使用 Akka Actor 进行文件操作

Question

使用 Akka Actor 与普通的 File 操作方法相比有什么优势？我试图计算分析日志文件所花费的时间。操作是查找登录次数超过50次的IP地址并显示出来。与 Akka Actor 模型相比，正常文件操作更快。为什么会这样？

使用普通文件操作

public static void main(String[] args) {
        // TODO Auto-generated method stub
        //long startTime = System.currentTimeMillis();
        File file = new File("log.txt");
        Map<String, Long> ipMap = new HashMap<>();

        try {

                FileReader fr = new FileReader(file);
                BufferedReader br = new BufferedReader(fr);
                String line = br.readLine();

                while(line!=null) {
                    int idx = line.indexOf('-');
                    String ipAddress = line.substring(0, idx).trim();
                    long count = ipMap.getOrDefault(ipAddress, 0L);
                    ipMap.put(ipAddress, ++count);
                    line = br.readLine();
                }

                 System.out.println("================================");
                 System.out.println("||\tCount\t||\t\tIP");
                 System.out.println("================================");

                 fr.close();
                 br.close();
                 Map<String, Long> result = new HashMap<>();

                    // Sort by value and put it into the "result" map
                    ipMap.entrySet().stream()
                            .sorted(Map.Entry.<String, Long>comparingByValue().reversed())
                            .forEachOrdered(x -> result.put(x.getKey(), x.getValue()));

                    // Print only if count > 50
                    result.entrySet().stream().filter(entry -> entry.getValue() > 50).forEach(entry ->
                        System.out.println("||\t" + entry.getValue() + "   \t||\t" + entry.getKey())
                    );

//                  long endTime = System.currentTimeMillis();
//                  System.out.println("Time: "+(endTime-startTime));

            } catch (FileNotFoundException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }

    }

Using Actors:
1. The Main Class
 public static void main(String[] args) {
        long startTime = System.currentTimeMillis();
        // Create actorSystem
        ActorSystem akkaSystem = ActorSystem.create("akkaSystem");

        // Create first actor based on the specified class
        ActorRef coordinator = akkaSystem.actorOf(Props.create(FileAnalysisActor.class));

        // Create a message including the file path
        FileAnalysisMessage msg = new FileAnalysisMessage("log.txt");

        // Send a message to start processing the file. This is a synchronous call using 'ask' with a timeout.
        Timeout timeout = new Timeout(6, TimeUnit.SECONDS);
        Future<Object> future = Patterns.ask(coordinator, msg, timeout);

        // Process the results
        final ExecutionContext ec = akkaSystem.dispatcher();
        future.onSuccess(new OnSuccess<Object>() {
            @Override
            public void onSuccess(Object message) throws Throwable {
                if (message instanceof FileProcessedMessage) {
                    printResults((FileProcessedMessage) message);

                    // Stop the actor system
                    akkaSystem.shutdown();
                }
            }

            private void printResults(FileProcessedMessage message) {
                System.out.println("================================");
                System.out.println("||\tCount\t||\t\tIP");
                System.out.println("================================");

                Map<String, Long> result = new LinkedHashMap<>();

                // Sort by value and put it into the "result" map
                message.getData().entrySet().stream()
                        .sorted(Map.Entry.<String, Long>comparingByValue().reversed())
                        .forEachOrdered(x -> result.put(x.getKey(), x.getValue())); 

                // Print only if count > 50
                result.entrySet().stream().filter(entry -> entry.getValue() > 50).forEach(entry ->
                    System.out.println("||\t" + entry.getValue() + "   \t||\t" + entry.getKey())
                );
                long endTime = System.currentTimeMillis();
                System.out.println("Total time: "+(endTime - startTime));
            }

        }, ec);

    }

2.文件分析器类

public class FileAnalysisActor extends UntypedActor {

    private Map<String, Long> ipMap = new HashMap<>();
    private long fileLineCount;
    private long processedCount;
    private ActorRef analyticsSender = null;

    @Override
    public void onReceive(Object message) throws Exception {
        /*
            This actor can receive two different messages, FileAnalysisMessage or LineProcessingResult, any
            other type will be discarded using the unhandled method
         */
            //System.out.println(Thread.currentThread().getName());
        if (message instanceof FileAnalysisMessage) {

            List<String> lines = FileUtils.readLines(new File(
                    ((FileAnalysisMessage) message).getFileName()));

            fileLineCount = lines.size();
            processedCount = 0;

            // stores a reference to the original sender to send back the results later on
            analyticsSender = this.getSender();

            for (String line : lines) {
                // creates a new actor per each line of the log file
                Props props = Props.create(LogLineProcessor.class);
                ActorRef lineProcessorActor = this.getContext().actorOf(props);

                // sends a message to the new actor with the line payload
                lineProcessorActor.tell(new LogLineMessage(line), this.getSelf());
            }

        } else if (message instanceof LineProcessingResult) {

            // a result message is received after a LogLineProcessor actor has finished processing a line
            String ip = ((LineProcessingResult) message).getIpAddress();

            // increment ip counter
            Long count = ipMap.getOrDefault(ip, 0L);
            ipMap.put(ip, ++count);

            // if the file has been processed entirely, send a termination message to the main actor
            processedCount++;
            if (fileLineCount == processedCount) {
                // send done message
                analyticsSender.tell(new FileProcessedMessage(ipMap), ActorRef.noSender());
            }

