Category Archives: Software development

Retrieving Stock Prices using AWS Lambda

AWS Lambda functions are great for simple logic running periodically (among other things). In this article, we’ll create a simple AWS Lambda function in Python that retrieves stock prices from a REST API every minute. Let’s get straight to it!

Create a Lambda Function

First, we need to create a function. Follow the instructions illustrated below to do this.

From the AWS Console dashboard, locate the Lambda service. You can also do this via the Services drop-down panel at the top, or from your recently visited services (if you’ve already been using Lambda).
Once you are in the Lambda service, create a new function by clicking on the “Create a function” button as shown above.
Choose a name for the Lambda function, and also the runtime. We’re using Python 3.7 (which is the latest supported Python version in AWS Lambda at the time of writing this) for this example, but other options are available (e.g. Node.js, NET Core, Go, etc). Leave everything else as is and hit the Create function button.
Once the Lambda is created, you are taken to the new function itself. A green status message at the top indicates that it has been created successfully.

Editing the Lambda’s Code

The function’s configuration screen can seem quite confusing at first, but all you need to do is scroll down to get to the code editor. While there are a few different ways to add code to your Lambda, using the provided editor (which is the default option for Python) is the easiest.

Replace the default code in the editor with the following, and hit the Save button at the top-right.

from urllib.request import urlopen
from contextlib import closing
import json

def lambda_handler(event, context):
    with closing(urlopen("")) as responseData:
        jsonData =
        deserialisedData = json.loads(jsonData)
        price = deserialisedData['price']
    return price

Here we are simply retrieving Google’s stock price using the Financial Modeling Prep Stock Realtime Price API, which is open and doesn’t require any authentication.

Next to the Save button at the top-right, there’s a Test button. Click it, and the following screen comes up.

The Configure test event screen. Just enter a name and hit Create.

Just enter a name (e.g. “Test”) and hit the Create button further below. We’re not using the input JSON data, so you can just ignore it.

Next, click the Test button at the top-right again, and your Lambda function will be executed:

After clicking Test again, the lambda is executed and the results are shown below the code.

The results are shown below the code, and these include various metadata (such as a Request ID and execution time) as well as Google’s stock price of 1082.38, which we retrieved from the REST API and logged using the print statement in the code.

Running Periodically

We now have a working Lambda function, but so far we have to invoke it manually every time. Let’s set it up so that it runs every minute.

At the top of the screen, click CloudWatch Events on the left to add a CloudWatch trigger.

Scroll back to the top, and you’ll see a placeholder telling you to “Add triggers from the list on the left“. Following that advice, click on “CloudWatch Events” to the left.

A CloudWatch Event trigger is added to the function.

This has the effect of adding “CloudWatch Events” as a trigger in the slot where the placeholder text was, but what you might not notice at first is that the lower part of the page changes from the code editor to a “Configure triggers panel“. This can be quite confusing for those new to AWS Lambda who might not intuit right away that clicking on the boxes will affect the content in some other part of the page.

By scrolling down, we can configure the new trigger.

Here we use a Schedule expression of rate(1 minute) to make the function run every minute.

Filling in most of the settings (e.g. choosing a name) is easy, bearing in mind that there are some restrictions (e.g. some characters, such as spaces, are restricted in the name).

The only tricky part is where we specify how frequently we want the function to be executed. For this, we can use cron or rate expressions (refer to AWS documentation: Schedule Expressions Using Rate or Cron). By using an expression of rate(1 minute), we configure the function to run every minute, which is the smallest supported interval.

Once this is all set up, click the Add button to set up the trigger. Then, don’t forget to click the Save button at the top-right of the page to apply the changes to the Lambda function.

Checking Output in CloudWatch

After waiting a few minutes for the function to run a few times, we can go into CloudWatch and check the output of each execution.

CloudWatch logs.

From the AWS Services, locate CloudWatch. Go into Logs from the left menu, and locate the log group for our Lambda function (in this case it’s /aws/lambda/StockChecker).

Select the most recent log stream (the one at the top), and if you scroll to the end, you should see logs showing the function’s execution every minute, as well as whatever we’re writing to standard output (in this case, Google’s stock price).

CloudWatch logs show that the Lambda function is executing every minute.

We can see that the function is executing every minute, and we’re logging a stock price each time. The US stock market is closed right now, and that’s why the stock price is always the same (you’d expect it to change frequently when the market is active).


At this point, we have a simple, working AWS Lambda function (written in Python) that runs every minute and retrieves Google’s stock price. To keep things simple, we’re just writing it to standard output, which means we can see the value in CloudWatch – but we could also expand the code to build something useful from this.

Getting Started with Angular 8

Angular is an open-source framework built and maintained by Google, which is mainly used to develop Single-Page Applications (SPAs). It provides a structured approach towards creating front-end web applications.

Originally known as AngularJS, the framework underwent a complete rewrite that resulted in Angular 2.0 (dropping the -JS suffix from the name). The versions that came after 2.0 (with Angular 8 being the latest, released just over two weeks ago) are incremental upgrades, thus it is possible to upgrade between them. However, AngularJS is a different beast and there is no easy way to upgrade from AngularJS from Angular 2.0+.

