wcMapReduceActor = system.actorOf(props, "WCMapReduceActor");}/*** @param args*/public static void main(String[] args) {new WCMapReduceServer(50, 50);}}

服务端的入口程序,定义了一个50个actor的单词统计服务.并使用轮询模式来分发客服端接收到的统计任务.

以上就是整个DEMO的所有的代码.当执行这个程序后,会在控制台打印:

服务器端:

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Got Display Message
{minerals=5, half=35, exceeding=5, spoke=25, mince=5, hall=5, disproportion=5, youth=20, guards=5, wreck=5, begins=10, approved=20, imperfect=5, drunk=15, framed=10, pick=5,......}

客户端:

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All lines send !
聚合完成ClientActor estimate:       Calculation time:   467 MS

到此,我们就实现了通过AKKA-remoting 来进行map/reduce的简单计算.

Cluster调用

原理

Akka除了remoting远程调用外,还提供了支持去中心化的基于P2P的集群服务,并且不会出现单点故障.Akka的集群是基于Gossip协议实现的,支持服务自动失效检测,能够自动发现出现问题而离开集群的成员节点,通过事件驱动的方式,将状态传播到整个集群的其他成员节点中去.Gossip协议是点对点通信协议的一种,它受社交网络中的流言传播的特点所启发,解决了在超大规模集群下其他方式无法解决的单点等问题.

一个Akka集群是由一组成员节点组成的,每一个成员节点都是通过hostname:port:uid来唯一标识,并且每一个成员节点间是完全解耦合的.

节点状态

Akka集群内部为集群中的成员定义了6种状态,并提供了状态转换矩阵,这6种状态分别是:

  • Joining : 正在加入集群的状态
  • Up : 正常提供服务的状态
  • Leaving : 正在离开集群的状态
  • Down : 节点服务下线的状态
  • Exiting : 节点离开状态
  • Removed : 节点被删除状态

在Akka集群中的每一个成员节点,都只可能处在这6种状态中的一种中.当节点状态发生变化的时候,会发出节点状态事件.需要注意的是,除了Down和Removed状态外,其他状态是有可能随时变为Down状态的,即节点故障而无法提供服务.处于Down状态的节点如果想要再次加入Akka集群中,需要重新启动,并加入Joining状态,然后才能进行后续状态的变化,加入集群.

故障监控

在Akka集群中,集群的每一个成员节点,都会被其他另外一组节点(默认是5个)所监控,这一组节点会通过心跳来检测被监控的节点是否处于Unreachable状态,如果不可达则这一组节点会将被监控的节点的Unreachable状态向集群中的其他所有节点传播,最终使集群中的每个成员节点都知道被监控的节点已经故障.

Akka集群中任一一个成员节点都有可能成为集群的Leader,这是基于Gossip协议收敛过程得到的确定性结果,并不是通过选举产生,从而避免了单点故障.在Akka集群中,Leader只是一种角色,在各轮Gossip收敛过程中Leader可能是不断变化的.Leader的职责就是让成员节点加入和离开集群.一个成员节点最开始是处于Joining状态,一旦所有其他节点都看到了新加入的该节点,则Leader会设置这个节点的状态为up.如果一个节点安全离开Akka集群,那么这个节点的状态会变为Leaving状态,当Leader看到该节点为Leaving状态,会将其状态修改为Exiting,然后通知所有其他节点,当所有节点看到该节点状态为exiting后,Leader将该节点移除,状态修改为removed状态.

