Enhancement: Can override Freeze Window to start a build.
Previously, users could not start builds during a freeze window unless they made changes to the freeze window setting in the screwdriver.yaml configuration. Now, you can start a build by entering a reason in the confirmation modal. This can be useful for users needing to push out an urgent patch or hotfix during a freeze window.
Store
Feature: Build cache now supports local disk-based cache in addition to S3 cache.
Queue Worker
Bugfix: Periodic build timeout check
Enhancement: Prevent re-enqueue of builds from same event.
Compatibility List
In order to have these improvements, you will need these minimum versions:
UI – v1.0.479
API – v0.5.835
Store – v3.10.3
Launcher – v6.0.42
Queue-Worker – v2.9.0
Contributors
Thanks to the following contributors for making this feature possible:
Screwdriver now has the ability to cache and restore files and directories from your builds to either s3 or disk-based storage. Rest all features related to the cache feature remains the same, only a new storage option is added. Please DO NOT USE this cache feature to store any SENSITIVE data or information.
The graph below is our Internal Screwdriver instance build-cache comparison between disk-based strategy vs aws s3.
Build cache – get cache – (disk strategy)
Build cache – get cache – (s3)
Build cache – set cache – (disk strategy)
Build cache – set cache – (s3)
Why disk-based strategy?
Based on the cache analysis, 1. The majority of time was spent pushing data from build to s3, 2. At times the cache push fails if the cache size is big (ex: >1gb). So, simplified the storage part by using a disk cache strategy and using filer/storage mount as a disk option. Each cluster will have its own filer/storage disk mount.
NOTE: When a cluster becomes unavailable and if the requested cache is not available in the new cluster, the cache will be rebuilt once as part of the build.
Cache Size:
Max size limit per cache is configurable by Cluster admins.
Retention policy:
Cluster admins are responsible to enforce retention policy.
Cluster Admins:
Screwdriver cluster-admin has the ability to specify the cache storage strategy along with other options like compression, md5 check, cache max limit in MB
Screwdriver is an open-source build automation platform designed for Continuous Delivery. It is built (and used) by Yahoo. Don’t hesitate to reach out if you have questions or would like to contribute: http://docs.screwdriver.cd/about/support.
The Spinnaker community has grown significantly after launching as an open source project in 2015. The project maintainers increasingly look for ways to help the community better understand how Spinnaker is used, and to help contributors prioritize future improvements.
Today, feature development is guided by industry experts, community discussions, Special Interest Groups (SIGs), and events like the recently held Spinnaker Summit. In August 2019, the community published an RFC, which proposed the tooling that will enable everyone to make data-driven decisions based on product usage across all platforms. We encourage Spinnaker users to continue providing feedback, and to review and comment on the RFC.
Following on from this RFC, the Spinnaker 1.18 release includes an initial implementation of statistics collection capabilities that are used to collect generic deployment and usage information from Spinnaker installations around the world. Before going into the details, here are some important facts to know:
No personally identifying information (PII) is collected or logged.
All stats collection code is open source and can be found in the Spinnaker stats, Echo, and Kork repos found on GitHub.
Users can disable statistics collection at any time through a single Halyard command.
Community members that want to work with the underlying dataset and/or dashboard reports can request and receive full access.
This feature exists in the Spinnaker 1.18 release,but is disabled by default while we finalize testing of the backend and fine-tune report dashboards. The feature will be enabled by default in the Spinnaker 1.19 release (scheduled for March 2020).
All data will be stored in a Google BigQuery database, and report dashboards will be publicly available from the Community Stats page. Community members can request access to the collection data.
Data collected as part of this effort allows the entire community to better monitor the growth of Spinnaker, understand how Spinnaker is used “in the wild”, and prioritize feature development across a large community of Spinnaker contributors. Thank you for supporting Spinnaker and for your help in continuing to make Spinnaker better!
Tekton Pipelines, the core component of the Tekton project, is moving to beta status with the release of v0.11.0 this week. Tekton is an open source project creating a cloud-native framework you can use to configure and run continuous integration and continuous delivery (CI/CD) pipelines within a Kubernetes cluster.
