Tekton Reviews

Tekton is the orchestration engine within OpenShift, which is our on-premise platform. Since we are not on the cloud yet, OpenShift plays a strategic role, and Tekton is a significant part of it. It serves as an orchestrator.

In my experience of the last two years using Tekton and OpenShift pipelines, I haven't encountered many issues. As an orchestrator, Tekton works best. It's just one component of the larger OpenShift platform. Tekton consists of multiple components like events, trigger bindings, and more. However, when it comes to the overall OpenShift platform, being a platform as a service, most aspects are taken care of. 

Tekton plays a primary role as an orchestrator. When we receive a webhook from any Git repository, such as Azure Git or GitLab, Tekton triggers the pipeline and performs tasks like code retrieval, running SonarQube or Fortify tasks, and creating and deploying images to multiple environments.

So we can have multiple promoted environments, starting from dev to SIT, then to UAT, and finally to production. We follow a continuous flow branching approach, allowing us to promote changes from smaller environments to larger ones like dev to SIT, SIT to UAT, and UAT to production, which is our master branch. This helps us maintain a smooth workflow and ensures reliable deployment.

Tekton is an orchestrator. It provides seamless integration for our pipelines. It offers robust support for executing tasks within the pipeline, allowing us to set up and run pipelines quickly.

Additionally, Tekton's underlying architecture with OpenShift enables us to create, implement, and run end-to-end pipelines. We can integrate various automation tools like Fortify or SonarQube for testing, code scanning, regression testing, and more. All these tasks can be executed within the pipeline using Tekton.

There might be occasional issues with storage or cluster-level logging, which can affect production. But as a component, Tekton performs flawlessly.

As an orchestrator, Tekton effectively executes most tasks. However, there are instances where we feel that YAML files, which Tekton reads, could benefit from increased flexibility. You see, in OpenShift, everything revolves around YAML. We have different components specified in YAML files, and when we put them together in an OpenShift pipeline, it generally works fine. However, occasionally we encounter difficulties when editing these YAML files.

I have been working with Tekton since we implemented it in 2020, so it's been almost two years.

We haven't encountered any stability issues with it. It has been reliable and available.

Tekton is scalable.

Red Hat's support has been quite excellent. We have a close partnership with Red Hat, as our DevOps strategy heavily relies on OpenShift as a core component. 

Since our entire architecture is on-premise, we have made significant investments in OpenShift. Setting up the OpenShift cluster and configuring different components, including Tekton, has been smooth and hassle-free for us, thanks to Red Hat's support.

It's not solely about Tekton itself. We chose OpenShift as a platform as a service because we opted for on-premise implementation instead of the cloud. The implementation of OpenShift includes the incorporation of Tekton.

The initial setup is actually easy. Tekton is just one of the underlying components in OpenShift pipelines. It's a technology and engine with a straightforward architecture, so the setup process is quite simple.

We have a command-line setup where we use the OpenShift client to connect to Tekton. It's like talking to the cluster, and Tekton executes the tasks on that specific cluster. It's an efficient and streamlined process.

The entire OpenShift platform is supported by just two DevOps engineers.

But we might need to expand the team in the future. Two resources are not sufficient considering the workload and stress we handle.

The pricing is based on OpenShift's vCPU licenses. We pay according to the number of virtual CPUs, which can be costly. 

However, it's important to note that Tekton is just one of the underlying components in OpenShift. Therefore, the pricing and licensing considerations are more related to OpenShift as a whole rather than Tekton alone.

We have evaluated multiple vendors, including Red Hat, whose DevOps architecture includes Tekton as an underlying component. However, other vendors also offer similar orchestration components in their architectures. 

So, there are various tools available from different vendors that serve the same purpose as Tekton.

There are several vendors in the market who provide their own versions of orchestration components for DevOps architectures, apart from Red Hat. They implement their own approaches and name their components accordingly, but the purpose is similar to Tekton.

I would recommend Tekton as an orchestrator because it works well within the OpenShift environment. While there may be similar orchestrator components offered by other vendors in different DevOps architectures, Tekton's integration with other OpenShift components makes it a strong choice.

I would rate Tekton a seven. The only drawback I've experienced is the difficulty of modifying YAML files on the fly and making changes, as it doesn't work well in that aspect. However, apart from that, Tekton performs well in other areas.

We are using Tekton for continuous integration and continuous delivery to deploy our code to production.

By using Tekton, we have streamlined our deployment process and enabled continuous integration and delivery, which is crucial for maintaining our production systems.

The most valuable aspect of Tekton is its Kubernetes maintenance, allowing us to use it anywhere that supports Kubernetes. It is an open-source tool, which makes it highly adaptable.

