Red Hat OpenShift OpenShift Commons Briefings, 19 Apr 2021

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From YouTube: Event-driven Applications with Kogito Serverless Workflows and Knative - Ricardo Zanini (Red Hat)

Description

Event-driven Applications with Kogito Serverless Workflows and Knative
Guest Speaker - Ricardo Zanini (Red Hat)
hosted by Diane Mueller (Red Hat)
2021-04-19
OpenShift Commons Briefing
#AMA #Upstream
see full calendar here: https://commons.openshift.org/events.html
link to slides:
https://github.com/openshift-cs/commons.openshift.org/blob/master/briefings/slides/Event-driven%20Applications%20with%20Kogito%20Serverless%20Workflows%20and%20Knative%20%5B2021%5D.pdf

A

All right, everybody welcome back to another openshift commons briefing. Today we have ricardo, zanini who's from red hat and a senior software engineer and we're going to have a topic that I don't really know a lot about, because I haven't heard a lot about it, which is why we've invited him here to talk about event, driven applications using pagido, server-less workflows with nk native and so okay native we've had a lot of talks about, but is, I think, I'm pronouncing it mashing up the name wrong, but we'll?

A

Let ricardo tell us a lot more about this and then we'll have live q a um after the presentation and the demo that he's going to give so take it away. Ricardo and um introduce yourself tell us what you do at red hat and um tell us all about this.

B

Sure, yes, uh thanks for having me guys uh well, my name is ricardo, like diana said uh today we're going to talk about even driven applications and how we can create those. You know business applications using the serverless, workflow specification and the okay native uh platform. uh Well, I'm a software engineer.

B

I work with the cogito actually uh project and uh I'm also helping the cncf serverless workflow uh project specification as well, so uh we work in the both ends in the doing the specification side, you know the specific specifying the workflow uh dsl and also in the implementation in the coaching engine.

B

Well, let's get it started. Let me see if I can.

C

B

Right uh in the genome, today I'm going to introduce you guys the servos workflow uh specification and what we are doing there. You know just a brief uh uh introduction to the to the project. I I will try to not talk too much about it, uh because there's a lot of facilities that you, you should take a look in the specification and figure yourself for all other details and uh about the implementation itself that we are working on right now.

B

That is the coach, the project that we call uh it is a business application uh project for you know for the for designing, creating uh business applications. And after that uh I will introduce a small use case. You know if, for someone to feel comfortable, uh it is like a online store or the processing. So it is super simple use case.

B

Everyone is super familiar, I hope so and uh we are going to run a super short demo as well uh exemplifying how the the workflow is reacting from events and what he is doing uh on a k native overall uh platform.

B

So about the the cncf serverless workflow specification. uh We we say cncf because the serverless workflow project it is a um a sandbox uh project on cncf uh institution. So you can go there in the sandbox project and you see the server's workflow specification project in there and we are accepting contributions and then it is open source patch to licensing. You can go there and then you know figure things out, ask questions and.

B

Maybe introduce some new features that you like, or, if you're for him, to implement the specification as well. We can help uh we have sdks. We have a lot of tools around the specification that can help. You know to understand, to implement maybe use the specification in your company as well. So what it is, the um the service workflow specification. It is something that we are targeting. It is uh to create a declarative, workflow language so uh based and to the target for a specifically serverless computing technology domain.

B

So um that means uh reading events producing events consuming event. Correlating them call functions outside the the scope.

B

um Have all those things run smoothly, we can um out escalate the the functions you know up and down and do all those features that you already know about service computing, but within you know having this in mind in the in the in the workflow language as well. uh This quote, I I I brought from the website, so you can go there and visit the website and understand what it is uh with more. You know details about that. uh Why why we start with this no crazy project?

B

Why why it is important for us well, first, that we believe that workflows can capture and organize business requirements. So what it is you can? um You know instead of grab your maybe java, go or python code and bring to your business analyst and explain today: hey here's. What we are doing this service here is what is going on there.

B

um You know look at this, these conditionals here we are doing this and that, instead of that, you can just bring the workflow um to your business to your business person and explain to them what is happening there and they can understand. They can even help you, you know design their workflow or they can even uh create it work for themselves and handle it to you in order to have that run into uh a runtime. Maybe so that's uh one of the the the idea behind it.

B

um The specification also targets the uh be a vendor neutral platform, independent uh workflow language. So what this means means that we do not tie the specification with a specific vendor. It is not code. It's not anything like that. It is just the specification anyone out there can take the specification implements in your runtime and have a workflow runtime running. uh You know, based on the specification it is platform dependent because we're not tied with any specific technology.

