Enforcing Referential Integrity Between Documents with Marten and PostgreSQL

Even though Marten is a library to enable document database storage semantics, because it’s built on top of (the advanced JSON features) of PostgreSQL, and PostgreSQL itself is relational, we can add constraints between document instances just as we would do in relational normalised databases.

For example, the following two simplified classes represent an order that points to a customer document:

class Customer
    public int Id { get; set; }
    public string Name { get; set; }
    // etc.

class Order
    public int Id { get; set; }
    public int CustomerId { get; set; }
    public int Quantity { get; set; }
    // etc.

By default there is no referential integrity enforcing the idea that the CustomerId in the Order class must point to an existing Customer document in the database. This means that the following code succeeds with no errors:

const string connectionString = "host = localhost; database = OrderDb; password = g7qo84nck22i; username = postgres";

var store = DocumentStore.For(connectionString);

using (var session = store.OpenSession())
    var order = new Order
        CustomerId = 34567 // non-existent


    session.SaveChanges(); // no error

The preceding code will result in an order document being stored with an invalid CustomerId.

Marten can be configured to enforce this by either adding the [ForeignKey(typeof(Customer))] attribute to the CustomerId property or by configuring the document store as the following code shows:

var store = DocumentStore.For(configure =>
    configure.Schema.For<Order>().ForeignKey<Customer>(x => x.CustomerId);

Now running the code again will result in an exception: insert or update on table "mt_doc_order" violates foreign key constraint "mt_doc_order_customer_id_fkey”.

If the code is modified to create a valid customer in the same session, Marten will generate a customer Id that can then be used in the order. When SaveChanges() is called, Marten will save the dependent customer document first and then the order document that points to it. The following code executes successfully:

using (var session = store.OpenSession())
    var customer = new Customer {Name = "Sarah"};

    // customer now has an Id auto-generated 

    var order = new Order
        Quantity = 42,
        CustomerId = customer.Id // valid existing customer ID



To learn more about the document database features of Marten check out my Pluralsight courses: Getting Started with .NET Document Databases Using Marten and Working with Data and Schemas in Marten.

Creating a Tweet Buffer with Azure Queues and Microsoft Flow

There are apps and services that allow the scheduling or buffering of the sending of Tweets. Using the features of Microsoft Flow, it’s possible to create a solution that allows Tweets to be quickly created as simple text files in a OneDrive folder and these will then be buffered to be sent every 15 minutes (or whatever schedule you fancy). The actual buffering mechanism used below is an Azure Queue.

There are two Flows as part of this solution: Flow 1 to pick up text files from OneDrive, extract the content and write a new message to an Azure Queue. Flow 2 runs on a schedule, picks a message off the queue, grabs the message content and sends it a s a Tweet.

Flow 1: Queuing Tweets

The first step is to create an Azure Storage account and create an Azure Queue. The easiest way to create a new queue is to use the Azure Storage Explorer. Once installed and connected, creating a queue is a simple right-click operation:

Using Azure Storage Explorer to create a new Azure Queue

We’ll call the queue “tweet-queue”.

We’ll also create OneDrive folders: OneDrive\FlowDemo\TweetQ\In

Now we can create a new Flow that grabs files from this path and adds them to tweet-queue as the following screenshot shows (notice we're also deleting the file after adding to the queue):

Microsoft Flow reading a file from OneDrive and adding to Azure Queue

Now if we create a .txt file (for example with the content “Testing - this Tweet came from Microsoft Flow via OneDrive and an Azure Queue” in the OneDrive\FlowDemo\TweetQ\In directory, wait for the Flow to run and check out the queue in Storage Explorer we can see a new message as the following screenshot shows:

Azure Storage Explorer showing Azure Queue message content

Now we have a way of queuing Tweets we can create a second flow to send them on a timer.

Flow 2: Sending Tweets

The second Flow will be triggered every 15 minutes, grab a message from the queue, use the message body as the Tweet content, then delete the message from the queue.

