We Keep Coding

Does the Lazy class clear the reference to the factory delegate after having generated the value?

Consider the following C# code using MemoryCache to generate a new value for a given key if not already preset in the cache:
private static MemoryCache _memoryCache = new MemoryCache();
public T Apply<T>(string key, Func<T> factory)
{
var expiration ...
var newValue = new Lazy<T>(factory);
var value = (Lazy<T>)_memoryCache.AddOrGetExisting(key, newValue, expiration);
return (value ?? newValue).Value;
}
Consider now this:
var hugeObject = new HugeObject();
return cache.Apply("SomeKey", () =>
{
return hugeObject.GetValue();
});
The factory will be invoked "immediately" after AddOrGetExisting or never, so the question is:
Does the Lazy class clear the reference to the factory delegate after having generated the value (so all the resources used by the factory like, in this case, hugeObject, can be released)?

Looking at the reference source, I believe it does release the factory, and call out why:
// We successfully created and stored the value. At this point, the value factory delegate is
// no longer needed, and we don't want to hold onto its resources.
m_valueFactory = ALREADY_INVOKED_SENTINEL;
There is quite a lot of threading code in there so I'm not sure it does so every time, but you'd hope that if they've realise they need to, they will have done so properly.

Why does my thread-safe dictionary implementation produce a data race?

I'm currently implementing a thread-safe dictionary in C# which uses immutable AVL trees as buckets internally. The idea is to provide fast read access without a lock because in my application context, we add entries to this dictionary only at startup and afterwards, values are mostly read (but there still are a few number of writes).
I've structured my TryGetValue and GetOrAdd methods in the following way:
public sealed class FastReadThreadSafeDictionary<TKey, TValue> where TKey : IEquatable<TKey>
{
private readonly object _bucketContainerLock = new object();
private ImmutableBucketContainer<TKey, TValue> _bucketContainer;
public bool TryGetValue(TKey key, out TValue value)
{
var bucketContainer = _bucketContainer;
return bucketContainer.TryFind(key.GetHashCode(), key, out value);
}
public bool GetOrAdd(TKey key, Func<TValue> createValue, out TValue value)
{
createValue.MustNotBeNull(nameof(createValue));
var hashCode = key.GetHashCode();
lock (_bucketContainerLock)
{
ImmutableBucketContainer<TKey, TValue> newBucketContainer;
if (_bucketContainer.GetOrAdd(hashCode, key, createValue, out value, out newBucketContainer) == false)
return false;
_bucketContainer = newBucketContainer;
return true;
}
}
// Other members omitted for sake of brevity
}
As you can see, I don't use a lock in TryGetValue because reference assignment in .NET runtimes is an atomic operation by design. By copying the reference of the field _bucketContainer to a local variable, I'm sure I can safely access the instance because it is immutable. In GetOrAdd, I use a lock to access the private _bucketContainer so I can ensure that a value is not created twice (i.e. if two or more threads are trying to add a value, only one can actually create a new ImmutableBucketContainer with the added value because of the lock).
I use Microsoft Chess for testing concurrency and in one of my tests, MCUT (Microsoft Concurrency Unit Testing) reports a data race in GetOrAdd when I exchange the new bucket container with the old one:
[DataRaceTestMethod]
public void ReadWhileAdd()
{
var testTarget = new FastReadThreadSafeDictionary<int, object>();
var writeThread = new Thread(() =>
{
for (var i = 5; i < 10; i++)
{
testTarget.GetOrAdd(i, () => new object());
Thread.Sleep(0);
}
});
var readThread = new Thread(() =>
{
object value;
testTarget.TryGetValue(5, out value);
Thread.Sleep(0);
testTarget.TryGetValue(7, out value);
Thread.Sleep(10);
testTarget.TryGetValue(9, out value);
});
readThread.Start();
writeThread.Start();
readThread.Join();
writeThread.Join();
}
MCUT reports the following message:
23> Test result: DataRace
23> ReadWhileAdd() (Context=, TestType=MChess): [DataRace]Found data race at GetOrAdd:FastReadThreadSafeDictionary.cs(68)
which is the assignment _bucketContainer = newBucketContainer; in GetOrAdd.
My actual question is: why is the assignment _bucketContainer = newBucketContainer a race condition? Threads currently executing TryGetValue always make a copy of the _bucketContainer field and thus shouldn't be bothered with the update (except that the searched value might be added to the _bucketContainer just after the copy takes place, but this doesn't matter with the data race). And in GetOrAdd, there is an explicit lock to prevent concurrent access. Is this a bug in Chess or am I missing something very obvious?

