In the SDLC, deployment is the final lever that must be pulled to make an application or system ready for use. Whether it's a bug fix or new release, the deployment phase is the culminating event to see how something works in production. This Zone covers resources on all developers’ deployment necessities, including configuration management, pull requests, version control, package managers, and more.
Deep Dive Into Terraform Provider Debugging With Delve
Convert Your Code From Jupyter Notebooks To Automated Data and ML Pipelines Using AWS
Jenkins allows you to automate everything, from building and testing code to deploying to production. Jenkins works on the principle of pipelines, which can be customized to fit the needs of any project. After installing Jenkins, we launch it and navigate to the web interface, usually available at http://localhost:8080. On the first launch, Jenkins will ask you to enter a password, which is displayed in the console or located in a file on the server. After entering the password, you are redirected to the plugin setup page. To work with infrastructure pipelines, you will need the following plugins: Pipeline: The main plugin for creating and managing pipelines in Jenkins. Git plugin: Necessary for integration with Git and working with repositories. Docker Pipeline: Allows you to use Docker within Jenkins pipelines. Also, in the Jenkins settings, there is a section related to the configuration of version control systems, and there you need to add a repository. For Git, this will require specifying the repository URL and account credentials. Now you can create an infrastructure pipeline, which is a series of automated steps that transform your code into production-ready software. The main goal of all this is to make the software delivery process as fast as possible. Creating a Basic Pipeline A pipeline consists of a series of steps, each of which performs a specific task. Typically, the steps look like this: Checkout — extracting the source code from the version control system Build — building the project using build tools, such as Maven Test — running automated tests to check the code quality Deploy — deploying the built application to the target server or cloud Conditions determine the circumstances under which each pipeline step should or should not be executed. Jenkins Pipeline has a "when" directive that allows you to restrict the execution of steps based on specific conditions. Triggers determine what exactly triggers the execution of the pipeline: Push to repository — the pipeline is triggered every time new commits are pushed to the repository. Schedule — the pipeline can be configured to run on a schedule, for example, every night for nightly builds. External events — the pipeline can also be configured to run in response to external events. To make all this work, you need to create a Jenkinsfile — a file that describes the pipeline. Here's an example of a simple Jenkinsfile: Groovy pipeline { agent any stages { stage('Checkout') { steps { git 'https://your-repository-url.git' } } stage('Build') { steps { sh 'mvn clean package' } } stage('Test') { steps { sh 'mvn test' } } stage('Deploy') { steps { // deployment steps } } } post { success { echo 'The pipeline has completed successfully.' } } } Jenkinsfile describes a basic pipeline with four stages: checkout, build, test, and deploy Parameterized Builds Parameterized builds allow you to dynamically manage build parameters. To start, you need to define the parameters in the Jenkinsfile used to configure the pipeline. This is done using the "parameters" directive, where you can specify various parameter types (string, choice, booleanParam, etc.). Groovy pipeline { agent any parameters { string(name: 'DEPLOY_ENV', defaultValue: 'staging', description: 'Target environment') choice(name: 'VERSION', choices: ['1.0', '1.1', '2.0'], description: 'App version to deploy') booleanParam(name: 'RUN_TESTS', defaultValue: true, description: 'Run tests?') } stages { stage('Initialization') { steps { echo "Deploying version ${params.VERSION} to ${params.DEPLOY_ENV}" script { if (params.RUN_TESTS) { echo "Tests will be run" } else { echo "Skipping tests" } } } } // other stages } } When the pipeline is executed, the system will prompt the user to fill in the parameters according to their definitions. You can use parameters to conditionally execute certain pipeline stages. For example, only run the testing stages if the RUN_TESTS parameter is set to true. The DEPLOY_ENV parameter can be used to dynamically select the target environment for deployment, allowing you to use the same pipeline to deploy to different environments, such as production. Dynamic Environment Creation Dynamic environment creation allows you to automate the process of provisioning and removing temporary test or staging environments for each new build, branch, or pull request. In Jenkins, this can be achieved using pipelines, Groovy scripts, and integration with tools like Docker, Kubernetes, Terraform, etc. Let's say you want to create a temporary test environment for each branch in a Git repository, using Docker. In the Jenkinsfile, you can define stages for building a Docker image, running a container for testing, and removing the container after the tests are complete: Groovy pipeline { agent any stages { stage('Build Docker Image') { steps { script { // For example, the Dockerfile is located at the root of the project sh 'docker build -t my-app:${GIT_COMMIT} .' } } } stage('Deploy to Test Environment') { steps { script { // run the container from the built image sh 'docker run -d --name test-my-app-${GIT_COMMIT} -p 8080:80 my-app:${GIT_COMMIT}' } } } stage('Run Tests') { steps { script { // steps to run tests echo 'Running tests against the test environment' } } } stage('Cleanup') { steps { script { // stop and remove the container after testing sh 'docker stop test-my-app-${GIT_COMMIT}' sh 'docker rm test-my-app-${GIT_COMMIT}' } } } } } If Kubernetes is used to manage the containers, you can dynamically create and delete namespaces to isolate the test environments. In this case, the Jenkinsfile might look like this: Groovy pipeline { agent any environment { KUBE_NAMESPACE = "test-${GIT_COMMIT}" } stages { stage('Create Namespace') { steps { script { // create a new namespace in Kubernetes sh "kubectl create namespace ${KUBE_NAMESPACE}" } } } stage('Deploy to Kubernetes') { steps { script { // deploy the application to the created namespace sh "kubectl apply -f k8s/deployment.yaml -n ${KUBE_NAMESPACE}" sh "kubectl apply -f k8s/service.yaml -n ${KUBE_NAMESPACE}" } } } stage('Run Tests') { steps { script { // test the application echo 'Running tests against the Kubernetes environment' } } } stage('Cleanup') { steps { script { // delete the namespace and all associated resources sh "kubectl delete namespace ${KUBE_NAMESPACE}" } } } } } Easily Integrate Prometheus The Prometheus metrics can be set up in Jenkins through "Manage Jenkins" -> "Manage Plugins." After installation, we go to the Jenkins settings, and in the Prometheus Metrics section, we enable the exposure of metrics — enable Prometheus metrics. The plugin will be accessible by default at the URL http://<JENKINS_URL>/prometheus/, where <JENKINS_URL> is the address of the Jenkins server. In the Prometheus configuration file prometheus.yml, we add a new job to collect metrics from Jenkins: YAML scrape_configs: - job_name: 'jenkins' metrics_path: '/prometheus/' static_configs: - targets: ['<JENKINS_IP>:<PORT>'] Then, through Grafana, we can point to the Prometheus source and visualize the data. The Prometheus integration allows you to monitor various Jenkins metrics, such as the number of builds, job durations, and resource utilization. This can be particularly useful for identifying performance bottlenecks, tracking trends, and optimizing your Jenkins infrastructure. By leveraging the power of Prometheus and Grafana, you can gain valuable insights into your Jenkins environment and make data-driven decisions to improve your continuous integration and deployment processes. Conclusion Jenkins is a powerful automation tool that can help streamline your software delivery process. By leveraging infrastructure pipelines, you can easily define and manage the steps required to transform your code into production-ready software.
