As software development moves to the cloud, there’s an increasing number of tools that can be used to build, test and deploy applications. While they’re not all directly competing with each other, it can be hard to decide which tool is right for your team.
In this article, we’ll take a look at how GitHub Actions and AWS CodePipeline/CodeBuild compare when it comes to workflow management and isolated job execution.
We’ll also cover pricing so you can make an informed decision about which tool is right for your team.
AWS CodePipeline and GitHub Actions are both ways to build and deploy applications. They differ in the way they are used but are similar in many other ways. This article compares and contrasts the two services to help you decide which one is right for your project.
AWS CodePipeline is Amazon’s offering in the continuous integration and delivery space. It provides a managed workflow service that allows developers to build, test, and deploy applications with ease. It integrates with various third-party tools such as GitHub, Slack, JIRA, and many others.
The pipeline can be configured to run on an hourly basis or whenever there is a change on a particular branch of code in your repository.
GitHub Actions is a new service from GitHub that lets you automate all aspects of your workflow from building code to deploying it in production environments.
The actions can be triggered by contributors submitting pull requests or pushes to branches on your repositories hosted on GitHub; however, unlike CodePipeline, these actions happen on individual repositories instead of across multiple projects at once time like CodePipeline does (which could be an advantage depending on what kind of workflows you’re looking for).
One important item to compare between these to discuss workflow management and isolated job execution.
Workflow Management
Both AWS CodePipeline and GitHub Actions offer easy-to-use tools for creating a workflow to run your code through different steps in the CI/CD process. CodePipeline offers more features for visualizing your workflow, while GitHub Actions’ visualizations are less detailed but easier to use.
Isolated Job Execution
GitHub Actions lets you run jobs immediately after they are pushed to a branch, while CodePipeline requires you to wait until all jobs have been completed before starting them again—which means that if one job fails, all other jobs will be stopped as well.
This can be particularly problematic if your build requires access to external resources that may change over time (such as an Amazon S3 bucket).
How to Use AWS Code Build for Continuous Integration and Delivery
AWS Code Build is a service that helps you build and test your code on AWS. You can use it to compile code, run unit tests, and even deploy artifacts to other services like Amazon Elastic Container Service or Amazon API Gateway.
You can also use it to generate reports based on your automated builds. It’s an easy way to automate many of the tasks performed by Jenkins or CircleCI without having to install any software or worry about managing servers yourself.
What is AWS Code Build?
AWS Code Build is a fully managed service that lets you build software projects hosted on GitHub or Bitbucket in a few clicks without having to install, configure, or operate any servers. AWS CodeBuild uses Amazon EC2 Container Service (ECS) to build your application into containers.
It also provides an API that allows you to integrate AWS CodeBuild into your CI/CD process so that you can automatically build and test when commits are pushed to the repository.
You will find that AWS Code Build builds your code faster than any other cloud service; it’s fast, simple, and easy to use. AWS Code Build can build and test your entire application on a large scale without you having to worry about deploying it.
You can also trigger builds based on events from other AWS services like Amazon Lambda or Amazon EC2.
Examples of how to get started with AWS Code Build.
Here are three examples of how to get started with AWS Code Build.
Set up an AWS Code Build project, then link it to your Github repository. After that, you can set up a building job and trigger builds based on events from other AWS services like Amazon EC2 or Amazon Lambda. You can then trigger builds based on events from other AWS services, such as Amazon EC2 or Amazon Lambda. Triggering builds by an event is useful when you want to run tests, like making sure your application is always in a deployable state before deploying it.
Link code build project to a different repo.
Create a GitHub webhook for automatic builds
1: Set up an AWS Code Build project, then link it to Github.
Create the code build project and link a Github repo, then push some code.
In the AWS CodeBuild console, choose Create project and enter a project name.
Under Repositories, enter the URL of your source repository and select whether to use SSH or HTTPS for git. You can also create an IAM user with access permissions for this specific repository if you don’t want to make all of your repositories publicly accessible from within AWS CodeBuild (more on that later).
Click Save to finish creating your new build project and open it in edit mode:
2: Link a code build project to a different repo.
You can link a code build project to a different repo. This is useful if you want to test and build against the latest version of your source code, but are using a branch that isn’t currently available in the master branch.
To link a different commit or commit hash:
Select Link Source from the Actions menu for an existing CodeBuild project in AWS CodePipeline; then choose your preferred source from the drop-down list.
Choose Repository Branch, Commit Hash, or Tag Name (depending on your choice) and enter values as needed; then choose Save Changes when finished.
