FastCache.Cached

The fastest cache library written in C# for items with set expiration time. Easy to use, thread-safe and light on memory.

Optimized to scale from dozens to millions of items. Features lock-free reads and writes, allocation-free reads and automatic eviction.

Credit to Vladimir Sadov for his implementation of NonBlocking.ConcurrentDictionary which is used as an underlying store.

When to use FastCache.Cached over M.E.C.M.MemoryCache

• When cache is used for expirable items only or does not need complex behavior (eviction delegates, multiple expiration modes, etc.), ~80% use cases fall into this category
• When there are strict memory limits (381MB for 10 million items vs 2136MB for MemoryCache)
• When you need pseudo-memoization: cached.Save(value, TimeSpan.MaxValue)
• When deployed to highly-parallel systems (much better multi-core scaling vs all alternatives thanks to lock-free reads/writes)
• When you need convenient limit API: cached.Save(value, TimeSpan.FromSeconds(180), limit: 50_000) (auto trim logic included)
• When you need the fastest solution - read/write paths are hand tuned to be as short as possible, no interface calls, all static dispatch, etc.
• When global static cache is an acceptable choice (vs instanced, works best for medium and fine-grained highly loaded microservices)

Quick start

Install

dotnet add package FastCache.Cached or Install-Package FastCache.Cached

How to use

Get cached value or save a new one with expiration of 60 minutes

public SalesReport GetReport(Guid companyId)
{
  if (Cached<SalesReport>.TryGet(companyId, out var cached))
  {
    return cached;
  }

  var report = // Expensive operation: retrieve and compute data

  return cached.Save(report, TimeSpan.FromMinutes(60));
}

Get cached value or call a method to compute and cache it

var report = Cached.GetOrCompute(companyId, GetReport, TimeSpan.FromMinutes(60));

Async version (works with Task<T> and ValueTask<T>)

var report = await Cached.GetOrCompute(companyId, GetReportAsync, TimeSpan.FromMinutes(60));

Use multiple arguments as key (up to 7)

public async Task<Picture> GetPictureOfTheDay(DateOnly date, FeedKind kind, bool compressed)
{
  if (Cached<Picture>.TryGet(date, kind, compressed, out var cached))
  {
    return cached;
  }

  var api = GetApiService(kind);
  var picture = await api.GetPictureOfTheDay(date, compressed);

  return cached.Save(picture, TimeSpan.FromHours(3));
}

Use multiple arguments with GetOrCompute

var expiration = TimeSpan.FromHours(3);
var picture = await Cached.GetOrCompute(date, kind, compressed, GetPictureOfTheDay, expiration);

Save the value to cache (if it fits) and keep the cached items count below specified limit

public SalesReport GetReport(Guid companyId)
{
  if (Cached<SalesReport>.TryGet(companyId, out var cached))
  {
    return cached;
  }
  ...
  return cached.Save(report, TimeSpan.FromMinutes(60), limit: 500_000);
}

// GetOrCompute with maximum cache size limit.
// RAM is usually plenty but what if the user runs Chrome?
var report = Cached.GetOrCompute(companyId, GetReport, TimeSpan.FromMinutes(60), limit: 500_000);

Add new data without accessing cache item first

Cached<SalesReport>.Save(companyId, report, TimeSpan.FromMinutes(60));

// Same as above but via extension method for more concise syntax
using FastCache.Extensions;
...
report.Cache(companyId, TimeSpan.FromMinutes(60));

Save an entire range of values in one call. Fast for IEnumerable, extremely fast for lists, arrays and ROM/Memory.

using FastCache.Collections;
...
var reports = ReportsService
  .GetReports(11, 2022)
  .Select(report => (report.CompanyId, report));

CachedRange<SalesReport>.Save(reports, TimeSpan.FromMinutes(60));

Save range of cached values with multiple arguments as key

var februaryReports = reports.Select(report => ((report.CompanyId, 02, 2022), report));

CachedRange<SalesReport>.Save(februaryReports, TimeSpan.FromMinutes(60));

var companyId = februaryReports.First().CompanyId;
var reportFound = Cached<SalesReport>.TryGet(companyId, 02, 2022, out _);
Assert.True(reportFound);