B

We do not say hey uh the the workflow must be implemented in java, for instance, this is not the case, so anyone can just bring the specification and share and use that as they please in the in their in their implementations.

B

So uh imagine that having this common way of describing our workflow, we can, you know, potentialize and create creating some common libraries to be shared among uh all the the the runtimes implement, the implementations itself. uh We can create tooling uh around it influx infrastructure around it around all the workflow and having all of them.

B

You know to have a common way to describe something and and create something together. That's the the the the nice of the being on open source uh specification so uh like, like I said, being vendor neutral, uh we increase portability, productivity and learning curve. You don't have to learn all of those uh workflow language that is, that is out there, because no nowadays, uh if you go uh in the iit news, you see that every day every week we have a new uh workflow engine out there.

B

So, instead of that, uh we are proposing this like on a specification that can be run in in lots of runtimes and those runtimes can be. You know, offered by uh to be run on kubernetes on openshift. You know, google cloud can have uh a runtime for the specification as well iws azure, whatever cloud provided out there, they can offer uh their own uh workflow um service b. Based on the specification.

B

That's a our wingy situation that we believe you know that everyone uh implements the the the same uh dsl for specifying workflows, so the users would uh gain a lot of that.

B

uh The specification is based on standards, so uh we looked around and saw a lot of standards out there so, for instance, to define an event like I want to consume this event.

B

In order to start my workflow or I want to produce an event at the end of my of my workflow or I want to produce an event if I go to through this branch on my workflow and uh we use the cloud event specification to define this event uh uh in the workflow uh definition uh file in the workflow definition itself, you can also run and and not run but call uh external restful services for ins, uh for instance, using the workflow specification, and uh we use the open api to declare how you can.

B

You know create uh those calls from the workflow itself. So when you, when you, for instance, you you want to call when you you receive an order, you want to call an external service to validate your order to do something else, and it is a restful uh end point you can. You know express that call using the open api specification. So you you you, you bring uh the open api interface into the to the workflow when you specify uh which um function in there.

B

That is defined in the in the open api specification that you you wish to call uh using the workflow uh dsl. Also. It is, of course, based on workflow patterns, so there is execution, handle uh order, error, handling data management, data transformation. So there's a lots of uh patterns that we envision in the that we review as useful to be to to have in the dsl.

B

uh In you know, in the specification itself, you can see plots uh all those informations is in this link here uh that it goes to the specification and you see all the features that we have in there.

B

Well, uh the workflow itself is based on a state machine diagram. It is, I say that it's based, because you know uh we have start and end uh conditions in in our in our workflow. So when you, you start and you go from stage to state, you know in a fluent manner, so each state is responsible to do something.

B

It is responsible, for instance, to produce an event or to call an external function or to decide uh to split between uh two brands or to go to one branch or to go to another branch.

B

I can call not another subflow, for instance, and there's a lot of uh type of states that we that we we have in the specification and you, as I said you can go to the specifications see in the details, everyone that we support that that we have in there and if you, if you feel like that, we don't have something that it might be interesting.

B

You can propose, you, know a pr or send or reach out out to us and say hey this is this should be nice to have a specification, and we can, you know, talk uh together and figure that out uh for the data processing itself, it is like you know, uh you enter with data and you go out with data so uh and in between you have this pipeline of states.

B

You know uh going through one after each other and then you can have like uh a data processing uh with the data that you that you're expecting to receive um on your workflow.

B

uh The specification has like those three main: uh this is the backbone of a workflow. Let's say this way: uh we have functions, events and, of course, the control logic of the work. Well, the the first thing is the functions uh we define. The functions is, uh uh for instance, a call to a restful service or a car to a grfc uh api as well.

B

So you can define the functions, the the the spec file of the open api, for instance, the id of the function that you'd like to call uh in this section of the workflow. uh In the event section, you declare the how you're going to consume and how you're going to produce uh the those events uh within your workflow. So, for instance, you you you're listening to our ordered event and you're, producing maybe shipping events or you're producing approved order, approved events or in um in a scenario like a bid service.

B

You were listening to bid events and you're, producing maybe notifications for users that are listening to that feed, for instance. So you can, you know, go crazy and do whatever declaration of events and uh as you please, as your business needs uh in this section of the workflow and it states it is not the least, but not the least important uh part of the the workflow control launch.

B

That is where you describe the workflow and what this workflow is doing, like uh the the data processing and how things are are, uh are going to to go they're like calling these dysfunctions or producing these events that are declaring there or uh consuming events that that you declare or branching the the the workflow.

B

You know the the the run of the workflow into uh more subflows, of the whatever control logic that you come up uh with, or your business need um in this uh part the section of the workflow, uh as as always uh always like that that I I linked here uh to a specific session in the in the in the in the specification.

