Azure WebJob with Azure Queue


Cron job is essential part of complex systems to execute of certain script or program at a specific time interval. Traditionally, developer or system administrator create Windows Scheduled Task to execute scheduled job within the operating system.

In one project, I used to have multiple .exe programs scheduled to update the production database during mid night for various use cases such as expiring user credit. This gets the job done within application context but this is not the cleanest way when my system administrator need to take care of 20 other cron jobs coming from different machines and different operating systems.

The next thing I have implemented is to expose a cron job through WCF API endpoint. For example, I opened a WCP API endpoint to be triggered on a functionality in sending email notification. This end point will map user’s saved criteria and business inventory on a daily basis. (Yes, this is the annoying 9.00AM email spam notification you get everyday. Sorry!) The WCF API endpoint does not do anything if no one hits it. It is a simple HTTP endpoint waiting for something to tell him to get up and work.

The reason to expose the cron job as WCF API endpoint is to allow my system administrator to have a centralized system to trigger and monitor all the cron jobs in one place rather than logging into multiple servers (operating systems) to monitor and troubleshoot. This works alright except that now I have my cron job stuck in a WCF project instead of simple script or a lightweight .exe program.

Azure WebJob

The next option is Azure WebJob. Azure WebJob enables me to run programs or scripts in web app context as background processes. It runs and scales as part of Azure Web Apps. With Azure WebJob, now I can write my cron job as simple script or a light-weight .exe rather than WCF. With Azure WebJob, my system administrator can also have a centralized interface, Azure Portal to monitor and configure all the cron jobs. In fact, it’s pretty cool that I can trigger a .exe program using a public HTTP URL using the Web Hook property in WebJob.

Azure WebJob goes beyond the traditional cron job definition (timer based). Azure WebJobs can run continuously, on demand or on a schedule.

The following file types are accepted:

  • .cmd, .bat, .exe (using windows cmd)
  • .ps1 (using powershell)
  • .sh (using bash)
  • .php (using php)
  • .py (using python)
  • .js (using node)
  • .jar (using java)

I will use C#.NET to create a few .exe to demonstrate how Azure WebJob works.

Prerequisites (nice to have)

Preferably, you should have Microsoft Azure SDK installed on your machine. At the point of writing this, I’m running Visual Studio 2015 Update 3, so I have the SDK for VS2015 installed using Web Platform Installer.

Note that this is NOT a MUST have. You can still write your WebJob in any above-mentioned language and upload you job manually in Azure Portal. The reason I recommend you to install it is to make your development and deployment much easier.

Working with Visual Studio

If you have Microsoft Azure SDK installed for Visual Studio, you will see Azure WebJob template. We are going to start with a simple Hello World program to get started.

Your WebJob project will be pre-loaded with some sample codes. As usual, you will still need to build it once for NuGet to resolve the packages. 

The following packages are part of your packages.config if you started the project with Azure WebJobs template. No big deal if you didn’t, you can install them manually, although it’s a little tedious.

For now, we will ignore the fancy built-in SDK support for various Azure services. We will create a simple Hello World program and deploy it to Azure to get an end-to-end experience.

Remove everything else and left with Program.cs writing a simple message:

Go to your Azure Portal. Click on Get publish profile to download your publishing profile. You will need it to import into your Visual Studio later when publishing your WebJob.

Import your publish profile into your project in Visual Studio. The Import dialog will kick in at the first time when you publish your project as WebJob.

Right click on your project and select “Publish as Azure WebJob”, you will see the following dialog to set up your publishing profile.

Import the earlier downloaded publishing profile setting file into your WebJob project in Visual Studio.

Validate your connection.

Click on “Publish” to ship your application to Azure.

Upon successful publishing, you should be seeing a message “Web App was published successfully…”

Go to your Azure portal and verify that your WebJob is indeed listed.

Select your WebJob, click on the Logs button on top to see the following page.

The impressive part about using WebJob in Azure is the following WebJobs monitoring page. You can use this page to monitor multiple WebJobs status and drill down deeper into the respective logs. No extra cost, coding or configuration, all work out of the box!

Now we have our first Hello World application running in Azure. We have deployed our WebJob to run continuously, which means it will get triggered automatically every 60 seconds. Once the first round is completed, status will change to PendingRestart and wait for the next 60 seconds to kick in.

WebJob SDK sample project in GitHub demonstrates comprehensively how you can work with WebJob through Azure Queue, Azure Blob, Azure Table, Azure Service Bus. In this article, we will do a little bit more coding by using WebJob to interact with Azure Queue.

Azure WebJob with Azure Queue Storage

Microsoft.Azure.WebJobs namespace provides QueueTriggerAttribute. We will use it to trigger a method in our WebJob.

This works by whenever a new message is added into the queue, the WebJob will be triggered to pick up the message in the queue.

Before we continue in our codes, we first need to create a Azure Storage account to host the queue. Here, I have a storage account name “danielfoo”.

We will use Microsoft Azure Storage Explorer to get visual on our storage account. It’s a handy tool to visualize your data. If you do not have it, no worry, just imagine the queue message in your mind 🙂

Let’s add a new console application project in our solution to put some messages in our queue.

There are two packages that you’ll need to install into your queue project:

We will write the following simple codes to initialize a queue and put a message into the queue.

Of course, you will have to configure your StorageConnectionString in app.config for codes to recognize the connection string.

You can get your account name and key from Azure Portal.

Let’s execute our console application to test if our queue can be created and whether a message can be placed into the queue properly.

After execution, look at Storage Explorer to verify if the message is already in the queue.

Now we will dequeue this message so that it will not interfere with the actual QueueTrigger in our exercise later.

Next, we will create a new WebJob project that get triggered whenever a message is added into the queue by using QueueTriggerAttribute under Microsoft.Azure.WebJobs namespace.

This time we do not remove Functions.cs nor modify Program.cs.

Make sure that your Functions.cs method parameter contains the same queue name as what you defined earlier in your Queue.MessageGenerator project. In this example, we are using the name “danielqueue”.


Remember to fill up your App.config on the following connection string. This is to allow the WebJob to know which storage account to monitor.

Now, let’s start WebJob.QueueTrigger project as a new instance and allow it to wait for a new message add into “danielqueue”.

Then, we will start Queue.MessageGenerator project as a new instance to drop a message into the queue for WebJob.QueueTrigger to pick up.

Yes! Our local debug is has detected a new message is added into “danielqueue” hence hit the ProcessQueueMessage function.

Let’s publish our WebJob.QueueTrigger to Azure to see it processing the queue message in Azure context instead of local machine. After successful publishing, we now have 2 WebJobs.

Select QueueTrigger (the WebJob we just published) and click on Logs button on top. You will see the following log on queue message processing.

If you drill down into particular message, you will be redirected to the Invocation Details page

We have just setup our WebJob to work with Azure Queue!

That wraps up everything I want to show you in working with Azure WebJob and Azure Queue.

Obviously in reality you will write something more complex than simply output the log in your WebJob. You may write some logic to perform certain task. You may even use this to trigger another more complex job sitting in another service.

In the queue, obviously you also wouldn’t write a real “message” like I did. You will probably create one queue for very specific purpose. For example, you will create a queue to store a list of ID, where each of the ID is required for another type of process such as indexing. The queue will index the entity (represented by the ID) in batches (let’s say 4 messages at a time) instead of having a large surge of load in a short period of time.