The following screenshot shows the first 2 phases:

Getting Azure Queue messages on a timer

Even though we’ve specified 1 message, when we add the next action in the Flow, we’ll automatically get an “Apply to each” added as the following screenshot shows:

Posting Tweet from Azure Queue

Notice in the preceding screenshot that we also need to add an action to delete the message from the queue.

Now once we save this Flow, every 15 minutes a message will be retrieved and posted as a Tweet:

Triggering a Microsoft Flow from an HTTP Post

Microsoft Flow allows the building of workflows in the cloud. One way to trigger a Flow is to set up a HTTP endpoint that can be posted to.

For example, a Flow can be created that takes some JSON data and writes it out to OneDrive or Dropbox.

The first step is to create a new Flow and add a Request trigger. When the Flow is saved, a URL will be generated that can be posted to.

As part of this Request trigger, a JSON schema can be specified that allows individual JSON properties to be surfaced and referenced by name in later actions.

For example the following JSON schema could be specified:

  "$schema": "http://json-schema.org/draft-04/schema#",
  "title": "Customer",
  "description": "A generic customer",
  "type": "object",
  "properties": {
    "id": {
      "description": "The unique identifier for a customer",
      "type": "integer"
    "name": {
      "description": "The full name of the customer excluding title",
      "type": "string"
  "required": [

Now in later steps, the “id” and the “name” properties from the incoming JSON can be used as dynamic content.

Next action(s) can be added that make use of the the data in these properties when an HTTP post occurs. For example we could create a file in OneDrive where the filename is {id}.txt that contains the customer name. This is a simple example but serves to demonstrate the flexibility.

The following screenshot shows the full flow and the JSON schema properties in use:

Microsoft Flow using request trigger and OneDrive

We can now post to the generated URL. For example the following screenshot shows a test post using Postman and the resulting file that was created in OneDrive:

HTTP post to trigger a Microsoft Flow

Serverless Computing and Workflows with Azure Functions and Microsoft Flow

Microsoft Flow is a tool for creating workflows to automate tasks. It’s similar in concept to If This Then That but feels like it exists more towards the end of the spectrum of the business user rather than the end consumer – though both have a number of channels/services in common. Flow has a number of advanced features such as conditions, loops, timers, and delays.

Flow has a number of services including common ones such as Dropbox, OneDrive, Twitter, and Facebook. There are also generic services for calling HTTP services, including those created as Azure Functions. Essentially, services are the building blocks of a Flow.

Screenshot of Microsoft Flow Services

Once the free sign up is complete you can create Flows from existing templates or create your own from scratch.

Screenshot of Microsoft Flow pre-built templates

To create a new custom Flow, the web-based workflow designer can be used.

Integrating a Flow with Azure Functions

In the following example, a Flow will be created that picks up files with a specific naming convention from a OneDrive folder, sends the text content to an Azure Function that simply converts to uppercase and returns the result to the Flow. The Flow then writes out the uppercase version to another OneDrive folder.

Reading Files From OneDrive

The first step in the Flow is to monitor a specific OneDrive folder for new files.

A Flow triggered by new OneDrive files

As an example of conditions, an “if statement” can be added to only process files that contain the word “data”:

Microsoft Flow condition

Now if the filename is correct we can go ahead and call an Azure Function (or other HTTP endpoint).

Calling an Azure Function from Microsoft Flow

Now that we are reading specific files, we want to call an Azure Function to convert the text content of the file to upper case.

The following code and screenshot shows the function that will be called – this code is stripped down and doesn’t contain any error checking/handling code for simplicity:

Azure Function app screenshot

using System.Net;

public static async Task<HttpResponseMessage> Run(HttpRequestMessage req, TraceWriter log)
    log.Info($"C# HTTP trigger function processed a request. RequestUri={req.RequestUri}");

    dynamic data = await req.Content.ReadAsAsync<object>();
    string text = data.text;

    return  req.CreateResponse(HttpStatusCode.OK, text.ToUpperInvariant());

We can test the API in Postman:

Calling Azure Function from Postman

Now that we have a working function we can add a new action of type “HTTP” to the Flow and pass the contents of the OneDrive file as JSON data in the request. The final step is to take the response of calling the Azure Function and writing out to a new file in OneDrive as the following screenshot shows:

Calling Azure Function passing OneDrive file content as JSON data

Now we can create a file “OneDrive\FlowDemo\In\test1data.txt”, the Flow will be trigged, and the output file “OneDrive\FlowDemo\Out\test1data.txt” created.