As mentioned by #CodesInChaos in the comments of the question, I missed a volatile read in TryGetValue. The method now looks like this:
public bool TryGetValue(TypeKey typeKey, out TValue value)
{
var bucketContainer = Volatile.Read(ref _bucketContainer);
return bucketContainer.TryFind(typeKey, out value);
}
This volatile read is necessary because different threads accessing this dictionary might cache data and reorder instructions independently from each other, which might lead to a data race. Additionally, the CPU architecture that is running the code also matters, e.g. x86 and x64 processors perform volatile reads by default, while this might not be true for other architectures like ARM or Itanium. That's why the read access has to be synchronized with other threads using a Memory Barrier, which is performed internally in Volatile.Read (note that lock statements also use memory barriers internally). Joseph Albahari wrote a comprehensive tutorial on this here: http://www.albahari.com/threading/part4.aspx

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. */;
}

Locking pattern for proper use of .NET MemoryCache

I assume this code has concurrency issues:
const string CacheKey = "CacheKey";
static string GetCachedData()
{
string expensiveString =null;
if (MemoryCache.Default.Contains(CacheKey))
{
expensiveString = MemoryCache.Default[CacheKey] as string;
}
else
{
CacheItemPolicy cip = new CacheItemPolicy()
{
AbsoluteExpiration = new DateTimeOffset(DateTime.Now.AddMinutes(20))
};
expensiveString = SomeHeavyAndExpensiveCalculation();
MemoryCache.Default.Set(CacheKey, expensiveString, cip);
}
return expensiveString;
}
The reason for the concurrency issue is that multiple threads can get a null key and then attempt to insert data into cache.
What would be the shortest and cleanest way to make this code concurrency proof? I like to follow a good pattern across my cache related code. A link to an online article would be a great help.
UPDATE:
I came up with this code based on #Scott Chamberlain's answer. Can anyone find any performance or concurrency issue with this?
If this works, it would save many line of code and errors.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Runtime.Caching;
namespace CachePoc
{
class Program
{
static object everoneUseThisLockObject4CacheXYZ = new object();
const string CacheXYZ = "CacheXYZ";
static object everoneUseThisLockObject4CacheABC = new object();
const string CacheABC = "CacheABC";
static void Main(string[] args)
{
string xyzData = MemoryCacheHelper.GetCachedData<string>(CacheXYZ, everoneUseThisLockObject4CacheXYZ, 20, SomeHeavyAndExpensiveXYZCalculation);
string abcData = MemoryCacheHelper.GetCachedData<string>(CacheABC, everoneUseThisLockObject4CacheXYZ, 20, SomeHeavyAndExpensiveXYZCalculation);
}
private static string SomeHeavyAndExpensiveXYZCalculation() {return "Expensive";}
private static string SomeHeavyAndExpensiveABCCalculation() {return "Expensive";}
public static class MemoryCacheHelper
{
public static T GetCachedData<T>(string cacheKey, object cacheLock, int cacheTimePolicyMinutes, Func<T> GetData)
where T : class
{
//Returns null if the string does not exist, prevents a race condition where the cache invalidates between the contains check and the retreival.
T cachedData = MemoryCache.Default.Get(cacheKey, null) as T;
if (cachedData != null)
{
return cachedData;
}
lock (cacheLock)
{
//Check to see if anyone wrote to the cache while we where waiting our turn to write the new value.
cachedData = MemoryCache.Default.Get(cacheKey, null) as T;
if (cachedData != null)
{
return cachedData;
}
//The value still did not exist so we now write it in to the cache.
CacheItemPolicy cip = new CacheItemPolicy()
{
AbsoluteExpiration = new DateTimeOffset(DateTime.Now.AddMinutes(cacheTimePolicyMinutes))
};
cachedData = GetData();
MemoryCache.Default.Set(cacheKey, cachedData, cip);
return cachedData;
}
}
}
}
}

