Load Balancing Azure Web Apps with Nginx

nginx-ubuntu-azurevm.png

This morning, my friend messaged me a Chinese article about how to do clustering with Linux + .NET Core + Nginx. As we are geek first, we are going to try it out with different approaches. While my friend was going to set up on RaspberryPi, as a developer who loves playing with Microsoft Azure, I proceed to do load balancing of Azure Web Apps in different regions with Nginx.

Setup Two Azure Web Apps

Firstly, I deployed the same ASP .NET Core 2 web app to two different Azure App Services. One of them is deployed at Australia East; another one is deployed at South India (Huuray, Microsoft opens Azure India to the world in April 2017!).

The homepage of my web app, Index.cshtml, is as follows to display the information in Request.Headers.

 

Index.png
Since WordPress cannot show the HTML code properly, I show the code as an image here.

 

In the code above, Request.Headers[“X-Forwarded-For”] is used to get the actual visitor’s IP address instead of the IP address of the Nginx load balancer. To allow this to work, we need to have the following codes added in Startup.cs.

app.UseForwardedHeaders(new ForwardedHeadersOptions
{
    ForwardedHeaders = 
        ForwardedHeaders.XForwardedFor | ForwardedHeaders.XForwardedProto
});
azure-regions.png
In this article, we will set up load balancer in Singapore for websites hosting in India and Australia.

Configure Linux Virtual Machine on Azure

Secondly, as described in the Chinese article mentioned above, the Nginx needs to be set up on a Linux server. The OS used in my case is Ubuntu 17.04.

installing-ubuntu-server-17-on-azure.png
Creating a new Ubuntu server running on Microsoft Azure virtual machine.

The Authentication Type that was chosen is the SSH Public Key option. Hence, we need to create public and private keys using OpenSSL tool. There is a tutorial from Microsoft showing steps on how to generate the keys using Git Bash and Putty.

Installing Nginx

After that, I installed Nginx by using the following command.

sudo apt-get install nginx

After installing it, in order to test whether Nginx is installed properly, I visited the public IP address of the virtual machine. However, it turns out that I couldn’t visit the server because the port 80 by default is not opened on the virtual machine.

Hence, the next step I need to do is opening port using Azure Portal by adding a new inbound security rule for the port 80 and then associate it to the subnet of the virtual network of the virtual machine.

Then when I revisited the public IP of the server, I could finally see the “Welcome to Nginx” success page.

successfully-opened-port-and-installed-nginx.png
Nginx is now successfully running on our Ubuntu server!

Mission: Load Balancing Azure Web Apps with Nginx

As the success page mentioned, further configuration is required. So, we need to edit the configuration file by first opening it up with the following command.

sudo nano /etc/nginx/sites-available/default

The first section that I added is the Cache Configuration.

# Cache configuration
proxy_temp_path /var/www/proxy_tmp;
proxy_cache_path /var/www/proxy_cache levels=1:2 keys_zone=my_cache:20m inactive=60m max_size=500m;

The proxy_temp_path is the path to the directory where the temporary files should be stored at when the response from the upstream server cannot fit into the configured buffers.

The proxy_cache_path is about in which directory the cache should be stored at. The levels=1:2 means that the cache will be stored in a single-character directory with a two-character subdirectory. The keys_zone parameter defines a my_cache cache zone which can store 20MB of keys at most but with the maximum size of the actual data to be 500MB. The inactive=60m means the maximum inactive time cache can be stored, which is 60 minutes in this case.

Next, upstream needs to be defined as follows.

# Cluster sites configuration
upstream backend {
    server dotnetcore-clustering-web01.azurewebsites.net fail_timeout=30s;
    server dotnetcore-clustering-web02.azurewebsites.net fail_timeout=30s;
}

For the default server configuration, we need to make a few modifications to it.

# Default server configuration
# 
server {
    listen 80 default_server;
    listen [::]:80 default_server;
    server_name localhost;
    
    ...
    
    location / {
        proxy_pass http://backend;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        try_files $uri $uri/ =404;
    }
}

Now, we just need to restart the Nginx with the following command.

sudo service nginx restart

Then when we visit the Ubuntu server again, we will realize that we sort of able to reach Azure Web Apps but not really so because it says 404!

404-on-azure.png
Oops, the Nginx routes the visitor to 404 land.