In this article, we’re going to go through the steps necessary to start working with Angular. In order to keep this concise, there won’t be a lot of background.


On Windows, download the Node.js installer from their website. Either version should be fine to get started.

The first thing we need to do is get npm, a package manager for JavaScript libraries. On Windows, download and install Node.js. On Linux or Mac, use the relevant package manager for your system (e.g. apt-get on Linux Ubuntu), possibly along with the sudo command for elevated privileges, to install npm.

Angular CLI

The Angular CLI homepage shows the commands you need to set up and use the Angular CLI tools.

Next, we need the Angular CLI to help us with our development workflow. Use npm to install it as a global tool, as follows (prefix this with sudo if using Linux or Mac):

npm install -g @angular/cli
Using npm to install the Angular CLI.

ng is the command-line tool we just installed. Use ng --version to make sure it’s in working order:

After executing ng --version, we can see some “Angular CLI” ASCII art and other information. This means that it’s working fine.

Creating a Project

Use ng new to create an Angular app from a template. You’ll be asked some questions to determine what features you need, but for now just press ENTER at each question to use the defaults.

ng new myproject
ng new myproject creates a folder called myproject with the Angular files in it. Press ENTER when asked questions to use defaults for now.

Note: when I first ran this, I got an error along the lines of “EPERM: operation not permitted, unlink“, even when using an elevated command prompt. The problem was likely caused by an old version of npm I had on my machine before, and I fixed it by running npm cache clean --force.

Running the application

Go into the project directory you’ve just created (e.g. myproject), and use ng serve to run the web application you just generated:

cd myproject
ng serve
ng serve runs a web server that you can use to access the running web application. Look in the output for the endpoint to use in your browser.

When ng serve is done building the project, it runs a web server hosting the web application. The output tells you where to access it, in this case http://localhost:4200/. Put that in your browser’s address bar, and you should see the homepage from the project template that we set up earlier:

A simple page shows us that Angular is in fact working.

Data Binding Illustration

We’ve created and run a web application using Angular, so we’re done in terms of getting started. However, let’s make a small change to the web application to get a little more comfortable with it and see something working.

Visual Studio Code is a popular choice for frontend development, despite having been made by Microsoft.

With ng serve still running, locate the src/app directory under your project’s root directory. Using a text editor or IDE of your choice, add the lines highlighted below to app.component.html:

<!--The content below is only a placeholder and can be replaced.-->
<div style="text-align:center">
    Welcome to {{ title }}!
  <img width="300" alt="Angular Logo" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNTAgMjUwIj4KICAgIDxwYXRoIGZpbGw9IiNERDAwMzEiIGQ9Ik0xMjUgMzBMMzEuOSA2My4ybDE0LjIgMTIzLjFMMTI1IDIzMGw3OC45LTQzLjcgMTQuMi0xMjMuMXoiIC8+CiAgICA8cGF0aCBmaWxsPSIjQzMwMDJGIiBkPSJNMTI1IDMwdjIyLjItLjFWMjMwbDc4LjktNDMuNyAxNC4yLTEyMy4xTDEyNSAzMHoiIC8+CiAgICA8cGF0aCAgZmlsbD0iI0ZGRkZGRiIgZD0iTTEyNSA1Mi4xTDY2LjggMTgyLjZoMjEuN2wxMS43LTI5LjJoNDkuNGwxMS43IDI5LjJIMTgzTDEyNSA1Mi4xem0xNyA4My4zaC0zNGwxNy00MC45IDE3IDQwLjl6IiAvPgogIDwvc3ZnPg==">
  <input type="text" [(ngModel)]="name" />
  <br />{{ name }}
<h2>Here are some links to help you start: </h2>
    <h2><a target="_blank" rel="noopener" href="">Tour of Heroes</a></h2>
    <h2><a target="_blank" rel="noopener" href="">CLI Documentation</a></h2>
    <h2><a target="_blank" rel="noopener" href="">Angular blog</a></h2>

Then, add the lines highlighted below to app.module.ts:

import { BrowserModule } from '@angular/platform-browser';
import { NgModule } from '@angular/core';
import { FormsModule } from '@angular/forms';

import { AppComponent } from './app.component';

  declarations: [
  imports: [
  providers: [],
  bootstrap: [AppComponent]
export class AppModule { }

When ng serve detects these changes, it should reload the web application (in your browser) automatically, so you don’t need to stop and start it again whenever you change something.

We’ve added the text input box under the image. When you type in it, the text below it is automatically updated accordingly.

Thanks to the changes we made, we now have a text input box under the Angular logo. When you type in it, the text below it is synchronised with it.

The changes we made might seem alien at first, but we’ve actually used two important features of Angular: data binding and string interpolation. While explaining these is beyond the scope of this introductory article, I hope that seeing this power at work — with such a small change — has given a taste of why Angular is so useful.

Woodchuck Translation with Amazon Translate

This article is an attempt to have fun with Amazon Translate, and is not intended to be taken as any sort of serious review.

