Using Streams With Httpclient To Improve Performance And Memory Usage
To download a source code, you can visit our Using Streams with HttpClient repository. Hope you learned something from this post and know where to start looking for optimizations. If you’re reading this post you most likely have read about the HttpClient, HttpClientFactory, whether you should reuse the HttpClient or not.
HttpClient is the standard way to make API calls in .NET Core so most people are familiar with it. Make sure your async/await code is looking right. If you code async/await wrong your app will most likely suck no matter what.
In our second article of the series, we have learned how to send a POST request using HttpClient. In that example, we were serializing our payload into a JSON string before we send the request. As soon as we have our stream, we call the JsonSerializer.DeserializeAsync method to read from a stream and deserialize the result into the list of company objects. When the debugger hits a breakpoint, you can open the memory view in a separate tab of the Debug window.
If you use it on Windows to collect memory dumps you can review the dumps in WindDbg or DebugDiag or any dump debugging tool. Remember you can always scale horizontally by increasing the number of replicas to handle more traffic. Another metric we noticed during our spike window was that the memory for the pods shut through the roof.
Best Practices To Keep A Net Applications Memory Healthy
The suggestion was to switch from Server GC to Workstation GC, which optimizes for lower memory usage. The switch can be done by adding this flag to our csproj file. I have some ASP.NET core project and I was looking on memory that consumes. I have a few controllers and it consumes about 350MB of Ram and that’s a big amount of memory for the webserver.
- I did not have metrics for everything here, but I tried one thing after another – inspecting that the application behaved good/better and continued.
- This part of managed memory is not compacted automatically due to the cost of moving larger objects.
- The only thing you need to worry about is the collections.
- The framework just adds objects to this memory wherever it finds some free space.
- In the client application, we can use streams to prepare a request body or to read from a response regardless of the API implementation.
- This means that memory consumption should be more or less the same in the long run.
- With JetBrains Rider, you can explore the managed heap while debugging and look into the memory space that is used by your application.
The dump is a snapshot of all the memory used by the process at a given point in time. These things are essential to know when trying to understand memory usage and “wellbeing” of you application, so I thought i’ll mention them. These are all decisions that the runtime will take no matter what; and to improve them we must first understand them. I’m a software developer, C# enthusiast, author, and a blogger. I write about C#, .NET, memory management, performance, and solving difficult problems in .NET. One way to relieve some of that memory pressure is by using mutable cache objects.
I did not have metrics for everything here, but I tried one thing after another – inspecting that the application behaved good/better and continued. Unfortunately I don’t have updated graphs in-between each step I took. I did have a feeling that we were not going things right in our code, so I started to search for “pitfalls”. If you are like me – having an app runing inside Kubernetes – you might also have questions such as “is my app behaving well?”. It’s impossible to cover everything related to a well performing app, but this post will give you some guidance at least. We also tried various different ways to reproduce the problem in our development environment.
The Stream class in C# is an abstract class that provides methods to transfer bytes – read from or write to the source. Since we can read from or write to a stream, this enables us to skip creating variables in the middle that can increase memory usage or decrease performance. With the continuous allocation and release of memory, fragmentation may occur in memory. This is because objects must be allocated in continuous memory blocks. In order to alleviate this problem, whenever the garbage collector releases some memory, it will try to defragment. The memory view keeps track of the difference in object count between breakpoints.
We used to say that careful use of structs could achieve the same as escape analysis. But lets be honest, almost nobody has the time for that. And even if we did, the libraries we’re using usually don’t have both a class and struct version of each type. The platform to power synchronized digital experiences in realtime. QCon San Francisco Understand the emerging software trends you should pay attention to.
Using Streams With Httpclient To Improve Performance And Memory Usage
So as long as that List is on the stack, and the Product is part of the list etc. we won’t garbage collect it, because it is still in use. Once you are in the tool, it can feel a bit daunting if you’ve never used it before, but typing the command help will list all the commands you can use. The commands are extremely similar to the sos commands you use in WinDbg (except you don’t have to start the commands with ! since it is not an extension here).
Poorly designed applications inevitably have larger memory footprints than is strictly necessary. State buckets such as session caches encourage developers to create unnecessary and long-lived memory dumps. Over-aggressive caching can start to resemble to symptoms of a memory leak. Although the details of memory management may be largely hidden from developers, they still have a responsibility to protect finite memory resources and code as efficiently as possible.
Unless the class is not implemented properly . The figure below shows a relatively small 5K RPS Load generation , In order to understand how memory allocation is affected by GC Influence . Once the application starts , The application shows some memory and GC Statistics , The page refreshes every second . Specific API Interface performs a specific memory allocation pattern .
