.NET and Amazon EventBridge

As briefly mentioned in an earlier post, Amazon EventBridge is a serverless event bus service designed to deliver data from applications and services to a variety of targets. It uses a different methodology than does SNS to distribute events.

The event producers submit their events to the service bus. From there, a set of rules determines what messages get sent to which recipients. This flow is shown in Figure 1.

Figure 1. Message flow through Amazon EventBridge.
Figure 1. Message flow through Amazon EventBridge.

The key difference between SNS and EventBridge is that in SNS you send your message to a topic, so the sender makes some decisions about where the message is going. These topics can be very broadly defined and domain-focused so that any application interested in order-related messages subscribes to the order topic, but this still obligates the sender to have some knowledge about the messing system.

In EventBridge you simply toss messages into the bus and the rules sort them to the appropriate destination. Thus, unlike SNS where the messages themselves don’t really matter as much as the topic; in EventBridge you can’t define rules without an understanding of the message on which you want to apply rules. With that in mind, we’ll go in a bit of a different order now and go into using EventBridge within a .NET application, that way we’ll have a definition of the message on which we want to apply rules.

.NET and Amazon EventBridge

The first step to interacting with EventBridge from within your .NET application is to install the appropriate NuGet package, AWSSDK.EventBridge. This will also install AWSSDK.Core. Once you have the NuGet package, you can access the appropriate APIs by adding several using statements:

using Amazon.EventBridge;
using Amazon.EventBridge.Model;

You will also need to ensure that you have added:

using System.Collections.Generic;
using System.Text.Json;

These namespaces provide access to the AmazonEventBridgeClient class that manages the interaction with the EventBridge service as well as the models that are represented in the client methods. As with SNS, you can manage all aspects of creating the various EventBridge parts such as service buses, rules, endpoints, etc. You can also use the client to push events to the bus, which is what we do now. Let’s first look at the complete code and then we will walk through the various sections.

static void Main(string[] args)
    var client = new AmazonEventBridgeClient();

    var order = new Order();

    var message = new PutEventsRequestEntry
        Detail = JsonSerializer.Serialize(order),
        DetailType = "CreateOrder",
        EventBusName = "default",
        Source = "ProDotNetOnAWS"

    var putRequest = new PutEventsRequest
        Entries = new List<PutEventsRequestEntry> { message }

    var response = client.PutEventsAsync(putRequest).Result;
$"Request processed with ID of  

The first thing we are doing in the code is newing up our AmazonEventBridgeClient so that we can use the PutEventsAsync method, which is the method used to send the event to EventBridge. That method expects a PutEventsRequest object that has a field Entries that are a list of PutEventsRequestEntry objects. There should be a PutEventsRequestEntry object for every event that you want to be processed by EventBridge, so a single push to EventBridge can include multiple events.

Tip: One model of event-based architecture is to use multiple small messages that imply different items of interest. Processing an order, for example, may result in a message regarding the order itself as well as messages regarding each of the products included in the order so that the inventory count can be managed correctly. This means the Product domain doesn’t listen for order messages, they only pay attention to product messages. Each of these approaches has its own advantages and disadvantages.

The PutEventsRequestEntry contains the information to be sent. It has the following properties:

·         Detail – a valid JSON object that cannot be more than 100 levels deep.

·         DetailType – a string that provides information about the kind of detail contained within the event.

·         EventBusName – a string that determines the appropriate event bus to use. If absent, the event will be processed by the default bus.

·         Resources – a List<string> that contains ARNs which the event primarily concerns. May be empty.

·         Source – a string that defines the source of the event.

·         Time – a string that sets the time stamp of the event. If not provided EventBridge will use the time stamp of when the Put call was processed.

In our code, we only set the Detail, DetailType, EventBusName, and Source.

This code is set up in a console, so running the application gives results similar to that shown in Figure 2

Figure 2. Console application that sent a message through EventBridge
Figure 2. Console application that sent a message through EventBridge

We then used Progress Telerik Fiddler to view the request so we can see the message that was sent. The JSON from this message is shown below.

            "Detail": "{\"Id\":0,
            "DetailType": "CreateOrder",
            "EventBusName": "default",
            "Source": "ProDotNetOnAWS"

Now that we have the message that we want to process in EventBridge, the next step is to set up EventBridge. At a high level, configuring EventBridge in the AWS console is simple.

Configuring EventBridge in the Console

You can find Amazon EventBridge by searching in the console or by going into the Application Integration group. Your first step is to decide whether you wish to use your account’s default event bus or create a new one. Creating a custom event bus is simple as all you need to provide is a name, but we will use the default event bus.

Before going any further, you should translate the event that you sent to the event that EventBridge will be processing. You do this by going into Event buses and selecting the default event bus. This will bring you to the Event bus detail page. On the upper right, you will see a button Send events. Clicking this button will bring you to the Send events page where you can configure an event. Using the values from the JSON we looked at earlier, fill out the values as shown in Figure 3.

Figure 3. Getting the “translated” event for EventBridge
Figure 3. Getting the “translated” event for EventBridge

Once filled out, clicking the Review button brings up a window with a JSON object. Copy and paste this JSON as we will use it shortly. The JSON that we got is displayed below.

  "version": "0",
  "detail-type": "CreateOrder",
  "source": "ProDotNetOnAWS",
  "account": "992271736046",
  "time": "2022-08-21T19:48:09Z",
  "region": "us-west-2",
  "resources": [],
  "detail": "{\"Id\":0,\"OrderDate\":\"0001-01-01T00:00:00\",\"CustomerId\":0,\"OrderDetails\":[]}"

The next step is to create a rule that will evaluate the incoming messages and route them to the appropriate recipient. To do so, click on the Rules menu item and then the Create rule button. This will bring up Step 1 of the Create rule wizard. Here, you define the rule by giving it a name that must be unique by event bus, select the event bus on which the rule will run, and choose between Rule with an event pattern and Schedule. Selecting to create a schedule rule will create a rule that is run regularly on a specified schedule. We will choose to create a rule with an event pattern.

Step 2 of the wizard allows you to select the Event source. You have three options, AWS events or EventBridge partner events, Other, or All events. The first option references the ability to set rules that identify specific AWS or EventBridge partner services such as SalesForce, GitHub, or Stripe, while the last option allows you to set up destinations that will be forwarded every event that comes through the event bus. We typically see this when there is a requirement to log events in a database as they come in or some special business rule such as that. We will select Other so that we can handle custom events from our application(s).

You next can add in a sample event. You don’t have to take this action, but it is recommended to do this when writing and testing the event pattern or any filtering criteria. Since we have a sample message, we will select Enter my own and paste the sample event into the box as shown in Figure 4.

Figure 4. Adding a Sample Event when configuring EventBridge
Figure 4. Adding a Sample Event when configuring EventBridge

Be warned, however, if you just paste the event directly into the sample event it will not work as the matching algorithms will reject it as invalid without an id added into the JSON as highlighted by the golden arrow in Figure 4.

Once you have your sample event input, the next step is to create the Event pattern that will determine where this message should be sent. Since we are using a custom event, select the Custom patterns (JSON editor) option. This will bring a JSON editor window in which you enter your rule. There is a drop-down of helper functions that will help you put the proper syntax into the window but, of course, there is no option for simple matching – you have to know what that syntax is already. Fortunately, it is identical to the rule itself, so adding an event pattern that will select every event that has a detail-type of “Create Order” is:

  "detail-type": ["CreateOrder"]

Adding this into the JSON editor and selecting the Test pattern button will validate that the sample event matched the event pattern. Once you have successfully tested your pattern select the Next button to continue.

You should now be on the Step 3 Select Target(s) screen where you configure the targets that will receive the event. You have three different target types that you can select from, EventBridge event bus, EventBridge API Destination, or AWS Service. Clicking on each of the different target types will change the set of information that you will need to manage that detail the target. We will examine two of these in more detail, the EventBridge API destination, and the AWS Service, starting with the AWS service.

Selecting the AWS service radio button brings up a drop-down list of AWS services that can be targeted. Select the SNS target option. This will bring up a drop-down list of the available topics. Select the topic we worked with in the previous section and click the Next button. You will have the option configure Tags and then can Create rule.

Once we had this rule configured, we re-ran our code to send an event from the console. Within several seconds we received the email that was sent from our console application running on our local machine to EventBridge where the rule filtered the event to send it to SNS which then configured and sent the email containing the order information that we submitted from the console.

Now that we have verified the rule the fun way, let’s go back into it and make it more realistic. You can edit the targets for a rule by going into Rules from the Amazon EventBridge console and selecting the rule that you want to edit. This will bring up the details page. Click on the Targets tab and then click the Edit button. This will bring you back to the Step 3 Select Target(s) screen. From here you can choose to add an additional target (you can have up to 5 targets for each rule) or replace the target that pointed to the SNS service. We chose to replace our existing target.

Since we are looking at using EventBridge to communicate between various microservices in our application we will configure the target to go to a custom endpoint. To do so requires that we choose a Target type of EventBridge API destination. We will then choose to Create a new API destination which will provide all of the destination fields that we need to configure. These fields are listed below.

·         Name – the name of the API destination. Destinations can be reused in different rules, so make sure the name is clear.

·         Description – optional value describing the destination.

·         API destination endpoint – the URL to the endpoint which will receive the event.

·         HTTP Method – the HTTP method used to send the event, can be any of the HTTP methods.

·         Invocation rate limit per second – an optional value, defaulted to 300, of the number of invocations per second. Smaller values mean that events may not be delivered.

The next section to configure is the Connection. The connection contains information about authorization as every API request must have some kind of security method enabled. Connections can be reused as well, and there are three different Authorization types supported. These types are:

·         Basic (Username/Password) – where a username and password combination is entered into the connection definition.

·         OAuth Client Credentials – where you enter the OAuth configuration information such as Authorization endpoint, Client ID, and Client secret.

·         API Key – which adds up to 5 key\value pairs in the header.

Once you have configured your authorization protocol you can select the Next button to once again complete moving through the EventBridge rules creation UI.

There are two approaches that are commonly used when creating the rule to target API endpoint mapping. The first is a single endpoint per type of expected message. This means that, for example, if you were expecting “OrderCreated” and “OrderUpdated” messages then you would have created two separate endpoints, one to handle each message. The second approach is to create a generic endpoint for your service to which all inbound EventBridge messages are sent and then the code within the service evaluates each message and manages it from there.

Modern Event Infrastructure Creation

So far, we have managed all the event management through the console, creating topics and subscriptions in SNS and rules, connections, and targets in EventBridge. However, taking this approach in the real world will be extremely painful. Instead, modern applications are best served by modern methods of creating services; methods that can be run on their own without any human intervention. There are two approaches that we want to touch on now, Infrastructure-as-Code (IaC) and in-application code.