Troubleshooting 404 Error

According to another article which is written by Issac Lázaro, he said this was due to the fact that Azure App Service uses cookies to do ARR (Application Request Routing), hence we need to have the Ubuntu server to pass the header to the web apps by modifying our Nginx configuration to the following.

# Cluster sites configuration
upstream backend {
    server localhost:8001 fail_timeout=30s;
    server localhost:8002 fail_timeout=30s;
}
...

server {
    listen 8001;
    server_name web01;

    location / {
        proxy_set_header Host dotnetcore-clustering-web01.azurewebsites.net;
        proxy_pass http://dotnetcore-clustering-web01.azurewebsites.net;
    }
}

server {
    listen 8002;
    server_name web02;
    
    location / {
        proxy_set_header Host dotnetcore-clustering-web02.azurewebsites.net;
        proxy_pass http://dotnetcore-clustering-web02.azurewebsites.net;
    }
}

Then when we refresh the page, we shall see the website is loaded correctly with the content will be delivered from either web01 or web02.

success.png
Yay, we make it!

Yup, that’s all about setting up a simple Nginx to load balance multiple Azure Web Apps. You can refer to the following articles for more information about Nginx and load balancing.

References

  1. How to open ports to a virtual machine with the Azure portal
  2. Can’t start Nginx – Job for nginx.service failed
  3. Linux+.NetCore+Nginx搭建集群
  4. Understanding Nginx HTTP Proxying, Load Balancing, Buffering, and Caching
  5. Module ngx_http_upstream_module
  6. How To Set Up Nginx Load Balancing with SSL Termination

 

[KOSD Series] Code Review and VSTS

KOSD, or Kopi-O Siew Dai, is a type of Singapore coffee that I enjoy. It is basically a cup of coffee with a little bit of sugar. This series is meant to blog about technical knowledge that I gained while having a small cup of Kopi-O Siew Dai.

kosd-vsts-azure.png

Code reviews are a best practice for software development projects but it’s normally ignored in startups and SMEs because

  • the top management doesn’t understand the value of doing so;
  • the developers have no time to do code reviews and even unit testing.

So, in order to improve our code quality and management standards, we decided to introduce the idea of code reviewing by enforcing pull requests creating in our deployment procedure, even though our team is very small and we are working in a startup environment.

Firstly, we set up two websites on Azure App Service, one for UAT and another for the Production. We enabled Continuous Deployment feature for two of them by configuring Azure App Service integration with our Git repository on Visual Studio Team Services (VSTS).

Secondly, we have two branches in the Git repository of the project, i.e. master and development-deployment. Changes pushed to the branches will automatically be deployed to the Production and the UAT websites, respectively.

In order to prevent that our codes are being deployed to even the UAT site without code reviews, we created a new branch known as the development branch. The development branch allows all the relevant developers (in the example below, we call them Alvin and Bryan) to pull/push their local changes freely from/to it.

git-flow-on-vsts.png

Once any of the developers is confident with his/her changes, he/she can create a new pull request on VSTS.

creating-pull-request.png
Creating a new pull request on VSTS.

We then proceed to make use of the new capability on VSTS, which is to set policies for the branches. In the policy setting, we checked the option “Require a minimum number of reviewers” to prevent direct pushes to both master and development-deployment branches.

branch-policies.png
Enabled the code review requirement in each pull request to protect the branch.

So for every deployment to our UAT and Production websites, the checking step is in place to make sure that the deployments are all properly reviewed and approved. This is not just to protect the system but also to protect the developers by having a standardized quality checking across the development team.

This is the end of this episode of KOSD series. If you have any comment or suggestion about this article, please shout out. Hope you enjoy this cup of electronic Kopi-O Siew Dai. =)

MS SQL on AWS: Amazon RDS

amazon-rds-ms-sql-server

There are some startups and SMEs hosting their databases on AWS. However, most of them choose to use Amazon EC2 because doing so is similar to running a SQL Server on-premise at data centres. So, to them, it’s something that they are familiar with back in the old days. However, doing so actually increases their cost of hosting services on AWS. The companies also need to hire experts to do database administration such as database backup and recovery and OS patching.