Few more thoughts…

  1. By default, JobHostConfiguration.QueuesConfiguration.BatchSize handles 16 queue messages concurrently. I recommend you to override the default value with a smaller value (let’s say, 4) to ensure the other end which does the more heavy processing (for example indexing a document in Solr or Azure Search) is able to handle the load. The maximum value for JobHostConfiguration.QueuesConfiguration.BatchSize is 32. If having WebJob to handle 32 message at a go is not sufficient for you, you can further tweak the performance by setting a short JobHostConfiguration.QueuesConfiguration.MaxPollingInterval time to make sure you do not accumulate too many message before the processing kicks in.
  2. If for whatever reason you have max out the built-in configuration (such as BatchSize, MaxPollingInterval) and yet it is not good enough, a quick win will be to scale up your WebApp. Note that you cannot scale your WebJob alone because WebJob sits under the context of WebApp. If scaling up WebApp for the sake WebJob sound like an inefficient way, consider migrating your jobs to Worker Role.
  3. WebJobs are good for lightweight processing. They are good for tasks that only need to be run periodically, scheduled, or triggered. They are cheap and easy to setup and run. Worker Roles are good for more resource intensive workloads or if you need to modify the environment where they are running (for example .NET framework version). Worker Roles are more expensive and slightly more difficult to setup and run, but they offer significantly more power when you need to scale. There is a pretty comprehensive blog post by kloud comparing WebJob and Worker Role.
  4. Azure Storage Queue has no guarantee on message Ordering. In other words, a message get placed into the queue first does not necessary get processed first. Delivery for Azure Queue is At-Least-Once but not At-Most-Once. In other words, a message potentially get processed more than once. The application codes will need to handle the duplication of what happens after a message is picked up. If this troubles you, you should consider Service Bus Queue. The Ordering is First-In-First-Out (FIFO) and delivery is At-Least-Once and At-Most-Once. If you are wondering then why people still use Azure Storage Queue, it is because Storage Queue is designed to handle super large scale queuing. For example, maximum queue size for Storage Queue is 200 TB while Service Bus Queue is 1 GB to 80 GB; maximum number of queues for Storage Queue is Unlimited while for Service Bus Queue is 10,000. For complete comparison reference, please refer to Microsoft doc.

I hope you have enjoyed reading this article. If you find it useful, please share it with your friends who might benefit from this. Cheers!

Tech Talk 2016


2016 has been a fruitful year for me in software development. Apart from my day job in Sitecore as a lead developer, I also have a lot fun with services in Azure cloud in my spare time.

Another new “adventure” I tried in 2016 is speaking for Tech Talk in local tech communities – from my own office, university, Microsoft office to being an online panelist in Google Hangout discussion.

Microsoft Malaysia Level 26, Tower 3

Scaling SQL Server @ Microsoft Malaysia Level 26, Tower 3


Software Industry Career Advice @ Multimedia University, Computing Faculty Lecture Hall, Cyberjaya

Sitecore Malaysia - Daniel Foo on Azure Search

Working with Azure Search @ Sitecore Malaysia, Level 18

Standardizing DevOps Across Organization @ Continuous Discussions (#c9d9) Google Hangout

It has been a rewarding experience to contribute and making a difference in tech communities through Tech Talk. It amazed me when some audiences asked me follow up questions based on what I have shared earlier. It is truly satisfying to learn that I have inspired some audiences with useful information or ideas in general which they can benefit from.

It has been an honor to share the stages with many of the knowledgeable speakers. As much as I enjoyed sharing, I have learned equally a lot from them. Thanks to those who invited me over to speak, those who helped me out to make the Tech Talks possible and those who came and supported me. Thank you! I am truly lucky to have you all amazing people as friends in the tech communities.

Signing off for 2016… It has been a fantastic year, looking forward to 2017!

Standardizing and Scaling DevOps Across the Organization


Large, complex organizations implementing DevOps find that they need to balance between supporting small teams and an agile way of working,  and finding ways to standardize their DevOps processes, tooling and implementation — in order to scale DevOps throughout the organization (and save costs).

On Tuesday I participated in an online panel on the subject of Standardizing and Scaling DevOps Across the Organization, as part of Continuous Discussions (#c9d9), a series of community panels about Agile, Continuous Delivery and DevOps. Watch a recording of the panel:

Continuous Discussions is a community initiative by Electric Cloud, which powers Continuous Delivery and Release Management at businesses like SpaceX, Cisco, GE and E*TRADE by automating their build, test and deployment processes.

Working with Azure Search in C#.NET


What is Azure Search

Azure Search is a service in Microsoft Azure Cloud Platform providing indexing and querying capabilities. Azure Search is intended to provide developers with complex search capabilities for mobile and web development while hiding infrastructure requirements and search algorithm complexities.

I like the idea of having an index service provider being part of my solution as it allows developers to perform searches quickly and effortlessly without going through the pain of writing essay-length SQL query.

The key capabilities of Azure Search include scalable full-text search over multiple languages, geo-spatial search, filtering and faceted navigation, type-ahead queries, hit highlighting, and custom analyzers.


Why Azure Search (or index provider in general)

  1. It’s Brilliant – Having an index provider sitting in between application and database is a great way to shield the database from unnecessary or inefficient Read requests. This allows database I/O and computing power to be reserved for operations that truly matters.
  2. It’s Modern – Traditional search approach requires developers to write essay-length SQL query to retrieve simple aggregated data. Index provider such as Azure Search allows developers to initiate a search request without writing complicated search algorithm (for example geo-spatial search) and no complex SQL query is required (for example faceted navigation).
  3. It Scales – Solutions that gain the most benefited from index provider are relatively larger enterprise system. Such system often require scaling to certain extain. Scaling Azure Search in Microsoft Azure Cloud Platform is several clicks away compared to having an on-premise service provider such as Solr.
  4. It’s FREE – Microsoft Azure provides a free tier for Azure Search. Developers can create and delete Azure Search service for development and self-learning purpose.

Use Case

A car classified site is a good example to make use of index service provider such as Azure Search. I will use a local car classified site, to illustrate several features the system can potentially tap into.

Type-ahead Queries allow developer to implement auto suggestions as user types. In the following example, as I was typing “merce”, the site returns me a list of potential Mercedes-Benz car model that I might be interested in.


Facet allow developers to retrieve aggregated data (such as car make, car model, car type, car location, car price range, etc) without writing writing complex SQL query, which also means saving the Read load at database. In the following example, the site returns me a list of Mercedes-Benz model and the count in the bracket to indicate how many classified ads are available for the specific model.


Filter allows developer to retrieve documents that fit the searching criteria without writing complex SQL queries with endless INNER JOIN and WHERE clauses. In the following example, I specified that I want all the used Mercedes-Benz, model of E-Class E200, variant of Avantgarde from Kuala Lumpur with the price range of RM100,000 to RM250,000. You can imagine the kind of INNER JOIN and WHERE clauses the poor developer has to design dynamically if this were to be retrieved from a database directly.


Another feature the car classified site can potentially tap into is Geo-spatial Search although it is not seen implemented. For example if I were to search for a specific car in a dealership, the portal can suggest similar cars from other dealerships nearby to the dealership I’m looking at. That way when I make a trip to visit a dealership, I can also visit other nearby dealerships that have the similar cars.

