Format Currency Sample

25-03-22 7

如果您想了解FormatCurrencySample的相关知识，那么本文是一篇不可错过的文章，我们将为您提供关于"Format"didnotcompletenormally.Pleaseseethel

如果您想了解Format Currency Sample的相关知识，那么本文是一篇不可错过的文章，我们将为您提供关于"Format" did not complete normally. Please see the log for more information. 解决、Android Studio error: format not a string literal and no format arguments [-Werror=format-security]、C# form表单提交enctype="multipart/form-data" 与 enctype="application/x-www-form-urlenco...、Concurrency and Fault Tolerance Made Easy: An Akka Tutorial with Examples的有价值的信息。

本文目录一览：

Format Currency Sample
"Format" did not complete normally. Please see the log for more information. 解决
Android Studio error: format not a string literal and no format arguments [-Werror=format-security]
C# form表单提交enctype="multipart/form-data" 与 enctype="application/x-www-form-urlenco...
Concurrency and Fault Tolerance Made Easy: An Akka Tutorial with Examples

Format Currency Sample

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>JQuery FormatCurrency Sample</title> <script type="text/javascript" src="scripts/jquery-1.2.6.js"></script> <script type="text/javascript" src="scripts/jquery.formatCurrency.js"></script> <style type="text/css"> body, div  { margin:0px auto; padding:0px; }

            .main { margin:40px; }
            
            .sample { float:left; margin:10px; padding:4px; border:1px solid #888; width:350px; }
            
            .sample h3 { margin:-4px; margin-bottom:10px; padding:4px; background:#555; color:#eee; }
            
            .currencyLabel { display:block; } </style> <script type="text/javascript"> // Sample 1 $(document).ready(function()
            {
                $(''#currencyButton'').click(function()
                {
                    $(''#currencyField'').formatCurrency();
                    $(''#currencyField'').formatCurrency(''.currencyLabel'');
                });
            }); // Sample 2 $(document).ready(function()
            {
                $(''.currency'').blur(function()
                {
                    $(''.currency'').formatCurrency();
                });
            }); </script> </head> <body> <div class="main"> <div class="formPage"> <h1>Format Currency Sample</h1> <div class="sample"> <h3>Formatting Using Button Click</h3> <input type="textbox" id="currencyField" value="$1,220.00" /> <input type="button" id="currencyButton" value="Convert" /> <div> Formatting Currency to an Html Span tag. <span class="currencyLabel">$1,220.00</span> </div> </div> <div class="sample"> <h3>Formatting Using Blur (Lost Focus)</h3> <input type="textbox" id="currencyField" class=''currency'' value="$1,220.00" /> </div> </div> </div> </body> </html>

"Format" did not complete normally. Please see the log for more information. 解决

在使用 MyEclipse 编写 web.xml 时使用 Ctrl+Shift+F 无法格式排版代码，出现 "Format" did not complete normally. Please see the log for more information.

Overlapping text edits,

主要可能原因是 <web-app></web-app> 中多了些空格所致，试着将其删掉之后问题完美解决

Android Studio error: format not a string literal and no format arguments [-Werror=format-security]

使用 android studio, 如果编译 jni 过程出现如下错误：

error: format not a string literal and no format arguments [-Werror=format-security]

解决方法：
在你的 ndk 目录下修改 build/core/default-build-commands.mk

TARGET_FORMAT_STRING_CFLAGS := -Wformat -Werror=format-security

为

TARGET_FORMAT_STRING_CFLAGS := -Wformat   #-Werror=format-security

也就是把后面部分注释掉

C# form表单提交enctype=

C# form表单提交enctype="multipart/form-data" 与 enctype="application/x-www-form-urlenco...

1.application/x-www-form-urlencoded

GET方式，会将表单中的数据（键值对）经过urlencode编码后追加到url中。

POST方式，会将表单中的数据经过urlencode编码后放在request body 中。

2.multipart/form-data

当需要在表单内上传文件时（二进制流数据）时，就需要使用 multipart/form-data。

"application/x-www-form-urlencoded"，他是默认的MIME内容编码类型，一般可以用于所有的情况。但是他在传输比较大的二进制或者文本数据时效率极低。这种情况应该使用"multipart/form-data"。如上传文件或者二进制数据和非ASCII数据。

在 application/x-www-form-urlencoded 消息中:

......

name=ryan+ou&email=ryan@rhythmtechnology.com

......