Hence, if I’m given the opportunity, I usually recommend the small companies with limited resources to consider Amazon RDS (or Azure SQL) first. Amazon RDS is a fully managed service which provides cost-efficient and resizable capacity while automating time-consuming database administration tasks.

Multi-AZ Deployments for MS SQL Server

Starting from May 2014, Amazon RDS also provides a highly available database solution with the synchronous Multi-AZ replication for MS SQL. Multi-AZ deployments for MS SQL database instances use SQL Server Mirroring.

Currently, Amazon RDS only supports Standard Edition and Enterprise Edition of SQL Server 2008 R2, 2012, 2014, and 2016. Amazon RDS also does not support Multi-AZ with Mirroring for the following regions yet:

  • US West (N. California);
  • Asia Pacific (Singapore);
  • European Union (Frankfurt);
  • AWS GovCloud (US);
  • Asia Pacific (Sdyney): Supported for DB instances in VPCs only;
  • Asia Pacific (Tokyo): Supported for DB instances in VPCs only;
  • South America (São Paulo): Supported for all DB instance classes except m1/m2.

It’s quite unfortunate that Singapore Region is one of them.

use-multi-az-deployment-for-production-sql-server-se.png
In N. Virginia Region, we’re able to specify to use Multi-AZ Deployment in Production SQL Server SE.

DB Instance Class

We can specify the DB Instance Class that allocates the computational, network, and memory capacity required by planned workload of the database instance.

available-instance-class-for-ms-sql.png
DB Instance Classes available in MS SQL 2016 on AWS.

Standard (db.m4) instances offer a balance of compute, memory, and network resources, and are a good choice for many applications.

Memory Optimized (db.r3) instances are designed to deliver fast performance for workloads that process large data sets in memory. The instances are well suited for the applications, such as high performance relational databases, in-memory analytics, and enterprise applications (for example, Microsoft SharePoint).

Burst Capable (db.t2) instances are instances that provide baseline performance level with the ability to burst to full CPU usage.

Storage Types

Most of the Amazon RDS are using Amazon EBS (Elastic Block Store) volumes for database and log storage. There are currently two main Storage Types available when setting up MS SQL database instances, as listed below.

General Purpose (SSD) storage, aka gp2, offers cost-effective storage which is suitable for a broad range of database workloads. Hence, it’s ideal for small to medium-sized databases. It provides baseline of 3 IOPS/GB and ability to burst to 3,000 IOPS for extended periods of time. Its volume can range from 20GB to 4TB for MS SQL database instances. However, provisioning less than 100 GB of General Purpose (SSD) storage for high throughput workloads could result in higher latencies upon exhaustion of the initial General Purpose (SSD) I/O Credit balance.

Provisioned IOPS (SSD) storage, aka io1, is suitable for I/O intensive database workloads which pay attention to storage performance and consistency in random access I/O throughput. It provides flexibility to provision I/O ranging from 1,000 to 30,000 IOPS. MS SQL can have provisioned IOPS volumes between 100GB (Express/Web edition) or 200GB (Standard/Enterprise edition) and 4TB.

amazon-ebs-pricing.png
Amazon Elastic Block Store (EBS) Pricing for Singapore region.

Allocated Storage and I/O Credits

General Purpose (SSD) storage performance is controlled by the volume size. Larger volumes have higher base performance levels and can accumulate I/O Credits faster. The more storage, the greater the base performance is and the faster it replenishes the credit balance.

For General Purpose (SSD) storage, the DB instance has an initial I/O Credits balance of 5.4 million. When the storage requires more than the base performance I/O level, it uses I/O credits in the credit balance to burst to the required performance level, up to a maximum of 3,000 IOPS. If the storage uses all of its I/O credit balance, its maximum performance will remain at the base performance level until I/O demand drops below the base level and unused credits are added to the I/O credit balance at the baseline performance rate of 3 IOPS/GB of volume size. Hence, we can use the formula below to calculate the Burst Duration.

burst-duration-formula.png

burst-duration-tabular.png

Thus, for production application that requires fast and consistent I/O performance, it’s recommended to use Provisioned IOPS (SSD) storage that is optimized for I/O intensive, online transaction processing workloads that have consistent performance requirements. Note that we cannot decrease storage allocated for a DB instance.