CI/CD (Continuous Integration and Continuous Delivery) is an essential part of modern software development. CI/CD tools help developers automate the process of building, testing, and deploying software, which saves time and improves code quality. GitLab and Jenkins are two popular CI/CD tools that have gained widespread adoption in the software development industry. In this article, we will compare GitLab and Jenkins and help you decide which one is the best CI/CD tool for your organization. What Are GitLab and Jenkins? Before we get down to brass tacks, let's quickly go over some definitions in order to give you a clearer picture of each tool's purpose and capabilities. GitLab: GitLab is a web-based DevOps lifecycle tool that provides a complete DevOps platform, including source code management, CI/CD pipelines, issue tracking, and more. It offers an integrated environment for teams to collaborate on projects, automate workflows, and deliver software efficiently. Jenkins: Jenkins is an open-source automation server that enables developers to build, test, and deploy software projects continuously. It offers a wide range of plugins and integrations, making it highly customizable and adaptable to various development environments. Jenkins is known for its flexibility and extensibility, allowing teams to create complex CI/CD pipelines tailored to their specific needs. The Technical Difference Between GitLab and Jenkins FEATURE GITLAB JENKINS Version Control Git N/A (requires integration with a separate VCS tool). Continuous Integration Yes, built-in. Yes, built-in. Continuous Delivery Yes, built-in. Requires plugins or scripting. Security Built-in security features. Requires plugins or scripting. Code Review Built-in code review features. Requires plugins or scripting. Performance Generally faster due to built-in Git repository May require additional resources for performance Scalability Scales well for small to medium-sized teams. Scales well for large teams. Cost Free for self-hosted and cloud-hosted versions. Free for self-hosted and has a cost for cloud-hosted. Community Active open-source community and enterprise support. Active open-source community and enterprise support. GitLab vs Jenkins: Features and Performance 1. Ease of Use GitLab is an all-in-one platform that provides a comprehensive solution for CI/CD, version control, project management, and collaboration. It has a simple and intuitive user interface that makes it easy for developers to set up and configure their CI/CD pipelines. On the other hand, Jenkins is a highly customizable tool that requires some technical expertise to set up and configure. It has a steep learning curve, and new users may find it challenging to get started. 2. Integration GitLab and Jenkins support integration with a wide range of tools and services. However, GitLab offers more native integrations with third-party services, including cloud providers, deployment platforms, and monitoring tools. This makes it easier for developers to set up their pipelines and automate their workflows. Jenkins also has a vast library of plugins that support integration with various tools and services. These plugins cover a wide range of functionalities, including source code management, build triggers, testing frameworks, deployment automation, and more. 3. Performance GitLab is known for its fast and reliable performance. It has built-in caching and parallel processing capabilities that allow developers to run their pipelines quickly and efficiently. Jenkins, on the other hand, can suffer from performance issues when running large and complex pipelines. It requires manual optimization to ensure it can handle the load. 4. Security GitLab has built-in security features that ensure code is secure at every pipeline stage. It provides features, like code scanning, vulnerability management, and container scanning, that help developers identify and fix security issues before they make it into production. Jenkins relies heavily on plugins for security features. This can make it challenging to ensure your pipeline is secure, especially if you are using third-party plugins. 5. Cost GitLab offers free and paid plans. The free plan includes most features a small team would need for CI/CD. The paid plans include additional features like deployment monitoring, auditing, and compliance. Jenkins is an open-source tool that is free to use. However, it requires significant resources to set up and maintain, which can add to the overall cost of using the tool. GitLab vs Jenkins: Which One Is Best? GitLab and Jenkins are two popular tools used in the software development process. However, it’s difficult to say which one is better as it depends on the specific needs of your project and organization. GitLab may be a better choice if you want an integrated solution with an intuitive interface and built-in features. Jenkins could be the better option if you want a customizable and extensible automation server that can be easily integrated with other tools in your workflow. GitLab is a complete DevOps platform that includes source code management, continuous integration/continuous delivery (CI/CD), and more. It offers features such as Git repository management, issue tracking, code review, and continuous integration/continuous delivery (CI/CD) pipelines. GitLab also has a built-in container registry and Kubernetes integration, making it easy to deploy applications to container environments. On the other hand, Jenkins is a popular open-source automation server widely used for continuous integration and continuous delivery (CI/CD) pipelines. It offers several plugins for various functionalities, such as code analysis, testing, deployment, and monitoring. Jenkins can be easily integrated with other tools in the software development process, such as Git, GitHub, and Bitbucket. Ultimately, the choice between GitLab and Jenkins will depend on your specific needs and preferences. GitLab is an all-in-one solution, while Jenkins is more flexible and can be customized with plugins. Conclusion GitLab and Jenkins are excellent CI/CD tools that offer a range of features and integrations. However, GitLab has the edge when it comes to ease of use, integration, performance, security, and cost. GitLab’s all-in-one platform makes it easy for developers to set up and configure their pipelines, while its native integrations and built-in features make it more efficient and secure than Jenkins. Therefore, if you are looking for a CI/CD tool that is easy to use, cost-effective, and reliable, GitLab is the best option for your organization.
Let’s face it, most Java developers cringe and run when they see the term “Visual Studio” with the misconception that the tool will be for .NET users only. I have to confess; I was one of those guilty Java developers until I discovered that it is actually a powerful solution for any language or platform to utilize during project release cycles. The Azure DevOps service offers several features for such as adding team members, Kanban boards, Repo options, Build/Release Pipelines, Test Plans, Build Artifacts (e.g. Maven) and much more. It also integrates with your favorite tools like Eclipse, IntelliJ, Jenkins or Chef. For microservices, Java developers will be happy to know that it supports container build services like Docker, Kubernetes, Cloud Foundry etc. This tutorial will demo the use of Azure DevOps to set up a release automation to build and deploy a Java web application. Prerequisites Register or log into your Azure DevOps account Copy and save the Git URL for the sample Java code from GitHub Create an Azure Web App (Note: choose the following options for this Java web app. OS = Linux, Runtime Stack = JRE 8) How to Build CI/CD Pipelines for Java Using Azure DevOps 1. Create a Project and Git Repository On the Azure DevOps projects page, click "New Project" then enter your project name. Select "Git" in the "Version Control" drop-down, then click the "Create" button to continue Click on the "import" button below the "or import a repository" to paste in the Git URL for the sample Java code above. Then click on "Import" to continue 2. Create a Build Definition for Maven The build definition in Azure DevOps automatically executes all the tasks in the build each time there’s a commit in the Java source application. In this example, Azure DevOps will use Maven to build a Java Spring Boot project. Click on the Build and Release tab on top of your Azure DevOps project page. Then select the "Builds" link Click on the New Pipeline button, and then "Continue" to start defining your build tasks Select "Maven"from the list of templates, then click on the "Apply" button to build your Java project Use the drop-down menu for the Agent pool field to select Hosted Linux Preview option. This informs Azure DevOps which build server to use. You can also use your private customized build server To configure your build for continuous integration, select the Triggers tab and check the Enable continuous integrationcheckbox. Use the Save & queue drop-down menu to select the Save & queue option. In the pop-up window, verify that "Hosted Linux Preview" is select as the Agent pool field. Then click on the queue button to build the java application. Verify that the build tasks completed successfully by clicking on the generated build number. 3. Create a Release Definition the Java Web App The release pipeline in Azure DevOps automatically triggers the deployment of build artifacts to any target environment like Azure as soon as the Build process completes successfully. The release pipeline can be created for dev, test, staging or production environments. Click on the Build and release tab on top of your project page. Then select the “Releases” link. Click on the New pipeline button. On top of the list of templates, then click on the “Empty job” link Enter a "Stage name" (e.g Dev, Test, Staging or Production). Then click on the “X” button to close the pop-up window Click on the "+ Add" button in the Artifacts section. This will link artifacts from the build definition (e.g. Maven build) to this release definition. Use the drop-down menu for the "Source (build pipeline)" to select your build definition. Then click the "Add" button to continue. Click on the "Tasks" tab on the pipeline. Then, select your stage name. Click on the "+" link on the Agent Job section to add a deployment task. Select the "Azure App Service Deploy" task under the list of tasks, then click on the "Add" button On the "Azure App Service Deploy" page, set the version to "4.* (preview)" in the drop-down menu Use the "Azure subscription" drop-down menu to select your Azure subscription ID. Select "Web App on Linux" from the "App Service type" drop-down menu Select the name of the Azure Web App instance you created above in the "App service name" drop-down menu. For the "Package or folder" field, navigate to the project generated .jar file Select the "JRE 8" in the "Runtime Stack" drop-down. Note: click the refresh button next to the field if you don’t see the JRE 8 in the list. Click on the "Agent job" link on the left navigation bar. Select "Hosted Linux Preview" in the Agent pool drop-down menu To enable continuous deployment, click the "Pipelines" tab In the Artifacts section, click on the lightning icon. Then set the "Continuous deployment trigger" to Enabled. Click on the "Save" button, then the "OK" button. Click on the "+ Release" drop-down menu, then select "Create a release." In the pop-up window, click on the "Create" button. Click on the generated “release number” to check the status of your deployment. 4. Test Your Deployed Java Web Application Open a web browser and paste your web app URL:https://{your-app-service-name}.azurewebsites.net The web page should display the Spring Music Albums: Summary It's nice to see Microsoft making strides to enable all developers by taking simple steps like naming changes that are more attractive and embrace many developer communities. Azure DevOps provides developers and teams with a robust solution to collaborate and automatically generate CI/CD pipelines to deliver applications faster.
