A LRU Cache is a key-value based data container that is constrained by size and/or age, removing the least recently used objects first. This algorithm requires keeping track of the most recent time each object is accessed, which can be expensive to ensure the algorithm always discards the least recently used item.
Other LRU Algorithms
Most LRU Caching algorithms consist of two parts: a dictionary and a list. The dictionary guarantees quick access to your data, and the list, ordered by age of the objects, controls the lifespan of objects and determines which objects are to be removed first.
A simple LRU Cache implementation uses a doubly linked list; adding new items to the head, removing items from the tail, and moving any existing items to the head when referenced (touched). This algorithm is good for single threaded applications but becomes very slow in a multi-threaded environment. In a multi-threaded app, every time the linked list is modified, it must be locked. This requires thread locks on insert, read, and delete operations, causing the slowness.
Another algorithm is to mark each item’s age with a timestamp (DateTime or incremented Integer) value when touched. When the cache becomes full, the list of items must be sorted and truncated. This keeps insert and read operations very fast because no locks are required, but makes delete painfully slow (normally O(N * log N), for sorting).
Overview
The algorithm I choose, divides items into time slices I call AgeBags. Items are added to the current AgeBag until the bag becomes full or the designated time span expires, then that AgeBag is closed and the next AgeBag becomes current. Items that are touched are marked with an index of the current bag. When the cache gets too full/old, the oldest AgeBag is emptied, moving any nodes that have been touched to the correct AgeBag and removing the rest of the nodes in the bag. This allows reads to be performed without locking, and makes deletes much faster because only items that are in the oldest AgeBag are ever moved and no real sorting is ever performed.
In this system, even though the exact order of items is not maintained, this is still a true LRU Cache because everything that remains in the oldest bag (after moves are made for the touched items) is removed at the same time. Therefore, no item remains in the cache that is older than the removed items.
My implementation of LRUCache contains two distinct sections which are coded as subclasses. The LifespanMgr class tracks the item usage and determines which items to remove when the cache gets full/old. The Index class provides Dictionary key/value access to all objects stored in the cache. The Index has delegates to calculate the key of any item added to the cache, and to load an item by key/value from an external source.
Instead of storing item nodes in the Index, the Index holds WeakReference objects that point at the item nodes. By doing this, the Index does not prevent items from being garbage collected. This also allows old items to be removed from the AgeBag without having to remove the items from the index. Once a Node is removed from the LifespanMgr, it becomes eligible for garbage collection. The Node is not removed from the Indexes immediately. If an Index retrieves the node prior to garbage collection, it is reinserted into the current AgeBag’s node list. If it has already been garbage collected, a new object gets loaded. If the Index size exceeds twice the cache’s capacity, the Index is cleared and rebuilt.
Locking
Before I get into the code, I should provide a brief explanation of all the locking mechanisms used by the LRUCache:
The first and simplest is the Interlocked class which ensures that increment and decrement operations occur without context switching. This is all that is required to make increment and decrement operations thread safe, and is much faster than other locking methods.
The next locking mechanism is the Monitor class. This class is called whenever you use the C#lock keyword. One method of note is Monitor.TryEnter(obj), which immediately returns a bool specifying if a lock was acquired. Unlike the standard Enter() method, after the first thread gets the lock, all other threads will skip the operation instead of waiting for the first thread to complete. This is good for cases where speed is more important than the action being locked. It is also ideal for cases when the locked code only needs to be performed by one thread.
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if(Monitor.TryEnter(obj))
try {
//do something if lock is not busy
}
finally {
Monitor.Exit(obj);
}
The last locking mechanism used is ReaderWriterLock. At any given time, it allows either concurrent read access for multiple threads, or write access for a single thread. A ReaderWriterLock provides better throughput than Monitor because of its concurrent read access. Because using ReaderWriter locks can get a little tricky, I use a wrapper class that simplifies locking a provided delegate.
Code Review
Because the LifespanMgr is the most interesting part of the code, I will spend most of the remaining article discussing it; however, the code is highly commented so you shouldn’t have any trouble following the rest of the code.
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publicvoid Touch()
{
if( _value != null && ageBag != _mgr._currentBag )
{
if( ageBag == null )
lock( _mgr )
if( ageBag == null )
{
// if node.AgeBag==null then the object is not// currently managed by LifespanMgr so add it
next = _mgr._currentBag.first;
_mgr._currentBag.first = this;
Interlocked.Increment( ref _mgr._owner._curCount );
}
ageBag = _mgr._currentBag;
Interlocked.Increment( ref _mgr._currentSize );
}
_mgr.CheckValid();
}
Each time an indexed item is added or referenced, LifespanMgr.Node.Touch() is called which (re)inserts the node if needed, and points the node's ageBag variable at the current AgeBag. Also, in the touch method, LifespanMgr.CheckValid() is called.