For MS SQL Server, Amazon RDS does not currently support increasing storage. Hence, we need to provision storage based on anticipated future storage growth. If we predict it wrongly, then we need to increase the storage of an existing SQL Server DB instance by first exporting the data, creating a new database instance with increased storage, and then importing the data into the new database instance.

Specifying Database Instance Specification

After understanding key concepts above, we can then proceed to setup our database instance.

specifying-db-instance-specifications.png
Although there is Free Tier available but allocating storage > 20GB or adding provisioned IOPS will disqualify the databse instance from being eligible for the Free Tier.

Network and Security: VPC (Virtual Private Cloud)

Amazon RDS database instances can be hosted on either EC2-VPC platform or the legacy EC2-Classic platform, the original platform used by Amazon RDS. Amazon VPC launches AWS resources, such as database instances, into a virtual private cloud.

Nowadays, if we are creating a database instance in a region that we have not used before, we normally are already on the EC2-VPC platform.

rds-supported-platforms.png
We are already on EC2-VPC platform.

There are many scenarios for accessing a database instance in a VPC. Today, I will only focus on having an EC2 web server to access the database instance in the same VPC.

web-server-and-db-instance-in-the-same-vpc.png
A database instance in a VPC accessed by an EC2 instance in the same VPC (Source: AWS Documentation)

In such scenario, Amazon RDS database instance normally needs to be available to the web server, and not to the public Internet. Hence, we can create a VPC with both public and private subnets. The web server will be hosted in the public subnet so that it is accessible by the public. The database instance is hosted in the private subnet so that it won’t be available to the public Internet, providing greater security.

The Security Group used to restrict access to the database instances can have a custom rule that allows TCP access using the port 1433 and an IP address we will use to access the database instance for development or other purposes. In addition, we also need to set the Public Accessible option to Yes first (It is recommended to set the option to No for production database instance to limit the potential thread with no public routes).

Encryption of Database Instances using Key Management Service (KMS)

Amazon RDS for MS SQL supports the encryption of database instances with encryption keys managed in AWS KMS. Once the data is encrypted, Amazon RDS handles authentication of access and decryption of the data transparently without having the need to change our database client applications.

enable-database-encryption.png
Currently, encryption of database instances (Data-in-Rest Protection) is not available for those which are running SQL Server Express Edition.

Backup and Maintenance

Amazon RDS automatically backup our database instances. It creates a storage volume snapshot of our database instance, backing up the entire database instance and not just individual databases. We can setup and modify our preferred Backup Window from time to time. During the automatic backup window, storage I/O might be suspended briefly while the backup process initializes (typically under a few seconds). For SQL Server, I/O activity is suspended briefly during backup for Multi-AZ deployments.

By default, Amazon RDS has a 30-minute backup window randomly selected from an 8-hour block (Singapore region will be 14:00–22:00 UTC).

Periodically, Amazon RDS also automatically does maintenance work such as, updating the databse instance’s or database cluster’s OS. We can choose to manually apply maintenance, or wait for the automatic maintenance process initiated during our preferred maintenance window. There is one thing to take note is that the maintenance window determines when pending operations start, but does not limit the total execution time of these operations.

By default, Amazon RDS also has a 30-minute maintenance window randomly selected from an 8-hour block (Singapore region will be 14:00–22:00 UTC).

maintenance-window-collide-with-backup-window.png
We’re not allowed to make the maintenance window and the backup window overlap.

CloudWatch

Amazon RDS sends metrics to CloudWatch for each active database instance every minute. Detailed monitoring is enabled by default.

cloudwatch.png
Amazon RDS Metrics

When setting up the database instance, there is an option for us to specify whether to enable Enhanced Monitoring or not. Enhanced Monitoring is not exactly like CloudWatch. CloudWatch gathers metrics about CPU utilization from the hypervisor for a database instance, and Enhanced Monitoring gathers its metrics from an agent on the instance.

enable-enhanced-monitoring.png
Enhanced monitoring requires permission to act on our behalf to send OS metric information to CloudWatch Logs.

Conclusion

It’s true that AWS allows us to deploy our MS SQL Server database on either Amazon RDS and Amazon EC2. However, it’s very crucial to analyze our needs and our application before deciding which one to use. In general, it is still recommended to consider Amazon RDS first so that developers can focus on high-level tasks and business logic implementation.