Improvements could include introducing a UI-based pipeline development feature, such as drag and drop, which would help individuals with limited technical knowledge start building pipelines. However, I acknowledge that might be too ambitious.

I have been using Tekton for over six months.

We have experienced some performance issues with our clusters, leading the deployment team to create new clusters for enhanced performance. Teams are in the process of migrating to these new clusters.

Scalability-wise, Tekton should be adequate since it supports Kubernetes natively. Although I have limited knowledge in this area, it should scale by adding pods and clusters.

I have only received communications regarding migration from customer service. There haven't been any significant issues requiring their contact.

Positive

Previously, I was not involved in using any CI/CD platform, as there was a separate deployment team handling it. Therefore, Tekton is my first experience with a CI/CD solution.

The initial setup was challenging due to the various core concepts involved and multiple required connections, such as GitHub and cloud. Understanding triggers, events, and pipelines was important, but it posed a learning curve.

The setup process was managed by involving a senior colleague with experience, which aided in deploying successfully.

It is an excellent choice for CI/CD, primarily due to being open source and Kubernetes-native, which avoids vendor lock-in and allows flexibility in backend changes with minimal settings adjustments.

I'd rate the solution eight out of ten.

In my company, we use Tekton for the pipeline, specifically Bitbucket pipelines. We have some cluster setups as well. In order to run some testing of the cluster, we chose to handle pipelines via Tekton.

The best feature of Tekton is that it is a Kubernetes-native tool for CI/CD. The tool offers flexibility and compatibility. If we look under the Kubernetes-native area, Tekton is built to run natively on Kubernetes, so it leverages Kubernetes 'scalability, security, and robustness, making it an ideal cloud-native application. I can relate to the pipeline customization in Tekton. It provides a highly customizable and reusable pipeline component. You can define tasks and pipelines that suit your specific workflow needs and easily reuse them as a different service platform. If you see the interoperability, Tekton is designed to indicate that there are other tools and services in the Kubernetes ecosystem. It makes it easy to incorporate things into the existing workflow.

Earlier, I used to use Jenkins pipeline, but compared to Jenkins, Tekton is better. Tekton is quite a user-friendly tool. The tool offers good dashboard access, and it is extremely user-friendly. If you are a beginner, then accessing the flexibility part can be overwhelming. We think the learning curve of the tool is steep, especially for those who are not already familiar with Kubernetes. There is a need to improve the recommendations and create more tutorials for the tool. The tool is very user-friendly for advanced users and not for beginners. There are some limitations, especially since the tool primarily operates through YAML configurations and command lines with limited graphical user interface options. While there are some third-party tools that look like Tekton's dashboard, there are still basic and compact major systems like Jenkins or GitLab.With more advanced built-in CGI and GUI, as a user, we can make it easier to monitor and manage Python faster. We can also improve the data handling and debugging. The debugging of failed pipelines in Tekton can be challenging. The error messages are not very descriptive, and the tool has limited support for step-by-step debugging. So, the improvement in error handling and data handling for debugging needs to be improved so that it could significantly improve the developer experience.

I have been using Tekton for three years. In our company, we have been using the tool for more than eight months. I am just a user of the tool.

In our company, there are around more than 5,000 employees. There are actually many things under the cloud and platform delivery and infrastructure. We don't know the exact number of employees who are using the tool. I believe over 100 people in my company use the tool.

In my entire career, I never went to the support team for help. If I had any doubts or any kind of features, I was able to Google it or use ChatGPT.

The solution is deployed on the cloud.

Beginners should go through the product documentation. There are many tutorials available on the product that can help a person to configure the pipeline and task functions. Once you get a proper idea of the architecture of Tekton and how it works, only after knowing how to use the tool will a person get to apply the practical knowledge in the Tekton tools. I suggest that businesses get an idea about Python and how it works, as they need to understand the architecture of Tekton.

I rate the tool an eight out of ten.

The CI solution of the team I worked for was based on Tekton. Since Tekton is a Kubernetes native framework, we can easily configure it within a Kubernetes cluster, which has many benefits.

I would say the customization ability that Tekton provides is good. As an example, if we look at the pipeline structure of Tekton, a Tekton Pipeline was built with Tekton Tasks. We can specify a Tekton Task within the pipeline YAML. Otherwise, we can specify the task in a separate YAML and add it as a reference within the pipeline so that we can use the Tekton Task as a reusable component in other pipelines as well. Another thing is that we can execute a Tekton Task based on conditions such as "when" expressions. Furthermore, Tekton provides Tekton Triggers. We use Tekton Triggers to dynamically execute a pipeline based on an event, which is also a very useful feature for us. These customization features of Tekton made it more valuable and flexible to use for us.

The product's documentation is an area of concern, making it an area where improvements are required.