B

So you can go there and see for yourself all of the details of each session and understand um more in details of what what we are going trying to express in here I'll try to be uh brief, because uh the the presentation can be long. uh If I stay in on one or other details.

B

Well, um every time that we like present the specification, talk about specification events, people ask about implementation so um well, it is nice to have a specification, and it is also have now it's nice to have an implementation right, because uh that's the point of having a specification, you don't you don't just create a specification to be in there. You create specification implemented.

B

So um we we took this specification uh now uh talking about the coached projects. We thought we took the the specification say: hey, uh there's a petition here when we can actually implement this a specification and offer to our users a way of then to declare uh uh their workflow using json remo files in a declarative way, and also if there are other vendors out there, implementing this specification, they can migrate to kojita uh easily.

B

You know using the same the same thing that they learn uh with this with this dsl uh in other implementations, with kojitsu as well. So uh this is a work in progress, so uh we are in in tech preview. Actually, uh right now, um you know community community wise. Of course, there is uh some limitations of the of the implementation that we that we also we are working towards to implement, but uh the basic things uh are there and I'm going to show you guys what about a little bit uh about the code itself.

B

It is. uh I I like to call this like a cloud native solution. You know um it is a project with lots of components. So I'm calling like a cloud native solution for you to build business applications, so business applications that you can uh solve business problems and then what I mean by that it is like uh we have an engine uh with that is capable to process rules in a specific domain or even the assault.

B

So uh imagine that you're, your insurance company and you want to define uh how you're going to process that you know super uh details, lots of computations and calculations that it is more or less cumbersome to have that. You know in your in your in your code base, so you're externalizing rules and you have all all of them defined on those on those rules, and you can give to the engineer in order to run it. um You have also. We have also an engine um capable to run complex decisions based on the dmn um standard.

B

uh We have, of course, serverless orchestration uh feature that is super brand new, something that we are working now, that is, the implementation of the cnscf uh service workforce. In a specification we also have an engine capable to run bpmn uh based uh files, um and we offer some tools uh focused on cloud for so far you can bring everything all your business applications that you created with conjured to the cloud, so um we have a specific tooling for that.

B

um We have like editors, since it is a business application driven project, and it is uh of course we we have editors for you to design and create your bpmn uh process for you to create and design your dmn um decisions as well your your rules and that you can download on a vs code uh marketplace. We have an online editor as well. If you, if you go bpmn.new or dmn.new, you see our online editor there and you can start. uh You know, scratching your process or your dma right away.

B

uh You can also download from our website this standalone editors. uh We are an engine like I said, based on drew's gbpm and optic planner. It's you, um of course, if you're a long-term java developer. Of course, you should uh heard about those uh during your career like through pm, they're super uh famous yeah popular. Let's see uh uh that's correct, the correct word: uh there's super popular uh frameworks out there and children rules and process babies on vpn.

B

um We are also working on supporting services uh capable of you know, giving support for your business application. So, for instance, if you run a process- and you like to see what is happening inside that process, we have some supporting services that is capable to run on cloud.

B

That can give you a glimpse of the process itself and what's going on out there or even a user task, for instance, in in a given process, you can go uh uh in this supporting service and see the tasks and then you know approve a task or go forward with a process and stuff and etc. So there is a lot of features out there in those supporting services. As well and uh like I said some cloud tools, uh we have a a specific kubernetes operator for deploying codesystem.

B

Applications on openshift are on kubernetes and I'm going to use the the in in the demonstration. um So we we bring and pack this all together and uh that's what we call the cogito project. So this is a a feature of you know, a handful components to help you design and create your business applications and deploy it on the cloud.

B

Let's go to the to the use case straightforward. It is imagine that uh you have this online store right and uh after new new orders, uh you create an an event in your in your architecture, every new order. We create a new event and then this event is complete, is being consumed by this order. Approval service, so the the services will be some sort of rules, and you know some sort of business requirements. They say if it's approved or not.

B

So that's the the the state of the after architecture right now and uh john doe, the ceo come to you and say hey uh before approving the the order I'd like to to go through a shipping and fraud verification first, so uh whatever comes uh a new water before approving it, we've been using whatever uh features that you have now um do a shipping and a fraud verification for me, please and um I'd like to to run that you know in parallel, because uh we can then uh concentrate the the approval in the in the in the end of the of this no order processing so and you came up with okay.

B

So uh what what about like? Creating a pre-processing workflow? So when it comes a new order event uh we are, we can, we are able to. You know, uh split uh this process inside. Like two events, you can create two events. uh After uh of this, this uh workflow um within this workflow, we create a shipping handling uh event and a fraud evaluation event. So uh each component in the architecture can listen to these events and react upon it and do whatever they want uh with that.

