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Thread synchronisation among multithreads accessing singleton object

I want to find out if there is a better way for the code below in terms of robust code and clean code.
// Encryption is singleton via IoC
public class Encryption{
private volatile ConcurrentDictionary<string, string> _blobs = new ConcurrentDictionary<string, string>();
private object _encryptedDataLock = new object();
public string CreateEncryptedData(string key)
{
string encryptedData = string.Empty;
if (_cloudBlobs.ContainsKey(key))
encryptedData = _blobs[key];
lock (_encryptedDataLock)
{
if (!_cloudBlobs.ContainsKey(key))
{
encryptedData = CalulateEncryptedData();
_blobs.TryAdd(key, encryptedData);
return encryptedData;
}
}
return encryptedData;
}
}
Update
I am looking for solution that is not using lock object, double checking, volatile, as less code as possible explicitly.

If two threads call CreateEncryptedData with the same key at the same time, there are three possible outcomes:
CalulateEncryptedData() is called exactly once, both threads get the result, and that result is stored in _blobs.
CalulateEncryptedData() is called twice, but the result of one of the calls is discarded. Both threads get the same result, and that result is also stored in _blobs.
CalulateEncryptedData() is called twice, and both threads get different results. One of the results is stored in _blobs. Obviously this is undesirable.
If you need case 1, then you need a separate lock object, but you can write it like this:
public string CreateEncryptedData(string key)
{
if (_blobs.TryGetValue(key, out var value))
return value;
lock (_encryptedDataLock)
{
return _blobs.GetOrAdd(key, x => CalulateEncryptedData());
}
}
If case 2 is OK, you can further simplify to this:
public string CreateEncryptedData(string key)
{
return _blobs.GetOrAdd(key, x => CalulateEncryptedData());
}

Caching Pattern for Fetching Collections

Is there a caching strategy/pattern that can be used for fetching multiple items, where only some may be cached? Does it make sense to use a cache in such scenarios?
More Detail
Where the cache would return a single (or known number of) result(s) caching's simple; if it's in the cache we return it, if not we fetch it, add it to the cache, then return it:
//using System.Runtime.Caching;
ObjectCache cache = MemoryCache.Default;
TimeSpan cacheLifetime = new TimeSpan(0, 20, 0);
SomeObjectFinder source = new SomeObjectFinder();
public SomeObject GetById(long id)
{
return GetFromCacheById(id) ?? GetFromSourceById(id);
}
protected SomeObject GetFromCacheById(long id)
{
return (SomeObject)cache.Get(id.ToString(),null);
}
protected SomeObject GetFromSourceById (long id)
{
SomeObject result = source.GetById(id);
return result == null ? null : (SomeObject)cache.AddOrGetExisting(id.ToString(), result, DateTimeOffset.UtcNow.Add(cacheLifetime), null);
}
However, where we don't know how many results to expect, if we fetched the results from the cache we wouldn't know that we'd fetched everything; only what's been cached. So following the above pattern, if none of or all of the results had been cached we'd be fine; but if half of them had been, we'd get a partial result set.
I was thinking the below may make sense; but I've not played with async/await before, and most of what I've read implies that calling async code from synchronous code is generally considered bad; so this solution's probably not a good idea.
public IEnumerable<SomeObject> GetByPartialName(string startOfName)
{
//kick off call to the DB (or whatever) to get the full result set
var getResultsTask = Task.Run<IList<SomeObject>>(async() => await GetFromSourceByPartialNameAsync(startOfName));
//whilst we leave that to run in the background, start getting and returning the results from the cache
var cacheResults = GetFromCacheByPartialName(startOfName);
foreach (var result in cacheResults)
{
yield return result;
}
//once all cached values are returned, wait for the async task to complete, remove the results we'd already returned from cache, then add the remaining results to cache and return those also
var results = getResultsTask.GetAwaiter().GetResult();
foreach (var result in results.Except(cacheResults))
{
yield return CacheAddOrGetExistingByName(result.Name, result);
}
}
protected async Task<IList<SomeObject>> GetFromSourceByPartialNameAsync(string startOfName)
{
return source.GetByPartialName(startOfName);
}
My assumption is that the answer's going to be "in this scenario either cache everything beforehand, or don't use cache"... but hopefully there's some better option.

