You can download the complete guide on “Continuous Deployment using Microsoft Azure Web Sites” from the Microsoft Architecture site.
One of the easiest ways to implement continuous deployment with web sites is to use Git. Developers can write Git hooks that push the deployable repository to the web site repository. When we take this approach, it is important to fully script the creation and configuration of the web site. It is not a good practice to “manually” create and configure it. This might not be apparent, but it is crucial for supporting disaster recovery, creating parallel versions of different releases, or deploying releases to additional data centers. Further, the separation of configuration and settings from the deployable artifacts makes it easy to guard certificates and other secrets, such as connection strings.
The proposed approach is to create a web site (including staging slot) for each releasable branch. This allows deployment of new release candidates by simply pushing the Git repository to the staging web site. After testing, this can be switched to the production environment.
As described above, it is recommended that we create two repositories, one for the creation and configuration of the web site and one for the deployable artifacts. This allows us to restrict access to sensitive data stored in the configuration repository. The configuration script must be idempotent, so it produces the same outcome regardless of if it runs the first or the hundredth time. Once the web site has been created and configured, the deployable artifacts can be deployed using Git push to the staging web site’s Git repository. This push should take place with every commit to the release repository.
It is important that all web site dependencies, such as connection strings and URLs, are sourced from the web site’s application and connection string settings. (Do not make them part of the deployable artifacts!) This allows us to deploy the same artifacts across different web sites without interference. For this example, assume we have an application that consists of two sites, one serving as the frontend and the other as the backend. The backend site also uses storage services (Figure 1).
The first step is to split the application into independent deployable components. Each component has its own source repository. Because the backend is the only component that accesses the storage service, we can group them together. The configuration script creates the web site for each component as well as the containing resources, such as storage accounts or databases. Further, it configures all dependencies. In the example below, the script for site 1 will configure the site 2 URL as an application setting. Splitting an application into independent deployable components (Figure 2).
There are different strategies to handle code branches when releasing new functionality. The following two are commonly used:
- Keep the master always deployable and use short-lived branches for feature work.
- Create long-lived branches for releases and integrate feature work directly into the master.
In this series of posts I will focus on the second approach—creating long-lived branches for every new release. The benefit of this approach resides in the fact that there is a 1:1 relationship between a specific release and its corresponding web site creation and configuration script. This makes deploying previous versions extremely simple because we just run the respective script and then deploy the component. It also allows us to easily run multiple releases of the same component in parallel, which is great for A/B testing.
The next posts will cover how to manage long-lived branches for releases while working on features on master. So stay tuned…
deployment doesn’t equal release
As part of continuous deployment, we have to build an automated deployment pipeline that allows us to frequently deploy and test new functionality and bug fixes. We will first test the deployment on a staging environment. Only if a deployment passed all tests, we will release it to the production environment:
- running unit tests (triggered by check-in)
- running integration builds
- running integration tests
- deploy the artifacts to the staging environment
- perform more tests (e.g. smoke and acceptance tests)
- If all tests are passed, the new release can be rolled out across the production environment.
Having such a deployment strategy in place becomes very handy when instant releases (e.g. bug fixes) are required. The goal is to fully automate this deployment pipeline to shorten the time (and pain) from check-in to release. While doing so, the solution needs to be able to respond to requests at all times, even when in the process of deploying or testing a new release. To achieve zero downtime, we commonly take advantage of the two deployment strategies “blue-green deployment” and “canary releasing”.
It is important to understand that the risk is exponentially greater the more check-ins that occur between releases. So actually, launching with new releases more frequently is less risky because the scope of the changes are better understood. This is counterintuitive to many people.
Blue-green deployments are based on two identical deployment environments – one for production and one for staging. The key is to ensure that the two environments are truly identical, including the data it manages. Zero downtime releases are achieved by deploying to the staging environment. After smoke testing the new deployment, traffic will be routed to the staging environment which now becomes the production environment. While blue-green deployments provide a simple and powerful way to test a deployment before going into production, it might require staging environments of similar size and capacity to perform capacity tests, which might not be an economically feasible option for large scale services. Microsoft Azure Web Sites and its staging slots provide an out-of-the-box experience for “blue-green deployments”. It basically provides two deployment slots which can be swapped. In most scenarios, this will be the default deployment strategy.
