C# ArrayPool Memory Leaks: Why Finalizers Fail and How to Fix Them

INTRODUCTION: How did a C# finalizer cause memory spikes in an IoT platform?

While working on a high-throughput industrial IoT platform, we encountered a severe architectural bottleneck. The system was designed to ingest massive volumes of raw sensor data, process it across multiple background threads and route the results to a cloud storage layer. To avoid putting excessive pressure on the .NET Garbage Collector (GC), the team correctly utilized ArrayPool<byte>.Shared to recycle memory buffers.

However, during load testing, we realized the application’s memory consumption was steadily climbing, eventually leading to intermittent OutOfMemory exceptions and application crashes. The GC logs showed massive allocation spikes on the Large Object Heap (LOH).

Upon investigating the codebase, we discovered a common but dangerous pattern: because the sensor payloads were being handed off between multiple asynchronous threads, the original developers struggled to determine exactly when a buffer was no longer needed. To “play it safe,” they omitted explicit disposal and instead relied on the class’s finalizer to return the rented arrays back to the ArrayPool.

This situation perfectly illustrates a widespread misunderstanding in .NET memory management regarding managed resources and finalization. It inspired this article to help other engineering teams understand why treating memory pools as a valid exception to the “never touch managed resources in a finalizer” rule is a critical mistake.

PROBLEM CONTEXT: Why use ArrayPool for managed resources in C#?

In enterprise-grade .NET applications, continuously allocating large arrays (typically over 85,000 bytes) places them on the Large Object Heap. Frequent allocations here cause fragmentation and force expensive Gen 2 garbage collections. Using ArrayPool<T> is the standard industry practice to mitigate this by renting and returning arrays.

In our client’s IoT system, the data wrapper looked conceptually similar to this sanitized example:

public class SensorPayload : IDisposable
{
    private bool _isDisposed;
    public SensorPayload(ISensorDevice sensor, int timeoutMs, int payloadSize) 
    {
         Size = payloadSize;     
         Data = ArrayPool<byte>.Shared.Rent(payloadSize); 
         sensor.ReadData(Data, timeoutMs);
    }
    public byte[] Data { get; private set; } 
    private int Size { get; }
    public void Dispose()
    {
        if (_isDisposed) return;
        _isDisposed = true;
        if (Data != null)
        {
            // Returning managed resource back to the pool
            ArrayPool<byte>.Shared.Return(Data);
            Data = null;
        }
        GC.SuppressFinalize(this);
    }
    ~SensorPayload()
    {
        Dispose();
    }
}

Because the SensorPayload object was shared across varying threads, developers found it impossible to use a simple using statement. They assumed that adding a finalizer would act as a safety net, ensuring the array always made it back to the pool even if explicit disposal was missed.

WHAT WENT WRONG: Is it safe to access managed resources in a C# finalizer?

The standard C# rule is: never touch managed resources in a finalizer. But developers often question the assumptions behind this advice. What if the resource wasn’t allocated with new() but rented from a static ArrayPool<T>.Shared? Since the pool is static and globally rooted, won’t it survive longer than the finalized object?

Technically, the static ArrayPool might still be accessible during a standard GC cycle. However, relying on this behavior led to catastrophic failures in our production environment for several reasons:

• Pool Starvation: Finalizers do not run immediately. They run non-deterministically on a dedicated finalizer thread. When high-throughput threads abandon arrays, they sit in memory waiting for GC. By the time the finalizer runs to return the array, the pool has already been starved, forcing it to allocate new large arrays to satisfy incoming requests. This entirely defeats the purpose of pooling.
• Finalizer Queue Blocking: Returning to the ArrayPool involves thread synchronization and internal state management. Executing this on the single .NET finalizer thread slows down the finalization of all other objects in the application, causing a massive backlog and memory leaks.
• AppDomain and Shutdown Tearing: During application shutdown, the order of finalization is undefined. If the runtime attempts to finalize the SensorPayload after it has already begun tearing down other static or managed structures, accessing the pool can throw unpredictable exceptions like NullReferenceException or ObjectDisposedException.

HOW WE APPROACHED THE SOLUTION: What are the alternatives to C# finalizers for memory pools?