You can use the Decorator and strategy pattern, here is the link to Steve Smith post regarding building a cached repository, and for individual items work as well, so you can use a decorator using redis and if not found it goes to the database and then save it to database, after that querying the data will be really fast.
Here is some sample code:
public class CachedAlbumRepository : IAlbumRepository
{
private readonly IAlbumRepository _albumRepository;
public CachedAlbumRepository(IAlbumRepository albumRepository)
{
_albumRepository = albumRepository;
}
private static readonly object CacheLockObject = new object();
public IEnumerable<Album> GetTopSellingAlbums(int count)
{
Debug.Print("CachedAlbumRepository:GetTopSellingAlbums");
string cacheKey = "TopSellingAlbums-" + count;
var result = HttpRuntime.Cache[cacheKey] as List<Album>;
if (result == null)
{
lock (CacheLockObject)
{
result = HttpRuntime.Cache[cacheKey] as List<Album>;
if (result == null)
{
result = _albumRepository.GetTopSellingAlbums(count).ToList();
HttpRuntime.Cache.Insert(cacheKey, result, null,
DateTime.Now.AddSeconds(60), TimeSpan.Zero);
}
}
}
return result;
}
}

Synchronize threads on per-item base

While this question is about the MemoryCache class, I can imagine the same need with a Dictionary or ConcurrentDictionary.GetOrAdd where the valueFactory-lambda is also a lengthy operation.
In essence I want to synchronize/lock threads on a per-item base. I know MemoryCache is thread safe, but still, checking if an item exists and add the item when it doesn't exist, still needs to be synchronized.
Consider this sample code:
public class MyCache
{
private static readonly MemoryCache cache = new MemoryCache(Guid.NewGuid().ToString());
public object Get(string id)
{
var cacheItem = cache.GetCachedItem(id);
if (cacheItem != null) return cacheItem.Value;
var item = this.CreateItem(id);
cache.Add(id, item, new CacheItemPolicy
{
SlidingExpiration = TimeSpan.FromMinutes(20)
});
return item;
}
private object CreateItem(string id)
{
// Lengthy operation, f.e. querying database or even external API
return whateverCreatedObject;
}
}
As you can see, we need to synchronize cache.GetCachedItem and cache.Add. But since CreateItem is a lengthy operation (hence the MemoryCache), I don't want to lock all threads as this code would do:
public object Get(string id)
{
lock (cache)
{
var item = cache.GetCachedItem(id);
if (item != null) return item.Value;
cache.Add(id, this.CreateItem(id), new CacheItemPolicy
{
SlidingExpiration = TimeSpan.FromMinutes(20)
});
}
}
Also, having no lock is not an options, as then we could have multiple threads calling CreateItem for the same id.
What I could do is create a unique named Semaphore per id, so locking happens on per-item basis. But this will be a system-resource killer, as we do not want to register +100K named semaphores on our system.
I'm sure I'm not the first that needs this kind of synchronization, but I didn't find any question/answer that fits this scenario.
My question is if someone can come up with a different, resource friendly approach for this problem?
Update
I've found this NamedReaderWriterLocker class that looks promising at first but is dangerous to use as two threads can potentially get a different ReaderWriterLockSlim instance for the same name when both threads get into the ConcurrentDictionary's valueFactory at the same time. Maybe I can use this implementation with some additional lock inside the GetLock method.

Since your key is a string, you could lock on string.Intern(id).
MSDN documentation: System.String.Intern
i.e.
lock (string.Intern(id))
{
var item = cache.GetCachedItem(id);
if (item != null)
{
return item.Value;
}
cache.Add(id, this.CreateItem(id), new CacheItemPolicy
{
SlidingExpiration = TimeSpan.FromMinutes(20)
});
return /* some value, this line was absent in the original code. */;
}

How to deal with costly building operations using MemoryCache?

On an ASP.NET MVC project we have several instances of data that requires good amount of resources and time to build. We want to cache them.
MemoryCache provides certain level of thread-safety but not enough to avoid running multiple instances of building code in parallel. Here is an example:
var data = cache["key"];
if(data == null)
{
data = buildDataUsingGoodAmountOfResources();
cache["key"] = data;
}
As you can see on a busy website hundreds of threads could go inside the if statement simultaneously until the data is built and make the building operation even slower, unnecessarily consuming the server resources.
There is an atomic AddOrGetExisting implementation in MemoryCache but it incorrectly requires "value to set" instead of "code to retrieve the value to set" which I think renders the given method almost completely useless.
We have been using our own ad-hoc scaffolding around MemoryCache to get it right however it requires explicit locks. It's cumbersome to use per-entry lock objects and we usually get away by sharing lock objects which is far from ideal. That made me think that reasons to avoid such convention could be intentional.
So I have two questions:
Is it a better practice not to lock building code? (That could have been proven more responsive for one, I wonder)
What's the right way to achieve per-entry locking for MemoryCache for such a lock? The strong urge to use key string as the lock object is dismissed at ".NET locking 101".

