devopsy.kinsta.cloud

Category: GitOps

  • Source Code Management Best Practices

    Source Code Management Best Practices

    Source code is, of course, the foundation of any software development practice. Managing that source code is the first task tackled by modern software development pipelines, with all subsequent stages of the pipeline dependent on the source code for their success and functionality. Thus, it is critical that source code is properly managed without introducing bottlenecks or defects into the delivery pipeline. To do so, there are some best practices to follow when interacting with source code.

    Regularly Commit Changes

    While this may seem obvious, commits often are done sparingly, as most developers tend to commit only after a significant change. The commit frequency depends on the workflow and it is acceptable to commit only after major modifications if that commit triggers another task.

    However, it is better to commit changes—even minor ones—regularly. It not only keeps an auditable history of changes but also enables easy comparisons between commits for troubleshooting and ensures that the code is up to date with the version control system (VCS). However, it does not mean that users should make commits for every small change; instead, adapt a development style that includes frequent commits as part of the workflow. Even if there are many commits, most VSCs provides users with the option to combine multiple commits to a single commit using a rebase for better management.

    Always Ensure That the Repository is Up to Date

    Most of the time, development is a team effort with multiple developers working on a single codebase. Thus, there will be frequent updates to the source code. So it is always advisable to use git pull or fetch to obtain an updated copy of the code before making any changes. This practice not only mitigates merge conflicts but also helps users to avoid errors while pushing the code, such as ‘failed to push some refs to‘ errors which occur when the local copy of the repository is behind the remote.

    Create Detailed Commit Messages

    Commit messages should be meaningful and include the ability for anyone reading the message to grasp the reason behind the commit quickly. It does not mean that users should create lengthy commit messages; instead, they should describe the commit in a concise yet meaningful manner. The commit message should include why the code was committed and not how it can be identified by comparing the files.

    A good commit message can include details such as bug fix ID or requirement ID, which reference the specifications of that code change in project management platforms like Jira and Azure Boards.

    Review Changes Before Committing

    Always make sure to review the code before making commits—this is imperative if you are committing to a shared repository. Typically, code reviews are a part of the delivery pipeline before merging code with the master branch or a specific repository via a review system or a pull request.

    Code reviews put a second set of eyes on code modifications and help detect any issues in the code. It also acts as a quality control mechanism that helps organically improve the overall code quality while helping to increase the awareness of each code change.

    Implement a Proper Workflow

    A proper workflow with standardized practices must be implemented across the development team even while using a source code management (SCM) tool. Some considerations for a standardized workflow:

    • Decide on a branching strategy and branching best practices like naming conventions for specific development types. For example, using a prefix to indicate the type of development feature or bugfix
    • Ensure proper use of scopes to decide who has access to each repository
    • Make sure the review workflow includes which users have the final say in the review process and proper authorization to merge the changes.

    A properly defined workflow describes the complete source code management cycle from creating new branches to making changes and, finally, merging the code to the master branch after review. This process helps organizations simplify the overall code management by pushing users to follow a specific workflow. This can also help easily onboard new developers.

    Implement Proper Security Practices

    This practice goes hand-in-hand with proper workflows, as security will be one of the primary considerations when defining the workflow. It should start from developer authentication and authorization, which controls who has access and what areas of the source code they have access to. Additionally, security practices should provide visibility into all the user activities with auditable trails to pinpoint any unusual activity in the source code.

    Security practices coupled with a good backup and disaster recovery strategy are vital for the continuous operation of the development pipeline.

    These best practices can help developers manage their source code securely and efficiently. A properly implemented source code management system will lead to fewer issues in the preceding stages of the pipeline and ensure smoother operation in the long run.

  • Extending GitOps to the Enterprise

    Extending GitOps to the Enterprise

    GitOps is a relatively new term, but it continues to gain momentum as more organizations embrace the paradigm. In DevOps, GitOps lands on the software engineering side of the development and operations continuum. Even though GitOps is more likely to be adopted by smaller organizations, larger enterprises can successfully implement GitOps with just a few tweaks to the overarching framework.