That’s all for my first trip to Amazon RDS. As a frequent user of Microsoft Azure, I never host MS SQL Server on AWS platform. So, if there is any mistake made in this article, kindly feedback to me. Thanks in advance!

Further Reading

Deploying Microsoft SQL Server on Amazon Web Services

Magical Experience with Beacons

One month ago on 27th of March, my friend passed me a box of Estimote Proximity Beacons. That day marks the beginning of my journey towards a greater understanding of beacons and IoT.

Since the day I joined travel industry, I have always been thinking of providing a fun travel experience with beacon technology. When I joined Changi Airport team in 2015, I proposed to my manager the possibility of applying beacons in the airport. The idea was rejected. Now, I finally get the chance to build something with the small little Estimote Proximity Beacons.

estimote-beacons.png
We forcefully opened up the beacons and replaced the batteries.

Claiming Beacons

Every Estimote beacons are shipped with an unique ID which we can modify. By default, the beacon ID is in iBeacon format and consists of 3 values:

The three values are hierarchical. The purpose of UUID is to distinguish our beacons from all other beacons in the network. Major and Minor values allow us to label the beacons with higher accuracy.

ibeacon-format
An example of how a chain of retail shops will deploy and label their beacons. (Source: Estimote Developer Docs)

The iBeacon ID can be changed. One way is to use the Estimote app to do it. Since I wasn’t the owner of the beacons, my first step is to claim the beacon using the app. After I successfully claim the beacons, I can then proceed to retrieve detailed info of the beacons and modify their info.

claiming-beacons-and-changing-broadcasting-power.png
Claiming beacon and modifying its info, such as its range (by default it’s ~3.5m).

Google Beacon Platform

After configuring our beacons, we can then proceed to claim the ownership of our beacons on the Google Beacon Registry. There is a mobile app called Beacon Tools available from Google to help us registering our beacons on Google Beacon Registry. There is a very interesting video interviewing Peter Lewis in the Coffee with a Googler season talking about the steps of beacon registration.

google-beacon-registry.png
Peter shares about Google Beacon Registry and Google Beacon Platform. (Source: YouTube)

After that, we can associate a lot of information with our beacons. To do so, we first are recommended to use Google Beacon Dashboard. There is a very simple tutorial guiding us to use the Google Beacon Dashboard to associate the attachments with the beacons.

attachments
My beacon project, Icy Marshmallow, and the attachments of a beacon in the project.

Read Attachments

I’m using the Nearby Messages API to retrieve the attachments from the beacons. I did a small little Android app (which is properly configured following the recommended steps) with the codes as shown below to achieve this.

package gclprojects.icymarshmallow;

...
import com.google.android.gms.common.ConnectionResult;
import com.google.android.gms.common.api.GoogleApiClient;
import com.google.android.gms.Nearby;
import com.google.android.gms.nearby.messages.Message;
import com.google.android.gms.nearby.messages.MessageListener;
import com.google.android.gms.nearby.messages.Strategy;
import com.google.android.gms.nearby.messages.SubscribeOptions;

public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    
    private GoogleApiClient mGoogleApiClient;
    private MessageListener mMessageListener;
    ...

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);

        ...

        mGoogleApiClient = new GoogleApiClient.Builder(this)
                .addApi(Nearby.MESSAGES_API)
                .addConnectionCallbacks(this)
                .enableAutoManage(this, this)
                .build();

        mMessageListener = new MessageListener() {
            @Override
            public void onFound(final Message message) {
                // Called when a new message is found.
                // Use message.getType().toString() to read the attachment Type
                // Use new String(message.getContent()) to read the attachment Value
            }
        }
    }

    @Override
    protected void onStart() {
        super.onStart();
        mGoogleApiClient.connect();
    }

    @Override
    protected void onStop() {
        super.onStop();
        mGoogleApiClient.disconnect();
    }

    @Override
    public void onConnected(@Nullable Bundle bundle) {
        if (mGoogleApiClient != null && mGoogleApiClient.isConnected()) {
            subscribe();
        }
    }

    ...

    private void subscribe() {
        SubscribeOptions options = new SubscribeOptions.Builder()
                .setStrategy(Strategy.BLE_ONLY)
                .build();

        Nearby.Messages.subscribe(mGoogleApiClient, mMessageListener);
    }
}

With the codes above, when beacon gets detected by the mobile app, the onFound method gets called for each of the attachment associated with the beacons. If we print the variable message into Log, we shall see something as follows.

