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CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
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This RewriteRule matches nodes with the add instruction attribute. The method creates a new element with the same attributes, but removes the instruction attribute. It also transforms the child nodes and appends a child node. Let s run this transformation:
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scala> new RuleTransformer(addIt).transform(xmlBooks)
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res2: Seq[scala.xml.Node] = <books instruction="update"> <book instruction="remove" status="" name="book1"></book> <book status="" name="book2"><added>I added this</added></book> </books>
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Note that the book2 book has child nodes. We can run the transformation with both of the RewriteRules:
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scala> new RuleTransformer(addIt, removeIt).transform(xmlBooks)
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res3: Seq[scala.xml.Node] = <books instruction="update"> <book status="" name="book2"><added>I added this</added></book> </books>
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In this section, we ve explored creating, parsing, and rewriting XML. Scala s awesome XML support makes it super-simple to write complex web-based applications. You can create Atom or RSS feeds in just a few lines that transform database records into the appropriate XML. You can parse incoming XML using the same constructs you use for other data sequences, which makes transformation from XML into data structures and objects easy and fast. Finally, Scala provides simple ways to transform XML. In this chapter, we ve explored Scala s immutable data types and the power and simplicity that they afford you. Next, let s see how we can use the immutable data structures in a highly concurrent situation. We re going to do concurrency without synchronization and see how immutability helps us out, big time.
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CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
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Writing multithreaded programs is a challenge. While simple data structures such as multithreaded queues or mailboxes are easy to write and relatively low in defects, most Java programs don t lend themselves to such simple abstractions. This is due in large part to passing around mutable data structures. Any data structure that can be changed without creating a new reference is a potential defect in a multithreaded application. The problem is exacerbated by the transitory nature of thread-related defects: it s hard to test for them. There are many strategies for dealing with concurrency. One can synchronize everything, but this often leads to deadlocks because two threads are locking resources that depend on each other. Another strategy is to copy everything that s passed to a thread. This strategy uses memory and CPU to work around the threading issue. Every time a resource is needed by a thread, the resource is copied for the thread s use. This means each Array, Hashtable, and so on that s requested by a given thread is copied. That way, the current thread does not have to synchronize the data. Scala and immutable data structures offer another alternative. In this example, we re going to write a synchronize-free program (even the underlying data structures contain no synchronization) that has 2,000 threads each updating shared data every 100 milliseconds and a master thread that summarizes the shared data structure every second. Create a file called Multics.scala and put the code from Listing 3-8 in it. We ll go through the code line by line after the listing.
Listing 3-8. Multics.scala import java.util.concurrent.atomic.{AtomicReference => AtomR, AtomicLong} import java.util.Random import scala.collection.immutable.TreeHashMap object Multics { type MT = Map[String, Int] val info: AtomR[MT] = new AtomR(TreeHashMap.empty) val clashCnt = new AtomicLong def main(argv: Array[String]) { runThread { repeatEvery(1000) { println("Clash Count: "+clashCnt+" Total: "+ info.get.foldLeft(0)(_ + _._2)) } }
CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
for (i <- 1 to 2000) runThread { var cnt = 0 val ran = new Random val name = "K"+i doSet(info){old => old + (name -> 0)} repeatEvery(ran.nextInt(100)) { doSet(info){old => old + (name -> (old(name) + 1))} cnt = cnt + 1 if (cnt != info.get()(name)) throw new Exception("Thread: "+name+" failed") } } } def runThread(f: => Unit) = (new Thread(new Runnable {def run(): Unit = f})).start def doSet[T](atom: AtomR[T])(update: T => T) { val old = atom.get if (atom.compareAndSet(old, update(old))) () else { clashCnt.incrementAndGet doSet(atom)(update) } } def repeatEvery(len: => Int)(body: => Unit): Unit = { try { while(true) { Thread.sleep(len) body } } catch { case e => e.printStackTrace; System.exit(1) } } }
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