devopsy.kinsta.cloud

Author: Gilad David Maayan

  • Automation Hacks for DevOps Teams

    Automation Hacks for DevOps Teams

    DevOps teams are expected to do more work in less time. They need to plan, test, build, secure and release applications at a fast-paced rate. Of course, the DevOps team needs to achieve all of this while maintaining high software quality. This is why automation is a crucial part of the DevOps workflow. The goal is to delegate repetitive tasks to the machine, and free up dev time for more complex jobs. 

    Below are some helpful automation hacks you can apply to boost the effectiveness of your DevOps practices.

    Automating Regulatory Compliance

    Properly managing regulatory compliance is vital for many organizations to avoid fines and operate legally. Although compliance standards vary by industry, most focus on ensuring privacy and confidentiality. To meet these standards, companies must secure data and prove they are taking the appropriate measures. This proof is commonly accomplished with the maintenance of a clear digital trail.

    You can use DevOps automation to log and store digital access trails for your protected data. You can also use automated testing and deployment to ensure that only code that passes compliance tests is deployed. This prevents compliance issues from being pushed to production and helps keep your data secure.

    Automating Development Environments

    DevOps workflows require frequent creation and elimination of environments, for testing, deployment and development. These environments can be created manually but it is more efficient to create environments automatically. This can be done using a golden image with your required configurations.

    Using standardized base images ensures that you are working with consistent environments and standardizes configurations and privileges. These images can be used for local or remote machines and can be initialized automatically.

    There are a variety of tools you can use to create and automate environment creation. Chef and Vagrant are two of the most popular. Both of these tools offer an accessible API and enable you to script automation using common languages and syntax.

    Automating Code Review

    Code review is an essential part of DevOps processes but it is also one of the lengthier and more tedious processes. Automation can help you perform this process more efficiently. Automatic code review works similarly to manual review except it is done using tools that integrate with development environments. These tools can check for styling, security, duplication, complexity and code coverage, and can auto-suggest corrections. Some popular tools for this automation include Codebeat, Code climate and Codacy.

    Automating File Synchronization and File Uploads

    Many DevOps and development teams use the public cloud for quickly spinning up development and test environments. However, what happens when the production environment, or other critical resources used in the dev/test process, are deployed on-premises? Having a fast, automated and reliable way to synchronize files between the on-premise data center and the cloud can save time and headaches.

    There are a few approaches to synchronize files:

    • Scripting file uploads and downloads. The most basic method is to use shell scripts and FTP or SFTP to upload and download files between local and cloud environments. However, this can be fragile, unreliable and difficult to scale.
    • Using APIs of cloud storage services. You can move data to and from the cloud using cloud storage APIs, such as the Amazon S3 API, the Box API or specialized hosting services such as Cloudinary.
    • Hybrid cloud. The ultimate solution is to create a hybrid cloud architecture where applications, developers and users view the local environment and the public cloud as one unit, and data is synchronized transparently in the background.

    Collaboration Tools for DevOps Teams

    There are a number of collaborative tools that teams can use to further boost gains from automation. These tools can help bridge gaps in automation and processes and ensure smooth workflows.

    Slack: a chat tool that DevOps teams can integrate with automation tooling. It includes features for segregating discussions by topic, team or purpose; uploading shared files and documents; and initiating or receiving alerts from automated processes via integrations.

    Jira: a tool that enables you to create and assign tasks. It includes features for scrum and kanban methodologies, unified reporting and backlog tracking. You can integrate it with many common DevOps tools, such as Jenkins.

    Trello: a cloud-based Kanban board tool. It enables you to create project boards that can be shared across teams to track project progress. Through integrations, you can automate the updating of cards or initiate processes when cards are manually moved.

    Asana: another cloud-based Kanban tool. Like Trello, it enables you to track and collaborate on projects across a team. Typically, when teams use tools such as Asana, they create multiple boards for different project aspects. These boards can then be viewed individually or from a central dashboard.

    Conclusion

    Hopefully, this article helped you figure out how to leverage automation for improved efficiency. There are many automation options for DevOps, including automated regulatory compliance, automated infrastructure, automated code review and automated file synchronization.

    For DevOps project management, you can use collaboration tools such as Slack, Jira, Trello and Asana. Remember to make sure that any tool you introduce can be simply and easily integrated into your current environment. The tools of your choice also need to fit the skillset of all collaborators. For ease of use, keep your tooling ecosystem simple and minimalistic.

    — Gilad David Maayan

  • Service Meshes: Improving Security, Delivery and Availability

    Service Meshes: Improving Security, Delivery and Availability

    Containerized microservices are taking over the software world. These services provide developers a way to quickly create and deploy cloud-native applications. However, containers aren’t perfect. Orchestrating and managing container deployments can be a serious challenge. 