B

So in our example, we have the international international shipping validation service and we have the domestic shipping validation service. They are listening for the shipping events and we have the fraud evaluation service that is listening for the fraud evaluation service.

B

uh Once we finish uh with the shipping validation, we meet a a new event, saying like shipping verified or something like that and after the fraud evaluation service, you know is finished. Like you can say, hey this. This looks like a fraud does not look like looks like a fraud can be anything and in the end we have our order approval service again, and you can correlate all those events you know by like, for instance, the order id and say hey. I received the shipping approval.

B

I received the evaluation approval and I received this new order event, so I can correlate the three of them and then they can proceed doing whatever they want. So this is a a small excerpt for of a small processing use case in our example. So um I have like five uh services running around. None of them knows about each other. They just listen about events, so can be. You know a uh uh an event-driven architecture and even driven applications.

B

They they're just reacting upon events and emitting new events, and you can add new components in your architecture. So you can you you have this flexibility of creating things around your architecture, uh removing an event adding another. So, for instance, tomorrow you won't ship internationally anymore, and you just you know, remove the international shipping validation service.

B

There's no use anymore right. You can't you, you won't have to you, know, code, something new or anything like that. Just remove an event from the from your uh you just remove a service from your architecture, and you stop doing that thing or no. Well, I wish to add some new capabilities. uh So I add a new service um in order to listen this event and do whatever they want.

B

So this is more or less uh uh what uh an event-driven architecture can the benefits of an even driven architecture can give to. You know: async processing, this kind of flexibility. You know, composition and orchestration of events. That's why uh we we are talking today. You know like about the workflows, for instance, so let's try to zoom in into this first service, the water pre-processing workflow.

B

Let's see uh how it is doing uh its thing, so uh I draw this workflow like uh in order to pre-process my order, so I receive disney water event and I will process the order. I split into uh into two parallel uh um states saying I will handle fraud in one state and I will handle shipping in another state, so I can figure if I, uh which kind of events I need to you know produce uh for my architecture, so this is happening inside that uh preprocessing workflow service.

B

So this is more or less what looks like uh my workflow, let's see um how this workflow works and it is described how I'm describing this workflow um in a yearbook format using the serverless workflow specification.

B

So uh I I broke the workflow into three workflows. I have the main workflow, the water workflow, and uh I have one state here that we call the parlour state. You know calling those two subfloors, um the fraud handling workflow the shipping handling. So let's take a look at it. um The first uh part of the workflow it is the I call the headers, you know the when we we describe.

B

We we give some information about uh the workflow, so the name of the workflow, the identification of the workflow version for you to control the version of the workflow, um some description for it, and I I I will say uh which state I will start my workflow in this case the receive order.

B

The second part I have the events uh definition when I define how I I will consume that event. So let's say that in in this particular use case, I have the order event, type cloud event type. So someone out there, you know any service out there is creating, is producing uh uh this ordered event cloud event for me and I'm and I'm capable of listening to it.

B

So the first state that receives the receive order, it is capable of you, know, listen listening to this event, the other event here that we tie with the name like order event name. I I have the the the event by his name. It is the order event here and uh when I receive this event, I say transition to process order and this state process order, which I call 5 parallel. I will create the branch fraud handling shipping handling. Let's say my engine will call.

B

You know at the same time, in parallel those two sub subflows and once they finished, I end my workflow and um to create those uh workflows, I'm using the serverless workflow uh plugin and you can download it in the in in the vs code marketplace.

B

uh It is maintained by by the service workflow community, the specification community and you can go there, um see the source code do whatever you want, um download it, and this h5 uh the the plugin itself- and we have this nice feature that is, uh you can generate diagram using uh any workflow file, so it uses the you know this def simplification of the event uh diagram from the uml. So uh we have a start. We receive the order, it is a an event state.

B

We process the order in a parallel state and we create two branches. um So let's take a look into the first one, the fraud handling workflow same thing. I have the name the, which state that we started the version and the events that I'm going to produce. So I'm going to produce a type event: orders, dot, fraud, evaluation and, let's take a look into the states.

B

So we have a data conditioning here which is when we we start this workflow. uh We analyze the data inside the the the input of the of my workflow and we perform a json path expression into this into this data, and uh if the total of my order is is greater than one thousand, uh I would say that uh we need uh fraud, verification if it is under 1000. uh I I won't need front verification. Of course. This is super lame uh an example in real use case. You won't uh do things like that.

B

You can like create an estate before that, like you can call uh a service to evaluate uh some aspects of you know, taxes or um you know, uh behavior of the the customer. Whatever thing you want, you know, and then you can say to the the workflow hey this, this order looks like they need to perform, um is to be validated the defraud, or something like that.

