For the last few months, here at KIE team we’ve been hard at work. Today I am proud to announce that our cloud-native business automation platform is hitting a major milestone. Today we release Kogito 1.0!
- the Drools rule language and decision platform,
- the jBPM workflow and process automation engine,
- the OptaPlanner constraint satisfaction solver;
and it brings along several new capabilities
- our fresh new unified BPMN and DMN editors and VSCode-based extension
- the new vendor-neutral Serverless Workflow Specification
- business-relevant insights on machine-assisted decisions through the contributions of the TrustyAI initiative
- automated deployment through the Kogito Operator and the
- noSQL persistence through the Infinispan and the MongoDB addons
- GraphQL as the query language for process data
- microservice-based data indexing and timer management
- completely revisited UIs for task and process state
- CloudEvent for event handling
I believe there is a lot to be proud of, but I want to talk more about another thing that makes Kogito special, and that is the heavy reliance on code-generation.
In Kogito code-generation has a double purpose:
- we generate code ahead-of-time to avoid run-time reflection;
- we automatically generate domain-specific services from user-provided knowledge assets.
Together, Kogito delivers a truly low-code platform for the design and implementation of knowledge-oriented REST services.
In Kogito, we load, parse, analyze your knowledge assets such as rules, decisions or workflow definitions during your build-time. This way, your application starts faster and it consumes less memory, and, at run-time, it won’t do more than what’s necessary.
Compare this to a more traditional pipeline, where instead the all the stages of processing of a knowledge asset would occur at run-time:
The Cloud, albeit allegedly being «just someone else’s computer», is a deployment environment that we have to deal with. More and more businesses are using cloud platforms to deploy and run their services. Thus, because they are paying for the resources they use, they are caring more and more about them.
This is why application density is becoming increasingly more important: we want to fit more application instances in the same space, because we want to keep costs low. If your application has a huge memory footprint and high CPU requirements, it will cost you more.
While we do support Spring Boot (because, hey, you can’t really ignore such a powerhouse), we chose Quarkus as our primary runtime target, because through its extension system, it lets us truly embrace ahead-of-time code generation.
Whichever you choose, be it Spring, or Quarkus, Kogito will move as much processing as possible at build time. But if you want to get the most out of it, we invite you to give Quarkus a try: through its simplified support to native image generation, allows Kogito to truly show its potential, producing the tiniest native executables. So tiny and cute, they are the envy of a gopher.
Kogito cuts the fat, but you won’t lose flavor. And if you pick Quarkus, you’ll get live code reload for free.
Although build-time processing is a characterizing trait of Kogito, code-generation is also key to another aspect. We automatically generate a service starting from the knowledge assets that users provide.
You write rules, a DMN decision, a BPMN process or a serverless workflow: in all these cases, in order for these resources to be consumed, you need an API to be provided. In the past, you had full access to the power of our engines, through a command-based REST API for remote execution or through their Java programmatic API, when embedding them in a larger application.
While programmatic interaction will always be possible (and we are constantly improving it in Kogito to make it better, with a new API), in Kogito we aim for low-code. You drop your business assets in a folder, start the build process, and you get a working service running.
In the animation you see that a single DMN file is translated into an entire fully-functional service, complete with its OpenAPI documentation and UI.
Through the Kogito Operator you are also able to go from a knowledge asset to a fully-working service in a matter of one click or one command. In this animation you can see the
kogito cli in action: the operator picks up the knowledge assets, builds a container and deploys it to OpenShift with just 1 command!
For local development, the [Kogito Quarkus extension][qex] in developer mode extends Quarkus’ native live code reloading capabilities going further from reloading plain-text source code (a feature in Quarkus core) to adding support to hot reload of graphical models supported by our modeling tools. In this animation, for instance you can see hot-reload of a DMN decision table.
In this animation, we update a field of the decision table. As a result, the next time we invoke the decision, the result is different. No rebuild process is necessary, as it is all handled seamlessly by the Kogito extension. You get the feeling of live, run-time processing, but under the hood, Quarkus and Kogito do the heavy lifting of rebuilding, reloading and evaluating the asset.
In the future we plan to support customization of these automatically-generated services, with a feature we call scaffolding. With scaffolding you will also be able to customize the code that is being generated. You can already get a sneak peek of this preview feature by following the instructions in the manual.
Kogito 1.0 brings a lot of new features, we are excited for reaching this milestone and we can’t wait to see what you will build! Reach out for feedback on all our platforms!