Amazon Web Services (AWS) includes a machine translation service called Amazon Translate:

“Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms. Amazon Translate allows you to localize content – such as websites and applications – for international users, and to easily translate large volumes of text efficiently.” — Amazon Translate homepage

Sounds pretty cool. If you log into the AWS Console and select the Amazon Translate service, it gives you an interface where you can easily play with it:

If we hit the “Swap languages” button, this translates the result back to the original language. In this really simple case, it matches perfectly:

However, this is not always the case. Translating back and forth will often result in things being “lost in translation”, for various reasons ranging from context to linguistic differences.

Since we all know machine translation isn’t perfect, I could not resist being a complete bastard and testing AWS Translate against none other than… How much wood would a woodchuck chuck:

Translating back and forth for each of the supported languages yields some interesting and often hilarious results before the translation converges (translating back and forth starts giving you the same thing every time). Let’s take a look at the results of this little experiment.

English <-> German

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Wie viel Holz würde ein Holzfutter, wenn ein Holzfutter Holz abwerfen könnte?
  3. How much wood would a wood fodder if a wood fodder could drop wood?
  4. Wie viel Holz würde ein Holzfutter, wenn ein Holzfutter Holz fallen könnte?
  5. How much wood would a wood lining if a wood lining could fall wood?
  6. Wie viel Holz würde eine Holzverkleidung, wenn eine Holzverkleidung Holz fallen könnte?
  7. How much wood would a wood panelling if a wood paneling could fall wood?

It’s easy to see how, as we translate back and forth, we not only lose the sense of the original question (if it ever had any), but the result also makes no grammatical sense. However, what really catches my eye is the two different spellings of “panelling”/”paneling” in the same phrase.

English <-> French

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Combien de bois un mandrin de bois serait-il un mandrin de bois si un mandrin de bois pouvait mander du bois ?
  3. How much wood would a wood mandrel be a wood mandrel if a wood mandrel could mandrel wood?
  4. Combien de bois un mandrin de bois serait-il un mandrin de bois si un mandrin de bois pouvait mandrin de bois ?
  5. How much wood would a wood chuck be a wood chuck if a wood chuck could wood chuck?

What’s really interesting about this one is that after going on a nice big detour (becoming a “wood mandrel”), the English phrase eventually returns to something relatively close to the original (even if still wrong).

English <-> Portuguese

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Quanta madeira uma marmota manteria se uma marmota pudesse atirar madeira?
  3. How much wood would a groundhog keep if a groundhog could throw wood?

This one’s pretty good – it got the animal right. The only mistake is on the first translation of “chuck” becoming “keep” – you’ll notice that all other translations also interpret the different “chuck”s differently.

English <-> Spanish

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. ¿Cuánta madera haría un Woodchuck si un Woodchuck pudiera tirar madera?
  3. How much wood would a Woodchuck make if a Woodchuck could throw wood?
  4. ¿Cuánta madera ganaría un Woodchuck si un Woodchuck pudiera tirar madera?
  5. How much wood would a Woodchuck gain if a Woodchuck could throw wood?

The Spanish Mr. Woodchuck is a businessman, apparently.

English <-> Arabic

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. كم من الخشب يمكن أن تشوك الخشب إذا كان الخشب يمكن أن يرمي الخشب؟
  3. How much wood can a wood fork if the wood can throw wood?
  4. كم الخشب يمكن شوكة الخشب إذا الخشب يمكن رمي الخشب؟
  5. How much wood can a wood fork if wood can throw wood?

Okay, this is starting to get pretty weird.

English <-> Chinese

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. 如果木头可以木头卡住木头,木头卡住多少木头?
  3. If wood can be stuck in wood, how many wood is stuck?
  4. 如果木头可以被卡在木头上,有多少木头被卡住?
  5. If wood can be stuck on wood, how many wood is stuck?
  6. 如果木头可以卡在木头上,有多少木头被卡住?
  7. If the wood can be stuck on the wood, how many wood is stuck?


English <-> Japanese

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. ウッドチャックが木材をチャックできるなら、どのくらいの木材をチャックしますか?
  3. If a Woodchuck can chuck wood, how much wood would you chuck?
  4. もしウッドチャックが木材をチャックできるなら、どのくらいの木材をチャックしますか?
  5. If the wood chuck can chuck wood, how much wood would you chuck?
  6. 木材チャックが木材をチャックできるなら、どれくらいの木材をチャックしますか?
  7. If a wood chuck can chuck wood, how much wood do you chuck?
  8. 木材チャックが木材をチャックできる場合、どれくらいの木材をチャックしますか?
  9. If a wood chuck can chuck wood, how much wood do you chuck?

I don’t know… how much wood would you chuck?

English <-> Russian

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Сколько дров будет завалить, если дрочак сможет заправлять древесину?
  3. How much wood will it fill if the wok can fill the wood?
  4. Сколько древесины она заполнит, если вок сможет заполнить дерево?
  5. How much wood will it fill if the wok can fill the tree?
  6. Сколько дерева она заполнит, если вок сможет заполнить дерево?
  7. How much tree would she fill if the wok could fill the tree?

Image credit: source

Sounds like one of Google’s interview questions.