Output file

Microsoft Flow also has a really nice visual representation of runs (individual executions) of Flows:

Microsoft Flow run visualization

Microsoft Flow by itself enables a whole host of workflow scenarios, and combined with all the power of Azure Functions (and other Azure features) could enable some really interesting uses.

To jump-start your Azure Functions knowledge check out my Azure Function Triggers Quick Start Pluralsight course.

.NET Document Databases with Marten

Document databases are a form of NoSQL database that store items (objects) as single documents rather than potentially splitting the data among multiple relational database tables.

Marten is a .NET library that enables the storing, loading, deleting, and querying of documents. Objects in the application can be stored into the document database and retrieved back as an object from the database. In this approach there is no requirement for the additional “plumbing code” of ORMs.

Marten is not a database itself but rather a library that interacts with the (advanced JSON features) of the open source, cross platform PostgreSQL database.

Once the Marten NuGet package is installed there are a number of steps to storing .NET objects as documents.

First the “document” to be stored is defined. At the most basic level this is a class that has a field or property that represents the document identity as stored in the database. There are a number of ways to define  identity, one of which is to follow the naming convention “Id” as the following class shows:

class Customer
    public int Id { get; set; }
    public string Name { get; set; }
    public List<Address> Addresses { get; set; } = new List<Address>();

internal class Address
    public string Line1 { get; set; }
    public string Line2 { get; set; }
    public string Line3 { get; set; }

Notice in the preceding code that the Address class does not have an Id. This is because the address information will be storing in the overall Customer document, rather than for example in a relational database as rows in a separate Address table.

To work with the database a document store instance is required, this can be created with additional configuration or with the simple code shown below:

var connectionString = "host = localhost; database = CustomerDb; password = YOURPASSWORD; username = postgres";

var store = DocumentStore.For(connectionString);

Working with documents happens by way of a session instance, there are a number of types/configurations of sessions available.

To create a new customer object and store it the following code could be used:

// Store document (& auto-generate Id)
using (var session = store.LightweightSession())
    var customer =  new Customer
        Name = "Arnold",
        Addresses =
            new Address {Line1="Address 1 Line 1", Line2="Address 1 Line 2",Line3="Address 1 Line 3"},
            new Address {Line1="Address 2 Line 1", Line2="Address 2 Line 2",Line3="Address 2 Line 3"}

    // Add customer to session

    // Persist changes to the database

Once the above code executes, the customer will be stored in a PostgreSQL table called “mt_doc_customer”. The Customer object will be serialized to JSON and stored in a special JSONB field in PostgreSQL. The JSON data also contains all the addresses.

Screenshot of pgAdmin showing Marten table

  "Addresses": [
      "Line3": "Address 1 Line 3",
      "Line2": "Address 1 Line 2",
      "Line1": "Address 1 Line 1"
      "Line3": "Address 2 Line 3",
      "Line2": "Address 2 Line 2",
      "Line1": "Address 2 Line 1"
  "Id": 1,
  "Name": "Arnold"

There are a number of ways to retrieve documents and convert them back into .NET objects. One method is to use the Marten LINQ support as the following code shows:

// Retrieve by query
using (var session = store.QuerySession())
    var customer = session.Query<Customer>().Single(x => x.Name == "Arnold");

When executed, this code displays the retrieved customer details as the following screenshot shows:

Console application showing Marten document data

To learn more about the document database features of Marten check out my Pluralsight courses: Getting Started with .NET Document Databases Using Marten and Working with Data and Schemas in Marten.

Sending a Regular SMS with Azure Functions and Twilio

Azure Functions allow the creation of Serverless event driven applications with minimal effort. We can create Azure Functions using C# (or other languages) right inside the Azure Web app.