We solved this issue by combining Lazy<T> with AddOrGetExisting to avoid a need for a lock object completely. Here is a sample code (which uses infinite expiration):
public T GetFromCache<T>(string key, Func<T> valueFactory)
{
var newValue = new Lazy<T>(valueFactory);
// the line belows returns existing item or adds the new value if it doesn't exist
var value = (Lazy<T>)cache.AddOrGetExisting(key, newValue, MemoryCache.InfiniteExpiration);
return (value ?? newValue).Value; // Lazy<T> handles the locking itself
}
That's not complete. There are gotchas like "exception caching" so you have to decide about what you want to do in case your valueFactory throws exception. One of the advantages, though, is the ability to cache null values too.

For the conditional add requirement, I always use ConcurrentDictionary, which has an overloaded GetOrAdd method which accepts a delegate to fire if the object needs to be built.
ConcurrentDictionary<string, object> _cache = new
ConcurrenctDictionary<string, object>();
public void GetOrAdd(string key)
{
return _cache.GetOrAdd(key, (k) => {
//here 'k' is actually the same as 'key'
return buildDataUsingGoodAmountOfResources();
});
}
In reality I almost always use static concurrent dictionaries. I used to have 'normal' dictionaries protected by a ReaderWriterLockSlim instance, but as soon as I switched to .Net 4 (it's only available from that onwards) I started converting any of those that I came across.
ConcurrentDictionary's performance is admirable to say the least :)
Update Naive implementation with expiration semantics based on age only. Also should ensure that individual items are only created once - as per #usr's suggestion. Update again - as #usr has suggested - simply using a Lazy<T> would be a lot simpler - you can just forward the creation delegate to that when adding it to the concurrent dictionary. I'be changed the code, as actually my dictionary of locks wouldn't have worked anyway. But I really should have thought of that myself (past midnight here in the UK though and I'm beat. Any sympathy? No of course not. Being a developer, I have enough caffeine coursing through my veins to wake the dead).
I do recommend implementing the IRegisteredObject interface with this, though, and then registering it with the HostingEnvironment.RegisterObject method - doing that would provide a cleaner way to shut down the poller thread when the application pool shuts-down/recycles.
public class ConcurrentCache : IDisposable
{
private readonly ConcurrentDictionary<string, Tuple<DateTime?, Lazy<object>>> _cache =
new ConcurrentDictionary<string, Tuple<DateTime?, Lazy<object>>>();
private readonly Thread ExpireThread = new Thread(ExpireMonitor);
public ConcurrentCache(){
ExpireThread.Start();
}
public void Dispose()
{
//yeah, nasty, but this is a 'naive' implementation :)
ExpireThread.Abort();
}
public void ExpireMonitor()
{
while(true)
{
Thread.Sleep(1000);
DateTime expireTime = DateTime.Now;
var toExpire = _cache.Where(kvp => kvp.First != null &&
kvp.Item1.Value < expireTime).Select(kvp => kvp.Key).ToArray();
Tuple<string, Lazy<object>> removed;
object removedLock;
foreach(var key in toExpire)
{
_cache.TryRemove(key, out removed);
}
}
}
public object CacheOrAdd(string key, Func<string, object> factory,
TimeSpan? expiry)
{
return _cache.GetOrAdd(key, (k) => {
//get or create a new object instance to use
//as the lock for the user code
//here 'k' is actually the same as 'key'
return Tuple.Create(
expiry.HasValue ? DateTime.Now + expiry.Value : (DateTime?)null,
new Lazy<object>(() => factory(k)));
}).Item2.Value;
}
}