Using C#.NET to work with Azure Search

Let’s roll our sleeves and hack some codes. I will be using a C#.NET console application to illustrate how we can design and create an index, upload documents into the index and perform several types of searches on the index. This solution will be used to simulate some of the potential codes required by a car classified portal.

First, we create a solution name AzureSearchSandbox.

We will need “Microsoft.Azure.Search” NuGet package from Open your Package Manager Console and run the following command:

Upon successful installation, you will see several NuGet packages are added into your packages.config file in your solution.

Note that you will only need to install “Microsoft.Azure.Search”, the other packages are dependencies. The dependencies are resolved automatically. 

In your solution, add a new class Car.cs

This Car object will be used to represent your Azure Search document.

Next we create a static Helper.cs class to take care of the initialization of the index in Azure Search.

Initialize() is the main method to kick start our Azure Search index. Unlike other on-premise index service that require certain amount of setup such as Solr, it doesn’t take long to have our index up and running. In Solr, I have to install Solr using NuGet, install the right Java version, set the environment variable and finally create a core in Solr admin portal. With Azure Search, no upfront set up is required.

The index is created in CreateIndex() method, where we tell Azure Search client SDK that we want an index with the fields we define in our Index object.

To ensure this set of code is running on a fresh index, we have DeleteIfIndexExist() method to ensure the previous index is removed. We call this right before CreateIndex() method.

Next, we add a new class Uploader.cs to deal with the documents we are about to upload into our Azure Search index.

PrepareDocuments() is a simple method to construct a list of dummy Car object for our searches later on.

Upload() method gets the dummy Car objects from PrepareDocuments() and pass these object into Azure Search client SDK to upload the documents into index in a batch. Note that we added a 2000 millisecond sleep time to allow our service to upload and process car documents properly before moving on to next part of the code, which is search. However in practical sense, we would not want to add sleep time in our upload code. Instead, the component that takes care of searching should expect index is not available immediately. We also catch IndexBatchException implicitly to handle the index in case the batch upload of index failed. In this example, we merely output the index key. In practical sense, we should implement a retry or at least logging the failed index.

Once the index upload operation is completed, we will add another class Searcher.cs to take care of the searching capability.

SearchDocuments() method is to handle the searching mechanism on the index we created earlier. No fancy algorithm, only passing specific instruction to Azure Search client SDK on what we are looking for and display them. In this method, we take care simple text search, filter and facets. There are much more capabilities Azure Search client SDK can provide. Feel free to explore the SearchParameters and response object on your own.

Putting them all together in Program.cs

First we define index service name and API key to create a search index client instance. The instance is returned by Helper.Initialize(). We will make use of this instance for both search and upload later.

After initializing the index, we call Upload() method to upload some dummy car documents to the index.

Next, we perform the following searches:

  1. Simple text search. We will search for the text “Perodua” in the documents.

The result as following. Azure Search index returns 2 documents which contains the keyword “Perodua”


2. Using Filter. A more targeted and efficient approach to look for documents. In the following example, first we look for Category field which is equal to ‘Hatchback’; second we look for Price field which is greater than 100,000 and is a ‘Sedan’ category. More details on how to write expression syntax in Azure search.

The result as following: Car with the category of Hatchback and car cost more than 100,000 and is a Sedan.


3. Searching facets. With facets, developer will no longer need to write long query that combines Count() and endless GROUP BY clauses.

If we have a traditional database table that represent the dummy car data, this is equivalent to “SELECT Category, Count(*) FROM Car GROUP BY Category”.

Result as following:


This example might not look like a big deal if you have a small data set or simple data structure. Facet is super handy and fast when you have large number of data and when your query is getting more complex. The ability to define which facet is required in C# codes make the”query” much cleaner and easier to maintain.

One last thought…

You can clone the above source code from my GitHub repo. I will be happy to accept Pull Request if you want to demonstrate how to make use of other capabilities in Azure Search client SDK. Remember to change the API key and service name before compiling. Have fun hacking Azure Search!

Scaling SQL Server


Scaling database refers the ability to serve significantly more request to both read and write data without compromising performance. In many enterprise applications, performance bottleneck often happens at database, hence scaling database is a critical part on improving system performance. In the last article on Microservices, we discussed scaling database horizontally and vertically on high level. In this article we will talk more in-depth about:

  1. Three types of replication in SQL Server
  2. Distribute database load using log shipping
  3. Tools to shield your database from being hit.

3 Types of Replication

In typical enterprise applications, Read request significantly outnumber Write request. By implementing Replication, you effectively offload the bulk of Read request to the Subscribers while reserving Publisher for Writing.

Transactional Replication

Transactional Replication is the simplest form of replication to understand and to implement. Transactional Replication is implemented by having a Publisher to publish the changes. One or more than one Subscriber will replay the transaction log. Data changes and schema modifications made at the Publisher are delivered to the Subscriber(s) as they occur (almost real time). In this way, transactional consistency is guaranteed.

The incremental changes in Publisher will be propagated to Subscribers as they occur. If a row changes for 3 times in Publisher, the Subscriber will also change for 3 times. It is not just the net data change that get propagated over.

For example, if a row in Product table changes price three times from $1.00, to $1.10, to $1.20 and finally to $1.30, transactional replication allows an application to respond to each change. Perhaps, send a notification to user when the price hit $1.20. It is not simply the net data change to the row by changing the price from $1.00 to $1.30. This is ideal for applications that require access to intermediate data states. For example, a stock market price alert application that tracks near real time stock price changes to send price alert to users.

Is it possible to scale your Publisher horizontally? Yes, Bidirectional Transactional Replication and Peer-to-Peer Transactional Replication will help you to achieve it. However, Microsoft strongly recommend that write operations for each row be performed at only one node – for 2 reasons. First, if a row is modified at more than one node, it can cause a conflict or even a lost update when the row is propagated to other nodes. Second, there is always some latency involved when changes are replicated. For applications that require the latest change to be seen immediately, dynamically load balancing the application across multiple nodes can be problematic.

From experience, the most optimum solution is to scale your Subscriber horizontally by having multiple nodes. Keep your Publisher in one node and scale the Publisher vertically, when you really have to.



Merge Replication

In Merge Replication, Subscriber synchronizes with the Publisher when connected to the network and exchanges all rows that have changed between the Publisher and Subscriber since the last synchronization occurred. You may see Merge Replication as a batch update from Subscriber to Publisher that propagates only the net data changes. For example, if a row changes five times at a Subscriber before it synchronizes with Publisher, the row will change only change once at the Publisher to reflect the net data change (which is the 5th value). Then, the unified changes in Publisher will be propagated back to other Subscribers.

Merge Replication is suitable for situation where Subscribers need to receive data, make changes offline, and later synchronize changes with the Publisher and other Subscribers. For example a nationwide POS (point of sale) system where retail branches are spread across multiple physical locations. The retail branches will first initialize a snapshot from the Publisher database, make local offline changes (for example, through sales) to Subscriber database. The sales are not required to be propagated back to Publisher immediate. The many other retail branches also do not need immediate update on the changes happened at another retail branch although a more recent update will be beneficial, for example knowing whether another branch nearby has the stock that the local branch has run out to recommend customers where to go accordingly. Once a day or multiple times a day, depending on business need, the sales number in retail branches (Subscribers) will be propagated back to HQ (Publisher) and the executives in the HQ office can view the daily sales report.


