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Learning Concurrent Programming in Scala
Learning Concurrent Programming in Scala

Learning Concurrent Programming in Scala: Dive into the Scala framework with this programming guide, created to help you learn Scala and to build intricate, modern, scalable concurrent applications

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AU$47.99 AU$53.99
eBook Nov 2014 366 pages 1st Edition
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Learning Concurrent Programming in Scala

Chapter 2. Concurrency on the JVM and the Java Memory Model

 

"All non-trivial abstractions, to some degree, are leaky."

 
 --Jeff Atwood

Since its inception, Scala has run primarily on top of the JVM, and this fact has driven the design of many of its concurrency libraries. The memory model in Scala, its multithreading capabilities, and its inter-thread synchronization are all inherited from the JVM. Most, if not all, higher-level Scala concurrency constructs are implemented in terms of the low-level primitives presented in this chapter. These primitives are the basic way to deal with concurrency—in a way, the APIs and synchronization primitives in this chapter constitute the assembly of concurrent programming on the JVM.

In most cases, you should avoid low-level concurrency in place of higher-level constructs introduced later, but we felt it important for you to understand what a thread is, that a guarded block is better than busy-waiting, or why a...

Processes and Threads

In modern, pre-emptive, multitasking operating systems, the programmer has little or no control over the choice of the processor on which the program will be executed. In fact, the same program might run on many different processors during its execution and sometimes even simultaneously on several processors. It is usually the task of the Operating System (OS) to assign executable parts of the program to specific processors—this mechanism is called multitasking, and it happens transparently for the computer user.

Historically, multitasking was introduced to operating systems to improve the user experience by allowing multiple users or programs to use resources of the same computer simultaneously. In cooperative multitasking, programs were able to decide when to stop using the processor and yield control to other programs. However, this required a lot of discipline on the programmer's part and programs could easily give the impression of being unresponsive...

Monitors and synchronization

In this section, we will study inter-thread communication using the synchronized statement in more detail. As we saw in the previous sections, the synchronized statement serves both to ensure the visibility of writes performed by different threads, and to limit concurrent access to a shared region of memory. Generally speaking, a synchronization mechanism that enforces access limits on a shared resource is called a lock. Locks are also used to ensure that no two threads execute the same code simultaneously; that is, they implement mutual exclusion.

As mentioned previously, each object on the JVM has a special built-in monitor lock, also called the intrinsic lock. When a thread calls the synchronized statement on an x object, it gains ownership of the monitor lock of the x object, given that no other thread owns the monitor. Otherwise, the thread is blocked until the monitor is released. Upon gaining ownership of the monitor, the thread can witness the memory...

Volatile variables

The JVM offers a more lightweight form of synchronization than the synchronized block, called volatile variables. Volatile variables can be atomically read and modified, and are mostly used as status flags; for example, to signal that a computation is completed or cancelled. They have two advantages. First, writes to and reads from volatile variables cannot be reordered in a single thread. Second, writing to a volatile variable is immediately visible to all the other threads.

Note

Reads and writes to variables marked as volatile are never reordered. If a write W to a volatile v variable is observed on another thread through a read R of the same variable, then all the writes that preceded the write W are guaranteed to be observed after the read R.

In the following example, we search for at least one ! character in several pages of the text. Separate threads start scanning separate pages p of the text written by a person that is particularly fond of a popular fictional hero...

The Java Memory Model

While we were never explicit about it throughout this chapter, we have actually defined most of the JMM. What is a memory model in the first place?

A language memory model is a specification that describes the circumstances under which a write to a variable becomes visible to other threads. You might think that a write to a variable v changes the corresponding memory location immediately after the processor executes it, and that other processors see the new value of v instantaneously. This memory consistency model is called sequential consistency.

As we already saw in the ThreadSharedStateAccessReordering example, sequential consistency has little to do with how processors and compilers really work. Writes rarely end up in the main memory immediately; instead, processors have hierarchies of caches that ensure a better performance, and guarantee that the data is only eventually written to the main memory. Compilers are allowed to use registers to postpone or avoid memory...

Summary

In this chapter, we showed how to create and start threads, and wait for their termination. We have shown how to achieve inter-thread communication by modifying the shared memory and by using the synchronized statement, and what it means for a thread to be in a blocked state. We have studied approaches to prevent deadlocks by imposing ordering on the locks and avoided busy-waits in place of guarded blocks. We have seen how to implement a graceful shutdown for thread termination and when to communicate using volatiles. We witnessed how the correctness of a program can be compromised by undesired interactions known as race conditions as well as data races due to the lack of synchronization. And, most importantly, we have learned that the only way to correctly reason about the semantics of a multithreaded program is in terms of happens-before relationships defined by the JMM.

The language primitives and APIs presented in this section are low-level; they are the basic building blocks...

Exercises

In the following set of exercises, you are required to implement higher-level concurrency abstractions in terms of basic JVM concurrency primitives. Some of these exercises introduce concurrent counterparts of sequential programming abstractions, and, in doing so, highlight important differences between sequential and concurrent programming. The exercises are not ordered in any particular order, but some of them rely on specific content from earlier exercises or this chapter.

  1. Implement a parallel method, which takes two computation blocks a and b, and starts each of them in a new thread. The method must return a tuple with the result values of both the computations. It should have the following signature:
    def parallel[A, B](a: =>A, b: =>B): (A, B)
  2. Implement a periodically method, which takes a time interval duration specified in milliseconds, and a computation block b. The method starts a thread that executes the computation block b every duration milliseconds. It should...
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Description

This book is a must-have tutorial for software developers aiming to write concurrent programs in Scala, or broaden their existing knowledge of concurrency. This book is intended for Scala programmers that have no prior knowledge about concurrent programming, as well as those seeking to broaden their existing knowledge about concurrency. Basic knowledge of the Scala programming language will be helpful. Readers with a solid knowledge in another programming language, such as Java, should find this book easily accessible.

What you will learn

  • Get to grips with the fundamentals of concurrent programming on modern multiprocessor systems, with a particular focus on the JVM concurrency model
  • Build highperformance concurrent systems from simple, lowlevel concurrency primitives
  • Express asynchrony in concurrent computations with futures and promises
  • Seamlessly accelerate sequential programs by using dataparallel collections
  • Implement reactive and eventbased programs declaratively with Rxstyle event streams
  • Design safe, scalable, and easytocomprehend inmemory transactional data models
  • Transparently create distributed applications that scale across multiple machines
  • Choose the correct concurrency abstraction and integrate different concurrency frameworks together in large applications

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Table of Contents

10 Chapters
1. Introduction Chevron down icon Chevron up icon
2. Concurrency on the JVM and the Java Memory Model Chevron down icon Chevron up icon
3. Traditional Building Blocks of Concurrency Chevron down icon Chevron up icon
4. Asynchronous Programming with Futures and Promises Chevron down icon Chevron up icon
5. Data-Parallel Collections Chevron down icon Chevron up icon
6. Concurrent Programming with Reactive Extensions Chevron down icon Chevron up icon
7. Software Transactional Memory Chevron down icon Chevron up icon
8. Actors Chevron down icon Chevron up icon
9. Concurrency in Practice Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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