Kraken CI vs Other CI Systems
Introduction
In this section, three CI/CD tools will be compared with Kraken
CI. These tools are among the most popular systems:
1. Jenkins
2. GitHub Actions
3. GitLab CI
The most significant difference between Kraken CI and these three
systems, as well as other CI/CD systems, is that they lack test result
analytics. All three systems barely touch upon the topic and are only
able to report results acquired during a build.
Having only information about passes and failures, it is difficult to
make a decision. It is not known if this failure has appeared
before. Maybe it has been failing for several months, or perhaps it is
sporadic and only fails from time to time. Alternatively, it could be
a significant regression that requires urgent fixing.
This requires either using other third-party tools that can fill this
gap or developing custom scripts that can analyze the results and
generate comprehensive reports.
In contrast, Kraken CI focuses on this topic and excels in both
execution and results analytics. It can show:
- fixes and regressions,
- age of test result,
- stability of a test case,
- importance of negative test result,
- history of a test case in a table and in a chart,
- total results chart ie. pass ratio over time,
- pass ratio chart for selected subset of tests (e.g. tests results obtain on particular operating system).
Furthermore, it allows users to comment on test results, such as
referencing a submitted bug or identifying the root cause, among other
things.
The details are presented in Test Results Basics and Test Results Tracking
of Kraken CI docs.
But now let’s discuss the differences between specific CI/CD systems.
Jenkins
Jenkins is an open-source on-premises
automation server used for building, testing, and deploying
software. It offers a broad range of plugins and integrations that
support continuous integration and continuous delivery
workflows. Jenkins is widely used in software development and is
recognized for its flexibility and extensibility.
Bright Side
Jenkins has many powerful features, with one of the most prominent
being its flexible and convenient method for defining pipelines using
scripting code.
Another very positive aspect of Jenkins is its community. The
community, besides providing lots of blog posts and tutorials about
Jenkins, also develops many plugins that greatly improve Jenkins.
Dark Side
1. Plugin Overload and Compatibility Issues
Jenkins relies heavily on plugins for extended functionality. However,
the sheer number of plugins available can make it challenging to
select, manage, and maintain the right set of plugins. Plugin
compatibility and stability issues can also arise, leading often to
conflicts or performance problems.
Kraken CI has a different approach. All of its features are built-in,
which ensures compatibility and thorough cross testing. Users are
encouraged to contribute directly to the core of Kraken CI. The only
extensions available to users are tools, which enable
the integration of third-party tools with Kraken CI. This is similar
to third-party actions in GitHub Actions.
2. Bad User Experience in UI
Jenkins’ user interface (UI) is often criticized for being outdated
and not user-friendly. There is a dichotomy between the classic UI and
the Ocean Blue UI. Both show more or less the same things, but one is
better in some areas, while the other is better in other areas. This
leads to confusion.
Many UI pages in Jenkins lack usability. Some areas are exceptionally
bad, such as presenting logs. When the logs are very large, Jenkins
shows them very slowly, especially if timestamps are
enabled. Sometimes the UI freezes completely and cannot show
anything. There is also a page called `Pipeline Steps` that shows logs
for each step separately, but there are so many internal steps
presented that it is very hard to navigate.
Presenting logs in Kraken CI is impressive. They are divided by
default into steps that the user has defined in a workflow, or they
are displayed in a flat format. Users can enable internal logs to see
details, and loading logs is paged, so it is not necessary to load the
entire log at once. However, it is still possible to download the
entire log in raw format. Logs can be filtered by multiple fields and
viewed at the individual step level, job level, run, flow, branch, or
project levels. ANSI codes are colored, and there are many more
features. Check out these two blog posts that present these features: Standing out Logs Handling and Further Logging Improvements.
There are more examples like that. For instance, navigation, browsing
test results, and so on. In Kraken CI, all of these aspects were
thoughtfully reevaluated and engineered with user experience (UX) in
mind.
3. Reliance on Groovy Scripting
Jenkins pipelines, which are used to define CI/CD workflows, are
written in Groovy scripting language. While this provides flexibility
and extensibility, it requires from developers to learn and use Groovy,
which can be an additional learning curve for teams not familiar with
Groovy.
In the case of Kraken CI, Python is used as the language to define
workflows. According to the TIOBE Index, Python is ranked number 1, making it the most popular programming language, while Groovy is ranked 44th.
GitHub Actions
GitHub Actions is a powerful and flexible automation platform that
allows you to streamline and optimize your software development
workflows. With GitHub Actions, you can automate tasks, build, test,
and deploy your applications directly from your GitHub
repositories.
Bright Side
The biggest advantage of GH Actions is that it is integrated with
GitHub repositories, and it is free up to a certain usage level. In
addition, it allows for the extension of defined workflows by using
community-developed actions. The number of available third-party
actions is quite substantial.
It is also possible to expand the computing power by connecting your
own “self-hosted” machines to the pool of all machines. Therefore,
workflows can run on both GitHub’s machine cluster and on your own
on-premises machines.
Aside from that, GitHub Actions can do more than just CI/CD. Workflows
can be triggered for various project events, such as a new issue being
submitted, a new comment being added to an issue, a repository being
forked, a release being created, and more.