Deployment strategies provide a systematic approach to releasing software changes, minimizing risks, and maintaining consistency across projects and teams. Without a well-defined strategy and systematic approach, deployments can lead to downtime, data loss, or system failures, resulting in frustrated users and revenue loss. Before we start exploring different deployment strategies in more detail, let’s take a look at the short overview of each deployment strategy mentioned in this article: All-at-once deployment: This strategy involves updating all the target environments at once, making it the fastest but riskiest approach. In-place deployment: This involves stopping the current application and replacing it with a new version, directly affecting availability. Blue/Green deployment: A zero-downtime approach that involves running two identical environments and switching from old to new Canary deployment: Introduces new changes incrementally to a small subset of users before a full rollout Shadow deployment: Mirrors real traffic to a shadow environment where the new deployment is tested without affecting the live environment All-At-Once Deployment All-at-once deployment strategy, also known as the "Big Bang" deployment strategy, involves simultaneously releasing your application's new version to all servers or environments. This method is straightforward and can be implemented quickly, as it does not require complex orchestration or additional infrastructure. The primary benefit of this approach is its simplicity and the ability to immediately transition all users to the new version of the application. However, the all-at-once method carries significant risks. Since all instances are updated together, any issues with the new release immediately impact all users. There is no opportunity to mitigate risks by gradually rolling out the change or testing it with a subset of the user base first. Additionally, if something goes wrong, the rollback process can be just as disruptive as the initial deployment. Despite these risks, all-at-once deployment can be suitable for small applications or environments where downtime is more acceptable and the impact of potential issues is minimal and is used pretty often. It is also useful in scenarios where applications are inherently simple or have been thoroughly tested to ensure compatibility and stability before release. In-Place (Recreate) Deployment In-place or recreate deployment strategy is another strategy that is used pretty often when developing projects. It is the simplest and does not require additional infrastructure. Its essence lies in the fact that when we deploy a new version, we stop the application and start it with new changes. The disadvantage of this approach is that the service we are updating will experience downtime that will affect its users. Also, in case of problems with new software changes, we might need to roll back the latest changes, which will lead to service downtime. To avoid downtime and be able to roll back changes without it during the deployment process, there are deployment strategies that are created for this purpose and used in the industry. Blue/Green Deployment The first zero downtime deployment strategy we’re going to talk about is the Blue/Green deployment strategy. Its main goal is to minimize downtime and risks while deploying new software versions. This is done by having 2 identical environments of our service. One environment contains the original application (Blue environment) that serves users' requests and the other environment (Green environment) is where new software changes are deployed. This allows us to verify and test new changes with near zero downtime for users and the service, with the ability to safely roll back in case of any problems, except for some cases that we will discuss a bit later. Typically, the process is the following: after verifying and testing the new changes in the Green environment, we reroute traffic from the Blue environment to our identical Green environment with the new changes. Sounds easy, isn’t it? ... it depends. The problem is that we can easily reroute traffic between environments only when our services are stateless. If they interact with any data sources, things get more complicated, and here's why: Our identical Green and Blue environments share common data source(s). While sharing data sources such as NoSQL databases or object stores (AWS S3, for example) between our identical environment is easier to accomplish, this is completely not true for relational databases because it requires additional efforts (NoSQL also might require some effort) to support Blue/Green deployments. Since approaches to handle schema updates without downtime are out of the scope of this article, you can check out the article, "Upgrading database schema without downtime," to learn more about updating schemas without downtime (if you have any interesting resources on updating schemas without downtime - please, share with us in the comments). A general recommendation is that If your services are not stateless and use data sources with schemas, implementing a Blue/Green deployment strategy is not always recommended because of the additional risk and failure points this can introduce minimizing the benefits of the Blue/Green deployment strategy. But if you’ve decided that you need to integrate a Blue/Green deployment strategy and your infrastructure is running on Amazon Web Services, you might find this document by AWS on how to implement Blue/Green deployments and its infrastructure useful. Canary Deployment The idea of the Canary deployment strategy is to reduce the risks of deploying new software versions in production by rolling out new changes to users slowly. In the same manner, as we do in the Blue/Green deployment strategy, we roll out new software versions to our identical environment; but instead of completely rerouting traffic from one environment to another, we, for example, route a portion of users to our environment with new software version using a load balancer. The size of the portion of users getting new software versions and/or the criteria used to determine them - may be specific for every company/project. Some roll out new changes only to their internal stuff first, some determine users randomly and some may use algorithms to match users based on some criteria. Pick anything that best suits your needs. Shadow Deployment Shadow deployment strategy is the next strategy I find interesting, personally. This strategy also uses the concept of identical environments, just as the Blue/Green and Canary deployment strategies do. The main difference is that instead of completely rerouting or rerouting only a portion of real users we duplicate entire traffic to our second environment where we deployed our new changes. This way, we can test and verify our changes without negatively affecting our users, thus mitigating risks of broken software updates or performance bottlenecks. Conclusion In this article, we walked through five different deployment strategies, each with its own set of advantages and challenges. The all-at-once and in-place deployment strategies stand out for their speed and minimal effort required to deploy new versions of software. While these two strategies will be your go-to deployment strategies in most cases, it’s still useful to understand and know about more complex and resource-intensive strategies. Ultimately, implementing any deployment strategy requires careful consideration of the potential impact on both the system and its users. The choice of deployment strategy should align with your project’s needs, risk tolerance, and operational capabilities.
With the rise of high-frequency application deployment, CI/CD has been adopted by the modern software development industry. But many organizations are still looking for a solution that will give them more control over the delivery of their applications such as the Canary deployment method or even Blue Green. Called Progressive Delivery, this process will give organizations the ability to run multiple versions of their application and reduce the risk of pushing a bad release. In this post, we will focus on Canary deployment as there’s a high demand for organizations to run testing in production with real users and real traffic which Blue Green deployment cannot do. ArgoCD vs. Flagger: Overview A Canary deployment will be triggered by ArgoCD Rollout and Flagger if one of these changes ais applied: Deployment PodSpec (container images, commands, ports, env, resources, etc) ConfigMaps mounted as volumes or mapped to environment variables Secrets mounted as volumes or mapped to environment variables Why Not Use Kubernetes RollingUpdate? Kubernetes offers by default their RollingUpdate deployment strategy, but it can be limited due to: No fine-grained control over the speed of a new release, by default Kubernetes will wait for the new pod to get into a ready state and that’s it. Can’t manage traffic flow, without traffic split, it is impossible to send a percentage of the traffic to a newer release and adjust its percentage. No ability to verify external metrics such as Prometheus custom metrics to verify the status of a new release. Unable to automatically abort or rollback the update What Is ArgoCD Rollout? Just a year after ArgoCD creation, in 2019 the group behind the popular ArgoCD decided to overcome these limitations from Kubernetes by creating ArgoCD Rollout as a Kubernetes Controller used to achieve Canary, Blue Green, Canary analysis, experimentation, and progressive delivery features to Kubernetes with the most popular service mesh and ingress controllers. What Is Flagger? Created in 2018 by the FluxCD community, FluxCD has been growing massively since its creation and offers Flagger as one of its GitOps components to deal with progressive delivery on Kubernetes. Flagger helps developers solidify their production releases by applying canary, A/B testing, and Blue Green deployment strategies. It has direction integration with service mesh such as Istio and Linkerd but also ingress controllers like NGINX or even Traefik. How ArgoCD Rollout and Flagger Work With Istio If you are using Istio as a service mesh to deal with traffic management and want to use Canary as a deployment strategy: ArgoCD Rollout will transform your Kubernetes Deployment as a ReplicaSet. To start, you would need to create the Istio DestinationRule and Virtual Service but also the two Kubernetes Services (stable and canary) The next step would be creating your rollout, ArgoCD Rollout will manage the Virtual Service to match with the current desired canary weight and your DestionationRule that will contain the label for the canary ReplicaSet. Example: YAML apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: reviews-rollout namespace: default spec: replicas: 1 selector: matchLabels: app: reviews version: stable template: metadata: labels: app: reviews version: stable service.istio.io/canonical-revision: stable spec: serviceAccountName: bookinfo-reviews containers: - name: reviews image: docker.io/istio/examples-bookinfo-reviews-v1:1.18.0 imagePullPolicy: IfNotPresent env: - name: LOG_DIR value: "/tmp/logs" ports: - containerPort: 9080 volumeMounts: - name: tmp mountPath: /tmp - name: wlp-output mountPath: /opt/ibm/wlp/output securityContext: runAsUser: 1000 volumes: - name: wlp-output emptyDir: {} - name: tmp emptyDir: {} strategy: canary: canaryService: reviews-canary stableService: reviews-stable trafficRouting: istio: virtualService: name: reviews destinationRule: name: reviews canarySubsetName: canary stableSubsetName: stable steps: - setWeight: 20 - pause: {} # pause indefinitely - setWeight: 40 - pause: {duration: 10s} - setWeight: 60 - pause: {duration: 10s} - setWeight: 80 - pause: {duration: 10s} Here’s a documentation link for the Istio ArgoCD Rollout integration. Flagger relies on a k8s custom resource called Canary, example below: YAML apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: reviews namespace: default spec: # deployment reference targetRef: apiVersion: apps/v1 kind: Deployment name: reviews # the maximum time in seconds for the canary deployment # to make progress before it is rollback (default 600s) progressDeadlineSeconds: 60 service: # service port number port: 9080 analysis: # schedule interval (default 60s) interval: 15s # max number of failed metric checks before rollback threshold: 5 # max traffic percentage routed to canary # percentage (0-100) maxWeight: 50 # canary increment step # percentage (0-100) stepWeight: 10 As seen on L11, you don’t have to define your deployment but can call its name so the k8s deployment is managed outside of the Canary custom resource. Once you apply this, Flagger will automatically create the Canary resources: # generated deployment.apps/reviews-primary service/reviews service/reviews-canary service/reviews-primary destinationrule.networking.istio.io/reviews-canary destinationrule.networking.istio.io/reviews-primary virtualservice.networking.istio.io/reviews As you can see, it created the Istio Destinationrule and Virtual service to achieve traffic management for canary deployment. How Does ArgoCD Rollout Compare to Flagger? Both solutions support the same service mesh and share a very similar analysis process but there are a few features that can make the difference in choosing your progressive delivery tool for Kubernetes ArgoCD Rollout Flagger + - Great UI/dashboard to manage releases - ArgoCD dashboard (not Rollout dashboard) can interact with ArgoCD Rollout to approve promotions. - Kubectl plugin which makes it easy to query via a CLI rollout status. - Automatically creates the Kubernetes Services, Istio DestinationRule, and Virtual Service. - Load tester can run advanced testing scenarios. - - ArgoCD Rollout needs you to create Kubernetes Services, Istio DestinationRules, and Vertical Services manually. - No authentication or RBAC for the Rollout dashboard. - CLI only, no UI/dashboard. - Logs can lack information, in addition to being difficult to visualize. - No Kubectl plugin to easily fetch deployment information. - Documentation may not be as detailed as ArgoCD Rollout. Conclusion Both solutions are backed up by strong communities so there’s not a bad option that really stands out. You may already be using FluxCD. In this case, Flagger makes sense as an option to achieve progressive delivery and the same goes for ArgoCD and ArgoCD Rollout We hope this helps you get an idea of how ArgoCD Rollout and Flagger work with Canary deployments and Istio, in addition to giving you a general overview of the two solutions.