Store common type (string) in a shared cache store (other users may share the cache for the same <K, V> type, this time it's <int, string>)

// GetOrCompute<...V> where V is string.
// To save some other string for the same 'int' number simultaneously, look at the option below :)
var userNote = Cached.GetOrCompute(userId, GetUserNoteString, TimeSpan.FromMinutes(5));

Or in a separate one by using value object (Recommended)

readonly record struct UserNote(string Value);

// GetOrCompute<...V> where V is UserNote
var userNote = Cached.GetOrCompute(userId, GetUserNote, TimeSpan.FromMinutes(5));

// This is how it looks for TryGet
if (Cached<UserNote>.TryGet(userId, out var cached))
{
  return cached;
}
...
return cached.Save(userNote, TimeSpan.FromMinutes(5));

Features and design philosophy

• In-memory cache for items with expiration time and automatic eviction
• Little to no ceremony - no need to configure or initialize, just add the package and you are ready to go. Behavior can be further customized via env variables
• Focused design allows to reduce memory footprint per item and minimize overhead via inlining and static dispatch
• High performance and scaling covering both simplest applications and highly loaded services. Can handle 1-100M+ items with O(1) read/write time and up to O(n~) memory cost/cpu time cost for full eviction
• Lock-free and wait-free reads/writes of cached items. Performance will improve with threads, data synchronization cost is minimal thanks to NonBlocking.ConcurrentDictionary
• Multi-key store access without collisions between key types. Collisions are avoided by statically dispatching on the composite key type signature e.g. (string, CustomEnum, int) together with the type of cached value - composite keys are structurally evaluated for equality, different combinations will correspond to different cache items
• Handles timezone/DST updates on most platforms by relying on system uptime timestamp for item expiration - Environment.TickCount64 which is also significantly faster than DateTime.UtcNow

Performance

BenchmarkDotNet=v0.13.1, OS=Windows 10.0.22000
AMD Ryzen 7 5800X, 1 CPU, 16 logical and 8 physical cores
.NET 6.0.5 (6.0.522.21309), X64 RyuJIT

TLDR: FastCache.Cached vs Microsoft.Extensions.Caching.Memory.MemoryCache

Library	Lowest read latency	Read throughput (M/1s)	Lowest write latency	Write throughput (M/1s)	Cost per item	Cost per 10M items
FastCache.Cached	15.63 ns	114-288M MT / 9-72M ST	33.75 ns	39-81M MT / 6-31M ST	40 B	381 MB
MemoryCache	56.93 ns	41-46M MT / 4-10M ST	203.32 ns	11-26M MT / 2-6M ST	224 B	2,136 MB
CacheManager	87.54 ns	N/A	~436.85 ns	N/A MT / 1.5-5M ST	(+alloc)360 B	1,602 MB

+CachedRange.Save(ReadOnlySpan<(K, V)>) provides parallelized bulk writes out of box

++CacheManager doesn't have read throughput results because test suite would take too long to run to include CacheManager and LazyCache. Given higher CPU usage by CacheManager and higher RAM usage by LazyCache it is reasonable to assume they would score lower and scale worse due to higher number of locks

Read/Write lowest achievable latency

Method	Mean	Error	StdDev	Median	Ratio	Gen 0	Allocated
Get: FastCache.Cached	15.63 ns	0.452 ns	1.334 ns	14.61 ns	1.00	-	-
Get: MemoryCache	56.93 ns	1.179 ns	1.904 ns	55.73 ns	3.68	-	-
Get: CacheManager*	87.54 ns	1.751 ns	2.454 ns	89.32 ns	5.68	-	-
Get: LazyCache	73.43 ns	1.216 ns	1.138 ns	73.25 ns	4.71	-	-
Set: FastCache.Cached	33.75 ns	0.861 ns	2.539 ns	31.92 ns	2.18	0.0024	40 B
Set: MemoryCache	203.32 ns	4.033 ns	6.956 ns	199.77 ns	13.23	0.0134	224 B
Set: CacheManager*	436.85 ns	8.729 ns	19.160 ns	433.97 ns	28.10	0.0215	360 B
Set: LazyCache	271.56 ns	5.428 ns	7.785 ns	274.19 ns	17.58	0.0286	480 B