        } else {
            // Ignore message
            this.unhandled(message);
        }
    }
}

3.Logline处理器类

public class LogLineProcessor extends UntypedActor {

    @Override
    public void onReceive(Object message) throws Exception {
        if (message instanceof LogLineMessage) {
            // What data each actor process?
            //System.out.println("Line: " + ((LogLineMessage) message).getData());
            // Uncomment this line to see the thread number and the actor name relationship
           //System.out.println("Thread ["+Thread.currentThread().getId()+"] handling ["+ getSelf().toString()+"]");

            // get the message payload, this will be just one line from the log file
            String messageData = ((LogLineMessage) message).getData();

            int idx = messageData.indexOf('-');
            if (idx != -1) {
                // get the ip address
                String ipAddress = messageData.substring(0, idx).trim();

                // tell the sender that we got a result using a new type of message
                this.getSender().tell(new LineProcessingResult(ipAddress), this.getSelf());
            }
        } else {
            // ignore any other message type
            this.unhandled(message);
        }
    }
}

消息类

1. 文件分析消息

   公共类 FileAnalysisMessage {

   private String fileName;
   
   public FileAnalysisMessage(String file) {
       this.fileName = file;
   }
   
   public String getFileName() {
       return fileName;
   }
   

   }

2.文件处理消息

public class FileProcessedMessage {

    private Map<String, Long> data;

    public FileProcessedMessage(Map<String, Long> data) {
        this.data = data;
    }

    public Map<String, Long> getData() {
        return data;
    }
}

1. LineProcessing结果

   公共类 LineProcessingResult {

   private String ipAddress;
   
   public LineProcessingResult(String ipAddress) {
       this.ipAddress = ipAddress;
   }
   
   public String getIpAddress() {
       return ipAddress;
   }
   

   }

4.Logline消息

public class LogLineMessage {

    private String data;

    public LogLineMessage(String data) {
        this.data = data;
    }

    public String getData() {
        return data;
    }
}

我正在为文件中的每一行创建一个演员。

Answer 1

对于所有并发框架，始终需要在部署的并发量与每个并发单元所涉及的复杂性之间进行权衡。阿卡也不例外。

在您的非 akka 方法中，您对每一行都有一个相对简单的步骤序列：

1. 从文件中读取一行
2. 用'-'分割行
3. 将 ip 地址提交到 hashmap 并增加计数

相比之下，你的 akka 方法对于每一行都要复杂得多：

1. 创建一个演员
2. 创建LogLineMessage 消息
3. 将消息发送给参与者
4. 用'-'分割行
5. 创建LineProcessingResult 消息
6. 将消息发送回协调参与者
7. 将 ip 地址提交到 hashmap 并增加计数

如果我们天真地假设上述每个步骤都花费了相同的时间，那么您将需要 2 个带有 akka 的线程才能以与没有 akka 的 1 个线程相同的速度运行。

让每个并发单元做更多的工作

不是每 1 行有 1 个Actor，而是让每个 Actor 将 N 行处理成自己的子哈希映射（例如，每个 Actor 处理 1000 行）：

public class LogLineMessage {

    private String[] data;

    public LogLineMessage(String[] data) {
        this.data = data;
    }

    public String[] getData() {
        return data;
    }
}

那么Actor就不会发回IP地址这样简单的东西了。相反，它将为其行子集发送计数哈希：

public class LineProcessingResult {

    private HashMap<String, Long> ipAddressCount;

    public LineProcessingResult(HashMap<String, Long> count) {
        this.ipAddressCount = Count;
    }

    public HashMap<String, Long> getIpAddress() {
        return ipAddressCount;
    }
}

协调 Actor 可以负责组合所有不同的子计数：

//inside of FileAnalysisActor
else if (message instanceof LineProcessingResult) {
    HashMap<String,Long>  localCount = ((LineProcessingResult) message).getIpAddressCount();

    localCount.foreach((ipAddress, count) -> {
        ipMap.put(ipAddress, ipMap.getOrDefault(ipAddress, 0L) + count);
    })

然后，您可以改变 N 来查看您的特定系统在哪里获得最佳性能。

不要将整个文件读入内存

并发解决方案的另一个缺点是它首先将整个文件读入内存。这对 JVM 来说是不必要的，而且很费力。

改为一次读取文件 N 行。如前所述，一旦您将这些行放入内存中，就会从 Actor 中生成。

FileReader fr = new FileReader(file);
BufferedReader br = new BufferedReader(fr);

String[] lineBuffer;
int bufferCount = 0;
int N = 1000;

String line = br.readLine();

while(line!=null) {
    if(0 == bufferCount)
      lineBuffer = new String[N];
    else if(N == bufferCount) {
      Props props = Props.create(LogLineProcessor.class);
      ActorRef lineProcessorActor = this.getContext().actorOf(props);

      lineProcessorActor.tell(new LogLineMessage(lineBuffer),
                              this.getSelf());

      bufferCount = 0;
      continue;
    }

    lineBuffer[bufferCount] = line;
    br.readLine();
    bufferCount++;
}

//handle the final buffer
if(bufferCount > 0) {
    Props props = Props.create(LogLineProcessor.class); 
    ActorRef lineProcessorActor = this.getContext().actorOf(props);

    lineProcessorActor.tell(new LogLineMessage(lineBuffer),
                            this.getSelf());
}

这将允许文件 IO、行处理和子图组合并行运行。