This is my 2nd iteration of the code. Because MemoryCache is thread safe you don't need to lock on the initial read, you can just read and if the cache returns null then do the lock check to see if you need to create the string. It greatly simplifies the code.
const string CacheKey = "CacheKey";
static readonly object cacheLock = new object();
private static string GetCachedData()
{
//Returns null if the string does not exist, prevents a race condition where the cache invalidates between the contains check and the retreival.
var cachedString = MemoryCache.Default.Get(CacheKey, null) as string;
if (cachedString != null)
{
return cachedString;
}
lock (cacheLock)
{
//Check to see if anyone wrote to the cache while we where waiting our turn to write the new value.
cachedString = MemoryCache.Default.Get(CacheKey, null) as string;
if (cachedString != null)
{
return cachedString;
}
//The value still did not exist so we now write it in to the cache.
var expensiveString = SomeHeavyAndExpensiveCalculation();
CacheItemPolicy cip = new CacheItemPolicy()
{
AbsoluteExpiration = new DateTimeOffset(DateTime.Now.AddMinutes(20))
};
MemoryCache.Default.Set(CacheKey, expensiveString, cip);
return expensiveString;
}
}
EDIT: The below code is unnecessary but I wanted to leave it to show the original method. It may be useful to future visitors who are using a different collection that has thread safe reads but non-thread safe writes (almost all of classes under the System.Collections namespace is like that).
Here is how I would do it using ReaderWriterLockSlim to protect access. You need to do a kind of "Double Checked Locking" to see if anyone else created the cached item while we where waiting to to take the lock.
const string CacheKey = "CacheKey";
static readonly ReaderWriterLockSlim cacheLock = new ReaderWriterLockSlim();
static string GetCachedData()
{
//First we do a read lock to see if it already exists, this allows multiple readers at the same time.
cacheLock.EnterReadLock();
try
{
//Returns null if the string does not exist, prevents a race condition where the cache invalidates between the contains check and the retreival.
var cachedString = MemoryCache.Default.Get(CacheKey, null) as string;
if (cachedString != null)
{
return cachedString;
}
}
finally
{
cacheLock.ExitReadLock();
}
//Only one UpgradeableReadLock can exist at one time, but it can co-exist with many ReadLocks
cacheLock.EnterUpgradeableReadLock();
try
{
//We need to check again to see if the string was created while we where waiting to enter the EnterUpgradeableReadLock
var cachedString = MemoryCache.Default.Get(CacheKey, null) as string;
if (cachedString != null)
{
return cachedString;
}
//The entry still does not exist so we need to create it and enter the write lock
var expensiveString = SomeHeavyAndExpensiveCalculation();
cacheLock.EnterWriteLock(); //This will block till all the Readers flush.
try
{
CacheItemPolicy cip = new CacheItemPolicy()
{
AbsoluteExpiration = new DateTimeOffset(DateTime.Now.AddMinutes(20))
};
MemoryCache.Default.Set(CacheKey, expensiveString, cip);
return expensiveString;
}
finally
{
cacheLock.ExitWriteLock();
}
}
finally
{
cacheLock.ExitUpgradeableReadLock();
}
}

There is an open source library [disclaimer: that I wrote]: LazyCache that IMO covers your requirement with two lines of code:
IAppCache cache = new CachingService();
var cachedResults = cache.GetOrAdd("CacheKey",
() => SomeHeavyAndExpensiveCalculation());
It has built in locking by default so the cacheable method will only execute once per cache miss, and it uses a lambda so you can do "get or add" in one go. It defaults to 20 minutes sliding expiration.
There's even a NuGet package ;)

I've solved this issue by making use of the AddOrGetExisting method on the MemoryCache and the use of Lazy initialization.
Essentially, my code looks something like this:
static string GetCachedData(string key, DateTimeOffset offset)
{
Lazy<String> lazyObject = new Lazy<String>(() => SomeHeavyAndExpensiveCalculationThatReturnsAString());
var returnedLazyObject = MemoryCache.Default.AddOrGetExisting(key, lazyObject, offset);
if (returnedLazyObject == null)
return lazyObject.Value;
return ((Lazy<String>) returnedLazyObject).Value;
}
Worst case scenario here is that you create the same Lazy object twice. But that is pretty trivial. The use of AddOrGetExisting guarantees that you'll only ever get one instance of the Lazy object, and so you're also guaranteed to only call the expensive initialization method once.