Taking the top answer into C# 7, here's my implementation that allows storage from any source type T to any return type TResult.
/// <summary>
/// Creates a GetOrRefreshCache function with encapsulated MemoryCache.
/// </summary>
/// <typeparam name="T">The type of inbound objects to cache.</typeparam>
/// <typeparam name="TResult">How the objects will be serialized to cache and returned.</typeparam>
/// <param name="cacheName">The name of the cache.</param>
/// <param name="valueFactory">The factory for storing values.</param>
/// <param name="keyFactory">An optional factory to choose cache keys.</param>
/// <returns>A function to get or refresh from cache.</returns>
public static Func<T, TResult> GetOrRefreshCacheFactory<T, TResult>(string cacheName, Func<T, TResult> valueFactory, Func<T, string> keyFactory = null) {
var getKey = keyFactory ?? (obj => obj.GetHashCode().ToString());
var cache = new MemoryCache(cacheName);
// Thread-safe lazy cache
TResult getOrRefreshCache(T obj) {
var key = getKey(obj);
var newValue = new Lazy<TResult>(() => valueFactory(obj));
var value = (Lazy<TResult>) cache.AddOrGetExisting(key, newValue, ObjectCache.InfiniteAbsoluteExpiration);
return (value ?? newValue).Value;
}
return getOrRefreshCache;
}
Usage
/// <summary>
/// Get a JSON object from cache or serialize it if it doesn't exist yet.
/// </summary>
private static readonly Func<object, string> GetJson =
GetOrRefreshCacheFactory<object, string>("json-cache", JsonConvert.SerializeObject);
var json = GetJson(new { foo = "bar", yes = true });

Here is simple solution as MemoryCache extension method.
public static class MemoryCacheExtensions
{
public static T LazyAddOrGetExitingItem<T>(this MemoryCache memoryCache, string key, Func<T> getItemFunc, DateTimeOffset absoluteExpiration)
{
var item = new Lazy<T>(
() => getItemFunc(),
LazyThreadSafetyMode.PublicationOnly // Do not cache lazy exceptions
);
var cachedValue = memoryCache.AddOrGetExisting(key, item, absoluteExpiration) as Lazy<T>;
return (cachedValue != null) ? cachedValue.Value : item.Value;
}
}
And test for it as usage description.
[TestMethod]
[TestCategory("MemoryCacheExtensionsTests"), TestCategory("UnitTests")]
public void MemoryCacheExtensions_LazyAddOrGetExitingItem_Test()
{
const int expectedValue = 42;
const int cacheRecordLifetimeInSeconds = 42;
var key = "lazyMemoryCacheKey";
var absoluteExpiration = DateTimeOffset.Now.AddSeconds(cacheRecordLifetimeInSeconds);
var lazyMemoryCache = MemoryCache.Default;
#region Cache warm up
var actualValue = lazyMemoryCache.LazyAddOrGetExitingItem(key, () => expectedValue, absoluteExpiration);
Assert.AreEqual(expectedValue, actualValue);
#endregion
#region Get value from cache
actualValue = lazyMemoryCache.LazyAddOrGetExitingItem(key, () => expectedValue, absoluteExpiration);
Assert.AreEqual(expectedValue, actualValue);
#endregion
}

Sedat's solution of combining Lazy with AddOrGetExisting is inspiring. I must point out that this solution has a performance issue, which seems very important for a solution for caching.
If you look at the code of AddOrGetExisting(), you will find that AddOrGetExisting() is not a lock-free method. Comparing to the lock-free Get() method, it wastes the one of the advantage of MemoryCache.
I would like to recommend to follow solution, using Get() first and then use AddOrGetExisting() to avoid creating object multiple times.
public T GetFromCache<T>(string key, Func<T> valueFactory)
{
T value = (T)cache.Get(key);
if (value != null)
{
return value;
}
var newValue = new Lazy<T>(valueFactory);
// the line belows returns existing item or adds the new value if it doesn't exist
var oldValue = (Lazy<T>)cache.AddOrGetExisting(key, newValue, MemoryCache.InfiniteExpiration);
return (oldValue ?? newValue).Value; // Lazy<T> handles the locking itself
}

Here is a design that follows what you seem to have in mind. The first lock only happens for a short time. The final call to data.Value also locks (underneath), but clients will only block if two of them are requesting the same item at the same time.
public DataType GetData()
{
lock(_privateLockingField)
{
Lazy<DataType> data = cache["key"] as Lazy<DataType>;
if(data == null)
{
data = new Lazy<DataType>(() => buildDataUsingGoodAmountOfResources();
cache["key"] = data;
}
}
return data.Value;
}

Caching in C#/.Net

I wanted to ask you what is the best approach to implement a cache in C#? Is there a possibility by using given .NET classes or something like that? Perhaps something like a dictionary that will remove some entries, if it gets too large, but where whose entries won't be removed by the garbage collector?

If you are using .NET 4 or superior, you can use MemoryCache class.

If you're using ASP.NET, you could use the Cache class (System.Web.Caching).
Here is a good helper class: c-cache-helper-class
If you mean caching in a windows form app, it depends on what you're trying to do, and where you're trying to cache the data.
We've implemented a cache behind a Webservice for certain methods
(using the System.Web.Caching object.).
However, you might also want to look at the Caching Application Block. (See here) that is part of the Enterprise Library for .NET Framework 2.0.