Canary releasing addresses the challenge of testing a new release with only a subset of the servers. This approach can also be used for A/B testing: small percentage of the users will be routed to the new service while the majority still works against the “old” version. This allows the team to get direct feedback without being at risk of impacting the majority of users. It is actually possible to have multiple versions running in parallel. The same approach can be used to perform capacity tests without routing actual users to the release which is under test (basically test the new version in production without routing actual users to it). While “blue-green deployments” are simple, doing “canary releases” is more complicated, because all instances within a Web Site are based on the same deployment. As part of this series, I will discuss the use of a custom router which acts as a reverse proxy. Using this approach allows to route certain users to the “canary deployment” while the majority of users work against older releases.
During the last couple of months I had many discussions on DevOps and especially continuous deployment. These discussions were not only about technology - they were also about a cultural change:
Delivering a service instead of shipping software requires a cultural change in how development teams and operation teams interact – there has to be a joint accountability to deliver the SLA as one team. A central part of continuous delivery is automation, from the check-in of new code to build, test and deploy – automation is the key to continuously deliver a service. Gone are the days of the waterfall process where the developer hands the code to the test department to hand it to the ops guys. It is one team that is responsible to develop, test and operate the service.
Over the next couple of weeks I plan to blog concrete guidance on how to build an automated deployment pipeline using Azure Web Sites. Here’s a rough outline of the topics to come:
- Deployment and release strategies
Common release and deployment strategies independent of the underlying technologies
- Deployment and release strategies for Azure Web Sites
Release and deployment strategies for Microsoft Azure Web Sites and the use of Git and Visual Studio Online
- Script the configuration and deployment of Azure Web Sites
Details on how to build an automated deployment pipeline using Azure CLI and PowerShell scripting
- Manage releases using the Service Gateway
Introducing the Service Gateway to implement advanced release strategies
- Scripting examples
Examples that detail the discussed topics using PowerShell and CLI scripts
Stay tuned …
Looking across the app landscape, I see still too many apps that persist data just on devices without synchronizing it to a service. While this approach makes app development straight forward – it lacks two major capabilities that I expect from a modern app:
- Seamless experience across devices –most of us have more than one device and expect a seamless experience across all of them. First and foremost I want to have the option to keep my data in-synch across my devices.
- Friction free device replacement/addition – my devices get replaced on an annual or bi-annual base and new devices join my portfolio. This has to be friction free. Losing data – and if it is just the state of a game – is just not an option. As a positive side effect – this makes losing a device just a bit less painful – at least the data is not lost.
Designing such apps
It’s crucial to focus on the user and it’s data and not on the device – it really is not the center of attention – the data and the users are. This is why apps should authenticate users and securely store it’s data on a service. Doing this is really no rocket science:
Windows Azure Mobile Services provide the service platform and the client sdk to authenticate users using OAuth and to store and retrieve data securely. And not to forget – this works for iOS and Android too!
There is no excuse for writing apps which don’t work seamless across devices or give me a headache when I replace my device – no excuse – I just won’t use them anymore.
Recently I strolled over the following disk ad from the early 80’s:
This was the trigger to play with some numbers:
What would it cost to provide 1GB geo-redundant high availability storage (similar to Windows Azure storage) using these ancient disks?
- Windows Azure stores data 6 times across two geo-redundant locations
- Which means that storing 1GB of data requires 6GB of storage capacity
- Taking the disk from the early 80s – storing 6GB of data would have required 600 10MB disks
- This would have cost $2M+!
- Let’s say we would have gotten a 50% discount on those drives, we would still have to pay around $1M
- And that would be just the cost for the disks…
Today, Windows Azure provides geo-redundant storage for $0.095 per GB/month. Which means we can store a 1GB of data over 5 years and it costs less than $6.
This is 166’000 times cheaper than 30 years ago, not even considering that we’re not just getting the disks but a complete storage service.
While Apps are usually monetized through marketplaces or advertisement – the predominant model for services is subscription based.
So…what is the best business model for a devices and services scenario?
As a rule of thumb – monetization per download (the model of many App marketplaces) makes the most sense for scenarios which include none or very little service capabilities. Prime examples of that approach are some games: For instance Rovio monetizes Angry Birds through the many different marketplaces – they get paid for the App download and usually no further revenue occurs after that.
On the other hand – if a solution contains service capabilities – it is much better to gear the revenue towards service monetization. This generates recurring revenue and provides the opportunity for up and x-selling across different subscription levels. For instance, the Swedish company Spotify provides three subscription levels for their music as a service offering: While the entry level is free, only the premium subscription enables the seamless music experience across multiple devices. Because monetization is based on their own service, the Spotify Apps can be downloaded for free. In such scenarios, the App marketplace is only used as a distribution channel.