Tekton development began as Knative Build before becoming a founding project of the CD Foundation under the Linux Foundation last year.
The Tekton project follows the Kubernetes deprecation policies. With Tekton Pipelines upgrading to beta, most Tekton Pipelines CRDs (Custom Resource Definition) are now at beta level. This means overall beta level stability can be relied on. Please note, Tekton Triggers, Tekton Dashboard, Tekton Pipelines CLI and other components are still alpha and may continue to evolve from release to release.
Tekton encourages all Tekton projects and users to migrate their integrations to the new apiVersion. Users of Tekton can see the migration guide on how to migrate from v1alpha1 to v1beta1.
Now is a great time to contribute. There are many areas where you can jump in. For example, the Tekton Task Catalog allows you to share and reuse the components that make up your Pipeline. You can set a Cluster scope, and make tasks available to all users in a namespace, or you can set a Namespace scope, and make it usable only within a specific namespace.
Continuous Delivery (CD) and Runbook Automation are standard means to deploy, operate and manage software artifacts across the software life cycle. Based on our analysis of many delivery pipeline implementations, we have seen that on average seven or more tools are included in these processes, e.g., version control, build management, issue tracking, testing, monitoring, deployment automation, artifact management, incident management, or team communication. Most often, these tools are “glued together” using custom, ad-hoc integrations in order to form a full end-to-end workflow. Unfortunately, these custom ad-hoc tool integrations also exist in Runbook Automation processes.
Processes usually integrate multiple tools and exist in multiple permutations
Problem: Point-to-Point Integrations are Hard to Scale and Maintain
Not only is this approach error-prone but maintenance and troubleshooting of these integrations in all its permutations is time-intensive too. There are several factors that prevent organizations from scaling this across multiple teams:
Number of tools: Although the great availability of different tools always allows having the appropriate tool in place, the numberof required integrations explodes.
Tight coupling: The tool integrations are usually implemented within the pipeline, which results in a tight coupling between the pipeline and the tool.
Copy-paste pipeline programming: A common approach we are frequently seeing is that a pipeline with a working tool integration is often used as a starting point for new pipelines. If now the API of a used tool changes, all pipelines have to catch up to stay compatible and to prevent vulnerabilities.
Let’s imagine an organization with hundreds of copy-paste pipelines, which all contain a hard-coded piece of code for triggering Hey load tests. Now this organization would like to switch from Hey to JMeter. Therefore, they would have to change all their pipelines. This is clearly not efficient!
In order to solve these challenges, we propose introducing interoperability interfaces, which allow abstract tooling in CD and Runbook Automation processes. These interfaces should trigger operations in a tool-agnostic way.
For example, a test interface could abstract different testing tools. This interface can then be used within a pipeline to trigger a test without knowing which tool is executing the actual test in the background.
Interface abstracts the actual tooling
These interoperability interfaces are important and this is confirmed by the fact that the Continuous Delivery Foundation has implemented a dedicated working group on Interoperability, as well as the open-source project Eiffel, which provides an event-based protocol enabling a technology-agnostic communication especially for Continuous Integration tasks.
Use Events as Interoperability Interfaces
By implementing these interoperability interfaces, we define a standardized set of events. These events are based on CloudEvents and allow us to describe event data in a common way.
The first goal of our standardization efforts is to define a common set of CD and runbook automation operations. We identified the following common operations (please let us know if we are missing important operations!):
Operations in CD processes: deployment, test, evaluation, release, rollback
Operations in Runbook Automation processes: problem analysis, execution of the remediation action, evaluation, and escalation/resolution notification
For each of these operations, an interface is required, which abstracts the tooling executing the operation. When using events, each interface can be modeled as a dedicated event type.
The second goal is to standardize the data within the event, which is needed by the tools in order to trigger the respective operation. For example, a deployment tool would need the information of the artifact to be deployed in the event. Therefore, the event can either contain the required resources (e.g. a Helm chart for k8s) or a URI to these resources.