With the rapid development of how applications are built and shipped, adopting the right deployment strategy is pivotal for ensuring strong Continuous Deployment (CD) and maintaining high software quality standards. Deployment strategies play a crucial role in DevOps practices, offering varied approaches to software release and infrastructure management. In this blog, we will explore several key deployment strategies, emphasizing their relevance in Continuous Integration and Continuous Deployment pipelines, before focusing on the blue-green deployment method, particularly its implementation on AWS using Terraform, a leading Infrastructure as Code (IaC) tool. Rolling deployment: This technique, integral to continuous deployment, involves incrementally updating servers with the new version. It’s highly compatible with Agile methodologies, ensuring minimal downtime and facilitating a stable continuous delivery process. Canary deployment: A strategic fit for continuous deployment, canary deployment targets a small segment of the production environment first. Its gradual approach aligns well with Agile and DevOps principles, allowing for real-time monitoring and quick rollback if needed. A/B testing deployment: This strategy is crucial for user-centric continuous deployment, providing direct feedback on user engagement and experience. It’s a data-driven approach, often used in conjunction with continuous testing practices. Recreate deployment: Simple yet effective, this strategy involves downtime but is sometimes used in continuous deployment when zero downtime isn’t a critical factor. It’s straightforward and suitable for applications with flexible availability requirements. Shadow deployment: Often used in continuous deployment and continuous testing, this strategy involves duplicating real traffic to a shadow version. It’s excellent for performance testing under real conditions without impacting the end-user experience. Focusing on blue-green deployment, this strategy is used for continuous deployment with zero downtime. It involves maintaining two identical environments: the Blue (current production) and Green (new version). At any given time, only one of these environments is live, serving all production traffic. When it’s time to release a new version of the software, the update is first deployed to the inactive environment (e.g., green). The switch from blue to green ensures minimal downtime and provides a quick rollback mechanism in case of issues, aligning seamlessly with continuous deployment and continuous integration (CI) practices. Integrating Terraform, a prominent infrastructure-as-code tool, into blue-green deployment on AWS enhances the strategy. Terraform automates the creation and management of both environments, ensuring consistency and alignment with DevOps, continuous integration, and continuous deployment principles. This integration is particularly beneficial in AWS cloud environments, where managing complex infrastructures requires both precision and flexibility. When To Use Blue-Green Deployment There are several benefits to using blue-green deployment: Zero downtime: By routing traffic to the new environment before taking the old one out of service, you can ensure that there is no disruption to the end users. Easy rollback: If there are any issues with the new version of the software, you can quickly roll back by routing traffic back to the old environment. Improved reliability: By testing the new version of the software in a separate environment before releasing it to production, you can catch and fix any issues before they affect the end users. Confidence in release: Blue-green deployment allows you to release software updates with confidence, knowing that you have a fallback plan in case anything goes wrong. Integrating Terraform With EC2 Autoscaling for Blue-Green Deployments While blue-green deployments offer significant advantages, integrating this strategy with tools like Terraform and EC2 Autoscaling groups presents its own set of challenges. In this section, I’ll delve into these challenges and outline the effective solutions I’ve developed. The Problem With Terraform and EC2 Autoscaling Groups When implementing blue-green deployment using Terraform on AWS a key challenge emerges with EC2 Auto Scaling groups and how Terraform operates. This challenge is crucial for DevOps engineers and cloud architects who rely on Terraform for infrastructure as code (IaC) practices and AWS CodeDeploy for seamless deployment processes. Addressing this issue is essential for optimizing Continuous Integration/Continuous Deployment (CI/CD) pipelines and ensuring efficient cloud resource management. The core of the problem lies in how Terraform interacts with AWS Auto Scaling groups during a blue-green deployment orchestrated by AWS CodeDeploy. AWS CodeDeploy, a critical service in AWS for automating software deployments, plays a vital role in this setup. According to the AWS CodeDeploy documentation, during a blue-green deployment, a new Auto Scaling group is created to transition to the new version of the application. However, when Terraform is used to create and manage these Auto Scaling groups, it does not automatically recognize or incorporate the new Auto Scaling group created by CodeDeploy into its state management. This discrepancy leads to Terraform attempting to recreate the Auto Scaling group with its original configuration during subsequent terraform apply operations. As a result, cloud engineers face errors and inconsistencies, which can disrupt the deployment process and lead to potential downtime or resource mismanagement. To delve deeper into this topic, it’s essential to understand the intricacies of Terraform’s state management and how it interacts with AWS services. Terraform’s state file is crucial for tracking the current state of the infrastructure it manages. When external changes are made to the infrastructure that Terraform manages (in this case, by AWS CodeDeploy), Terraform’s state file does not automatically update to reflect these changes. This leads to a state mismatch, causing Terraform to try to enforce the configuration as defined in its code, which doesn’t account for the new Auto Scaling group. Solution To Seamless Blue-Green Deployment for EC2 Autoscaling Groups With Terraform and AWS CodeDeploy To navigate this challenge, we’ve developed an approach that ensures Terraform, AWS CodeDeploy, and EC2 Autoscaling groups work in harmony. This section provides a detailed step-by-step implementation of the solution. 1. Modify the official terraform module (here Terraform-aws-module) to accommodate the solution requirements Add support for an additional variable to ignore resource tag-related changes. GitHub Flavored Markdown # Add new variable to variables.tf file variable "ignore_tags" { description = "Determines whether the `tags` value is ignored after initial apply. See README note for more details" type = bool default = true } Whenever blue/green deployments are done by AWS CodeDeploy, a new autoscaling group is created on every deployment with a new name and additional tags like deployment ID. These details are not present in the Terraform state since the AWS Code triggered the change deploy service and not Terraform. To avoid the Terraform state deviation after each deployment, I created the AWS EC2 autoscaling group’s name with a unique tag ID. Add lifecycle policy in resource "aws_autoscaling_group to ignore_changes in tag property here –Terraform-aws-autoscaling. GitHub Flavored Markdown lifecycle { create_before_destroy = true ignore_changes = [tag] } } 2. Sample Terraform code to deploy an EC2 Autoscaling group GitHub Flavored Markdown data "aws_autoscaling_groups" "app" { filter { name = "tag:id" values = ["app-asg"] } } module "asg-app" { source = "../modules/asg/" name = length(data.aws_autoscaling_groups.app.names) > 0 ? data.aws_autoscaling_groups.app.names[0] : "app-asg" use_name_prefix = false desired_capacity = 2 min_size = 2 max_size = 4 health_check_type = "EC2" vpc_zone_identifier = ["pvt-subnet-1-id", "pvt-subnet-2-id"] ## replace with the VPC private subnet IDs target_group_arns = ["alb_target_group_arn"] ## replace with ARN of the ALB Target Group # Launch template launch_template_name = "app-launch-template" launch_template_description = "Launch Template for application" image_id = "ami-id" ## replace with the AMI ID of youe application instance_type = "t3.large" ebs_optimized = true enable_monitoring = false security_groups = [" sg-xxxxxxx "] ## Add the security group IDs to attach to this ASG instances key_name = "ssh-key-pair" ## Keypair used to launch instances in ASG iam_instance_profile_name = "ec2-role-for-s3-ssm-secret-manager" user_data = base64encode("#!