Using AWS CloudFormation or AWS Cloud Development Kit within the build and release process allows developers to manage the growth of their event infrastructure as their usage of events grows. Typically, you would see the work breakdown as being the teams building the systems sending events are responsible for creating the infrastructure required for sending, and the teams for the systems listening for events need to manage the creation of that infrastructure. Thus, if you are planning on using SNS then the sending system would have the responsibility for adding the applicable topic(s) while the receiving system would be responsible for adding the appropriate subscription(s) to the topics in which they are interested.

Using IaC to build out your event infrastructure allows you to scale your use of events easily and quickly. It also makes it easier to manage any changes that you may feel are necessary, as it is very common for the messaging approach to be adjusted several times as you determine the level of messaging that is appropriate for the interactions needed within your overall system.

In-Application Code

In-Application code is a completely different approach from IaC as the code to create the infrastructure resides within your application. This approach is commonly used in “configuration-oriented design”, where configuration is used to define the relationship(s) that each application plays. An example of a configuration that could be used when an organization is using SNS is below.

     “sendrules”:[{“name”:”Order”, “key”:”OrdersTopic”}],
     “receiverules”: [{“name”:” ProductUpdates”, 
                       “key”:” Products”,

The code in the application would then ensure that every entry in the sendrules property has the appropriate topic created, so using the example above the name value represents the topic name and the key value represents the value that will be used within the application to map to the “Order” topic in SNS. The code in the application would then evaluate the receiverules value and create subscriptions for each entry.

This seems like a lot of extra work, but for environments that do not support IaC then this may be the easiest way to allow developers to manage the building of the event’s infrastructure. We have seen this approach built as a framework library included in every application that used events, and every application provided a configuration file that represented the messages they were sending and receiving. This framework library would evaluate the service(s) to see if there was anything that needed to be added and if so then add them.

.NET and Amazon Simple Notification Service (SNS)

SNS, as you can probably guess from its name, is a straightforward service that uses pub\sub messaging to deliver messages. Pub\Sub, or Publish\Subscribe, messaging is an asynchronous communication method. This model includes the publisher who sends the data, a subscriber that receives the data, and the message broker that handles the coordination between the publisher and subscriber. In this case, Amazon SNS is the message broker because it handles the message transference from publisher to subscriber.

Note – The language used when looking at events and messaging as we did above can be confusing. Messaging is the pattern we discussed above. Messages are the data being sent and are part of both events and messaging. The term “message” is considered interchangeable with notification or event – even to the point where you will see articles about the messaging pattern that refer to the messages as events.

The main responsibility of the message broker is to determine which subscribers should be sent what messages. It does this using a topic. A topic can be thought of as a category that describes the data contained within the message. These topics are defined based on your business. There will be times that a broad approach is best, so perhaps topics for “Order” and “Inventory” where all messages for each topic are sent. Thus, the order topic could include messages for “Order Placed” and “Order Shipped” and the subscribers will get all of those messages. There may be other times where a very narrow focus is more appropriate, in which case you may have an “Order Placed” topic and an “Order Shipped” topic where systems can subscribe to them independently. Both approaches have their strength and weaknesses.

When you look at the concept of messaging, where one message has one recipient, the advantage that a service like SNS offers is the ability to distribute a single message to multiple recipients as shown in Figure 1, which is one of the key requisites of event-based architecture.

Figure 1. Pub\Sub pattern using Amazon SNS
Figure 1. Pub\Sub pattern using Amazon SNS

Now that we have established that SNS can be effectively used when building in an event-based architecture, let’s go do just that!

Using AWS Toolkit for Visual Studio

If you’re a Visual Studio user, you can do a lot of the configuration and management through the toolkit. Going into Visual Studio and examining the AWS Explorer will show that one of the options is Amazon SNS. At this point, you will not be able to expand the service in the tree control because you have not yet started to configure it. Right-clicking on the service will bring up a menu with three options, Create topic, View subscriptions, and Refresh. Let’s get started by creating our first topic. Click on the Create topic link and create a topic. We created a topic named “ProDotNetOnAWS” – it seems to be a trend with us. Once you save the topic you will see it show up in the AWS Explorer.

Right-click on the newly created topic and select to View topic. This will add the topic details screen into the main window as shown in Figure 2.

Figure 2. SNS topic details screen in the Toolkit for Visual Studio
Figure 2. SNS topic details screen in the Toolkit for Visual Studio

In the details screen, you will see a button to Create New Subscription. Click this button to bring up the Create New Subscription popup window. There are two fields that you can complete, Protocol and Endpoint. The protocol field is a dropdown and contains various choices.

HTTP or HTTPS Protocol Subscription

The first two of these choices are HTTP and HTTPS. Choosing one of these protocols will result in SNS making an HTTP (or HTTPS) POST to the configured endpoint. This POST will result in a JSON document with the following name-value pairs.

·         Message – the content of the message that was published to the topic

·         MessageId – A universally unique identifier for each message that was published

·         Signature – Base64-encoded signature of the Message, MessageId, Subject, Type, Timestamp, and TopicArn values.

·         SignatureVersion – version of the signature used

·         SigningCertUrl – the URL of the certificate that was used to sign the message

·         Subject – the optional subject parameter used when the notification was published to a topic. In those examples where the topic is broadly-based, the subject can be used to narrow down the subscriber audience.

·         Timestamp – the time (GMT) when the notification was published.

·         TopicARN – the Amazon Resource Name (ARN) for the topic

·         Type – the type of message being sent. For an SNS message, this type is Notification.

At a minimum, your subscribing system will care about the message, as this message contains the information that was provided by the publisher. One of the biggest advantages of using an HTTP or HTTPS protocol subscription is that the system that is subscribed does not have to do anything other than accept the message that is submitted. There is no special library to consume, no special interactions that must happen, just an endpoint that accepts requests.

Some considerations as you think about using SNS to manage your event notifications. There are several different ways to manage the receipt of these notifications. The first is to create a single endpoint for each topic to which you subscribe. This makes each endpoint very discrete and only responsible for handling one thing; usually considered a plus in the programming world. However, this means that the subscribing service has some limitations as there are now external dependencies on multiple endpoints. Changing an endpoint URL, for example, will now require coordination across multiple systems.

On the other hand, there is another approach where you create a single endpoint that acts as the recipient of messages across multiple topics. The code within the endpoint identifies the message and then forwards it through the appropriate process. This approach abstracts away any work within the system, as all of those changes happen below this single broadly bound endpoint. We have seen both of those approaches working successfully, it really comes down to your own business needs and how you see your systems evolving as you move forward.

Other Protocol Subscriptions

There are other protocol subscriptions that are available in the toolkit. The next two in the list are Email and Email (JSON). Notifications sent under this protocol are sent to the email address that is entered as the endpoint value. This email is sent in two ways, where the Message field of the notification becomes the body of the email or where the email body is a JSON object very similar to that used when working with the HTTP\HTTPS protocols. There are some business-to-business needs for this, such as sending a confirmation to a third party upon processing an order; but you will generally find any discussion of these two protocols under Application-to-Person (A2P) in the documentation and examples.

The next protocol that is available in the toolkit is Amazon SQS. Amazon Simple Queue Service (SQS) is a queue service that follows the messaging pattern that we discussed earlier where one message has one recipient and one recipient only.

The last protocol available in the toolkit is Lambda. Choosing this protocol means that a specified Lambda function will be called with the message payload being set as an input parameter. This option makes a great deal of sense if you are building a system based on serverless functions. Of course, you can also use HTTP\HTTPS protocol and make the call to the endpoint that surfaces the Lambda method; but using this direct approach will remove much of that intermediate processing.

Choosing either the SQS or Lambda protocols will activate the Add permission for SNS topic to send messages to AWS resources checkbox as shown in Figure 3.

Figure 3. Create New Subscription window in the Toolkit for Visual Studio
Figure 3. Create New Subscription window in the Toolkit for Visual Studio

Checking this box will create the necessary permissions allowing the topic to interact with AWS resources. This is not necessary if you are using HTTP\HTTPS or Email.

For the sake of this walk-through, we used an approach that is ridiculous for enterprise systems; we selected the Email (JSON) protocol. Why? So we could easily show you the next few steps in a way that you could easily duplicate. This is important because all you can do in the Toolkit is to create the topic and the subscription. However, as shown in Figure 4, this leaves the subscription in a PendingConfirmation state.

Figure 4. Newly created SNS topic subscription in Toolkit for Visual Studio
Figure 4. Newly created SNS topic subscription in Toolkit for Visual Studio

Subscriptions in this state are not yet fully configured, as they need to be confirmed before they are able to start receiving messages. Confirmation happens after a SubscriptionConfirmation message is sent to the endpoint, which happens automatically when creating a new subscription through the Toolkit. The JSON we received in email is shown below:

  "Type" : "SubscriptionConfirmation",
  "MessageId" : "b1206608-7661-48b1-b82d-b1a896797605",
  "Token" : "TOKENVALUE", 
  "TopicArn" : "arn:aws:sns:xxxxxxxxx:ProDotNetOnAWS",
  "Message" : "You have chosen to subscribe to the topic arn:aws:sns:xxxxxxx:ProDotNetOnAWS.\nTo confirm the subscription, visit the SubscribeURL included in this message.",
  "SubscribeURL" : "https://sns.us-west-2.amazonaws.com/?Action=ConfirmSubscription&TopicArn=xxxxxxxx",
  "Timestamp" : "2022-08-20T19:18:27.576Z",
  "SignatureVersion" : "1",
  "Signature" : "xxxxxxxxxxxxx==",
  "SigningCertURL" : "https://sns.us-west-2.amazonaws.com/SimpleNotificationService-56e67fcb41f6fec09b0196692625d385.pem"

The Message indicates the action that needs to be taken – you need to visit the SubscribeURL that is included in the message. Clicking that link will bring you to a confirmation page in your browser like that shown in Figure 5.

Figure 5. Subscription confirmation message displayed in browser
Figure 5. Subscription confirmation message displayed in browser

Refreshing the topic in the Toolkit will show you that the PendingConfirmation message is gone and has been replaced with a real Subscription ID.

Using the Console

The process for using the console is very similar to the process we just walked through in the Toolkit. You can get to the service by searching in the console for Amazon SNS or by going into the Application Integration group under the services menu. Once there, select Create topic. At this point, you will start to see some differences in the experiences.