(不同的field会用"&"符号连接;空格被替换成"+";field和value间用"="联系,等等)

再看multipart/form-data 消息中:

......

-----------------------------7cd1d6371ec

Content-Disposition: form-data; name="name"

ryan ou

-----------------------------7cd1d6371ec

Content-Disposition: form-data; name="email"

ryan@rhythmtechnology.com

-----------------------------7cd1d6371ec

Content-Disposition: form-data; name="Logo"; filename="D:\My Documents\My Pictures\Logo.jpg"

Content-Type: image/jpeg

......

(每个field被分成小部分，而且包含一个value是"form-data"的"Content-Disposition"的头部；一个"name"属性对应field的ID,等等)

-------------------------------------------------

application/x-www-form-urlencoded

1、它是post的默认格式，使用js中URLencode转码方法。包括将name、value中的空格替换为加号；将非ascii字符做百分号编码；将input的name、value用‘=’连接，不同的input之间用‘&’连接。

2、百分号编码什么意思呢。比如汉字‘丁’吧，他的utf8编码在十六进制下是0xE4B881，占3个字节，把它转成字符串‘E4B881’，变成了六个字节，每两个字节前加上百分号前缀，得到字符串“%E4%B8%81”，变成九个ascii字符，占九个字节（十六进制下是0x244534254238253831）。把这九个字节拼接到数据包里，这样就可以传输“非ascii字符的 utf8编码的十六进制表示的字符串的百分号形式”，^_^。

3、同样使用URLencode转码，这种post格式跟get的区别在于，get把转换、拼接完的字符串用‘?’直接与表单的action连接作为URL使用，所以请求体里没有数据；而post把转换、拼接后的字符串放在了请求体里，不会在浏览器的地址栏显示，因而更安全一些。

---------------------

multipart/form-data

1、对于一段utf8编码的字节，用application/x-www-form-urlencoded传输其中的ascii字符没有问题，但对于非ascii字符传输效率就很低了（汉字‘丁’从三字节变成了九字节），因此在传很长的字节（如文件）时应用multipart/form-data格式。smtp等协议也使用或借鉴了此格式。

2、此格式表面上发送了什么呢。用此格式发送一段一句话和一个文件，请求体如下

同时请求头里规定了Content-Type: multipart/form-data; boundary=----WebKitFormBoundarymNhhHqUh0p0gfFa8

可见请求体里不同的input之间用一段叫boundary的字符串分割，每个input都有了自己一个小header，其后空行接着是数据。

3、此格式实际上发送了什么呢。fiddler抓包如下

右边明显看到了一段乱码，为什么呢，以汉字‘丁’为例，其utf8编码为0xE4B881，这三个字节会直接拼接到数据包中，即其在实际发送时只占三字节，上图右边是逐字节转为ascii字符显示的，因此会显示为三个乱码字符。

4、由上可见，multipart/form-data将表单中的每个input转为了一个由boundary分割的小格式，没有转码，直接将utf8字节拼接到请求体中，在本地有多少字节实际就发送多少字节，极大提高了效率，适合传输长字节。

---------------------

原文链接：https://blog.csdn.net/u013827143/article/details/86222486

Concurrency and Fault Tolerance Made Easy: An Akka Tutorial with Examples

MY Tips:

Actors本身变量安全，但涉及到传递变量就需要仔细考虑,如文中的fileSender
异常处理策略仍然需要研究下，例如将文中示例改为异常状况

class StringCounterActor extends Actor {
  def receive = {
    case ProcessStringMsg(string) => {
      val wordsInLine = string.split(" ")(100)
      sender ! StringProcessedMsg(1)
    }
    case _ => println("Error: message not recognized")
  }
}

BY DIEGO CASTORINA - SOFTWARE ENGINEER @ TOPTAL

The challenge

Writing concurrent programs is hard. Having to deal with threads, locks, race conditions, and so on is highly error-prone and can lead to code that is difficult to read, test, and maintain.