I assume this code has concurrency issues:
Actually, it's quite possibly fine, though with a possible improvement.
Now, in general the pattern where we have multiple threads setting a shared value on first use, to not lock on the value being obtained and set can be:
Disastrous - other code will assume only one instance exists.
Disastrous - the code that obtains the instance is not can only tolerate one (or perhaps a certain small number) concurrent operations.
Disastrous - the means of storage is not thread-safe (e.g. have two threads adding to a dictionary and you can get all sorts of nasty errors).
Sub-optimal - the overall performance is worse than if locking had ensured only one thread did the work of obtaining the value.
Optimal - the cost of having multiple threads do redundant work is less than the cost of preventing it, especially since that can only happen during a relatively brief period.
However, considering here that MemoryCache may evict entries then:
If it's disastrous to have more than one instance then MemoryCache is the wrong approach.
If you must prevent simultaneous creation, you should do so at the point of creation.
MemoryCache is thread-safe in terms of access to that object, so that is not a concern here.
Both of these possibilities have to be thought about of course, though the only time having two instances of the same string existing can be a problem is if you're doing very particular optimisations that don't apply here*.
So, we're left with the possibilities:
It is cheaper to avoid the cost of duplicate calls to SomeHeavyAndExpensiveCalculation().
It is cheaper not to avoid the cost of duplicate calls to SomeHeavyAndExpensiveCalculation().
And working that out can be difficult (indeed, the sort of thing where it's worth profiling rather than assuming you can work it out). It's worth considering here though that most obvious ways of locking on insert will prevent all additions to the cache, including those that are unrelated.
This means that if we had 50 threads trying to set 50 different values, then we'll have to make all 50 threads wait on each other, even though they weren't even going to do the same calculation.
As such, you're probably better off with the code you have, than with code that avoids the race-condition, and if the race-condition is a problem, you quite likely either need to handle that somewhere else, or need a different caching strategy than one that expels old entries†.
The one thing I would change is I'd replace the call to Set() with one to AddOrGetExisting(). From the above it should be clear that it probably isn't necessary, but it would allow the newly obtained item to be collected, reducing overall memory use and allowing a higher ratio of low generation to high generation collections.
So yeah, you could use double-locking to prevent concurrency, but either the concurrency isn't actually a problem, or your storing the values in the wrong way, or double-locking on the store would not be the best way to solve it.
*If you know only one each of a set of strings exists, you can optimise equality comparisons, which is about the only time having two copies of a string can be incorrect rather than just sub-optimal, but you'd want to be doing very different types of caching for that to make sense. E.g. the sort XmlReader does internally.
†Quite likely either one that stores indefinitely, or one that makes use of weak references so it will only expel entries if there are no existing uses.

Somewhat dated question, but maybe still useful: you may take a look at FusionCache ⚡🦥, which I recently released.
The feature you are looking for is described here, and you can use it like this:
const string CacheKey = "CacheKey";
static string GetCachedData()
{
return fusionCache.GetOrSet(
CacheKey,
_ => SomeHeavyAndExpensiveCalculation(),
TimeSpan.FromMinutes(20)
);
}
You may also find some of the other features interesting like fail-safe, advanced timeouts with background factory completion and support for an optional, distributed 2nd level cache.
If you will give it a chance please let me know what you think.
/shameless-plug