The first is that you have a choice on the topic Type, as shown in Figure 6. You can select from FIFO (first-in, first-out) and Standard. FIFO is selected by default. However, selecting FIFO means that the service will follow the messaging architectural approach that we went over earlier where there is exactly-once message delivery and message ordering is strictly preserved. The Standard type, on the other hand, supports “at least once message delivery” which means that it supports multiple subscriptions.

Figure 6. Creating an SNS topic in the AWS Console
Figure 6. Creating an SNS topic in the AWS Console

Figure 6 also displays a checkbox labeled Content-based message deduplication. This selection is only available when FIFO type is selected. When selected, the message being sent is assumed to be unique and SNS will not provide a unique deduplication value. Otherwise, SNS will add a unique value to each message that it will use to determine whether a particular message has been delivered.

Another difference between creating a topic in the console vs in the toolkit is that you can optionally set preferences around message encryption, access policy, delivery status logging, delivery retry policy (HTTP\S), and, of course, tags. Let’s look in more detail at two of those preferences. The first of these is the Delivery retry policy. This allows you to set retry rules for how SNS will retry sending failed deliveries to HTTP/S endpoints. These are the only endpoints that support retry. You can manage the following values:

·         Number of retries – defaults to 3 but can be any value between 1 and 100

·         Retries without delay – defaults to 0 and represents how many of those retries should happen before the system waits for a retry

·         Minimum delay – defaults to 20 seconds with a range from 1 to the value of the Maximum delay.

·         Maximum delay – defaults to 20 seconds with a range from the Minimum delay to 3,600.

·         Retry backoff function – defaults to linear. There are four options, Exponential, Arithmetic, Linear, and Geometric. Each of those functions processes the timing for retries differently. You can see the differences between these options at https://docs.aws.amazon.com/sns/latest/dg/sns-message-delivery-retries.html.

The second preference that is available in the console but not the toolkit is Delivery status logging. This preference will log delivery status to CloudWatch Logs. You have two values to determine. This first is Log delivery status for these protocols which presents a series of checkboxes for AWS Lambda, Amazon SQS, HTTP/S, Platform application endpoint, and Amazon Kinesis Data Firehouse. These last two options are a preview of the next big difference between working through the toolkit or through the console.

Additional Subscriptions in the Console

Once you have finished creating the topic, you can then create a subscription. There are several protocols available for use in the console that are not available in the toolkit. These include:

·         Amazon Kinesis Data Firehouse – configure this subscription to go to Kinesis Data Firehouse. From there you can send notifications to Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party service providers such as Datadog, New Relic, MongoDB, and Splunk.

·         Platform-application endpoint – this protocol sends the message to an application on a mobile device. Push notification messages sent to a mobile endpoint can appear in the mobile app as message alerts, badge updates, or even sound alerts. Go to https://docs.aws.amazon.com/sns/latest/dg/sns-mobile-application-as-subscriber.html for more information on configuring your SNS topic to deliver to a mobile device.

·         SMS – this protocol delivers text messages, or SMS messages, to SMS-enabled devices. Amazon SNS supports SMS messaging in several regions, and you can send messages to more than 200 countries and regions. An interesting aspect of SMS is that your account starts in a SMS sandbox or non-production environment with a set of limits. Once you are convinced that everything is correct you must create a case with AWS support to move your account out of the sandbox and actually start sending messages to non-limited numbers.

Now that we have configured our SNS topic and subscription, lets next look at sending a message.

.NET and Amazon SNS

The first step to interacting with SNS from within your .NET application is to install the appropriate NuGet package, AWSSDK.SimpleNotificationService. This will also install AWSSDK.Core. Once you have the NuGet package, you can access the appropriate APIs by adding several using statements

using Amazon.SimpleNotificationService;
using Amazon.SimpleNotificationService.Model;

These namespaces provide access to the AmazonSimpleNotificationServiceClient class that manages the interaction with the SNS service as well as the models that are represented in the client methods. There are a lot of different types of interactions that you can support with this client. A list of the more commonly used methods is displayed below:

·         PublishAsync – Send a message to a specific topic for processing by SNS

·         PublishBatchAsync – Send multiple messages to a specific topic for processing by SNS.

·         Subscribe – Subscribe a new endpoint to a topic

·         Unsubscribe – Remove an endpoint’s subscription to a topic.

These four methods allow you to add and remove subscriptions as well as publish messages. There are dozens of other methods available from that client, including the ability to manage topics and confirm subscriptions.

The code below is a complete console application that sends a message to a specific topic.

static void Main(string[] args)
    string topicArn = "Arn for the topic to publish";
    string messageText = "Message from ProDotNetOnAWS_SNS";

    var client = new AmazonSimpleNotificationServiceClient();

    var request = new PublishRequest
        TopicArn = topicArn,
        Message = messageText,
        Subject = Guid.NewGuid().ToString()

    var response = client.PublishAsync(request).Result;

       $"Published message ID: {response.MessageId}");


As you can see, the topic needs to be described with the Arn for the topic rather than simply the topic name. Publishing a message entails the instantiation of the client and then defining a PublishRequest object. This object contains all of the fields that we are intending to send to the recipient, which in our case is simply the subject and message. Running the application presents a console as shown in Figure 7.

Figure 7. Console application that sent message through SNS
Figure 7. Console application that sent message through SNS

The message that was processed can be seen in Figure 8. Note the MessageId values are the same as in Figure 7.

Figure 8. Message sent through console application
Figure 8. Message sent through console application

We have only touched on the capabilities of Amazon SNS and its capacity to help implement event-driven architecture. However, there is another AWS service that is even more powerful, Amazon EventBridge. Let’s look at that next.

Modern .NET Application Design

In this post we will go over several modern application design architectures, with the predominant one being event and message-based architecture. After this mostly theoretical discussion, we will move into practical implementation. We will do this by going over two different AWS services. The first of these services, Amazon Simple Notification Service (SNS), is a managed messaging service that allows you to decouple publishers from subscribers. The second service is Amazon EventBridge, which is a serverless event bus. As we are going over each, we will also review the inclusion of these services into a .NET application so that you can see how it works.

Modern Application Design

The growth in the public cloud and its ability to quickly scale computing resources up and down has made the building of complex systems much easier. Let’s start by looking at what the Microservice Extractor for .NET does for you. For those of you unaware of this tool, you can check out the user guide or see a blog article on its use. Basically, however, the tool analyzes your code and helps you determine what areas of the code you can split out into a separate microservice. Figure 1 shows the initial design and then the design after the extractor was run.

Figure 1. Pre and Post design after running the Microservice Extractor
Figure 1. Pre and Post design after running the Microservice Extractor

Why is this important? Well, consider the likely usage of this system. If you think about a typical e-commerce system, you will see that the inventory logic, the logic that was extracted, is a highly used set of logic. It is needed to work with the catalog pages. It is needed when working with orders, or with the shopping cart. This means that this logic may act as a bottleneck for the entire application. To get around this with the initial design means that you would need to deploy additional web applications to ease the load off and minimize this bottleneck.

Evolving into Microservices

However, the extractor allows us to use a different approach. Instead of horizontally scaling the entire application, scale the set of logic that gets the most use.  This allows you to minimize the number of resources necessary to keep the application optimally running. There is another benefit to this approach as you now have an independently managed application, which means that it can have its own development and deployment processes and can be interacted with independently of the rest of the application stack. This means that a fully realized microservices approach could look more like that shown in Figure 2.

Figure 2. Microservices-based system design
Figure 2. Microservices-based system design

This approach allows you to scale each web service as needed. You may only need one “Customer” web service running but need multiples of the “Shopping Cart” and “Inventory” running to ensure performance. This approach means you can also do work in one of the services, say “Shopping Cart” and not have to worry about testing anything within the other services because those won’t have been impacted – and you can be positive of that because they are completely different code lines.

This more decoupled approach also allows you to manage business changes more easily.

Note – Tightly coupled systems have dependencies between the systems that affect the flexibility and reusability of the code. Loosely coupled, or decoupled, systems have minimal dependencies between each other and allow for greater code reuse and flexibility.

Consider Figure 3 and what it would have taken to build this new mobile application with the “old-school” approach. There most likely would have been some duplication of business logic, which means that as each of the applications evolve, they would likely have drifted apart. Now, that logic is in a single place so it will always be the same for both applications (excluding any logic put into the UI part of the application that may evolve differently – but who does that?)

Figure 3. Microservices-based system supporting multiple applications
Figure 3. Microservices-based system supporting multiple applications

One look at Figure 3 shows how this system is much more loosely coupled than was the original application. However, there is still a level of coupling within these different subsystems. Let’s look at those next and figure out what to do about them.

Deep Dive into Decoupling

Without looking any deeper into the systems than the drawing in Figure 3 you should see one aspect of tight coupling that we haven’t addressed. The “Source Database.” Yes, this shared database indicates that there is still a less than optimal coupling between the different web services. Think about how we used the Extractor to pull out the “Inventory” service so we could scale that independently of the regular application. We did not do the same to the database service that is being accessed by all these web services. So, we still have that quandary, only at the database layer than at the business logic layer.

The next logical step in decoupling these systems would be to break out the database responsibilities as well, resulting in a design like that shown in Figure 4.

Figure 4. Splitting the database to support decoupled services
Figure 4. Splitting the database to support decoupled services

Unfortunately, it is not that easy. Think about what is going on within each of these different services; how useful is a “Shopping Cart” or an “Order” without any knowledge of the “Inventory” being added to the cart, or sold? Sure, those services do not need to know everything about “Inventory”, but they need to either interact with the “Inventory” service or go directly into the database to get information. These two options are shown in Figure 5.

Figure 5. Sharing data through a shared database or service-to-service calls
Figure 5. Sharing data through a shared database or service-to-service calls

As you can see, however, we have just added back in some coupling, as in either approach the “Order” service and “Shopping Cart” service now have dependencies on the “Inventory” service in some form or another. However, this may be unavoidable based on certain business requirements – those requirements that mean that the “Order” needs to know about “Inventory.” Before we stress out too much about this design, let’s further break down this “need to know” by adding in an additional consideration about when the application needs to know about the data. This helps us understand the required consistency.  

Strong Consistency

Strong consistency means that all applications and systems see the same data at the same time. The solutions in Figure 3 represent this approach because, regardless of whether you are calling the database directly or through the web service, you are seeing the most current set of the data, and it is available immediately after the data is persisted. There may easily be requirements where that is required. However, there may just as easily be requirements where a slight delay between the “Inventory” service and the “Shopping Cart” service knowing information may be acceptable.