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publicvoid CheckValid()
{
DateTime now = DateTime.Now;
// if lock is currently held then skip and let next Touch perform cleanup.if( (_currentSize > _bagItemLimit || now > _nextValidCheck)
&& Monitor.TryEnter( this ) )
try
{
if( (_currentSize > _bagItemLimit || now > _nextValidCheck) )
{
// if cache is no longer valid throw contents// away and start over, else cleanup old itemsif( _current > 1000000 || (_owner._isValid != null
&& !_owner._isValid()) )
_owner.Clear();
else
CleanUp( now );
}
}
finally
{
Monitor.Exit( this );
}
}
CheckValid only performs an action if the AgeBag gets full or the designated time span expires. When either of those cases are met, an optional IsValid() delegate is called which checks the health of the overall cache. If the cache is invalid or out of date, all items in the cache are removed and items will be reloaded the next time an index accesses them. If IsValid returns true, the LifespanMgr.Cleanup() method is called.
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publicvoid CleanUp( DateTime now )
{
if( _current != _oldest )
lock( this )
{
//calculate how many items should be removed
DateTime maxAge = now.Subtract( _maxAge );
DateTime minAge = now.Subtract( _minAge );
int itemsToRemove = _owner._curCount - _owner._capacity;
AgeBag bag = _bags[_oldest % _size];
while( _current != _oldest && (_current-_oldest>_size - 5
|| bag.startTime < maxAge || (itemsToRemove > 0
&& bag.stopTime > minAge)) )
{
// cache is still too big / old so remove oldest bag
Node node = bag.first;
bag.first = null;
while( node != null )
{
Node next = node.next;
node.next = null;
if( node.Value != null && node.ageBag != null )
if( node.ageBag == bag )
{
// item has not been touched since bag was// closed, so remove it from LifespanMgr
++itemsToRemove;
node.ageBag = null;
Interlocked.Decrement( ref _owner._curCount );
}
else
{
// item has been touched and should// be moved to correct age bag now
node.next = node.ageBag.first;
node.ageBag.first = node;
}
node = next;
}
// increment oldest bag
bag = _bags[(++_oldest) % _size];
}
OpenCurrentBag( now, ++_current );
CheckIndexValid();
}
}
Cleanup does the grunt work of moving touched items out of the last bag and deleting the rest. It also calls CheckIndexValid() to see if indexes need to be recreated, and OpenCurrentBag() which sets up the next current AgeBag.
Usage
The UserCache.cs file contains an example of how the cache can be used:
Brian Agnes has been professionally programming for 15 years using a variety of languages. Brian started using C# in 2002 and is a MCAD charter member.
Brian is currently living in Denver Colorado and working as a Senior Developer for NewsGator.com (a leader in RSS aggregation).
Brian has been a regular presenter at local INETA dot net user groups.
declare @n int
set @n = 10
select TOP(@n) * from orders
2. 分页
不知各位过去用SQL Server 2000是怎么分页的，大多都用到了临时表。SQL Server 2005一句话就支持分页，性能据说也非常不错。
–按Freight从小到大排序，求20到30行的结果
select * from(select orderId, Freight, ROW_NUMBER() OVER(order by Freight) as row from orders) a
where row between 20 and 30
3. 排名
select * from(select orderId, Freight, RANK() OVER(order by Freight) as rank from orders) a
where rank between 20 and 30
4. try … catch
SQL Server 2000没有异常，T-SQL必须逐行检查错误代码，对于习惯了try catch程序员，2005是不是更加亲切：
SET XACT_ABORT ON — 打开 try功能
BEGIN TRY
begin tran
insert into orders(CustomerId) values(-1)
commit tran
print 'commited'
END TRY
BEGIN CATCH
rollback
print 'rolled back'
END CATCH
5. 通用表达式CTE
通过表达式可免除你过去创建临时表的麻烦。
–例子：结合通用表达式进行分页
WITH orderFreight AS(
select orderId, Freight, ROW_NUMBER() OVER(order by Freight) as row from orders
)
select orderId, Freight from orderFreight where row between 10 and 20
public static class ThreadPool
{
public static void GetMinThreads(out int workerThreads, out int completionPortThreads);
public static bool SetMinThreads(int workerThreads, int completionPortThreads);
}
public static class ThreadPool
{
public static void GetMaxThreads(out int workerThreads, out int completionPortThreads);
public static bool SetMaxThreads(int workerThreads, int completionPortThreads);
public static void GetAvailableThreads(out int workerThreads, out int completionPortThreads);
}
Mikel
United States
