IoT Hub First Peek

The Internet of Things (IoT) is here today, and it begins with the data, devices, and services already at work in your organization. When your “things” are connected to each other and to the cloud, you create new ways to improve efficiency, enable innovation, and transform your business.

This line is printed on the front page of a Microsoft booklet distributed during the lunchtime workshop “Connecting and Building the Internet of Things (IoT)” conducted by Gerald Goh, Microsoft Technical Evangelist. Gerald shared with us technologies such as AMQP, MQTT, Message Broker in Azure, Device Explorer, and so on.

Gerald is sharing Azure IoT Hub during the lunchtime workshop.

IoT hasn’t gone totally mainstream, however, and we have yet to feel its impact. In many ways it is roughly where the big data movement was few years ago — consisting mainly of a buzzword that’s not yet widely understood.

Nevertheless, Gerald’s workshop does give me, a web developer who doesn’t know much about this field, a helpful quick start about IoT. After reading and experimenting, I learn more about the capability of Microsoft Azure in IoT and thus I’d like to share with you about what I’ve learnt so far about Azure IoT Hub.

Message Broker

I’m working in Changi Airport. In the airport, we have several shops serving the travelers and visitors. Most of the shops have a back-end system that integrates several systems such as the retail system, e-commerce website, payment system, Changi Rewards system, inventory management system, the finance system.

So there will be cases where, when a customer buys something at the shop, the retail system needs to send as request to the payment system. Then when the purchase is successful, another purchase request will be sent to the inventory management system and the finance system.

I’m not too sure how the shops link different systems, especially this kind of point-to-point integration will cause a large number of connections among the systems. Hence, the developers of their system may find Message Broker useful.

Message Broker is a physical component that handles the communication between systems. A system sends a message to the message broker, providing the logical name of the receiving systems. The message broker will then search for the receiving systems and then passes the message to them.

A message broker mediating the communication between systems. (Image Credit: Message Broker – MSDN)

Messaging Protocols: AMQP and MQTT

Sending a message between systems seems to be an easy task, however, doing it in a reliable and secure manner can be a challenging work.

As shown in the article “Scalable Eventing over Mesos!”, Autodesk is using AMQP (Advanced Message Queuing Protocol) as messaging protocols between two parties with the following main characteristics as goals.

  • Security
  • Reliability
  • Interoperability
  • Standard
  • Open
AMQP communication between two parties (Image Credit: Autodesk)

AMQP 1.0 is the current specification version. It is also the primary protocol of Azure Event Hubs and Azure Service Bus Messaging after the SBMP (Service Bus Messaging Protocol), the TCP-based protocol which is used inside of .NET client library, is phased out.

Besides AMQP, MQTT (Message Queue Telemetry Transport) is another open protocol based on TCP/IP for asynchronous message queuing which has been developed and matured over past few years.

Dr Andy Stanform-Clark from IBM invented the MQTT protocol. (Image Source: IBM – Wikipedia)

While AMQP is designed to provide the full vibrancy of messaging scenarios, MQTT is designed as an extremely lightweight publish/subscribe message transport for small and simple devices sending small messages on low-bandwidth networks. Hence, MQTT is said to be ideal for mobile applications because of its low power usage and minimized data packets.

MQTT is also simple because it just has five API methods:

  • Connect to an MQTT broker;
  • Disconnect from an MQTT broker;
  • Subscribe to an MQTT topic filter;
  • Unsubscribe from an MQTT topic filter;
  • Publish MQTT messages.
“By maintaining an MQTT connection and routing messages through our chat pipeline, we were able to often achieve phone-to-phone delivery in the hundreds of milliseconds, rather than multiple seconds.” — Lucy Zhang, Facebook software engineer

If you are interested to know more about the comparison of AMQP and MQTT, there is a detailed white paper from StormMQ discussing the difference between AMQP and MQTT.

Brokered Messaging – Service Bus Messaging

When two or more systems want to exchange information, they need a communication facilitator. This is where Microsoft Azure Service Bus comes into picture.

Azure Service Bus is a reliable information delivery service, which is similar to a postal service in the physical world.

One of the messaging patterns offered in Azure Service Bus is called Service Bus Messaging, or Brokered Messaging. By using it, both senders and receivers do not have to be available at the exact same time.

AMQP 1.0 support is available in the Service Bus SDK since its version 2.1. Since the Service Bus .NET client library by default using a dedicated SOAP-based protocol, to use AMQP 1.0, we need to specify in the Service Bus Connection String as highlighted below in bold.

<?xml version="1.0" encoding="utf-8" ?>
            value="Endpoint=sb://[namespace];SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=[SAS key];TransportType=Amqp" /> 

In AMQP transport mode, the client library of sender will serialize the brokered message into an AMQP message so that the message can be received and interpreted by a receiver running on a different platform.

Azure Event Hub

When our event-based messaging needs to be handled at a very huge scale, we can either continue to pay even more to use Azure Service Bus or we can switch to use Event Hub. Event Hub is a cheaper way for us to be able to deal with huge bursts of messages and retain messages for a longer period of time.

Event Hub is cheaper, reliable and also fully managed. (Full video: Azure Service Bus Event Hubs 101 with Dan Rosanova)

Although Event Hub does not support MQTT, it does support AMQP (and HTTP) where there could be at most 5,000 concurrent AMQP connections.