For example, consider how a change in inventory availability (the quantity available for the sale of a product) may affect the shopping cart system differently than the order system. The shopping cart represents items that have been selected as part of an order, so inventory availability is important to it – it needs to know that the items are available before those items can be processed as an order. But when does it need to know that? That’s where the business requirements come into play. If the user must know about the change right away, that will likely require some form of strong consistency. If, on the other hand, the inventory availability is only important when the order is placed, then strong consistency is not as necessary. That means there may be a case for eventual consistency.

Eventual Consistency

As the name implies, data will be consistent within the various services eventually – not right away. This difference may be as slight as milliseconds or it can be seconds or even minutes, all depending upon business needs and system design. The smaller the timeframe necessary, the more likely you will need to use strong consistency. However, there are plenty of instances where seconds and even minutes are ok. An order, for example, needs some information about a product so that it has context. This could be as simple as the product name or more complex relationships such as the warehouses and storage locations for the products. But the key factor is that changes in this data are not really required to be available immediately to the order system. Does the order system need to know about a new product added to the inventory list? Probably not – as it is highly unlikely that this new product will be included in an order within milliseconds of becoming active.  Being available within seconds should be just fine. Figure 6 shows a time series graph of the differences between strong and eventual consistency.

Figure 6. Time series showing the difference between strong and eventual consistency
Figure 6. Time series showing the difference between strong and eventual consistency

What does the concept of eventual consistency mean when we look at Figure 3 showing how these three services can have some coupling? It gives us the option for a paradigm shift. Our assumption up to this time is that data is stored in a single source, whether all the data is stored in a big database or whether each service has its own database – such as the Inventory service “owning” the Inventory database that stores all the Inventory information. Thus, any system needing inventory data would have to go through these services\databases in some way.

This means our paradigm understands and accepts the concept of a microservice being responsible for maintaining its own data – that relationship between the inventory service and the inventory database. Our paradigm shift is around the definition of the data that should be persisted in the microservices database. For example, currently, the order system stores only data that describes orders – which is why we need the ability to somehow pull data from the inventory system. However, this other information is obviously critical to the order so instead of making the call to the inventory system we instead store that critical inventory-related data in the order system. Think what that would be like.

Oh No! Not duplicated data!

Yes, this means some data may be saved in multiple places. And you know what? That’s ok. Because it is not going to be all the data, but just those pieces of data that the other systems may care about. That means the databases in a system may look like those shown in Figure 7 where there may be overlap in the data being persisted.

Figure 7. Data duplication between databases
Figure 7. Data duplication between databases

This data overlap or duplication is important because it eliminates the coupling that we identified when we realized that the inventory data was important to other systems. By including the interesting data in each of the subsystems, we no longer have that coupling, and that means our system will be much more resilient.

If we continued to have that dependency between systems, then an outage in the inventory system means that there would also be an outage in the shopping cart and order systems, because those systems have that dependency upon the inventory system for data. With this data being persisted in multiple places, an outage in the inventory system will NOT cause any outage in those other systems. Instead, those systems will continue to happily plug along without any concern for what is going on over in inventory-land.  It can go down, whether intentionally because of a product release or unintentionally, say by a database failure, and the rest of the systems continue to function. That is the beauty of decoupled systems, and why modern system architectural design relies heavily on decoupling business processes.

We have shown the importance of decoupling and how the paradigm shift of allowing some duplication of data can lead to that decoupling. However, we haven’t touched on how we would do this. In this next section, we will go into one of the most common ways to drive this level of decoupling and information sharing.

Designing a messaging or event-based architecture

The key to this level of decoupling requires that one system notify the other systems when data has changed. The most powerful method for doing this is through either messaging or events. While both messaging and events provide approaches for sending information to other systems, they represent different forms of communication and different rules that they should follow.


Conceptually, the differences are straightforward. Messaging is used when:

·         Transient Data is needed – this data is only stored until the message consumer has processed the message or it hits a timeout or expiration period.

·         Two-way Communication is desired – also known as a request\reply approach, one system sends a request message, and the receiving system sends a response message in reply to the request.

·         Reliable Targeted Delivery – Messages are generally targeted to a specific entity. Thus, by design, a message can have one and only one recipient as the message will be removed from the queue once the first system processes it.

Even though messages tend to be targeted, they provide decoupling because there is no requirement that the targeted system is available when the message is sent. If the target system is down, then the message will be stored until the system is back up and accepting messages. Any missed messages will be processed in a First In – First Out process and the targeted system will be able to independently catch up on its work without affecting the sending system.

When we look at the decoupling we discussed earlier, it becomes apparent that messaging may not be the best way to support eventual consistency as there is more than one system that could be interested in the data within the message. And, by design, messaging isn’t a big fan of this happening. So, with these limitations, when would messaging make sense?

Note: There are technical design approaches that allow you to send a single message that can be received by multiple targets. This is done through a recipient list, where the message sender sends a single message and then there is code around the recipient list that duplicates that message to every target in the list. We won’t go into these approaches here.

The key thing to consider about messaging is that it focuses on assured delivery and once and once-only processing. This provides insight into the types of operations best supported by messaging. An example may be the web application submitting an order. Think of the chaos if this order was received and processed by some services but not the order service. Instead, this submission should be a message targeted at the order service. Sure, in many instances we are handling this as an HTTP Request (note the similarities between a message and the HTTP request) but that may not always be the best approach. Instead, our ordering system sends a message that is assured of delivery to a single target.


Events, on the other hand, are traditionally used to represent “something that happened” – an action performed by the service that some other systems may find interesting. Events are for when you need:

·         Scalable consumption – multiple systems may be interested in the content within a single event

·         History – the history of the “thing that happened” is useful. Generally, the database will provide the current state of information. The event history provides insight into when and what caused changes to that data. This can be very valuable insight.

·         Immutable data – since an event represents “something that already happened” the data contained in an event is immutable – that data cannot be changed. This allows for very accurate tracing of changes, including the ability to recreate database changes.

Events are generally designed to be sent by a system, with that system having no concern about whether other systems receive the event or act upon it. The sender fires the event and then forgets about it.

When you consider the decoupled design that we worked through earlier, it becomes quickly obvious that events are the best approach to provide any changed inventory data to the other systems. In the nest article we will jump right into Amazon Simple Notification Service (SNS), and talk more about events within our application using SNS as our guide.

Deploying New Container Using AWS App2Container

In our last article, we went through the containerization of a running application. The last step of this process is to deploy the container. The default approach is to deploy a container image to ECR and then create the CloudFormation templates to run that image in Amazon ECS using Fargate. If you would prefer to deploy to Amazon EKS instead, you will need to go to the deployment.json file in the output directory. This editable file contains the default settings for the application, ECR, ECS, and EKS. We will walk through each of the major areas in turn.

The first section is responsible for defining the application and is shown below.

"a2CTemplateVersion": "1.0",
"applicationId": "iis-tradeyourtools-6bc0a317",
"imageName": "iis-tradeyourtools-6bc0a317",
"exposedPorts": [
              "localPort": 80,
              "protocol": "http"
"environment": [],

The applicationId and the imageName are values we have seen before when going through App2Containers. The exposedPorts value should contain all of the IIS ports configured for the application. The one used in the example was not configured for HTTPS, but if it was there would be another entry for that value. The environment value allows you to enter any environment variables as key/value pairs that may be used by the application. Unfortunately, App2Container is not able to determine those because it does its analysis on running code rather than the code base. In our example, there are no environmental variables that are necessary.

Note – If you aren’t sure whether there are environment variables that your application may access, you can see which variables are available by going into the System -> Advanced system settings -> Environment variables. This will provide you with a list of available variables and you can evaluate those as to their relevance to your application.

The next section is quite small and contains the ECR configuration. The ECR repository that will be created is named with the imageName from above and then versioned with the value in the ecrRepoTag as shown below.

"ecrParameters": {
       "ecrRepoTag": "latest"

We are using the value latest as our version tag.

There are two remaining sections in the deployment.json file. The first is the ECS setup information with the second being the EKS setup information. We will first look at the ECS section. This entire section is listed below.

"ecsParameters": {
       "createEcsArtifacts": true,
       "ecsFamily": "iis-tradeyourtools-6bc0a317",
       "cpu": 2,
       "memory": 4096,
       "dockerSecurityOption": "",
       "enableCloudwatchLogging": false,
       "publicApp": true,
       "stackName": "a2c-iis-tradeyourtools-6bc0a317-ECS",
       "resourceTags": [
                     "key": "example-key",
                     "value": "example-value"
       "reuseResources": {
              "vpcId": "vpc-f4e4d48c",
              "reuseExistingA2cStack": {
                     "cfnStackName": "",
                     "microserviceUrlPath": ""
              "sshKeyPairName": "",
              "acmCertificateArn": ""
       "gMSAParameters": {
              "domainSecretsArn": "",
              "domainDNSName": "",
              "domainNetBIOSName": "",
              "createGMSA": false,
              "gMSAName": ""
       "deployTarget": "FARGATE",
       "dependentApps": []

The most important value here is createEcsArtifacts, which if set to true means that deploying with App2Container will deploy the image into ECS. The next ones to look at are cpu and memory. These values are only used for Linux containers. In our case, these values do not matter because this is a Windows container. The next two values, dockerSecurityOption and enableCloudwatchLogging are only changed in special cases, so they will generally stay at their default values. The next value, publicApp, determines whether the application will be configured into a public subnet with a public endpoint. This is set to true because this is our hoped-for behavior. The next value, stackName, defines the name of the CloudFormation stack while the value after that, resourceTags, are the custom tags that should be added to the ECS task definition. There is a default set of key/values in the file, but those will not be used if kept in; only keys that are not defined as example-key will be added.

The next section, reuseResources, is where you can configure whether you wish to use any pre-existing resources, namely VPC – which is added to the vpcId value. When left blank, as shown below, App2Container will create a new VPC.

"reuseResources": {
     "vpcId": "",
     "reuseExistingA2cStack": {
            "cfnStackName": "",
            "microserviceUrlPath": ""
     "sshKeyPairName": "",
     "acmCertificateArn": ""

Running the deployment with these settings will result in a brand new VPC being created. This means that, by default, you wouldn’t be able to connect in or out of the VPC without making changes to the VPC. If, however, you have an already existing VPC that you want to use, update the vpcId key with the ID of the appropriate VPC.

Note: App2Container requires that the included VPS has a routing table that is associated with at least two subnets and an internet gateway. The CloudFormation template for the ECS service requires this so that there is a route from your service to the internet from at least two different AZs for availability. Currently, there is no way for you to define these subnets. You will receive a Resource creation failures: PublicLoadBalancer: At least two subnets in two different Availability Zones must be specified message if your VPC is not set up properly.