There is no size fits all business model – every solution requires a thorough analysis of the market opportunity , sales channels and the respective pricing. Alex Osterwalder’s Business Model Canvas (BMC) provides a great tool to create your very own business model. It is a graphical representation of 9 building blocks – with the value proposition in its center:
The right part of the model describes the value towards the customers while the left part focuses on the efficiency in delivering it. Here a short description of the 9 building blocks:
- Value Proposition - the value created for a specific customer segment
- Customer Segment - different group and people which will be served/sold to
- Channels - how to communicate value proposition to customer segments
- Customer Relationship - the type of relationships established with customer segments
- Revenue Streams - cash generated per customer segment
- Key Resources - the most important assets to make this model work
- Key Activities - the most important activities to make this model work
- Key Partners - the required partners to make this model work
- Cost Structure - cost to operate this model
Alex’s book “Business Model Generation” is a must have if you want to learn more about BMCs and even more importantly – want to create your own model based on it.
In this blog post I will discuss some communication options for device and services scenarios. Looking at it from a higher level, we can differentiate between device initiated and service initiated communication. However on most devices, there is a fundamental difference between the two:
- the service has the capability to listen for incoming requests and therefore implementing device initiated communication is as straight forward as sending the request (REST, WS-*, …) to the service endpoint
- most device platforms don’t provide the capability of exposing a service endpoint and dis-encourage from listening/polling for requests, which makes pushing data to a device quite a bit more challenging
Actively pushing information to mobile devices is a common requirement. That’s why the different device platforms offer capabilities which take care of push notifications in a bandwidth and battery friendly way. This is achieved through a client component which takes care of receiving the message and then dispatches it to the App on one side, and a service component which facilitates the interaction with the client component.
Let’s have a look how this works for Windows Store Apps:
- to receive push notifications, the Windows Store App simply requests a so called channel URI from the Notification Client Platform (which represents the client component of Windows 8 notification capabilities).
- this URI is used to identify the device and App and needs to be shared with services that should send notifications to this App. To do so, the service provides a function which allows the App to register its channel URI (in other words, the service simply receives and stores the different channel URIs)
- to actually send a notification to a Windows Store App, the service authenticates itself to the Windows Push Notification Service (which is a service run by Microsoft) and makes the request to send the notification message to a specific channel
- the Windows Push Notification Service sends the message to the requested device (there is not guarantee for delivery)
- on the client side, the Notification Client Platform simply dispatches the message according to the channel URI
Since there is a strong coupling between the client and the service component, it shouldn’t come as a surprise that the different device platforms provide you with different notification services:
- Windows 8 – Windows Push Notification Service (WNS)
- Windows Phone – Microsoft Push Notification Service (MPNS)
- iOS – Apple Push Notification Service (APNS)
- Android – Google Cloud Messaging (GCM)
However the really good news is that Windows Azure Mobile Services makes sending push notifications to the above mentioned platforms very easy: It not only provides you with an easy way to configure the services on its portal, but it also provides objects for implementing the service and SDKs for the client. This makes the request for a Windows Store Channel URI as simple as the following line of code:
channelURI = pushNotificationChannelManager.
Once the mobile service knows about the channelURI, it simply can send a push notification using the server side object model:
push.wns.sendToastText04(channelURI, “this is my message”);
As already mentioned, the server side scripting of Mobile Services doesn’t only provide a push object for Windows Store Apps but also one for APNS, GCM and MPNS.
I’m lovin’ it…
My previous blog post covered the need for handling state across devices/users and introduced the different Windows Azure storage options. In this post, I want to discuss the approach to data architecture in more detail.
Why not just use SQL databases?
While SQL databases provide many of the functionality known from a RDBMS they come with a higher price point and pretty hard size limitations (150GB as of March 2013). This makes them great for solutions with a predictable amount of data and scenarios which benefit from RDMBS capabilities such as Transact-SQL support. Another benefit might be the reuse of your client libraries because tabular data stream (TDS) being the communication protocol for both SQL Server and SQL databases.
However most services will have the need to store and query an increasing amount of data which pushes a single database at its scale up limitations. Since cloud computing is based on scale out we’re soon confronted with the challenge to partition our data across multiple storage nodes or different storage technologies (such as Tables, Blobs, Hadoop on Azure, SQL databases, …).
While traditional reasons for partioning where predominately about horizontal partitioning (e.g. sharding) the cloud provides new reasons for data partitioning such as cost optimization through the usage of different storage technologies or the ability to only temporarily store data (e.g. when running a Monte Carlo simulation on a Hadoop cluster on Windows Azure).