3: Create a GitHub webhook for automatic builds.
Next, set up a webhook in GitHub that automatically triggers a build when you push code changes to your repository (you can also do this with Bitbucket or GitLab).
Select Settings and then Webhooks & Services under the Integrations section.
Click Add Webhook at the bottom of the page, which will open up a form where you can configure some options:
Select “GitHub hook” as your trigger type.
Set “Frequency” to “Always.” This tells AWS CodeBuild that it should always watch for new commits and build them whenever they happen—no matter what time of day or how often you push updates. The other option here would be “Polling,” which means checking for webhooks about once per hour on average; this is useful if your team is actively working on projects but not committing too often (for example, just after lunch).
AWS Code Build can become a powerful tool in your CI/CD pipeline.
AWS Code Build is a fully managed service that can be used to build and test your code. It also can deploy your code, so you don’t have to worry about setting up any servers or managing them once they’re up.
Once you set up AWS Code Build for continuous integration, it will automatically run tests on your new commits and update your application if there are any errors. This saves developers time and ensures their code works before merging into the master branch.
AWS Code Build gives you the power to create custom pipelines that can integrate with other services like AWS CodePipeline or JenkinsCI and even run tests on Amazon EC2 instances! The possibilities are endless, but ultimately it comes down to knowing where your company stands regarding DevOps practices.
Fine-tune your EC2 instance selections based on CPU and Memory usage. Even if you had chosen the instance type carefully, note that the application and its usage pattern might change over time.
Select always new instance generation. You will get enhanced performance at a lower cost. Look at how instances differ across generations. Instance prices decline as the new generations come. Upgrading to M5 generation from M3 saves you 27.8% per hour.
Upgrading to the new generation also gives you a performance increase from C3 to C5 large the cost per ECU decreases by %43 and from M3 to M5d.large it decreases by 44%
Of course, upgrading to a new generation instance might require some additional work; however, it will save money in the long run.
Selecting across the same instance type
2. Instance Selection over Different Types:
Instance Selection over Different Types
Amazon EC2 offers a wide range of EC2 instance types and pricing options offer significant advantages if you select instances carefully.
To compare instances across types, you need to calculate per compute units like vCPU, ECU and Memory. The below table lists the most common low-mid-end instance types sorted by cost per vCPU.
The below table is sorted by Cost per Memory.
Considering both CPU and Memory options, interesting cost-saving opportunities arise. Here t3.large instances are cheaper than m5.large instances by 13.3%. Both have 2vCPUs and 8 GiB of memory. T3 might be the better selection considering the burstable option.
M4 large is priced as C4 large. Although the M4 has just less CPU performance than C4 (6.5 vs 8 vCPU), it has a signification memory advance (3.75 vs. 8 GiB) so if your applications do not very heavy on CPU, having M4 over C4 large more than double what you get from memory with the same price.
If your current application runs on Computation-optimized C types and you notice that you might need extra memory before increasing the size in the same class, always consider M types.
Here for example if your application runs on C4 large, before considering moving to C4 xlarge or C5 xlarge, consider M5 large where you will get 8 GiB memory at a lower price.
This is similar to even C5 large. You can consider moving to M5 Large. If you don’t require extra ECUs) you can select M5 large and then move to C5 xlarge.
3. Comparing Processors:
Amazon EC2 provides Intel, AMD EPYC and AWS Graviton types of processors where a selection of the processors directly affects the cost.
Below compares different processor types. In general, the cost for AMD processors is around 10% cheaper than Intel instances.
4. Generate Scheduling Plans:
Review your workload to find instances that can be scheduled. Especially development and test environments are of this kind.
You won’t need those resources always up&running. You can use AWS Instance Schedule which will only cost you $5 per month in AWS Lambda for each schedule, and $0.9 Cloudwatch schedule tracking will be added per schedule.
AWS Scheduler is based on schedule not a number of instances, so create as less as possible scheduler covering as many as your instances.
For on-demand requests, you can create scripts to make your environment up & down if needed.
5. Monitor data transfer costs:
One of the important pitfalls is underestimating the cost of data transfer on VPCs. VPC peering is a cost-effective method for transferring data across VPCs, if you won’t use VPC peering, then transfer happens via public IP, resulting in higher costs.
6. Regularly check CPU and Memory utilization:
Instances that run with CPU and memory utilization under 50% can be considered a candidate for resizing.
You can choose to deploy smaller and less expensive instances or totally terminate the instance by moving the workload to containers.
7. Check unallocated EBS or EIPs :
Terminating an instance does not always terminate EBS or EIPs. Ensure that EBS volumes are terminated and that “Delete on Termination” is selected during the creation of the instance.