/bin/bashecho \"Hello\"") block_device_mappings = [ { device_name = "/dev/sda1" no_device = 0 ebs = { delete_on_termination = true encrypted = true volume_size = 30 volume_type = "gp3" } } ] scaling_policies = { dynamic_TTS_policy = { policy_type = "TargetTrackingScaling" target_tracking_configuration = { predefined_metric_specification = { predefined_metric_type = "ASGAverageCPUUtilization" } target_value = 70.0 } } } tags = { Name = "app-asg" terraform = "true" id = "app-asg" } } To avoid the AWS Autoscaling group module from creating auto-scaling groups with random names, I have set use_name_prefix to false Then using the terraform data source feature, we fetched the name of the new auto-scaling group with the help of tags and referred to it while calling the module again for any changes. This code snippet assumes that the VPC network and AWS Application Loadbalancers are already created. 3. I have used Terraform to create the AWS code deploy service resources also and its configurations GitHub Flavored Markdown ## Codedeploy main.tf resource "aws_iam_role" "codedeploy_service_role" { name = "codedeploy_service_role" assume_role_policy = jsonencode({ Version = "2012-10-17", Statement = [ { Action = "sts:AssumeRole", Effect = "Allow", Principal = { Service = "codedeploy.amazonaws.com" }, }, ], }) } resource "aws_iam_policy" "codedeploy_access_policy" { name = "codedeploy_access_policy" policy = jsonencode({ Version = "2012-10-17", Statement = [ { Action = [ "autoscaling:CompleteLifecycleAction", "autoscaling:DeleteLifecycleHook", "autoscaling:DescribeAutoScalingGroups", "autoscaling:DescribeLifecycleHooks", "autoscaling:PutLifecycleHook", "autoscaling:RecordLifecycleActionHeartbeat", "ec2:CreateTags", "ec2:DeleteTags", "ec2:DescribeInstances", "ec2:DescribeTags", "ec2:DetachInstances", "ec2:AttachInstances", "elasticloadbalancing:DeregisterInstancesFromLoadBalancer", "elasticloadbalancing:DescribeInstanceHealth", "elasticloadbalancing:DescribeLoadBalancers", "elasticloadbalancing:RegisterInstancesWithLoadBalancer" ], Effect = "Allow", Resource = "*" }, ], }) } resource "aws_iam_role_policy_attachment" "codedeploy_access_policy_attachment" { role = aws_iam_role.codedeploy_service_role.name policy_arn = aws_iam_policy.codedeploy_access_policy.arn } resource "aws_codedeploy_app" "my_app" { compute_platform = "Server" name = "my_app" } resource "aws_codedeploy_deployment_group" "blue" { app_name = aws_codedeploy_app.my_app.name deployment_group_name = "blue" service_role_arn = aws_iam_role.codedeploy_service_role.arn } resource "aws_codedeploy_deployment_group" "green" { app_name = aws_codedeploy_app.my_app.name deployment_group_name = "green" service_role_arn = aws_iam_role.codedeploy_service_role.arn } This terraform code snippet creates an IAM Role and Policy for CodeDeploy that grants AWS CodeDeploy the necessary permissions to perform deployments across EC2 instances and Autoscaling groups. This role will be assumed by the CodeDeploy service. It also creates the CodeDeploy Application and sets up deployment groups (one for each of the blue and green environments. ) 4. Solution to the terraform state deviation problem: I also created a script that needs to be run before any terraform operations. This will import new auto-scaling groups created by AWS Codedeploy Service’s Blue-Green Deployment strategy and replace the older auto-scaling group details. Now the terraform plan and terraform apply will not create a new auto-scaling group after the CI/CD deployments. terraform_before_apply.sh GitHub Flavored Markdown terraform refresh # setting variables for auto scaling groups and policies location in state file asg_location=module.asg.aws_autoscaling_group.id[0] # checking the status of asgs in terraform state if there are changes then the new asg will be imported in place of that # importing the updates in asg terraform state show $asg_location | grep $(terraform output -raw asg_name) > /dev/null 2>&1 if [ $? != 0 ] then terraform state rm $asg_location terraform import $asg_location $(terraform output -raw asg_name) terraform refresh terraform state rm 'module.asg.aws_autoscaling_policy.this["dynamic_TTS_policy"]' terraform import 'module.asg.aws_autoscaling_policy.this["dynamic_TTS_policy"]' $(terraform output -raw asg_name)/dynamic_TTS_policy echo "updated asg" fi Let us go through all the commands in this script: a.terraform refresh command refreshes the state of the Terraform to identify any changes. b. This command will match the name in outputs and state GitHub Flavored Markdown terraform state show $asg_location | grep $(terraform output -raw asg_name) > /dev/null 2>&1 c. If the above command gives an exit status code value other than “0”, it means the autoscaling group’s name has changed as a part of CI/CD runs. d. The next commands will then remove the existing autoscaling group from the terraform state and import the new one. Along with the auto-scaling group, we need to import a new auto-scaling policy as well that is associated with the new autoscaling group created by AWS Codedeploy. Conclusion In this blog, I navigated through the challenges of setting up blue-green deployments using AWS, Terraform, and AWS CodeDeploy. Blue-green deployment is more than just a deployment strategy; it’s a pathway to ensuring zero downtime, enhancing the reliability of your applications, and providing a safety net through easy rollbacks.
Implementing CI/CD pipelines for Docker applications, especially when deploying to AWS environments like Lambda, requires a well-thought-out approach to ensure smooth, automated processes for both development and production stages. The following outlines how to set up a CI/CD pipeline, using AWS services and considering a Docker application scheduled to execute on AWS Lambda every 12 hours. Overview The goal is to automate the process from code commit to deployment, ensuring that any updates to the application are automatically tested and deployed to the development environment and, following approval, to production. AWS services like CodeCommit, CodeBuild, CodeDeploy, and Lambda, along with CloudWatch for scheduling, will be instrumental in this setup. Application Containerization With Docker Application containerization with Docker is a pivotal step in modernizing applications, ensuring consistent environments from development to production, and facilitating continuous integration and continuous deployment (CI/CD) processes. This section expands on how to effectively containerize an application using Docker, a platform that packages an application and all its dependencies into a Docker container to ensure it runs uniformly in any environment. Understanding Docker Containers Docker containers encapsulate everything an application needs to run: the application's code, runtime, libraries, environment variables, and configuration files. Unlike virtual machines, containers share the host system's kernel but run in isolated user spaces. This makes them lightweight, allowing for rapid startup and scalable deployment practices. Dockerizing an Application: The Process Creating a Dockerfile: A Dockerfile is a text document containing all the commands a user could call on the command line to assemble an image. Creating a Dockerfile involves specifying a base image (e.g., Python, Node.js), adding your application code, and defining commands to run the application. Example: For a Python-based application, your Dockerfile might start with something like FROM python:3.8-slim, followed by COPY . /app to copy your application into the container, and CMD ["python", "./app/my_app.py"] to run your application. Building the Docker Image: Once the Dockerfile is set up, use the Docker build command to create an image. This image packages up your application and its environment. Command Example: docker build -t my_app:1.0 . This command tells Docker to build an image named my_app with a tag of 1.0 based on the Dockerfile in the current directory (.). Running Your Docker Container: After building the image, run your application in a container using Docker's run command. Command Example: docker run -d -p 5000:5000 my_app:1.0 This command starts a container based on the my_app:1.0 image, mapping port 5000 of the container to port 5000 on the host, allowing you to access the application via localhost:5000. Best Practices for Dockerizing Applications Minimize Image Size: Use smaller base images and multi-stage builds to reduce the size of your Docker images, which speeds up build times and deployment. Leverage .dockerignore: Similar to .gitignore, a .dockerignore file can help you exclude files not relevant to the build (like temporary files or dependencies that should be fetched within the Dockerfile), making builds faster and more secure. Parameterize Configuration: Use environment variables for configuration that changes between environments (like database connection strings), making your containerized application more portable and secure. Logging and Monitoring: Ensure your application logs to stdout/stderr, allowing Docker to capture logs effectively, which can then be managed and monitored by external systems. Health Checks: Implement health checks in your Dockerfile to help Docker and orchestration tools like Kubernetes know if your application is running correctly. Source Control With GitLab Integrating a CI/CD pipeline with GitLab for deploying