English <-> Traditional Chinese

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. 如果一個木頭能扔木頭的話一個木頭會扔多少木頭
  3. How much wood would a wood throw if one could throw wood?
  4. 如果能扔木頭的話木頭會扔多少木頭
  5. How much wood would it throw out if we could throw it?
  6. 如果我們能扔掉的話會丟多少木頭
  7. How much wood would we lose if we could throw it away?
  8. 如果我們能把它扔掉我們會損失多少木頭
  9. How much wood would we lose if we could get rid of it?
  10. 如果能除掉它我們會損失多少木頭
  11. How much wood would we lose if we could get rid of it?

I think we’ve found the Spanish woodchuck businessman’s rival.

English <-> Italian

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Quanta legna manderebbe una marmotta se una marmotta potesse buttare legna?
  3. How much wood would a groundhog send if a groundhog could throw out wood?
  4. Quanto legna manderebbe una marmotta se una marmotta potesse buttare la legna?
  5. How much wood would a groundhog send if a groundhog could throw out the wood?
  6. Quanto legna manderebbe una marmotta se una marmotta potesse buttare via la legna?
  7. How much wood would a groundhog send if a groundhog could throw away the wood?

This one is interesting as there are a lot of very subtle changes before convergence.

English <-> Turkish

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Eğer bir dağ sıçanı odunları çöpe atabilseydi, bir dağ sıçanı ne kadar ağaç gönderirdi?
  3. If a groundhog could throw wood away, how many trees would a groundhog send?
  4. Eğer bir dağ sıçanı tahta atabilseydi, bir dağ sıçanı kaç ağaç gönderirdi?
  5. If a groundhog could throw a throne, how many trees would a groundhog send?
  6. Eğer bir dağ sıçanı tahtı atabilseydi, bir dağ sıçanı kaç ağaç gönderirdi?
  7. If a groundhog could throw the throne, how many trees would a groundhog send?

It is really bizarre to see how “wood” transitions into “throne” and “trees” in two different parts of the same question.

English <-> Czech

  1. How much wood would a woodchuck chuck if a woodchuck could chuck wood?
  2. Kolik dřeva by dřevorubec sklízl, kdyby dřevorubec mohl sklíčit dřevo?
  3. How much wood would a lumberjack harvest if a lumberjack could deceive the wood?
  4. Kolik dřeva by dřevorubec sklízel, kdyby dřevorubec mohl klamat dřevo?
  5. How much wood would a lumberjack harvest if a lumberjack could deceive wood?

Image credit: source


I had fun playing around with Amazon Translate and seeing how the woodchuck tongue-twister degenerates when translated across different languages. I hope it was just as much fun for you to read this.

Please do not make any judgements about the accuracy of Amazon Translate based on this, for the following reasons:

  1. This is a very specific case and certainly doesn’t speak for the accuracy across entire languages.
  2. Translation isn’t easy. We’ve all heard of situations where things got “lost in translation”. Translation depends very much on context and linguistic differences. Hopefully the varying performance across languages is an illustration of this.
  3. Machine translation isn’t easy either. There’s a reason why it’s considered a field of artificial intelligence.

Microsoft Orleans 2.0.4 Released

Those using (or learning about) Microsoft Orleans, especially the newer 2.0.x releases that target .NET Standard and are cross-platform, might be interested to know that version 2.0.4 has just been released.

This release includes a couple of important bugfixes:

  • A number of Orleans users observed grain calls getting really slow after the silo has been running for around 12 hours. The long issue discussion reveals a lot of collective findings and ultimately provides the means to reproduce the problem. The root cause was traced to a bug in BlockingCollection<T> in .NET Core, which can lead to memory leaks and even lost items (Orleans messages in this case). A workaround has been implemented to sort this out.
  • Another issue prevented Orleans build-time code generation from being built when targeting .NET Core 2.1. This has also been fixed.

If you’re using Orleans 2.0.x, it’s therefore a good idea to upgrade to 2.0.4, especially if you are running Orleans in production.

Microsoft Orleans Use Case: Reservations System

Microsoft Orleans is an implementation of the actor model, and many people have leveraged it to build highly scalable distributed systems while completely avoiding the pain of multithreaded programming.

The actor model is still not a very mainstream thing, and people who come across it are often confused about what it is and why it is useful.

In order to address this, .NET contractor Jakub Konecki (Twitter | GitHub) has kindly agreed to share with us how he has been using Microsoft Orleans in his own particular use case. You can also learn more about his project from the Orleans Virtual Meetups in which he presented (Meetup #1: Event Sourced Grains, and  Meetup #12: Deploying Orleans).

DD: What is the problem you’re addressing with Microsoft Orleans?

JK: Currently I’m working for a company in the hospitality domain that manages bookings for a number of luxury resorts in the Caribbean.

I’m responsible for designing and delivering a greenfield system for a multi-tenant system for managing reservations.

The main features of the system are:

  • ability to register resort accommodation,
  • ability to manage pricing: rate plans, special offers,
  • integration with third-party marketplace used by tour operators
  • integration with third-party systems for flight searching and ticket purchasing
  • integration with property management systems used by resorts
  • a bespoke website that resort customers can use to search for and make their reservations.

The non-functional requirements include elastic scaling to allow for easy onboarding of new tenants and allow flexibility for existing tenants – for example the traffic may change drastically when special offers are introduced.