Functions sit inside a function app and each function can be configured to be executed in response to a number of triggers such as a BlobTrigger, EventHubTrigger, etc. One of the triggers is the Timer trigger that can be configured to execute the function on a specified schedule.

To get get started, once you’ve created an Azure account and logged in, head to http://functions.azure.com/ and this will allow a quickstart experience to develop your function (you may be asked to create a new Function App as a container for your functions so go ahead and follow the prompts to do this).


Click “Create this function” and you’ll be taken to the editor.


In the code section (within the Run method) we can write code that is executed when the function executes.

Clicking on the Integrate tab allows the specification of the schedule in CRON format.


To have the function execute every five minutes we would specify: 0 */5 * * * *

A function can have Outputs, one of which is a Twilio output. At the time of writing this is in an early beta/experimental phase and I couldn’t get it to work correctly. However because we can write C#, we can send an SMS by using the Twilio REST API. You need to sign up for a Twilio trial account and this will give you a Twilio AccountSID and an authorisation token.

Click the Develop tab and add the following code (using your Twilio SID and auth token):

using System;
using Twilio;

public static void Run(TimerInfo myTimer, TraceWriter log)
    log.Info($"C# Timer trigger function executed at: {DateTime.Now}");    

    string accountSid = "YOUR SID HERE";
    string authToken = "YOUR TOKEN HERE";

    var client = new TwilioRestClient(accountSid, authToken);

            "+614xxxxxxxx", // Insert your Twilio from SMS number here
            "+614xxxxxxxx", // Insert your verified (trial) to SMS number here
            "hello from Azure Functions!" + DateTime.Now            

(+614 is the dialling code for Australian mobiles so you should replace this with whatever makes sense for the Twilio account phone numbers you’ve created.)

Click Save and you’ll get some compilation errors because we haven’t yet added the Twilio NuGet package to be used by our code.

Click the View Files link under the code editing window and add a new file (by clicking the plus icon). Add a file called project.json with the following content:


Click Save and in the logs section you should see the package installed:


Now every five minutes you’ll get a text message sent!


Just remember to disable/delete the function or you will continue to get messages sent. You can do this by clicking on the Manage tab and choosing the appropriate option:


New Pluralsight Course: Testing C# Code in Production with Scientist.NET

My latest Pluralsight course is now available for viewing. It demonstrates how to use the Scientist.NET library to execute candidate code in production alongside the existing production code. This allows the new candidate code to be additionally verified as able to work in production (for example with production data that may be of variable quality) and offers an additional check in addition to automated tests that have been executed in the development/QA environment.

From the course description: “Errors in production code can be costly to fix, more stressful for the development team, and frustrating for the end-user. In this course, Testing C# Code in Production with Scientist.NET, you will get to see how Scientist.NET allows sections of the application to be changed more safely by running both the existing code alongside the new code in production. You'll begin with an introduction to Scientist.NET before installing it and writing your first experiment. Next, you'll learn how to customize the configuration of experiments. Finally, you'll learn how to publish experiment data to SQL Server and analyze experiment results…”

You can check out the new course here.

Refactoring Production Code With Experiments and Scientist.NET

When refactoring a part of an application we can use the existing tests to give a level of confidence that the refactored code still produces the same result, i.e. the existing tests still pass with the new implementations.

A system that has been in production use for some time is likely to have amassed a lot of data that is flowing through the “legacy” code. This means that although the existing tests may still pass, when used in production the new refactored code may produce errors or unexpected results.

It would be helpful as an experiment to run the existing legacy code alongside the new code to see if the results differ, but still continue to use the result of the existing legacy code. Scientist.NET allows exactly this kind of experimentation to take place.

Scientist.NET is a port of the Scientist library for Ruby. It is currently in pre-release and has a NuGet package we can use.