Event Hubs event and telemetry handling capabilities, such as ingesting millions of events per second, make it especially usefu for IoT scenarios. However, since it is ingestion only thus Event Hub has no facility for sending traffic, for example, from the cloud back to the devices (C2D).

Azure IoT Hub

Since Event Hubs only enable event ingress, i.e. C2D, Azure offers another service, IoT Hub, for both C2D and D2C (Device-to-Cloud) communications which are reliable and secure. Not only allowing bi-directional communication, IoT Hub also supports AMQP, HTTP, and MQTT.

IoT Hub has an identity registry storing information about devices which are given the permission to connect to the IoT Hub. Before a device can connect to an IoT Hub, there must be an entry for that device in the identity registry of the IoT Hub.

In a Hello World tutorial of connecting stimulated device to IoT Hub using C#, there is a way to add device and retrieve device identity programmatically as shown below.

private static async Task AddDeviceAsync()
    string deviceId = "gclRasPi2";
    Device device;

        device = await registryManager.AddDeviceAsync(new Device(deviceId));
    catch (DeviceAlreadyExistsException)
        device = await registryManager.GetDeviceAsync(deviceId);

    Console.WriteLine("Generated device key: {0}", device.Authentication.SymmetricKey.PrimaryKey);

The Registry Manager, which is connecting to the IoT Hub using a Connection String with proper Policy, will add an device identity with the Device ID “gclRasPi2” to the Device Explorer in Azure.

The device “gclRasPi2” is now in the Device Explorer.

After doing so, a message then can be sent from (stimulated) device to the IoT Hub. For example, the device wants to send data about the temperature and humidity at that moment using MQTT, we can use the following code.

var deviceClient = DeviceClient.Create(
    new DeviceAuthenticationWithRegistrySymmetricKey("gclRasPi2", deviceKey), 

var telemetryDataPoint = new
    deviceId = "gclRasPi2",
    temperature = currentTemperature,
    humidity = currentHumidity

var messageString = JsonConvert.SerializeObject(telemetryDataPoint);

var message = new Message(Encoding.ASCII.GetBytes(messageString));
message.Properties.Add("temperatureAlert", (currentTemperature > 30) ? "true" : "false");

await deviceClient.SendEventAsync(message);

To read the message, please follow the steps shared by the tutorial on setting up to read data-point messages.

Message Routing

Besides reading normal data-point messages, what really interests me is another tutorial about message processing with Message Routing.

Message Routing (Image Source: Microsoft Azure Blog)

According to the tutorial, we first need to setup a Service Bus queue in the same Azure subscription and region as our IoT Hub.

Created a Queue in the Service Bus.

We can then add an Endpoint in the IoT Hub for the queue we just created. As shown in the following screenshot, there is a message saying that “You may have up to 1 endpoint on the IoT hub.” This is because I am using the free IoT Hub. For its paid versions, only at most 10 custom endpoints are allowed.

Interestingly, each Azure subscription can only have at most 10 IoT Hubs, and only 1 free IoT Hub.

Adding a new endpoint to the IoT Hub.

After adding endpoint, we need to setup the Message Routing. For free version, we can only have 5 routing rules.

Creating new route with query string following special syntax.

In the query string, I used temperatureAlert = “true” as the condition. Also, as shown on the screenshot above, there is a line saying “Messages which do not match any rules will be written to the ‘Events (messages/events)’ endpoint.” Hence, the following two console applications will show different results: The left one is connecting to the messages/events endpoint while the right one is showing messages that match the CustomizedMessageRoutingRule created above.

Only data with temperatureAlert = “true” will be sent to the “CustomizedMessageRoute”.

Now if we visit the Service Bus Queue page and IoT Hub page again, we will see some updates on the numbers.

Usage statistics in Service Bus Queue.
2% of 8k messages sent from the stimulated device console application.


That’s all about my first try of Azure IoT Hub after attending the workshop delivered by Gerald. It’s a great lunchtime workshop.

For those who are interested, there is an article on Microsoft sharing the benefits of using Azure IoT Hub service, you can read it to understand more.

This is just the beginning of my IoT learning journey. There are still more things for me to learn, such as Azure Stream Analysis and Microsoft Azure IoT Suite which is briefly brought up in the booklet mentioned above.

If you spot any mistake in this article or you have more to talk about IoT and in particular IoT in Azure ecosystem, please share with me. =)

Exploring Azure Functions for Scheduler


During my first job after finishing my undergraduate degree in NUS, I worked in a local startup which was then then the largest bus ticketing portal in Southeast Asia. In 2014, I worked with a senior to successfully migrate the whole system from on-premise to Microsoft Azure Virtual Machines, which is the IaaS option. Maintaining the virtual machines is a painful experience because we need to setup the load balancing with Traffic Manager, database mirroring, database failover, availability set, etc.

In 2015, when I first worked in Singapore Changi Airport, with the support of the team, we made use of PaaS technologies such as Azure Cloud Services, Azure Web Apps, and Azure SQL, we successfully expanded our online businesses to 7 countries in a short time. With the help of PaaS option in Microsoft Azure, we can finally have a more enjoyable working life.

Azure Functions

Now, in 2017, I decided to explore Azure Functions.

Azure Functions allows developers to focus on the code for only the problem they want to solve without worrying about the infrastructure like we do in Azure Virtual Machines or even the entire applications as we do in Azure Cloud Services.

There are two important benefits that I like in this new option. Firstly, our development can be more productive. Secondly, Azure Functions has two pricing models: Consumption Plan and App Service Plan, as shown in the screenshot below. The Consumption Plan lets us pay per execution and the first 1,000,000 executions are free!