It is difficult to choose which one is better; lock or ReaderWriterLockSlim. You need real world statistics of read and write numbers and ratios etc.
But if you believe using "lock" is the correct way. Then here is a different solution for different needs. I also include the Allan Xu's solution in the code. Because both can be needed for different needs.
Here are the requirements, driving me to this solution:
You don't want to or cannot supply the 'GetData' function for some reason. Perhaps the 'GetData' function is located in some other class with a heavy constructor and you do not want to even create an instance till ensuring it is unescapable.
You need to access the same cached data from different locations/tiers of the application. And those different locations don't have access to same locker object.
You don't have a constant cache key. For example; need of caching some data with the sessionId cache key.
Code:
using System;
using System.Runtime.Caching;
using System.Collections.Concurrent;
using System.Collections.Generic;
namespace CachePoc
{
class Program
{
static object everoneUseThisLockObject4CacheXYZ = new object();
const string CacheXYZ = "CacheXYZ";
static object everoneUseThisLockObject4CacheABC = new object();
const string CacheABC = "CacheABC";
static void Main(string[] args)
{
//Allan Xu's usage
string xyzData = MemoryCacheHelper.GetCachedDataOrAdd<string>(CacheXYZ, everoneUseThisLockObject4CacheXYZ, 20, SomeHeavyAndExpensiveXYZCalculation);
string abcData = MemoryCacheHelper.GetCachedDataOrAdd<string>(CacheABC, everoneUseThisLockObject4CacheXYZ, 20, SomeHeavyAndExpensiveXYZCalculation);
//My usage
string sessionId = System.Web.HttpContext.Current.Session["CurrentUser.SessionId"].ToString();
string yvz = MemoryCacheHelper.GetCachedData<string>(sessionId);
if (string.IsNullOrWhiteSpace(yvz))
{
object locker = MemoryCacheHelper.GetLocker(sessionId);
lock (locker)
{
yvz = MemoryCacheHelper.GetCachedData<string>(sessionId);
if (string.IsNullOrWhiteSpace(yvz))
{
DatabaseRepositoryWithHeavyConstructorOverHead dbRepo = new DatabaseRepositoryWithHeavyConstructorOverHead();
yvz = dbRepo.GetDataExpensiveDataForSession(sessionId);
MemoryCacheHelper.AddDataToCache(sessionId, yvz, 5);
}
}
}
}
private static string SomeHeavyAndExpensiveXYZCalculation() { return "Expensive"; }
private static string SomeHeavyAndExpensiveABCCalculation() { return "Expensive"; }
public static class MemoryCacheHelper
{
//Allan Xu's solution
public static T GetCachedDataOrAdd<T>(string cacheKey, object cacheLock, int minutesToExpire, Func<T> GetData) where T : class
{
//Returns null if the string does not exist, prevents a race condition where the cache invalidates between the contains check and the retreival.
T cachedData = MemoryCache.Default.Get(cacheKey, null) as T;
if (cachedData != null)
return cachedData;
lock (cacheLock)
{
//Check to see if anyone wrote to the cache while we where waiting our turn to write the new value.
cachedData = MemoryCache.Default.Get(cacheKey, null) as T;
if (cachedData != null)
return cachedData;
cachedData = GetData();
MemoryCache.Default.Set(cacheKey, cachedData, DateTime.Now.AddMinutes(minutesToExpire));
return cachedData;
}
}
#region "My Solution"
readonly static ConcurrentDictionary<string, object> Lockers = new ConcurrentDictionary<string, object>();
public static object GetLocker(string cacheKey)
{
CleanupLockers();
return Lockers.GetOrAdd(cacheKey, item => (cacheKey, new object()));
}
public static T GetCachedData<T>(string cacheKey) where T : class
{
CleanupLockers();
T cachedData = MemoryCache.Default.Get(cacheKey) as T;
return cachedData;
}
public static void AddDataToCache(string cacheKey, object value, int cacheTimePolicyMinutes)
{
CleanupLockers();
MemoryCache.Default.Add(cacheKey, value, DateTimeOffset.Now.AddMinutes(cacheTimePolicyMinutes));
}
static DateTimeOffset lastCleanUpTime = DateTimeOffset.MinValue;
static void CleanupLockers()
{
if (DateTimeOffset.Now.Subtract(lastCleanUpTime).TotalMinutes > 1)
{
lock (Lockers)//maybe a better locker is needed?
{
try//bypass exceptions
{
List<string> lockersToRemove = new List<string>();
foreach (var locker in Lockers)
{
if (!MemoryCache.Default.Contains(locker.Key))
lockersToRemove.Add(locker.Key);
}
object dummy;
foreach (string lockerKey in lockersToRemove)
Lockers.TryRemove(lockerKey, out dummy);
lastCleanUpTime = DateTimeOffset.Now;
}
catch (Exception)
{ }
}
}
}
#endregion
}
}
class DatabaseRepositoryWithHeavyConstructorOverHead
{
internal string GetDataExpensiveDataForSession(string sessionId)
{
return "Expensive data from database";
}
}
}