The GC is optimized to have many Gen 0 collections, fewer Gen 1 collections, and very few Gen 2 collections. But if you have many objects that get promoted to a higher generation then you’ll have the reverse effect. This leads to memory pressure and poor performance.
You’ll also see best practices to optimize garbage collections and make your application very fast. This system of memory management is “deterministic”. By careful analysis, you can determine exactly when an object will be deleted. This in turn means that you can automatically free resources such as database connections. Contrast this with .NET, where you need a separate mechanism (i.e. IDisposable/using) to ensure the non-memory resources are released in a timely manner. Ultimately you have finite resources so you should get used to the discipline of only using memory when you have to.
With the continuous allocation and release of memory , Fragmentation can occur in memory . This is because objects must be allocated in contiguous asp net usage blocks of memory . In order to Alleviate the problem , Whenever the garbage collector releases some memory , Will try to defragment .
Running A Net Core Console Application As A Windows Service
A few months ago, Raygun was featured on the Microsoft website with how we increased throughput by 2,000 percent with a change from Node.js to .NET Core. My context is null, although I can access it to seed the database during ConfigureWebHost. This is a API endpoints , It creates a new instance in each request and then releases . You can also use the file in the published application runtimeconfig.json Set up System.GC.Server Properties to complete . Distinguish between the effects of the two patterns , We can modify the project file (.csproj) in ServerGarbageCollection Parameters , mandatory Web Application and use Workstation GC. But for the sake of simplicity , This article will not use these , Instead, it presents some in app Real-time charts .
Sharing Libraries Between Net Core And net Framework Applications
And if the counter reaches zero, the object is deleted, freeing the memory to use elsewhere. One is reserved for small objects and is regularly compacted to ensure optimal memory usage. If an object is 85k or larger then it is allocated onto the “large” object heap.
The same problem can easily happen in user code , Release class incorrectly or forget to call Need to release the object’s Dispose() Method . Fortunately, .NET Provides IDisposable Interface allows developers to actively release native memory . Even if Dispose() Not called in time , Classes are usually executed automatically when the finalizer runs …
The problem with compression is , The larger the object , The slower you move . When it reaches a certain size , The time it takes to move it makes moving it less effective . For these large objects Memory Area , Become Large object heap . Greater than 85,000 bytes The object of It was placed there , Uncompressed , And only in 2 Release on collection .
Indeed we did see a gain, although in both cases memory was fairly static. I am a London-based technical architect who has spent more than twenty five years leading development across start-ups, digital agencies, software houses and corporates. Over the years I have built a lot of stuff including web sites and services, systems integrations, data platforms, and middleware.
This is because the .NET Core runtime doesn’t want to allocate hundred of threads just because of a traffic spike. Remember those .Result nightmare-calls that blocks the thread. There are two GC modes, “Workstation mode” and “Server mode”.
Workstation GC made the application be much more conservative in terms of memory usage, and decreased the average from ~600 MB to ~ MB. These graphs show the memory usage of two of our APIs, you can see that they keep increasing until they reach the memory limit, when Kubernetes restarts them. The GC does generation 0 collections multiple times per second instead of every two seconds. Back when .NET was new, many people complained that the non-deterministic performance of .NET’s garbage collector would harm performance and be difficult to reason about.
For example, you might have objects that should have been collected but remain alive and still have code executing in them, which results in incorrect behavior. It might be a server that serves requests, a service that pulls messages from a queue, a desktop application with a lot of screens. During this time your application constantly creates new objects, performs some operations, and then frees those objects and returns to a normal state. This means that memory consumption should be more or less the same in the long run. Sure, it might reach high levels in peak time or during heavy operation, but it should return to normal once it’s done. There are three major downsides to reference counting garbage collectors.
Also, we use the Seek method to set a position at the beginning of the stream. Then, we initialize a new instance of the HttpReqestMessage object with the required arguments and set the accept header to application/json. In this method, we use the GetAsync shortcut method to fetch the data from the API. But as we have explained in the first article of this series, you can use the HttpRequestMessage class to do the same thing with higher control over the request. Also, pay attention that this time we are wrapping our response inside the using directive since we are working with streams now. The stream represents an abstraction of a sequence of bytes in the form of files, input/output devices, or network traffic.
GC Allocation by segment , Each segment is a contiguous range of memory . The objects in it are divided into three generations 0, 1, 2. I decided GC Frequency of attempts to free memory on managed objects that are no longer referenced by the application – The smaller the number, the higher the frequency . The green line is average response times.This web application handles roughly 1k requests per minute, so that is very low compared to what ASP.NET Core is benchmarked against. This means that for this web application to have a good throughput we cannot queue up too many dependency calls.