Tekton Reviews

I use Tekton generally for CI purposes and automation APIs.

We have created a Helm template for Tekton, and we're currently working on scripts. This allows us to create pipelines on the fly, which has significantly streamlined our workflow.

I have full control over it, actually, in my team. Another thing is that Tekton is based on Kubernetes. In Kubernetes, we have the concept of cron jobs which run on a time-to-time basis. In Tekton, we have the concept called task or task runs. We can expose an API and use the trigger and trigger-binding concept, allowing us to trigger tasks whenever needed. This mechanism is very nice.

RBAC is really needed for Tekton. Also, if you have too many PVCs and don't clean them up, it may stop working due to the limited number of PVCs you can create in a cluster.

I have been using it for the past two years.

Tekton is a stable product. One issue we might run into is when there are too many PVCs that haven't been cleaned up. We have mitigated this by creating a CronJob that runs every midnight to clear those tasks.

Tekton is scalable if well-planned. However, it is not a plug-and-play solution for now. You need to write the tasks according to your business case, which might differ from team to team or service to service.

While setting up, I was initially active on their Slack channel where I was able to get help. They were responsive.

Neutral

We were using Jenkins before Tekton.

The initial setup was daunting and took me four months to set it up and get the flow. There is a steep learning curve, especially with concepts like event listeners, event triggers, and trigger binding.

Tekton was initially very daunting, and it took me a long time to get the flow. However, once you get it, it’s awesome.

I'd rate the solution six out of ten.

I used Tekton to integrate data functionality within a major automobile industry. I created Tekton pipelines in Kubernetes clusters to complete the CI/CD end-to-end from the source code repository in the Google Cloud Platform. This involved taking the ML training model and deploying it in the Kubernetes clusters, Cloud Run, and App Engines.

Tekton has provided flexibility and robustness in our processes. However, I faced challenges with the integration with the source code repository. The tool is very robust and provides great flexibility once you understand how to use it, but it is initially difficult to learn and understand.

The most valuable feature is the pipeline. When you run a pipeline, all the jobs are called. It is a very valuable feature that enhances job maintenance overall. 

Another valuable feature is the standalone task that sticks to isolation. This feature is very good for our needs.

I faced significant challenges with integration, particularly with Git or any source code management and Workload Identity Federation. The documentation is also not mature, making it hard to use. Improvement is needed in the documentation and the overall integration process. 

Additionally, I am not sure if Tekton can be used efficiently in multi-cloud scenarios such as Anthos.

I have been working with Tekton for the last 12 months.

The stability of Tekton heavily depends on the developer's knowledge of Kubernetes deployments and managing Tekton parts. For instance, handling the logs, maintaining the load, and managing resource allocation are essential skills required for ensuring stability.

Scalability isn't perfect yet; Tekton is not very mature in the market with only a few companies using it. The ability of Tekton to adapt to needs is somewhat limited. Overall, I would rate its scalability as a seven. The ability to create different pipelines and reuse jobs seems to be the most robust scalability feature.

I have not communicated with technical support for Tekton. I followed the official documentation, created my own YAML files, and started deploying pipelines and operators.

Neutral

I used different tools for similar use cases based on the requirements of my engineers and the clients. Tekton was introduced to me in 2022, and I used it accordingly. I did not make the decision to switch; I just used what was provided.

The initial setup is simple and involves a few commands. The configuration process, however, might cause some challenges, particularly with integrating third-party tools or handling CI/CD pipelines.

I was the only person in my team handling the deployment and application of Tekton. A single person with knowledge of tools like Ansible, CI/CD tools, DevOps, and cloud services can manage this process.

It is essential to have a straightforward, end-to-end sample for implementation, including steps involving job, trigger, and pipeline settings. Syntax and URL validation validators are important for the effective use of Tekton. They will help check the correctness of the code and improve efficiency.

I'd rate the solution six out of ten.

We use Tekton primarily as a CI/CD tool. We develop the product, and after developing the product, we deploy it to the cloud. Before deployment, Tekton is used as a CI/CD tool to validate and check our test cases, vulnerabilities, and licensing issues. If any test cases are written, they will first validate Tekton cases. Once the test cases pass, it moves on to check for vulnerabilities and licensing issues using Fosa, which is one of our QA and Check Point tools.

Tekton has significantly improved our organization, especially in the area of reusability. When we moved to Tekton from Jenkins, I felt that the tasks could be reused for any number of pipelines within the same cluster, which was not the case with Jenkins. This reusability is a major benefit. Moreover, it has streamlined our CI/CD pipelines, making the deployment process much more efficient. Tekton provides a clean UI interface and allows us to run multiple tasks simultaneously, which has enhanced our efficiency and productivity.

The most valuable features of Tekton include the reusability of tasks, the clean UI interface, and the binding of services and secrets. The tasks can be reused for multiple pipelines, making it very efficient. The UI interface appears very clean and user-friendly. The binding of services and secrets is also very clear and well-organized.