    Let’s start at the beginning. Wouldn’t it be great if we could combine DevOps and GitOps approaches and use the Git distributed version control system as the ultimate source of truth? Then, when there’s a dispute over the correct state, people know where to go for the correct version.


     

    In GitOps, the system’s desired configuration is stored in a revision control system such as Git. Any difference between the desired state stored in Git and the system’s actual state indicates to DevOps teams that not all changes have been deployed. These changes can be reviewed and approved through standard revision control processes such as pull requests (PRs). When a PR is approved and merged to the main branch, an operator software process is responsible for changing the system’s current state to the desired state based on the configuration in Git.

    GitOps doesn’t require a particular set of tools, but the tools must:

    1. Operate according to the desired system state stored in Git.
    2. Detect differences between the desired and actual states.
    3. Perform required operations on the infrastructure to synchronize the actual and desired states. 

    In an ideal implementation of GitOps, manual changes to the system are not permitted and all changes to configuration must be made to files stored in Git. The infrastructure and operations engineers’ roles in a GitOps model shift from performing infrastructure changes and application deployments to developing and maintaining GitOps automation and helping teams review and approve changes through Git. 

    GitOps is great for non-critical environments, but enterprises are less likely to adopt GitOps due to a number of challenges facing GitOps users.

    Challenges with GitOps

    GitOps is applicable only to a subset of the software development life cycle (SDLC). This is important because GitOps tools are sometimes marketed as a one-size-fits-all solution that will solve all release problems—this is simply not true. First, GitOps requires that your deployment artifacts are already there. This means that tasks such as

    • Compiling code
    • Running unit/integration tests
    • Security scanning
    • Static analysis

    are not a concern of GitOps tools and are assumed to already be in place. GitOps also doesn’t address promotion of releases between environments.

    Auditability

    A default GitOps process is not fully auditable because of force push. Force push essentially allows users to remove any unwanted blocks of commits from Git history on the central repository.

    Business Approvals

    PRs are a typical approval model used in GitOps. Developers make changes, create PRs and then an approver may accept that PR. This would be recorded and a change would be deployed. A PR-based approval model assumes peers are already using Git and are familiar with PRs. 

    Generally this works great until we need to include business approvals. Business personnel may not be familiar with Git or PRs. If a team needs business approvals, they have to build a process on top of GitOps. This drastically increases lead time. 

    Governance 

    GitOps is dependent on a pure PR-approval based model, one in which the PR approver is responsible for reviews. After a PR is approved, there is no way to enforce enterprise policy rules. Enterprises need to control what end users can do on specific clusters to ensure those clusters are in compliance with policies to meet governance, legal requirements or enforce best practices.

    An Enterprise Approach to GitOps

    However, there are ways to solve this problem at the enterprise level.

    Alerting on Drift Detection 

    Enterprise customers get alerted whenever there is a drift in their repository and, based on that, customers can choose to approve or reject the drift. If rejected, the merged PR should be reverted to the previous state to ensure that Git is in sync with the desired state of the cluster.

    Use a Pipeline for Promotion of Releases Between Environments

    To promote an artifact between environments, users would have the ability to use a pipeline with deployment stages, performance monitoring and log analysis/approvals. 


     

    Let’s say the developer commits the source code and the GitOps deployment happens in the QA environment. The PR can be automatically approved upon meeting the policy standards set by the company. Then it can be pushed to a production environment.

    Governance 

    There are many reasons why developers and platform engineers integrate policy-as-code into their current GitOps processes. For one, this strategy helps to accelerate application development and deployment because it helps solve many of the change management hurdles that slow development pipelines.

    On the development side, policy-as-code helps app developers understand and abide by the company’s configuration, security and compliance policies. For example, a developer may not remember—or may have no reason to know—when deploying a load balancer onto Kubernetes in AWS is or is not sanctioned. Policy-as-code solves this problem automatically.

    The ability to run policies during GitOps deployments and adhere to those policies as a part of the deployment and upon any violations—alert, audit, notify and automatically roll back the changes from Git to the previous successful compliant state so that Git remains in sync with the cluster.