配置

要在项目中使用Akka集群,首先需要的就是在项目的Maven中引入akka-Cluster:

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<dependency><groupId>com.typesafe.akka</groupId><artifactId>akka-cluster_2.11</artifactId><version>2.4.1</version></dependency>

然后在application.conf中配置必要的参数:

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akka {actor {#表示Actor的提供者是Cluster集群provider = "akka.cluster.ClusterActorRefProvider"}#同akka-remoting,表示远程协议是什么remote {log-remote-lifecycle-events = offnetty.tcp {hostname = "127.0.0.1"port = 0}}#集群特有的配置cluster {#种子节点seed-nodes = ["akka.tcp://ClusterSystem@127.0.0.1:2551","akka.tcp://ClusterSystem@127.0.0.1:2552"]#自动down掉不可达的成员节点auto-down-unreachable-after = 10s}
}

上面的配置需要特别注意的就是种子节点,种子节点是akka集群中的一种特殊的节点角色.
种子节点最主要的用处是提供Cluster的初始化和加入点,同时也为其他节点作为中间联系人.要启动Akka-Cluster就必须配置一些列的种子节点.这些种子节点就是一开始就能够预料到的节点,有节点加入的时候,会等种子节点的返回确认才算是加入成功.

更多的集群配置请参见:config-akka-cluster

集群事件

正如上文所说,当节点发生变化的时候,Leader会发送状态的事件给集群中的所有成员节点.因此,接收和处理这些事件也是非常重要的.

  • MemberUp : 成员节点上线
  • MemberExited : 成员节点下线
  • MemberRemoved : 成员节点被剔除
  • UnreachableMember : 成员节点无法到达
  • ReachableMember : 成员节点可到达
  • LeaderChanged : Leader变化
  • RoleLeaderChanged : 角色Leader变化
  • ClusterShuttingDown : 集群关闭
  • ClusterMetricsChanged :

要说明这些节点的变化可以参考官方给出的最最简单的Akka集群的Demo,它不仅列出了一个最简单的Akka的集群要如何构建,也说明了这几个事件状态的变化.

集群事件Demo

在官方的文档中,编写了一个最简单的Akka-Cluster的例子,这个例子就是启动三个Akka的节点,并且监听了节点的所有事件,接收到事件后,打印出来.

demo6.conf

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akka {actor {provider = "akka.cluster.ClusterActorRefProvider"}remote {log-remote-lifecycle-events = offnetty.tcp {hostname = "127.0.0.1"port = 0}}cluster {seed-nodes = [#先启动了两个种子节点,这样当有新的节点加入时,会把事件通知给这两个节点."akka.tcp://ClusterSystem@127.0.0.1:2551","akka.tcp://ClusterSystem@127.0.0.1:2552"]auto-down-unreachable-after = 10s}
}

SimpleClusterListener.java

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public class SimpleClusterListener extends UntypedActor {/*记录日志*/LoggingAdapter log = Logging.getLogger(getContext().system(), this);/*创建,获取集群*/Cluster cluster = Cluster.get(getContext().system());//订阅集群中的事件@Overridepublic void preStart() {//region subscribecluster.subscribe(getSelf(), ClusterEvent.initialStateAsEvents(),MemberEvent.class, UnreachableMember.class);//endregion}//re-subscribe when restart@Overridepublic void postStop() {cluster.unsubscribe(getSelf());}@Overridepublic void onReceive(Object message) {/*当接收到不同的事件的时候,打印出不同的信息*/if (message instanceof MemberUp) {MemberUp mUp = (MemberUp) message;log.info("Member is Up: {}", mUp.member());} else if (message instanceof UnreachableMember) {UnreachableMember mUnreachable = (UnreachableMember) message;log.info("Member detected as unreachable: {}", mUnreachable.member());} else if (message instanceof MemberRemoved) {MemberRemoved mRemoved = (MemberRemoved) message;log.info("Member is Removed: {}", mRemoved.member());} else if (message instanceof MemberEvent) {// ignorelog.info("Member Event: {}", ((MemberEvent) message).member());} else {unhandled(message);}}
}