Screen Shot 2017-02-01 at 2.22.01 PM.png
Two hosting plans in Azure Functions: Consumption Plan vs. App Service Plan

After setting up the Function App, we can choose “Quick Start” to have a simpler user interface to get started with Azure Function.

Under “Quick Start” section, there are three triggers available for us to choose, i.e. Timer, Data Processing, and Webhook + API. Today, I’ll only talk about Timer. We will see how we can achieve the scheduler functionality on Microsoft Azure.

Screen Shot 2017-02-05 at 11.16.40 PM.png
Quick Start page in Azure Function.

Timer Trigger

Timer Trigger will execute the function according to a schedule. The schedule is defined using CRON expression. Let’s say if we want our function to be executed every four hours, we can write the schedule as follows.

0 0 */4 * * *

This is similar to how we did in the cron job. The CRON expression consists of six fields. The first one is second (0-59), followed by minute (0 – 59), followed by hour (0 – 23), followed by day of month (1 – 31), followed by month (1 – 12) and day of week (0-6).

Similar to the usual Azure Web App, the default time zone used in Azure Functions is also UTC. Hence, if we would like to change it to use another timezone, what we need to do is just add the WEBSITE_TIME_ZONE application setting in the Function App.

Companion File: function.json

So, where do we set the schedule? The answer is in a special file called function.json.

In the Function App directory, there always needs a function.json file. The function.json file will contain the configuration metadata for the function. Normally, a function can only have a single trigger binding and can have none or more than one I/O bindings.

The trigger binding will be the place we set the schedule.

    "bindings": [
            "name": "myTimer",
            "type": "timerTrigger",
            "direction": "in",
            "schedule": "0 0 */4 * * *"

The name attribute is to specify the name of the parameter used in the C# function later. It is used for the bound data in the function.

The type attribute specifies the binding time. Our case here will be timerTrigger.

The direction attribute indicates whether the binding is for receiving data into the function (in) or sending data from the function (out). For scheduler, the direction will be “in” because later in our C# function, we can actually retrieve info from the myTimer parameter.

Finally, the schedule attribute will be where we put our schedule CRON expression at.

To know more about binding in Azure Function, please refer to the Azure Function Developer Guide.

Function File: run.csx

2nd file that we must have in the Function App directory is the function itself. For C# function, it will be a file called run.csx.

The .csx format allows developers to focus on just writing the C# function to solve the problem. Instead of wrapping everything in a namespace and class, we just need to define a Run method.

#r "Newtonsoft.Json"

using System;
using Newtonsoft.Json;

public static async Task Run(TimerInfo myTimer, TraceWriter log)

Assemblies in .csx File

Same as how we always did in C# project, when we need to import the namespaces, we just need to use the using clause. For example, in our case, we need to process the Json file, so we need to make use of the library Newtonsoft.Json.

using Newtonsoft.Json;

To reference external assemblies, for example in our case, Newtonsoft.Json, we just need to use the #r directive as follows.

#r "Newtonsoft.Json"

The reason why we are allowed to do so is because Newtonsoft.Json and a few more other assemblies are “special case”. They can be referenced by simplename. As of Jan 2017, the assemblies that are allowed to do so are as follows.

  • Newtonsoft.Json
  • Microsoft.WindowsAzure.Storage
  • Microsoft.ServiceBus
  • Microsoft.AspNet.WebHooks.Receivers
  • Microsoft.AspNet.WebHooks.Common
  • Microsoft.Azure.NotificationHubs

For other assemblies, we need to upload the assembly file, for example MyAssembly.dll, into a bin folder relative to the function first. Only then we can reference is as follows.

#r "MyAssembly.dll"

Async Method in .csx File

Asynchronous programming is recommended best practice. To make the Run method above asynchronous, we need to use the async keyword and return a Task object. However, developers are advised to always avoid referencing the Task.Result property because it will essentially do a busy-wait on a lock of another thread. Holding a lock creates the potential for deadlocks.

Inputs in .csx File and DocumentDB

This section will display the top four latest Facebook posts pulled by Azure Function.

For our case, the purpose of Azure Function is to process the Facebook Group feeds and then store the feeds somewhere for later use. The “somewhere” here is DocumentDB.

To gets the inputs from DocumentDB, we first need to have 2nd binding specified in the functions.json as follows.

    "bindings": [
            "type": "documentDB",
            "name": "inputDocument",
            "databaseName": "feeds-database",
            "collectionName": "facebook-group-feeds",
            "id": "41f7adb1-cadf-491e-9973-28cc3fca57df",
            "connection": "dotnetsg_DOCUMENTDB",
            "direction": "in"

In the DocumentDB input binding above, the name attribute is, same as previous example, used to specify the name of the parameter in the C# function.

The databaseName and collectionName attributes correspond to the names of the database and collection in our DocumentDB, respectively. The id attribute is the Document Id of the document that we want to retrieve. In our case, we store all the Facebook feeds in one document, so we specify the Document Id in the binding directly.

The connection attribute is the name of the Azure Function Application Setting storing the connection string of the DocumentDB account endpoint. Yes, Azure Function also has Application Settings available. =)

Finally, the direction attribute must be “in”.