B

um So for this case for to to simplify uh the conditions, um we have this event type switch, and I have these conditions in here very lame, but um it is. It is super nice and I inject into the into my data state, and you can a new a new attribute to my uh my workflow. The fraud evaluation equals true, um and then I produce the event throughout the evaluation, and this event contains in the data uh attributes everything that I created here uh in their workflow.

B

So it goes with the fraud evaluation equals true and the order information as well. So anyone that is interested in in the in their orders that fraud, evaluation and event can do something with it all right. So let's do the same thing that we did and let's generate diagram um using the the the word, the plugin and as you see, we see that we have like a fraud, handling switch state and, uh depending on the condition condition.

B

I'm going to you know uh say that we we're going to produce a fraud evaluation, if not otherwise, we just end uh our workflow. Well, very simple, and hopefully you you could understand uh what is happening there.

B

uh The other uh workflow is the shipping handling workflow that is uh being executed at the same time as the other one, more or less the same thing. We have now two types of events. We have international shipping order event and we have domestic shipping other events. So, depending on the context of my order, I will emit uh reproduce an event like an international event: a shipping order event or a domestic shipping event and more or less the same thing that we did there.

B

We have also sweet case, a sweet state case here and the first condition it is like uh it if, if it is uh within u.s, is it domestic shipping? If it is not, it is an international shipping. You know again um super lane uh an example in a real use case you can like call a shipping, maybe you can call it.

B

Maybe a google uh service, for you know for localization to find um the address the correct address and then to figure some other things out or whatever, and enhance uh your your order, data with some uh output for this service, for instance, uh but in this case after uh if we realize that this is a u.s um order, so we we transition to domestic shipping if it is international or outside u.s uh we handle for international shipping so for the automatic shipping.

B

I'm going to add a new attribute to my to my data, that is, shipping domestic. Otherwise, I'm adding a shipping international to my data and I will produce an international ship in order and I will produce a domestic shipping order again. uh Super simple, but uh you can, you know, see the the powerful work of something so imagine that you that you're talking with a business person and they can bring the ds out to them and explain hey. This is uh what is happening um inside the these microservices.

B

This is uh what what which kind of events that we are creating there you're consuming and what we are doing uh with it. So, for instance, in a real use case, you can say: hey, I'm calling this uh google service and we can receive this kind of uh information and we can process the information as as we please using the workflow.

B

Let's see how it looks, seeing the generated.

B

Diagram here, so we have this: uh the shipping handling state, it is a switch state and we go with it can be a domestic shipping or can be on international uh shipping as well as you see. In the end, we are going to um produce an event being international or domestic shipping whatsoever and we are going to win.

B

Well, uh that's what looked like uh the event when you when you, when you see it in this. In this perspective, and um of course this is the when you are designing and we are creating your workflow when it turns out to you, know, to deploy and to uh into a kubernetes or on our low bishops cluster, for instance, or in the cloud you can use the capabilities of the co2 we have cli.

B

So in this case you can deploy uh the workflow using this command line like conjugate, deploy. That is the the actual the verb of what we are doing order uh surplus workflow.

B

That is the name of the service that I gave and you can add all those emo files you know uh into the cli and the cli will push in this case, one openshift cluster and in there uh we are going to to generate code uh based on those files and we will create a an image according to crystal process runtime image uh into our internal web registry and the code operator uh will be capable to create the kubernetes resources and the canadian uh to deploy and and have this and to manage this workflow.

B

um You know running uh within uh the platform itself and, of course, uh for this use case specifically for this working with events with k native. We need the the serverless platform. This also works on kubernetes, but in kubernetes you, you can build uh within a cluster. You have to build the image yourself and then push the image to the cluster and the clutch to operator. You take your image and you do you perform the same steps?

B

You create the kubernetes resources, you create the native resources for you and you know for for your service to be connected uh with the keynative broker. um In this case, uh diane, I see that we have some. You know chat going on there uh you'd like me to stop here and or we can answer the questions after the.

A

Let's answer the questions at the end um and most of the chat is me trying to get people to ask questions. So this is a good point if people have questions type them into chat and um all right we'll make it happen at the end, thanks.

C

All right, no problem, yeah, I know if I'm I'm rushing or not. So you tell me uh all right.

B

When also we have the the service deployed on openshift, this is more sorry about the other technical details, but this is how it looks like uh from the cogito operator perspective, how we deploy uh the service in there, so uh our service will be like an event source or this event source can be any other thing.