SimpleClusterApp.java

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public class SimpleClusterApp {public static void main(String[] args) {if (args.length == 0)/*启动三个节点*/startup(new String[]{"2551", "2552", "0"});elsestartup(args);}public static void startup(String[] ports) {for (String port : ports) {// 重写配置中的远程端口Config config = ConfigFactory.parseString("akka.remote.netty.tcp.port=" + port).withFallback(ConfigFactory.load("demo6"));// 创建ActorSystem,名称需要和conf配置文件中的相同ActorSystem system = ActorSystem.create("ClusterSystem", config);// 创建集群中的Actor,并监听事件system.actorOf(Props.create(SimpleClusterListener.class),"clusterListener");}}
}

这个简单的例子启动了三个节点,并创建了一个Actor来监听集群中的各种事件,执行这个Demo,会在控制台打印:

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[INFO] [02/11/2016 22:14:42.051] [main] [akka.remote.Remoting] Starting remoting
[INFO] [02/11/2016 22:14:42.347] [main] [akka.remote.Remoting] Remoting started; listening on addresses :[akka.tcp://ClusterSystem@127.0.0.1:2551]
[INFO] [02/11/2016 22:14:42.356] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Starting up...
[INFO] [02/11/2016 22:14:42.425] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Registered cluster JMX MBean [akka:type=Cluster]
[INFO] [02/11/2016 22:14:42.425] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Started up successfully
[INFO] [02/11/2016 22:14:42.429] [ClusterSystem-akka.actor.default-dispatcher-14] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Metrics will be retreived from MBeans, and may be incorrect on some platforms. To increase metric accuracy add the 'sigar.jar' to the classpath and the appropriate platform-specific native libary to 'java.library.path'. Reason: java.lang.ClassNotFoundException: org.hyperic.sigar.Sigar
[INFO] [02/11/2016 22:14:42.432] [ClusterSystem-akka.actor.default-dispatcher-14] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Metrics collection has started successfully
[INFO] [02/11/2016 22:14:42.454] [main] [akka.remote.Remoting] Starting remoting
[INFO] [02/11/2016 22:14:42.460] [main] [akka.remote.Remoting] Remoting started; listening on addresses :[akka.tcp://ClusterSystem@127.0.0.1:2552]
[INFO] [02/11/2016 22:14:42.461] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2552] - Starting up...
[INFO] [02/11/2016 22:14:42.464] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2552] - Started up successfully
[INFO] [02/11/2016 22:14:42.464] [ClusterSystem-akka.actor.default-dispatcher-4] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2552] - Metrics will be retreived from MBeans, and may be incorrect on some platforms. To increase metric accuracy add the 'sigar.jar' to the classpath and the appropriate platform-specific native libary to 'java.library.path'. Reason: java.lang.ClassNotFoundException: org.hyperic.sigar.Sigar
[INFO] [02/11/2016 22:14:42.464] [ClusterSystem-akka.actor.default-dispatcher-4] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2552] - Metrics collection has started successfully
[INFO] [02/11/2016 22:14:42.486] [main] [akka.remote.Remoting] Starting remoting
[INFO] [02/11/2016 22:14:42.491] [main] [akka.remote.Remoting] Remoting started; listening on addresses :[akka.tcp://ClusterSystem@127.0.0.1:57726]
[INFO] [02/11/2016 22:14:42.492] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:57726] - Starting up...
[INFO] [02/11/2016 22:14:42.494] [main] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:57726] - Started up successfully
[INFO] [02/11/2016 22:14:42.494] [ClusterSystem-akka.actor.default-dispatcher-3] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:57726] - Metrics will be retreived from MBeans, and may be incorrect on some platforms. To increase metric accuracy add the 'sigar.jar' to the classpath and the appropriate platform-specific native libary to 'java.library.path'. Reason: java.lang.ClassNotFoundException: org.hyperic.sigar.Sigar
[INFO] [02/11/2016 22:14:42.494] [ClusterSystem-akka.actor.default-dispatcher-3] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:57726] - Metrics collection has started successfully