MemoryCache in the framework is a good place to start, but you might also like to consider the open source library LazyCache because it has a simpler API than memory cache and has built in locking as well as some other developer friendly features. It is also available on nuget.
To give you an example:
// Create our cache service using the defaults (Dependency injection ready).
// Uses MemoryCache.Default as default so cache is shared between instances
IAppCache cache = new CachingService();
// Declare (but don't execute) a func/delegate whose result we want to cache
Func<ComplexObjects> complexObjectFactory = () => methodThatTakesTimeOrResources();
// Get our ComplexObjects from the cache, or build them in the factory func
// and cache the results for next time under the given key
ComplexObject cachedResults = cache.GetOrAdd("uniqueKey", complexObjectFactory);
I recently wrote this article about getting started with caching in dot net that you may find useful.
(Disclaimer: I am the author of LazyCache)

The cache classes supplied with .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. 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.
The practicality is this - if you need to store a few hundred thousand instances - use MS cache. Does not matter if your objects are 2-field or 25 field - its about the number of references.
On the other hand there are cases when large RAMs, which are common these days, need to be utilized, i.e. 64 GB.
For that we have created a 100% managed memory manager and cache that sits on top of it.
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.
Here is the code: NFX Pile (Apache 2.0)
And video:
NFX Pile Cache - Youtube

You can use the ObjectCache.
See http://msdn.microsoft.com/en-us/library/system.runtime.caching.objectcache.aspx

For Local Stores
.NET MemoryCache
NCache Express
AppFabric Caching
...