    GitOps + AIOps

    There is a notion that once GitOps is in place, everything will work seamlessly with no problems. Not so much; it is important to continually monitor deployment state to ensure everything stays as it should. That is where AIOps comes into picture.

    GitOps enables DevOps and development teams to see any manifest change, infrastructure change or artifact change. Post-deployment, the GitOps operator can instantly understand the changes in the service health, see what caused those changes and automate the process of investigating them using AIOps. If any severe anomalies are found, the operator can determine the current state of the system and, should anything differ, revert to the previous compliant Git commit. 

  • Why You Should Use GitOps to Experiment With AI

    Why You Should Use GitOps to Experiment With AI

    As the pace at which artificial intelligence (AI) models are being constructed and inevitably updated starts to increase, it’s becoming more apparent that the pace at which data science teams are currently experimenting also needs to dramatically quicken.

    Many data science teams today are fortunate if they manage to successfully deploy two AI models in a production environment over the course of 12 months. It’s already apparent, however, thanks in part to the rise of digital business transformation, that there are potentially hundreds of use cases for AI models within each enterprise IT environment. Truth be told, it’s hard to envision an application being developed today that would not incorporate one or more AI models. The challenge is, the demand for AI capabilities is now far outpacing the ability of data science teams to deliver.

    The best way to solve this issue is to recognize that AI models and the components employed to construct them are, in reality, just another kind of software artifact. In fact, the need to accelerate the development of applications using artifacts is largely a problem already solved. Rather than reinventing the wheel, the time has come to apply what we already know to the development of AI models.

    The Rise of GitOps-Based Processes

    Modern software development teams today routinely make use of Git-based repositories to share code and collaborate. These repositories are jam-packed with vetted software artifacts that development teams continuously reuse via an opinionated instance of a DevOps workflow based on a set of GitOps best practices.

    As a subset of DevOps, a GitOps workflow enables data science teams to employ a Git repository that already exists within their software development environment to manage artifacts. Git enables versions of files to be created using what is known as a Git branch. They also employ concepts known as tags (Git Tag) and remotes (where the server is located). Those three different concepts, or references, exist under the hood in Git. In fact, the entire flow is based on those concepts. When a new feature is implemented, a new branch is created and then merged into the production branch with a tag that says something like “This is a new version that needs to be deployed.”

    An AI model, like any other software artifact, needs to be updated. As new data sources become available, an AI model is likely to drift beyond the parameters originally intended. A rapid change in business conditions may also require changes to an AI model. The same processes that DevOps teams apply to update any software artifact can also be applied to rapidly update an AI model. Employing a Git repository to also manage AI artifacts extends the benefits that software development teams enjoy to data science teams.

    Often, organizations will have an MLOps solution to streamline AI model development that is completely separate from their DevOps tools. These solutions will have dedicated features like version tracking for data and experiments specifically for AI models. But many MLOps solutions exist in a closed ecosystem, meaning they don’t work off of existing tools like GitHub used on the DevOps side. 

    Also, data science teams have to separately buy, deploy, and manage these closed MLOps solutions. Going with a GitOps-oriented solution streamlines management of added MLOps tools and makes it easier for both application developers and data science teams to gain visibility into each other’s respective efforts. The end result is not just faster AI experimentation at lower cost; the likelihood that more AI models will successfully be deployed in a production environment increases dramatically. 

    Software and AI Teams Working Together

    The convergence of DevOps and MLOps is already inevitable. The longer organizations continue to employ isolated MLOps platforms to build and deploy AI models, the more expensive it will become to meld MLOps and DevOps processes. 

    Data science teams are conservative by nature. The difference between success and failure often comes down to how thoroughly an AI model is validated. However, time to market doesn’t have to be sacrificed to ensure an AI model works as promised. There is no such thing as a great AI model that arrives late when rivals are racing to develop similar AI capabilities—there are only missed opportunities. Speed enabled by the reusability of artifacts that drives faster experimentation using GitOps workflows is of the essence now more than ever.