One area where Tekton can improve is log management. Currently, logs are often deleted after a day, making it challenging to refer back to logs from previous pipelines. It would be beneficial to have access to logs for at least the past two or three days. 

Additionally, stability needs to be improved. Sometimes Tekton crashes or freezes during important deployments, and this can cause delays. Enhancing stability and log management would greatly benefit users.

I have been using Tekton for almost three years, approximately from the time our organization migrated from Jenkins to Tekton.

The stability of Tekton needs improvement. Occasionally, Tekton crashes or freezes during important deployments, which requires waiting and troubleshooting. This instability can be problematic, especially during critical launches.

Tekton's scalability is generally good, but there is room for improvement. The resource quotas are somewhat limited, and increasing the resource quotas could enhance scalability further. The ability to run pipelines in parallel is a good feature, but expanding the resources would improve scalability even more.

Our organization has sufficient technical support for Tekton. However, there are times when more support would be beneficial, especially when dealing with production launches and critical deployments.

Neutral

Before Tekton, we used Jenkins. Our organization decided to switch due to the cost, performance, and the ability to leverage new tools and applications.

The initial setup of Tekton was somewhat challenging. It took a while to understand the various components, such as service binding, role binding, and how to create routes and bind services. It was a bit difficult at first because I was the first person in my team to explore Tekton, but after gaining an understanding, it became much easier.

The deployment and implementation were handled within our organization. A few team members, including myself, explored the functionalities and shared our knowledge with the rest of the teams.

The return on investment from Tekton is primarily seen in time savings. Once the initial setup is complete, the automated processes reduce the time needed for deployments and streamline the workflow.

I'd rate the solution seven out of ten.

I use the platform to manage development pipelines. Our infrastructure is integrated with Kubernetes, and we utilize it within an OpenShift environment for automation. I have developed various pipelines and plugins for both front-end and back-end applications, including Angular for the front end and Quarkus, a Java framework for microservices, for the back end. The platform supports tasks such as code cloning, testing, and deployment by integrating SonarQube, Argo, and Nexus tools.

The product's best feature is its ease of implementation. It is well-suited for microservices architecture and integrates seamlessly with Docker and other tasks. The orientation towards microservices makes managing different tasks and projects efficient.

Initially, working with YAML configuration can be challenging using the solution. 

I have been using Tekton for two years.

I have not encountered any stability issues with the platform.

Approximately four people in our organization use Tekton. It is a scalable platform. 

The setup process is simplified by its integration with Kubernetes and its examples for various tasks.

I suggest utilizing available tasks and resources, such as copying and using tools like ChatGPT, to write and configure tasks. 

The platform is user-friendly and provides good support for managing different pipelines.

I rate it an eight out of ten. 

Generally, we are using Tekton for deployments instead of Jenkins. We create pipelines in Tekton for a number of services, each with different logs and requirements, especially in our DevOps practices. I have been using this Tekton pipeline for nearly six months.

The main benefits we have seen from using Tekton include saving time compared to Jenkins. The user interface is easier to use, and there are no plugins required for installation, unlike Jenkins. Time savings and ease of use are significant advantages.

Tekton has a user interface that facilitates time savings, making it more user-friendly than Jenkins. It is easier to handle and eliminates the need for plugin installations.

The user interface of Tekton could be improved. Sometimes, it takes more time during production deployments than expected.

I have been using the solution for nearly six months.

Tekton is stable, easy to use, and currently provides good stability.

Tekton is scalable, and most of our team members are using the Tekton pipeline.

As of now, I haven't asked for technical support. I would approach technical support if I encounter any problems.

Positive

Before Tekton, we used Jenkins. Tekton is better than Jenkins in terms of user interface and ease of integration without the need for plugins.

The initial setup took about one week, and there were some doubts on how to operate and execute deployments. But after learning about the tools, the setup was smooth, and everything was configured properly.

Four teams were involved in the deployment process: the ECS team, tenant ops team, DevOps team, and development team.

There are efficiency gains due to time savings in pipeline setup compared to Jenkins. This is a major point of efficiency for Tekton.

I would recommend Tekton to other users who are considering its implementation. It's better compared to Jenkins.

I'd rate the solution eight out of ten.

I primarily use Tekton for continuous integration purposes. When we build applications, we use Tekton for CI. For deployments, we use ArgoCD. Most of the time, we use Tekton to facilitate the CI process.

Tekton is primarily a time-saving solution. While it does take some time to set up initially, it is very efficient once running. It helps manage deployments and separates processes accordingly, which benefits both developers and me. Developers don't have to worry about the deployment or integration part as Tekton takes care of those aspects, making it easier for me to manage the pipeline.