[INFO] [02/11/2016 22:14:42.645] [ClusterSystem-akka.actor.default-dispatcher-4] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Node [akka.tcp://ClusterSystem@127.0.0.1:2551] is JOINING, roles []
[INFO] [02/11/2016 22:14:42.665] [ClusterSystem-akka.actor.default-dispatcher-4] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Leader is moving node [akka.tcp://ClusterSystem@127.0.0.1:2551] to [Up]
[INFO] [02/11/2016 22:14:42.672] [ClusterSystem-akka.actor.default-dispatcher-16] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2551, status = Up)
[INFO] [02/11/2016 22:14:47.662] [ClusterSystem-akka.actor.default-dispatcher-3] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Node [akka.tcp://ClusterSystem@127.0.0.1:2552] is JOINING, roles []
[INFO] [02/11/2016 22:14:47.662] [ClusterSystem-akka.actor.default-dispatcher-3] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Node [akka.tcp://ClusterSystem@127.0.0.1:57726] is JOINING, roles []
[INFO] [02/11/2016 22:14:47.665] [ClusterSystem-akka.actor.default-dispatcher-18] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Joining)
[INFO] [02/11/2016 22:14:47.666] [ClusterSystem-akka.actor.default-dispatcher-4] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Joining)
[INFO] [02/11/2016 22:14:47.731] [ClusterSystem-akka.actor.default-dispatcher-21] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:57726] - Welcome from [akka.tcp://ClusterSystem@127.0.0.1:2551]
[INFO] [02/11/2016 22:14:47.731] [ClusterSystem-akka.actor.default-dispatcher-17] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2552] - Welcome from [akka.tcp://ClusterSystem@127.0.0.1:2551]
[INFO] [02/11/2016 22:14:47.731] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2551, status = Up)
[INFO] [02/11/2016 22:14:47.732] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Joining)
[INFO] [02/11/2016 22:14:47.732] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Joining)
[INFO] [02/11/2016 22:14:47.733] [ClusterSystem-akka.actor.default-dispatcher-5] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2551, status = Up)
[INFO] [02/11/2016 22:14:47.733] [ClusterSystem-akka.actor.default-dispatcher-5] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Joining)
[INFO] [02/11/2016 22:14:47.745] [ClusterSystem-akka.actor.default-dispatcher-18] [akka://ClusterSystem/user/clusterListener] Member Event: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Joining)
[INFO] [02/11/2016 22:14:48.464] [ClusterSystem-akka.actor.default-dispatcher-15] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Leader is moving node [akka.tcp://ClusterSystem@127.0.0.1:2552] to [Up]
[INFO] [02/11/2016 22:14:48.464] [ClusterSystem-akka.actor.default-dispatcher-15] [akka.cluster.Cluster(akka://ClusterSystem)] Cluster Node [akka.tcp://ClusterSystem@127.0.0.1:2551] - Leader is moving node [akka.tcp://ClusterSystem@127.0.0.1:57726] to [Up]
[INFO] [02/11/2016 22:14:48.465] [ClusterSystem-akka.actor.default-dispatcher-15] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Up)
[INFO] [02/11/2016 22:14:48.465] [ClusterSystem-akka.actor.default-dispatcher-15] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Up)
[INFO] [02/11/2016 22:14:49.463] [ClusterSystem-akka.actor.default-dispatcher-16] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Up)
[INFO] [02/11/2016 22:14:49.463] [ClusterSystem-akka.actor.default-dispatcher-16] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Up)
[INFO] [02/11/2016 22:14:49.481] [ClusterSystem-akka.actor.default-dispatcher-21] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:2552, status = Up)
[INFO] [02/11/2016 22:14:49.481] [ClusterSystem-akka.actor.default-dispatcher-21] [akka://ClusterSystem/user/clusterListener] Member is Up: Member(address = akka.tcp://ClusterSystem@127.0.0.1:57726, status = Up)

从日志中就可以看出各种状态的变化

集群实践

阶乘服务Demo

这里通过一个简单的阶乘计算,来展示Akka-Cluster的使用.
这个Demo分为了前台和后台两个部分,前台只用来输入阶乘的大小以及打印计算的结果,后台节点负责真正的阶乘的计算.

