Speaker 1: Okay, imagine this scenario. You launch your amazing new application
Speaker 1: and boom, overnight, it just goes completely viral. The dream, right,
Speaker 1: absolutely every developer's dream. But then that little bit of
Speaker 1: panic starts creeping in. Is your system actually ready? Can
Speaker 1: it handle say, one hundred x traffic spike without just
Speaker 1: falling over?
Speaker 2: Yeah, that's the kind of challenge that definitely keeps people
Speaker 2: up at night exactly.
Speaker 1: So this deep dive, this is your shortcut really to
Speaker 1: understanding how Kubernetes and containers can turn that potential nightmare
Speaker 1: scenario into a well, a robust, scalable reality.
Speaker 2: We're basically going to pull apart the key ideas from
Speaker 2: a really great resource, William Dennis's Kubernetes for Developers.
Speaker 1: Yeah, we'll unpack the essentials, starting from the real basics
Speaker 1: like why you even use containers in.
Speaker 2: The first place, all the way up to the more
Speaker 2: advanced stuff you know, strategies for running your applications in
Speaker 2: a proper professional production environment.
Speaker 1: Our mission here is simple. We want to equip you
Speaker 1: with the knowledge you need to confidently, deploy, manage, and
Speaker 1: crucially scale your applications.
Speaker 2: Making you feel well informed and hopefully ready for pretty
Speaker 2: much anything.
Speaker 1: Okay, so let's dive right in. Let's start right at
Speaker 1: the beginning. Why why are containers and kubernetes suddenly such
Speaker 1: a huge deal? What problem were they actually designed to solve?
Speaker 2: Well, it's really been an evolution, hasn't it. I mean
Speaker 2: you think back. We went from just throwing multiple apps
Speaker 2: onto a single server.
Speaker 1: Oh yeah, the bad old days totally.
Speaker 2: Then we moved to isolating them in these heavyweight virtual machines,
Speaker 2: and now finally we've landed on containers.
Speaker 1: And the key differences.
Speaker 2: It's all about that lightweight isolation. See a VM, it
Speaker 2: duplicates an entire operating system, kernel and all super heavy, right,
Speaker 2: But a container it just packages your application and only
Speaker 2: its direct dependencies, nothing else.
Speaker 1: Okay, So the benefits there are huge benefits.
Speaker 2: Language flexibility. For one, you can run like different versions
Speaker 2: of Java or Python on the very same host machine
Speaker 2: without them clashing.
Speaker 1: That alone is pretty big.
Speaker 2: Yeah it is, and maybe even bigger is true reproducibility.
Speaker 2: The docker file that's like the recipe for your container.
Speaker 2: It records every single step needed to create that exact environment.
Speaker 1: So no more, Well it worked on my machine exactly.
Speaker 2: That whole headache just goes away consistency every single time.
Speaker 1: Okay, that reproducibility sounds like a lifesaver personally. So containers
Speaker 1: give us this perfectly packaged portable app Where does Kubernetes
Speaker 1: fit into this picture? What's its role?
Speaker 2: So if containers are the perfectly packed boxes, Kubernetes is
Speaker 2: kind of like the super smart automated warehouse management system
Speaker 2: for all those boxes.
Speaker 1: I like that analogy.
Speaker 2: It orchestrates them, but at a workload level, it hits
Speaker 2: this really nice sweet spot. You know, you're not drowning
Speaker 2: in the details of every single machine, but you're also
Speaker 2: not stuck in a really high level, sometimes limiting platform
Speaker 2: as a service or pot environment.
Speaker 1: So how does it do that? What are the components
Speaker 1: it uses?
Speaker 2: These really clever composable building blocks. You've got pods. They're
Speaker 2: the smallest thing you can deploy, often just one container,
Speaker 2: but could be a few tightly coupled ones working together.
Speaker 2: Then you have deployments. They manage your stateless applications, the
Speaker 2: ones that don't hold onto data long term. They make
Speaker 2: sure you always have the right number of replicas running.
Speaker 1: Got it? And how do they talk to each other?
Speaker 1: Were they outside world?
Speaker 2: Ah? That's services. Services expose your applications either internally or externally,
Speaker 2: and they handle all the load balancing between your pods.
Speaker 1: The real magic, though, sounds like the automation.
Speaker 2: Oh absolutely, that's the core of it. It delivers automation.