Conflict can happen in Merge Replication and conflict will happen. Good news is, conflicts are resolved without the need of user intervention because SQL Server has built-in mechanism to resolve conflicts on data changes. However if you have unique use cases where you want to ensure SQL Server is doing exactly what you intended while resolving a conflict, you can view the conflict at Microsoft Replication Conflict Viewer and the outcome of the resolution can be modified.

Snapshot Replication

Snapshot Replication propagate data exactly as it appears at a specific moment in time and does not monitor for updates to the data. When synchronization occurs, the entire snapshot is generated and is sent to Subscribers. In simpler terms, Snapshot Replication takes a snapshot of the Publisher data state and overwrite it at the Subscribers. No conflict will happen as this replication basically overwrite the whole data set. Snapshot Replication is also used in both Transactional Replication and Merge Replicate to initialize the database at the Subscribers.


Snapshot Replication is suitable for system where

  • Subscriber deal with data that does not change frequently.
  • Subscriber do not require the most recent set of data for a long time.
  • The data set is small.

I was working on a small project with an advertising agency where the requirement is to analyze the trend of discussion and sentiment related to Telcos in a community forum. I quickly hack some web scraping codes to scrap what I needed and populate them into my database. As a result, I got 730 discussion topics and the total size of the database is about 5MB. From there I need to write some more algorithm to find out the trend and sentiment of discussion. During the development of my algorithm, I did not need the most updated dataset that reflects what is happening in the live forum. I pleasantly work (read) on a Subscriber node to develop and test out my algorithm. Few weeks later when the forum is updated with a lot more discussion topics, I simply replicate the changes in Publisher to my Subscriber in a fairly short amount of time. On production, knowing the analytical codes read database extensively, I pointed my codes to run on the Subscriber node. My Publisher node will not get any hit apart from getting the necessary insert operation from my web scraping service. Since my users do not need to know what was being discussed on a daily basis or on real time basis (because accurate trend and sentiment require months and months worth of data), I have configured my Snapshot Replication to happens on a monthly basis. The replication can be completed in a fairly short amount of time. On monthly basis the users will get a fresh copy of the trend and sentiment report base on at least 1 year worth of backdated forum discussion data without any performance downgrade. By implementing Snapshot Replication, I allow my web scraping service to write to Publisher without worrying if anyone need the database for reading to generate report at any particular time. Through Subscriber, I have also set up the foundation to make it possible to generate much more sophisticated reports without downgrading the performance by spinning up more Subscriber nodes when and if I need to.

Log Shipping

Log Shipping is used to automatically send transaction log backups from primary database to one or more secondary databases. The primary and secondary database should sit on different nodes. The transaction log backups are applied to each of the secondary database. Log Shipping is often used as part of disaster recovery strategy but creative database administrator often use Log Shipping for various other purposes *wink*.

In one of the projects I was working on, the SQL Server database was storing 130k registered users and their related activities such as payment history, credit spending history, Account & Contact relationship, products, login audit, etc. The company was at a rapid expansion stage where the CFO decided it’s time to get in a Business Intelligence guy to churn out some reports to give a sense how the business is doing on daily basis. The obvious thing to do here is to replicate a database for the BI colleague to run his heavy queries because running the reporting queries on production database is going to kill the poor database. The most suitable type of replication will be Transactional Replication. However the challenge was Replication requires SQL Server Enterprise Edition and we were running on SQL Server Standard Edition. The bad news is, the company did not have a lot of cash lying around for our disposal. We will have to find an alternative. After getting the green light from CTO, I implemented Log Shipping for BI reporting. In essence, I was “scaling” the SQL Server using a disaster recovery technique by offloading the reporting query load to a secondary database by replaying the transaction log to simulate Transactional Replication on an interval basis. It was the most optimum option we have in order to satisfy various stakeholders while keeping the cost low.


You can use this trick as long as your client does not require real time data. Take note on the following practical issues while “scaling” your SQL Server using Log Shipping.

  1. Understand that Log Shipping is a 3 steps process. First, primary database backup the transaction log at the primary server instance. Second, copy the transaction log file to the secondary server instance. Third, restore the log backup on the secondary server instance. From experience, third step Restore is the most fragile step where it broke often. To find out why Restore fails, go to SQL Server Agent to view the job history to see the detail error message.
  2. Understand your transaction backup cycle. If you have another Transaction Log backup automation happening by another agent / service, your Log Shipping will stop working fairly quickly (note: not immediately). Log Shipping works by taking all the transaction log since the last Full backup and clear off the log. It is important for SQL Server to match to log sequence. If there is another Transaction Log backup happened somewhere else, the tail of your newest Transaction Log will not match the head of last Transaction Log hence fail. If you need to use Log Shipping, disable / stop all other Transaction Log backup.
  3. Move your agent job interval up gradually. Note that Backup, Copy, Restore jobs are run by SQL Server agent on an interval. When you are setting up your Log Shipping, after the initial full database backup is restored at secondary server, your Transaction Log is ready for action. When do your Backup, Copy, and Restore jobs kick in depend on what is the interval you set while configuring your Log Shipping. I recommend you set a super short interval in the beginning so that you can monitor the failure in your setup fairly quickly. You do not want to wait for 6 hours for your Backup, Copy, Restore jobs to kick in to find out they failed, you then make some changes and wait for another 6 hours. I always start with 1 minute. Then, 5 minutes then to the actual time frame depending on business requirement. In my case, it was 2 hours.
  4. Enable Standby mode so that your client can read the data. I highly recommend you to check “Disconnect user in the database when restoring backups.”. SQL Server Agent Restore job will handle the Transaction Log intelligently by replaying the log that was missed previously. However as I mentioned earlier, Restore job is the most fragile step. Sometimes when Restore job breaks, you have to resetup the whole log shipping mechanism. Imagine a database size of 300GB (the actual size I was working with), it is pretty painful to wait for the whole process to complete. Hence, to ensure the integrity of the Transaction Log sequence, I would rather terminate all open connection to ensure my Restore step can be executed successfully. standby-mode
  5. Monitoring your Log Shipping continuity. Again, Log Shipping is pretty fragile especially if this is the first time you are doing it. You need certain mechanism to monitor your Log Shipping to ensure they are still running as expected 6 months down the road. You can open up SQL Server Agent to view the job history ensuring they are all green or configure Alert Job to raise an alert when job does not complete successfully. But personally, nothing is more assuring than knowing the data actually changes in the secondary database during specific time frame. What I do is monitoring a table column that is supposed to change after replay of Transaction Log. In my case, I monitor the latest user login time because I know this table is updated fairly frequently. The probability that no one login in the last 2 hours is close to zero. I make sure that every 2 hour the value in column changes. If you do not have a user login audit, you can make use of any table that you are sure the data will most likely change, for example your CreatedOn column on the highest transaction table.

Shielding your database

Shielding your database is a great way to keep your database load low so that you can serve more requests. But if you shield the database, where does the data come from? The data has to come from somewhere and that somewhere is known as index provider. I’m not referring to building more indexes within SQL Server because indexes served from SQL Server still add load to SQL Server and not to mention additional storage on disk. The index provider I was referring to are fast-searching services such as Solr, Elasticsearch, Azure Search, or even Redis.

This approach is to drastically offload the reading at database level to another service which are built for super fast searching. Not only the response time is much faster, you will save the poor programmers from write essay-length query to retrieve data.