B

In this case we have the the serverless workflow in here and we are listening to the new ordered events coming from the canadian broker and we consuming uh using canada eventing triggers and that triggers uh you know an event for us to listen to it. So uh the art element comes here into into the broker and we listen to it and we start uh our workflow. Our workflow will produce events to the broker uh using sync bindings, so uh we produce uh events to the candidate broker and any other service out there.

B

Any other you know uh can be a canadian service can be any deployable uh pod or anything else. You know in the in the in the spectrum of the kind of servant platform to be listening to this event and to react upon it, and that this is what we are going to see in this short demonstration. Let me see, um I think this is not in. Let me try to zoom that.

B

Well, can you guys see this.

A

Yep looks great.

C

B

So uh I'm I'm producing an event uh to the platform uh and can in the country, is u.s um if the total value is 1001 and is an iphone 12 uh order. So I'm going to push this event to the platform and you see uh what what what will happen uh in there so in in this um terminal. Here I have three uh listeners of our k native uh of ours, our workflow event producing events. So uh in the top most we have the fraud evaluation service.

B

So we are listening to the events from the front evaluation service. We have the um I'm sorry, uh the shipping international here in the left and the domestic shipping here in the right. So uh us one event comes to the either through the fraud evaluation or to the international or domestic shipping uh uh services. You will see the the produced event in here.

B

So let me push the event.

B

It is like a fire and forget uh uh type of event. It's taking some time, because my internet connection is not that good. So I push the event and we can see that uh we didn't have any pods and listening to that, because those are creative services and uh we receive a fraud evaluation because the the value is above uh a thousand dollars and uh we received a domestic shipping because the country that we ship the our um our iphone it is within u.s. It is our water from the u.s.

B

So that's why it is a domestic shipping and you can see me there and we we go to the to the openshift cluster. You can see. Hopefully you can see if my international connect, my international connection, helps help help.

A

It is a live demo, damn.

D

B

You you already saw those like you could, because I'm I'm I'm fetching for the pods of the shipping international service, and you see there is no resources in there and yeah. I don't know what is happening. I don't know if I need to uh refresh this, but whatever- uh and you see that is this event coming in here and this event coming in here.

B

So there is some extensions and as well, so we have some information about the the the process itself and here you can see that the fraud evaluation service received the fraud evaluation equals true and the domestic shipping service received the shipping domestic. You know and this shipping domestically does not. It is in here, but the front evaluation is not in here because we we run one after the other.

B

Well, let's see, if yeah we can see the deposit here and you and because we were talking uh canada will, you know, uh destroy the pods because they are not being used. So that's why we are seeing terminating me here.

B

So let's say that uh we have, I don't know, maybe um a new order from italy and that's above uh a thousand dollars as well, when I'm gonna send it quickly this time, because the pod is already there, uh I'm creating the ship international uh pod in order to to listen to this event and react upon it and the same thing, you won't see fraud nor domestic here, because we entering that that branch that we just receive you know from the international ship so now try to imagine this in a real use case where you you have.

B

You know to equalize your resources, you have to save resources in order to have you know a more profit, uh a a company. So uh in this case let's say that you receive, I don't know, maybe nine percent of your orders. They are there. They are domestic, so you don't have to have like the international uh shipping handling. You know, services, be you know up there, all the time uh you can have the workflow to you know, control all the logics and can break through into category categories.

B

You can break your domain into more specific things and you know uh be flexible enough to distribute your services around your your architecture and you won't have all the you know. uh You want waste resources into something that you want. You know need uh for some time, uh so this is a a a nice, a nice example where a nice use case for you to maybe uh reevaluate some of those things that we are doing because normally people create, like oh huge microservices.

B

They are doing a lot of things and maybe sometimes it is nicer just to break into you, know um specific parts and and make them you know run into this fashion like uh I can create, like lots of domestic orders or lots of international orders and having uh this part of my system not producing uh services on producing events, not whatsoever, so I can uh reduce uh my cost and sometimes that will help a lot. Well, let's get back, uh there's some much more things that I'd like to discuss.

B

There is no lots of uh of details and studies uh about it. I know that was you know, super quick and brief things. I introduced lots of concepts. You know, like uh the cloud events, uh sorry, the the the the service reform specification, the coach, the project, the houses deployed, the native there's lots of things out there and also lots of resources. You can uh go there in this on our websites and see what we are doing in the the the use case that we are capable to.

B

You know to help you uh solve your business problem so uh reach out to me as well. um I have twitter account. You can send me a dm message or send an email or reach out to into github or whatever I'm up there. uh You can see uh and our I I'd like. I would like also to you know, uh to ask you guys to take a look in the blog as well.