Message{namespace='icy-marshmallow', type='string', content=[29 bytes]}

As shown above, the Value of the attachment is base64 encoded. So to read it, we just need to use new String(message.getContent()).

In the subscribe method, since we are only interested in messages attached to BLE (Bluetooth Low Energy) beacons, we use Strategy.BLE_ONLY.

Problem #1: Unsubscribe Method

When the app is running and another app comes into the foreground, we also need to stop subscribing to messages from the beacons. Otherwise, when we navigate back to the app, the messages can no longer be received even though we re-trigger the subscribe method.

So, I added the following codes.

@Override
protected void onPause() {
    if (mGoogleApiClient != null && mGoogleApiCLient.isConnected) {
        unsubsribe();
    }

    super.onPause();
}

@Override
protected void onResume() {
    if (mGoogleApiClient != null && mGoogleApiClient.isConnected) {
        subscribe();
    }

    super.onResume();
}

private void unsubscribe() {
    Nearby.Messages.unsubscribe(mGoogleApiClient, mMessageListener);
}

Problem #2: Stop Receiving Messages After Few Minutes

Another problem I notice is that the messages will stop be “found” after one to two minutes. However, if I re-trigger the mobile app, then I can start seeing the messages being detected for another one or two minutes.

To solve this issue, I use a simple timer which helps to check whether it has been quite some time the app doesn’t detect the beacons. If it’s more than 1 minute, then the timer will do a unsubscribe-then-subscribe-again action. This will help the mobile app to keep receiving the messages from the beacons. It also solve the problem of the mobile app re-visiting the beacons.

Problem #3: Geo-Location

This is not a real problem if we don’t need the geo-location information of the beacons. However, if we need to know the geo-location of the beacon, one simple way is to just use the LocationManager which provides periodic updates of the mobile geographical location.

package gclprojects.icymarshmallow;

...
import android.location.Location;
import android.location.LocationListener;
import android.location.LocationManager;

public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    LocationManager locationManager;
    ...

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);

        ...

        LocationListener locationListener = new LocationListener() {
            public void onLocationChanged(Location location) {
                // Record down the latitude and longitude of the mobile
            }

            ...
        }

        locationManager = (LocationManager) this.getSystemService(Context.LOCATION_SERVICE);
    
        int permissionCheck = ContextCompat.checkSelfPermission(this, Manifest.permission.ACCESS_FINE_LOCATION);
        if (permissionCheck == PackageManager.PERMISSION_GRANTED) {
            locationManager.requestLocationUpdates(LocationManager.NETWORK.PROVIDER, 0, 0, locationListener);

            ...
        }
    }
}

Writing Data to Firebase

This step is optional unless the data collected needs to be stored for future use.

I use the following codes to write the beacon data to Firebase database.

package gclprojects.icymarshmallow;

...
import com.google.firebase.database.DatabaseReference;
import com.google.firebase.database.FirebaseDatabase;

public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    private DatabaseReference mDatabase;
    ...

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);

        ...
        
        mDatabase = FirebaseDatabase.getInstance().getReference();

        mMessageListener = new MessageListener() {
            @Override
            public void onFound(final Message message) {
                ...

                Beacon beaconInfo = new Beacon(...);

                Format formatter = new SimpleDateFormat("yyyy-MM-dd-HH:mm:ss");
                mDatabase.child("Person A")
                        .child(formatter.format(new Date())
                        .setValue(beaconInfo);
            }
        }
    }
}

...

public class Beacon { ... }
firebase.png
Successfully recorded the data from beacons in my Firebase database!

To integrate our Android app with Firebase, our friendly Android Studio comes with a tool called the Firebase Assistance which will help us connect to the Firebase. The assistance also comes with short getting-started tutorial to show us how to cinfigure and add realtime database to our mobile app.

beacon-in-changi-airport.png
Spot the beacon. =)

Installing Beacons in Changi Airport

Installing beacons in our Changi Airport is always one of my dreams to enhance the experience of millions of travelers flying in and out of the airport. In fact, currently the Armsterdam city is already making use of beacon technology to build a powerful beacon networks to give the people a better experience when they are walking around in the city. So why can’t we do the same in our friendly Changi Airport? =)