Effective DevOps with AWS: Ship faster, scale better, and deliver incredible productivity

It wasn't long ago that tech companies, small and large, had to have a proper technical operations organization able to build out infrastructures.

The process went a little bit like this:

Not only is this a full-time job for a big team, but it also takes a lot of time and money to even get there.

As you will see in this book, getting new servers up and running with AWS will take us minutes. In fact, more than just providing a server within minutes, you will soon see how to deploy and run a service in minutes and just when you need it.

From a cost standpoint, deploying in a cloud infrastructure such as AWS usually ends up being a lot cheaper than buying your own hardware. If you want to deploy your own hardware, you have to pay upfront for all the hardware mentioned previously (servers, network equipment, and so on) and sometimes licensed software as well. In a cloud environment, you pay as you go. You can add or remove servers in no time and only be charged for the duration the servers were running for. Also, if you take advantage of PaaS and SaaS applications, you usually end up saving even more money by lowering your operating costs as you don't need as many staff to administrate your database, storage, and so on. Most cloud providers, AWS included, also offer tiered pricing and volume discount. As your service gets bigger and bigger, you end up paying less for each unit of storage, bandwidth, and so on.

As you just saw, when deploying in the cloud, you only pay for the resources you provision. Most cloud companies use this to their advantage to scale their infrastructure up or down as the traffic to their site changes.

This ability to add or remove new servers and services in no time and on demand is one of the main differentiators of an effective cloud infrastructure. In the example below, we can see the amount of traffic hitting Amazon.com during the month of November. Thanks to Black Friday and Cyber Monday, the traffic triples at the end of the month:

© 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved.

If the company was hosting their service in an old-fashioned way, they would need to have enough servers provisioned to handle this traffic such that only 24% of their infrastructure used on an average during the month:

© 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved.

however, thanks to being able to scale dynamically, they are able to provide only what they really need and dynamically absorb the spikes in traffic that Black Friday and Cyber Monday trigger:

© 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Here at Medium, you also see, on a very regular basis, the benefits of having fast auto-scaling capabilities. Very often, stories become viral and the amount of traffic going on Medium drastically changes. On January 21, 2015, to our surprise, the White House posted a transcript of the State of the Union minutes before President Obama started his speech:

http://bit.ly/2sDvseP

As you can see in the following graph, thanks to being in the cloud and having auto-scaling capabilities, our platform was able to absorb five times the instant spike of traffic that the announcement made by doubling the number of servers our front service used. Later, as the traffic started to drain naturally, you automatically removed some hosts from our fleet:

Cloud computing is often broken down into three different types of services, as follows:

This book covers a fair amount of services of PaaS and SaaS types. When building an application, relying on these services make a big difference when compared to the more traditional environment outside of the cloud.

Another key element to success when deploying or migrating to a new infrastructure is to adopt a DevOps mindset.

DevOps is a new movement that officially started in 2009 in Belgium, when a group of people met at the first DevOpsDays conference, organized by Patrick Debois, to talk about how to apply some agile concepts to infrastructure.

Agile methodologies transformed the way software is developed. In a traditional waterfall model, a product team would come up with specifications, a design team would then create and define a certain user experience and user interface, the engineering team would then start implementing the requested product or feature and hand off the code to a QA team, who would test and make sure that the code behaves correctly according to the design specifications. Once all the bugs are fixed, a release team would package the final code that would be handed off to the technical operations team, who would deploy the code and monitor the service over time:

The increasing complexity of developing certain software and technologies showed some limitations with this traditional waterfall pipeline.

The agile transformation addressed some of these issues, allowing for more interaction between the designers, developers, and testers. This change increased the overall quality of the product as these teams now had the opportunity to iterate more on product development; but apart from this, you would still be in a very classical waterfall pipeline:

All the agility added by this new process didn't extend past the QA cycles, and it was time to modernize this aspect of the software development life cycle. This foundational change with the agile process, which allows for more collaboration between the designers, developers, and QA teams, is what DevOps was initially after, but very quickly the DevOps movement started rethinking how developers and operations team could work together.

In a non-DevOps culture, developers are in charge of developing new products and features and maintaining the existing code, but ultimately they are rewarded when their code is shipped. The incentive is to deliver as fast as possible.

On the other hand, the operations team, in general, have the responsibility to maintain the uptime of the production environments. For these teams, "change is evil." New features and services increase the risk of having an outage, and therefore it is important to move with caution.

To minimize the risks of having outages, operations team usually need to schedule any deployment ahead of time so that they can stage and test any production deployment and maximize their chances of success. It is also very common for the enterprise type of software companies to schedule maintenance windows and, in these cases, this means production changes can only be done a few times a quarter.

Unfortunately, a lot of time deployments won't succeed, and there are many possible reasons for that.