B

uh We are trying to update a blog like um twice a week um with new content and to give people an understanding of what we are doing in cogito and the business applications in general and how we can you can solve uh your business problems and, if you're curious about the demonstration the example itself, you can go into this we're holding here and see all the source code. uh You see the the the workflow I create some scripts in there for you to deploy in your kubernetes instance.

B

um I have we have the the image already in there. You know the docker file, sorry already in there, so you can go download the docker file run, uh use the image run, the the image locally. um There's you can play around this demonstration, whatever you want- and uh I was I'd like us to invite you to see this. uh The the the presentation from last year that t homier and I did uh tumor- is much better than me explaining uh the the cloud.

B

uh The service growth was specification and there uh we go through more details about the specification and, if you're curious about it, just go in there and, of course, the servos workflow.io website. You have all the links that you that you need to know there, our slack channel uh our main list, um our weekly meeting and everything else uh you see in the resources. Okay. Well, uh thank you! That's it! uh I don't know if we have any questions.

A

Or not, we've there's a couple of them in the chat so um and I'm going to try and I'm going to do them in reverse order, because I think you answered the earlier questions already um and eric um is asking. Why was k native chosen to increase the workload capabilities and not an auto scaler?

A

B

Just for the demonstration purposes, uh actually um uh we can use autoscaler. This is not that's, not a restriction whatsoever. I just I just.

B

I just found that um more simple to you enough to to to show to the to users uh what we can like what we can do and then in the example, you will see uh all the kind of resources for you to replicate uh the exact same uh thing into the into the into the in your um in your environment as well, so yeah, no, not particularly uh any any any reason whatsoever, but uh sorry, but for the canadian inventing part it is because uh the the coached engine, actually we we have these connectors and our the crystal operator as well uh is coded, and we have this support for the canadian uh eventing platform.

B

So we created a specific native eventing resources for it. So for the canadian venting part, that's that's why, but for the canadian serving part, no can be whatever thing uh the author's killer can be. You know regular deployments, whatever that's no particular reason cool.

A

All right and um that there's one other question from ilian: is it possible to create custom code.

B

Oh yeah, of course, because this is a a java uh project, it's a straightforward, maven java project in our in the in the example itself. I have the a redmi file here that I explained how, uh let's see how you can create this uh uh serverless workflow code to project uh from the from the scratch using uh using java. So you have to know java but yeah, you can add custom code and you can call this custom code uh using a workflow. We have a specific in the code implementation.

B

We have specific functions uh that you can declare in here, uh for you know like the you can give a name and then can in the metadata space we we have a. uh What do you call like a service uh internal calling? So you can you can call what any java being uh from the workflow so yeah you can do it, but uh from the from the specification point of view uh we have the jq capabilities.

B

So uh you can. You can like create a function expression and then you know externalize this. You know we can define here the function. That is an expression as a function function, and uh you can, you know, declare any if you're, if your custom code, it is now related to data uh handling with the the input you can just create uh the expression and in here in the workflow itself, and that will work.

B

A

Yes, so um and he's assuming you can call out to other services during um execution. Alien would like to be able to call out to fraud prevention api that they have so.

B

Yeah, absolutely let me let me share. Let me let me show you the specification itself yeah you can. You can call uh any any any functions. You know we have three in the specification. We expect the restful service, invocation, rfc, service, invocation and also expressions like I said before in the restful, uh you can define like a function like this. uh Let's say same order: confirmation and the operation of this function.

B

It is a uh an open api uh definition file and after the hashtag here you have the the the method I the the operation id of that particular uh open api. So um for it, for example, let me see if I can find it.

A

At the famous pet store.

B

Yeah yeah, so imagine that you have like this one here. You know this uh open api spec here and you would like to call the list paths uh function. So you just say hey. This is my file, and this is my operation id, so the cogito engine will re. We will we read this the this this specification and we generate code client code to call this rest service. So yeah you can call it. You know a third-party api and do whatever you want uh is within your workflow.

B

So after receiving the the the payload of the the the response of the list paths, you can do whatever you want in our workflow yeah. That's correct.

A

Okay and so um someone's now asking where are correlation keys states stored if used well,.

B

um This, the correlation uh id it is, we we are working on a stateful uh implementation, so we we don't, have it yet on coach to site, uh but in this specification uh we have the part where that we define. Let me see if I can find it real quickly.

B

Oh very, on top of the hour, but let me- but this is the yeah here in the events you can. You declare the correlation in here so in in this use case that I that I brought in the end in the final order process. So if that process would be like uh workflow service as well, because I don't know what it is.

B

It's just an example that I gave, but uh if it is a workflow uh as well, you can you can create the correlation in here in the your workflow and say the event say: hey uh the the attribute name. It is order id for instance, and then you can res when you receive everything else, you would do whatever you want uh in code. We don't have uh stateful yet for the for the for the service workflow, but we are working on it.