In horizontal partitioning, we spread all data across similar nodes to achieve massive scale out of data and load. In such a scenario, all queries within a partition are fast and simple while querying data cross-partitions becomes expensive. An example of horizontal partitioning is the distribution of an order table according to the customer which placed the order. In this example we partition the order table using the customer as the partition key. This would make it very efficient for retrieving orders that belong to a specific customer but very ineffective to retrieve information that involves cross customer queries such us “What are the customers that ordered product xyz”.
In vertical partitioning, we spread data across dis-similar nodes to take advantage of different storage capabilities within a logical dataset. By doing so, we can leverage more expensive indexed storage for frequently queried data but store large data entities in cheaper storage (such as blob and tables). For instance, we could store all order information in a SQL database except the order documents, which we store as pdf in blob storage. The downside of this approach is that retrieving a whole row requires more than just one query.
In hybrid partitioning we take advantage of horizontal and vertical partitioning within the same logical dataset. For instance leverage horizontal partitioning across multiple similar SQL databases (sharding) but use blob storage to store the order documents.
To take advantage of cheap cloud storage we must partition our data.
In all partitioned scenarios it is cheap to query data within a partition but expensive to query it across multiple partitions or storage types. However since storage is fairly cheap and available in unlimited capacity, it is a very common approach to aggressively duplicate data to ensure every query includes a partition key. By doing so, we optimize the service for data retrieval. For example, if we have an order table which is partitioned by customers, it is expensive to retrieve a list of customers which ordered product xyz. This is because we can’t provide the query with a partition key. One way to address this problem is to create a second table which duplicates the data but uses product as the partition key. We basically optimize our service for data retrieval and not for data inserts. Which is a fundamental change for many of us used to SQL databases.
Too many Apps are designed for single device usage and they don’t allow me to share and store data across devices. This not only makes the configuration of a new/additional device painful but in my case, it also makes me decide against a re-purchase of certain Apps. Take for instance Angry Birds: When I switched from my HTC to my Nokia, I basically lost all my unlocked levels. I wouldn’t mind purchasing the game a second time but I have definitely no interest in replaying all the different levels again… this would be just too painful. While upgrading a device is normally not a daily routine, the inability to share data across devices becomes a painful shop-stopper for sequential and simultaneous device usage. There are examples of Apps which preserve state across devices but the trend will go towards seamless cross device usage which will lead to the ability of sequential and simultaneous device usage:
For gaming/entertainment that means PLAY – PAUSE – RESUME
carry the game progress across screens
For productivity this means WORK – SAVE – SYNC
carry the workflow state across Screens
Unfortunately, today’s reality looks different: Only a few Apps take advantage of services but most store their data directly on the device. The reason for this is either the App doesn’t need to share/store information or more likely the reduced complexity to develop and test the App because there is no need to establish a communication with the service and no user authentication/authorization is required. Beside not supporting cross device and App scenarios, many devices have limited storage capacity and query capabilities, so it might become tricky to either store all collected data and/or making good use out of it.
On the other side, a service enables a seamless cross device and upgrade experiences and helps to overcome local storage constraints. This doesn’t mean that the only storage is in the cloud. It’s a best practice to reduce network dependency and leverage a combination of local storage and service capabilities.
- Tables are designed for large scale NoSQL data and have a very favorable price point (7 cents / GB). Storing large scale of data requires the developer to understand the concepts of data partitioning (more about this in a future post). Tables can store up to 100 TB and support either local or geographical redundancy. A unique storage account key grants access via REST and managed APIs.
- Blobs are the preferred way to store files , whether these are images, text or media documents. Similar to tables, blobs can store up to 100 TB, support local or geographical redundancy and the storage account key grants access via REST and managed APIs.
- Queues are a great way to implement reliable, persistent messaging between apps and services. Each message can store up to 64KB. The number of messages is unlimited. As with tables and blobs, a unique storage account key grants access via REST and managed APIs.
- SQL databases provide the capabilities of a fully fletched relational database-as-a-service. The rich transactional support helps writing LOB services. Another great feature is SQL Data Sync, which enables hybrid scenarios through the synchronization of Windows Azure SQL databases and on-premise SQL servers. The current size limitation of SQL databases is 150GB and the cost per GB is between 10$ (the first GB) and 1$ (each GB above 50GB). The database connection can be established using ADO.NET, ODBC, JDBC, Entity Framework and php drivers for SQL server.
But with all these options, how do I pick the one which suits me the best?
Since there is no simple answer to this question, I will cover this is in a future post