LockAndMoveToEnd will ignore it, since it is detached. contains an item that is not in the linked list. This means that the dictionary at this moment Remove from the dictionary outside the lock. Var node = new LinkedListNode(new LruItem(key, valueFactory(key))) Public V GetOrAdd(K key, Func valueFactory) Interlocked.Increment(ref requestHitCount) If (dictionary.TryGetValue(key, out node)) Interlocked.Increment(ref requestTotalCount) Public double HitRatio => (double)requestHitCount / (double)requestTotalCount If (capacity >(concurrencyLevel, this.capacity + 1, comparer)
Public ClassicLru(int concurrencyLevel, int capacity, IEqualit圜omparer comparer) : this(Defaults.ConcurrencyLevel, capacity, Equalit圜omparer.Default) Private readonly LinkedList linkedList = new LinkedList() Private readonly ConcurrentDictionary> dictionary
ICACHING PROVIDERS .NET CODE
A cache replacement policy then determines which object to discard when the cache is full.īelow is the source code for a simple cache with least recently used eviction policy: public sealed class ClassicLru An alternative approach to caching is to bound the number of objects in the cache (rather than estimating memory used). Your workload will determine the degree to which these things are problematic. Contains perf counters that cannot be disabled (which incur overhead).Does not scale well with concurrent writes.This can result in memory pressure, page faults, induced GC or when running under IIS, recycling the process due to exceeding the private bytes limit. if some automated process is rapidly looping through all the items in that exist, the cache size can grow too fast for the background thread to keep up. This can really add up in a server application when items are 'hot'. Keys are strings, so if the key type is not natively string, you will be forced to constantly allocate strings on the heap.A background thread then periodically 'trims' entries. All of these caches bound size in terms of memory used, and attempt to estimate memory used by tracking how total physical memory is increasing relative to the number of cached objects. NET Framework is Memor圜ache and the various related implementations in Microsoft NuGet packages (e.g. Our solution can easily store 300,000,000 object in-memory in-process without taxing GC at all - this is because we store data in large (250 mb) byte segments.Īs mentioned in other answers, the default choice using the. 64 GB.įor that we have created a 100% managed memory manager and cache that sits on top of it. On the other hand there are cases when large RAMs, which are common these days, need to be utilized, i.e. Does not matter if your objects are 2-field or 25 field - its about the number of references. The practicality is this - if you need to store a few hundred thousand instances - use MS cache. They work great if you cache a few thousand objects, but the moment you move into millions and keep them around until they propagate into GEN2 - the GC pauses would eventually start to be noticeable when you system comes to low memory threshold and GC needs to sweep all gens. NET are handy, but have a major problem - they can not store much data (tens of millions+) of objects for a long time without killing your GC.
(Disclaimer: I am the author of Laz圜ache) I recently wrote this article about getting started with caching in dot net that you may find useful. and cache the results for next time under the given keyĬomplexObject cachedResults = cache.GetOrAdd("uniqueKey", complexObjectFactory) Get our ComplexObjects from the cache, or build them in the factory func Declare (but don't execute) a func/delegate whose result we want to cacheįunc complexObjectFactory = () => methodThatTakesTimeOrResources() Uses Memor圜ache.Default as default so cache is shared between instances To give you an example: // Create our cache service using the defaults (Dependency injection ready). Memor圜ache in the framework is a good place to start, but you might also like to consider the open source library Laz圜ache because it has a simpler API than memory cache and has built in locking as well as some other developer friendly features.
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