An Example Experiment

Suppose there is an interface as shown in the following code that describes the ability to sum a list if integer values and return the result.

interface ISequenceSummer
    int Sum(int numberOfTerms);

This interface is currently implemented in the legacy code as follows:

class SequenceSummer : ISequenceSummer
    public int Sum(int numberOfTerms)
        var terms = new int[numberOfTerms];

        // generate sequence of terms
        var currentTerm = 0;
        for (int i = 0; i < terms.Length; i++)
            terms[i] = currentTerm;

        // Sum
        int sum = 0;
        for (int i = 0; i < terms.Length; i++)
            sum += terms[i];

        return sum;

As part of the refactoring of the legacy code, this implementation is to be replaced with a version that utilizes LINQ as shown in the following code:

class SequenceSummerLinq : ISequenceSummer
    public int Sum(int numberOfTerms)
        // generate sequence of terms
        var terms = Enumerable.Range(0, 5).ToArray();
        // sum
        return terms.Sum();

After installing the Scientist.NET NuGet package, an experiment can be created using the following code:

int result;            

result = Scientist.Science<int>("sequence sum", experiment =>
    experiment.Use(() => new SequenceSummer().Sum(5)); // current production method

    experiment.Try("Candidate using LINQ", () => new SequenceSummerLinq().Sum(5)); // new proposed production method

}); // return the control value (result from SequenceSummer)

This code will run the .Use(…) code that contains the existing legacy implementation. It will also run the .Try(…) code that contains the new implementation. Scientist.NET will store both results for reporting on and then return the result from the .Use(…) code for use by the rest of the program. This allows any differences to be reported on but without actually changing the implementation of the production code. At some point in the future, if the results of the legacy code (the control) match that of the new code (the candidate), the refactoring can be completed by removing the old implementation (and the experiment code) and simply calling the new implementation.

To get the results of the experiment, a reporter can be written and configured. The following code shows a custom reporter that simply reports to the Console:

public class ConsoleResultPublisher : IResultPublisher
    public Task Publish<T>(Result<T> result)
        Console.ForegroundColor = result.Mismatched ? ConsoleColor.Red : ConsoleColor.Green;

        Console.WriteLine($"Experiment name '{result.ExperimentName}'");
        Console.WriteLine($"Result: {(result.Matched ? "Control value MATCHED candidate value" : "Control value DID NOT MATCH candidate value")}");
        Console.WriteLine($"Control value: {result.Control.Value}");
        Console.WriteLine($"Control execution duration: {result.Control.Duration}");
        foreach (var observation in result.Candidates)
            Console.WriteLine($"Candidate name: {observation.Name}");
            Console.WriteLine($"Candidate value: {observation.Value}");
            Console.WriteLine($"Candidate execution duration: {observation.Duration}");

        if (result.Mismatched)
            // saved mismatched experiments to event log, file, database, etc for alerting/comparison

        Console.ForegroundColor = ConsoleColor.White;

        return Task.FromResult(0);

To plug this in, before the experiment code is executed:

Scientist.ResultPublisher = new ConsoleResultPublisher();

The output of the experiment (and the custom reporter) look like the following screenshot:

screenshot of console application using Scentist.NET with custom reporter

To learn more about Scientist.NET check out my Pluralsight course: Testing C# Code in Production with Scientist.NET.

New Pluralsight Course: Working with Nulls in C#

My latest Pluralsight course is now available for viewing. It covers the fundamental aspects of working with nulls in C# code.

From the course description: “Unexpected null values and NullReferenceExceptions can be a constant source of bugs resulting in wasted time and out of hours support callouts. In this course, Working with Nulls in C#, you're going to learn about the different ways that null values pop up in C# code and how to deal with them. First, you're going to learn the fundamentals of why you get null values and the difference between value and reference types. Next you'll learn how you can use the various C# operators to check for and manipulate nulls. Finally, you'll learn how to think about nulls at a higher abstraction level in your object-oriented code. By the end of this course, you'll understand the different types of objects in C#, how to correctly create nullable value types, how to use C# operators to work with nulls with fewer lines of code, and how to implement the Null Object pattern to remove the need to write repetitive null checking code.”

You can check out the course here.