a Dockerized application to AWS Lambda involves several key steps, from setting up your GitLab repository for source control to automating deployments through GitLab CI/CD pipelines. In the context of our example — an e-commerce platform's price update microservice scheduled to run every 12 hours — let's break down how to set up source control with GitLab and provide a code example for the Lambda function. Initialize a GitLab Repository: Start by creating a new project in GitLab for your application. This repository will host your application code, Dockerfile, buildspec.yml, and .gitlab-ci.yml files. Push Your Application to GitLab: Clone your newly created repository locally. Add your application files, including the Dockerfile and any scripts or dependencies it has. Commit and push these changes to your GitLab repository. PowerShell git clone <your-gitlab-repository-url> # Add your application files to the repository git add . git commit -m "Initial commit with application and Dockerfile" git push -u origin master Set up .gitlab-ci.yml: The .gitlab-ci.yml file defines your CI/CD pipeline in GitLab. For deploying a Dockerized Lambda function, this file needs to include steps for building the Docker image, pushing it to Amazon ECR, and updating the Lambda function to use the new image. Code Example for AWS Lambda Function Before setting up the CI/CD pipeline, let's define the Lambda function. Assuming the microservice is written in Python, the function might look like this: Python import requests import boto3 def update_pricing_data(event, context): # Your code to fetch new pricing data pricing_data = requests.get("https://api.example.com/pricing").json() # Logic to update the database with new pricing data # For simplicity, we'll assume it's a direct call to an RDS instance or DynamoDB # Note: Ensure your Lambda function has the necessary permissions for database access db_client = boto3.client('dynamodb') for product in pricing_data['products']: # Example of updating DynamoDB (simplified) db_client.update_item( TableName='ProductPrices', Key={'productId': {'S': product['id']}, UpdateExpression='SET price = :val', ExpressionAttributeValues={':val': {'N': str(product['price'])} ) return { 'statusCode': 200, 'body': 'Product pricing updated successfully.' } This function fetches pricing data from an external API and updates a DynamoDB table with the new prices. Integrating AWS Lambda With GitLab CI/CD Dockerfile: Ensure your Dockerfile is set up to containerize your Lambda function correctly. AWS provides base images for Lambda which you can use as your starting point. Dockerfile # Example Dockerfile for a Python-based Lambda function FROM public.ecr.aws/lambda/python:3.8 # Copy function code and requirements.txt into the container image COPY update_pricing.py requirements.txt ./ # Install the function's dependencies RUN python3.8 -m pip install -r requirements.txt # Set the CMD to your handler CMD ["update_pricing.update_pricing_data"] .gitlab-ci.yml Example: Define the pipeline in .gitlab-ci.yml for automating the build and deployment process. YAML stages: - build - deploy build_image: stage: build script: - $(aws ecr get-login --no-include-email --region us-east-1) - docker build -t my_ecr_repo/my_lambda_function:latest . - docker push my_ecr_repo/my_lambda_function:latest deploy_lambda: stage: deploy script: - aws lambda update-function-code --function-name myLambdaFunction --image-uri my_ecr_repo/my_lambda_function:latest only: - master This CI/CD pipeline automates the process of building your Docker image, pushing it to Amazon ECR, and updating the AWS Lambda function to use the new image. Make sure to replace placeholders like my_ecr_repo/my_lambda_function with your actual ECR repository URI and adjust AWS CLI commands based on your setup and region. By following these steps and leveraging GitLab's CI/CD capabilities, you can automate the deployment process for your Dockerized AWS Lambda functions, ensuring that your e-commerce platform's price update microservice is always running with the latest codebase. Deploying Docker Application on AWS Lambda Deploying a Docker application on AWS Lambda involves several steps, starting from containerizing your application to configuring the Lambda function to use the Docker image. This process enables you to leverage the benefits of serverless architecture, such as scalability, cost-efficiency, and ease of deployment, for your containerized applications. Here’s how you can deploy a Docker application on AWS Lambda: Containerize Your Application Create a Dockerfile: Begin by defining a Dockerfile in your application's root directory. This file specifies the base image, dependencies, and other configurations needed to containerize your application. Dockerfile # Example Dockerfile for a Python-based application FROM public.ecr.aws/lambda/python:3.8 # Copy the application source code and requirements.txt COPY app.py requirements.txt ./ # Install any dependencies RUN python3.8 -m pip install -r requirements.txt # Define the handler function CMD ["app.handler"] Build the Docker Image: With the Dockerfile in place, build the Docker image using the Docker CLI. Ensure the image is compatible with AWS Lambda's container image requirements. bash PowerShell docker build -t my-lambda-app . Push the Docker Image to Amazon ECR: Create an ECR Repository: If you haven't already, create a new repository in Amazon Elastic Container Registry (ECR) to store your Docker image. PowerShell aws ecr create-repository --repository-name my-lambda-app Authenticate Docker to Your ECR Registry: Authenticate your Docker CLI to the Amazon ECR registry to push images. PowerShell aws ecr get-login-password --region <your-region> | docker login --username AWS --password-stdin <your-aws-account-id>.dkr.ecr.<your-region>.amazonaws.com Tag and Push the Docker Image: Tag your local Docker image with the ECR repository URI and push it to ECR. PowerShell docker tag my-lambda-app:latest <your-aws-account-id>.dkr.ecr.<your-region>.amazonaws.com/my-lambda-app:latest docker push <your-aws-account-id>.dkr.ecr.<your-region>.amazonaws.com/my-lambda-app:latest Create and Configure the AWS Lambda Function: Create a New Lambda Function: Go to the AWS Lambda console and create a new Lambda function. Choose the "Container image" option as your source and select the Docker image you pushed to ECR. Configure Runtime Settings: Specify the handler information if required. For container images, the handler corresponds to the CMD or ENTRYPOINT specified in the Dockerfile. Adjust Permissions and Resources: Set the appropriate execution role with permissions that your Lambda function needs to access AWS resources. Also, configure memory, timeout, and other resources according to your application's requirements. Testing and Deployment Deploy and Test: With the Lambda function configured, deploy it and perform tests to ensure it's working as expected. You can invoke the Lambda function manually from the AWS console or using the AWS CLI. Set Up Triggers (Optional): Depending on your use case, set up triggers to automatically invoke your Lambda function. For a Docker application that needs to execute periodically (e.g., every 12 hours), you can use Amazon CloudWatch Events to schedule the function. PowerShell aws events put-rule --name "MyScheduledRule" --schedule-expression "rate(12 hours)" aws lambda add-permission --function-name "myLambdaFunction" --action "lambda:InvokeFunction" --principal events.amazonaws.com --source-arn <arn-of-the-scheduled-rule> aws events put-targets --rule "MyScheduledRule" --targets "Id"="1","Arn"="<Lambda-function-ARN>" Deploying Docker Application on AWS Lambda Container Image Support: Ensure your application fits within the Lambda container image guidelines. You may need to adjust your Dockerfile to meet Lambda's requirements. Upload to ECR: Push your Docker image to Amazon ECR, which will serve as the source for Lambda to pull and execute the container. Create Lambda Function: Configure a new Lambda function to use the container image from ECR as its source. Set the execution role with appropriate permissions for Lambda operations. Scheduling Execution with AWS CloudWatch CloudWatch Event Rule: Set up a CloudWatch Event rule to trigger your Lambda function every 12 hours. Use a cron expression for scheduling (e.g., cron(0 */12 * * ? *)). Monitoring and Rollback CloudWatch Metrics and Logs: Utilize CloudWatch for monitoring application logs and performance metrics. Set alarms for any critical thresholds to be notified of issues. Rollback Strategy: Ensure your CI/CD pipeline supports rolling back to previous versions in case of deployment failures or critical issues in production. Conclusion Implementing CI/CD for Docker applications deploying to AWS environments, including Lambda for scheduled tasks, enhances operational efficiency, ensures code quality, and automates deployment processes. By leveraging AWS services and Docker, businesses can achieve a highly scalable and reliable deployment workflow for their applications.