As mentioned in other answers, the default choice using the .NET Framework is MemoryCache and the various related implementations in Microsoft NuGet packages (e.g. Microsoft.Extensions.Caching.MemoryCache). 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. A background thread then periodically 'trims' entries.
MemoryCache etc. share some limitations:
Keys are strings, so if the key type is not natively string, you will be forced to constantly allocate strings on the heap. This can really add up in a server application when items are 'hot'.
Has poor 'scan resistance' - e.g. 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 result in memory pressure, page faults, induced GC or when running under IIS, recycling the process due to exceeding the private bytes limit.
Does not scale well with concurrent writes.
Contains perf counters that cannot be disabled (which incur overhead).
Your workload will determine the degree to which these things are problematic. An alternative approach to caching is to bound the number of objects in the cache (rather than estimating memory used). A cache replacement policy then determines which object to discard when the cache is full.
Below is the source code for a simple cache with least recently used eviction policy:
public sealed class ClassicLru<K, V>
{
private readonly int capacity;
private readonly ConcurrentDictionary<K, LinkedListNode<LruItem>> dictionary;
private readonly LinkedList<LruItem> linkedList = new LinkedList<LruItem>();
private long requestHitCount;
private long requestTotalCount;
public ClassicLru(int capacity)
: this(Defaults.ConcurrencyLevel, capacity, EqualityComparer<K>.Default)
{
}
public ClassicLru(int concurrencyLevel, int capacity, IEqualityComparer<K> comparer)
{
if (capacity < 3)
{
throw new ArgumentOutOfRangeException("Capacity must be greater than or equal to 3.");
}
if (comparer == null)
{
throw new ArgumentNullException(nameof(comparer));
}
this.capacity = capacity;
this.dictionary = new ConcurrentDictionary<K, LinkedListNode<LruItem>>(concurrencyLevel, this.capacity + 1, comparer);
}
public int Count => this.linkedList.Count;
public double HitRatio => (double)requestHitCount / (double)requestTotalCount;
///<inheritdoc/>
public bool TryGet(K key, out V value)
{
Interlocked.Increment(ref requestTotalCount);
LinkedListNode<LruItem> node;
if (dictionary.TryGetValue(key, out node))
{
LockAndMoveToEnd(node);
Interlocked.Increment(ref requestHitCount);
value = node.Value.Value;
return true;
}
value = default(V);
return false;
}
public V GetOrAdd(K key, Func<K, V> valueFactory)
{
if (this.TryGet(key, out var value))
{
return value;
}
var node = new LinkedListNode<LruItem>(new LruItem(key, valueFactory(key)));
if (this.dictionary.TryAdd(key, node))
{
LinkedListNode<LruItem> first = null;
lock (this.linkedList)
{
if (linkedList.Count >= capacity)
{
first = linkedList.First;
linkedList.RemoveFirst();
}
linkedList.AddLast(node);
}
// Remove from the dictionary outside the lock. This means that the dictionary at this moment
// contains an item that is not in the linked list. If another thread fetches this item,
// LockAndMoveToEnd will ignore it, since it is detached. This means we potentially 'lose' an
// item just as it was about to move to the back of the LRU list and be preserved. The next request
// for the same key will be a miss. Dictionary and list are eventually consistent.
// However, all operations inside the lock are extremely fast, so contention is minimized.
if (first != null)
{
dictionary.TryRemove(first.Value.Key, out var removed);
if (removed.Value.Value is IDisposable d)
{
d.Dispose();
}
}
return node.Value.Value;
}
return this.GetOrAdd(key, valueFactory);
}
public bool TryRemove(K key)
{
if (dictionary.TryRemove(key, out var node))
{
// If the node has already been removed from the list, ignore.
// E.g. thread A reads x from the dictionary. Thread B adds a new item, removes x from
// the List & Dictionary. Now thread A will try to move x to the end of the list.
if (node.List != null)
{
lock (this.linkedList)
{
if (node.List != null)
{
linkedList.Remove(node);
}
}
}
if (node.Value.Value is IDisposable d)
{
d.Dispose();
}
return true;
}
return false;
}
// Thead A reads x from the dictionary. Thread B adds a new item. Thread A moves x to the end. Thread B now removes the new first Node (removal is atomic on both data structures).
private void LockAndMoveToEnd(LinkedListNode<LruItem> node)
{
// If the node has already been removed from the list, ignore.
// E.g. thread A reads x from the dictionary. Thread B adds a new item, removes x from
// the List & Dictionary. Now thread A will try to move x to the end of the list.
if (node.List == null)
{
return;
}
lock (this.linkedList)
{
if (node.List == null)
{
return;
}
linkedList.Remove(node);
linkedList.AddLast(node);
}
}
private class LruItem
{
public LruItem(K k, V v)
{
Key = k;
Value = v;
}
public K Key { get; }
public V Value { get; }
}
}
This is just to illustrate a thread safe cache - it probably has bugs and can be a bottleneck under heavy concurrent workloads (e.g. in a web server).
A thoroughly tested, production ready, scalable concurrent implementation is a bit beyond a stack overflow post. To solve this in my projects, I implemented a thread safe pseudo LRU (think concurrent dictionary, but with constrained size). Performance is very close to a raw ConcurrentDictionary, ~10x faster than MemoryCache, ~10x better concurrent throughput than ClassicLru above, and better hit rate. A detailed performance analysis provided in the github link below.
Usage looks like this:
int capacity = 666;
var lru = new ConcurrentLru<int, SomeItem>(capacity);
var value = lru.GetOrAdd(1, (k) => new SomeItem(k));
GitHub: https://github.com/bitfaster/BitFaster.Caching
Install-Package BitFaster.Caching

Your question needs more clarification. C# is a language not a framework. You have to specify which framework you want to implement the caching. If we consider that you want to implement it in ASP.NET it is still depends completely on what you want from Cache. You can decide between in-process cache (which will keep the data inside the heap of your application) and out-of-process cache (in this case you can store the data in other memory than the heap like Amazon Elastic cache server). And there is also another decision to make which is between client caching or serve side caching. Usually in solution you have to develop different solution for caching different data. Because base on four factors (accessibility, persistency, size, cost) you have to make decision which solution you need.

