Aligning Microsoft Azure BizTalk Services Development with Enterprise Integration Patterns

by | Sep 25, 2014 | BizTalk Community Blogs via Syndication

We have just finished a fairly large effort in moving the QuickLearn TrainingTeam blog over to http://www.quicklearn.com/blog, as a result we had a mix-up with the link for our last post. This post has moved here: Aligning Microsoft Azure BizTalk … Continue reading →

The post Aligning Microsoft Azure BizTalk Services Development with Enterprise Integration Patterns appeared first on QuickLearn Blog.

Blog Post by: Nick Hauenstein

How to upload an R package to Azure Machine Learning

by | Sep 24, 2014 | BizTalk Community Blogs via Syndication

Azure Machine Learning (http://azure.com/ml) has a number of packages already installed by default. You can see them with this following sample experiment:

R script is:

data.set <-data.frame(installed.packages());
# Select data.frame to be sent to the output Dataset port
maml.mapOutputPort("data.set");

you’ll find a little more than 400 packages.

 

Still you may need to use a package which is not known by Azure ML. Here is how to upload it to the environment.

NB: This post takes skmeans (k-means with a cosine distance) as an example, but this works for other packages as well.

 

Let’s suppose you have this code in R Studio locally.

NB: you can find information on how to setup your environment in this post. It’s in French, but bing translator is your friend. 

library(skmeans)

set.seed(1234)
sample_data <- matrix(sample.int(1000, size = 20*500, replace = TRUE), nrow = 20, ncol = 500, 
                      dimnames=list(1:20, 1:500))

fit <- skmeans(sample_data,5)

result <- data.frame(list(rownames(sample_data), fit$cluster), row.names=NULL)
colnames(result) <- c("sample data row", "cluster")

print(result)

this will give this kind of result

If you try this in Azure ML, you’ll get the following result:

Here is how to have the script loading all the necessary packages in the Azure ML environment.

So let’s now see how you construct the skmeans_packages.zip and know which lines to write here:

 

 

On the local environment (in my case Windows), I remove the R packages that are installed in My Documents\R

then in R, I install the skmeans package:

install.packages("skmeans")

this gives the following result:

So I know I have to install the following packages in order:

  • slam
  • clue
  • skmeans

Then I go to the temp folder:

I Zip the zips:

and rename this new zip file as skmeans_packages.zip

I then can upload it Azure ML:

NEW, DATASET, FROM LOCAL FILE

 

Then you’ll be able to find it as a saved dataset in your workspace:

After it has been connected to the third dot of the Execute R Script module instance, you’ll be able to find the content in src/ folder:

so, in order  to install skmeans and its two dependencies, then reference the skmeans library, you just have to enter the following lines: 

install.packages("src/slam_0.1-32.zip", lib = ".", repos = NULL, verbose = TRUE)
install.packages("src/clue_0.3-48.zip", lib = ".", repos = NULL, verbose = TRUE)
install.packages("src/skmeans_0.2-6.zip", lib = ".", repos = NULL, verbose = TRUE)
library(skmeans, lib.loc=".", verbose=TRUE)

 

Azure ML has a pool of VM with docker-like containers (true Windows containers, named drawbridge) where the experiments run. So each time the script runs, it starts from a blank standard Azure ML environment. By bringing a zip, you add the files to that environment.

 

Hope this blog post will help you if you need R packages which are not in the 400+ preloaded ones in Azure Machine Learning!

 

Benjamin (@benjguin)

Blog Post by: Benjamin GUINEBERTIERE

How to use context data with Business Rules

by | Sep 15, 2014 | BizTalk Community Blogs via Syndication

 

I had never come across the need to execute rules based on message metadata until a colleague of mine asked me the other day. Which is surprising as we often define routing rules based on the message context.

The basic principle is simple, – Rather than using schema based facts, we use facts based on .Net assemblies. In this case IBaseMessageContext (Microsoft.BizTalk.Message.Interop). This works well in cases where you’d like to call the rule from within a pipeline, as the context of the pipeline message (IBaseMessage) is implementing the IBaseMessageContext interface.

