How to Find Memory Leaks in Production ASP.NET Core Applications?

dotnet-counters ps

dotnet-counters monitor \
  --process-id 1234 \
  --counters System.Runtime

dotnet-dump collect \
  --process-id 1234 \
  --output memory-growth.dmp

dotnet-dump analyze memory-growth.dmp

dumpheap -stat

public static class RequestHistory
{
    public static readonly List<string> Requests = new();
}

Set Explicit Cache Limits

Caching can improve performance, but the application needs a policy for how much data it retains.

ASP.NET Core provides IMemoryCache for in-process caching. Configure expiration and size limits according to the application’s requirements.

For example:

using Microsoft.Extensions.Caching.Memory;

builder.Services.AddMemoryCache(options =>
{
    options.SizeLimit = 10_000;
});

When a size limit is configured, each cached entry must specify a size:

public sealed class ProductCache
{
    private readonly IMemoryCache _cache;

    public ProductCache(IMemoryCache cache)
    {
        _cache = cache;
    }

    public void StoreProduct(
        string productId,
        string productName)
    {
        var options = new MemoryCacheEntryOptions()
            .SetSize(1)
            .SetAbsoluteExpiration(
                TimeSpan.FromMinutes(15));

        _cache.Set(productId, productName, options);
    }
}

The size value is application-defined. In this example, each entry counts as one unit, so the limit is an entry-count-style budget rather than a measurement in bytes.

For variable-sized objects, choose a sizing strategy that better represents memory consumption. Also, remember that a cache size limit does not automatically measure or limit the application’s entire memory usage.

If a shared IMemoryCache instance has a size limit, every component adding entries to that instance must supply a size. Otherwise, entries may fail to be added. Test this behavior before enabling a limit in an established application.

For large or distributed caches, consider whether an external cache is more appropriate than retaining all data inside each application process.

Check Event Handlers, Timers, and Long-Lived Services

Objects can remain in memory because a long-lived component holds references to them.

For example, an application may subscribe a request-scoped object to an event published by a singleton service. If the event subscription is never removed, the publisher can keep the subscriber alive after the request finishes.

The same issue can occur with timers, callbacks, background tasks, and collections of delegates.

Review these patterns when heap analysis reveals many objects of a type that should have a short lifetime.

Remove Event Subscriptions When They Are No Longer Needed

A simplified example shows how a subscriber can unsubscribe when its work is complete:

public sealed class NotificationListener : IDisposable
{
    private readonly NotificationPublisher _publisher;

    public NotificationListener(
        NotificationPublisher publisher)
    {
        _publisher = publisher;
        _publisher.NotificationReceived += OnNotificationReceived;
    }

    private void OnNotificationReceived(
        object? sender,
        EventArgs args)
    {
        // Handle the notification.
    }

    public void Dispose()
    {
        _publisher.NotificationReceived -=
            OnNotificationReceived;
    }
}

The important point is that the listener must actually be disposed at the correct lifecycle boundary. Implementing IDisposable alone does not guarantee that cleanup occurs.

In ASP.NET Core, dependency injection can dispose services it creates when their lifetime ends, but manually created objects and subscriptions still need an explicit ownership strategy.

Avoid registering request-specific objects with long-lived event publishers unless the subscription lifetime is carefully managed.

Review Dependency Injection Lifetimes

Incorrect dependency injection lifetimes can keep objects alive longer than intended.

ASP.NET Core commonly uses three service lifetimes:

• Transient: a new instance is created when the service is requested.
• Scoped: one instance is used within a scope, commonly an HTTP request.
• Singleton: one instance is shared for the application’s lifetime.

A singleton that stores references to request-specific objects can prevent those objects from becoming eligible for collection.

A particularly problematic pattern is injecting a scoped service into a singleton and retaining it beyond its intended lifetime. ASP.NET Core’s scope validation can identify some lifetime errors, but not every memory-retention problem is a dependency injection error.

Review singleton fields, static references, background-service state, and long-lived collections when investigating object retention.

If a background service needs scoped dependencies, create a scope for each unit of work and release it when that work finishes.

Investigate Large Allocations and the Large Object Heap

The .NET runtime manages large objects differently from smaller allocations. Large allocations can increase memory pressure and may be expensive when they occur frequently.

Potential causes include:

• Reading entire files into memory when streaming would work.
• Creating large byte arrays for HTTP responses.
• Serializing large object graphs repeatedly.
• Building large strings through repeated transformations.
• Processing many records at once instead of in batches.