Too much code changing at once

There is a certain correlation that can be made between the size of the change and the risk of introducing critical bugs in the product, as follows:

As we just said, a DevOps culture relies on a certain number of principles: source control everything, automate everything, and measure everything.

Measure everything

Measure everything is the last major principle that DevOps-driven companies adopt. As Edwards Deming said, "You can't improve what you can't measure." DevOps is an ever-evolving process that feeds off of those metrics to assess and improve the overall quality of the product and the team working on it.

From a tooling and operating standpoint, here are some of the metrics most organizations look at:

• Check how many builds a day are pushed to production
• Check how often do you need to roll back production in your production environment (this indicated when your testing didn't catch an important issue)
• The percentage of code coverage
• Frequency of alerts resulting in paging the on-call engineers for immediate attention
• Frequency of outages
• Application performance
• Mean Time to Resolution (MTTR) which is the speed at which an outage or a performance issue can be fixed

At the organizational level, it is also interesting to measure the impact of shifting to a DevOps culture. While it is a lot harder to measure, you can consider the following points:

• The amount of collaboration across teams
• Team autonomy
• Cross-functional work and team efforts
• Fluidity in the product
• Happiness among engineers
• Attitude toward automation
• Obsession with metrics

As you just saw, having a DevOps culture means, first of all, changing the traditional mindset that developers and operations are two separate silos and make both teams collaborate more during all phases of the software development life cycle.

In addition to a new mindset, DevOps culture requires a specific set of tools geared toward automation, deployment, and monitoring:

http://www.pwc.com/us/en/technology-forecast/2013/issue2/features/cios-agility-stability-paradox.html

Amazon with AWS offers a number of services of PaaS and SaaS types that will let us do just that.

Amazon services are like Lego pieces. If you can picture your final product, then you can explore the different services and start combining them the way you would assemble a Lego, in order to build the supporting stack needed to quickly and efficiently build your product. Of course, in this case, the "if" is a big if, and unlike Lego, understanding what each piece can do is a lot less visual and colorful than Lego pieces. This is why this book is written in a very practical way; throughout the different chapters, we are going to take a web application and deploy it like it's our core product. We will see how to scale the infrastructure supporting it so that millions of people can use it and finally make it more secure. And, of course, we will do this following DevOps best practices.

By going through that exercise, you will learn how AWS provides a number of managed services and systems to perform a number of common tasks such as computing, networking, load balancing, storing data, monitoring, programmatically managing infrastructure and deployment, caching, and queueing.

As you saw earlier in this chapter, having a DevOps culture is about rethinking how engineering teams work together by breaking these development and operations silos and bringing a new set of new tools to implement the best practices.

AWS helps in many different ways to accomplish this. For some developers, the world of operations can be scary and confusing, but if you want better cooperation between engineers, it is important to expose every aspect of running a service to the entire engineering organization. As an operations engineer, you can't have a gatekeeper mentality toward developers; instead, it's better to make them comfortable accessing production and working on the different components of the platform. A good way to get started with this in the AWS console:

While a bit overwhelming, it is still a much better experience for people not familiar with this world to navigate this web interface than referring to constantly out-of-date documentation, using SSH and random plays, to discover the topology and configuration of the service.

Of course, as your expertise grows and your application becomes more complex, the need to operate it faster increases, and the web interface starts to show some weaknesses. To get around this issue, AWS provides a very DevOps-friendly alternative: an API. Accessible through a command-line tool and a number of SDKs (which include Java, JavaScript, Python, .NET, PHP, Ruby Go, and C++), the SDKs let you administrate and use the managed services.

Finally, as you have seen a bit in the previous section, AWS offers a number of services that fits DevOps methodologies and will ultimately allow us to implement complex solutions in no time.

Some of the major services you will use are, at the compute level, EC2, the service to create virtual servers. Later, as you start looking into how to scale our infrastructure, you will discover Auto Scaling Groups, a service that let you scale pools on EC2 instances to handle traffic spikes and host failure. You will also explore the concept of containers with Docker via ECS. Lastly, you will create serverless functions via Lambda to run custom code without having to host it on our servers.

To implement our continuous integration and continuous deployment system, you will rely on four services: S3, the object store service that will allow us to store our artifacts; CodeBuild,which will let us test our code; CodeDeploy, which will let us deploy artifacts to our EC2 instances; and finally CodePipeline, which will let you orchestrate how our code is built, tested, and deployed across environments.

To monitor and measure everything, you will rely on CloudWatch and later ElasticSearch/Kibana to collect, index, and visualize metrics and logs. To stream some of our data to these services, you will rely on AWS Kinesis. To send email and SMS alerts, you will use the SNS service.

For infrastructure management, you will heavily rely on CloudFormation, which provides the ability to create templates of infrastructure.

In the end, as you explore ways to better secure our infrastructure, you will encounter Inspector and Trusted Advisor, and explore in more detail the IAM and the VPC service.