DD: How did Microsoft Orleans help you develop a solution?

Image taken from Orleans Virtual Meetup #12 presentation and used with permission.

The system is designed using DDD principles and benefits from event sourcing and event-driven architecture.

An actor framework is a good fit for this kind of system – mapping between actors and aggregate roots is natural, and implementation of event sourcing is quite straightforward and encapsulated by actors.

The Microsoft Orleans framework was selected as it was the most advanced actor framework implementation at the time that used technologies familier to the team (C#, Azure), was battle-tested, and was implicitly backed by Microsoft. Open-sourcing Orleans (and an active community that emerged shortly after) was another argument for using it.

DD: What benefits did Microsoft Orleans provide, and what challenges did you face?

JK: The most important benefits for using Orleans are scalability and programming model. Scaling an Orleans solution is as easy as moving a slider in the Azure portal. The ability to specify auto-scaling triggers in Azure means that changes in load can be handled with ease. We haven’t run into any problems related to scaling – there is no difference between running a cluster in Azure and a single node on local machine during development.

This brings me nicely to Orleans’ programming model, which makes development of distributed systems straightforward. Orleans handles a lot of complexity allowing developers to concentrate on business logic within essentially single-threaded grains. On the other hand Orleans doesn’t go to the other extreme and pretend the issues inherently related to distributed systems do not exist. That balance allows for rapid development – we’ve seen senior developers being able to pick up Orleans fundamentals and be productive within a day or two.

AWS Lambda .NET Core 2.1 Support Released

Amazon Web Services (AWS) has just announced that its serverless function offering, AWS Lambda, now supports the .NET Core 2.1 runtime, which was released towards the end of May 2018.

Quoting the official announcement:

“Today we released support for the new .NET Core 2.1.0 runtime in AWS Lambda. You can now take advantage of this version’s more performant HTTP client. This is particularly important when integrating with other AWS services from your AWS Lambda function. You can also start using highly anticipated new language features such as Span<T> and Memory<T>.

“We encourage you to update your .NET Core 2.0 AWS Lambda functions to use .NET Core 2.1 as soon as possible. Microsoft is expected to provide long-term support (LTS) for .NET Core 2.1 starting later this summer, and will continue that support for three years. Microsoft will end its support for .NET Core 2.0 at the beginning of October, 2018[2]. At that time, .NET Core 2.0 AWS Lambda functions will be subject to deprecation per the AWS Lambda Runtime Support Policy. After three months, you will no longer be able to create AWS Lambda functions using .NET Core 2.0, although you will be able to update existing functions. After six months, update functionality will also be disabled.

“[1] See Microsoft Support for .NET Core for the latest details on Microsoft’s .NET Core support.
“[2] See this blog post from Microsoft about .NET Core 2.0’s end of life.”

The choice here seems obvious: upgrade and get faster HttpClient, new language features, and long-term support; or lose support for your functions targeting .NET Core 2.0 (whatever that actually means).

In order to migrate to .NET Core 2.1, you’ll need the latest tooling – either version of the AWS Toolkit for Visual Studio, or version 2.2.0 of the Amazon.Lambda.Tools NuGet package.

Check out the official announcement at the AWS blog for more information, including additional tips on upgrading.

Orleans 2.0 Stateless Worker Grains

In this article, we’ll see how to create grains that automatically scale up and down depending on load, in Microsoft Orleans 2.0.

The source code for this article is very similar to that in “Getting Started with Microsoft Orleans 2.0 in .NET Core“, with a few key differences:

  • It has been modified to gracefully stop the silo and gracefully close the client.
  • It uses the latest packages at the time of writing this article – Orleans 2.0.3 and OrleansDashboard 2.0.7.
  • It uses a slightly different example, and the load generation has been adapted accordingly.

Since there’s nothing really new in the client and silo setup, we’ll be focusing mainly on the grain and load generation parts. However, you may find the full source code for this article in the Orleans2StatelessWorkers folder in the Gigi Labs BitBucket repository.

Example Grain

For the sake of example, we’ll imagine that the job of our Orleans cluster is to provide hashing as a service. A client provides an input string, and we’ll have a grain that computes a hash of the string (it doesn’t really matter what hash function it is – we’ll use MD5 in the example) and returns it.

Based on this requirement, we can easily write a grain and its corresponding interface to perform the hash calculation:

    public interface IHashGeneratorGrain : IGrainWithIntegerKey
        Task<string> GenerateHashAsync(string input);

    public class HashGeneratorGrain : Grain, IHashGeneratorGrain
        private HashAlgorithm hashAlgorithm;

        public HashGeneratorGrain()
            this.hashAlgorithm = MD5.Create();

        public Task<string> GenerateHashAsync(string input)
            var inputBytes = Encoding.UTF8.GetBytes(input);
            var hashBytes = hashAlgorithm.ComputeHash(inputBytes);
            var hashBase64Str = Convert.ToBase64String(hashBytes);

            return Task.FromResult(hashBase64Str);