We already defined a first set of events https://github.com/keptn/spec, which is specifically designed for Keptn — an open-source project implementing a control plane for continuous delivery and automated operations. We know that these events are currently too tailored for Keptn and single tools. So, please
Let us Work Together on Standardizing Interoperability Interfaces
In order to work on a standardized set of events, we would like to ask you to join us in Keptn Slack.
We can use the #keptn-spec channel in order to work on standardizing interoperability interfaces, which eventually are directly interpreted by tools and will make custom tool integrations obsolete!
Originally posted on the Armory blog, by Rosalind Benoit
“Let Google’s CloudBuild handle building, testing, and pushing the artifact to your repository. #WithSpinnaker, you can go as fast as you want, whenever you’re ready.”
Calling all infrastructure nerds, SREs, platforms engineers, and the like: if you’ve never seen Kelsey Hightower speak in person, add it to your bucket list. Last week, he gave a talk at Portland’s first Spinnaker meetup, hosted at New Relic by the amazing PDX DevOps GroundUp. I cackled and cried at the world’s most poignant ‘Ops standup’ routine. Of course, he thrilled the Armory tribe with his praise of Spinnaker’s “decoupling of high level primitives,” and I can share some key benefits that Kelsey highlighted:
Even with many different build systems, you can consolidate deployments #withSpinnaker. Each can notify Spinnaker to pick up new artifacts as they are ready.
Spinnaker’s application-centric approach helps achieve continuous delivery buy-in. It gives application owners the control they crave, within automated guardrails that serialize your software delivery culture.
Building manual judgements into heavy deployment automation is a “holy grail” for some. #WithSpinnaker, we can end the fallacy of “just check in code and everything goes to prod.” We can codify the steps in between as part of the pipeline.
Spinnaker uses the perfect integration point. It removes the brittleness of scripting out the transition between a ‘ready-to-rock’ artifact and an application running in production.
Kelsey’s words have profound impact. He did give some practical advice, like “Don’t run Spinnaker on the same cluster you’re deploying to,” and of course, keep separate build and deploy target environments. But the way Kelsey talked about culture struck a chord. We called the meetup, “Serializing culture into continuous delivery,” and in his story, Kelsey explained that culture is what you do: the actions you take as you work; your steps in approaching problems.
Yes, please!
I’m reminded of working on a team struggling with an “agile transformation” through a series of long, circular discussions. I urged my team, “Scrum is just something that you do!” You go to standups, and do demos. You get better at pointing work over time. The ceremonies matter because you adapt by doing the work.
Kelsey says his doing approach starts with raising his hand and saying, “I would like to own that particular problem,” and then figuring it out as he goes. Really owning a problem requires jumping in to achieve a deep understanding of it. It’s living it, and sharing with others who have lived it. We can BE our culture by learning processes hands-on, digging into the business reasons behind constraints, and using that knowledge to take ownership. Hiding behind culture talk doesn’t cut it, since you have to do it before you can change it.
“The return on investment is totally worth it”
Another important way of doing: recognizing when you don’t know how to do it and need some help. Powerful open source projects like Kubernetes and Spinnaker can become incredibly complicated to implement in a way that faithfully serializes your culture. Responsible ownership means getting the help you need to execute.
I love how Kelsey juxtaposed the theatrics and hero mythology behind change management and outage “war rooms” with the stark truth of the business needs behind our vital services. As Kelsey shared his Ops origins story, I recalled my own – the rocket launch music that played in my head the first time I successfully restarted the java process for an LMS I held the pager for, contrasted with the sick feeling I got when reading the complaining tweets from university students who relied on the system and had their costly education disrupted by the outage. I knew the vast majority of our students worked full time and paid their own way, and that many had families to juggle as I do. This was the real story of our work. It drove home the importance of continuous improvement, and meant that our slow-moving software delivery culture frustrated the heck out of me.
Kelsey’s LOL simulation of the Word doc deployment guide at his first “real” job. Got a deployment horror story about a Word-copied command with an auto-replaced en-dash on a flag not triggered until after database modification scripts had already run? I do!