If, on the other hand, you want to execute the rule form an orchestration we can’t use an interface as a rule fact, as the CallRules Policy shape can’t work with interfaces. In those cases we’d need to use a custom class (the one the is used by BizTalk is internal).

Create the output fact schema

Before we create the rule you need to have your facts ready. The input fact is going to be the context object mentioned above but you need to create the output schema where you’re going to store the result if the rule evaluates to true.

Create the rule (Messaging scenario)

Open the Microsoft Business Rule Compose and create your policy and rule as you’d normally do. Then in the Fact Explorer select the .Net Classes tab, right-click the .Net Assemblies node and select Browse. The IBaseMessageContext interface is found in the Microsoft.BizTalk.Pipeline assembly. Select the assembly and expand the IBaseMessageContext in the tree view.

Drag the Read(String strName, String strNamespace) method to your rule condition:

 

 

Create the pipeline component (Messaging scenario)

If you haven’t developed a pipeline component before you can use a nuget package like Default Breeze BizTalk Pipeline Component. Simply create a class library project and install the nuget package. Follow the steps at the top of the pipeline component file before implementing the Execute method.

public IBaseMessage Execute(IPipelineContext pc, IBaseMessage inmsg)
{
    //Create a policy object based on the name of the Policy
    Policy policy = new Policy("MessageContextSamplePolicy");
            
    // Create the response document
    XmlDocument doc = new XmlDocument();
    doc.LoadXml(@"<ns0:BreTest xmlns:ns0=""http://BreWithPromotedPropertiesSample.BreTest""><Field></Field></ns0:BreTest>");
    var responseDoc = new TypedXmlDocument("BreWithPromotedPropertiesSample.BreTest", doc);
            
    //Execute the policy and pass the message context and the response docoument
    policy.Execute(new object[] { inmsg.Context, responseDoc });
            
    var responseXml = responseDoc.Document.OuterXml;
           
    // TODO
    // Take action on the response XML...

    return inmsg;
}

Create the rule (Orchestration scenario)

There are two problems to working with the context data together with orchestrations; Access the metadata and not being able to work with interfaces (CallRules Policy shape can’t work with interfaces).

Within the orchestration we don’t have the IBaseMessage from which we can easily access the context. Instead we got the XLANGMessage. The message context is not as easily accessible form the XLANGMessage as it is from the IBaseMessage. Thankfully Maxime Labelle provided insight of how this can be accomplished.

I solved these problems be creating my own class called XLANGMessageContext and from within the constructor I read through the metadata. The class also provide a Read method that will return the value for a specific property:

 

public class XLANGMessageContext
{
    Hashtable _messageContext = null;

    public XLANGMessageContext(XLANGMessage message, string name, string ns)
    {
        try
        {
            foreach (Segment segment in Service.RootService._segments)
            {
                IDictionary fields = Context.FindFields(typeof(XLANGMessage), segment.ExceptionContext);

                foreach (DictionaryEntry field in fields)
                {
                    XMessage msg = (field.Value as XMessage);
                    if (msg == null)
                        continue;

                    if (String.Compare(msg.Name, message.Name) != 0)
                        continue;

                    var key = new XmlQName(name, ns);
                    _messageContext= msg.GetContextProperties();
                       
                }
            }
        }
        catch (Exception /* e */)
        {
            // do not provoke failure
            // probably best to add some logging here
        }
    }

    public object Read(string strName, string strNamespace)
    {
        var key = new XmlQName(strName, strNamespace);
        var result = _messageContext[key];
        return result;
    }
}

Creating the rule is very similar to the messaging scenario, but instead of using the IBaseMessageContext object you’ll use the Read method from the XLANGMessageContext:

Use the XLANGMessageContext (Orchestration scenario)

In your orchestration, create  a BreHelperComponent.XLANGMessageContext valiable called xlangMessageContext, and construct it in an Expression shape:

xlangMessageContext = new BreHelperComponent.XLANGMessageContext(myMessage);

Lastly add a CallRules shape and provide the parameters:

 

You can download the sample here.

HTH

Mikael

Blog Post by: wmmihaa