You can also choose to reuse an existing stack created by App2Container. Doing this will ensure that the application is deployed into the already existing VPC and that the URL for the new application is added to the already created Application Load Balancer rather than being added to a new ALB.

The next value, sshKeyPairName, is the name of the EC2 key pair used for the instances on which your container runs. Using this rather defeats the point of using containers, so we left it blank as well. The last value, acmCertificateArn, is for the AWS Certificate Manager ARN that you want to use if you are enabling HTTPS on the created ALB. This parameter is required if you use an HTTPS endpoint for your ALB, and remember as we went over earlier this means that the request being forwarded into the application will be on port 80 and unencrypted because this would have been handled in the ALB.

The next set of configuration values are part of the gMSAParameters section. This becomes important to manage if your application relies upon group Managed Service Account (gMSA) Active Directory groups. This can only be used if deploying to EC2 and not Fargate (more on this later). These individual values are:

·         domainSecretsArn – The AWS Secrets Manager ARN containing the domain credentials required to join the ECS nodes to Active Directory.

·         domainDNSName – The DNS Name of the Active Directory the ECS nodes will join.

·         domainNetBIOSName – The NetBIOS name of the Active Directory to join.

·         createGMSA – A flag determining whether to create the gMSA Active Directory security group and account using the name supplied in the gMSAName field.

·         gMSAName – The name of the Active Directory account the container should use for access.

There are two fields remaining, deployTarget and dependentApps. For deployTarget there are two valid values for .NET applications running on Windows; fargate and ec2. You can only deploy to Fargate if your container is Windows 2019 or more recent. This would only be possible if your worker machine, the one you used for containerizing, was running Windows 2019+. Also, you cannot deploy to Fargate if you are using gMSA.

The value dependentApps is interesting, as it handles those applications that AWS defines as “complex Windows applications”. We won’t go into it in more details here, but you can go to https://docs.aws.amazon.com/app2container/latest/UserGuide/summary-complex-win-apps.html if you are interested in learning more about these types of applications.

The next section in the deployment.json file is eksParameters. You will see that much of these parameters are the same as what we went over when talking about the ECS parameters. The only differences are the createEksArtifacts parameter, which needs to be set to true if deploying to EKS, and in the gMSA section, the gMSAName parameter has inexplicably been changed to gMSAAccountName.

Once you have the deployment file set as desired, you next deploy the container:

PS C:\App2Container> app2container generate app-deployment --application-id APPID --deploy

This process takes several minutes, and you should get an output like Figure 1. The gold arrow points to the URL where you can go see your deployed application – go ahead and look at it to confirm that it has been successfully deployed and is running.

Figure 1. Output from generating an application deployment in App2Container

Logging in to the AWS console and going to Amazon ECR will show you the ECR repository that was created to store your image as shown in Figure 2.

Figure 2. Verifying the new container image is available in ECR

Once everything has been deployed and verified, you can poke around in ECS to see how it is all put together. Remember though, if you are looking to make modifications it is highly recommended that you use the CloudFormation templates, make the changes there, and then re-upload them as a new version. That way you will be able to easily redeploy as needed and not worry about losing any changes that you may have added. You can either alter the templates in the CloudFormation section of the console or you can find the templates in your App2Container working directory, update those, and then use those to update the stack.

Containerizing a Running Application with AWS App2Container

Now that we have gone through containerizing an already existing application where you have access to the source code, let’s look at containerizing a .NET application in a different way. This is for those applications you may have that are running and where you may not have access to the source code, or you don’t deploy it, or there are other reasons where you don’t want to change the source code as we just went over earlier. Instead, you want to containerize the application by just “picking it up off its server” and moving it into a container. Up until recently, that was not a simple thing to do. However, AWS created a tool to help you do just that. Let’s look at that now.

What is AWS App2Container?

AWS App2Container is a command-line tool that is designed to help migrate .NET web applications into a container format. You can learn more about and download this tool at https://aws.amazon.com/app2container/.  It also does Java, but hey, we’re all about .NET, so we won’t talk about that anymore! You can see the process in Figure 1, but at a high level, there are five major steps.

Figure 1. How AWS App2Container works

These steps are:

1.      Inventory – This step goes through the applications running on the server looking for running applications. At the time of writing, App2Container supports ASP.NET 3.5, and greater, applications running in IIS 7.5+ on Windows.

2.      Analyze – A chosen application is analyzed in detail to identify dependencies including known cooperating processes and network port dependencies. You can also manually add any dependencies that App2Container was unable to find.

3.      Containerize – In this step, all the application artifacts discovered during the “Analyze” phase are “dockerized.”

4.      Create – This step creates the various deployment artifacts (generally as CloudFormation templates) such as ECS task or Kubernetes pod definitions.

5.      Deploy – Store the image in Amazon ECR and deploy to ECS or EKS as desired.

There are three different modes in which you can use App2Container. The first is a mode where you perform the steps on two different machines. If using this approach, App2Container must be installed on both machines. The first machine, the Server, is the machine on which the application(s) that you want to containerize is running. You will run the first two steps on the server. The second machine, the Worker, is the machine that will perform the final three steps of the process based on artifacts that you copy from the server. The second mode is when you perform all the steps on the same machine, so it basically fills both the server and worker roles. The third mode is when you run all the commands on your worker machine, connecting to the server machine using the Windows Remote Management (WinRM) protocol. This approach has the benefit of not having to install App2Container on the server, but it also means that you must have WinRM installed and running. We will not be demonstrating this mode.

App2Container is a command-line tool that has some prerequisites that must be installed before the tool will run. These prerequisites are listed below.

·         AWS CLI – must be installed on both server and worker

·         PowerShell 5.0+ – must be installed on both server and worker

·         Administrator rights – You must be running as a Windows administrator

·         Appropriate permissions – You must have AWS credentials stored on the worker machine as was discussed in the earlier articles when installing the AWS CLI.

·         Docker tools – Docker version 17.07 or later must be installed on worker

·         Windows Server OS – Your worker system must run on Windows OS versions that support containers, namely Windows Server 2016 or 2019. If working in server\worker mode, the server system must be Windows 2008+.

·         Free Space – 20-30 GB of free space should be available on both server and worker

The currently supported types of applications are

·         Simple ASP.NET applications running on a single server

·         A Windows service running on a single server

·         Complex ASP.NET applications that depend on WCF, running on a single server or multiple servers

·         Complex ASP.NET applications that depend on Windows services or processes outside of IIS, running on a single server or multiple servers

·         Complex, multi-node IIS or Windows service applications, running on a single server or multiple servers

There are also two types of applications that are not supported:

·         ASP.NET applications that use files and registries outside of IIS web application directories

·         ASP.NET applications that depend on features of a Windows operating system version prior to Windows Server Core 2016

Now that we have described App2Container as well as the .NET applications on which it will and will not work, the next step is to show how to use the tool.

Using AWS App2Container to Containerize an Application

We will first describe the application that we are going to containerize. We have installed a .NET Framework 4.7.2 application onto a Windows EC2 instance that supports containers; the AMI we used is shown in Figure 2. Please note that since EC2 regularly revises its AMIs, you may see a different Id.

Figure 2. AMI used to host the website to containerize

The application is connected to an RDS SQL Server instance for database access using Entity Framework, and the connection string is stored in the web.config file.

The next step, now that we have a running application, is to download the AWS App2Container tool. You can access the tool by going to https://aws.amazon.com/app2container/ and clicking the Download AWS App2Container button at the top of the page. This will bring you to the Install App2Container page in the documentation which has a link to download a zip file containing the App2Container installation package. Download the file and extract it to a folder on the server. If you are doing the work using the server\worker mode, then download and extract the file on both servers. After you unzip the downloaded file, you should have 5 files, one of which is another zipped file.

Open PowerShell and navigate to the folder containing App2Container. You must then run the install script.

PS C:\App2Container> .\install.ps1

You will see the script running through several checks and then present some terms and conditions text that will require you to respond with a y to continue. You will then be able to see the tool complete its installation.

The next step is to initialize and configure App2Container. If using server/worker mode, then you will need to do this on each machine. You start the initializing with the following command.

PS C:\App2Container> app2container init

It will then prompt you for a Workspace directory path for artifacts value. This is where the files from the analysis and any containerization will be stored. Click enter to accept the default value or enter a new directory. It will then ask for an Optional AWS Profile. You can click enter if you have a default profile setup or you can enter the name of the profile to use if different.

Note: It is likely that a server running the application you want to containerize does not have the appropriate profile available. If not, you can set one up by running the aws configure command to set up your CLI installation that App2Container will use to create and upload the created container.

Next, the initialization will ask you for an Optional S3 bucket for application artifacts. Providing a value in this step will result in the tool output also being copied to the provided bucket. You can click enter to use the default of “no bucket” however, at the time of this writing you must have this value configured so that it can act as storage for moving the container image into ECR. We used an S3 bucket called “prodotnetonaws-app2container”. The next initialization step is whether you wish to Report usage metrics to AWS? (Y/N). No personal or confidential information is gathered, so we recommend that you click enter to accept the default of “Y”. The following initialization prompt asks if you want to Automatically upload logs and App2Container generated artifacts on crashes and internal errors? (Y/N). We want AWS to know as soon as possible if something went wrong so we selected “y”. The last initialization prompt is asking whether to Require images to be signed using Docker Content Trust (DCT)? (Y/N). We selected the default value, “n”. The initialization will then display the path in which the artifacts will be created and stored. Figure 3 shows our installation when completed.

Figure 3. Output from running the App2Container initialization

For those of you using the server/worker mode approach, take note of the application artifact directory displayed in the last line of the command output as this will contain the artifacts that you will need to move to the worker machine. Now that the application is initialized, the next step is to take the inventory of eligible applications running on the server. You do this by issuing the following command:

PS C:\App2Container> app2container inventory

The output from this command is a JSON object collection that has one entry for each application. The output on our EC2 server is shown below:

     "iis-demo-site-a7b69c34": {
          "siteName": "Demo Site",
          "bindings": "http/*:8080:",
          "applicationType": "IIS"
      "iis-tradeyourtools-6bc0a317": {
          "siteName": "TradeYourTools",
          "bindings": "http/*:80:",
          "applicationType": "IIS"

As you can see, there are two applications on our server, the “Trade Your Tools” app we described earlier as well as another website “Demo Site” that is running under IIS and is bound to port 8080. The initial key is the application ID that you will need moving forward.

Note: You can only containerize one application at a time. If you wish to containerize multiple applications from the same server you will need to repeat the following steps for each one of those applications.

The next step is to analyze the specific application that you are going to containerize. You do that with the following command, replacing the application ID (APPID) in the command with your own.