So what do you do if you’re Kelsey? You become an expert at serializing a company’s decisions around software delivery and telling them, as a quietly functioning story, with the best-in-class open source and Google tooling of the moment. He tells the story of his first automation journey: “So I started to serialize culture,” he says, when most of the IT department left him to fend for himself over the winter holidays. Without trying to refactor applications, he set to work codifying the software delivery practices he had come to understand through Ops. He automated processes, using tools his team felt comfortable with.
He said, “We never walked around talking about all of our automation tools,” and that’s not a secrecy move, it’s his awareness of cognitive dissonance and cognitive overload. Because he had created a system based on application owners expectations, their comfort zone, he didn’t need to talk about it! It just worked (better and more efficiently over time, of course), and fulfilled the business case. Like Agile, this approach limits the scope of what one has to wrap their brain around to be excellent. Like Spinnaker, it empowers developers to focus on what they do best.
Instead of talking about the transformation you need, start by starting. Then change will begin.
By Forest Jing, Jenkins Ambassador and JAM organizer in China
On February 29, 2020, the first CI/CD Meetup in China was successfully held online. The atmosphere of this online live streaming event was hot and welcomed. There were more than 5,000 people and 27,000 pageviews! Several CI/CD experts have shared the practices about CI, CD, and DevOps. Although affected by the COVID-19, but it could not stop everyone’s passion of learning.
CI/CD Meetup is a global community event hosted by the Continuous Delivery Foundation (CDF), which aims to build a CI/CD ecosystem and promote CI/CD related practices and open source projects. The CI/CD Meetup in China is co-organized by Jenkins Ambassador Shi Xuefeng, Lei Tao, and Jing Yun who are also organizers of Jenkins Area Meetup in China. And DevOps Times community and GreatOps community are co-organizer of the event. We hope we could introduce CI/CD to more Chinese IT companies to improve their IT performance.
More than 5,000 people online and 27,000 pageviews Everyone is enthusiastic to leave messages and interact
Everyone likes the content and is curious to ask the lecturers: “As a programmer, why do you all have so luxuriant hairs?”
Details from the live broadcast content
Topic 1· CI/CD Practice of Large Mobile App
Shi Xuefeng, Engineering Efficiency Director of JD.COM,Jenkins Ambassador and Core author of DevOps Capability Maturity Model
First of all, Shi Xuefeng brought the wonderful topic of “Large Mobile App CI/CD.”
In the mobile era, mobile applications have become the main battlefield of business. In this activity, Xuefeng shared how is the CI/CD of a super large app is designed and implemented.
Topic 2· The implementation and practice of Agile && DevOps at CITIC Bank
Shi Lilong,Senior Expert, Software Development Center, CITIC Bank
Subsequently, Shi Lilong, a senior expert at the software development center of CITIC Bank, brought a wonderful sharing of “the implementation and practice of Agile and DevOps in CITIC Bank”.
Mr. Shi Lilong shared the overall promotion of CITIC Bank in Agile and DevOps, and the end-to-end tool chain of CITIC Bank.
Topic 3: How do large-scale financial and Internet companies conduct product library management?
Wang Qing,JFrog Chief Architect in China
Wang Qing, Chief Architect of JFrog China, brought a wonderful sharing of “How do large financial and Internet companies manage product libraries?”
Due to the large number of R & D personnel and large types of products delivered by large financial companies and Internet companies, the application dependent libraries and product libraries have become complicated and difficult to manage. After the implementation of many enterprise-level user product libraries, the advanced functions of the work-in-progress library solve the above problems and open up the second pulse of continuous delivery.
Topic 4: Watch out! 10 obstacles in DevOps Transformation
Shi Jingfeng, Senior DevOps expert in GreatOPS Community
Mr. Shi Jingfeng brought a wonderful sharing of “Watch out! 10 obstacles in DevOps Transformation.”