Load Generation

Typically, when we talk about actor models, the whole point is to have an instance of an actor (grain in Orleans) per entity ID. For instance, you’d have a grain instance for each Device, Vehicle, BlogPost, Game, User, or whatever other domain object you’re dealing with. In this case, however, our grain is completely stateless, and there is no difference in behaviour between one activation and another. In fact, since the grain ID doesn’t matter, we can just pass in 0 as a sort of convention when requesting a grain of this kind:

var hashGenerator = client.GetGrain<IHashGeneratorGrain>(0);

Once we have an instance of the grain, we can generate some load by creating random strings and invoking the relevant method on the grain repeatedly:

            while (true)
                var randomString = GenerateRandomString();
                var hash = await hashGenerator.GenerateHashAsync(randomString);

You can monitor the grain’s activity from the Orleans Dashboard (localhost:8080 by default), and as you’d expect, there is only one activation of the grain:

Stateless Worker Grains

This situation is a very good fit for Stateless Worker Grains.

Normally, when you request a grain with a particular ID, you get a single activation – and it is a singleton throughout the cluster, so you would never (bar edge cases involving failover scenarios) get more than one instance of that grain in the cluster. However, if you just add a [StatelessWorker] attribute on the grain…

    public class HashGeneratorGrain : Grain, IHashGeneratorGrain

…you’ll see very different behaviour:

Notice how there are now two activations of the HashGeneratorGrain, even though we’re still requesting an instance with ID 0.

When Orleans sees the [StatelessWorker] attribute, it will create a pool of grains behind the ID you specify. This is similar to a load balancer. Those grains are hidden behind that same ID, so you can’t access individual grains in the pool directly (it wouldn’t make any sense to do that). The number of grains will grow up to as many CPU cores are available on the machine, unless you pass an argument to the attribute specifying otherwise.

Aside from autoscaling, another important benefit of stateless worker grains is that they are always local. Orleans will always execute a request to a stateless worker on the same silo where the request was generated, spawning a new activation if necessary. This saves the overhead of potentially passing the request to an instance in a different silo (i.e. remote call), which makes a lot of sense for stateless workers that are pure logic and there’s no difference between activations running in different places.

Although stateless worker grains are best used for stateless logic (as one would expect), there is nothing preventing their use with state. However, coordination of state between multiple grain activations with the same ID can be complicated. The Stateless Worker Grains documentation describes some patterns where stateless worker grains with state make sense (although calling them that way is bizarre).


  • Use the [StatelessWorker] attribute to treat a grain as a stateless worker grain.
  • This creates a load-balanced autoscaling pool of grains with the same ID.
  • Requests to stateless worker grains are always local and never incur a remote call.
  • Stateless worker grains may have state, although this is unusual.

Accessing an ASP .NET Core Web Application Remotely

After setting up an empty ASP .NET Core Web Application, it’s easy to quickly run it and see something working, in the form of the usual “Hello World”:

When trying to deploy this somewhere though, you might be disappointed to notice that you can’t access the web application from another machine:

In fact, you’ll notice that you can’t even access it from the same machine if you use the actual hostname rather than localhost.

This is because by default, Kestrel will listen only on localhost. In order for another machine to access the web application using the server’s hostname, the web application must specify the endpoints on which Kestrel will listen to, using code or command-line arguments.

Note: you may also need to open a port in your firewall.

In code, this can be done by invoking UseUrls() in the webhost builder as follows:

        public static IWebHost BuildWebHost(string[] args) =>

Replace “myhostname” with the hostname of the server, and note that the localhost endpoint will still work even though it’s not specified explicitly here.

If you want to pass the the endpoint(s) via command line parameters instead, you can do so via the --urls argument. First, you need to change the BuildWebHost() method generated by the project template as per this GitHub comment, to allow command line parameters to be passed to the WebHostBuilder via configuration:

public static IWebHost BuildWebHost(string[] args)
    var configuration = new ConfigurationBuilder().AddCommandLine(args).Build();

    return WebHost.CreateDefaultBuilder(args)

Then, use the --urls argument when invoking dotnet run:

dotnet run --urls http://banshee:54691/

Either of these methods is fine to allow remote machines to access your ASP .NET Core web application.

.NET Core 3 to Support Desktop Applications… Kind of

A few days ago, Microsoft published a blog post titled “.NET Core 3 and Support for Windows Desktop Applications“. Just by reading the title, I’m pretty sure many of us jumped in their seats as thoughts like “WPF on Linux” became a source of excitement.

Image source: .NET Core 3 and Support for Windows Desktop Applications

Unfortunately however, the excitement turns into a disappointed “Oh. [Awkward silence] OK.” when reading that although .NET Core 3 is planned to support desktop applications built on technologies like Windows Forms and WPF, this support is for Windows only:

“Support for Windows desktop will be added as a set of “Windows Desktop Packs”, which will only work on Windows. .NET Core isn’t changing architecturally with this new version. We’ll continue to offer a great cross-platform product, focused on the cloud. We have lots of improvements planned for those scenarios that we’ll share later.”

The article does mention that this will bring several benefits ranging from performance improvements to deployment options, but this pales in comparison to the prospect of going cross-platform.

But given that they “are planning on releasing a first preview of .NET Core 3 later this year and the final version in 2019”, I can only wonder why they would spend a year doing a huge amount of work that most people won’t even care about, and pass it as a major release of .NET Core.

