Skip to main content

DataContract and XMLIgnore attribute

By
Intr-un post anterior am discutat despre cum putem sa serializam un IDictionary in format XML. In mod normal o clasa care nu este decorata cu atributul DataContract o sa poata fi serializata folosind DataContractSerializer fara nici o problema. In acest caz, toate proprietatiile si field-urile publice o sa fie serializate.
In specificatiile la DataContractSerializer, ne este specificat ca orice atribut folosit pentru serializare in format XML (XmlIgnore, XmlArray, Serializable etc.) sunt ignorate. Totusi cand obiectul de mai jos era serializat, atributul XmlIgnore este luat in considerare.
public Foo
{
    public string Name
    {
        get;
        set;
    }
    [XmlIgnore]
    public string Value
    {
        get;
        private set;
    }
}
In prima faza cea ce se intampla este destul de ciudat. In mod normal acesta nu ar trebui sa fie ignorat, dar practic proprietatea este ignorata in totalitate.
In realitate DataContractSerializer functioneaza asa cum ne-am astepta. Dar daca ne uitam mai atenti la modul in care este definita proprietatea Value, putem sa observam ca set-ul este private. DataContractSerializer serializeaza toate proprietatiile publice, iar Value nu este public. Aceasta are doar get-erul public, iar set-erul este private. Cu sau fara atributul XmlIgnore, aceasta proprietate nu este serializata.
Enjoy!
Labels:

Comments

Post a Comment

Popular posts from this blog

How to audit an Azure Cosmos DB

By
In this post, we will talk about how we can audit an Azure Cosmos DB database. Before jumping into the problem let us define the business requirement: As an Administrator I want to be able to audit all changes that were done to specific collection inside my Azure Cosmos DB. The requirement is simple, but can be a little tricky to implement fully. First of all when you are using Azure Cosmos DB or any other storage solution there are 99% odds that you’ll have more than one system that writes data to it. This means that you have or not have control on the systems that are doing any create/update/delete operations. Solution 1: Diagnostic Logs Cosmos DB allows us activate diagnostics logs and stream the output a storage account for achieving to other systems like Event Hub or Log Analytics. This would allow us to have information related to who, when, what, response code and how the access operation to our Cosmos DB was done. Beside this there is a field that specifies what was th...
Post a Comment
Read more

Why Database Modernization Matters for AI

By
  When companies transition to the cloud, they typically begin with applications and virtual machines, which is often the easier part of the process. The actual complexity arises later when databases are moved. To save time and effort, cloud adoption is more of a cloud migration in an IaaS manner, fulfilling current, but not future needs. Even organisations that are already in the cloud find that their databases, although “migrated,” are not genuinely modernised. This disparity becomes particularly evident when they begin to explore AI technologies. Understanding Modernisation Beyond Migration Database modernisation is distinct from merely relocating an outdated database to Azure. It's about making your data layer ready for future needs, like automation, real-time analytics, and AI capabilities. AI needs high throughput, which can be achieved using native DB cloud capabilities. When your database runs in a traditional setup (even hosted in the cloud), in that case, you will enc...
Post a Comment
Read more

AI ROI without hype: a practical way to measure value using risk adjustment + Azure Copilot example

By
Most people know what ROI means, but it’s harder to calculate for AI projects. The numbers are less predictable than with traditional platforms because many AI projects never reach stable production. IDC says only about 44% of custom AI apps and 53% of third-party AI apps make it from proof of concept to production. That’s why it’s important to look at ROI through a risk lens, not just cost versus benefit. One useful approach is to use a risk-adjusted formula: AI ROI = (AI Business Value Income / (Initial Investment + Annual Costs)) × Success Probability where, >AI Business Value Income (over N years) Consider a 2 to 3 year period and include both direct and indirect value: Direct: time saved, fewer tickets, higher conversion, lower fraud. Indirect: improved customer or employee experience and quicker decisions. For these, use measurable stand-ins like CSAT, churn, time to resolution, or hours saved, and estimate conservatively. >Initial Investment This covers more than just buil...
Post a Comment
Read more