Skip to main content

Putem sa inlocuim switch ... case ?

Sunt locuri in aplicatii unde suntem obligati sa scriem ceva de genul:
switch (value)
{
case "start":
...
break;
case "end":
...
break;
case "continue":
...
break;
default:
...
break;
}

M-am intrebat de mai multe ori daca am putea oare sa eliminam switch. Nu mi-a placut niciodata de acest "statement". Este foarte util, este usor de inteles, dar parca ar putea sa fi inlocuit cu altceva.
O solutie pe care pot sa o propun este inlocuirea acestuia cu un dictionar unde cheile sa reprezinta valorile posibile pe care le poate avea variabila, iar valorea din dictionar poate sa fie reprezentata de o functie anonima(lambda expression) sau de un pointer spre o metoda ce contine codul ce trebuie executat.

Toate bune si frumoase, o solutie perfecta pentru a scrie un cod "trendy" folosind ceea ce a adus nou .NET 3.5. Dar uitam un lucru de baza, performanta. Switch ... case este optimizat pentru asa ceva si orice am face, oricum am scrie, nu o sa putem inlocui un switch clasic cu nimic care sa ruleze mai repede.
Putem in schimb sa înfrumusețam codul, fiecare case sa contina doar un apel la o metoda care contine codul ce trebuie executat, astfel switch o sa poata fi inteles si vizualizat in intregime mult mai usor.
Solutia pe care am propus-o, folosind un dictionar, ar putea sa functioneaze cand vrem sa adaugam la runtime cate un "case", dar din pacate nu o sa fie mai rapida.

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