During the these days, many companies have started to work from home. Various obstacles appeared on the first day of WFH. The conference system was unstable, VPN connection was not available, remote desktops were queued, and the phone was busy. The implementation of DevOps seemed make all of these very easy . Jingfeng thinks that DevOps is like a journey, there are both beautiful attractions and obstacles. It is difficult to save yourself by not paying attention to the obstacles? How these pain points are addressed based on the DevOps Capability Maturity Model.
DevOps Capability Maturity Model
Experts Q&A
The last topic is a CI/CD expert question and answer part. All experts will answer the questions raised.
Experts solve problems for everyone online
Finally, the last group photo of the experts, the CI/CD Meetup online salon was successfully held.
This event was co-sponsored by the CDF, DevOps Times community, and GreatOPS community. Thanks to the strong support of JFrog and Tencent Cloud Community.
The last story of the first CI/CD Meetup in China.
Jenkins Ambassadors
Shi Xuefeng (BC), Lei Tao and Forest Jing are the Jenkins Ambassador who are always organizing JAM in China. We all visited DevOps World Lisbon. At the event, we met Kohsuke Kawaguchi and Alyssa Tong. So we discussed to introduce CI/CD Meetup into China. It is a fantastic event.
Chinese DevOps Experts with KK in DevOps World Lisbon
From Dailymotion, a French video-sharing technology platform with over 300 million unique monthly users
At Dailymotion, we are hosting and delivering premium video content to users all around the world. We are building a large variety of software to power this service, from our player or website to our GraphQL API or ad-tech platform. Continuous Delivery is a central practice in our organization, allowing us to push new features quickly and in an iterative way.
We are early adopters of Kubernetes: we built our own hybrid platform, hosted both on-premise and on the cloud. And we heavily rely on Jenkins to power our “release platform”, which is responsible for building, testing, packaging and deploying all our software. Because we have hundreds of repositories, we are using Jenkins Shared Libraries to keep our pipeline files as small as possible. It is an important feature for us, ensuring both a low maintenance cost and a homogeneous experience for all developers – even though they are working on projects using different technology stacks. We even built Gazr, a convention for writing Makefiles with standard targets, which is the foundation for our Jenkins Pipelines.
In 2018, we migrated our ad-tech product to Kubernetes and took the opportunity to set up a Jenkins instance in our new cluster – or better yet move to a “cloud-native” alternative. Jenkins X was released just a few months before, and it seemed like a perfect match for us:
It is built on top and for Kubernetes.
At that time – in 2018 – it was using Jenkins to run the pipelines, which was good news given our experience with Jenkins.
It comes with features such as preview environments which are a real benefit for us.
And it uses the Gitops practice, which we found very interesting because we love version control, peer review, and automation.
While adopting Jenkins X we discovered that it is first a set of good practices derived from the best performing teams, and then a set of tools to implement these practices. If you try to adopt the tools without understanding the practices, you risk fighting against the tool because it won’t fit your practices. So you should start with the practices. Jenkins X is built on top of the practices described in the Accelerate book, such as micro-services and loosely-coupled architecture, trunk-based development, feature flags, backward compatibility, continuous integration, frequent and automated releases, continuous delivery, Gitops, … Understanding these practices and their benefits is the first step. After that, you will see the limitations of your current workflow and tools. This is when you can introduce Jenkins X, its workflow and set of tools.
We’ve been using Jenkins X since the beginning of 2019 to handle all the build and delivery of our ad-tech platform, with great benefits. The main one being the improved velocity: we used to release and deploy every two weeks, at the end of each sprint. Following the adoption of Jenkins X and its set of practices, we’re now releasing between 10 and 15 times per day and deploying to production between 5 and 10 times per day. According to the State of DevOps Report for 2019, our ad-tech team jumped from the medium performers’ group to somewhere between the high and elite performers’ groups.
But these benefits did not come for free. Adopting Jenkins X early meant that we had to customize it to bypass its initial limitations, such as the ability to deploy to multiple clusters. We’ve detailed our work in a recent blog post, and we received the “Most Innovative Jenkins X Implementation” Jenkins Community Award in 2019 for it. It’s important to note that most of the issues we found have been fixed or are being fixed. The Jenkins X team has been listening to the community feedback and is really focused on improving their product. The new Jenkins X Labs is a good example.