We can then now enhance our Run method to include inputs from DocumentDB as follows. What it does is basically just reading existing feeds from the document and then update it with new feeds found in the Singapore .NET Facebook Group

#r "Newtonsoft.Json"

using System;
using Newtonsoft.Json;

private const string SG_DOT_NET_COMMUNITY_FB_GROUP_ID = "1504549153159226";

public static async Task Run(TimerInfo myTimer, dynamic inputDocument, TraceWriter log)
    string sgDotNetCommunityFacebookGroupFeedsJson = 
        await GetFacebookGroupFeedsAsJsonAsync(SG_DOT_NET_COMMUNITY_FB_GROUP_ID);

    var existingFeeds = JsonConvert.DeserializeObject(inputDocument.ToString());

    // Processing the Facebook Group feeds here...
    // Updating existingFeeds here... = existingFeeds.Feeds;

Besides getting input from DocumentDB, we can also have DocumentDB output binding as follows to, for example, write a new document to DocumentDB database.

    "bindings": [
            "type": "documentDB",
            "name": "outputDocument",
            "databaseName": "feeds-database",
            "collectionName": "facebook-group-feeds",
            "id": "41f7adb1-cadf-491e-9973-28cc3fca57df",
            "connection": "dotnetsg_DOCUMENTDB",
            "createIfNotExists": true,
            "direction": "out"

We don’t really use this in our case. However, as we can see, there are only two major differences between DocumentDB input and output bindings.

Firstly, we have a new createIfNotExists attribute which specify whether to create the DocumentDB database and collection if they don’t exist or not.

Secondly, we will have to set the direction attribute to be “out”.

Then in our function code, we just need to have a new parameter with “out object outputDocument” instead of “in dynamic inputDocument”.

You can read more at the Azure Functions DocumentDB bindings documentation to understand more about how they work together.

Application Settings in Azure Functions

Yes, there are our familiar features such as Application Settings, Continuous Integration, Kudu, etc. in Azure Functions as well. All of them can be found under “Function App Settings” section.

Screen Shot 2017-02-18 at 4.40.24 PM.png
Azure Function App Settings

As what we have been doing in Azure Web Apps, we can also set the timezone, store the App Secrets in the Function App Settings.

Deployment of Azure Functions with Github

We are allowed to link the Azure Function with variety of Deployment Options, such as Github, to enable the continuous deployment option too.

There is one thing that I’d like to highlight here is that if you are also starting from setting up your new Azure Function via Azure Portal, then when in the future you setup the continuous deployment for the function, please make sure that you first create a folder having the same name as the name of your Azure Function. Then all the files related to the function needs to be put in the folder.

For example, in case, we have the Azure Function called “TimerTriggerCSharp1”. we will have the following folder structure.

Screen Shot 2017-02-18 at 4.49.11 PM.png
Folder structure of the TimerTriggerCsharp1.

When I first started, I made a mistake when I linked Github with Azure Function. I didn’t create the folder with the name “TimerTriggerCSharp1”, which is the name of my Azure Function. So, when I deploy the code via Github, the code in the Azure Function on the Azure Portal is not updated at all.

In fact, once the Continuous Deployment is setup, we are no longer able to edit the code directly on the Azure Portal. Hence, setting up the correct folder structure is important.

Screen Shot 2017-02-18 at 4.52.17 PM.png
Read only once we setup the Continuous Deployment in Azure Function.

If you would like to add in more functions, simply create new folders at the same level.


Azure Function and the whole concept of Serverless Architecture are still very new to me. However, what I like about it is the fact that Azure Function allows us to care about the codes to solve a problem without worrying about the whole application and infrastructure.

In addition, we are also allowed to solve the different problems using the programming language that best suits the problem.

Finally, Azure Function is cost-saving because we can choose to pay only for the time our code is being executed.

If you would like to learn more about Azure Functions, here is the list of references I use in this learning journey.

You can check out my code for TimerTriggerCSharp1 above at our Github repository:

Never Share Your Secrets (Secret Manager and Azure Application Settings)


It’s important to keep app secrets out of our codes. Most of the app secrets are however still found in .config files. This way of handling app secrets becomes very risky when the codes are on public repository.

Thus, they are people put some dummy text in the .config files and inform the teammates to enter their respective app secrets. Things go ugly when this kind of “common understanding” among the teammates is messed up.

The moment when your app secrets are published on Github public repo. (Image from “Kono Aozora ni Yakusoku o”)

Secret Manager Tool

So when I am working on the website, which is an ASP .NET Core project, I use the Secret Manager tool.It offers a way to store sensitive data such as app secrets in our local development machine.

To use the tool, firstly, I need to add it in project.json as follows.

    "userSecretsId": "aspnet-CommunityWeb-...",
    "tools": {
        "Microsoft.Extensions.SecretManager.Tools": "1.0.0-preview2-final"

Due to the fact that the Secret Manager tool makes use of project specific configuration settings kept in user profile, we need to specify a userSecretsId value in the project.json as well.

After that, I can start storing the app secrets in the Secret Manager tool by entering the following command in the project directory.

$ dotnet user-secrets set AppSettings:MeetupWebApiKey ""

Take note that currently (Jan 2017) the values stored in the Secret Manager tool are not encrypted. So, it is just for development only.

As shown in the example above, the name of the secret is “AppSettings:MeetupWebApiKey”. This is because in the appsettings.json, I have the following.

    "AppSettings": {
        "MeetupWebApiKey": ""

Alright, now the API key is stored in the Secret Manager tool, how is it accessed from the code?