Apart from the documentation, there is one more thing that I believe needs to improve in Tekton. In the Tekton pipeline structure, there is an optional feature called finally section, where we can add all the core Tekton Tasks, which have to be executed even in a pipeline failure scenario. But the issue is that we cannot give a sequence for the tasks in the finally section, so all the tasks are running simultaneously. I believe if we had a feature to help users add a sequence for the tasks in the finally section, then it would make our lives easier.

The scalability could improve a lot in the future.

I have been using Tekton for a year. I am a user of the tool.

Sometimes, we face some version compatibility issues, but when we upgrade the version, it gets rectified. Stability-wise, I rate the solution an eight out of ten.

I know that there are more than 20 people using Tekton within our company. As the CI solution in our company is based on Tekton, many people, including new joiners, also have to learn Tekton. Scalability-wise, I rate the solution a seven out of ten.

When our company faced challenges with the tool, we had to do our own trials and errors to find a solution. Sometimes, we had to go through some GitHub issues related to the challenges we faced, but most of the time, we couldn't find an exact solution by looking at the GitHub issues. So we had to try to resolve the issues we faced in Tekton with the knowledge we had. We did not contact Tekton's support team.

Tekton's integration capabilities have helped our company's development workflow. Tekton is a Kubernetes native framework, so most of the command lines that we use in Kubernetes could also be used within Tekton framework. Basically, configuring Tekton Pipelines within our Kubernetes cluster was very easy compared to using other solutions. Overall, the development workflow was easy when using Tekton.

As a beginner who is trying to use Tekton, it will take some effort to learn it because the documentation in Tekton has to improve a lot. There are no tutorials to learn about Tekton because I personally learned it from my seniors and my experience and even when we faced challenges in the development, we couldn't find a solution on the internet. We had to do our own trial and error to find a solution, which was one of the main challenges while using Tekton.

I recommend the tool to others as using Tekton makes our lives easier. As far as I know, many companies don't know about Tekton. If they knew that there is something called Tekton, they would definitely adjust to it. 

I rate the tool a nine out of ten.

Our primary use case is for cloud integration and cloud deployment. Tekton is a cloud-native tool that we utilize in Kubernetes and OpenShift. It helps us with code integrations and code deployment. We use it for automating various processes and integrating it with our DevOps environment.

Tekton has improved our performance by allowing us to define metrics and resource limits, enhancing the performance of our whole pipeline. The ability to customize tasks and use custom containers has also allowed us to tailor our workflows to our needs.

The customization is valuable. We can make a lot of customizations, and data sharing between tasks using workspaces is very convenient. Moreover, the flexibility and ease of use in creating and attaching new tasks make it very user-friendly. The Tekton UI and user interactions are very good.

Tekton could improve by adding better documentation. The documentation needs improvement to clarify supported features and required integrations. Additionally, while it is currently a cloud-native solution limited to Kubernetes, an option to run it locally on our own systems would be very useful. Security integrations and third-party integrations also need enhancement.

We have been using the Tekton solution for nearly two years.

Tekton is stable and performs well. However, it would benefit from adding new features quickly to stay relevant and helpful.

Tekton is highly scalable due to its cloud-native nature.

We have not escalated any queries to Tekton itself. Since Tekton comes as an integral part of Red Hat, we rely on existing documentation and haven't had any issues requiring direct technical support.

Neutral

In the past, we used Jenkins for CI/CD. Each tool has its own use case and pros and cons, but Tekton's cloud-native capabilities and ease of deployment and maintenance make it a better fit for our needs.

The initial setup of Tekton is very straightforward. We install it as an operator in our cluster, making maintenance tasks like auto-upgrades and updates easier.

There is no specific cost related to resource optimization provided by Tekton. We need to manage the resource limits and requests ourselves to prevent resource exhaustion, especially when running on cloud platforms like AWS, Azure, or GCP.

I recommend Tekton to anyone closely interacting with Kubernetes. It is particularly useful for larger teams with complex application deployment requirements. However, for smaller teams or simpler use cases, it may not be necessary.

I'd rate the solution six out of ten.

I am using Tekton for continuous integration in our project.

The feature that I like most about Tekton is that it is built for Kubernetes, so we can utilize our resources effectively.

Tekton should have more accessibility for some rare use cases so that we can have more references when applying some techniques to our pipeline.

I have been using Tekton since February.

If we set it up correctly and know exactly what we have done, it is very stable because it runs on top of Kubernetes.

I can't give an opinion right now because I haven't applied the scale to our deployment. It's only in our staging environment.

I haven't contacted support yet since most of my errors are addressed in their GitHub repository.

Positive

I had some experience using Tekton when my company migrated the CI panel from another platform.