Tekton allows you to categorize tasks according to your needs and minimizes the amount of code needed. For instance, you can separate tasks to suit your requirements. Additionally, when integrating with Terraform, Tekton makes it easy to trigger only specific parts of the infrastructure, which is beneficial for ops engineers.

For infrastructure deployment, integration is somewhat complex, especially when using Terraform with Tekton. It would be beneficial if this process were simplified.

I have been working with Tekton for about one year now.

Sometimes, when there are updates and patches, it can cause issues. For example, building a Docker image may require changing the base image due to upgrade issues. However, understanding Tekton properly helps in resolving such issues easily.

Tekton is very scalable. It's particularly effective for managing resources across multiple clusters and regions, integrating resources from different cloud providers.

Before Tekton, I used Jenkins and GitHub Actions for CI/CD pipelines. I also used Bitbucket pipelines for smaller web-related projects. These options required maintaining multiple pipeline files, unlike Tekton, which allows for streamlined categorization and management.

For infrastructure deployment, we categorize each Terraform file according to the specific components needed, such as VPCs and subnets. We maintain separate repositories for Tekton files and deploy specific components by providing the necessary paths and executing the appropriate files. Depending on whether it's a development or production environment, we maintain the appropriate pipelines.

I'd rate the solution eight out of ten.

We use Tekton to build and deploy business apps, which include analytics programming in their strategy. Our DeepLab setup involves routers that trigger Tekton within our clusters to build and deploy their strategy as needed.

Tekton is also used in Flutter, eliminating the need for GitLab CI for our setup. While GitLab Runners can handle build and deployment tasks, Tekton offers strong integration with EKS and Kubernetes clusters. 

Tekton is useful for us, especially since we use it for all other deployments and in the cloud. In terms of build and deployment capabilities, it can function similarly to GitLab.

The Helm Chart for Tekton doesn't come with all the manifests needed for installation; you must apply them manually. I use ArgoCD to upgrade and manage Tekton, but manual updates to the Helm charts are still required.

I have been using Tekton for one year.

We have analytics that utilize a negative tone. Around 50 people use this in the cloud. When they need to deploy or release a strategy, they tag it and create a new Tekton task. Tekton then handles building and integrating its strategy within our clusters.

It is open source, like DeepLab Community Edition.

Tekton is easy to use, but updating from older versions can be challenging due to method changes with each release. I've spent a lot of time upgrading it. Once you start using Tekton, you can manage it with just a few files and apply them easily. 

The best documentation for Tekton is their CRD for the operator.

Overall, I rate the solution a six out of ten.

We have our automation pipeline in Tekton. We create pipelines, basically pipelines, to run automated customer scenarios through our automation, Bash scripts, and Golang. To execute these tests routinely, we have implemented those pipelines in Tekton.

Tekton's integration capabilities enhance our development workflows. The product we are testing is also based on Kubernetes and containers, as is Tekton. At the end of the day, we use similar files and languages. 

So, it integrates pretty well. We use it through OpenShift pipelines on the OpenShift platform. The updates are automated, so we don't have to worry about them too much.

I like that you can parallelize tasks. If two tests are not conflicting, you can run them in parallel. This saves time. You can also test pipelines locally if they are not working. 

Additionally, I like the branching and visualization tools for the pipeline. I can easily share an image with someone, and they can understand the pipeline's structure.

Compared to traditional tools like Jenkins, Tekton lacks a lot of features. It's not efficient in managing conflicts between multiple pipelines. If I execute a test and someone else executes a test, we face lots of conflicts and overwriting of each other's work. That's one area for improvement.

Also, the reliability of Tekton is sometimes an issue. I think it's more of a Kubernetes-related issue than a Tekton issue. There are more outages than we expect. 

We have a strict requirement of running tests four to five times a day, and sometimes it's not able to provision the pipeline on time, so we lose test runs.

I have been using it for two years. 

We have had stability issues, but on my side, it was more dependent on Kubernetes, the platform we used. But it depends on the deployment model. 

I would rate the stability a seven out of ten. 

No issues with scalability. I can estimate that around 20 teams, with an average of four people per team, use it, so around 80 people use it. 

The customer service and support have been excellent. We have an internal deployment of Tekton, so the support team is also internal.

Positive

It's easy, but it depends on which distribution you use. The Google one, I haven't tried. Because it is easy on RedHat one. 

Challenges:

There are too many files to change if you have to make any minor source code changes. It becomes difficult to review (if someone has changed something) and assess the impact of those changes on other tasks or pipelines. There's too much friction in terms of making small changes.

Tekton is an open-source tool, and we use it directly. Red Hat has its own distribution of Tekton.

We have our own internal deployment, so we don't pay anything.

Overall, I would rate the solution a seven out of ten because there are lots of areas for improvement.

I would recommend it to someone who is well-versed in Kubernetes and can debug internal issues, not to any newcomers. I would suggest they explore traditional legacy tools first.