缓存设计

最新推荐文章于 2025-07-23 13:59:28 发布

转载最新推荐文章于 2025-07-23 13:59:28 发布 · 276 阅读

本文深入探讨了如何通过Memoization技术优化Expensive Function的性能，利用ConcurrentHashMap和FutureTask实现了计算结果的缓存，显著提升了计算效率并减少了重复计算。介绍了实现细节及性能提升效果。

public interface Computable<A, V> {

V compute(A arg) throws InterruptedException;

}

public class ExpensiveFunctuion implements Computable<String, Integer> {

   public Integer compute(String arg) throws InterruptedException {
       //do..............
       return new Integer(arg);
   }

}

public class Memoizer3<A, V> implements Computable<A, V> {

   private final ConcurrentMap<A, FutureTask<V>> cache = new ConcurrentHashMap<A, FutureTask<V>>();
   private final Computable<A, V> c;

   // performance is good
   public V compute(final A arg) throws InterruptedException {
       FutureTask<V> f = cache.get(arg);
       if (f == null) {
           Callable eval = new Callable() {
               public Object call() throws Exception {
                   return c.compute(arg);
               }
           };
           FutureTask<V> ft = new FutureTask<V>(eval);
           f = cache.putIfAbsent(arg, ft);
           if(f==null){
               f=ft;
               ft.run();
           }
           ft.run();
       }

       try {
           return f.get();
       } catch (Exception e) {
           cache.remove(arg,f);
       }
       return null;
   }

   public Memoizer3(Computable<A, V> c) {
       this.c = c;
   }

}