By default, appsettings.json is already loaded in startup.cs. However, we still need to add the following bolded lines in startup.js to enable User Secrets as part of our configuration in the Startup constructor.

public class Startup
    public Startup(IHostingEnvironment env)
        var builder = new ConfigurationBuilder()
            .AddJsonFile("appsettings.json", optional: true, reloadOnChange: true)
            .AddJsonFile($"appsettings.{env.EnvironmentName}.json", optional: true);
        if (env.IsDevelopment())


        Configuration = builder.Build();

Then in the Models folder, I create a new class called AppSettings which will be used later when we load the app secrets:

public class AppSettings
    public string MeetupWebApiKey { get; set; }


So, let’s say I want to use the key in the HomeController, I just need to do the following.

public class HomeController : Controller
    private readonly AppSettings _appSettings;

    public HomeController(IOptions appSettings appSettings)
        _appSettings = appSettings.Value;

    public async Task Index()
        string meetupWebApiKey = _appSettings.MeetupWebApiKey;

Azure Application Settings

Just now Secret Manager tool has helped us on managing the app secrets in local development environment. How about when we deploy our web app to Microsoft Azure?

For, I am hosting the website with Azure App Service. What so great about Azure App Service is that there is one thing called Application Settings.

Screen Shot 2017-01-29 at 11.19.42 PM.png
Application Settings option is available in Azure App Service.

For .NET applications, the settings in the “App Settings” will be injected into the AppSettings at runtime and override existing settings. Thus, even though I have empty strings in appsettings.json file in the project, as long as the correct values are stored in App Settings, there is no need to worry.

Thus, when we deploy web app to Azure App Service, we should never put our app secrets, connection strings in our .config and .json files or even worse, hardcode them.

Application Settings and Timezone

Oh ya, one more cool feature in App Settings that was introduced in 2015 is that we can change the server time zone for web app hosted on Azure App Service easily by just having a new entry as follows in the App Settings.

WEBSITE_TIME_ZONE            Singapore Standard Time

The setting above will change the server time zone to use Singapore local time. So DateTime.Now will return the current local time in Singapore.


If you would like to read more about the topics above, please refer to following websites.

Deploy ASP .NET Core Directly via Git


You can deploy ASP .NET Core web apps to Azure App Service directly using Git.

This is actually part of the Continuous Deployment workflow for apps in Azure App Service. Currently, Azure App Service integrate with not only Github, but also Visual Studio Team Services, BitBucket, Dropbox, OneDrive, and so on.

Available deployment source options in Azure App Service.

Although source code is on Github, choosing the “GitHub” option cannot detect its repository. This is because the Github option only lists the repositories on my personal Github account. The repo whereas is under the sg-dotnet Github Organization account. Hence, I have to choose “External Repository” as the deployment source instead.

Screen Shot 2017-01-30 at 1.21.03 PM.png
Setting up External Repository (Git) as deployment source in Azure App Service.

After that, whenever there is a new commit, if we do “Sync”, it will create a new deployment record, as shown in the screenshot below. We can anytime revert back to the previous deployment by right-clicking on the desired deployment record and select “Redeploy”.

Screen Shot 2017-01-30 at 1.13.35 PM.png
Deployment history in Azure App Service.


So what if we want to customize the deployment process?

Before going into that, the first thing we need to say hi to is Kudu. What is Kudu? Kudu is the engine behind Git deployment in Azure App Service. It is also a set of troubleshooting and analysis tools for use with Azure App Service. It can capture hang dump for worker process for performance analyzing purposes.

On Kudu, we can also download the deployment script, deploy.cmd. We can then edit the file with any custom step we have and put the file under the root of repository.

There is another simpler way which is using a file with the filename “.deployment” at the root of repository. Then in the content of the file, we can specify our command to run during deployment as follows.


To learn more about Kudu, please watch the following video clip from Channel 9.


If you would like to read more about the topics above, please refer to following websites.

Exploring Azure Search


Last year, Riza shared about his small little algorithm to do smart auto complete in WPF in Singapore .NET Developers Community March meetup. Riza has his project for this, SmartSuggestions, available on Github. What it does is that it will prompt user for smart suggestion of typos and find the similar words for suggestion.

Riza Marhaban is sharing his SmartSuggestion algorithm to the audience during the community meetup. (Photo Credit: Singapore .NET Developers Community)

I find his program to be very interesting. In fact, I did a similar task when I was working in Easibook as well. By calculating the Levenshtein Distance of user input and the records in database, the small JavaScript code I wrote is able to suggest the places even user keys in the place name wrongly.

Soon after Riza’s talk about his SmartSuggestion, I read the announcement of general availability of Azure Search from Microsoft team.

Azure Search is generally available!

Azure Search

Azure Search is a fully managed search-as-a-service in Microsoft Azure. It offers scalable full-text search for the program. Hence, with its help, developers do not need to re-invent the text-searching capability in their programs and websites.

Azure Search currently provides two ways of querying text. One is using Simple Query Syntax where user can do keywords searching, phrase searching, suffix searching, etc. AND/OR/NOT operator is also available for use.

Another way of querying will be Lucene Query Parser. What interests me the most in Lucene Query Syntax is the use of Damerau–Levenshtein Distance in its Fuzzy Search, which does more than the Levenshtein Distance that only allows insertion, deletion, and substitution operations.

Try It Out!

In order to try out this feature, I have decided to create a demo program to test its functionality.