Example 1: You have a clean 3rd normalized form database. With SQL Profiler you observed that clients have been issuing long query such as a query with 15 JOIN and 5 GROUP BY on very large tables. Not only the that query result comes back slowly, it also drags down the server performance and other queries are badly affected. You have discussed with your best SQL query guru to review the queries, studied the execution plans, revisited the data structure and the conclusion is that is really what it takes the get the desired result. So, what do you do now?

Index provider will come into rescue when you design your schema for your query. The joining is no longer required because the index has flatten the defined fields during indexing. The grouping is also no longer required because that info come from facets. Now instead of the JOIN and GROUP BY happen in SQL Server level, you get the data from an index lightening fast – as a simple read provided you have designed your index schema properly.


Example 2: You are working with 2 microservices where each of the service has its own database keeping different domain data. Your application requires data from both the services and both of them are responding to you slowly. Again, after reviewing the request with respective microservice owner and you all came to the conclusion that both the codes and SQL statement sare at the most optimum form, what do you do now?

Index provider will come into rescue when you index both the data from different microservices into one index provider and you query from the index provider instead. Not only you bypass the two slow microservices, you are also querying from one index instead of multiple databases. Of course, this is easier said than done because now it involves the two microservices to index the required data and additional effort to maintain the index provider.


Here are some of the practical tips you can consider while implementing index provider to shield your database from getting hit:

  1. Initial indexing will take a lot of time, especially from various sources. This is one of the reason why some deployments take long time. A trick to overcome this is to not relying on the on-the-fly index API call provided by index provider. Export your data as .csv and import them for indexing. Do it manually if your data is large enough. Automate this process if full reindexing happen on recurring basis.
  2. Minimize your index schema changes. Every schema change will require you to reindex your indexes, which usually take time and lead to longer down time. The key is when designing your schema, think holistically what is the use case and potential use cases instead of designing a schema just for one client and one use case. In the meantime, you also do not want to design an overly generic schema else it will be inefficient. The art is finding this balance and the way to do it is to understand your domain and context well before designing your schema.
  3. Ensure your stakeholders are aware that index is a reflection of your database. Your database remains as the single source of truth. Expect delay in your index. Eventual consistent is what you aim to achieve. Often, it is acceptable to have a few seconds or even minutes delay depending on how critical the data is. Ensure you get the acknowledgement from your stakeholders.
  4. Queue your indexing. A simple phone number update into database could result in 100 of reindexing request. Have a queueing mechanism to protect your index provider from sudden surge of reindexing request.
  5. Take note of the strongly type fields. Index provider such as Solr takes everything as string while index provider such as Azure Search has strongly-typed field (Edm.Boolean, Edm.String, Edm.Int32, etc). If you plan to switch index provider in the future, take care of the data type from day one else you will end up having an additional layer of mapper to deal with the data type later on.

Hope these help you in your journey to scale your SQL Server. Have fun!



I was first introduced to Microservices Architecture in 2014. At that point of time, I have no idea what I was doing is known as Microservices. We designed our system that way simply because it made practical sense. We started off with 1 PHP web service, 1 PHP frontend application, 1 .NET web service, 2 .NET frontend applications and a CRM. The number of Microservices grow along with the business needs. Since then I learned that managing Microservices is as interesting and fun as building Microservices.

Microservices are relatively small applications that interacts with each other to achieve specific business requirement. In each Microservice, they are designed to do one thing and to do it really well. Sometimes, they are independent on their own but they often work together to accomplish more complex business requirement. The opposite of Microservices is a monolithic system, the kind of system where you have 5,000,000 line of codes in single code base.

Why do we use Microservices?

Technology Heterogeneity – It means the diversity of technology within a solution. Your Microservices have independent stacks of technology. You can choose the most suitable stack of technology depending on the problem you are solving. For example, a Photo Album Printing business might have a PHP frontend application (because the want to tap into WordPress as CMS), a .NET backend business rules exposed as Web API (because there is a legacy logic and SQL Server database), a Java image processing engine (because there are proprietary image processing libraries written in Java), and an R application to crunch big data on customer sentiment. In Microservices architecture, you can have different stacks of technologies that work together seamlessly. They interact through set of API exposed to each other.


This reason also align with Scrum. Each Scrum team potentially owns one Microservice and there will be multiple Scrum teams based on the technological domain. By the time the Scrum team gets too big, it also serves as an indicator it is time to break the Microservice to be smaller. Ideally you do not want to wait for the Microservice to be too big before you break it. You should be alert on not to stuff your Microservice to be bloated in the first place. Kick start another Microservice whenever you can logically scope the context boundary into a separate Microservice.

Scaling – The fundamental of scaling boils down to 2 approaches: Vertical and Horizontal. Vertical scaling is quick and easy but could get very expensive especially when hitting the top tiers of resources. Horizontal scaling is cheaper but could be difficult to implement if the solution is not designed to scale horizontally. For example, a stateful monolithic system. As a general rule of thumb, always design your solution to scale horizontally. To put this into perspective, one large virtual machine could be substantially more costly than three small virtual machines that provide the equal amount of processing power, depending on which cloud provider you are working with.

Building solution as Microservices provides the foundation to scale horizontally. Using the earlier Photo Album Printing system, say there are many users who submit photos in bulk for processing during 9.00 AM to 12.00 PM. The DevOps guy only need to scale up the Java image processing engine service.

9.00 AM-12.00 PM

Scaling for 9.00 AM-12.00 PM

At 6.00PM to 11.00PM, say there are many visitors come to the website to browse the photos. The DevOps guy only need to scale up the PHP frontend application.


Scaling for 6.00PM-11.00PM

If we have a gigantic monolithic system, we have to scale the entire system regardless of which component is being utilized most. To put this into perspective, imagine you keep your car engine running just because you want the air-cond. Heads will not roll, it is just not the most efficient way to use your technologies.

Ease of Deployment – If you have tried waking up 2.00 AM in the morning for a “major deployment”, or been through a 20-hour deployment, you probably will agree it is important to have clean and quick deployment. I can vividly remember how nervous my CIO got whenever we have a “major deployment”. Sometimes he will come in early morning together with us give us moral support by supplying us with coffee and McDonalds. Despite the heart-warming breakfast, it was really stressful for everyone go through such deployment. Long story short, we have improved our deployment to be able making 4 productions deployment within a day with 0 down time. It is not possible (or significantly more difficult) if we have not built our codes on Microservices architecture.

Deployment for Microservices is definitely easier compared to a gigantic monolithic system. The database that the Microservice using is simpler which make altering database schema changes less painful. The amount of code is lesser which indirectly means there are less configuration to deal with during deployment. The scope of what the Microservices is designed for is smaller which makes post-deployment (both automated or manual) testing faster. In worse case scenario, rolling back a small service is significantly straightforward compared to rolling back a monolithic system with 25 other dependencies where some of they need to be rolled back together.

Scaling Microservices

The secret to scaling Microservices is: start small, think big. You might start your Microservice as a small service coded by a solo developer in 2 weeks. Although a service could be small, you need to think about how to deal with it when your audience size grow 10 times larger. As we discussed earlier, scaling vertically is easy but could be fairly expensive when you get nearer to the top tiers. You want to design your Microservice to scale horizontally from day one.