PS C:\App2Container> app2container analyze --application-id APPID

You will see a lot of flashing that shows the progress output as the tool analyzes the application, and when it is complete you will get output like that shown in Figure 4.

 Figure 4. Output from running the App2Container analyze command

The primary output from this analysis is the analysis.json file that is listed in the command output. Locating and opening that file will allow you to see the information that the tool gathered about the application, much of which is a capture of the IIS configuration for the site running your application. We won’t show the contents of the file here as it is several hundred lines long, however, much of the content of this file can be edited as you see necessary.

The next steps branch depending upon whether you are using a single server or using the server/worker mode.

When containerizing on a single server

Once you are done reviewing the artifacts created from the analysis, the next step is to containerize the application. You do this with the following command

PS C:\App2Container> app2container containerize --application-id APPID

The processing in this step may take some time to run, especially if, like us, you used a free-tier low-powered machine! Once completed, you will see output like Figure 5.

Figure 5. Output from containerizing an application in App2Container

At this point, you are ready to deploy your container and can skip to the next article, “Deploying…”, if you don’t care about containerizing using server/worker mode.

When containerizing using server/worker mode

Once you are done reviewing the artifacts created from the analysis, the next step is to extract the application. This will create the archive that will need to be moved to the worker machine for containerizing. Also, the tool will upload the archive to the S3 bucket provided during initialization. Since we didn’t provide a bucket, we must manually copy the file. The command to extract the application is:

PS C:\App2Container> app2container extract --application-id APPID

This command will process, and you should get a simple “Extraction successful” message.

Returning to the artifact directory that was displayed when initializing App2Container, you will see a new zip file named with your Application ID. Copy this file to the worker server.

Once you are on the worker server and App2Container has been initialized, the next step is to containerize the content from the archive. You do that with the following command

PS C:\App2Container> app2container containerize --input-archive PathToZip

The output from this step matches the output from running the containerization on a single server and can be seen in Figure 5 above.

The next article will show how to deploy this containerized application into AWS.

Containerizing a .NET Core-based Application for AWS

In our last post in this series, we talked about Containerizing a .NET 4.x Application for deployment onto AWS, and as you may have seen it was a somewhat convoluted affair. Containerizing a .NET Core type application is much easier, because a lot of the hoops that you must leap through to manage a Windows container will not be necessary. Instead, all AWS products, as well as IDEs, will support this out the gate.

Using Visual Studio

We have already gone through adding container support using Visual Studio, and that we are doing it now using a .NET Core-based application does not change that part of the process at all. What does change, however, is the ease of getting the newly containerized application into AWS. Once the Docker file has been added, the “Publish to AWS” options when right-clicking on the project name in the Solution Explorer is greatly expanded. Since our objective is to get this application deployed to Amazon ECR, make the choice to Push Container Images to Amazon Elastic Container Registry and click the Publish button. You will see the process walk through a few steps and it will end with a message stating that the image has been successfully deployed into ECR.

Using JetBrains Rider

The process of adding a container using JetBrains Rider is very similar to the process used in Visual Studio. Open your application in Rider, right-click the project, select Add, and then Docker Support as shown in Figure 1.

Figure 1. Adding Docker Support in JetBrains Rider

This will bring up a window where you select the Target OS, in this case, Linux.  Once you have this finished you will see a Dockerfile show up in your solution. Unfortunately, the AWS Toolkit for Rider does not currently support deploying the new container image to ECR. This means that any deployment to the cloud must be done with the AWS CLI or the AWS Tools for Powershell and would be the same as the upload process used when storing a Windows container in ECR that we went over in an earlier post.

As you can see, containerizing a .NET Core based application is much easier to do as well as easier to deploy into AWS.

Containerizing a .NET Framework 4.x Application for AWS

In this post we are going to demonstrate ways in which you can containerize your applications for deployment into the cloud, the next step in minimizing resource usage and likely saving money. This article is different from the previous entries in this series because those were a discussion of containers and running them within the AWS infrastructure while this post is much more practical and based upon getting to that point from an existing non-containerized application.

Using Visual Studio

Adding container support using Visual Studio is straightforward.

Adding Docker Support

Open an old ASP.NET Framework 4.7 application or create a new one. Once open, right-click on the project name, select Add, and then Docker Support as shown in Figure 1.

Figure 1. Adding Docker Support to an application.

Your Output view, when set to showing output from Container Tools, will show multiple steps being performed, and then it should finish successfully. When completed, you will see two new files added in the Solution Explorer, Dockerfile, and a subordinate .dockerignore file. You will also see that your default Debug setting has changed to Docker. You can see both changes in Figure 2.

Figure 2. Changes in Visual Studio after adding Docker support

You can test the support by clicking the Docker button. This will build the container, run it under your local Docker Desktop, and then open your default browser. This time, rather than going to a localhost URL you will instead go to an IP address, and if you compare the IP address in the URL to your local IP you will see that they are not the same. That is because this new IP address points to the container running on your system.

Before closing the browser and stopping the debug process, you will be able to confirm that the container is running by using the Containers view in Visual Studio as shown in Figure 3.

Figure 3. Using the Containers view in Visual Studio to see the running container

You can also use Docker Desktop to view running containers. Open Docker Desktop and select Containers / Apps. This will bring you to a list of the running containers and apps, one of which will be the container that you just started as shown in Figure 4.

Figure 4. Viewing a running container in Docker Desktop

Once these steps have been completed, you are ready to save your container in ECR, just as we covered earlier in this series.

Deploying your Windows Container to ECR

However, there are some complications with this, as the AWS Toolkit for Visual Studio does not support the container deployment options we saw earlier when looking at the toolkit when working with Windows containers. Instead, we are going to use the AWS PowerShell tools to build and publish your image to ECR. At a high level, the steps are:

·         Build your application in Release mode. This is the only way that Visual Studio puts the appropriate files in the right place, namely the obj\Docker\publish subdirectory of your project directory. You can see this value called out in the last line of your Dockerfile: COPY ${source:-obj/Docker/publish} .

·         Refresh your ECR authentication token. You need this later in the process so that you can login to ECR to push the image.

·         Build the Docker image.

·         Tag the image. Creates the image tag on the repository

·         Push the image to the server. Copy the image into ECR

Let’s walk through them now. The first step is to build your application in Release mode. However, before you can do that, you will need to stop your currently running container. You can do that through either Docker Desktop or the Containers view in Visual Studio. If you do not do this, your build will fail because you will not be able to override the necessary files. Once that is completed, your Release mode build should be able to run without problem.

Next, open PowerShell and navigate to your project directory. This directory needs to be the one that contains the Docker file. First thing we will do is to set the authentication context. We do that by first getting the command to execute, and then executing that command. That is why this process has two steps.

$loginCommand = Get-ECRLoginCommand -Region <repository region>

And then

Invoke-Expression $loginCommand.Command

This refreshed the authentication token into ECR. The remaining commands are based upon an existing ECR repository. You can access this information through the AWS Explorer by clicking on the repository name. This will bring up the details page as shown in Figure 5.

Figure 5. Viewing a running container in Docker Desktop

The value shown by the 1 is the repository name and by number 2 is the repository URI. You will need both of those values for the remaining steps. Build the image:

docker build -t <repository> .

The next step is to tag the image. In this example we are setting this version as the latest version by appending both the repository name and URI with “:latest”.

docker tag <repository>:latest <URI>:latest

The last step is to push the image to the server:

docker push <URI>:latest

You will see a lot of work going on as everything is pushed to the repository but eventually it will finish processing and you will be able to see your new image in the repository.

Note: Not all container services on AWS support Windows containers. Amazon ECS on AWS Fargate is one of the services that does as long as you make the appropriate choices as you configure your tasks. There are detailed directions to doing just that at https://aws.amazon.com/blogs/containers/running-windows-containers-with-amazon-ecs-on-aws-fargate/.

While Visual Studio offers a menu-driven approach to containerizing your application, you always have the option to containerize your application manually.

Containerizing Manually

Containerizing an application manually requires several steps. You’ll need to create your Docker file and then coordinate the build of the application so that it works with the Docker file you created. We’ll start with those steps first, and we’ll do it using JetBrains Rider. The first thing you’ll need to do is to add a Docker file to your sample application, called Dockerfile. This file needs to be in the root of your active project directory. Once you have this added to the project, right-click the file to open the Properties window and change the Build action to None and the Copy to output directory to Do not copy as shown in Figure 6.

Figure 6. Build properties for the new Docker file

This is important because it makes sure that the Docker file itself will not end up deployed into the container.

Now that we have the file, let’s start adding the instructions:

FROM mcr.microsoft.com/dotnet/framework/aspnet:4.8-windowsservercore-ltsc2019
ARG source
WORKDIR /inetpub/wwwroot

These commands are defining the source image with FROM, defining an argument, and then defining the directory and entry point where the code is going to be running on the container. The source image that we have defined includes support for ASP.NET and .NET version 4.8, mcr.microsoft.com/dotnet/framework/aspnet:4.8, and is being deployed onto Windows Server 2019, windowsservercore-ltsc2019. There is an image for Windows Server 2022, windowsservercore-ltsc2022, but this may not be usable for you if you are not running the most current version of Windows on your machine

The last part that we need to do is to configure the Docker file to include the compiled application. However, before we can do that, we need to build the application in such a way that we can access these deployed bits. This is done by publishing the application. In Rider, you publish the application by right-clicking on the project and selecting the Publish option. This will give you the option to publish to either a Local folder or Server. This brings up the configuration screen where you can select the directory in which to publish as shown in Figure 7.

Figure 7. Selecting a publish directory

It will be easiest if you select a directory underneath the project directory; we recommend within the bin directory so that the IDEs will tend to ignore it. Clicking the Run button will publish the app to the directory. The last step is to add one more command to the Dockerfile where you point the source command to the directory in which you published the application.

COPY ${source:-bin/release} .

Once you add this last line into the Dockerfile, you are ready to deploy the Windows container to ECR using the steps that we went through in the last section.

Now that we have walked through two different approaches for containerizing your older .NET Framework-based Windows application, the next step is to do the same with a .NET Core-based application. As you will see, this process is a lot easier because we will build the application onto a Linux-based container so you will see a lot of additional support in the IDEs. Let’s look at that next.

Amazon RDS Oracle for .NET Developers

The last database available in RDS that we will go over is the oldest commercial SQL-based database management system, Oracle. While originally strictly relational, Oracle is now considered a multi-model database management system, which means that it can support multiple data models, such as document, graph, relational, and key-value rather than simple supporting relational data like many of the systems we have been talking about up until now. It is also the database of choice for many different packaged software systems and is generally believed to have the largest RDBMS market share (based on revenue) – which means that it would not be surprising to be a .NET developer and yet be working with Oracle. And Amazon RDS makes it easy to do that in the cloud.