In the contemporary digital landscape, the amalgamation of cloud computing and DevOps methodologies stands as a beacon of innovation, reshaping the contours of software delivery. This confluence paves the way for a seamless, agile, and robust development process, fundamentally altering the traditional paradigms of software engineering. By exploring the depths of this integration, we can unveil the transformative potential it holds for businesses striving for efficiency and competitiveness. Unveiling the Fusion of Cloud and DevOps At the heart of this integration lies a mutual objective: to streamline the development and deployment processes, thereby enhancing productivity and operational flexibility. Cloud computing dismantles the conventional constraints of hardware infrastructure, offering scalable resources on an on-demand basis. Parallelly, DevOps cultivates a culture that bridges the gap between development and operations teams, emphasizing continuous improvement, automation, and swift feedback cycles. The synthesis of Cloud and DevOps injects dynamism into the development lifecycle, enabling a symbiotic relationship where infrastructure evolves in concert with the applications it hosts. Such an environment is ripe for adopting practices like Infrastructure as Code (IaC) and Continuous Integration/Continuous Deployment (CI/CD), which automate and accelerate deployment tasks, significantly reducing manual intervention and the margin for error. Extending Infrastructure Automation: A Comprehensive Example To further elucidate the practical implications of Cloud and DevOps synergy, consider an expanded scenario involving the deployment of a scalable and secure web application architecture in the cloud. This intricate Python script showcases the use of AWS CloudFormation to automate the deployment of a web application, complete with a front-end, a back-end database, a load balancer for traffic management, and an auto-scaling setup for dynamic resource allocation: Python import boto3 # Define a detailed CloudFormation template for a scalable web application architecture template = """ Resources: AutoScalingGroup: Type: 'AWS::AutoScaling::AutoScalingGroup' Properties: AvailabilityZones: ['us-east-1a'] LaunchConfigurationName: Ref: LaunchConfig MinSize: '1' MaxSize: '3' TargetGroupARNs: - Ref: TargetGroup LaunchConfig: Type: 'AWS::AutoScaling::LaunchConfiguration' Properties: ImageId: 'ami-0c55b159cbfafe1f0' InstanceType: 't2.micro' TargetGroup: Type: 'AWS::ElasticLoadBalancingV2::TargetGroup' Properties: Port: 80 Protocol: HTTP VpcId: 'vpc-123456' LoadBalancer: Type: 'AWS::ElasticLoadBalancingV2::LoadBalancer' Properties: Subnets: - 'subnet-123456' DatabaseServer: Type: 'AWS::RDS::DBInstance' Properties: DBInstanceClass: 'db.t2.micro' Engine: 'MySQL' MasterUsername: 'admin' MasterUserPassword: 'your_secure_password' AllocatedStorage: '20' """ # Initialize CloudFormation client cf = boto3.client('cloudformation') # Deploy the stack response = cf.create_stack( StackName='ScalableWebAppStack', TemplateBody=template, Parameters=[], TimeoutInMinutes=20, Capabilities=['CAPABILITY_IAM'] ) print("Stack creation initiated:", response) This script embodies the complexity and sophistication that Cloud and DevOps integration brings to infrastructure deployment. By orchestrating a multi-tier architecture complete with auto-scaling and load balancing, it illustrates how automated processes can significantly enhance application resilience, scalability, and performance. Expanding the Benefits The amalgamation of Cloud and DevOps extends beyond mere technical advantages, permeating various aspects of organizational culture and operational philosophy: Strategic Innovation This integration facilitates a strategic approach to innovation, allowing teams to experiment and iterate rapidly without the fear of failure or excessive costs, thus fostering a culture of continuous improvement. Market Responsiveness Businesses gain the agility to respond swiftly to market changes and customer demands, ensuring that they can adapt strategies and products in real time to maintain competitiveness. Security and Compliance Automated deployment models incorporate security best practices and compliance standards from the outset, embedding them into the fabric of the development process and minimizing vulnerabilities. Environmental Sustainability Cloud providers invest heavily in energy-efficient data centers, enabling organizations to reduce their carbon footprint by leveraging cloud infrastructure, contributing to more sustainable operational practices. Workforce Empowerment The collaborative nature of DevOps, combined with the flexibility of the Cloud, empowers teams by providing them with the tools and autonomy to innovate, make decisions, and take ownership of their work, leading to higher satisfaction and productivity. Navigating Towards a Digital Future The fusion of cloud computing and DevOps is not merely a trend but a fundamental shift in the digital paradigm, catalyzing the transformation of software delivery into a more agile, efficient, and responsive process. This synergy not only accelerates the pace of innovation but also enhances the ability of businesses to adapt to the ever-changing digital landscape, ensuring they remain at the forefront of their respective industries. As organizations navigate toward this digital future, the integration of Cloud and DevOps stands as a pivotal strategy. It enables the creation of resilient, scalable, and innovative software solutions that can meet the demands of the modern consumer and adapt to the challenges of the digital era. The comprehensive example provided illustrates the practical application of these principles, showcasing how businesses can leverage automation to streamline their development processes, reduce costs, and enhance service reliability. The journey towards embracing Cloud and DevOps requires a cultural shift within organizations, one that promotes collaboration, continuous learning, and a willingness to embrace new technologies. By fostering an environment that values innovation and agility, businesses can unlock the full potential of their teams and technologies, driving growth and sustaining competitiveness in an increasingly digital world. In conclusion, the convergence of Cloud and DevOps is more than just a technological evolution; it is a strategic imperative for any organization looking to thrive in the digital age. By adopting this integrated approach, businesses can enhance their software delivery processes, foster innovation, and achieve operational excellence. The future belongs to those who can harness the power of Cloud and DevOps to transform their ideas into reality, rapidly and efficiently.
Ansible is one of the fastest-growing Infrastructure as Code (IaC) and automation tools in the world. Many of us use Ansible for Day 1 and Day 2 operations. One of the best analogies to understand the phases/stages/operations is defined on RedHat's website: "Imagine you're moving into a house. If Day 1 operations are moving into the house (installation), Day 2 operations are the 'housekeeping' stage of a software’s life cycle." Simply put, in a software lifecycle: Day 0: Design/planning phase - This phase involves preparation, initial planning, brainstorming, and preparing for the project. Typical activities in this phase are defining the scope, gathering requirements, assembling the development team, and setting up the development environments. For example, the team discusses the CI/CD platform to integrate the project with, the strategy for project management, etc. Day 1: Development/deployment phase - This phase marks the actual development activities such as coding, building features, and implementation based on the requirements gathered in the planning phase. Additionally, testing will begin to ensure early detection of issues (in development lingo, "bugs"). Day 2: Maintenance phase - This phase in which your project/software goes live and you keep a tap on the health of the project. You may need to patch or update the software and file feature requests/issues based on user feedback for your development team to work on. This is the phase where monitoring and logging (observability) play a crucial role. Ansible is an open-source tool written in Python and uses YAML to define the desired state of configuration. Ansible is used for configuration management, application deployment, and orchestration. It simplifies the process of managing and deploying software across multiple servers, making it one of the essential tools for system administrators, developers, and IT operations teams. With AI, generating Ansible code has become simpler and more efficient. Check out the following article to learn how Ansible is bringing AI tools to your Integrated Development Environment: "Automation, Ansible, AI." RedHat Ansible Lightspeed with IBM Watsonx code assistant At its core, Ansible employs a simple, agentless architecture, relying on SSH to connect to remote servers and execute tasks. This eliminates the need for installing any additional software or agents on target machines, resulting in a lightweight and efficient automation solution. Key Features of Ansible Here is a list of key features that Ansible offers: Infrastructure as Code (IaC) Ansible allows you to define your infrastructure and configuration requirements in code, enabling you to version control, share, and replicate environments with ease. For example, say you plan to move your on-premises application to a cloud platform. Instead of provisioning the cloud services and installing the dependencies manually, you can define the required cloud services and dependencies for your application like compute, storage, networking, security, etc., in a configuration file. That desired state is taken care of by Ansible as an Infrastructure as Code tool. In this way, setting up your development, test, staging, and production environments will easily avoid repetition. Playbooks Ansible playbooks are written in YAML format and define a series of tasks to be executed on remote hosts. Playbooks offer a clear, human-readable way to describe complex automation workflows. Using playbooks, you define the required dependencies and desired state for your application. Modules Ansible provides a vast collection of modules for managing various aspects of systems, networks, cloud services, and applications. Modules are idempotent, meaning they ensure that the desired state of the system is achieved regardless of its current state. For example, ansible.bultin.command is a module that helps you to execute commands on a remote machine. You can either use modules that are built in, like dnf, yum, etc., as part of Ansible Core, or you can develop your own modules in Ansible. To further understand the Ansible modules, check out this topic on RedHat. Inventory Management Ansible uses an inventory file to define the hosts it manages. This inventory can be static or dynamic, allowing for flexible configuration management across different environments. An inventory file (.ini or .yaml) is a list of hosts or nodes on which you install, configure, or set up a software, add a user, or change the permissions of a folder, etc. Refer to how to build an inventory for best practices. Roles Roles in Ansible provide a way to organize and reuse tasks, variables, and handlers. They promote code reusability and help maintain clean and modular playbooks. You can group tasks that are repetitive as a role to reuse or share with others. One good example is pinging a remote server, you can move the tasks, variables, etc., under a role to reuse. Below is an example of a role directory structure with eight main standard directories. You