Many therefore prefer to avoid multithreading altogether. Instead, they employ single-threaded processes exclusively, relying on external services (such as databases, queues, etc.) to handle any needed concurrent or asynchronous operations. While this approach is in some cases a legitimate alternative, there are many scenarios in which it is simply not a viable option. Many real-time systems – such as trading or banking applications, or real-time games – don’t have the luxury of waiting for a single-threaded process to complete (they need the answer now!). Other systems are so compute- or resource-intensive that they would take an inordinate amount of time (hours or even days in some cases) to run without introducing parallelization into their code.

One fairly common single-threaded approach (widely used in the Node.js world, for example) is to use an event-based, non-blocking paradigm. While this does help performance by avoiding context switches, locks, and blocking, it still does not address the issues of using multiple processors concurrently (doing so would require launching, and coordinating between, multiple independent processes).

So does this mean you have no choice but to journey deep into the bowels of threads, locks, and race conditions in order to build a concurrent application?

Thanks to the Akka framework, the answer is no. This tutorial introduces Akka examples and explores the ways in which it facilitates and simplifies the implementation of concurrent, distributed applications.

What is the Akka Framework?

This post introduces Akka and explores the ways in which it facilitates and simplifies the implementation of concurrent, distributed applications.

Akka is a toolkit and runtime for building highly concurrent, distributed, and fault tolerant applications on the JVM. Akka is written in Scala, with language bindings provided for both Scala and Java.

Akka’s approach to handling concurrency is based on the Actor Model. In an actor-based system, everything is an actor, in much the same way that everything is an object in object-oriented design. A key difference, though – particularly relevant to our discussion – is that the Actor Model was specifically designed and architected to serve as a concurrent model whereas the object-oriented model is not. More specifically, in a Scala actor system, actors interact and share information, without any presupposition of sequentiality. The mechanism by which actors share information with one another, and task one another, is message passing.

All the complexity of creating and scheduling threads, receiving and dispatching messages, and handling race conditions and synchronization, is relegated to the framework to handle transparently.

Akka creates a layer between the actors and the underlying system such that actors simply need to process messages. All the complexity of creating and scheduling threads, receiving and dispatching messages, and handling race conditions and synchronization, is relegated to the framework to handle transparently.

Akka strictly adheres to the The Reactive Manifesto. Reactive applications aim at replacing traditional multithreaded applications with an architecture that satisfies one or more of the following requirements:

Event-driven. Using Actors, one can write code that handles requests asynchronously and employs non-blocking operations exclusively.
Scalable. In Akka, adding nodes without having to modify the code is possible, thanks both to message passing and location transparency.
Resilient. Any application will encounter errors and fail at some point in time. Akka provides “supervision” (fault tolerance) strategies to facilitate a self-healing system.
Responsive. Many of today’s high performance and rapid response applications need to give quick feedback to the user and therefore need to react to events in an extremely timely manner. Akka’s non-blocking, message-based strategy helps achieve this.

What is an Actor in Akka?

An actor is essentially nothing more than an object that receives messages and takes actions to handle them. It is decoupled from the source of the message and its only responsibility is to properly recognize the type of message it has received and take action accordingly.

Upon receiving a message, an actor may take one or more of the following actions:

Execute some operations itself (such as performing calculations, persisting data, calling an external web service, and so on)
Forward the message, or a derived message, to another actor
Instantiate a new actor and forward the message to it

Alternatively, the actor may choose to ignore the message entirely (i.e., it may choose inaction) if it deems it appropriate to do so.

To implement an actor, it is necessary to extend the akka.actor.Actor trait and implement the receive method. An actor’s receive method is invoked (by Akka) when a message is sent to that actor. Its typical implementation consists of pattern matching, as shown in the following Akka example, to identify the message type and react accordingly:

import akka.actor.Actor
import akka.actor.Props
import akka.event.Logging
 
class MyActor extends Actor {
  def receive = {
    case value: String => doSomething(value)
    case _ => println("received unknown message")
  }
}

Pattern matching is a relatively elegant technique for handling messages, which tends to produce “cleaner” and easier-to-navigate code than a comparable implementation based on callbacks. Consider, for example, a simplistic HTTP request/response implementation.