B

uh But in this case we are going to handle that in memory, so we receive that at the same time we correlate and we do uh all the other stuff. But uh we are looking forward to.

A

Implemented all right. Well, there's one earlier question: I think you might have answered it, but um someone's asking for an understanding of how this relates or differs, to camel k.

B

Well, uh chemo is, for um I'd, say, uh integration most of the time you know uh implementations of integrations and uh we do not target integrations. So uh we you cannot like uh ftp a file or send an email or those. You know things like that. It is not uh what we we are discussing here and, of course, you can implement a pipeline in came okay.

B

This is not the the idea, but uh from from our perspective, uh it is more or less a declarative, workflow of a feature that we that we are implementing in here. uh It is not like uh dealing with integration side of the things you know. I understand that is some some things that they can interconnect.

B

Or can we implement a uh uh an integration in here like, for instance, you can call a tweeted api using the rest uh function, of course, or you can uh in the came okay, you can create a route that you you go through every step, as we created in here, of course, but each I I understand that each it too has its particular strengths, and uh you should you know, leverage from that.

B

So uh from the workflow side, sorry uh I'd say that for this, this kind of declarative, workflows, business applications that you that we have this is a suitable um application for for for this kind of use case, that's the that's! That's my point. In there.

A

Okay, cool all right, and so um we are almost to the top of the hour, and the very first question was: where did the name come from.

B

Well, that's the that's something that people ask uh sometimes and then yeah. Oh I'm sorry you you know I! I can't explain it uh exactly why uh the the founders they they come up with this name, but every everything that we that we do in kojito. It is like uh related to knowledge. So uh we because are they the key? uh So the knowledge is everything, that's our motto and uh because you know all these rules decisions process, everything is related to you know.

B

Business applications and knowledge based- and um it is, uh I believe, is based on. Of course, the the fathers can explain you much better than I, uh but from this uh quote, uh cody turgoson from renee. uh This card is, you can read but uh yeah and the k because kubernetes you know uh uh it is a cloud-based platform. So that's why uh that's called kojita. I don't know if I answered your question. Hopefully.

A

I I think, that's great, you know what the best part is. It's not a greek or uh navigation term. So that's really the most wonderful thing. It's like. We have completely run out of k, words and.

B

A

In the kubernetes cloud native space, so you've done well, so um in we've got like four minutes left. Is there some new features or what's in the road map um that what's coming up next, that we should be watching out for.

B

Well, um like I said, questions is a work in progress of the serverless workflow specification, so the workflow specification is going super uh in the community. We are creating great things in their great uh uh big companies are interested in in the specification. This is a very nice thing uh from the community side. uh We are about to release 1.0 from in the specification. So this is a nice thing.

B

uh Hopefully we see more people trying to implementing it uh and from college to side uh we are keen to implement everything that we, that is there um in the specification to be 100 percent compatible with the the specification um we uh are about to to bring uh jq to the engine. So you can, you know, create jq expressions in your in your workflow, and so you can, you know, transform or you know, do whatever you want with the data within the workflow um and, of course, state.

B

uh This is a very important thing that we are very looking for. uh You know to implement in the in the specific in the in the engine as well, as was, as you know, error handling, compensation and all of those uh workflow patterns that we don't have yet from the workflow uh from the serverless workflow uh perspective, but yeah that that's there's, there's there's a lot of work um to do in there and from the specification and the information side.

A

Yeah, so if you can share again that resources page because I think that's sure to end and um if people want to get involved, um then you know follow the link.

B

Oh please, yes,.

B

Yeah in the in the front page of the specification, if you go here in a specification, you'll see our github page and um you can see. Oh I'm sorry.

B

All the information that you need uh mainly uh how to engage uh with the community. We have a slack uh canada under the cncf slack. So please go there and say hello, and uh if you have any other questions just reach out to us there, uh we are keen to help and uh looking for for contributors as well.

A

Well, thank you for making um this that really interesting and engaging talk. Ricardo will definitely have you back um to celebrate. Maybe.

B

A

Release and sure and and just keep it keep us in mind, and there were plenty of great questions today, so thank you, everybody for tuning in and chris short for producing in the background there. So I'm really pleased to have you here. Ricardo definitely come back and keep us posted and up to date, and everybody join us on the community slack channel. If you have questions that he didn't answer today, um we'll try and get them there all right. Well, thank you, everybody and again, ricardo! Thank you.

A

um I love the hand the hand waving while you're talking the most expressive um virtual event. So far,.

B

D

I'm trying hard.

A

All right well take care. Okay and thanks.

B

Everybody thank you guys.

B

D