As our usage of Jenkins X grows, we’re hitting more and more the limits of the single Jenkins instance deployed as part of Jenkins X. In a platform where every component has been developed with a cloud-native mindset, Jenkins is the only one that has been forced into an environment for which it was not built. It is still a single point of failure, with a much higher maintenance cost than the other components – mainly due to the various plugins.
In 2019, the Jenkins X team started to replace Jenkins with a combination of Prow and Tekton. Prow (or Lighthouse) is the component which handles the incoming webhook events from GitHub, and what Jenkins X calls the “ChatOps”: all the interactions between GitHub and the CI/CD platform. Tekton is a pipeline execution engine. It is a cloud-native project built on top of Kubernetes, fully leveraging the API and capabilities of this platform. No single point of failure, no plugins compatibility nightmare – yet.
Since the beginning of 2020, we’ve started an internal project to upgrade our Jenkins X setup – by introducing Prow and Tekton. We saw immediate benefits:
Faster scheduling of pipelines “runners” pods – because all components are now Kubernetes-native components.
Simpler pipelines – thanks to both the Jenkins X Pipelines YAML syntax and the ability to easily decouple a complex pipeline in multiple small ones that are run concurrently.
Lower maintenance cost.
While replacing the pipeline engine of Jenkins X might seem like an implementation detail, in fact, it has a big impact on the developers. Everybody is used to see the Jenkins UI as the CI/CD UI – the main entry point, the way to manually restart pipelines executions, to access logs and test results. Sure, there is a new UI and a real API with an awesome CLI, but the new UI is not finished yet, and some people still prefer to use web browsers and terminals. Leaving the Jenkins Plugins ecosystem is also a difficult decision because some projects heavily rely on a few plugins. And finally, with the introduction of Prow (Lighthouse) the Github workflow is a bit different, with Pull Requests merges being done automatically, instead of people manually merging when all the reviews and automated checks are green.
So if 2019 was the year of Jenkins X at Dailymotion, 2020 will definitely be the year of Tekton: our main release platform – used by almost all our projects except the ad-tech ones – is still powered by Jenkins, and we feel more and more its limitations in a Kubernetes world. This is why we plan to replace all our Jenkins instances with Tekton, which was truly built for Kubernetes and will enable us to scale our Continuous Delivery practices.
By Tracy Ragan, CEO of DeployHub, CD Foundation Board Member
Microservice pipelines are different than traditional pipelines. As the saying goes…
“The more things change; the more things stay the same.”
As with every step in the software development evolutionary process, our basic software practices are changing with Kubernetes and microservices. But the basic requirements of moving software from design to release remain the same. Their look may change, but all the steps are still there. In order to adapt to a new microservices architecture, DevOps Teams simply need to understand how our underlying pipeline practices need to shift and change shape.
Understanding Why Microservice Pipelines are Different
The key to understanding microservices is to think ‘functions.’ With a microservice environment the concept of an ‘application’ goes away. It is replaced by a grouping of loosely coupled services connected via APIs at runtime, running inside of containers, nodes and pods. The microservices are reused across teams increasing the need for improved organization (Domain Driven Design), collaboration, communication and visibility.
The biggest change in microservice pipeline is having a single microservice used by multiple application teams independently moving through the life cycle. Again, one must stop thinking ‘application’ and think instead think ‘functions’ to fully appreciate the oncoming shift. And remember, multiple versions of a microservice could be running in your environments at the same time.
Microservices are immutable. You don’t ‘copy over’ the old one, you deploy a new version. When you deploy a microservice, you create a Kubernetes deployment YAML file that defines the Label and the version of the image.
In the above example, our Label is dh-ms-general. When a microservice Label is reused for a new container image, Kubernetes stops using the old image. But in some cases, a second Label may be used allowing both services to be running at the same time. This is controlled by the configuration of your ingresses. Our new pipeline process must incorporate these new features of our modern architecture.
Comparing Monolithic to Microservice Pipelines
What does your life cycle pipeline look like when we manage small functions vs. a monolithic applications running in a modern architecture? Below is a comparison for each category and their potential shift for supporting a microservice pipeline.