How to build a horizontal-scale friendly Microservice? The most common reason some services cannot scale horizontally efficiently is due to session. When you have a session stuck in your service memory, your client will always have to go back to the same service, else you will discover all kind of weird behavior. Of course, you can overcome this problem by enabling stickiness in your load balancer, or have an additional SQL Server database to keep all the session (InProc mode). However why would you want to get yourselves into this situation in the first place? If having session within the service is going make your scaling effort more challenging, avoid relying on session from day one so that you can scale horizontally, effortlessly. Building your service base on RESTful principles is a good starting point.

If your Microservice really have to make use of session, make use of additional session service such as Redis instead of keeping your session in service memory.

Front your service with a load balancer. Having your service instances sit behind a load balancer acts as the foundation for horizontal scaling. Configure auto-scaling in whichever cloud provider you are using. By the time the additional load kicks in, your auto-scaling will automatically boot up additional hosts to serve the load.


Another advantage of having your Microservice instances sit behind a load balancer is to avoid single point of failure. You should consider having at least 2 hosts to avoid single point of failure. For example, perhaps your Microservice only need 1 medium size virtual machine computing power. While 2 small size virtual machines provide the equal computing power as 1 medium size virtual machine (you have to work out the Maths yourselves). Having 2 small size virtual machines sit behind a load balancer rather than 1 medium size virtual machine being connected directly is a good remedy to avoid single point of failure.

Scaling Databases

As the number of your Microservice instance grows, it usually means more load in your database. Database IO is the most common bottleneck in software performance. Unless you have put in explicit design to protect your database from getting hit, you will often end up with scaling your database vertically. However you might want to keep this option as your last card.

Out of the box, SQL Server provides you the option to do Transactional Replication, Merge Replication, and Snapshot Replication. You have to determine which mode is most optimum for your system. If you have no idea what all these are about, Transactional Replication is your safest bet. But if you are adventurous, you can mix and match different approaches.

Transactional Replication works by Publisher and Subscriber model. All Write will happen in Publisher while all Read will happen in Subscribers. In typical services, the number of Read far out number Write. In this set up, you distribute the Read load to multiple Subscriber hosts. You can continue to add more Subscriber hosts as you see fit.


The drawback is, you need to code your Microservice in a way to perform all data manipulation and insertion in the Publisher while the reading in the Subscriber(s), which requires conscious effort from developers to code in such manner. Another drawback for adopting replication is the skill set required to set up and maintain the replication. Replaying transactional log is pretty fragile from my experience. You need someone who understand the mechanism behind replication to troubleshoot the failure effectively.

I highly recommend you to give some forward thinking on how to avoid your database get hit unnecessary in the first place. Tap into search engines such as Solr and ElasticSearch when suitable. Identify how your data will be used up front. Keep the on-the-fly data aggregation to minimum. Design your search indexes accordingly. At the very minimum, make use of caching.

The key for scaling your data is to achieve eventual consistency. It is alright to have your data out of sync for a short period of time especially on non-mission critical system. As long as your data will be consistent eventually, you are heading to the right direction.

Scaling database could be tricky. If you need something to be done by tomorrow 9.00AM, the easiest option is to scale vertically. No code change and no SQL Server expert involved. Everyone will be happy… probably except the CFO.

Keep Failure in Mind

Failure is inevitable in software. In Microservices architecture, the chances for software to fail is even greater. The risk of failure is exponentially higher because your service no longer depend on yourselves alone. Every Microservice that your Microservice depends on could go wrong at any given time. Your Microservice need to expect other Mircroservices to fail and handle the failure gracefully.

Bake in your failure handling. Your Microservice depends on other Microservices at one point or another. Can your Microservice core features operate as usual when other Microservices start failing? For example, a CMS depends on a Comment Service. In an article page, if the Comment Service is not responding, how will that affect CMS ability to display the article? Will the CMS article page just crash when the visitor visits? Or will the CMS be able to handle the Comment Service failure gracefully by not showing the comments but the rest of the article is loaded as usual?

Another example, I was using Redis to keep my user token after every successful login. At one point, Redis decided not to keep token for me anymore by actively rejecting the new connection. My users could not login although they have entered the correct username and password. The users could not login simply because part of the non-critical authentication process has failed. We discovered the root cause later. However, in order to avoid such embarrassing moment from happening again, at code level we changed the interaction with Redis to an asynchronous call because creating a token in Redis is not the main criteria in authentication. By changing the Redis call to asynchronous, users can continue to utilize the core functionalities although minor portion of features that relies on Redis token will not work.

It is fine if you do not have a sophisticated failure handling mechanism. At the very minimum, code defensively. Expect failure to happen at every interaction point with other Microservices. Ideally we do not want any of the Microservices to fail. But when they do fail (and they will), defensive coding help your Microservice being minimally affected. It is better to have 70% of your core functionality working working than the whole service crashing down.

The Backends for Frontends

This is another concept I discovered by accident when I was hacking some codes in Android Studio in 2013. The Backends for Frontends design is extra practical for mobile application, although you can still apply the concept on any Microservices.

In essence, the Backends for Frontends design is to back your frontend application with a backend service. The primary objective of this backend service is to serve your frontend application. This is a very good choice for mobile application for several reasons.


First, mobile application is known for having connectivity limitation. Instead of asking your mobile app to connect to 7 different other Microservices to request various information and do the processing at the client (mobile) side, it makes more sense to get the Backend for Frontend service to make the necessary server-to-server calls, process data, then only send the necessary data back to mobile client.

Second, the Backend for Frontend service also serve as a security gateway. Obviously you do not want to expose all your backend core services (for example your CRM) to the public. You need to design your network to have your backend core services sit in a private network. Then, grant permission for your public facing Backend for Frontend service to access to this private network. By doing this, your backend core services are protected from public access yet there are explicit permission granted to specific Backend for Frontend service. You can implement whichever security model you find fit in the Backend for Frontend service where your client application must and can comply to.


Third, mobile application sits at client side, which makes updating the application more challenging. You want to minimize the logic in the client side. The Backend for Frontend service plays the perfect role for handling business logic. You can update the logic much easier in the Backend for Frontend service compared to the client application. In other words, your frontend application will be lightweight and is only responsible for UI presentation.

One Last Thought…

Microservices is a huge topic by itself. This article serves as a triggering point for you to get to know Microservices without going through at 400 pages book. If you would like to learn more, there are many books available. I recommend you to look at Building Microservices by Sam Newman. I hope you have discovered something new in this article. Until next time!

Powershell PathTooLongException


PathTooLongException could be triggered in various occasions when the path you are dealing with exceed 260 characters. For example while copying an innocent file to a destination with super long path. I hit PathTooLongException when I need to clean up a build in my Powershell build script.

For simplicity sake, I extracted the script that trigger PathTooLongException as following:

Here is our friendly error message:


Here is an example of what a (really) long path looks like:


We will not argue whether it is right or wrong to have such a long path. Often, we are given a piece of software to work on. We have little choice but to find the most efficient solution to deal with the situation.

PathTooLongException is known since 2006. It has been discussed extensively by Microsoft BLC Team in 2007 here. At the point of writing this, Microsoft has no plan to do anything about it. The general advice you will find is is: do not use such long path. (Wow! Really helpful!)

I have been digging around the web for solution but unfortunately there was little luck. Well, the truth is I wasn’t happy with many of the solutions or they are just unnecessary complicated. I eventually wrote my own “hack” after some discussion with Alistair Deneys.

The trick is to setup a short symbolic link to represent the root of the working directory. Pass the short symbolic link as target directory to Remove-Item. We will use mklink command to set up the short symbolic link.