In this program, I use the event data from the .NET Developers Community Singapore to demonstrate how Azure Search works. To do this, I have to integrate with the Meetup APIs in this program.

Currently, this demo application covers the following features in Azure Search.

  • Create Azure Search index;
  • Data upload;
  • Keywords query in both Simple Query Syntax and Lucene Query Syntax.

Here are some of the screenshots of querying using Azure Search.

For example, if I’d like to find out what the talks covering topic about Visual Studio are, I can just simply search by “visual studio” as a phrase, as shown in the following screenshot.

Phrase Searching in Azure Search

Or let’s say a user wants to search the meetup events about “Xamarin” but he doesn’t know its correct spelling is either Xamarin or Zamarin. So he can do a Fuzzy Search by keying in “Zamarin~”. Take note of the tilde “~” symbol at the end of the word. It means the search of the word will be done using Fuzzy Search.

Fuzzy Search in Azure Search

Holiday and Coding

Christmas is a public holiday in Singapore. Since Christmas is on Sunday, I get a day off on Monday. So besides taking a rest in my room, I did a quick research on Azure Search. It’s kind of fun because it helps me to learn new things which I don’t have chance to explore during work.

With Azure Search, we can now search with our minds at east. (Image Credit: Re:Zero Kara Hajimeru Isekai Seikatsu, KissAnime)

Anyway, I have uploaded my demo program project to Github. Feel free to check it out!


Burger and Cheese


As a web developer, I don’t have many chances to play with mobile app projects. So rather than limit myself to just one field, I love to explore other technologies, especially mobile app development.

Burger Project: My First Xamarin App

Last month, I attended a Xamarin talk at Microsoft Singapore office with my colleague. The talk was about authentication and authorization with social networks such as Facebook and Twitter via Azure App Service: Mobile App.

Ben Ishiyama-Levy is talking about how Xamarin and Microsoft Azure works together.
Ben Ishiyama-Levy is talking about how Xamarin and Microsoft Azure works together.

The speaker is Ben Ishiyama-Levy, a Xamarin evangelist. His talk inspired me to further explore how I could retrieve user info from social network after authenticating the users.

Because I am geek-first and I really want to find out more, so I continue to read more about this topic. With the help from my colleague, I developed a simple Xamarin.Android app to demonstrate the Authentication and logged-in user’s info retrieval.

The demo app is called Burger and it can be found on my Github repository:

Challenges in Burger Project

Retrieving user's info from social network.
Retrieving user’s info from social network.

In Burger project, the first big challenge is to understand how Azure App Service: Mobile App works in Xamarin. Luckily, with the material and tutorial given in the Xamarin talk from Ben, I was able to get a quick start on this.

My colleague also shared another tutorial which is about getting authenticated user’s personal details on Universal Windows Platform (UWP). It helps me a lot to understand about how mobile app and Azure App Service can work together.

My second challenge in this project is to understand Facebook Graph API. I still remember that I spent quite some time finding out why I could not retrieve the friend list of a logged-in Facebook user. With the introduction of the Facebook Graph API 2.0, access to a user’s friends list via /me/friends is limited to just friends using the same app. Hence after reading a few other online tutorials, I finally somehow able to get another subset of a user’s friends via /me/taggable_friends.

In this project, it’s also the first time I apply Reflection in my personal project. It helps me easily get the according social network login class with a neat and organized code.

Microsoft Developer Day at NUS, Singapore in May 2016

Cheese Project: When Google Speech Meets MS LUIS on Android

Few months ago, I’m fortunate to represent my company to attend Microsoft Developer Day 2016 in National University of Singapore (NUS).

The day is the first time Microsoft CEO Satya Nadella comes to Singapore. It’s also my first time learn about the powerful Cognitive Services and LUIS (Language Understanding Intelligence Service) in Microsoft Azure in Riza’s talk.

Riza’s presentation about Microsoft Cognitive APIs during Microsoft Developer Day.

Challenges in Cheese Project

Everyday, it takes about one hour for me to reach home from office. Hence, I will only have two to three hours every night to work on personal projects and learning. During weekends, when people are having fun out there, I will spend time on researching about some exciting new technologies.

There are many advance topics in LUIS. I still remember that when I was learning how LUIS works, my friend was actually playing the Rise of the Tomb Raider beside me. So while he was there phew-phew-phew, I was doing data training on LUIS web interface.

Microsoft LUIS (Language Understanding Intelligence Service) and Intents

Currently, I only worked on some simple intents, such as returning me current date and time as well as understanding which language I want to translate to.

My first idea in Cheese project is to build an Android app such that if I say “Please translate blah-blah to xxx language”, the app will understand and do the translation accordingly. This can be quite easily done with the help of both LUIS and Google Translate.

After showing this app to my colleagues, we realized one problem in the app. It’s too troublesome for users to keep saying “Please translate blah-blah to xxx language” every time they need to translate something. Hence, recently I have changed it to use GUI to provide language selection. This, however, reduces the role played by LUIS in this project.

VoiceText provides a range of speakers and voices with emotions!

To make the project even more fun, I implemented the VoiceText Web API from Japanese in the Android app. The cool thing about this TTS (Text-To-Speech) API is that it allows developers to specify the mood and characteristic of the voice. The challenge, of course, is to read the API written in Japanese. =P

Oh ya, this is the link to my Cheese repository on Github: I will continue to work on this project while exploring more about LUIS. Stay tuned.

languagelist    googlespeech    SuccessfullyTranslated.png

After-Work Personal Projects

There are still more things in mobile app development for me to learn. Even though most of the time I feel exhausted after long work day, working on new and exciting technologies helps me getting energized again in the evening.