To avoid the global lock, you can use SingletonCache to implement one lock per key, without exploding memory usage (the lock objects are removed when no longer referenced, and acquire/release is thread safe guaranteeing that only 1 instance is ever in use via compare and swap).
Using it looks like this:
SingletonCache<string, object> keyLocks = new SingletonCache<string, object>();
const string CacheKey = "CacheKey";
static string GetCachedData()
{
string expensiveString =null;
if (MemoryCache.Default.Contains(CacheKey))
{
return MemoryCache.Default[CacheKey] as string;
}
// double checked lock
using (var lifetime = keyLocks.Acquire(url))
{
lock (lifetime.Value)
{
if (MemoryCache.Default.Contains(CacheKey))
{
return MemoryCache.Default[CacheKey] as string;
}
cacheItemPolicy cip = new CacheItemPolicy()
{
AbsoluteExpiration = new DateTimeOffset(DateTime.Now.AddMinutes(20))
};
expensiveString = SomeHeavyAndExpensiveCalculation();
MemoryCache.Default.Set(CacheKey, expensiveString, cip);
return expensiveString;
}
}
}
Code is here on GitHub: https://github.com/bitfaster/BitFaster.Caching
Install-Package BitFaster.Caching
There is also an LRU implementation that is lighter weight than MemoryCache, and has several advantages - faster concurrent reads and writes, bounded size, no background thread, internal perf counters etc. (disclaimer, I wrote it).

Console example of MemoryCache, "How to save/get simple class objects"
Output after launching and pressing Any key except Esc :
Saving to cache!
Getting from cache!
Some1
Some2
class Some
{
public String text { get; set; }
public Some(String text)
{
this.text = text;
}
public override string ToString()
{
return text;
}
}
public static MemoryCache cache = new MemoryCache("cache");
public static string cache_name = "mycache";
static void Main(string[] args)
{
Some some1 = new Some("some1");
Some some2 = new Some("some2");
List<Some> list = new List<Some>();
list.Add(some1);
list.Add(some2);
do {
if (cache.Contains(cache_name))
{
Console.WriteLine("Getting from cache!");
List<Some> list_c = cache.Get(cache_name) as List<Some>;
foreach (Some s in list_c) Console.WriteLine(s);
}
else
{
Console.WriteLine("Saving to cache!");
cache.Set(cache_name, list, DateTime.Now.AddMinutes(10));
}
} while (Console.ReadKey(true).Key != ConsoleKey.Escape);
}

public interface ILazyCacheProvider : IAppCache
{
/// <summary>
/// Get data loaded - after allways throw cached result (even when data is older then needed) but very fast!
/// </summary>
/// <param name="key"></param>
/// <param name="getData"></param>
/// <param name="slidingExpiration"></param>
/// <typeparam name="T"></typeparam>
/// <returns></returns>
T GetOrAddPermanent<T>(string key, Func<T> getData, TimeSpan slidingExpiration);
}
/// <summary>
/// Initialize LazyCache in runtime
/// </summary>
public class LazzyCacheProvider: CachingService, ILazyCacheProvider
{
private readonly Logger _logger = LogManager.GetLogger("MemCashe");
private readonly Hashtable _hash = new Hashtable();
private readonly List<string> _reloader = new List<string>();
private readonly ConcurrentDictionary<string, DateTime> _lastLoad = new ConcurrentDictionary<string, DateTime>();
T ILazyCacheProvider.GetOrAddPermanent<T>(string dataKey, Func<T> getData, TimeSpan slidingExpiration)
{
var currentPrincipal = Thread.CurrentPrincipal;
if (!ObjectCache.Contains(dataKey) && !_hash.Contains(dataKey))
{
_hash[dataKey] = null;
_logger.Debug($"{dataKey} - first start");
_lastLoad[dataKey] = DateTime.Now;
_hash[dataKey] = ((object)GetOrAdd(dataKey, getData, slidingExpiration)).CloneObject();
_lastLoad[dataKey] = DateTime.Now;
_logger.Debug($"{dataKey} - first");
}
else
{
if ((!ObjectCache.Contains(dataKey) || _lastLoad[dataKey].AddMinutes(slidingExpiration.Minutes) < DateTime.Now) && _hash[dataKey] != null)
Task.Run(() =>
{
if (_reloader.Contains(dataKey)) return;
lock (_reloader)
{
if (ObjectCache.Contains(dataKey))
{
if(_lastLoad[dataKey].AddMinutes(slidingExpiration.Minutes) > DateTime.Now)
return;
_lastLoad[dataKey] = DateTime.Now;
Remove(dataKey);
}
_reloader.Add(dataKey);
Thread.CurrentPrincipal = currentPrincipal;
_logger.Debug($"{dataKey} - reload start");
_hash[dataKey] = ((object)GetOrAdd(dataKey, getData, slidingExpiration)).CloneObject();
_logger.Debug($"{dataKey} - reload");
_reloader.Remove(dataKey);
}
});
}
if (_hash[dataKey] != null) return (T) (_hash[dataKey]);
_logger.Debug($"{dataKey} - dummy start");
var data = GetOrAdd(dataKey, getData, slidingExpiration);
_logger.Debug($"{dataKey} - dummy");
return (T)((object)data).CloneObject();
}
}

