Publisher by : Packt Publishing Ltd Format Available : PDF, ePub, Mobi Total Read : 41 Total Download : File Size : 46,9 Mb Description : Build fault tolerant concurrent and distributed applications with Akka About This Book Build networked applications that self-heal Scale out your applications to handle more traffic faster An easy-to-follow guide with a number of examples to ensure you get the best start with Akka Who This Book Is For This book is intended for beginner to intermediate Java or Scala developers who want to build applications to serve the high-scale user demands in computing today. If you need your applications to handle the ever-growing user bases and datasets with high performance demands, then this book is for you. Learning Akka will let you do more for your users with less code and less complexity, by building and scaling your networked applications with ease. What You Will Learn Use Akka to overcome the challenges of concurrent programming Resolve the issues faced in distributed computing with the help of Akka Scale applications to serve a high number of concurrent users Make your system fault-tolerant with self-healing applications Provide a timely response to users with easy concurrency Reduce hardware costs by building more efficient multi-user applications Maximise network efficiency by scaling it In Detail Software today has to work with more data, more users, more cores, and more servers than ever. Akka is a distributed computing toolkit that enables developers to build correct concurrent and distributed applications using Java and Scala with ease, applications that scale across servers and respond to failure by self-healing. Akka is written in Scala, which has become the programming language of choice for development on the Akka platform.

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So the assignment was not as easy as it seemed. So what can be done? Naturally, I realize that I needed to parallelize the task. Approach 2: Threaded Java Program Threads always seemed really complex to me. So I tried to understand this concept. Java monitors support two kind of thread synchronization: mutual exclusion and cooperation. Cooperation, is achieved by wait and notify. Monitor Region When a thread arrives at the beginning of a monitor region, it is placed into an entry set for the associated monitor.

This set works like the queue at the bank counter. A person gets to make a transaction when they reach the front of the line. Every lecture or article on Threading always mentions a Consumer and producer problem. So my problem is similar to this, right?

I have a reader producer which reads a line and gives it to the JDBC layer consumer to write it to the database. Java already provides a blocking queue, making the implementation problem easier.

I need something like a thread pool to limit the number of threads. The solution looked good and architecturally sound. Then I realized I forgot about error handling. So is their any other way to do it? Approach 3: Java Program With Actors Doing the above assignment made me realize that with increasing complexity it will be very difficult to maintain this code. Also, Java uses a system thread for every thread spawned.

So there is limit to spawning threads. What I needed was a framework which takes care of concurrency for me, and I can only concentrate on the business logic part of it. I did find such a framework: Akka. Akka is based on the Erlang actor model. If you read how the above problem is implemented, it is implemented using a pull strategy, where what a consumer thread will do is pull in a new task after it is finished with the current task.

So we need to make it wait until the producer is ready with something. For every event, an event handler should be ready to do the work. So thinking in a similar analogy to the bank, previously we used to stand in a queue for our turn, and the bank had a headache of maintaining this queue.

Sometimes customers got tired of standing in line and left. So what a bank can do is to take this problem to a third party vendor and ask for a solution. The vendor suggests a token system. Let all customers sit in chairs until their token number comes up.

It sounds like a good solution for the bank, and to add icing to the cake, the vendor was even ready to maintain this system free of cost. Think about the joy a bank would feel. I felt a similar joy after Akka. Akka is based on actors, so what are actors? Actors Simple and high-level abstractions for concurrency and parallelism. Asynchronous, non-blocking, and highly performant event-driven programming models. Very lightweight event-driven processes several million actors per GB of heap memory.

Using Akka is very easy. It can be added as dependency to our project. Simple jar file. So let us get our hands dirty and write a Hello World program.

Examples are taken from the Akka documentation. It just needs to implement the onRecieve method so it reacts to a tell call. So Akka gives a guarantee that the OnReceive method will be called only once at a time. Akka is a very scalable piece of software, not only in the context of performance but also in the size of applications it is useful for.

The core of Akka, akka-actor, is very small and easily dropped into an existing project where you need asynchronicity and lockless concurrency without hassle. Everything in Akka is designed to work in a distributed environment: all interactions of actors use pure message passing and everything is asynchronous. So my experience with using actors was very good and faster than traditional threads. Like This Article? Read More From DZone.



It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in front of you. Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions. The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial. The next module, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more.


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Tuktilar When a thread arrives at the beginning of a monitor region, it is placed into an entry set for the associated monitor. Java already provides a blocking queue, making the implementation problem easier. I need something like a thread pool to limit the number of threads. Introduction I started with an assignment to read a CSV file with columns and 0. The solution looked good and architecturally sound.


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The primary goal of streams is to provide a simple way to: build concurrent and memory bounded computations that can safely interact with various forms of non-blocking IO interfaces without involving blocking while embracing multi-core concurrency, and solving the typical pitfall of missing backpressure: faster producers overwhelm slower consumers that run on a separate thread, resulting in OutOfMemoryExceptions. In this post, I will explore how Akka Streams processing pipelines or graphs are transformed to actual multi-threaded execution. All of the code in the post assumes the akka-stream artifact of at least version 2. Always use the latest minor release — 2. ActorSystem import akka. In this view, a Source is not a static collection of elements; not like an Iterator, because computations can happen asynchronously, working concurrently with other computations. An Iterator always executes any chained computations on the caller thread.


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Shall So the boss tells the slave to do two withdraw orders and immediately leaves. Archived from the original on 13 August So I tried to understand this concept. Thank you for your interest in this question. Distributed systems without single points of failure. So instead of doing such mechanism, why not just drop the job in a queue? When we have different threads simultaneously accessing and modifying a variable, we have a race condition. Java Program With Actors Doing the above assignment made me realize that with increasing complexity it will be very difficult to maintain this code.

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