    To help ease this challenge, service meshes were developed. In this article, you’ll learn about service meshes components and architecture options. You’ll also learn how services meshes can improve your container deployments.

    What Is a Service Mesh?

    A service mesh is a dedicated communication layer that sits on top of your request/response layer. It works similarly to a Container Networking Interface (CNI), which controls the connection of your services to your network. A service mesh sits on top of your CNI, adding additional functionality, such as service discovery or security. A service mesh is not a replacement for a CNI or a service orchestration platform.

    Services meshes are designed for use with containerized microservices. You can use this technology with any container or container orchestration tools. However, the most active projects seem to favor developing service meshes for Kubernetes.

    Service Mesh Architectures

    When deploying a service mesh, there are three architectures you can choose from.

    Library 

    The library architecture employs a library that is imported in an application’s code and runs from inside the application. Using a library eliminates the need to call out to a remote service, making it more secure.

    Libraries make it easier to allocate resources since all work is contained in the microservice running the library. A library architecture works well for in-house applications that are written in one language. Third-party applications are difficult or impossible to add the library to.

    Node Agent 

    A node agent architecture uses a node agent or daemon that serves all containers on a particular node. A node agent architecture is language-agnostic since it doesn’t run from inside your microservice. It can be used with in-house and third-party applications. However, node agents must be coordinated with infrastructure processes, making this architecture more complex to deploy.

    Node agents require less memory than sidecars since information can be shared across a node. This architecture employs work resource sharing, with dynamically shared resources, making it easier to scale. The downside of sharing, however, is that it can be abused, with one service hogging all available memory.

    Sidecar Proxy 

    A sidecar proxy architecture uses sidecars that run alongside your containers. The sidecar handles all traffic into and out of the container it is attached to. Sidecars do not require coordination with your infrastructure since one proxy is assigned to each container. This lack of coordination makes sidecars easier to set up than node agents.

    Sidecars only serve one pod and are not influenced by other pods. This isolation makes this architecture better for zero-trust deployments as each container is accountable for its own actions. Sidecars can be added to containers at any time, enabling you to easily scale processes.

    Use Cases For Service Meshes

    Service meshes are an emerging technology, with new use cases being actively developed. The three use cases introduced here are just the ones that are currently most common.

    Improving Security

    Containers are isolated environments and generally considered to be more secure than traditional applications. However, containers still contain vulnerabilities, such as possible abuse of root access. 

    Service meshes can help address security concerns by providing:

    • Zero-trust security—any communication, either from inside or outside requires verification. Verification prevents compromised containers from interfering with other microservices or your broader system. It restricts a container’s actions and communications to that container alone.
    • Communication tracing—enables you to collect communications information and isolate errors or issues. If a container is communicating in an unexpected way or against protocol, you can identify it. Once identified, you can easily replace the container with an uncorrupted image or remedy the issue. Tracing enables you to pinpoint possible attacks, including where attackers came from and what they’re trying to accomplish.

    Improving Delivery

    Containers already improve on application and service delivery by making it easier to release and patch services. Service meshes expand upon these capabilities by improving application and performance monitoring functions. 

    Service meshes can also help improve service delivery by enabling:

    • A/B testing—provides advanced routing, enabling you to divide traffic between A and B versions. This routing makes it simpler to set up A/B testing. Additionally, tracing features enable you to gather and analyze user traffic and interactions.
    • Rapid versioning—new versions can be released with zero downtime and reduced risk. Containers can be started and verified healthy before traffic is gradually shifted between versions. If issues arise, changes can be rolled back without system-wide consequences. Easier corrections enable you to relax delivery requirements with minimal risk.

    Improving Availability

    Containers can provide improved availability, depending on your hosting configuration and resources. Service meshes can expand upon these abilities by improving load balancing, traffic management and health monitoring functionalities.

    Service meshes can also help improve availability by enabling:

    • Resilience testing—enables you to test how services respond with fault injections. Service meshes enable you to test your services’ fault tolerance. Increased understanding of how faults affect resilience helps you identify and correct vulnerabilities. Greater service resilience leads to greater availability.
    • Deployment and request shadowing—enables you to simulate anticipated traffic flow and see how services respond. Shadowing enables you to test the traffic and workload capacities of your services. With this information, you can determine whether you need to scale resources and connections. Proper scaling ensures that your traffic needs are met and prevents traffic bottlenecking and decreased availability.

    Conclusion

    As microservices become standard, teams are placing an increased focus on improving container performance and security. The development of service meshes is a result of this increased focus.

    Hopefully, this article helped you understand what service meshes are and how this technology can help improve your container deployments. If you’re interested in seeing how a service mesh might help you, check out this tutorial explaining how to build a service mesh.

    — Gilad David Maayan