I’m not as hardworking as my friends who are willing to sacrifice their sleep for their hobby projects and learning, hence the progress of my personal project development is kind of slow. Oh well, at least now I have my little app to help me talking to people when I travel to Hong Kong and Japan next year!

Azure Blob Storage and File API

Azure Blob Storage - Azure SDK - ASP .NET MVC - Entity Framework - HTML5

When my applications were hosted on Windows Azure Virtual Machines (VM), we stored the images uploaded via our web applications in the hard disks of the VMs (except the temporary disk). However, when we started load balancing, we soon encountered a problem that the uploaded images were only found in one of the VMs. So we needed to find a centralized storage for those images.

Recently, when we are using Azure PaaS (aka Cloud Service), even without load balancing, we already encounter the same issue. That is simply because the hard drives used in Cloud Service instances are not persistent. Hence, a persistent file storage on the cloud is needed.

IaaS vs. PaaS
IaaS vs. PaaS

Blob Storage

Azure Blob Storage, according to Azure Documentation, is a service for storing large amount of unstructured data that can be accessed everywhere via HTTP or HTTPS. Hence, it is an ideal tool that we can use as the persistent image cloud storage.

There are two types of blob, Page Blob and Block Blob. Page Blob is commonly used for storing VHD files for VMs because it is optimized for random read and write operations.

For most of the files uploaded, it’s recommended to store as Block Blobs because large files will be split into smaller blocks and then uploaded concurrently. Hence, Block Blob is designed to give us faster upload and better throughput, which is great for image upload.

The maximum size for a Block Blob is 64 MB. Hence, if the uploaded file is more than 64 MB, we must upload it as a set of blocks; otherwise, we will receive status code 413 (Request Entity Too Large). For my web applications, there is no need for uploading an image which is more than 5MB most of the time. Hence, I can just limit the size of images before the user uploads them.

HttpPostedFileBase imageUpload;
if (imageUpload.ContentLength > 0 && imageUpload.ContentLength <= 5242880)
    //warn the user to resize the image

Let’s Try Uploading Images

I’m going to share how to upload more than one image to the Azure Blob Storage from an ASP .NET MVC 5 application. If you are going to upload just one image, simply remove the for loop and change List to just DBPhoto in the codes below.

First of all, I create a class to handle upload to Azure Storage operation.

public class AzureStorage
    public static async Task UploadAndSaveBlobAsync(
        HttpPostedFileBase imageFile, CloudBlobContainer container)
        string blobName = Guid.NewGuid().ToString() + 

        CloudBlockBlob imageBlob = container.GetBlockBlobReference(blobName);
        using (var fileStream = imageFile.InputStream) 
            await imageBlob.UploadFromStreamAsync(fileStream);

        return imageBlob;

So, in my controller, I have the following piece of code which will be called when an image is submitted via web page.

public async Task Create(
    [Bind(Include = "ImageUpload")] PhotoViewModel model)
    var validImageTypes = new string[] { "image/jpeg", "image/pjpeg", "image/png" };
    if (ModelState.IsValid) 
        if (model.ImageUpload != null && model.ImageUpload.Count() > 0)
            var storageAccount = CloudStorageAccount.Parse 

            var blobClient = storageAccount.CreateCloudBlobClient();
            blobClient.DefaultRequestOptions.RetryPolicy = 
                new LinearRetry(TimeSpan.FromSeconds(3), 3);  

            var imagesBlobContainer = blobClient.GetContainerReference("images");
            foreach (var item in model.ImageUpload) 
                if (item != null) {
                if (validImageTypes.Contains(item.ContentType) && 
                    item.ContentLength > 0 && item.ContentLength <= 5242880)
                    var blob = await AzureStorage.UploadAndSaveBlobAsync(item, imagesBlobContainer);
                    DBPhoto newPhoto = new DBPhoto(); 
                    newPhoto.URL = blob.Uri.ToString();
                    // Show user error message 
                    return View(model); 
            // No image to upload
    return View(model);

In the code above, there are many new cool things.

Firstly, it is the connection string to Azure Blob Storage, which I store in StorageConnectionString in web.config. The format for secure connection string is as follows.

Retrieve the access keys to the Storage Account.
Retrieve the access keys to the Storage Account.

Secondly, it’s LinearRetry. It is basically a retry policy which states how many times the program will retry and how much time needed between retries. In my case, it will only wait for 3 seconds after each try up to 3 tries.

Thirdly, I get the URL of the image on the Azure Blob Storage via blob.Uri.ToString() and store it into the database table. The URL will be used later for displaying the image as well as deleting the image.

Fourthly, I actually check to see if model.ImageUpload has null entries. This is because if I submit the form without any image to upload, model.ImageUpload has one entry. Not zero, but one. The only one entry is actually null. So if I don’t check to see whether the entry in model.ImageUpload is null, there will be an exception thrown.

The controller has such a long code. Luckily the code needed in the model and view is short and simple.

For the model PhotoViewModel, I have the following.

public class PhotoViewModel
    [Display(Name = "Current Images")]
    public List AvailablePhotos { get; set; }

For view, it is easy to allow selecting multiple files in the same view page. The “multiple = “true”” is to make sure more than one file can be selected in the File Explorer. You can omit this attribute if you only want at most one file being selected.