demo7.conf

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include "demo6"# //#min-nr-of-members
akka.cluster.min-nr-of-members = 3
# //#min-nr-of-members# //#role-min-nr-of-members
akka.cluster.role {frontend.min-nr-of-members = 1backend.min-nr-of-members = 2
}
# //#role-min-nr-of-members# //#adaptive-router
akka.actor.deployment {/factorialFrontend/factorialBackendRouter = {router = adaptive-group# metrics-selector = heap# metrics-selector = load# metrics-selector = cpumetrics-selector = mixnr-of-instances = 100routees.paths = ["/user/factorialBackend"]cluster {enabled = onuse-role = backendallow-local-routees = off}}
}
# //#adaptive-router

FactorialResult.java

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public class FactorialResult implements Serializable {public final int n;public final BigInteger factorial;FactorialResult(int n, BigInteger factorial) {this.n = n;this.factorial = factorial;}@Overridepublic String toString() {return "FactorialResult{" +"n=" + n +", factorial=" + factorial +'}';}
}

FactorialBackend.java

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public class FactorialBackend extends UntypedActor {@Overridepublic void onReceive(Object message) {//如果是数字if (message instanceof Integer) {final Integer n = (Integer) message;/*使用akka的future功能,异步的计算阶乘*/Future<BigInteger> f = future(() -> factorial(n), getContext().dispatcher());/*合并计算的结果*/Future<FactorialResult> result = f.map(new Mapper<BigInteger, FactorialResult>() {public FactorialResult apply(BigInteger factorial) {return new FactorialResult(n, factorial);}}, getContext().dispatcher());/*把结果返回Sender*/pipe(result, getContext().dispatcher()).to(getSender());} else {unhandled(message);}}/*** 进行阶乘计算* @param n* @return*/BigInteger factorial(int n) {BigInteger acc = BigInteger.ONE;for (int i = 1; i <= n; ++i) {acc = acc.multiply(BigInteger.valueOf(i));}return acc;}
}

FactorialBackendMain.java

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public class FactorialBackendMain {public static void main(String[] args) {// 重写配置文件中的集群角色和端口final String port = args.length > 0 ? args[0] : "0";final Config config = ConfigFactory.parseString("akka.remote.netty.tcp.port=" + port).withFallback(ConfigFactory.parseString("akka.cluster.roles = [backend]")).withFallback(ConfigFactory.load("demo7"));ActorSystem system = ActorSystem.create("ClusterSystem", config);system.actorOf(Props.create(FactorialBackend.class), "factorialBackend");}}

FactorialFrontend.java

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public class FactorialFrontend extends UntypedActor {final int upToN;        //计算到多少final boolean repeat;       //是否重复计算LoggingAdapter log = Logging.getLogger(getContext().system(), this);/*获取到Backend的Router*/ActorRef backend = getContext().actorOf(FromConfig.getInstance().props(),"factorialBackendRouter");public FactorialFrontend(int upToN, boolean repeat) {this.upToN = upToN;this.repeat = repeat;}@Overridepublic void preStart() {//因为是在Start前就发送消息,所以必定超时.sendJobs();getContext().setReceiveTimeout(Duration.create(10, TimeUnit.SECONDS));}@Overridepublic void onReceive(Object message) {if (message instanceof FactorialResult) {FactorialResult result = (FactorialResult) message;if (result.n == upToN) {System.out.println("计算的结果:" + result);if (repeat)sendJobs();elsegetContext().stop(getSelf());}} else if (message instanceof ReceiveTimeout) {log.info("Timeout");sendJobs();} else {unhandled(message);}}void sendJobs() {log.info("Starting batch of factorials up to [{}]", upToN);for (int n = 1; n <= upToN; n++) {backend.tell(n, getSelf());}}}