Its a bit late, however...
Full implementation:
[HttpGet]
public async Task<HttpResponseMessage> GetPageFromUriOrBody(RequestQuery requestQuery)
{
log(nameof(GetPageFromUriOrBody), nameof(requestQuery));
var responseResult = await _requestQueryCache.GetOrCreate(
nameof(GetPageFromUriOrBody)
, requestQuery
, (x) => getPageContent(x).Result);
return Request.CreateResponse(System.Net.HttpStatusCode.Accepted, responseResult);
}
static MemoryCacheWithPolicy<RequestQuery, string> _requestQueryCache = new MemoryCacheWithPolicy<RequestQuery, string>();
Here is getPageContent signature:
async Task<string> getPageContent(RequestQuery requestQuery);
And here is the MemoryCacheWithPolicy implementation:
public class MemoryCacheWithPolicy<TParameter, TResult>
{
static ILogger _nlogger = new AppLogger().Logger;
private MemoryCache _cache = new MemoryCache(new MemoryCacheOptions()
{
//Size limit amount: this is actually a memory size limit value!
SizeLimit = 1024
});
/// <summary>
/// Gets or creates a new memory cache record for a main data
/// along with parameter data that is assocciated with main main.
/// </summary>
/// <param name="key">Main data cache memory key.</param>
/// <param name="param">Parameter model that assocciated to main model (request result).</param>
/// <param name="createCacheData">A delegate to create a new main data to cache.</param>
/// <returns></returns>
public async Task<TResult> GetOrCreate(object key, TParameter param, Func<TParameter, TResult> createCacheData)
{
// this key is used for param cache memory.
var paramKey = key + nameof(param);
if (!_cache.TryGetValue(key, out TResult cacheEntry))
{
// key is not in the cache, create data through the delegate.
cacheEntry = createCacheData(param);
createMemoryCache(key, cacheEntry, paramKey, param);
_nlogger.Warn(" cache is created.");
}
else
{
// data is chached so far..., check if param model is same (or changed)?
if(!_cache.TryGetValue(paramKey, out TParameter cacheParam))
{
//exception: this case should not happened!
}
if (!cacheParam.Equals(param))
{
// request param is changed, create data through the delegate.
cacheEntry = createCacheData(param);
createMemoryCache(key, cacheEntry, paramKey, param);
_nlogger.Warn(" cache is re-created (param model has been changed).");
}
else
{
_nlogger.Trace(" cache is used.");
}
}
return await Task.FromResult<TResult>(cacheEntry);
}
MemoryCacheEntryOptions createMemoryCacheEntryOptions(TimeSpan slidingOffset, TimeSpan relativeOffset)
{
// Cache data within [slidingOffset] seconds,
// request new result after [relativeOffset] seconds.
return new MemoryCacheEntryOptions()
// Size amount: this is actually an entry count per
// key limit value! not an actual memory size value!
.SetSize(1)
// Priority on removing when reaching size limit (memory pressure)
.SetPriority(CacheItemPriority.High)
// Keep in cache for this amount of time, reset it if accessed.
.SetSlidingExpiration(slidingOffset)
// Remove from cache after this time, regardless of sliding expiration
.SetAbsoluteExpiration(relativeOffset);
//
}
void createMemoryCache(object key, TResult cacheEntry, object paramKey, TParameter param)
{
// Cache data within 2 seconds,
// request new result after 5 seconds.
var cacheEntryOptions = createMemoryCacheEntryOptions(
TimeSpan.FromSeconds(2)
, TimeSpan.FromSeconds(5));
// Save data in cache.
_cache.Set(key, cacheEntry, cacheEntryOptions);
// Save param in cache.
_cache.Set(paramKey, param, cacheEntryOptions);
}
void checkCacheEntry<T>(object key, string name)
{
_cache.TryGetValue(key, out T value);
_nlogger.Fatal("Key: {0}, Name: {1}, Value: {2}", key, name, value);
}
}
nlogger is just nLog object to trace MemoryCacheWithPolicy behavior.
I re-create the memory cache if request object (RequestQuery requestQuery) is changed through the delegate (Func<TParameter, TResult> createCacheData) or re-create when sliding or absolute time reached their limit. Note that everything is async too ;)

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