First, let’s implement this using a callback-based paradigm in JavaScript:

route(url, function(request){
  var query = buildQuery(request);
  dbCall(query, function(dbResponse){
    var wsRequest = buildWebServiceRequest(dbResponse);
    wsCall(wsRequest, function(wsResponse) {
      sendReply(wsResponse);
    });
  });
});

Now let’s compare this to a pattern-matching-based implementation:

msg match {
  case HttpRequest(request) => {
    val query = buildQuery(request)
    dbCall(query)
  }
  case DbResponse(dbResponse) => {
    var wsRequest = buildWebServiceRequest(dbResponse);
    wsCall(dbResponse)
  }
  case WsResponse(wsResponse) => sendReply(wsResponse)
}

While the callback-based JavaScript code is admittedly compact, it is certainly harder to read and navigate. In comparison, the pattern-matching-based code makes it more immediately apparent what cases are being considered and how each is being handled.

The Actor System

Taking a complex problem and recursively splitting it into smaller sub-problems is a sound problem solving technique in general. This approach can be particularly beneficial in computer science (consistent with theSingle Responsibility Principle), as it tends to yield clean, modularized code, with little or no redundancy, that is relatively easy to maintain.

In an actor-based design, use of this technique facilitates the logical organization of actors into a hierarchical structure known as an Actor System. The actor system provides the infrastructure through which actors interact with one another.

An example of the way the actor system works in the Akka framework.

In Akka, the only way to communicate with an actor is through an ActorRef. An ActorRef represents a reference to an actor that precludes other objects from directly accessing or manipulating that actor’s internals and state. Messages may be sent to an actor via an ActorRef using one of the following syntax protocols:

! (“tell”) – sends the message and returns immediately
? (“ask”) – sends the message and returns a Future representing a possible reply

Each actor has a mailbox to which its incoming messages are delivered. There are multiple mailbox implementations from which to choose, with the default implementation being FIFO.

An actor contains many instance variables to maintain state while processing multiple messages. Akka ensures that each instance of an actor runs in its own lightweight thread and that messages are processed one at a time. In this way, each actor’s state can be reliably maintained without the developer needing to explicitly worry about synchronization or race conditions.

Each actor is provided with the following useful information for performing its tasks via the Akka Actor API:

sender: an ActorRef to the sender of the message currently being processed
context: information and methods relating to the context within which the actor is running (includes, for example, an actorOf method for instantiating a new actor)
supervisionStrategy: defines the strategy to be used for recovering from errors
self: the ActorRef for the actor itself

Akka ensures that each instance of an actor runs in its own lightweight thread and that messages are processed one at a time. In this way, each actor''s state can be reliably maintained without the developer needing to explicitly worry about synchronization or race conditions.

To help tie these tutorials together, let’s consider a simple example of counting the number of words in a text file.

For purposes of our Akka example, we’ll decompose the problem into two subtasks; namely, (1) a “child” task of counting the number of words on a single line and (2) a “parent” task of summing those per-line word counts to get the total number of words in the file.

The parent actor will load each line from the file and then delegate to a child actor the task of counting the words in that line. When the child is done, it will send a message back to the parent with the result. The parent will receive the messages with the word counts (for each line) and keep a counter for the total number of words in the entire file, which it will then return to its invoker upon completion.

(Note that the Akka tutorial code samples provided below are intended to be didactic only and therefore do not necessarily concern themselves with all edge conditions, performance optimizations, and so on. Also, a complete compilable version of the code samples shown below is available in this gist.)

Let’s first look at a sample implementation of the child StringCounterActor class:

case class ProcessStringMsg(string: String)
case class StringProcessedMsg(words: Integer)
 
class StringCounterActor extends Actor {
  def receive = {
    case ProcessStringMsg(string) => {
      val wordsInLine = string.split(" ").length
      sender ! StringProcessedMsg(wordsInLine)
    }
    case _ => println("Error: message not recognized")
  }
}

This actor has a very simple task: consume ProcessStringMsg messages (containing a line of text), count the number of words on the specified line, and return the result to the sender via a StringProcessedMsg message. Note that we have implemented our class to use the ! (“tell”) method to send the StringProcessedMsgmessage (i.e., to send the message and return immediately).

