I’ll give you:
- One self-contained Program.cs you can drop into a new project
- How to run it from command line
- Step-by-step: how to use dotMemory to detect the leak
- How to read dotMemory results & locate the leak
- How to fix it (clean version of the code)
1️⃣ Create the demo app
dotnet new web -n MemoryLeakDemo
cd MemoryLeakDemo
Code language: JavaScript (javascript)
Now replace the entire Program.cs with this:
This is self-sufficient: one file, no extra classes, no extra projects.
2️⃣ Run the app (command line)
From the MemoryLeakDemo folder:
dotnet run
It will listen on something like:
http://localhost:5000https://localhost:5001
(Use the HTTP one for simplicity: http://localhost:5000)
3️⃣ Generate the leak
In another terminal (or browser / Postman):
A. Start leaking memory
Run a loop (PowerShell):
1..50 | ForEach-Object {
curl "http://localhost:5000/leak"
}
Code language: JavaScript (javascript)
Or Git Bash:
for i in {1..50}; do curl -s "http://localhost:5000/leak" > /dev/null; done
Code language: JavaScript (javascript)
Then check stats:
curl "http://localhost:5000/stats"
Code language: JavaScript (javascript)
You should see Total stored MB growing (e.g. 250 MB).
B. Compare with non-leaking allocations
1..50 | ForEach-Object {
curl "http://localhost:5000/noleak"
}
curl "http://localhost:5000/gc"
curl "http://localhost:5000/stats"
Code language: JavaScript (javascript)
/noleakallocates memory but does not store it → GC can clean it./leakallocates memory and keeps it in a static list → cannot be GC’d.
4️⃣ Profile with dotMemory – step by step
Assuming you have dotMemory GUI (Rider/Standalone):
Step 1: Start the app
Make sure dotnet run for MemoryLeakDemo is running.
Step 2: Open dotMemory
- Start dotMemory
- Choose “Profile Running Process” (or similar wording)
- Select the
dotnet.exe/MemoryLeakDemo.dllprocess - Click Run / Attach
Step 3: Take a baseline snapshot
In dotMemory:
- Click “Get Snapshot” (or “Get Snapshot #1”).
- Name it “Baseline (before leak)”.
Step 4: Generate the leak
Back in terminal:
1..100 | ForEach-Object {
curl "http://localhost:5000/leak"
}
curl "http://localhost:5000/stats"
Code language: JavaScript (javascript)
You should see big total MB stored.
Step 5: Take snapshot after leak
In dotMemory:
- Click “Get Snapshot” again.
- Name it “After leak (static list)”.
Step 6: Optionally, force GC and snapshot
Back in terminal:
curl "http://localhost:5000/gc"
curl "http://localhost:5000/stats"
Code language: JavaScript (javascript)
Then in dotMemory:
- Take Snapshot #3: “After GC”.
5️⃣ Understanding dotMemory results (how to spot the leak)
Now the fun part: reading dotMemory.
A. Compare snapshots (Baseline vs After leak)
- In dotMemory, select “Baseline” and “After leak” snapshots.
- Click “Compare Snapshots”.
Look at:
- Heap size increased a lot
- New objects count is huge
- Top types by size will likely show
System.Byte[](byte arrays)
B. Drill into byte[] and find retention
Click on System.Byte[] and open:
- “Retention Graph”
- or “Shortest Paths to GC Roots”
You should see a path like:
Static field → LeakStore.Buffers → List<byte[]> → byte[]
Code language: CSS (css)
This tells you:
- The
byte[]objects are alive because they are referenced by a static list. - That static list is
LeakStore.Buffers.
This is your memory leak.
C. Check what happens after GC
Compare “After leak” vs “After GC” snapshots:
- If
byte[]objects are still around in large numbers/size → GC cannot free them. - Why? Because they’re still referenced by
LeakStore.Buffers.
That is exactly how dotMemory exposes real leaks:
Objects survive GCs and are retained by long-lived references (statics, singletons, caches, events, etc.).
6️⃣ Common real-world patterns similar to this leak
- Static lists/dictionaries that grow but never shrink
- Caches without eviction policies
- Event handlers where you forget to
-=unsubscribe - Long-lived singletons holding request-scoped data
TaskorTimercallbacks closing over large objects- In-memory queues that never get drained
dotMemory will show all of these as:
- Big retained size
- GC roots pointing to static fields, singletons, or event tables
7️⃣ How to fix this leak (clean code version)
Here’s a fixed version of the leak part:
Option A: Limit the cache
static class LeakStore
{
private const int MaxBuffers = 10;
private static readonly Queue<byte[]> Buffers = new();
public static void AddBuffer(byte[] buffer)
{
Buffers.Enqueue(buffer);
while (Buffers.Count > MaxBuffers)
{
Buffers.Dequeue(); // old buffers become eligible for GC
}
}
public static int Count => Buffers.Count;
}
Code language: PHP (php)
Then in /leak:
var buffer = new byte[bytes];
// ...
LeakStore.AddBuffer(buffer);
Code language: JavaScript (javascript)
Now dotMemory will show:
- Heap size not growing unbounded
- Byte arrays being collected over time
Option B: Don’t use static leaks at all
- Use scoped dependencies
- Avoid unbounded global collections
- Use proper cache with TTL / LRU (e.g., MemoryCache)
8️⃣ In-Code Metrics (like we did for TLS)
For this example, /stats already gives:
- Number of stored buffers
- Total MB stored
- Number of leak calls
You can show this in the terminal while dotMemory shows heap graph growth → this connects code behavior + profiler view.
I’m a DevOps/SRE/DevSecOps/Cloud Expert passionate about sharing knowledge and experiences. I have worked at Cotocus. I share tech blog at DevOps School, travel stories at Holiday Landmark, stock market tips at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow , and SEO strategies at Wizbrand.
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This article offers a clear and insightful explanation of memory leaks in .NET applications and how to effectively debug them using dotMemory. It highlights common causes of memory leaks, such as static references or event handlers that aren’t properly unsubscribed, which prevent the garbage collector from cleaning up memory. The practical walkthrough with dotMemory tools, including how to analyze memory snapshots, identify retained objects, and trace their references, provides valuable hands-on experience for developers. By demonstrating how to track down memory issues and optimize resource management, the post serves as an excellent resource for anyone looking to improve the performance and reliability of their .NET applications.