我正在为一些文档格式做文档查看器.为了更容易,让我们说这是一个PDF查看器,一个桌面应用程序.该软件的一个要求是渲染速度.所以,现在,当用户滚动文档时,我正在为下一页缓存图像.
这是有效的,UI非常敏感,似乎应用程序几乎可以立即呈现页面......但代价是:内存使用量有时会达到600MB.我将它全部缓存在内存中.
现在,我知道,我可以缓存到磁盘,但是这样做总是明显变慢.我想要做的是实现一些缓存(LRU?),其中一些缓存页面(图像对象)在内存中,大多数都在磁盘上.
在我开始这个之前,框架中有什么东西或某些库可以为我做这个吗?这似乎是一个非常普遍的问题.(这是一个桌面应用程序,而不是ASP.NET)
或者,您对此问题有其他想法吗?
Sam*_*ron 21
我写了一个LRU Cache和一些测试用例,随意使用它.
您可以在我的博客上阅读来源.
对于懒惰(这里是减去测试用例):
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
namespace LRUCache {
public class IndexedLinkedList<T> {
LinkedList<T> data = new LinkedList<T>();
Dictionary<T, LinkedListNode<T>> index = new Dictionary<T, LinkedListNode<T>>();
public void Add(T value) {
index[value] = data.AddLast(value);
}
public void RemoveFirst() {
index.Remove(data.First.Value);
data.RemoveFirst();
}
public void Remove(T value) {
LinkedListNode<T> node;
if (index.TryGetValue(value, out node)) {
data.Remove(node);
index.Remove(value);
}
}
public int Count {
get {
return data.Count;
}
}
public void Clear() {
data.Clear();
index.Clear();
}
public T First {
get {
return data.First.Value;
}
}
}
}
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LRUCache
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
namespace LRUCache {
public class LRUCache<TKey, TValue> : IDictionary<TKey, TValue> {
object sync = new object();
Dictionary<TKey, TValue> data;
IndexedLinkedList<TKey> lruList = new IndexedLinkedList<TKey>();
ICollection<KeyValuePair<TKey, TValue>> dataAsCollection;
int capacity;
public LRUCache(int capacity) {
if (capacity <= 0) {
throw new ArgumentException("capacity should always be bigger than 0");
}
data = new Dictionary<TKey, TValue>(capacity);
dataAsCollection = data;
this.capacity = capacity;
}
public void Add(TKey key, TValue value) {
if (!ContainsKey(key)) {
this[key] = value;
} else {
throw new ArgumentException("An attempt was made to insert a duplicate key in the cache.");
}
}
public bool ContainsKey(TKey key) {
return data.ContainsKey(key);
}
public ICollection<TKey> Keys {
get {
return data.Keys;
}
}
public bool Remove(TKey key) {
bool existed = data.Remove(key);
lruList.Remove(key);
return existed;
}
public bool TryGetValue(TKey key, out TValue value) {
return data.TryGetValue(key, out value);
}
public ICollection<TValue> Values {
get { return data.Values; }
}
public TValue this[TKey key] {
get {
var value = data[key];
lruList.Remove(key);
lruList.Add(key);
return value;
}
set {
data[key] = value;
lruList.Remove(key);
lruList.Add(key);
if (data.Count > capacity) {
data.Remove(lruList.First);
lruList.RemoveFirst();
}
}
}
public void Add(KeyValuePair<TKey, TValue> item) {
Add(item.Key, item.Value);
}
public void Clear() {
data.Clear();
lruList.Clear();
}
public bool Contains(KeyValuePair<TKey, TValue> item) {
return dataAsCollection.Contains(item);
}
public void CopyTo(KeyValuePair<TKey, TValue>[] array, int arrayIndex) {
dataAsCollection.CopyTo(array, arrayIndex);
}
public int Count {
get { return data.Count; }
}
public bool IsReadOnly {
get { return false; }
}
public bool Remove(KeyValuePair<TKey, TValue> item) {
bool removed = dataAsCollection.Remove(item);
if (removed) {
lruList.Remove(item.Key);
}
return removed;
}
public IEnumerator<KeyValuePair<TKey, TValue>> GetEnumerator() {
return dataAsCollection.GetEnumerator();
}
System.Collections.IEnumerator System.Collections.IEnumerable.GetEnumerator() {
return ((System.Collections.IEnumerable)data).GetEnumerator();
}
}
}
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rsb*_*rro 13
对于.NET 4.0,您也可以使用MemoryCache
from System.Runtime.Caching
.
http://msdn.microsoft.com/en-us/library/system.runtime.caching.aspx
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