OK, now let’s turn our attention to the parent WordCounterActor class:

1.  case class StartProcessFileMsg()
2.
3.  class WordCounterActor(filename: String) extends Actor {
4.  
5.    private var running = false
6.    private var totalLines = 0
7.    private var linesProcessed = 0
8.    private var result = 0
9.    private var fileSender: Option[ActorRef] = None
10.  
11.   def receive = {
12.     case StartProcessFileMsg() => {
13.       if (running) {
14.         // println just used for example purposes;
15.         // Akka logger should be used instead
16.         println("Warning: duplicate start message received")
17.       } else {
18.         running = true
19.         fileSender = Some(sender) // save reference to process invoker
20.         import scala.io.Source._
21.         fromFile(filename).getLines.foreach { line =>
22.           context.actorOf(Props[StringCounterActor]) ! ProcessStringMsg(line)
23.           totalLines += 1
24.         }
25.       }
26.     }
27.     case StringProcessedMsg(words) => {
28.       result += words
29.       linesProcessed += 1
30.       if (linesProcessed == totalLines) {
31.         fileSender.map(_ ! result)  // provide result to process invoker
32.       }
33.     }
34.     case _ => println("message not recognized!")
35.   }
36. }

Many things are going on in here, so let’s examine each of them in more detail (note that the line numbers referenced in the discussion that follows are based on the above code sample)…

First, notice that the name of the file to process is passed to the WordCounterActor constructor (line 3). This indicates that the actor is only to be used to process a single file. This also simplifies the coding job for the developer, by avoiding the need to reset state variables (running, totalLines, linesProcessed, and result) when the job is done, since the instance is only used once (i.e., to process a single file) and then discarded.

Next, observe that the WordCounterActor handles two types of messages:

StartProcessFileMsg (line 12)
- Received from the external actor that initially initiates the WordCounterActor.
- When received, the WordCounterActor first checks that it is not receiving a redundant request.
- If the request is redundant, WordCounterActor generates a warning and nothing more is done (line 16).
- If the request is not redundant:
  - WordCounterActor stores a reference to the sender in the fileSender instance variable (note that this is an Option[ActorRef] rather than an Option[Actor] - see line 9). This ActorRef is needed in order to later access and respond to it when processing the final StringProcessedMsg (which is received from a StringCounterActor child, as described below).
  - WordCounterActor then reads the file and, as each line in the file is loaded, a StringCounterActor child is created and a message containing the line to be processed is passed to it (lines 21-24).
StringProcessedMsg (line 27)
- Received from a child StringCounterActor when it completes processing the line assigned to it.
- When received, the WordCounterActor increments the line counter for the file and, if all lines in the file have been processed (i.e., when totalLines and linesProcessed are equal), it sends the final result to the original fileSender (lines 28-31).

Once again, notice that in Akka, the sole mechanism for inter-actor communication is message passing. Messages are the only thing that actors share and, since actors can potentially access the same messages concurrently, it is important for them to be immutable, in order to avoid race conditions and unexpected behaviors.

Case classes in Scala are regular classes that provide a recursive decomposition mechanism via pattern matching.

It is therefore common to pass messages in the form of case classes since they are immutable by default and because of how seamlessly they integrate with pattern matching.

Let’s conclude the example with the code sample to run the whole app.

object Sample extends App {
 
  import akka.util.Timeout
  import scala.concurrent.duration._
  import akka.pattern.ask
  import akka.dispatch.ExecutionContexts._
  
  implicit val ec = global
  
  override def main(args: Array[String]) {
    val system = ActorSystem("System")
    val actor = system.actorOf(Props(new WordCounterActor(args(0))))
    implicit val timeout = Timeout(25 seconds)
    val future = actor ? StartProcessFileMsg()
    future.map { result =>
      println("Total number of words " + result)
      system.shutdown
    }
  }
}

In concurrent programming, a "future" is a essentially a placeholder object for a result that is not yet known.

Notice how this time the ? method is used to send a message. In this way, the caller can use the returnedFuture to print the final result when this is available and to exit the program by shutting down the ActorSystem.

Akka fault tolerance and supervisor strategies

In an actor system, each actor is the supervisor of its children. If an actor fails to handle a message, it suspends itself and all of its children and sends a message, usually in the form of an exception, to its supervisor.

In Akka, supervisor strategies are the primary and straightforward mechanism for defining the fault tolerant behavior of your system.

In Akka, the way in which a supervisor reacts to and handles exceptions that percolate up to it from its children is referred to as a supervisor strategy. Supervisor strategies are the primary and straightforward mechanism by which you define the fault tolerant behavior of your system.