The initial setup is rather easy because the tutorial is easy to follow.

Tekton is free to use. It's open source.

I would recommend Tekton to my coworkers if they use Kubernetes most at work. However, if my colleagues don't use Kubernetes, I wouldn't recommend Tekton to them because it is very difficult to understand Tekton if you aren't familiar with Kubernetes.

I'd rate the solution eight out of ten.

I use Tekton for DevOps work, where developed applications will be deployed into a cloud Kubernetes or any Kubernetes platform. This process includes all the Docker builds and deployment into the cluster and the notification after deployment or any triggers.

Tekton has been used for a project where the client wanted their application to be deployed in a Kubernetes cluster, and Tekton is the tool that enables this deployment process.

I like the setup of the dashboard. We can see all the tasks, the pipelines, and all the triggers in one place. That's the main use case that I like. Also, the Tekton Hub was very useful with a huge community. It was very useful for sourcing tasks and pipelines.

Tekton is a good product with all the features I need. There isn't anything that needs improvement.

I have been using Tekton for about one year.

The stability of Tekton depends on correct implementation. If implemented correctly, it's a very good product. I haven't faced any stability issues myself.

Most of the time, the community is really helpful. I didn't need to contact support because the Tekton Hub and its community provided adequate support.

Positive

We used Flux before. However, we do not really use it now. Now, we use ArgoCD to deploy applications across multiple systems.

The initial setup is straightforward. However, users need to follow the documented steps and version requirements.

Some colleagues in my office had prior experience with Tekton, so I had to help with its implementation.

We have documentation for Tekton content creation for the CNCF course that can be forwarded to help others start using Tekton. I'd rate the solution ten out of ten.

We use the solution in CI/CD pipelines for application deployment. 

The tool's most valuable aspect is its compatibility with the cloud-native environment. It can be easily installed on Kubernetes and leverages its resources to run CI/CD pipelines.

Some of the tool's cons include its minimalistic dashboard, which lacks detailed information and control compared to other tools like Jenkins or GitLab. Additionally, it's primarily used by Japanese companies.

Another drawback is its dependency on the Kubernetes cluster for deployment, which could be a limitation. One feature I would like to see included or enhanced in Tekton is the ability to reuse specific pieces of pipelines as reusable modules.

I didn't choose this tool because they already used it when I joined this company. I don't like this tool because it has many limitations. I believe many other tools are better than Tekton.

Integration with other solutions involves mainly API calls. Tekton lacks a mature dashboard, making integration more challenging than tools like GitLab or Jenkins.

I encountered some challenges, particularly with compatibility between different versions of Tekton and Kubernetes. For example, the Tekton version couldn't be installed on Kubernetes version 1.24 due to compatibility issues. Some versions of Tekton don't support certain Kubernetes features, leading to installation issues. It depends on Kubernetes, and matching Tekton versions with compatible ones is essential.

The tool has increased our operational costs due to the need for repetitive tasks and code duplication. Unlike other tools like GitLab or Jenkins, it often requires duplicating resources across clusters or regions. This duplication elevates maintenance costs, as any code changes must be applied in multiple locations.

I have been working with the product for ten months. 

The solution is stable, and I haven't encountered any performance issues. 

I find Tekton to be scalable. It leverages Kubernetes as its underlying system, allowing for scalability. For instance, if you need to run numerous concurrent jobs, Tekton can accommodate this requirement. My company has 70-100 users. 

We have deployed Tekton across multiple locations, departments, and cross-tenants. It has been used for about three or four years and is utilized by various teams to deploy applications in different clusters and locations.

Since the tool is open-source, we resolve issues ourselves. 

The installation process is straightforward, which stands out as one of Tekton's notable advantages. Deploying a YAML file onto your Kubernetes cluster is all it takes, and the installation on Kubernetes is quick. I believe it's achievable in less than a minute. In contrast, other tools like GitHub or Jenkins often require significant setup time.

There is minimal ongoing maintenance required. However, upgrading to the latest version can sometimes be challenging due to compatibility issues between Tekton and Kubernetes. Upgrading Tekton may also necessitate upgrading the Kubernetes cluster, which can incur additional maintenance costs.

The tool increases our operational costs because it requires repetitive tasks. Other tools like GitLab or Jenkins can reduce costs, as they eliminate much of this repetition. Currently, we often need to duplicate resources. For example, if we want to install based on manifests in a new cluster, we must copy all the base modules to another cluster and keep them in the repository. This duplication can increase the maintainability costs, as any changes to the code must be made in multiple places.

The tool is open-source and free to use. 

I wouldn't recommend the product to others. I recommend GitLab for anyone looking to work on mature CI/CD pipelines. I rate the overall product a four out of ten.