I wrote this some time ago and it seems to work well. It allows you to differentiate different cache stores by using different Types: ApplicationCaching<MyCacheType1>, ApplicationCaching<MyCacheType2>....
You can decide to allow some stores to persist after execution and others to expire.
You will need a reference to the Newtonsoft.Json serializer (or use an alternative one) and of course all objects or values types to be cached must be serializable.
Use MaxItemCount to set a limit to the number of items in any one store.
A separate Zipper class (see code below) uses System.IO.Compression. This minimises the size of the store and helps speed up loading times.
public static class ApplicationCaching<K>
{
//====================================================================================================================
public static event EventHandler InitialAccess = (s, e) => { };
//=============================================================================================
static Dictionary<string, byte[]> _StoredValues;
static Dictionary<string, DateTime> _ExpirationTimes = new Dictionary<string, DateTime>();
//=============================================================================================
public static int MaxItemCount { get; set; } = 0;
private static void OnInitialAccess()
{
//-----------------------------------------------------------------------------------------
_StoredValues = new Dictionary<string, byte[]>();
//-----------------------------------------------------------------------------------------
InitialAccess?.Invoke(null, EventArgs.Empty);
//-----------------------------------------------------------------------------------------
}
public static void AddToCache<T>(string key, T value, DateTime expirationTime)
{
try
{
//-----------------------------------------------------------------------------------------
if (_StoredValues is null) OnInitialAccess();
//-----------------------------------------------------------------------------------------
string strValue = JsonConvert.SerializeObject(value);
byte[] zippedValue = Zipper.Zip(strValue);
//-----------------------------------------------------------------------------------------
_StoredValues.Remove(key);
_StoredValues.Add(key, zippedValue);
//-----------------------------------------------------------------------------------------
_ExpirationTimes.Remove(key);
_ExpirationTimes.Add(key, expirationTime);
//-----------------------------------------------------------------------------------------
}
catch (Exception ex)
{
throw ex;
}
}
//=============================================================================================
public static T GetFromCache<T>(string key, T defaultValue = default)
{
try
{
//-----------------------------------------------------------------------------------------
if (_StoredValues is null) OnInitialAccess();
//-----------------------------------------------------------------------------------------
if (_StoredValues.ContainsKey(key))
{
//------------------------------------------------------------------------------------------
if (_ExpirationTimes[key] <= DateTime.Now)
{
//------------------------------------------------------------------------------------------
_StoredValues.Remove(key);
_ExpirationTimes.Remove(key);
//------------------------------------------------------------------------------------------
return defaultValue;
//------------------------------------------------------------------------------------------
}
//------------------------------------------------------------------------------------------
byte[] zippedValue = _StoredValues[key];
//------------------------------------------------------------------------------------------
string strValue = Zipper.Unzip(zippedValue);
T value = JsonConvert.DeserializeObject<T>(strValue);
//------------------------------------------------------------------------------------------
return value;
//------------------------------------------------------------------------------------------
}
else
{
return defaultValue;
}
//---------------------------------------------------------------------------------------------
}
catch (Exception ex)
{
throw ex;
}
}
//=============================================================================================
public static string ConvertCacheToString()
{
//-----------------------------------------------------------------------------------------
if (_StoredValues is null || _ExpirationTimes is null) return "";
//-----------------------------------------------------------------------------------------
List<string> storage = new List<string>();
//-----------------------------------------------------------------------------------------
string strStoredObject = JsonConvert.SerializeObject(_StoredValues);
string strExpirationTimes = JsonConvert.SerializeObject(_ExpirationTimes);
//-----------------------------------------------------------------------------------------
storage.AddRange(new string[] { strStoredObject, strExpirationTimes});
//-----------------------------------------------------------------------------------------
string strStorage = JsonConvert.SerializeObject(storage);
//-----------------------------------------------------------------------------------------
return strStorage;
//-----------------------------------------------------------------------------------------
}
//=============================================================================================
public static void InializeCacheFromString(string strCache)
{
try
{
//-----------------------------------------------------------------------------------------
List<string> storage = JsonConvert.DeserializeObject<List<string>>(strCache);
//-----------------------------------------------------------------------------------------
if (storage != null && storage.Count == 2)
{
//-----------------------------------------------------------------------------------------
_StoredValues = JsonConvert.DeserializeObject<Dictionary<string, byte[]>>(storage.First());
_ExpirationTimes = JsonConvert.DeserializeObject<Dictionary<string, DateTime>>(storage.Last());