Oracle and .NET

Let’s first talk about using Oracle as a .NET developer. Since Oracle is a commercial database system, which is different from the rest of the systems we have talked about in this series, it has a lot of additional tools that are designed to help .NET developers interact with Oracle products. The first of these is the Oracle Developer Tools for Visual Studio.

Oracle Developer Tools for Visual Studio

There are a lot of .NET applications based upon Oracle, which means that it is to Oracle’s advantage to make that interaction as easy as possible. One of the ways that they did this was to create the Oracle Developer Tools for Visual Studio (ODT for VS). This tool runs within Visual Studio 2017 or 2019 (2022 was not supported at the time of this writing) and brings in features designed to provide insight and improve the developer experience. Examples of the features within this tool include:

·         Database browsing – Use Server Explorer to browse your Oracle database schemas and to launch the appropriate designers and wizards to create and alter schema objects.

·         Schema comparison – View differences between two different schemas and generate a script that can modify the target schema to match the source schema. You can do this by connecting to live databases or by using scripts within an Oracle Database project.

·         Entity Framework support – Use Visual Studio’s Entity Designer for Database First and Model First object-relational mapping. (“Code First” is also supported).

·         Automatic code generation– You can use various windows, designers, and wizards to drag and drop and automatically generate .NET code.

·         PL/SQL Editor and debugger– Allows you to take advantage of Visual Studio’s debugging features from within PL/SQL code, including seamlessly stepping from .NET code into your PL/SQL code and back out again.

You need to have a free Oracle account before you can download the tools from https://www.oracle.com/database/technologies/net-downloads.html. Please note that installing these tools will also install functionality to interact with Oracle Cloud, but those details are for a different article! Once the tools are downloaded and installed you will see a new section in your Tools menu as shown in Figure 1.

Figure 1. New features added to Tools menu by ODT for VS

You will also find four new project templates added to the Create a new project wizard:

·         Visual C# Oracle CLR project – creates a C#-based project for creating classes to use in Oracle database

·         Visual Basic Oracle CLR project – creates a Visual Basic project for creating classes to use in Oracle database

·         Oracle Database project – creates a project for maintaining a set of scripts that can be generated using Server Explorer menus. This project type does NOT support schema comparison.

·         Oracle Database project Version 2 – creates a project for maintaining a standardized set of SQL scripts that represent your Oracle database schema. This project type supports schema comparison.

There are additional features to these tools, so suffice to say that Oracle provides various ways to help .NET developers interact with their Oracle databases. Lots of ways. Many more than you will find for any of the other databases we have looked at in this series. And it should not surprise you to find that they also support connecting to Oracle databases from within your .NET application.

Oracle Data Provider for .NET (ODP.NET)

Where the ODT for VS is designed to help improve a developer’s productivity when interacting with Oracle databases, ODP.NET instead manages the interconnectivity between .NET applications and Oracle databases. ODP.NET does that by providing several NuGet packages, Oracle.ManagedDataAccess.Core and Oracle.EntityFrameworkCore, that support .NET 5 and more recent versions and several NuGet packages supporting .NET versions prior to 5.0, Oracle.ManagedDataAcess and Oracle.ManagedDataAccess.EntityFramework. Once you have the packages, the next thing that you need to do is to configure your application to use Oracle. You do this by using the UseOracle method when overriding the OnConfiguring method in the context class as shown below:

protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
    optionsBuilder.UseOracle("connection string here");

A connection string for Oracle has three required fields:

·         User Id – username for use with the connection

·         Password – password

·         Data Source – the Transparent Network Substrate (tns) name is the name of the entry in tnsnames.ora file for the database server. This file can be found in the $ORACLE_HOME/network/admin directory.

This makes it seem like this should be an easy task to manage a connection string. However, of course, there is a caveat – you must be willing to deploy a file that has to be in a very specific place on the server and contain a reference to the server to which you need to connect. If you are okay with that approach then this is a simple connection string – “user id=prodotnetonaws;password=password123;data source=OrcleDB”. However, since a lot of the flexibility inherent in the cloud will go away if you start making this a requirement (you are no longer simply deploying just your application), then you will have to build a much uglier connection string using a Connect Descriptor:

“user id=prodotnetonaws;password=password123;data source=”(DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=servernamehere)(PORT=1521))(CONNECT_DATA=(SID=databasename)))”

This means that we will need to build our connection string with additional values:

  • Host – The address of the server to which the application will connect
  • SID – The database, on the host server, to which the application is connecting

Let’s now setup our Oracle database and see where you get those values from.

Setting up an Oracle Database on Amazon RDS

Now that we know how to setup our .NET application to access an Oracle database, let’s go look at setting up an Oracle instance. First, log into the console, go to RDS, select Create database. On the Create Database screen, select Standard create and then Oracle. This will bring up the remainder of that section as shown in Figure 2

Figure 2. Options after selecting Oracle when creating a new RDS Database

As you can see, your next option is to select the Database management type, for which there are two options, the default Amazon RDS and Amazon RDS Custom. The Amazon RDS Custom management type requires you to upload your own installation files and patches to Amazon S3. Selecting that management type will change the UI as shown in Figure 3.

Figure 3. Selecting Amazon RDS Customs management type

In Amazon RDS Custom, a custom engine version (CEV) is a binary volume snapshot of a database engine and specific AMI. You first upload installation files and patches to Amazon S3 from which you create CEVs. These CEVs are used as the resources for your database. While this gives you much more control over the resources used by your database as well as managing the extra options you may have purchased as add-ons, it is out of scope for this article, so select Amazon RDS instead!

The next configuration option is a checkbox to Use multitenant architecture. This is a very interesting Oracle feature that allows for the concept of a container database (CDB) that contains one or more pluggable databases (PDB). A PDB is a set of schemas, objects, and related structures that appear logically to a client application as a separate, fully functional database. RDS for Oracle currently supports only 1 PDB for each CDB.

The next configuration option is the database Edition, with Oracle Enterprise Edition and Oracle Standard Edition Two as the only available choices currently. When selecting the Enterprise edition, you will see that you must bring your own license, however, selecting the Standard edition will allow you to bring your own license or to choose a license-included version. Standard edition is significantly less expensive, so you should consider that approach unless you need the full enterprise functionality. We chose the standard edition, license-included, most-recent version.

Once you have gone through those, all the remaining sections are ones that you have seen before as they are the same as are available on MySQL, MariaDB, and PostgreSQL (there is no serverless instance approach like was available with Amazon Aurora). However, this will not enable us to be able to automatically connect with our .NET application.

If we look back at our Oracle connection string:

“user id=prodotnetonaws;password=password123;data source=”(DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=servernamehere)(PORT=1521))(CONNECT_DATA=(SID=databasename)))”

There are two values that are needed, the servername and the databasename. We know that once the server has been created that there will be a servername, or host, but there is not yet a database with which to connect. Remember, this work you are doing right now is not to create the Oracle database, it is instead around getting the Oracle server set up and available. You can create an initial database by expanding the Additional Configuration section and filling out the Initial database name field in the Database options section as shown in Figure 4.

Figure 4. Creating an initial database during setup

Add in an initial database name and complete the set-up. Once you click the Create button then the process will start. However, since Oracle is a much more complicated server than any of the others, this initial creation and setup process will be considerably longer than it was with the other databases.

Once your database is available, clicking on the DB identifier will bring up the database details. This is where you will be able to see the endpoint of the server. Using that value plus the database name that you created during the setup process will finish the process for updating your application to use Oracle as its primary database.

Amazon RDS – Aurora for .NET Developers 

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database designed for the cloud. AWS claims that with some workloads Aurora can deliver up to 5x the throughput of MySQL and up to 3x the throughput of PostgreSQL without requiring any application changes. Aurora can do this because its storage subsystem was specifically designed to run on AWS’ fast distributed storage; in other words, Aurora was designed with cloud resources in mind, while those other “non-cloud only” databases are simply running on cloud resources. This design approach allows for automatic storage growth as needed, up to a cluster volume maximum size of 128 tebibytes (TiB) and offers 99.99% availability by replicating six copies of your data across three Availability Zones and backing up your data continuously to Amazon S3. It transparently recovers from physical storage failures; instance failover typically takes less than 30 seconds.

Note: A tebibyte (TiB) is a unit of measure used to describe computing capacity. The prefix tebi comes from the power-of-2 (binary) system for measuring data capacity. That system is based on powers of two. A terabyte (the unit normally seen on disk drives and RAM) is a power-of-10 multiplier, a “simpler” way of looking at the value. Thus, one terabyte = 1012 bytes, or 1,000,000,000,000 bytes as opposed to one tebibyte, which equals 240 bytes, or 1,099,511,627,776 bytes

Also, because of this customized design, Aurora can automate and standardize database replication and clustering. The last uniquely Aurora feature is the ability to use push-button migration tools to convert any already-existing RDS for MySQL and RDS for PostgreSQL applications to use RDS for Aurora instead. The argument for this ease in migration, and for Amazon Aurora in general, is that even though Aurora may be 20% more expensive than MySQL, Amazon claims that Aurora is 5x faster than MySQL, has 3x the throughput of standard PostgreSQL, and is able to scale to much larger datasets.

Creating an Amazon Aurora database in RDS

Let’s next look at creating a new Aurora database. First, log into the console, go to RDS, select Create database. On the Create Database screen, select Standard create and then Aurora.  This should bring up some Aurora-specific sections as shown in Figure 1.

Figure 1. Selecting edition and capacity type when building an Aurora database

The first selection, Edition, asks you to determine whether you wish a MySQL or PostgreSQL compatible edition.

MySQL compatible edition

The default selection when creating an Aurora database is MySQL, as shown above in Figure 1. By making this choice, values will be optimized for MySQL and default filters will be so set for the options within the Available versions dropdown. The next area, Capacity type, provides two choices: Provisioned and Serverless. Selecting a provisioned capacity type will require you to select the number and instance classes that you will need to manage your workload as well as determine your preferred Availability & durability settings as shown in Figure 2.

Figure 2. Settings for creating a provisioned database

Selecting the serverless capacity type, on the other hand, simply requires you to select a minimum and maximum value for capacity units as shown in Figure 3. A capacity unit is comparable to a specific compute and memory configuration. Based on the minimum capacity unit setting, Aurora creates scaling rules for thresholds for CPU utilization, connections, and available memory. Aurora then reduces the resources for the DB cluster when its workload is below these thresholds, all the way down to the minimum capacity unit.