In any means, a script that detects unallocated regularly and alerts teams will be very helpful.
8. Use EC2 Autoscaling and Fleet:
If your workload has variable loads, you can use EC2 Autoscaling & Fleet to save costs.
You can set the baseline capacity and scaling resources in terms of instances, vCPUs, or application-oriented units and also indicate how much of the capacity should be fulfilled by Spot Instances.
You should also select instance types that have reservations to use commitment discounts.
9. When you delete Ec2 Fleet, you need to stop or terminate the instances manually.
Be sure that if you delete the EC2 fleet, it only deletes the definition, not the actual EC2 instances. You need to stop or terminate them manually.
10. Set auto-scaling groups based on minimum resource capacity.
Be careful with setting up Auto Scaling groups. You should validate that the min resource capacity and desired capacity are not over-provisioned.
You should also define a scale-down policy for each scale-up policy. This can be based on CPU, Memory utilization.
11. Check the deleted instance if it belongs to Autoscaling group or not.
If a deleted instance belongs to an auto-scaling group, auto scaling group might spin up alternate instances to match the capacity. You need to change or delete group definitions before deleting the instance.
12. Be careful about 3rd party software licenses on auto scaling or EC2 fleet.
If you set up an autoscaling group or use software priced per CPU, you should check your workload before adding to the auto-scaling group.
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81% of Companies Have Multi-Cloud Strategies
The public cloud industry is truly massive. According to several reports, the cloud computing industry was just US$ 24.65 billion in 2010 and ten years later, the industry is set to cross the US 150 billion mark with ease. Globally, around 81% of companies have a multi-cloud strategy already in place and by the end of 2020, around 67% of enterprise-level IT infrastructure will be on cloud platforms. This will result in the cloud technology handling over 82% of the total enterprise workload. It is estimated that an astonishing 40 Zettabytes of data will be handled by cloud servers and networks by the end of 2020.
Business owners are always looking for solutions, like cloud technology, that can make their operations more efficient and easier. However, despite its increasing popularity there are still several thousand enterprises that are yet to adopt the innovative computing and storage technology. One of the main reasons that is still keeping several industries from adopting cloud technology is the suspected high cost of moving the existing enterprise applications and databases to the cloud servers. However, business owners need to know that overall, the cost of moving to cloud and using it as your database and processing platform is significantly low as compared to using on-site IT infrastructure.
Points to Consider when Finding Data Center Relocation Estimate
If you are hesitant to make the move to cloud platform because of suspected high cost, here are the simple ways you can figure out the precise data center relocation estimate for your business.
It is important to note that there are numerous small and large IT services and products that any business enterprise uses at a given time. The various products and services rack up a significant expense when accumulated. Hence, you need to carefully consider every aspect of expense that can be related to moving your enterprise database and applications to cloud platform.
We have classified the different expenses into several categories for your convenience:
Data Center Relocation Cost Estimate – expense categories
Pre-Migration Costs
You need to have maximum details about the existing cost of maintaining and running your business application on in-house IT systems as well as information from system performance data. This helps to decide the right-sized IT architecture you will need on the cloud platform and also show how cost-effective will be your business operations on the cloud server as compared to the existing on-site IT infrastructure. Some of the factors that determine pre-migration costs are:
On Site Data Center costs – Includes cost of maintaining and upgrading servers, power / utility bills, storage, IT labor, network, etc.
Hardware – Includes hardware specification, maintenance, upgrading, etc.
Software – Includes cost of licensing, support, purchase dates, contracts, warranties, etc.
Operations – Labor costs, network connections, system performance data, etc.
Post Migration Costs
Post-migration costs are expenses that are likely to be incurred when running business applications on cloud platform, such as:
Monitoring
Alerting
Monthly / annual licensing and support service
Administrations
Operations
System maintenance and operation
Maintenance
System updates
Software version patches and updates
Training
Migration Costs
The actual process of migration of database and applications requires several different expenses, such as:
Application migration cost which is the actual cost of moving applications from existing environment to cloud servers.
Configuration and infrastructural changes that are required to make applications compatible to run on cloud servers.
Integrating and testing migrated apps to ensure that they run smoothly on the new cloud environment.
Bottom Line
Once you have the details of the overall cost of maintaining and operating your business applications and databases on current on-site data banks and on cloud servers, you can get an even more accurate data center relocation cost estimate using the Cost Calculator provided on the website of leading cloud service providers, such as Amazon Web Services (AWS), Google, Microsoft, IBM, etc.
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