Change Request
Monolithic:
Logging a user problem ticket, enhancement request or anomaly based on an application.
Microservices:
This process will remain relatively un-changed in a microservice pipeline. Users will continue to open tickets for bugs and enhancements. The difference will be sorting out which microservice needs the update, and which version of the microservice the ticket was opened against. Because a microservice can be used by multiple applications, dependency management and impact analysis will become more critical for helping to determine where the issue lies.
Version Control
Monolithic:
Tracking changes in source code content. Branching and merging updates allowing multiple developers to work on a single file.
Microservices:
While versioning your microservice source code will still be done, your source code will be smaller, 100-300 lines of code versus 1,000 – 3,000 lines of code. This impacts the need for branching and merging. The concept of merging ‘back to the trunk’ is more of a monolithic concept, not a microservice concept. And how often will you branch code that is a few hundred lines long?
Artifact Repository
Monolithic:
Originally built around Maven, an artifact repository provides a central location for publishing jar files, node JS Packages, Java scripts packages, docker images, python modules. At the point in time where you run your build your package manager (maven, NPM, PIP) will perform the dependency management for tracking transitive dependencies.
Microservices:
Again, these tools supported monolithic builds and solved dependency management to resolve compile/link steps. We move away from monolithic builds, but we still need to build our container and resolve our dependencies. These tools will help us build containers by determining the transitive dependencies need for the container to run.
Builds
Monolithic:
Executes a serial process for calling compilers and linkers to translate source code into binaries (Jar, War, Ear, .Exe, .dlls, docker images). Common languages that support the build logic includes Make, Ant, Maven, Meister, NPM, PIP, and Docker Build. The build calls on artifact repositories to perform dependency management based on what versions of libraries have been specified by the build script.
Microservices:
For the most part, builds will look very different in a microservice pipeline. A build of a microservice will involve creating a container image and resolving the dependencies needed for the container to run. You can think of a container image to be our new binary. This will be a relatively simple step and not involve a monolithic compile/link of an entire application. It will only involve a single microservice. Linking is done at runtime with the restful API call coded into the microservice itself.
Software Configuration Management (SCM)
Monolithic:
The build process is the central tool for performing configuration management. Developers setup their build scripts (POM files) to define what versions of external libraries they want to include in the compile/link process. The build performs configuration management by pulling code from version control based on a ‘trunk’ or ‘branch. A Software Bill of Material can be created to show all artifacts that were used to create the application.
Microservices:
Much of what we use to do for configuring our application occurred at the software ‘build.’ But ‘builds’ as we know them go away in a microservice pipeline. This is where we made very careful decisions about what versions of source code and libraries we would use to build a version of our monolithic application. For the most part, the version and build configuration shifts to runtime with microservices. While the container image has a configuration, the broader picture of the configuration happens at run-time in the cluster via the APIs.
In addition, our SCM will begin to bring in the concept of Domain Driven Design where you are managing an architecture based on the microservice ‘problem space.’ New tooling will enter the market to help with managing your Domains, your logical view of your application and to track versions of applications to versions of services. In general, SCM will become more challenging as we move away from resolving all dependencies at the compile/link step and must track more of it across the pipeline.
Continuous Integration (CI)
Monolithic:
CI is the triggered process of pulling code and libraries from version control and executing a Build based on a defined ‘quiet time.’ This process improved development by ensuring that code changes were integrated as frequently as possible to prevent broken builds, thus the term continuous integration.
Microservices:
Continuous Integration was originally adopted to keep us re-compiling and linking our code as frequently as possible in order to prevent the build from breaking. The goal was to get to a clean ’10-minute build’ or less. With microservices, you are only building a single ‘function.’ This means that an integration build is no longer needed. CI will eventually go away, but the process of managing a continuous delivery pipeline will remain important with a step that creates the container.
Code Scanning
Monolithic:
Code scanners have evolved from looking at coding techniques for memory issues and bugs to scanning for open source library usage, licenses and security problems.