For example, loading an entire file with a single read operation may be unnecessary when the application can process it incrementally.

await using var input = File.OpenRead(filePath);
await using var output = File.Create(outputPath);

await input.CopyToAsync(
    output,
    cancellationToken);

Streaming reduces the need to hold the complete file contents in a single managed buffer. It does not guarantee constant memory usage for every processing pipeline, because downstream code may still buffer data.

Review large object allocations alongside heap statistics and allocation profiles. If the application repeatedly creates large temporary objects, reducing allocation volume may improve performance even when those objects are eventually collected.

Investigate Unmanaged Memory and Native Resources

If process memory grows while the managed heap remains relatively stable, investigate unmanaged allocations and native resources.

Possible causes include:

• Native libraries allocating memory.
• Buffers allocated outside the managed heap.
• Interoperability code that fails to release resources.
• Native handles or operating-system resources accumulating.
• Runtime or library components maintaining native state.

Managed memory tools may not fully explain these cases.

Review the native libraries used by the application and determine whether they require explicit release, disposal, or ownership transfer. For resources represented by managed wrappers, verify that the wrappers are disposed according to their documented lifetime requirements.

Also inspect process-level metrics, handle counts, container memory usage, and relevant operating-system diagnostics. Do not assume that calling GC.Collect() will solve the problem. Forced garbage collection cannot reclaim objects that are still reachable, and it does not replace correct cleanup of unmanaged resources.

Verify the Fix Under Production-Like Load

A memory-related code change should be validated over time rather than judged by a single successful request.

Use a representative workload and observe the application through several cycles of requests, background jobs, and garbage collection.

A useful verification process includes:

1. Record baseline heap size, process memory, allocation rate, and traffic.
2. Reproduce the growth pattern with a controlled workload.
3. Capture diagnostic evidence before and after the suspected growth period.
4. Identify the retaining reference or allocation source.
5. Apply the fix and repeat the same workload.
6. Confirm that memory reaches a stable operating range under comparable conditions.
7. Check that latency, error rates, and throughput remain acceptable.

The expected memory profile depends on the application. A service may retain a larger heap after a traffic spike without leaking memory. The important question is whether retained state continues growing beyond what the workload and caching policy justify.

For production services, also verify the effect of the fix on container memory limits, restart frequency, and the application’s ability to recover after traffic decreases.

Common Mistakes to Avoid

Assuming Every Increase Is a Leak

Memory may rise because of legitimate caching, increased traffic, or runtime allocation behavior. Compare heap and process-level metrics before drawing conclusions.

Restarting Without Capturing Evidence

A restart may restore service temporarily, but it can erase the state needed to diagnose the problem. Collect useful metrics and, where appropriate, a dump before restarting, provided doing so is safe for the application.

Calling GC.Collect() Repeatedly

Forced collection does not fix incorrect object retention and can reduce performance. Find and remove the references that keep unwanted objects alive.

Increasing Container Memory Without Investigation

Raising the memory limit may reduce immediate failures, but it can hide an unbounded cache or a growing object graph. Treat capacity changes as a mitigation, not proof that the underlying issue is resolved.

Ignoring Native Allocations

A stable managed heap does not rule out a process-level memory problem. Include native memory and operating-system resource usage in the investigation.

Storing Diagnostic Dumps Without Access Controls

Memory dumps can contain sensitive data. Restrict access, encrypt storage where appropriate, and apply the organization’s retention and deletion requirements.

Advantages and Limitations of Runtime Diagnostics

Runtime counters help identify memory trends with relatively low overhead, making them useful for continuous monitoring and initial triage. Memory dumps provide deeper visibility into object types and reference relationships, which can help identify retention problems that counters cannot explain.

However, dumps can be large, require specialized analysis, and expose sensitive in-memory information. Some memory problems also involve native allocations or external processes that are not fully represented in managed heap analysis.

The most effective approach combines lightweight monitoring, targeted diagnostic captures, source-code review, and repeatable load testing.

Summary

Diagnosing memory leaks in ASP.NET Core begins with distinguishing normal memory behavior from unwanted retention. Monitor runtime counters and process-level measurements, then collect memory dumps when the growth pattern is reproducible.

Inspect static collections, unbounded caches, event subscriptions, dependency injection lifetimes, large allocations, and unmanaged resources. After making a change, repeat the same workload and verify that memory stabilizes without introducing performance or reliability problems.

The goal is not to force the application to use as little memory as possible. It is to understand why memory is being retained, remove unnecessary retention, and keep memory usage predictable as the application handles production traffic.

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