There are many parts to successfully deploying your cloud-native application. To fully leverage the benefits of the cloud, you’ll need to consider many different tools and configuration options. To help accelerate your project, we’ve created this application deployment checklist.
Much of what you’ll do later on in this checklist depends on how you’ll serve or deploy your application. Basically, are you serving your app from a virtualization instance, or through a Docker-based container? Though both are cloud-based, they’re quite different.
If you’re running the application through virtualization or virtual machine (VM), you’ll require scaling. But if you’re running it from containers with an orchestration system — such as Kubernetes — you’ll get auto-scaling out-of-the-box.
This applies if you’re using a container orchestration system, such as Kubernetes.
You can use Jenkins for pure Continuous Integration (CI), and Spinnaker for pure Continuous Deployment (CD) tasks. Some will also use Jenkins for both CI/CD. You can deploy containers in different ways, so your choice of CI, CD, or CI/CD will depend on your needs.
If you’re using an auto-scaler, you’ll be able to estimate lower CPU, RAM, etc., requirements. In turn, you’ll save costs by not paying for surplus capacity.
In terms of Garbage Collection (GC) settings, you should test for 3-10X your app’s load — i.e., tune your GC settings based on your peak load.
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React.js is generally the preferred option for lightweight, single-page applications. Angular.js is the way forward for heavier applications. For home pages, you can use Vue.js.
Though there are several compile-to-JS languages, most use TypeScript.
As with compile-to-CSS languages, though you have several options, most use SASS.
Common steps include:
You have a range of relational databases to choose from, including MySQL, PostgreSQL, Oracle, SQL Server, and others. However, when selecting one, consider the following:
Whether you’re choosing MongoDB, Couchbase, or another solution, you should try leveraging managed cloud services. However, you should avoid trying to shoehorn databases into different platforms. For example, don’t migrate SQL datasets into MongoDB.
This requirement only applies to niche development environments, typically in BASH or trigger-based situations.
First, script out your infrastructure-as-code (IaC). This way, in case you need to recover your application data, you can just trigger the script and stand-up your hosting infrastructure.
Second, test your back-ups as part of your release management processes. Don’t assume they just work; you’re putting yourself at risk of failure during an actual crash/disaster.
In your application deployment plan, make sure you’re using more than one availability zone or region, especially for mission-critical applications. If one region sustains an outage, you’ll lose your application; focus on redundancy through multiple availability zones (AZ).
Based on your needs, you’ll need to pick either a monolithic or microservices-based architecture.
The goal behind these functions is to automate your application’s ability to identify and respond to a crash.
For example, with auto-scaling, if your application needs another instance, it’ll scale-up and start a new one. And once that demand recedes, the app can close the instance on its own. Likewise, with load balancing, the application will manage container requests coming into the same port.
If you’re deploying a microservices application, you might have a failure between several of your services. In this case, you should configure your application to handle these failures such that it doesn’t crash. To do this efficiently, you’ll need a service mesh such as the open-source Istio.
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Finally, test for resilience by running load tests against your infrastructure. You can also figure out the point at which your application will crash (e.g., its maximum load).
You could also use tools such as Chaos Monkey to randomly shut down specific services and other parts of your deployment process. This lets you gauge your ability to deal with downtime.
A critical part of deploying a cloud-native application is leveraging cloud automation tools.
You should narrow your options to some flavor of Git — e.g., GitHub, GitLab, etc.
When it comes to code reviews, it’s best to rely on peer code reviews. You can set-up a leveling system wherein if the code achieves a specific standard, you can commit to code changes. But you must have some type of code review in place before committing.
Standardize your Git comments when you commit code. This way, everyone in your organization understands the purpose of the code — the comments act like documentation for your code.
You’ll need a system to automatically compile your code. If you’re using Java, you’ll probably use Maven or Gradle. For Ruby, you’re looking at Rake, while Yarn works best for Node.js.
You’ll need a dependency management system — like Artifactory or Nexus — to create artifacts.
So that each time you compile your code, the system will generate an artifact containing the version history of your code. You can then organize those artifacts using a versioning system, e.g., you can have a major build, minor build, and an actual build number.
Next, under your release process, you should deploy immutable (i.e., unchangeable ) artifacts to one environment at a time. First, start with development, move to testing, and then send to your production environments.
In case of failure, your release automation system should be able to roll-back to an artifact in a good state.
The goal here is to ensure your application can run tools that check for performance, security, and other key elements.
First, you should be doing test-driven development (TDD). TDD involves a process called “red, green, refactor.” The basic idea of this process is to write your tests first, get them to pass, then refactor by improving upon the original code.
Second, you should test at the smallest unit possible to ensure your code works. Generally, you should test at least 70% of your overall code, but 100% of your functional code. In other words, test all code that does something.
Third, employ integrated tests and journey tests to ensure that the application as a whole runs the way you intended. If you have a build that fails any of your tests, kick that build out.
You can reduce the risk of downtime by using blue and green deployment techniques. Basically, you maintain two parallel production environments.
Keep one (e.g., blue) active for traffic, while also keeping the other (e.g., green) on standby. You can use green to test new builds/versions of your application. This way, you avoid putting blue at risk of bugs and, in turn, you can find and clean-up bugs on green.
Alternatively (or in conjunction with blue-green), you can roll-out updates to a subsection of your users first. In case there’s a problem, you can confine that problem to that specific user group — i.e., avoid downtime across your entire end-user base.
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Create custom virtual private clouds (VPC) and, in turn, subnets for each VPC. You should also set-up access control lists (ACL) to limit what traffic flows between your VPCs. Finally, configure security groups, static IPs, and DNS.
In terms of security, encrypt network connections. You should also set-up a Bastion Host to keep all of your instances behind a private subnet. Your instances shouldn’t directly be accessible to the internet. Finally, build a strong password management policy and do a security audit.
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The challenge with this — or any — software deployment checklist is that each task is, in a way, an entire development project in of itself. You need skilled experts and tools for each section of the development stack. If you lack resources in any (or all) of these areas we covered, then be sure to contact an experienced software development agency to plugin your gaps.
At Techolution, we help organizations deliver market-ready cloud-native applications in weeks. Don’t let a lack of in-house expertise or resources stall your cloud deployment efforts. Call us today to get started.