Figure 3. Capacity settings when creating a serverless database

You also have the ability to configure additional aspects around scaling using the Additional scaling configuration options. The first value is Autoscaling timeout and action. Aurora looks for a scaling point before changing capacity during the autoscaling process. A scaling point is a point in time when no transactions or long-running queries are in process. By default, if Aurora can’t find a scaling point within the specified timeout period, it will stop looking and keep the current capacity. You will need to choose the Force the capacity change option to make the change even without a scaling point. Choosing this option can affect any in-process transactions and queries. The last selection is whether you want the database to Scale the capacity to 0 ACUs when cluster is idle. The name of the option pretty much tells the story; when that item is selected then your database will basically shut off when not being used. It will then scale back up as requests are generated. There will be a performance impact on that first call, however, you will also not be charged any processing fees.

The rest of the configuration sections on this page are the same as they have been for the previous RDS database engines that we posted about earlier.

PostgreSQL compatible edition

Selecting to create a PostgreSQL-compatible Aurora database will give you very similar options as you would get when selecting MySQL. You have the option to select either a Provisioned or Serverless capacity type, however, when selecting the serverless capacity type you will see that the default values are higher. While the 1 ACU setting is not available, the ability to scale to 0 capacity units when the cluster is idle is still supported.

There is one additional option that is available when creating a provisioned system, Babelfish settings. Aurora’s approach towards building compatibility with the largest OSS relational database systems has proven to be successful for those using those systems. AWS took the first step into building compatibility with commercial software by releasing Babelfish for Aurora PostgreSQL. As briefly touched on earlier, Babelfish for Aurora PostgreSQL is a new capability that enables Aurora to understand commands from applications written for Microsoft SQL Server as shown in Figure 4. 

Figure 4. Accessing Amazon Aurora through Babelfish

With Babelfish, Aurora PostgreSQL now “understands” T-SQL and supports the SQL Server communications protocol, so your .NET apps that were originally written for SQL Server will work with Aurora – hopefully with minimal code changes. Babelfish is a built-in capability of Amazon Aurora and has no additional cost, although it does require that you be using a version greater than PostgreSQL 13.4, which at the time of this writing was not available on Serverless and is why this option is unable to be selected from that mode.

Amazon Aurora and .NET

As briefly touched on earlier, the primary outcome of your making a choice between PostgreSQL and MySQL is that the choice determines how you will interact with the database. This means that using the MySQL-compatible version of Aurora requires the use of the MySql.EntityFrameworkCore NuGet packages, while connecting to the PostgreSQL-compatible edition requires the Npgsql and Npgsql.EntityFrameworkCore.PostgreSQL packages, just like they were used earlier in those sections of this series. If you are considering using Babelfish with the PostgreSQL-compatible, then you would use the standard SQL Server NuGet packages as we worked with in the last few posts.

This means that moving from MySQL on-premises to MySQL-compatible Aurora Serverless would require no code changes to systems accessing the database; the only change you would have to manage would be the connection string so that you can ensure that you are talking to the database. Same for PostgreSQL and even SQL Server. This approach for compatibility has made it much easier to move from well-known database systems to Amazon’s cloud-native database, Aurora.

Amazon RDS – PostgreSQL for .NET Developers

PostgreSQL is a free, open-source database that emphasizes extensibility and SQL compliance and was first released in 1996. A true competitor to commercial databases such as SQL Server and Oracle, PostgreSQL supports both online transaction processing (OLTP) and online analytical processing (OLAP) and has one of the most advanced performance features available, multi-version concurrency control (MVCC). MVCC supports the simultaneous processing of multiple transactions with almost no deadlock, so transaction-heavy applications and systems will most likely benefit from using PostgreSQL over SQL Server, and there are companies that use PostgreSQL to manage petabytes of data.

Another feature that makes PostgreSQL attractive is that not only does it support your traditional relational database approach, but it also fully supports a JSON/JSONB key/value storage approach that makes it a valid alternative to your more traditional NoSQL databases; so, you can now use a single product to support the two most common data access approaches. Because of its enterprise-level of features and the amount of work it takes to manage and maintain those, even though it is also open source and free software like MySQL and MariaDB, it is slightly more expensive to run PostgreSQL on Amazon RDS than those other open-source products.

PostgreSQL and .NET

As with any database products that you will access from your .NET application, its level of support for .NET is important. Fortunately for us, there is a large community involved in helping ensure that PostgreSQL is relevant to .NET users.

Let’s look at what you need to do to get .NET and PostgreSQL working together. The first thing you need to do is to include the necessary NuGet packages, Npgsql and Npgsql.EntityFrameworkCore.PostgreSQL as shown in Figure 1.

Figure 1. NuGet packages required to connect to PostgreSQL

Once you have the packages, the next thing that you need to do is to configure your application to use PostgreSQL. You do this by using the UseNpgsql method when overriding the OnConfiguring method in the context class as shown below:

protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
    optionsBuilder.UseNpgsql("connection string here");

A connection string for PostgreSQL has six required fields:

  • server – server with which to connect
  • port – port number on which PostgreSQL is listening
  • user id – user name
  • password – password
  • database – database with which to connect
  • pooling – whether to use connection pooling (true or false)

When working in an ASP.NET Core application the connection string is added to the appsettings.json file as shown in Figure 2.

Figure 2. Adding a connection string to an ASP.NET Core application

Let’s now go create a PostgreSQL database.

Setting up a PostgreSQL Database on Amazon RDS

Now that we know how to set up our .NET application to access PostgreSQL, let’s go look at setting up a PostgreSQL instance. First, log into the console, go to RDS, select Create database. On the Create Database screen, select Standard create and then PostgreSQL. You then have a lot of different versions that you can select from, however, the NuGet packages that we used in our earlier example require a reasonably modern version of PostgreSQL, so unless you have any specific reason to use an older version you should always use the default, most updated version.

Once you have defined the version of PostgreSQL that you will use, your next option is to select the Template that you would like to use. Note that you only have two different templates to choose from:

·         Production – defaults are set to support high availability and fast, consistent performance.

·         Dev/Test – defaults are set in the middle of the range.

Note: Both MySQL and MariaDB had a third template, Free tier, that is not available when creating a PostgreSQL database. That does not mean that you must automatically pay, however, as the AWS Free Tier for Amazon RDS offer provides free use of Single-AZ Micro DB instances running PostgreSQL. It is important to consider that the free usage tier is capped at 750 instance hours per month across all your RDS databases.

Selecting the template sets defaults across the rest of the setup screen and we will call those values out as we go through those items.

Once you select a template, your next setup area is Availability and durability. There are three options to choose from:

·         Multi-AZ DB cluster – As of the time of writing, this option is in preview. Selecting this option creates a DB cluster with a primary DB instance and two readable standby instances, with each instance in a different Availability Zone (AZ). Provides high availability, data redundancy and increases capacity to serve read workloads.

·         Multi-AZ DB instance – This option creates a primary DB instance and a standby DB instance in a different AZ. Provides high availability and data redundancy, but the standby instance doesn’t support connections for read workloads. This is the default value if you chose the Production template.

·         Single DB instance– This option creates a single DB instance with no standby instances. This is the default value if you chose the Dev/Test template.

The next section, Settings, is where you provide the DB instance identifier, or database name, and your Master username and Master password. Your database identifier value must be unique across all the database instances you have in the current region, regardless of engine option. You also have the option of having AWS auto-generate a password for you.

The next section allows you to select the DB instance class. You have the same three filters that you had before of Standard classes, Memory optimized classes, and Burstable classes. Selecting one of the filters changes the values in the instance drop-down box, You need to select Burstable classes and then one of the instances with micro in the title, such as a db.t3.micro as shown in Figure 3.

Figure 3. Selecting a free-tier compatible DB instance

The next section in the setup is the Storage section, with the same options that you had available when going through the MySQL and MariaDB setups, though the default values may be different based upon the instance class that you selected. After the storage section are the Connectivity and Database authentication sections that we walked through earlier, so we will not go through them again now – they are standard across all RDS engine options. Selecting the Create database button will take you back to the RDS Databases screen where you will get a notification that the database is being created as well as a button that you can click to access the connection details. Make sure you get the password if you selected for AWS to create your administrative password. You will only be able to access the password this one time.

The pricing for PostgreSQL is slightly higher than MariaDB or MySQL when looking at compatible configurations, about 6% higher.

Selecting between PostgreSQL and MySQL/MariaDB

There are some significant differences between PostgreSQL and MySQL\MariaDB that can become meaningful when building your .NET application. Some of the more important differences are listed below. There are quite a few management and configuration differences, but those are not mentioned since RDS manages all of those for you!

·         Multi-Version Concurrency Control – PostgreSQL was the first DBMS to rollout multi-version concurrency control (MVCC), which means reading data never blocks writing data, and vice versa. If your database is heavily used for both reading and writing than this may be a significant influencer.

·         More types supported – PostgreSQL natively supports NoSQL as well as a rich set of data types including Numeric Types, Boolean, Network Address, Bit String Types, and Arrays. It also supports JSON, hstore (a list of comma-separated key/value pairs), and XML, and users can even add new types.

·         Sequence support – PostgreSQL supports multiple tables taking their ids from the same sequence while MySQL/MariaDB do not.

·         Index flexibility – PostgreSQL can use functions and conditional indexes, which makes PostgreSQL database tuning very flexible, such as not having a problem if primary key values aren’t inserted sequentially.

·         Spatial capability – PostgreSQL has much richer support for spatial data management, quantity measurement, and geometric topology analysis.

While PostgreSQL is considered one of the most advanced databases around, that doesn’t mean that it should automatically be your choice. Many of the advantages listed above can be considered advanced functionality that you may not need. If you simply need a place to store rarely changing data, then MySQL\MariaDB may still be a better choice. Why? Because it is less expensive and performs better than PostgreSQL when performing simple reads with simple join. As always, keep your use cases in mind when selecting your database.

Note: AWS contributes to an open-source project called Babelfish for PostgreSQL, which is designed to provide the capability for PostgreSQL to understand queries from applications written for Microsoft SQL Server. Babelfish understands the SQL Server wire-protocol and T-SQL. This understanding means that you can use SQL Server drivers for .NET to talk to PostgreSQL databases. As of this writing, this functionality is not yet available in the PostgreSQL version of RDS. It is, however, available for Aurora PostgreSQL. We will go over this in more detail later in the chapter. The project can be seen at https://www.babelfishpg.org.

MariaDB, MySQL, and PostgreSQL are all open-source databases that have existed for years and that you can use anywhere, including that old server under your desk. The next database we will talk about is only available in the cloud and within RDS, Amazon Aurora.