//-----------------------------------------------------------------------------------------
if (_ExpirationTimes != null && _StoredValues != null)
{
//-----------------------------------------------------------------------------------------
for (int i = 0; i < _ExpirationTimes.Count; i++)
{
string key = _ExpirationTimes.ElementAt(i).Key;
//-----------------------------------------------------------------------------------------
if (_ExpirationTimes[key] < DateTime.Now)
{
ClearItem(key);
}
//-----------------------------------------------------------------------------------------
}
//-----------------------------------------------------------------------------------------
if (MaxItemCount > 0 && _StoredValues.Count > MaxItemCount)
{
IEnumerable<KeyValuePair<string, DateTime>> countedOutItems = _ExpirationTimes.OrderByDescending(o => o.Value).Skip(MaxItemCount);
for (int i = 0; i < countedOutItems.Count(); i++)
{
ClearItem(countedOutItems.ElementAt(i).Key);
}
}
//-----------------------------------------------------------------------------------------
return;
//-----------------------------------------------------------------------------------------
}
//-----------------------------------------------------------------------------------------
}
//-----------------------------------------------------------------------------------------
_StoredValues = new Dictionary<string, byte[]>();
_ExpirationTimes = new Dictionary<string, DateTime>();
//-----------------------------------------------------------------------------------------
}
catch (Exception)
{
throw;
}
}
//=============================================================================================
public static void ClearItem(string key)
{
//-----------------------------------------------------------------------------------------
if (_StoredValues.ContainsKey(key))
{
_StoredValues.Remove(key);
}
//-----------------------------------------------------------------------------------------
if (_ExpirationTimes.ContainsKey(key))
_ExpirationTimes.Remove(key);
//-----------------------------------------------------------------------------------------
}
//=============================================================================================
}
You can easily start using the cache on the fly with something like...
//------------------------------------------------------------------------------------------------------------------------------
string key = "MyUniqueKeyForThisItem";
//------------------------------------------------------------------------------------------------------------------------------
MyType obj = ApplicationCaching<MyCacheType>.GetFromCache<MyType>(key);
//------------------------------------------------------------------------------------------------------------------------------
if (obj == default)
{
obj = new MyType(...);
ApplicationCaching<MyCacheType>.AddToCache(key, obj, DateTime.Now.AddHours(1));
}
Note the actual types stored in the cache can be the same or different from the cache type. The cache type is ONLY used to differentiate different cache stores.
You can then decide to allow the cache to persist after execution terminates using Default Settings
string bulkCache = ApplicationCaching<MyType>.ConvertCacheToString();
//--------------------------------------------------------------------------------------------------------
if (bulkCache != "")
{
Properties.Settings.Default.*MyType*DataCachingStore = bulkCache;
}
//--------------------------------------------------------------------------------------------------------
try
{
Properties.Settings.Default.Save();
}
catch (IsolatedStorageException)
{
//handle Isolated Storage exceptions here
}
Handle the InitialAccess Event to reinitialize the cache when you restart the app
private static void ApplicationCaching_InitialAccess(object sender, EventArgs e)
{
//-----------------------------------------------------------------------------------------
string storedCache = Properties.Settings.Default.*MyType*DataCachingStore;
ApplicationCaching<MyCacheType>.InializeCacheFromString(storedCache);
//-----------------------------------------------------------------------------------------
}
Finally here is the Zipper class...
public class Zipper
{
public static void CopyTo(Stream src, Stream dest)
{
byte[] bytes = new byte[4096];
int cnt;
while ((cnt = src.Read(bytes, 0, bytes.Length)) != 0)
{
dest.Write(bytes, 0, cnt);
}
}
public static byte[] Zip(string str)
{
var bytes = Encoding.UTF8.GetBytes(str);
using (var msi = new MemoryStream(bytes))
using (var mso = new MemoryStream())
{
using (var gs = new GZipStream(mso, CompressionMode.Compress))
{
CopyTo(msi, gs);
}
return mso.ToArray();
}
}
public static string Unzip(byte[] bytes)
{
using (var msi = new MemoryStream(bytes))
using (var mso = new MemoryStream())
{
using (var gs = new GZipStream(msi, CompressionMode.Decompress))
{
CopyTo(gs, mso);
}
return Encoding.UTF8.GetString(mso.ToArray());
}
}
}

If you are looking to Cache something in ASP.Net then I would look at the Cache class. For example
Hashtable menuTable = new Hashtable();
menuTable.add("Home","default.aspx");
Cache["menu"] = menuTable;
Then to retrieve it again
Hashtable menuTable = (Hashtable)Cache["menu"];

- Memory cache implementation for .Net core
public class CachePocRepository : ICachedEmployeeRepository
{
private readonly IEmployeeRepository _employeeRepository;
private readonly IMemoryCache _memoryCache;
public CachePocRepository(
IEmployeeRepository employeeRepository,
IMemoryCache memoryCache)
{
_employeeRepository = employeeRepository;
_memoryCache = memoryCache;
}
public async Task<Employee> GetEmployeeDetailsId(string employeeId)
{
_memoryCache.TryGetValue(employeeId, out Employee employee);
if (employee != null)
{
return employee;
}
employee = await _employeeRepository.GetEmployeeDetailsId(employeeId);
_memoryCache.Set(employeeId,
employee,
new MemoryCacheEntryOptions()
{
AbsoluteExpiration = DateTimeOffset.UtcNow.AddDays(7),
});
return employee;
}

You could use a Hashtable
it has very fast lookups, no key collisions and your data will not garbage collected