We needed to refactor the cross-thread memory ownership model to guarantee deterministic disposal. When you hire software developer teams with deep architectural experience, analyzing these tradeoffs is a core part of the process. We considered these solutions as well:

Solution 1: Forcing GC Collections

One initial suggestion was to leave the finalizer in place but force GC.Collect() at specific intervals to clear the finalizer queue. We immediately discarded this. Forcing garbage collection pauses the application threads, destroying performance in a high-throughput system.

Solution 2: Implementing Reference Counting

We considered building a custom reference counting mechanism (similar to COM AddRef/Release). Every time a thread took ownership of the payload, it would increment the counter. When finished, it would decrement it and the last thread would return the array. While valid, reference counting is notoriously difficult to maintain, prone to race conditions and introduces significant lock contention.

Solution 3: Utilizing IMemoryOwner and System.IO.Pipelines

The modern, idiomatic approach for .NET is to use IMemoryOwner<T> or the System.IO.Pipelines namespace. This enforces a strict producer-consumer ownership model. A producer allocates/rents the memory and hands it off to a channel or pipeline. The consumer reads it and becomes strictly responsible for disposing of it. This ensures exact, deterministic lifetime management without relying on the GC.

FINAL IMPLEMENTATION: How do you safely manage cross-thread ArrayPool lifecycles?

We chose to refactor the architecture to enforce strict ownership handoffs using channels and IMemoryOwner<byte>. By utilizing the built-in MemoryPool<T> (which wraps ArrayPool), we eliminated the need for finalizers entirely.

public class SensorDataProcessor
{
    // Using Channels for safe cross-thread handoff
    private readonly Channel<IMemoryOwner<byte>> _processingChannel = Channel.CreateBounded<IMemoryOwner<byte>>(100);
    public async Task ProduceDataAsync(ISensorDevice sensor, int payloadSize)
    {
        // Rent from the pool and immediately wrap in an IMemoryOwner
        IMemoryOwner<byte> owner = MemoryPool<byte>.Shared.Rent(payloadSize);
        
        try
        {
            sensor.ReadData(owner.Memory.Span);
            // Hand off ownership to the consumer
            await _processingChannel.Writer.WriteAsync(owner);
        }
        catch
        {
            // If handoff fails, producer cleans up immediately
            owner.Dispose();
            throw;
        }
    }
    public async Task ConsumeDataAsync(CancellationToken cancellationToken)
    {
        await foreach (var owner in _processingChannel.Reader.ReadAllAsync(cancellationToken))
        {
            try
            {
                // Process the data on the background thread
                ProcessSpan(owner.Memory.Span);
            }
            finally
            {
                // Consumer deterministically returns memory to the pool
                owner.Dispose();
            }
        }
    }
}

Validation Steps: After deploying this refactored pipeline, we monitored the LOH size and GC metrics. Pool starvation disappeared completely. Memory consumption flatlined to a predictable, steady state and the finalizer queue remained empty.

LESSONS FOR ENGINEERING TEAMS: What are the best practices for C# memory management?

For organizations looking to hire dotnet developers for enterprise modernization, mastering these low-level memory concepts is critical. Here are the actionable insights from this project:

• Never use finalizers as a fallback for memory pooling. Delaying the return of resources defeats the purpose of the pool and leads to memory exhaustion.
• Finalizers are for unmanaged resources only. The rule stands firm. SafeHandles or unmanaged pointers belong in finalizers; managed objects (like ArrayPools or database connections) do not.
• Enforce strict object ownership. If an object is passed across threads, clearly define whether the producer or the consumer is responsible for calling Dispose().
• Use modern .NET primitives. Prefer IMemoryOwner<T>, Span<T>, Memory<T> and Channels over bare byte arrays and custom locking logic when managing high-throughput data streams.
• Monitor LOH and Finalizer Queues. Anomalies in your finalizer queue length or LOH allocation rates are the first indicators of architectural flaws in your pooling strategy.

WRAP UP: How can expert engineering improve your .NET architecture?

Attempting to bend the rules of the .NET Garbage Collector rarely pays off. Relying on finalizers to clean up managed memory pools creates subtle, catastrophic bottlenecks in production. By refactoring toward strict ownership and leveraging modern memory primitives, we stabilized the IoT platform, completely eliminating memory spikes.

When you need to build or scale high-performance systems, bringing in developers who understand the underlying runtime behavior is essential. If you are looking to hire cloud backend developers for scalable systems who can navigate these exact challenges, contact us to learn how our dedicated engineering teams can support your goals.

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