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So whenever Argo CD upgrades your application on the cluster, you have the option of triggering a predefined or a presubscribed chaos workflow against it. That happens via a call to the chaos center from the event tracker service running in your cluster. |
So that is the relation that we have with Argo CD, and it is true for other popular GitOps tools as well. It could be Flux, or Keel, or you might have built in something with your own -- you might have written some tooling by yourself, using Helm... So you have the option of triggering Litmus experiments or workflows a... |
\[39:59\] There's another angle to it... Litmus also supports GitOps for the chaos artifacts. When you construct chaos scenarios, these workflow manifests can also be stored in Git or committed into Git automatically. When you make changes to the chaos workflows in your source, you will have those changes reflect on yo... |
**Gerhard Lazu:** Okay, that makes a lot of sense. I'm starting to form this mental model in my head of how all this fits together in our setup. I can start seeing the integration points... But what I'm wondering now, Uma, is if someone doesn't have Kubernetes, how would they start even using this? |
**Uma Mukkara:** So when you talk about Litmus, you need Kubernetes to run the chaos center, where the control plane of chaos engineering is put together, where the SREs and developers interact with it, and where you interact with the chaos experiments that are stored on a hub, or on your private Git repository - all t... |
**Break**: \[42:04\] |
**Gerhard Lazu:** So this is a very special topic for me... The reason why it's special is because I disagree with Kelsey Hightower about running databases on Kubernetes, and I learned it the hard way (again, pun intended), that if you run databases on Kubernetes, the database needs to be built for a distributed system... |
\[44:22\] So what do you think about running databases on Kubernetes, Uma? I know you have a bit of experience in this area, that's why I ask you first... |
**Uma Mukkara:** Yes. Litmus \[unintelligible 00:44:29.13\] trying to fix bugs when you're trying to run databases on Kubernetes. So I kind of have an opinion that you cannot have an option of not running databases on Kubernetes forever. Five years ago that was not a requirement; two years ago people thought it's very,... |
For example, my earlier project, OpenEBS, which is still a popular subject in this space, is having the concept of containerized storage. So you try to consider the storage as container an element that is built for running data on Kubernetes. And similarly, there is an element of local PV that is started by Kubernetes ... |
So I would say there are people who are running data on Kubernetes. Because the infrastructure also becomes a microservice, you need to understand that there are more failures that can happen. Storage is not guaranteed to be running in one place. It can \[unintelligible 00:46:30.01\] and how do you actually handle that... |
So hopefully in a few years from now there will be questions like "Oh, we thought data on Kubernetes is not \[unintelligible 00:47:09.29\] but I see many people running it. That would be what will happen, in my opinion. |
**Gerhard Lazu:** I would agree with that. I think there is a process of -- as you mentioned at the beginning of the interview, it's evolving, so I think the storage, the data layer is evolving on Kubernetes... But also the networking I think is evolving. Because in our case, the one that I mentioned earlier, it was ne... |
\[47:59\] In other cases, for the app itself, when we had a three-node Kubernetes cluster - by the way, we have a single-node one; I know it's very contentious, but guess what, it works better. So reality says and the practicality says it works better. The point is when we had three nodes, those volumes that should hav... |
What I'm wondering now, Karthik, is if there is such a stateful system, which was built to be distributed from day one, it understands that and it's in its DNA, is it easier to run in on Kubernetes? I'm thinking maybe a message broker that was built to be distributed. It still has some state, but it works as a distribu... |
**Karthik Satchitanand:** Yes, I think to a great degree it does, but the network problems are not going away anywhere, Gerhard. If you take a look at the Litmus Slack channel on the Kubernetes workspace, network latency and network loss are probably the most popular discussion items. People are trying those experiment... |
Message brokers is a good example, and in fact, when we're trying to build some illustration for application-specific chaos experiments with Litmus -- so application-specific chaos is a category of chaos experiments in which the experiment business logic has some native health checks that are specific to an app, and th... |
The first application-specific experiment that we considered was Kafka. We have some communities that are actually trying out Litmus against Kafka. Strimzi is one of the Kafka providers whom we are speaking with and trying to collaborate on, trying to find good scenarios that can be used as part of this thing. |
What is relevant in the message broker world is - let us say you have some very intelligent message broker that is capable of handling message queues, and doing failovers, and doing elections, and things like that... Because here also there is some amount of state involved, so you have storage at play, you have network... |
One of the scenarios that we got started with was killing a partition leader, which could also be a controller broker. Then you have a series of things happening. You have reelections happening, you basically trying to speak to Zookeeper, and you're trying to ensure that the failovers happen quick enough so the consume... |
\[52:16\] This experiment was a simple \[unintelligible 00:52:17.02\] You will have the need for chaos engineering in these environments as well, both to learn about the system, as well as prove some hypothesis that you might already have around timeouts and such settings that you have. So to come back to the earlier q... |