Dark Side
1. Poor, not Scalable UI/UX
The user interface is cluttered and does not scale well for larger
projects. When a workflow reuses other workflows, the job names become
excessively long and are frequently truncated in multiple areas within
the UI. This makes it difficult to identify which jobs we are
currently viewing. The problem is further compounded when nested
workflows are utilized.
The UI also does not scale well for a larger number of jobs. If there
are more than 30 jobs in a workflow, it becomes impossible to filter,
sort, or list them in pages. Instead, all jobs are just presented to
you in a flat list.
One more weakness is the logs panel. Initially, it appears to be
sleek. Nonetheless, if there are numerous steps and the logs are quite
extensive, containing thousands of lines, then navigation becomes
difficult. While jobs are in progress, the UI attempts to predict what
to display, causing it to jump from one location to another, making it
difficult to choose the correct step. Additionally, paging through the
logs is not feasible. They are all displayed at once and are
progressively loaded while scrolling down. This is a pretty
ineffective method of finding the appropriate location in the log if
the log contains hundreds of pages.
Kraken’s logging facilities in the UI were already described in the
Jenkins section. In terms of UI scalability, this is one of Kraken’s
goals. It is designed from the ground up to handle multiple jobs, up
to thousands or even tens of thousands. It provides means for
filtering and sorting jobs, and presenting workflows, even those that
are more complicated and involve multiple forks, in a more scalable
way than GH Actions.
2. Reusability Is Quite Hard
Initially, it seems that reusability is well supported in GH Actions
workflow using third-party actions and reusable workflows. However,
upon closer inspection, it is not as seamless as it
appears. Externalizing some parts of the workflow into reusable
actions or workflows can be quite tedious. If there is a need to
locally reuse a fragment of code, one would like to use the YAML
feature called anchors and aliases. Unfortunately, GH Actions does not
support this feature.
The situation becomes complicated if a user needs to maintain multiple
repositories. Certain parts of workflows can be reused, but the
framework of the workflow, including trigger definitions, global
environment variables, and main jobs (which may reference reusable
workflows), must be present in every repository. Ideally, one would
like to fully externalize the entire workflow to a single repository
and simply reference it in every other repository.
Kraken allows for defining a workflow for particular repository in
several way. The workflow definition can be stored
1. in Kraken server,
2. in the same repository,
3. in another repository.
In the first case, the entire workflow definition resides in the
Kraken server’s database. It is then possible to link the workflow
definition to any Git repository — whether it is the repository that
needs to be built and tested or any other repository. This makes it
easy to experiment with a workflow for any repository without the need
to make changes. Later on, it is easy to transfer that workflow to the
relevant repository. Furthermore, the entire workflow definition can
be extracted to a separate repository and reused in multiple other
repositories.
3. Cloud and Security
GitHub Actions is a cloud-based CI/CD tool, which means that its
reliability and availability are out of your control. If an outage
occurs, the only thing left for you to do is wait until GitHub’s team
fixes their services. This may last from a few minutes to a few hours.
Although GitHub Actions provides security features like access
controls, secrets management, and vulnerability scanning, it may not
fulfill the specific security or compliance requirements of all
organizations or industries. Organizations with stringent security or
compliance needs must evaluate GitHub Actions’ security features
carefully and ensure that they align with their requirements.
GitLab CI
GitLab CI is a comprehensive DevOps platform that combines source code
management, CI/CD pipelines, issue tracking, and more in a single
integrated solution. It enables organizations to streamline their
software development processes, promote collaboration, and achieve
faster and more efficient delivery of high-quality software.
Bright Side
GitLab CI enables the definition of pipelines as code by using a
YAML configuration file. This approach makes the CI/CD process
version-controlled, easily replicable, and auditable. Infrastructure
modifications, test scripts, and deployment steps can be defined
alongside the code, promoting transparency and collaboration.
Furthermore, GitLab is offered as software that can be installed
on-premises, as well as a service that can be used in the cloud. This
way, it combines the software aspect of Jenkins with the cloud service
aspect of GitHub Actions in one solution. The choice belongs to the user.
GitLab offers a wide range of features and integrations, with most of
them only available in the Enterprise Edition. These features include
code quality checks. Another strong aspect of GitLab is its
reusability, as it is easy to reuse fragments of YAML definitions
thanks to anchors in the language.
Dark Side
1. Too complex in advanced cases
The learning curve for GitLab CI/CD is rather steep, and some poor
design decisions have made configuring CI/CD pipelines in the
.gitlab-ci.yml file quite complex. The documentation is full of
notes that certain aspects are not obvious, discouraged, or may
behave unexpectedly. Furthermore, the issue tracker is filled with
problems related to excessively complicated features.
One of the problems is that a CI pipeline is triggered by all possible
events. Then it is user role to explicitly exclude which jobs should
be run or not for particular event. Sometimes it leads to situations
that a pipeline is triggered multiple times. There is a dedicated
section in the docs how to avoid such cases:
https://docs.gitlab.com/ee/ci/jobs/job_control.html#avoid-duplicate-pipelines
All of these factors make developing more advanced workflows quite
challenging.
In contrast, Kraken is more deterministic. Workflows are triggered by
user-indicated events, and it is clear why a given workflow was
started — this information is presented in the UI.