Read throughput detailed

Method	Count	Reads/1s	Mean	Error	StdDev	Ratio
Read(MT): FastCache	1,000	130.97M	7.635 us	0.1223 us	0.1144 us	1.00
Read(ST): FastCache	1,000	72.99M	13.700 us	0.2723 us	0.5562 us	1.78
Read(MT): MemoryCache	1,000	41.35M	24.183 us	1.2907 us	3.7853 us	2.68
Read(ST): MemoryCache	1,000	10.31M	96.943 us	0.9095 us	0.8063 us	12.71
Read(MT): FastCache	100,000	288.66M	346.418 us	5.2196 us	6.6011 us	1.00
Read(ST): FastCache	100,000	28.99M	3,449.865 us	66.4929 us	81.6593 us	9.96
Read(MT): MemoryCache	100,000	46.77M	2,138.400 us	175.2152 us	516.6259 us	6.32
Read(ST): MemoryCache	100,000	4.64M	21,540.964 us	394.9239 us	499.4523 us	62.20
Read(MT): FastCache	1,000,000	114.54M	8,730.009 us	173.8538 us	170.7476 us	1.00
Read(ST): FastCache	1,000,000	9.74M	102,580.795 us	926.3173 us	866.4778 us	11.76
Read(MT): MemoryCache	1,000,000	41.46M	24,114.261 us	369.3612 us	308.4334 us	2.76
Read(ST): MemoryCache	1,000,000	3.92M	254,619.996 us	2,585.3079 us	2,291.8081 us	29.17
Read(MT): FastCache	10,000,000	112.89M	88,584.244 us	1,709.9078 us	1,599.4488 us	1.00
Read(ST): FastCache	10,000,000	9.70M	1,030,431.980 us	9,874.4883 us	9,236.6025 us	11.64
Read(MT): MemoryCache	10,000,000	42.84M	233,410.703 us	2,945.8464 us	2,299.9231 us	2.63
Read(ST): MemoryCache	10,000,000	4.13M	2,421,159.114 us	35,280.8135 us	31,275.5222 us	27.33

Further reading

• Keys and composite keys performance estimation: Code / Results

Notes

• FastCache.Cached defaults provide highest performance and don't require spending time on finding a way to use API optimally. The design goal is to nudge a developer to use the fastest way to achieve his or her goals while strictly adhering to the principle of "pay for play".
• Comparison was made with a string-based key. Composite keys supported by FastCache.Cached have additional performance cost. Numeric keys have significantly higher performance in some instances (down to 5ns read latency on x86_64 where K is int or uint and V fits in a single register).
• *CacheManger documentation suggests using WithMicrosoftMemoryCacheHandle() by default which has terrible performance (and uses obsolete System.Runtime.Caching). We give it a better fighting chance by using WithDictionaryHandle() instead.
• **CachedRange.Save(ReadOnlyMemory<(K, V)>) (and CachedRange.Save(ReadOnlyMemory<K>, ReadOnlyMemory<V>)) automatically splits the range into slices of 1024 or length / cpu cores elements and saves them in parallel which dramatically improves throughput. This works especially well because the execution flow is lock-free and does not suffer from false sharing of CPU cache lines (except for the adjacent entries at the edges of slices)
• ***Forced single-threaded execution of CachedRange.Save(ReadOnlyMemory<(K, V)>)
• Overall, performance stays relatively comparable when downgrading to .NET 5 and decreases further by 15-30% when using .NET Core 3.1 with the difference ratio between libraries staying close to provided above.
• Non-standard platforms (the ones that aren't CLR based) use DateTime.UtcNow fallback instead of Environment.TickCount64, which will perform slower depending on the platform-specific implementation.

On benchmark data

Throughput saturation means that all necessary data structures are fully available in the CPU cache and branch predictor has learned branch patters of the executed code. This is only possible in scenarios such as items being retrieved or added/updated in a tight loop or very frequently on the same cores. This means that real world performance will not saturate maximum throughput and will be bottlenecked by memory access latency and branch misprediction stalls. As a result, you can expect resulting performance variance of 1-10x min latency depending on hardware and outside factors.

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