When a message signifying a failure reaches a supervisor, it can take one of the following actions:

Resume the child (and its children), keeping its internal state. This strategy can be applied when the child state was not corrupted by the error and it can continue functioning correctly.
Restart the child (and its children), clearing its internal state. This strategy can be used in the opposite scenario of the one just described. If the child state has been corrupted by the error, it is necessary the reset its state before it can be used in the Future.
Stop the child (and its children) permanently. This strategy can be employed in cases where the error condition is not believed to be rectifiable, but does not jeopardize the rest of the operation being performed, which can be completed in the absence of the failed child.
Stop itself and escalate the error. Employed when the supervisor does not know how to handle the failure and so it escalates it to its own supervisor.

Moreover, an Actor can decide to apply the action just to the failed children or to its siblings as well. There are two pre-defined strategies for this:

OneForOneStrategy: Applies the specified action to the failed child only
AllForOneStrategy: Applies the specified action to all of its children

Here’s a simple example, using the OneForOneStrategy:

import akka.actor.OneForOneStrategy
import akka.actor.SupervisorStrategy._
import scala.concurrent.duration._
 
override val supervisorStrategy =
 OneForOneStrategy() {
   case _: ArithmeticException      => Resume
   case _: NullPointerException     => Restart
   case _: IllegalArgumentException => Stop
   case _: Exception                => Escalate
 }

If no strategy is specified, the following default strategy is employed:

If there was an error while initializing the actor or if the actor was killed, the actor is stopped.
If there was any other kind of exception, the actor is simply restarted.

The Akka-supplied implementation of this default strategy is as follows:

final val defaultStrategy: SupervisorStrategy = {
  def defaultDecider: Decider = {
    case _: ActorInitializationException ⇒ Stop
    case _: ActorKilledException         ⇒ Stop
    case _: Exception                    ⇒ Restart
  }
  OneForOneStrategy()(defaultDecider)
}

Akka allows for the implementation of custom supervisor strategies, but as the Akka documentation warns, do so with caution as incorrect implementations may lead to problems such as blocked actor systems (i.e. permanently suspended actors).

Location transparency

The Akka architecture supports location transparency, enabling actors to be entirely agnostic to where the messages that they receive originated. The sender of the message may reside in the same JVM as the actor or in a separate JVM (either running on the same node or a different node). Akka enables each of these cases to be handled in a manner that is completely transparent to the actor (and therefore the developer). The only caveat is that messages sent across multiple nodes must be serializable.

The Akka architecture supports location transparency, enabling actors to be entirely agnostic to where the messages that they receive originated.

Actor systems are designed to run in a distributed environment without requiring any specialized code. Akka only requires the presence of a configuration file (application.conf) that specifies the nodes to send messages to. Here’s a simple example of a configuration file:

akka {
  actor {
    provider = "akka.remote.RemoteActorRefProvider"
  }
  remote {
    transport = "akka.remote.netty.NettyRemoteTransport"
    netty {
      hostname = "127.0.0.1"
      port = 2552
    }
  }
}

A few parting tips…

We have seen how the Akka framework helps achieve concurrency and high performance. However, as this tutorial pointed out, there are a few points to keep in mind when designing and implementing your system in order to exploit the power of Akka to its fullest:

To the greatest extent possible, each actor should be assigned the smallest task possible (as previously discussed, following the Single Responsibility Principle)
Actors should handle events (i.e., process messages) asynchronously and should not block, otherwise context switches will happen which can adversely affect performance. Specifically, it is best to perform blocking operations (IO, etc.) in a Future so as not to block the actor; i.e.:
```
  case evt => blockingCall() // BAD
  case evt => Future {
    blockingCall()           // GOOD
  }
```
Be sure your messages are all immutable, since the actors that pass them to one another will all be running concurrently in their own threads. Mutable messages are likely to result in unexpected behavior.
Since messages sent between nodes must be serializable, it is important to bear in mind that the larger the messages are, the longer it will take to serialize, send, and deserialize them, which can negatively impact performance.

Conclusion

Akka, written in Scala, simplifies and facilitates the development of highly-concurrent, distributed, and fault tolerant applications, hiding much of the complexity from the developer. Doing Akka full justice would require much more than this single tutorial, but hopefully this introduction and its examples were sufficiently captivating to get you to want to read more.

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