will learn about a tool to generate this defined structure in the next section of this article. Shell roles/ common/ # this hierarchy represents a "role" tasks/ # main.yml # <-- tasks file can include smaller files if warranted handlers/ # main.yml # <-- handlers file templates/ # <-- files for use with the template resource ntp.conf.j2 # <------- templates end in .j2 files/ # bar.txt # <-- files for use with the copy resource foo.sh # <-- script files for use with the script resource vars/ # main.yml # <-- variables associated with this role defaults/ # main.yml # <-- default lower priority variables for this role meta/ # main.yml # <-- role dependencies library/ # roles can also include custom modules module_utils/ # roles can also include custom module_utils lookup_plugins/ # or other types of plugins, like lookup in this case webtier/ # same kind of structure as "common" was above, done for the webtier role monitoring/ # "" fooapp/ Beyond Automation Ansible finds applications in several areas. Configuration management: Ansible simplifies the management of configuration files, packages, services, and users across diverse IT infrastructures. Application deployment: Ansible streamlines the deployment of applications by automating tasks such as software installation, configuration, and version control. Continuous Integration/Continuous Deployment (CI/CD): Ansible integrates seamlessly with CI/CD pipelines, enabling automated testing, deployment, and rollback of applications. Orchestration: Ansible orchestrates complex workflows involving multiple servers, networks, and cloud services, ensuring seamless coordination and execution of tasks. Security automation: Ansible helps enforce security policies, perform security audits, and automate compliance checks across IT environments. Cloud provisioning: Ansible's cloud modules facilitate the provisioning and management of cloud resources on platforms like IBM Cloud, AWS, Azure, Google Cloud, and OpenStack. The list is not exhaustive, so only a subset of applications is included above. Ansible can act as a security compliance manager by enforcing security policies and compliance standards across infrastructure and applications through patch management, configuration hardening, and vulnerability remediation. Additionally, Ansible can assist in setting up monitoring and logging, automating disaster recovery procedures (backup and restore processes, failovers, etc.,), and integrating with a wide range of tools and services, such as version control systems, issue trackers, ticketing systems, and configuration databases, to create end-to-end automation workflows. Tool and Project Ecosystem Ansible provides a wide range of tools and programs like Ansible-lint, Molecule for testing Ansible plays and roles, yamllint, etc. Here are additional tools that are not mentioned in the Ansible docs: Ansible Generator: Creates the necessary folder/directory structure; comes in handy when you create Ansible roles AWX: Provides a web-based user interface, REST API, and task engine built on top of Ansible; Comes with an awx-operator if you are planning to set up on a container orchestration platform like RedHat OpenShift Ansible VS Code extension by Red Hat: Syntax highlighting, validation, auto-completion, auto-closing Jinja expressions ("{{ my_variable }") etc. The Ansible ecosystem is very wide. This article gives you just a glimpse of the huge set of tools and frameworks. You can find the projects in the Ansible ecosystem on Ansible docs. Challenges With Ansible Every tool or product comes with its own challenges. Learning curve: One of the major challenges with Ansible is the learning curve. Mastering the features and best practices can be time-consuming, especially for users new to infrastructure automation or configuration. Complexity: Initially, understanding the terminology, folder structure, and hierarchy challenges the user. Terms like inventory, modules, plugins, tasks, playbooks, etc., are hard to understand in the beginning. As the number of nodes/hosts increases, the complexity of managing the playbooks, and orchestrating increases. Troubleshooting and error handling: For beginners, troubleshooting errors and debugging playbooks can be challenging. Especially, understanding error messages and identifying the root cause of failures requires familiarity with Ansible's syntax and modules, etc. Conclusion In this article, you learned that Ansible as an open-source tool can be used not only for automation but also for configuration, deployment, and security enablement. You also understood the features, and challenges and learned about the tools Ansible and the community offers. Ansible will become your go-to Infrastructure as Code tool once you pass the initial learning curve. To overcome the initial complexity, here's a GitHub repository with Ansible YAML code snippets to start with. Happy learning. If you like this article, please like and share it with your network.
Nowadays, it’s critical to get your releases out fast. Gone are the days when developers could afford to wait weeks for their code to be deployed to a testing environment. More than ever, there is great demand for rapid deployment cycles that seamlessly take code from development to production without any hiccups. Yet, the reality is that developers often find themselves bogged down by the complexities of infrastructure management and the tediousness of manual deployment processes. They crave a solution that allows them to focus solely on their code, leaving the intricacies of deployment to automation. That's where Continuous Integration and Continuous Deployment (CI/CD) pipelines come in. These automated workflows streamline the entire deployment process, from code compilation to testing to deployment, enabling developers to deliver updates at lightning speed. However, implementing a robust CI/CD pipeline has historically been challenging, particularly for organizations with legacy applications. Why Kubernetes for Deployment? This is where Kubernetes, the leading container orchestration platform, shines. Kubernetes has revolutionized the deployment landscape by providing a scalable and flexible infrastructure for managing containerized applications. When combined with Helm, the package manager for Kubernetes, developers gain a powerful toolkit for simplifying application deployment and management. In this article, we delve into the intricacies of setting up a fully automated CI/CD pipeline for containerized applications using Jenkins, Helm, and Kubernetes. We'll walk you through the process of configuring your environment, optimizing your pipeline for efficiency, and provide a practical template for customizing your own deployment workflows. By the end of this guide, you'll be equipped with the knowledge and tools necessary to accelerate your software delivery cycles and stay ahead in today's competitive landscape. Let's dive in! Automating CI/CD Pipeline Setup This 6-step workflow will easily automate your CI/CD pipeline for quick and easy deployments using Jenkins, Helm, and Kubernetes. In order to get familiar with the Kubernetes environment, I have mapped the traditional Jenkins pipeline with the main steps of my solution. Note: This workflow is also applicable when implementing other tools or for partial implementations. Setting Up the Environment Configure the Software Components Before you create your automated pipeline, you need to set up and configure your software components according to the following configuration: Software Components Recommended Configuration A Kubernetes Cluster Set up the cluster on your data center or on the cloud. A Docker Registry Find a solution for hosting a private Docker registry. Consider requirements like privacy, security, latency, and availability when choosing a solution. A Helm Repository Find a solution for hosting a private Helm repository. Consider requirements like privacy, security, latency, and availability when choosing a solution. Isolated Environments Create different namespaces or clusters for Development and Staging Create a dedicated and isolated cluster for Production Jenkins Master Set up the master with a standard Jenkins configuration. If you are not using slaves, the Jenkins master needs to be configured with Docker, Kubectl, and Helm. Jenkins Slave(s) It is recommended to run the Jenkins slave(s) in Kubernetes to be closer to the API server which promotes easier configuration. Use the Jenkins Kubernetes plugin to spin up the slaves in your Kubernetes clusters. Prepare Your Applications Follow these guidelines when preparing your applications: Package your applications in a Docker Image according to the Docker Best Practices. To run the same Docker container in any of these environments: Development, Staging or Production, separate the processes and the configurations as follows: For Development: Create a default configuration. For Staging and Production: Create a non-default configuration using one or more: Configuration files that can be mounted into the container during runtime. Environment variables that are passed to the Docker container. The 6-Step Automated CI/CD Pipeline in Kubernetes in Action General Assumptions and Guidelines These steps are aligned with the best practices when running Jenkins agent(s). Assign a dedicated agent for building the App, and an additional agent for the deployment tasks. This is up to your good judgment. Run the pipeline for every branch. To do so, use the Jenkins Multibranch pipeline job. The Steps Get code from Git Developer pushes code to Git, which triggers a Jenkins build webhook. Jenkins pulls the latest code changes. Run build and unit tests Jenkins runs the build. Application’s Docker image is created during the build.- Tests run against a running Docker container. Publish Docker image and Helm Chart Application’s Docker image is pushed to the Docker registry. Helm chart is packed and uploaded to the Helm repository. Deploy to Development Application is deployed to the Kubernetes development cluster or namespace using the published Helm chart. Tests run against the deployed application in Kubernetes development environment. Deploy to Staging Application is deployed to Kubernetes staging cluster or namespace using the published Helm chart. Run tests against the deployed application in the Kubernetes staging environment. [Optional] Deploy to Production The application is deployed to the production cluster if the application meets the defined criteria. Please note that you can set up as a manual approval step. Sanity tests run against the deployed application. If required, you can perform a rollback. Create Your Own Automated CI/CD Pipeline Feel free to build a similar implementation using the following sample framework that I have put together just for this purpose: A Jenkins Docker image for running on Kubernetes. A 6-step CI/CD pipeline for a simple static website application based on of official nginx Docker image. Conclusion Automating your CI/CD pipeline with Jenkins, Helm, and Kubernetes is not just a trend but a necessity in today's fast-paced software development landscape. By leveraging these powerful tools, you can streamline your deployment process, reduce manual errors, and accelerate your time-to-market. As you embark on your journey to implement a fully automated pipeline, remember that continuous improvement is key. Regularly evaluate and optimize your workflows to ensure maximum efficiency and reliability. With the right tools and practices in place, you'll be well-equipped to meet the demands of modern software development and stay ahead of the competition.
John Vester
Staff Engineer,
Marqeta
Raghava Dittakavi
Manager , Release Engineering & DevOps,
TraceLink