FactorialFrontendMain.java

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public class FactorialFrontendMain {public static void main(String[] args) {final int upToN = 10;final Config config = ConfigFactory.parseString("akka.cluster.roles = [frontend]").withFallback(ConfigFactory.load("demo7"));final ActorSystem system = ActorSystem.create("ClusterSystem", config);system.log().info("Factorials will start when 2 backend members in the cluster.");//#registerOnUpCluster.get(system).registerOnMemberUp((Runnable) () -> system.actorOf(Props.create(FactorialFrontend.class, upToN, false),"factorialFrontend"));//#registerOnUp}}

FactorialApp.java

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public class FactorialApp {public static void main(String[] args) {// starting 3 backend nodes and 1 frontend nodeFactorialBackendMain.main(new String[] { "2551" });FactorialBackendMain.main(new String[] { "2552" });FactorialBackendMain.main(new String[0]);FactorialFrontendMain.main(new String[0]);}
}

通过执行FactorialApp.java可以启动整个演示. 它创建了3个后台节点和一个前台节点.
前台节点启动后,创建FactorialFrontendActor,这个Actor负责发送计算数给后台节点,以及接受计算的结果并打印出来.后台节点的ActorFactorialBackend负责计算阶乘,并返回结果.

执行这个DEMO后,控制台会打印:

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...
...
[INFO] [02/11/2016 22:45:04.580] [ClusterSystem-akka.actor.default-dispatcher-17] [akka://ClusterSystem/user/factorialFrontend] Starting batch of factorials up to [10]
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [1] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [2] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [3] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [4] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [5] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.583] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [6] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.584] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [7] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.584] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [8] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.584] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [9] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:04.584] [ClusterSystem-akka.actor.default-dispatcher-2] [akka://ClusterSystem/deadLetters] Message [java.lang.Integer] from Actor[akka://ClusterSystem/user/factorialFrontend#1317512767] to Actor[akka://ClusterSystem/deadLetters] was not delivered. [10] dead letters encountered, no more dead letters will be logged. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [02/11/2016 22:45:14.600] [ClusterSystem-akka.actor.default-dispatcher-7] [akka://ClusterSystem/user/factorialFrontend] Timeout
[INFO] [02/11/2016 22:45:14.601] [ClusterSystem-akka.actor.default-dispatcher-7] [akka://ClusterSystem/user/factorialFrontend] Starting batch of factorials up to [10]
[WARN] [02/11/2016 22:45:14.607] [ClusterSystem-akka.remote.default-remote-dispatcher-8] [akka.serialization.Serialization(akka://ClusterSystem)] Using the default Java serializer for class [java.lang.Integer] which is not recommended because of performance implications. Use another serializer or disable this warning using the setting 'akka.actor.warn-about-java-serializer-usage'
[WARN] [02/11/2016 22:45:14.622] [ClusterSystem-akka.remote.default-remote-dispatcher-5] [akka.serialization.Serialization(akka://ClusterSystem)] Using the default Java serializer for class [cn.sunxiang0918.akka.demo7.FactorialResult] which is not recommended because of performance implications. Use another serializer or disable this warning using the setting 'akka.actor.warn-about-java-serializer-usage'
[WARN] [02/11/2016 22:45:14.622] [ClusterSystem-akka.remote.default-remote-dispatcher-6] [akka.serialization.Serialization(akka://ClusterSystem)] Using the default Java serializer for class [cn.sunxiang0918.akka.demo7.FactorialResult] which is not recommended because of performance implications. Use another serializer or disable this warning using the setting 'akka.actor.warn-about-java-serializer-usage'
[WARN] [02/11/2016 22:45:14.622] [ClusterSystem-akka.remote.default-remote-dispatcher-5] [akka.serialization.Serialization(akka://ClusterSystem)] Using the default Java serializer for class [cn.sunxiang0918.akka.demo7.FactorialResult] which is not recommended because of performance implications. Use another serializer or disable this warning using the setting 'akka.actor.warn-about-java-serializer-usage'
计算的结果:FactorialResult{n=10, factorial=3628800}

转载于:https://my.oschina.net/xiaohui249/blog/829884

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