Time will tell, but one can get an idea of how people feel about this from the comments in the blog post.

My guess is that this could be part of a long-term strategy to retire the full .NET Framework, rather than bringing any real value to .NET Core or desktop applications.

Orleans 2.0 Dependency Injection

Dependency Injection (DI) has become a cornerstone of any well-designed and testable application nowadays, and Microsoft Orleans applications are no exception. In the 2.0 release, Microsoft Orleans has replaced some of its old internal frameworks (such as logging and dependency injection) with the corresponding Microsoft packages; thus these will be familiar for those who already worked with ASP .NET Core.

In this article we’ll focus on setting up dependency injection in the silo so that we can pass dependencies into our grains. However, if you read the dependency injection documentation page for Orleans 2.0, you’ll see that you can also have DI on the client side.

The source code for this article is the Orleans2DependencyInjection folder at the Gigi Labs BitBucket repository. Be careful not to confuse it with the OrleansDependencyInjection folder which targets Orleans 1.4.x.

TL;DR if you just want to quickly see how to do DI without going through the whole example, jump to the Registering Dependencies section.

Update 30th June 2018: The source code for this article needs a little adjusting, in order to gracefully stop the silo and gracefully close the client. Counterintuitively, directly disposing a silo or client is non-graceful and is generally discouraged.


We’ll start off with a project structure based on the Getting Organised article. Once that is in place, we can build an example representing a blog’s comment system. In the Grains project, we’ll add a grain representing a blog post, and that will be responsible for saving and retrieving all comments for that blog post.

    public class BlogPostGrain : Grain, IBlogPostGrain
        private ICommentRepository repo;
        private ITimeService time;

        public BlogPostGrain(ICommentRepository repo, ITimeService time)
            this.repo = repo;
            this.time = time;

        public Task SaveCommentAsync(int blogPostId, InputComment comment)
            var storedComment = new StoredComment()
                Name = comment.Name,
                EmailAddress = comment.EmailAddress,
                Body = comment.Body,
                Timestamp = this.time.UtcNow

            return this.repo.SaveCommentAsync(blogPostId, storedComment);

        public Task<List<StoredComment>> GetCommentsAsync(int blogPostId)
            => this.repo.GetCommentsAsync(blogPostId);

There are a few classes and interfaces in here that we haven’t created yet, but let’s understand what we’re doing here. We have a dependency on a repository where the comments will be held (whatever that is – we don’t care about the implementation at this stage). The grain acts mostly as a pass-through to this repository for storage and retrieval of comments, but when saving, we transform it by adding a timestamp. We use different DTOs for input comments and stored comments so that it is not possible to supply a timestamp with the input data.

We also have a second dependency on something called a time service. While you could just use DateTime.UtcNow in your code, time is typically one of the dependencies you want to factor out of your unit tests because it can affect the results. So we wrap DateTime.UtcNow in something we can mock, just for the sake of unit tests later.


In the Contracts project, we’ll add all our interfaces and DTOs. Let’s start with our dependencies:

    public interface ITimeService
        DateTime UtcNow { get; }

    public interface ICommentRepository
        Task SaveCommentAsync(int blogPostId, StoredComment comment);
        Task<List<StoredComment>> GetCommentsAsync(int blogPostId);

Then we have our grain interface:

    public interface IBlogPostGrain : IGrainWithIntegerKey
        Task SaveCommentAsync(int blogPostId, InputComment comment);
        Task<List<StoredComment>> GetCommentsAsync(int blogPostId);

And finally our DTOs:

    public class InputComment
        public string Name { get; set; }
        public string EmailAddress { get; set; }
        public string Body { get; set; }

    public class StoredComment : InputComment
        public DateTime Timestamp { get; set; }

Dependency Implementations

In the Silo, we can create implementations for our dependencies.

To keep it simple, we’ll implement our repository using a ConcurrentDictionary. This is a volatile, in-memory implementation that is for demonstration only, but it allows us to focus on what we’re doing with Orleans, rather than distracting us with store-specific details.

Note: We could also use Orleans storage providers, but that’s out of scope here.

    public class MemoryCommentRepository : ICommentRepository
        private ConcurrentDictionary<int, List<StoredComment>> dict;

        public MemoryCommentRepository()
            this.dict = new ConcurrentDictionary<int, List<StoredComment>>();

        public Task<List<StoredComment>> GetCommentsAsync(int blogPostId)
            this.dict.TryGetValue(blogPostId, out var comments);
            return Task.FromResult(comments);

        public Task SaveCommentAsync(int blogPostId, StoredComment comment)
                addValue: new List<StoredComment>() { comment },
                updateValueFactory: (postId, commentsList) => {
                    return commentsList;

            return Task.CompletedTask;

The time service is really simple: it just wraps DateTime.UtcNow.

    public class TimeService : ITimeService
        public DateTime UtcNow => DateTime.UtcNow;

Registering Dependencies

All the above was setting up the example, and now we get to the part we’ve all been waiting for.