Microservices:
Code scanners will continue to be important in a microservice pipeline but will shift to scanning the container image more than the source. Some will be used during the container build focusing on scanning for open source libraries and licensing while others will focus more on security issues with scanning done at runtime.
Continuous Testing
Monolithic:
Continuous testing was born out of test automation tooling. These tools allow you to perform automated test on your entire application including timings for database transactions. The goal of these tools is to improve both the quality and speed of the testing efforts driven by your CD workflow.
Microservices:
Testing will always be an important part of the life cycle process. The difference with microservices will be understanding impact and risk levels. Testers will need to know what applications depend on a version of a microservice and what level of testing should be done across applications. Test automation tools will need to understand microservice relationships and impact. Testing will grow beyond testing a single application and instead will shift to testing service configurations in a cluster.
Security
Monolithic:
Security solutions allow you to define or follow a specific set of standards. They include code scanning, container scanning and monitoring. This field has grown into the DevSecOps movement where more of the security activities are being driven by Continuous Delivery.
Microservices:
Security solutions will shift further ‘left’ adding more scanning around the creation of containers. As containers are deployed, security tools will begin to focus on vulnerabilities in the Kubernetes infrastructure as they relate to the content of the containers.
Continuous Delivery Orchestration (CD)
Monolithic:
Continuous Delivery is the evolution of continuous integration triggering ‘build jobs’ or ‘workflows’ based on a software application. It auto executes workflow processes between development, testing and production orchestrating external tools to get the job done. Continuous Delivery calls on all players in the lifecycle process to execute in the correct order and centralizes their logs.
Microservices:
Let’s start with the first and most obvious difference between a microservice pipeline and a monolithic pipeline. Because microservices are independently deployed, most organizations moving to a microservice architecture tell us they use a single pipeline workflow for each microservice. Also, most companies tell us that they start with 6-10 microservices and grow to 20-30 microservices per traditional application. This means you are going to have hundreds if not thousands of workflows. CD tools will need to include the ability to template workflows allowing a fix in a shared template to be applied to all child workflows. Managing hundreds of individual workflows is not practical. In addition, plug-ins need to be containerized and decoupled from a version of the CD tool. And finally, look for actions to be event driven, with the ability for the CD engine to listen to multiple events, run events in parallel and process thousands of microservices through the pipeline.
Continuous Deployments
Monolithic:
This is the process of moving artifacts (binaries, containers, scripts, etc.) to the physical runtime environments on a high frequency basis. In addition, deployment tools track where an artifact was deployed along with audit information (who, where, what) providing core data for value stream management. Continuous deployment is also referred to as Application Release Automation.
Microservices:
The concept of deploying an entire application will simply go away. Instead, deployments will be a mix of tracking the Kubernetes deployment YAML file with the ability to manage the application’s configuration each time a new microservice is introduced to the cluster. What will become important is the ability to track the ‘logical’ view of an application by associating which versions of the microservices make up an application. This is a big shift. Deployment tools will begin generating the Kubernetes YAML file removing it from the developer’s to-do list. Deployment tools will automate the tracking of versions of the microservice source to the container image to the cluster and associated applications to provide the required value stream reporting and management.
Conclusion
As we shift from managing monolithic applications to microservices, we will need to create a new microservice pipeline. From the need to manage hundreds of workflows in our CD pipeline, to the need for versioning microservices and their consuming application versions, much will be different. While there are changes, the core competencies we have defined in traditional CD will remain important even if it is just a simple function that we are now pushing independently across the pipeline.
About the Author
Tracy Ragan is CEO of DeployHub and serves on the Continuous Delivery Foundation Board. She is a microservice evangelist with expertise in software configuration management, builds and release. Tracy was a consultant to Wall Street firms on build and release management for 7 years prior to co-founding OpenMake Software in 1995. She was a founding member of the Eclipse organization and served on the board for 5 years. She is a recognized leader and has been published in multiple industry publications as well as presenting to audiences at industry conferences. Tracy co-founded DeployHub in 2018 to serve the microservice development community.