The bad news is, mklink is a Operating System level command. It is not recognized in Powershell. I will have to open Command Prompt to set up the symbolic link before proceeding my Powershell build script. As my build script is supposed to be fully automated without manual intervention, manually setting up the symbolic link using Command Prompt is obviously an ugly option.

The following is the command we need to execute to set up the short symbolic link using Command Prompt:

The good news is we can call Command Prompt from Powershell to set up the symbolic link fairly easily. How easy?

Of course, we will need to ensure there isn’t a folder or another symbolic link which is already being named the same as our target symbolic link. (Else you will get another exception.)

Once we created the symbolic link by executing the above command, there will be a “shortcut” icon.


Prior to deleting the directory with a really long path, we will set up the symbolic link on the fly to “cheat” Powershell that the path isn’t really that long 😉

Obviously we don’t want to leave any trace for another person to guess what this “shortcut” is about especially the symbolic link is no longer required after our operation. Hence, once we are done doing what we need to do (in this case, I’m deleting some files), we will need to clean up the symbolic link.

There isn’t any dedicated command to remove a symbolic link, we will use the bare bone approach to remove the symbolic link / shortcut created earlier.

The interesting thing about removing a symbolic link / shortcut is it will remove it as a shortcut but the shortcut becomes a real directory (What?!). Hence we will need to remove it one more time! I don’t know what is the justification for this behaviour and I do not have the curiouscity to find out for now. What I ended up doing is calling Remove-Item twice.

Then, things got a little more sticky because Remove-Item will throw ItemNotFoundException if the symbolic link or the actual directory is not there.


Theoretically this should not happen because all we need to do is create a symbolic link, delete the symbolic link 2 times and we are clear. However reality is not always as friendly as theory 🙂 So, we need to script our Remove-Item defensively. I ended up creating a function to handle the removing task really carefully:

The full script looks as following:

Here, my Powershell happily helped me to remove the files which the path exceed 260 characters and I have a fairly simple “hack” that I can embed into my Powershell build script.

One Last Thought…

During my “hacking” time, I have also tried Subst and New-PSDrive. You might be able to do the same using them. However in my case, some of the dependencies didn’t work well with the above. For example, AttachDatabase could not recognize the path set by Subst. That’s why I settled with Mklink.

This obviously this isn’t a bullet proof solution to handle PathTooLongException. Imagine if the “saving” you gain by representing the actual directory is still not sufficient – as in the path starting from the symbolic link is still more than 260 characters. However I have yet to encounter such situation and this “hack” works for me everytime so far. If you have a simpler way to get this to work, feel free to drop me a comment. Enjoy hacking your Powershell!

Continuous Integration and Continuous Delivery with NuGet


Continuous Integration (CI) is a development practice that requires developers to integrate codes into a shared repository. Each commit will then be verified by an automated build and sometimes with automated tests.

Why Continuous Integration is important? If you have been programming in a team, you probably encountered situation where one developer committed codes that cause every developer’s code base to break. It could be extremely painful to isolate the codes that broke the code base. Continuous Integration serves as a preventive measurement by building the latest code base to verify whether there is any breaking changes. If there is, raise an alert perhaps by sending out an email to the developer who last committed the codes or perhaps notify the whole development team or even to reject the commit. If there isn’t any breaking change, CI will proceed to run a set of unit test to ensure the last commit has not modify any logic in an unexpected manner. This process sometimes also known as Gated CI, which guarantees the sanity of the code base in a relatively short period of time (usually within few minutes).


The idea of Continuous Integration goes beyond validating the code base in a team of developers working on. If the code base utilizes other development teams’ components, it is also about continuously pulling the latest components to build against the current code base. If the code base utilizes other micro-services, then it is about continuously connecting to the latest version of the micro-services. On the other hand, if the code base output is being utilized by other development teams, it is also about continuously delivering the output so that other development teams can pull the latest to integrate with. If the code base output is a micro-service, then it is about continuously exposing the latest micro-service so that other micro-services can connect and integrate to the latest version. The process of delivering the output for other teams to utilize leads us to another concept known as Continuous Delivery.

Continuous Delivery (CD) is a development practice where development team build software in a manner the latest version of software can be released to production at any time. The delivery could mean the software being delivered to a staging or pre-production server or simply a private development NuGet feed.

Why Continuous Delivery is important? In today software development fast pace of change, stakeholders and customers wanted all the features yesterday. Product Managers do not want to wait 1 week for the team to “get ready” to release. Business expectation is as soon as the codes are written and functionalities are tested, software should be READY to ship. Development teams must establish an efficient delivery process where delivering software is as simple as pushing a button. A good benchmark is the delivery can be accomplished by anyone in the team. Perhaps to be done by a QA after he has verified the quality of the deliverable or by Product Manager when he thinks the time is right. In complex enterprise system, it is not always possible to ship codes to production quickly. Therefore complex enterprise system is often broken into smaller components or micro-services. In this case, the components or micro-services must be ready to be pushed to a shared platform so that other components or micro-services can consume the deliverable as soon as available. This delivery process must be at READY state at all time. The decision of whether to deliver the whole system or the smaller component should be a matter of business decision.

Note that Continuous Delivery does not necessary mean Continuous Deployment. Continuous Deployment is where every change goes through the pipeline and automatically gets pushed into production. This could lead to several production deployments every day, which is not always desirable. Continuous Delivery allows development team to do frequent deployments but may choose not to do it. In today’s standard for .NET development, NuGet package is commonly used for either delivering a component or a whole application.

NuGet is the package manager for the Microsoft development platform. A NuGet package is a set of well-managed library and the relevant files. NuGet packages can be installed and be added to .NET solution from GUI or command line. Instead of referencing to individual library in the form of .dll, developers can reference to a NuGet package which provides much better management in handling dependencies and assemblies versions. In a more holistic view, a NuGet package can even be an application deliverable by itself.

Real life use cases

Example 1: Micro-services

In a cloud based (software as a service) solution, domains are encapsulated in the respective micro-service. Every development team is responsible for their own micro-services.


Throughout the Sprint, developers commit codes into TFS. After every commit, TFS will build the latest code base. Once the building process is completed, unit tests will be executed to ensure existing logic are still intact. Several NuGet packages are then generated to represent several micro-services (WCF, Web application, etc). These services will be deployed by a deployment tool known as Octopus Deploy to a Staging environment (hosted in AWS EC2) for QA to perform testing. This process continues until the last User Story is completed by the developers.

In a matter of clicks, the earlier NuGet package can also be deployed to Pre-production environment (hosted in AWS EC2) for other types of testing. Lastly, with the blessing from Product Manager, DevOps team will use the same deployment tool to Promote the same NuGet packages that were tested by QA earlier into Production. Throughout this process, it is very important that there is no manual intervention (such as copying a dll, changing a configuration, etc) by hands to ensure the integrity of the NuGet package and deployment process. The entire delivery process must be pre-configured or pre-scripted to ensure the process is consistent, replicatable, and robust.

Example 2: Components

In a complex enterprise application, functionalities are split into components. Each component is a set of binary (dll) and other relevant files. A component is not a stand-alone application. The component has no practical usage until it sits on the larger platform. Development teams are responsible for their respective component.