We’ll set up our silo’s code similarly to what we’ve done in the past two articles, but this time, we’ll add a call to ConfigureServices() in order to register our dependencies:

            var siloBuilder = new SiloHostBuilder()
                .UseDashboard(options => { })
                .Configure<ClusterOptions>(options =>
                    options.ClusterId = "dev";
                    options.ServiceId = "Orleans2DependencyInjection";
                .Configure<EndpointOptions>(options =>
                    options.AdvertisedIPAddress = IPAddress.Loopback)
                .ConfigureServices(services =>
                    services.AddSingleton<ITimeService, TimeService>();
                    services.AddSingleton<ICommentRepository, MemoryCommentRepository>();
                .ConfigureLogging(logging => logging.AddConsole());

Note: as per the previous articles, C# 7.1 or above is needed in order to allow async/await in Main().

Since AddSingleton() is an extension method coming from Mirosoft.Extensions.DependencyInjection (already included as a dependency of Microsoft.Orleans.Core), you’ll need to add the following for this to work:

using Microsoft.Extensions.DependencyInjection;


We can complete this example by exposing the grain’s functionality via our Web API. For this, we’ll add the following controller:

    public class BlogPostsController : Controller
        private IClusterClient orleansClient;

        public BlogPostsController(IClusterClient orleansClient)
            this.orleansClient = orleansClient;

        public Task<List<StoredComment>> Get(int blogPostId)
            var grain = this.orleansClient.GetGrain<IBlogPostGrain>(blogPostId);
            return grain.GetCommentsAsync(blogPostId);

        public async Task Put(int blogPostId, InputComment comment)
            var grain = this.orleansClient.GetGrain<IBlogPostGrain>(blogPostId);
            await grain.SaveCommentAsync(blogPostId, comment);


Note: as I write this, I am noticing a quirk in this implementation. If you get a grain with a blogPostId, then why do you have to pass it again to call the method on the grain? The grain should know its ID already. Fair enough – that was an oversight on my part. But since grain IDs are retrieved using extension methods, and thus their retrieval would also need to be mocked, I’d rather not overcomplicate things in this example.

We can then add Swagger to the Web API and wire up the Orleans client as we did in the Getting Organised article (complete with retries):

       private IClusterClient CreateOrleansClient()
            var clientBuilder = new ClientBuilder()
                .Configure<ClusterOptions>(options =>
                    options.ClusterId = "dev";
                    options.ServiceId = "Orleans2DependencyInjection";
                .ConfigureLogging(logging => logging.AddConsole());

            var client = clientBuilder.Build();

            client.Connect(async ex =>
                await Task.Delay(3000);
                return true;

            return client;

        // This method gets called by the runtime. Use this method to add services to the container.
        public void ConfigureServices(IServiceCollection services)
            var orleansClient = CreateOrleansClient();

            services.AddSwaggerGen(c =>
                c.SwaggerDoc("v1", new Info { Title = "My API", Version = "v1" });


Manual Testing with Swagger

We can quickly add a couple of comments on a blog post and retrieve them to see that all this is working:

Note: seems like Swagger recently changed their UI. I liked it a lot better before.

Unit Testing

Dependency injection makes it easy for us to write unit tests. Let’s add a Grains.Tests project (.NET Core Console App), add a reference to the Grains project, and install the following packages:

Install-Package Microsoft.NET.Test.Sdk
Install-Package NUnit
Install-Package NUnit3TestAdapter
Install-Package Moq

Remove the auto-generated Program.cs file and add the following test class instead:

    public class BlogPostGrainTests
        public async Task SaveCommentTest()
            // arrange

            const int blogPostId = 1;

            var fixedDateTime = new DateTime(2018, 4, 29, 18, 28, 33, DateTimeKind.Utc);
            var mockRepo = new Mock<ICommentRepository>(MockBehavior.Strict);
            var mockTimeService = new Mock<ITimeService>(MockBehavior.Strict);

            mockRepo.Setup(x => x.SaveCommentAsync(blogPostId, It.IsAny<StoredComment>()))
            mockTimeService.Setup(x => x.UtcNow)

            var grain = new BlogPostGrain(mockRepo.Object, mockTimeService.Object);

            const string name = "George";
            const string emailAddress = "";
            const string body = "I'm hungry!";

            var comment = new InputComment()
                Name = name,
                EmailAddress = emailAddress,
                Body = body

            // act

            await grain.SaveCommentAsync(blogPostId, comment);

            // assert

            mockRepo.Verify(x => x.SaveCommentAsync(blogPostId, It.Is<StoredComment>(
                c => c.Name == name
                  && c.EmailAddress == emailAddress
                  && c.Body == body
                  && c.Timestamp == fixedDateTime

This test verifies that the submitted comment was passed on to the store with the generated timestamp. It should pass:


We’ve seen a complete example featuring dependency injection. Registering dependencies is easy; most of the effort in this article was around building the example to demonstrate that.

As you can see, you can write unit tests for grains just as you would for any other class, without having to resort to the Orleans TestCluster.

There are a number of ways you can take this further:

  1. Have the grain perform a validation against the email address, and write unit tests for that.
  2. Have the grain retrieve its own ID (removing the need to pass it as a parameter to its methods), and find a way to mock the grain retrieval.
  3. Try dependency injection in the Orleans client.