Skip to main content

Remote debug of an application that is on Windows Azure using VPN

One cool thing that I like when working with Azure is the way how you can do remotely debug. In a classic system is not very easily to do something like this. Azure facilitate this using Visual Studio and attached to process mechanism.
Theoretically you should not need to do remote debug, but in practice you need to do this from time to time.
To be able to do something like this you will need to create a virtual network. Using the virtual network you will be able to establish a VPN connection between the cloud machines (network) and you. Using this feature you can do more complicated stuff, for example integrate your cloud network with the one from your premise, but we don’t needs something like this if we want remote debugging.
Creating the virtual network is pretty simple. You need to go to New-> Network -> Virtual Network and create a new one. For this purpose you don’t need to make any kind of custom configurations. Don’t forget to select your affinity group – this should represent the cloud machines that you want to access remotely using VPN.
After this step you should create a custom certificate on your machine and upload to the certificates tab of your private network.
Now we are ready for the last step. Download the VPN configuration from the dashboard tab and run it. From now, you will be connect to your private network using the VPN connection. Based on the machine name you can connect to any machine or …
you can do remote debug of your application very easily. Using Visual Studio you can attach to any process of your machines and do real time debugging.
Until now I didn’t needed this feature, but it is pretty cool how easily you can do something like this. No custom configuration, no custom tools, no IT department from the on premise network.

Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP