What is GitOps, Where Did It Come From, and Why Should You Care?

“What is GitOps?” – a question which has seen increasing popularity on Google searches and blog posts in the last three years. If you want to know why then read on.

Quite simply, the coining of GitOps is credited to one individual, and pretty smart guy, Alexis Richardson. He’s so smart that he’s built a multi-award-winning consultancy, Weaveworks, and a bespoke product, Flux, around the GitOps concept.

Through the course of this post, we’re going to explore what GitOps is, its relevance today, why you should be aware of it (and maybe even implement it), and what GitOps and Observability mean together. 

What is GitOps?

The concept is quite simple. A source control repository holds everything required for a deployment including code, descriptions, instructions, and more. Every time you change what’s in the repository, an engineer can pull the change to alter or update the deployment. This principle can also be applied for new deployments. 

Of course, it’s a little more complex than that. GitOps relies on a single source of truth, automation, and software agents, to identify deltas between what is deployed and what is in source control. Then, through more automation, reconcilers, and a tool like Helm, clusters are updated or changes are rolled back, depending on a host of factors.

What’s crucial to understand is that GitOps and containerization, specifically with Kubernetes for orchestration and management, are symbiotic. GitOps has huge relevance for other cloud-native technologies but is revolutionary for Kubernetes.

The popularity of GitOps is closely linked to some of the key emerging trends in technology today: rapid deployment, containerization with Kubernetes, and DevOps.

Is GitOps Better Than DevOps?

GitOps and DevOps aren’t necessarily mutually exclusive. GitOps is a mechanism for developers to be far more immersed in the operations workflow, therefore making DevOps work more effectively. 

On top of that, because it relies on a central repository from which everything can be monitored and logged, it brings security and compliance to the heart of development and operations. Therefore, GitOps is also an enabler of good DevSecOps practices

The Four Principles of GitOps

Like any methodology, GitOps has some basic principles which define best practice. These four principles, defined by Alexis Richardson, capture the essence of ‘GitOps best practices’:

1. The Entire System Can Be Described Declaratively 

This is a simple principle. It means that your whole system can be described or treated through declarative commands. Therefore, applications, infrastructure, and containers are not only defined in code, but are also declared with code as well. All of this is version controlled within your central repository.

2. The Canonical Desired System State Versioned in Git

Building on the first principle, as your system is wholly defined within a single source of truth, like Git, you have one place where all changes and declarations are stored. ‘Git Revert’ makes rollbacks, upgrades, and new deployments seamless. You can make your entire workflow more secure by using an SSH key to certify changes that enforce your organization’s security requirements. 

3. Approved Changes Can Be Automatically Applied

CI/CD with Kubernetes can be difficult. This is largely attributed to the complexity of Kubectl, and the need to have your cluster credentials allocated to each system alteration. With the principles of GitOps above, the definition of your system is kept in a closed environment. That closed environment can be permissioned so pulled changes are automatically applied to the system. 

4. Software Agents for Correctness and Convergence 

Once the three above principles are applied, this final principle ensures that you’re aware of the delta (or divergence) between what is in your source control repository and what is deployed. When a change is declared, as described in the first principle, these software agents will automatically pick up on any changes in your repository and reconcile your cluster to match. 

When used with your repository, software agents can perform a vast array of functions. They ensure that there are automated fixes in place for outages, act as a QA process for your operations workflow, protect against human error or manual intervention, and can even take self-healing to another level.

GitOps and Scalability

So far, we have examined the foundational aspects of GitOps. These in themselves go some of the way in showing the inherent benefits GitOps has to offer. It brings additional advantages to organizations seeking simple scalability, in more than one way.

Traditionally, monolithic applications with multiple staging environments can take a while to spin up. If this causes delays, scaling horizontally and providing elasticity for critical services becomes very difficult. Deployment pipelines can also be single-use, wasting DevOps engineers’ time. 

Migrating to a Kubernetes-based architecture is a great step in the right direction, but this poses two problems. First, you have to build all-new deployment pipelines, which is time consuming. Second, you have to get the bulk of your DevOps engineers up to speed on Kubernetes administration. 

Scaling Deployment 

In a traditional environment, adding a new application means a new deployment pipeline, creating new repositories, and being responsible for brand new workflows. 

What GitOps brings to the table is simplicity in scalability. All you need to do is write some declarative code in your central repository, and let your software agents take care of the rest.

GitOps gives engineers, and developers in particular, the ability to self-serve when it comes to CI/CD. This means that engineers can employ both automation and scalability in their continuous delivery in much less time and without waiting for operational resources (human or technical).

Scaling Your Team

Because the principles of GitOps rely on a central repository and a pull-to-deploy mentality, it empowers your developers to act as DevOps engineers. 

Whereas you may have previously needed a whole host of Kubernetes administrators, that isn’t the case with GitOps. Version controlled configs for Kubernetes mean it’s possible for a skilled developer to roll out changes instantaneously. 

They can even roll them back again with a simple Git command. Thus, GitOps helps to create true “t-shaped” engineers, which is particularly valuable as your team or organization grows.

GitOps and Observability 

If you’ve read and understood the principles of GitOps above, then it should come as no surprise that observability is a huge consideration when it comes to adopting GitOps. But how does having a truly observable system relate to your GitOps adoption?

GitOps requires you to detail the state of the system that you want, and therefore your system has to be designed in a way that allows it to be truly understood. For example, when changes to a Kubernetes cluster are committed to your central repository, it’s imperative that that cluster remains understood at all times. This ensures that the divergence between the observed and desired cluster state can be measured and acted upon.

This really requires a purpose-built, cloud-native, and fully-wrapped SaaS observability platform. Coralogix’s Kubernetes Operator provides a simple interface between the end-user and a cluster, automatically acknowledging and monitoring the creation and cessation of resources via your code repository. With its ability to map, manage, and monitor a wide variety of individual services, Coralogix is the natural observability partner for an organization anywhere on its GitOps journey.

Summary

Hopefully, you can now answer the question posited at the start of this article. GitOps requires your system to be code-defined, stored in a central repository, and to be cloud-native to allow its pull-to-deploy functionality.

In return, you get a highly scalable, highly elastic, system that empowers your engineers to spend more time developing releases and less time deploying them. With built-in considerations for security and compliance, there’s no doubt that it’s worth adopting. 

However, be mindful that with such free-flowing spinning up and down of resources, an observability tool like Coralogix is a must-have companion to your GitOps endeavor.

DevSecOps vs DevOps: What are the Differences?

The modern technology landscape is ever-changing, with an increasing focus on methodologies and practices. Recently we’re seeing a clash between two of the newer and most popular players: DevOps vs DevSecOps. With new methodologies come new mindsets, approaches, and a change in how organizations run. 

What’s key for you to know, however, is, are they different? If so, how are they different? And, perhaps most importantly, what does this mean for you and your development team?

In this piece, we’ll examine the two methodologies and quantify their impact on your engineers.

DevOps: Head in the Clouds

DevOps, the synergizing of Development and Operations, has been around for a few years. Adoption of DevOps principles has been common across organizations large and small, with elite performance through DevOps practices up 20%.

The technology industry is rife with buzzwords, and saying that you ‘do DevOps’ is not enough. It’s key to truly understand the principles of DevOps.

The Principles of DevOps

Development + Operations = DevOps. 

There are widely accepted core principles to ensure a successful DevOps practice. In short, these are: fast and incremental releases, automation (the big one), pipeline building, continuous integration, continuous delivery, continuous monitoring, sharing feedback, version control, and collaboration. 

If we remove the “soft” principles, we’re left with some central themes. Namely, speed and continuity achieved by automation and monitoring. Many DevOps transformation projects have failed because of poor collaboration or feedback sharing. If your team can’t automate everything and monitor effectively, it ain’t DevOps. 

The Pitfalls of DevOps

As above, having the right people with the right hard and soft skills are key for DevOps success. Many organizations have made the mistake of simply rebadging a department, or sending all of their developers on an AWS course and all their infrastructure engineers on a Java course. This doesn’t work – colocation and constant communication (either in person, via Slack or Trello) are the first enablers in breaking down silos and enabling collaboration. 

Not only will this help your staff cross-pollinate their expertise, saving on your training budget, but it enables the organic and seamless workflow. No two organizations or tech teams are the same, so no “one size fits all” approach can be successfully applied.

DevSecOps: The New Kid On The Block

Some people will tell you that they have been doing DevSecOps for years, and they might be telling the truth. However, DevSecOps as a formal and recognized doctrine is still in its relative infancy. If DevOps is the merging of Development and Operations, then DevSecOps is the meeting of Development, Security, and Operations. 

Like we saw with DevOps adoption, it’s not just as simple as sending all your DevOps engineers on a security course. DevSecOps is more about the knowledge exchange between DevOps and Security, and how Security can permeate the DevOps process. 

When executed properly, the “Sec” shouldn’t be an additional consideration, because it is part of each and every aspect of the pipeline.

What’s all the fuss with DevSecOps?

The industry is trending towards DevSecOps, as security dominates the agenda of every board meeting of every big business. With the average cost of a data breach at $3.86 million, it’s no wonder that organizations are looking for ways to incorporate security at every level of their technology stack.

You might integrate OWASP vulnerability scanning into your build tools, use Istio for application and container-level security and alerting, or just enforce the use of Infrastructure as Code across the board to stamp out human error.

However, DevSecOps isn’t just about baking Security into the DevOps process. By shifting security left in the process, you can avoid compliance hurdles at the end of the pipeline. This ultimately allows you to ship faster. You also minimize the amount of rapid patching you have to do post-release, because your software is secure by design.

As pointed out earlier, DevOps is already a successful methodology. Is it too much of a leap to enhance this already intimidating concept with security as well? 

DevOps vs DevSecOps: The Gloves Are Off

What is the difference between DevOps and DevSecOps? The simple truth is that in the battle royale of DevOps vs DevSecOps, the latter, newer, more secure contender wins. Not only does it make security more policy-driven, more agile, and more enveloping, it also bridges organizational silos that are harmful to your overall SDLC.

The key to getting DevSecOps right lies in two simple principles – automate everything and have omnipotent monitoring and alerting. The reason for this is simple – automation works well when it’s well-constructed, but it still relies on a trigger or preceding action to prompt that next function. 

Every single one of TechBeacon’s 6 DevSecOps best practices relies on solid monitoring and alerting – doesn’t that say a lot?

Coralogix: Who You Want In Your Corner

Engineered to support DevSecOps best practices, Coralogix is the ideal partner for helping you put security at the center of everything

Alerts API allows you to feed ML-driven DevOps alerts straight into your workflows, enabling you to automate more efficient responses and even detect nefarious activity faster. Easy to query log data combined with automated benchmark reports ensure you’re always on top of your system health. Automated Threat Detection turns your web logs into part of your security stack. 

With battle-tested software and a team of experts servicing some of the largest companies in the world, you can rely on Coralogix to keep your guard up.

Where is Your Next Release Bottleneck? 

A typical modern DevOps pipeline includes eight major stages, and unfortunately, a release bottleneck can appear at any point:

devops pipeline

These may slow down productivity and limit a company’s ability to progress. This could damage their reputation, especially if a bug fix needs to be immediately deployed into production.

This article will cover three key ways using data gathered from your DevOps pipeline can help you find and alleviate bottlenecks in your DevOps pipeline.

1. Increasing the velocity of your team

To improve velocity in DevOps, it’s important to understand the end-to-end application delivery lifecycle to map the time and effort in each phase. This mapping is performed using the data pulled directly and continuously from the tools and systems in the delivery lifecycle. It helps detect and eliminate bottlenecks and ‘waste’ in the overall system. 

Teams gather data and metrics from build, configuration, deployment and release tools. This data can contain information such as release details, duration of each phase, whether the phase is successful and more. However, none of these tools paint the whole picture.

By analyzing and monitoring this data in aggregate, DevOps teams benefit from an actionable view of the end-to-end application delivery value stream, both in real-time and historically. This data can be used to streamline or eliminate the bottlenecks that are slowing the next release down and also enable continuous improvement in delivery cycle time.

2. Improving the quality of your code

Analyzing data from the testing performed for a release, enables the DevOps teams to see all the quality issues in new releases and remediate them before implementing into production. Ideally preventing post-implementation fixes.

In modern DevOps environments, most (if not all) of the testing process is achieved with automated testing tools. Different tools are usually used for ‘white box’ testing versus ‘black box’ testing. While the former aims to cover code security, dependencies, comments, policy, quality, and compliance testing, the latter covers functionality, regression, performance, resilience, penetration testing, and meta-analysis like code coverage and test duration.

Again, none of these tools paints the whole picture, but analyzing the aggregate data enables DevOps teams to make faster and better decisions about overall application quality, even across multiple QA teams and tools. This data can even be fed into further automation. For example:

  • Notify the development team of failing code which breaks the latest build. 
  • Send a new code review notification to a different developer or team.
  • Push the fix forward into UAT/pre-prod.
  • Implement the fix into production.

There are some great static analysis tools that can be integrated into the developers’ pipeline to validate the quality, security, and unit test coverage of the code before it even gets to the testers. 

This ability to ‘shift left’ to the Test stage in the DevOps loop (to find and prevent defects early) enables rapid go/no-go decisions based on real-world data. It also dramatically improves the quality of the code that is implemented into production, by ensuring failing or poor quality code is fixed or improved. Therefore reducing ‘defect’ bottlenecks in the codebase

3. Focusing in on your market

Data analytics from real-world customer experience enables DevOps teams to reliably connect application delivery with business goals. It is critical to connect technology and application delivery with business data. While technical teams need data on timings like website page rendering speeds, the business needs data on the impact of new releases.

This includes metrics like new users / closed accounts, completed sales/items stuck in the shopping cart and income. No single source provides a complete view of this data, as it’s isolated across multiple applications, middleware, web servers, mobile devices, APIs, and more.

Fail Fast, Fail Small, Fail Cheap!

Analyzing and monitoring the aggregate data to generate business-relevant impact metrics enables DevOps teams to:

  • Innovate in increments 
  • Try new things
  • Inspect the results
  • Compare with business goals
  • Iterate quickly

Blocking low-quality releases can ultimately help contain several bottlenecks that are likely to crop up further along in the pipeline. This is the key behind ‘fail fast, fail small, fail cheap’, a core principle behind successful innovation.

Bottlenecks can appear in a release pipeline. Individually, many different tools all paint part of the release picture. But only by analyzing and monitoring data from all these tools can you see a full end-to-end, data-driven approach that can assist in eliminating bottlenecks. This can improve the overall effectiveness of DevOps teams by enabling increased velocity, improved code quality and increased business impact.