Throughout the Sprint, developers commit codes into a Git repository. The repository is monitored by Team City (build server). Team City will pull the latest changes and execute a set of Powershell script. From the Powershell script, an instance of the platform is setup. The latest code base will be built and the output is placed on top of the platform. Various tests are executed on the platform to ensure the component functionality is intact. Then, a set of NuGet package will be generated from the Powershell script to be published as the artifacts. These artifacts will be used by QA to run other forms of tests. This process continues until the last User Story is completed by the developers.

When QA gives the green light and with the blessing from Product Manager, the NuGet packages will be promoted to ProGet (an internal NuGet feeds). This promotion process happens in a matter of clicks. No manual intervention (modifying the dependencies, version, etc) should happen to ensure the integrity of the NuGet package.

Once the NuGet package is promoted / pushed into ProGet, other components update this latest component into their components. In Scaled Agile, a release train is planned on frequent and consistent time frame. Internal release happens on weekly basis. This weekly build will always pull all of the latest components from ProGet to generate a platform installer.


From the examples, we can tell that Continuous Integration and Continuous Delivery are a fairly simple concepts. There is neither black magic nor rocket science in both the use cases. The choice of tools and approaches to accomplish largely depend on the nature of the software we are building. While designing software, it is always a good idea to keep Continuous Integration and Continuous Delivery in mind to maximize team productivity and to have quick and robust delivery.

Facebook Feed Widget in Sitecore Habitat


In the previous post we have a introductory view on what Sitecore Habitat is. In this post we will get our hands dirty creating a Facebook Widget in Sitecore Habitat.

Out of the box, Sitecore Habitat contains number of Features. One of the Features is “Social” and we will use Sitecore.Feature.Social project in Habitat solution for Facebook Feed Widget. Before we get started, let’s examine what is available in Sitecore.Feature.Social project.


From here we can see that Social comes with certain implementation for Twitter feed. If we visit our Habitat instance, we can see there is a Twitter feed shows in the following page:


From here, we will create a Facebook feed to replace the Twitter feed.

First step, we create a new data template to cater for the fields for Facebook feed.


Click on image for full size view

Then, we create an item in Content Editor named FacebookDetails for setting the actual properties for the Facebook feed. In this example, we are using Sitecore Facebook page for demonstration purpose.


Click on image for full size view

The complete properties are as following:

Type: timeline
Width: 350
Height: 600
Hide Facebook Cover: [unchecked]
Hide Face Pile: [checked]
Title: Sitecore Facebook Feed

Type is a Droplist. Facebook feed supports multiple types, namely timeline, events and messages. By having Type as a Droplist, user can select which kind of feed to fetch from Facebook. However for simplicity sake, you can also configure your Type as a Single-Line Text. Doing that you will have to type in the Type field yourself each time you were to change the feed type. Not a show stopper, but something nice to have 🙂

Next, we will move to Visual Studio to tie the fields to a FacebookFeed View that we are about to create.

Look for Template.cs under Sitecore.Feature.Social project. Add the following struct into the class. Do note that the GUID in your instance will be different from the GUID displayed here if you are creating the fields by yourself. Hence, remember to replace the GUID in these properties. If you installing the Sitecore package provided at the end of this post, the GUID will remain the same.

This is the code that “tie” the fields we created earlier in Content Editor. The next thing we will do is to create a view that consume these properties’ GUID.

Add a new FacebookFeed View in Sitecore.Feature.Social project under Social folder.


Add in the following code to construct a Facebook Feed widget.

Note all the HTML attributes are the fields we created earlier in data template and the values are the fields we filled in FacebookDetail item in Content Editor.

Now, compile the solution (or project). Ensure that FacebookFeed.cshtml and Sitecore.Feature.Social.dll are copied over to your Sitecore instance. Again, if you are installing the package provided at the end of this article, you will not need to copy the files over.

At this point, we have successfully created created a Facebook Feed Widget. However the widget has not been configured to display on any page on our instance. Now let’s create and configure the necessary items in Content Editor for our Facebook Feed Widget.

Create a view rendering item to represent the View (.cshtml) we have created earlier.

Click on image to view full size

Click on image to view full size

Add FacebookFeed rendering view into Layout that we want to display the View from. For simplicity sake, we use “col-wide-1” as this layout is readily available on Social page.


Click on image to view full size

Next, we will now move to Experience Editor to replace the Twitter Feed with Facebook Feed Widget we have just created.

Navigate to Social page using Breadcrumb and remove the existing Twitter Feed.


Add the new Facebook Feed into the page. Check the “Open the Properties dialog box immediately” option:


Click on Browse under Data Source:


Select FacebookDetails and click OK


On your browser, navigate to http://habitat.local/en/Modules/Feature/Social and Facebook Feed Widget will appear in your page:


Obviously, this is the most perfect Facebook Feed Widget we can build (just kidding!). You are welcome to modify any steps to cater for your own Sitecore instance.

The objective of this article is to guide you through the steps in creating a Facebook Feeds Widget in Sitecore Habitat. If you would like to have a Facebook Feeds Widget in Sitecore Habitat without going through (most of) the steps, feel free to download and install the ready made package.

Download Sitecore-Habitat-Facebook-Feed-Widget package.

Sitecore Habitat


Sitecore released Habitat on 18th December 2015. Habitat is a Sitecore solution example built on a modular architecture. Habitat architecture and methodology focus on few main concepts which are Simplicity, a simple, consistent and discoverable architecture. Flexibility, the ability to change and add quickly and without worry. Extensibility, the ability to simply add new features without steep learning curve.

Sitecore Habitat is a new “demo” site provided by Sitecore. Prior to that there is which is another Sitecore “demo” site where public can evaluate what Sitecore is capable of. The major difference between LaunchSitecore and Habitat is that LaunchSitecore is designed to allow public to explore the core functionalities in Sitecore, while Habitat is a modular architectural solution to build features.

To get a quick look and feel what Habitat is about, you can download Sitecore Habitat package and install it on your local Sitecore instance. Once installed, your Sitecore instance home page will have the following look:

sitecore habitat homepage

Now, let’s dive a little deeper into Habitat code base. You can clone a new repository of Habitat from GitHub.

Once cloned, you will see from Visual Studio there are 37 projects in the solution. The “magic” of Habitat sits at the Feature folder. 14 features are provided out of the box (at the point of writing). They are: Accounts, Demo, Identity, Language, Media, Metadata, Multisite, Navigation, News, PageContent, Person, Search, Social and Teasers.

sitecore habitat feature

The idea of the Habitat architecture is you can develop or use a feature independently. If one feature is broken, you can simply not use it and it will not affect the rest of the feature. Each feature has the consistent architecture to allow development practices to be more predictable.

If you are into developing a feature in Habitat, I highly recommend you to look at the videos prepared Thomas Eldblom on:

Sitecore Habitat Architecture – Introduction

This video explains how Sitecore Habitat is solution framework that focuses on streamlining development process and optimizing productivity. It also explains the how productivity is increased when developers simplify coupling.

Sitecore Habitat Architecture – Modules

This video explains the closure principles in Sitecore. It digs down deeper into Habitat code base by using Navigation as an example. It explains the Views, Controllers, Data Model and how the items are structured and how do they get built.

Sitecore Habitat Architecture – Layers

Focus on the layered architecture and how to couple modules to build the entire solution. It also explains how different layers fit together as a solution and how different modules and layer interact with each other.

This is a quick introduction to Sitecore Habitat. If you would like to find out more, feel free to visit the Habitat Wiki page.

On the next post, we will dive a little deeper into the code base to build a Facebook widget in Sitecore Habitat.