The adoption of data on Kubernetes can be accelerated, much in the way general Kubernetes \[unintelligible 00:52:53.20\] can be accelerated through chaos engineering. There are folks in the Litmus community, and I'm sure there are other projects speaking to such users as well, where they want to use Kubernetes in produ... |
The multi-attach error issue, as we typically like to call it, the volume not getting detached from one node, and therefore it doesn't get attached to the other node - this is something we've found very early in OpenEBS using the chaos experiment. And something has come up in the \[unintelligible 00:53:49.06\] to fix i... |
So I think both the application architecture, the data architecture becoming more distributed, as well as evolving chaos engineering practices will ensure that the adoption of databases into Kubernetes, as well as the general Kubernetes adoption itself will increase. |
**Gerhard Lazu:** I think the most important point that resonates with me that you've made, Karthik, is around the different platforms having different recovery times. I think that's really powerful, because if you are, for example, as we are, running on Linode, we cannot apply the same approaches that someone may be r... |
\[55:14\] And we all know that as much as we want to be confident from our staging experiments, the best failures happen in production. So as much as you can try to preempt things in staging, until you go into production, you won't see it. So maybe trying to generate production-level load, if it's possible? It's not al... |
So as a listener, if I had to remember one thing from this conversation, what would that be, Uma? |
**Uma Mukkara:** Yeah, so the last stage of reliability is to be able to confidently generate random triggers after you apply every change to your system in production. So you upgrade it, you have a good CI/CD system, and you apply the change in production, but also \[unintelligible 00:56:11.16\] to create a random fau... |
**Gerhard Lazu:** What do you think, Karthik? Do you agree with that? |
**Karthik Satchitanand:** Doing chaos engineering in production is the ultimate stage, the Nirvana of a very mature practice that you've set up in your organization... So start small, and explore a lot of failures, and establish a culture of continuous chaos at all levels. Chaos has become more democratic, more ubiquit... |
So go ahead and perform chaos, and then you will be able to confidently deploy your applications and sleep better at night. |
**Gerhard Lazu:** Thank you very much, Karthik, thank you very much, Uma. That was a great thought to end on. A very powerful one. So yeah, go forth and break things, that's what we're saying... In production, by the way. Because until you do that in production, it's okay, but it's not great. So for a proper challenge,... |
Thank you, Uma, thank you, Karthik. It's been a pleasure. I hope to see you again soon. |
**Uma Mukkara:** Thank you, Gerhard. |
**Karthik Satchitanand:** Thank you. |
• Disconnect between vision, actions, and reported outcomes |
• Arnaud's Y Combinator demo and its challenges in condensing a message into 60 seconds with one slide |
• Echoes' 60-second pitch to investors |
• Product Hunt launch as part of Y Combinator's advice on being public and launching early |
• The importance of shipping early and getting feedback from customers |
• Changes to Echoes since its launch at Y Combinator, including refining the ideal customer and messaging |
• The universal problem that Echoes aims to solve: connecting daily work as engineers to intent in companies with more than 20 engineers |
• The key value of Docker and Echoes is creating a shared understanding among teams and stakeholders. |
• Measuring the right level of information is crucial for success, but current metrics often fail to provide actionable insights. |
• Lines of code, PRs closed/merged, etc. are examples of metrics that don't accurately reflect quality or progress. |
• The true metric should be whether work is creating value for the business in a sustainable way. |
• Measuring intent (why something is being done) is essential, but difficult to capture with current systems. |
• Echoes aims to provide a central definition of intent and make it easy for engineers to express their reasons behind their work. |
• This will enable a consolidated view on what teams are working towards and how they're progressing towards those goals. |
• Infering code intent and developer habits may not be relevant or accurate |
• Importance of understanding and agreeing on "Why" in organizational goals and objectives |
• Arnaud's passion for developer empowerment and mission to help companies improve context for engineers |
• Differentiating between poor ("making money", "getting followers") and good ("customer satisfaction", "empathy") Why examples |
• Autonomous teams and product team organization model as a solution to complex interactions and information flow issues |
• The importance of clear vertical communication within organizations |
• Challenges in scaling technical organizations and maintaining business-technical alignment |
• The role of intent and money flows in driving decision-making in large companies |
• Structuring information for different layers to promote efficient communication |
• Using OKR (Objectives and Key Results) model to align individual goals with company objectives |
• Measuring and mitigating bias in open source communities |
• Differences between open source and commercial product intentions |
• Challenges of reconciling a company's interests with an open source project's needs |
• Importance of transparency and understanding motivations for using open source |
• Docker's experience with open sourcing its project and its subsequent challenges |
• Discussion on the current state of a company after a hype period has passed |
• Arnaud Porterie's experience and motivations for starting Echoes, a new project |
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