@Html.LabelFor(model => model.ImageUpload, new { style = "font-weight: bold;" })
@Html.TextBoxFor(model => model.ImageUpload, new { type = "file", multiple = "true" })
@Html.ValidationMessageFor(model => model.ImageUpload)

Image Size and HttpException

The image upload function looks fine. However, when images having size larger than a certain size is uploaded, HttpException will be thrown.

There is no way that having exception would be fun too! (Image Credit: Tari Tari)
There is no way that having exception would be fun too! (Image Credit: Tari Tari)

In order to prevent DOS attacks which upload huge files to the server, IIS by default only allows files which have size less than 4MB to be uploaded. Hence, although I earlier put a check to prevent image larger than 5MB to be uploaded, the exception will still be thrown if an image of size between 4 to 5MB is uploaded.

What if we just change the if clause above to allow only at most 4MB of image being uploaded? This won’t work because the exception is already thrown before the if condition is reached.

Then, can we just increase the IIS limit from 4MB to, let’s say, 100MB or something bigger? Sure. This can work. However, it still doesn’t stop someone uploads something bigger than the limit. Also, it makes attackers easier to exhaust your server with big files. Hence, expanding the upload size restriction is not really a full solution.

If you are interested, there are many good articles online discussing about this problem. I highlight some interesting ones below.

  1. Use HttpModule to Handle File Uploads;
  2. Use RIA (Rich Internet Application) Services in Silverlight (Seriously, we are talking about Silverlight in year 2015?);
  3. SubStatusCode = 13 in IIS 7;
  4. Catch the Exception in Global.asax.

I don’t really like the methods listed above, especially the 3rd and 4th options. It’s already too late to inform the user when the exception is thrown. Could we do something at client side before the images are being uploaded?

Luckily, we have File API in HTML 5. It allows to loop through the files in JavaScript to check their size. So, after the submit button is clicked, I will call a JavaScript method to check for the size of the images before they are being uploaded.

function IsFileSizeAcceptable() {
    if (typeof FileReader !== "undefined") {
        var filesBeingUploaded = document.getElementById('ImageUpload').files;
        for (var i = 0; i < filesBeingUploaded.length; i++) {
            if (filesBeingUploaded[i].size >= 4194304) { // Less than 4MB only
                alert('The file ' + filesBeingUploaded[i].name + ' is too large. Please remove it from your selection.');
                return false;
    return true;
File API is currently supported in major modern browsers. (Image Credit:
File API is currently supported in major modern browsers. (Image Credit:

Remove from Azure Blob Storage

It’s normal that files uploaded to storage will be removed later. So how are we going to implement this feature in our ASP .NET MVC 5 application?

First of all, I added the following code to my AzureStorage.cs.

public static async Task DeleteBlobAsync(Uri blobUri, CloudBlobContainer container)
    string blobName = blobUri.Segments[blobUri.Segments.Length - 1];
    CloudBlockBlob blobToDelete = container.GetBlockBlobReference(blobName);

    await blobToDelete.DeleteAsync(); 

Secondly, I just pass in the Azure Storage URL of the image that I would like to remove and then call the DeleteBlobAsync method.

Uri blobUri = new Uri();
await AzureStorage.DeleteBlobAsync(blobUri, imagesBlobContainer);

Then the image will be deleted from the Azure Storage successfully.

Global.asax.cs and Blob Container

In order to have my application to create a blob container automatically if it doesn’t already exist, I add a few lines in Global.asax.cs as follows.

var storageAccount = CloudStorageAccount.Parse(
var blobClient = storageAccount.CreateCloudBlobClient();
var imagesBlobContainer = blobClient.GetContainerReference("images");
if (imagesBlobContainer.CreateIfNotExists())
    imagesBlobContainer.SetPermissions(new BlobContainerPermissions
            PublicAccess = BlobContainerPublicAccessType.Blob

Write a Console Program to Upload File to Azure Storage

So, how is it done if we are developing a console application, instead of web application?

Windows Azure Storage NuGet Package needs to be installed first.
Windows Azure Storage NuGet Package needs to be installed first.

The codes below show how I upload an html file from my local hard disk to Azure Blob Storage. Then I can share the Azure Storage URL of the file to my friends so that they can read the web page.

Similar to what I do in web application, this is how I connect to the Storage account via https.

var azureStorageAccount = new CloudStorageAccount(
    new StorageCredentials("", ""), true);

This is how I access the container.

var blobClient = new CloudBlobClient(azureStorageAccount.BlobStorageUri, azureStorageAccount.Credentials);
var container = blobClient.GetContainerReference("myfiles");

Then the next thing I do is just upload the local file to Azure Storage by specifying the file name, content type, etc.

CloudBlockBlob blob = container.GetBlockBlobReference("mysimplepage.html");
using (Stream file = System.IO.File.OpenRead(@"C:\Users\ChunLin\Documents\mysimplepage.html")) 
    blob.Properties.ContentType = "text/html"; 

Yup, that’s all. =)


Hosting your files on cloud storage is sure convenience. However, Azure Blob Storage is not free. The following table shows the current pricing of Azure Block Blob Storage in South East Asia region. To get the latest pricing details, please visit Azure Storage Pricing page.

Azure Standard Block Blob Storage in SEA Pricing
Azure Standard Block Blob Storage in SEA Pricing

Summer 2015 Self-Learning Project

This article is part of my Self-Learning in this summer. To read the other topics in this project, please click here to visit the project overview page.

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