Speaker 2: You get self healing, so if a container crashes, Kupernettes
Speaker 2: just restarts it nice. If a whole server a node
Speaker 2: goes down, it reschedules your pods onto healthy nodes automatically.
Speaker 1: Wow.
Speaker 2: And massive scalability. I mean, think about projects like Niantic's
Speaker 2: Pokemon Go that ran on tens of thousands of CPU cords,
Speaker 2: scaling up and down like crazy. Kupernettes made that possible.
Speaker 1: That self healing, that automatic rescheduling, that's a game changer
Speaker 1: for reliability.
Speaker 2: It makes you think about those traditional PAWS platforms. Though
Speaker 2: people often say they're quicker to start with.
Speaker 1: They can be, yeah, initially.
Speaker 2: So what's the fundamental difference. What does Kupernetes offer that
Speaker 2: makes it worth Maybe a steeper initial learning curve.
Speaker 1: That's a great question. With a traditional pass you can
Speaker 1: get going fast, sure, but a lot of teams they
Speaker 1: eventually hit this well people call the clone of despair.
Speaker 1: Huh Okay, seriously, their needs just outgrow what the pass
Speaker 1: can easily do, and then they're stuck facing a complete,
Speaker 1: often really painful re architecture from scratch out. Yeah. Kuper Natties,
Speaker 1: on the other hand, while maybe taking a bit longer
Speaker 1: to learn upfront, gives you just expansive possibilities as you grow.
Speaker 1: The key is flexibility. You get deep control, but without
Speaker 1: sacrificing that automation we talked.
Speaker 2: About, So you don't paint yourself into a corner exactly.
Speaker 2: You won't need to just rip everything out and start
Speaker 2: over if your application of all. So you can run simple,
Speaker 2: stateless web apps, sure, but you can also migrate complex,
Speaker 2: stateful legacy apps, or even run your own.
Speaker 1: Databases, all on the same platform, all.
Speaker 2: On the same unified platform. That initial investment in learning Kubernetes,
Speaker 2: it really pays off long term as your system matures.
Speaker 1: All right, I'm definitely sold on the why. This makes
Speaker 1: a lot of sense. Now let's get practical. How do
Speaker 1: you actually get an application ready for Kubernetes. What's the
Speaker 1: very first step?
Speaker 2: The essential first step is getting your app into a container.
Speaker 2: We call it containerizing it, and the main tool for
Speaker 2: that is the.
Speaker 1: Docker file the recipe you mentioned earlier.
Speaker 2: Exactly, it's just a text file with a set of
Speaker 2: instructions step by step telling Docker how to build your
Speaker 2: container image. You start with a base image like say
Speaker 2: Python point three, instead of installing Python yourself on a boontu, right,
Speaker 2: saves you that step totally. Then you add your application code, installers, dependencies,
Speaker 2: configure things for apps that need compiling, like Java or Go.
Speaker 2: There's a really neat trick called multi stage builds.
Speaker 1: Oh yeah, how does that work?
Speaker 2: You basically use one stage with all the heavy build tools, compilers, SDKs, whatever.
Speaker 2: Then in a final stage, you copy only the compiled
Speaker 2: application into a clean, minimal base image.
Speaker 1: Ah, so you ditch all the build stuff exactly.
Speaker 2: You end up with these tiny, production ready images that
Speaker 2: only have what's strictly needed to run much smaller, more secure.
Speaker 1: Makes sense. And for server applications, what else do we
Speaker 1: need to keep in mind?
Speaker 2: Well, the main thing is your container needs to keep running, right.
Speaker 2: It's not just a script that finishes, it's.
Speaker 1: A long running process.
Speaker 2: Yeah, and you'll need to know how to map ports,
Speaker 2: like tell Docker to connect port eighty eighty on your
Speaker 2: local machine to port eighty inside the container. Using something
Speaker 2: like the natch P eighty eighty point eighty eight zo flag.
Speaker 1: Okay, standard stuff for web servers. So we've got our
Speaker 1: container image built, but surely we test this locally before
Speaker 1: throwing it onto a big cluster.
Speaker 2: Oh absolutely, you have to, and for local testing and development.
Speaker 2: Docker composed is just invaluable. It's really your secret weapon.
Speaker 2: It lets you define and run multi container applications easily,
Speaker 2: so you can spin up your app container, maybe a
Speaker 2: database container or reddis container, all linked together just like
Speaker 2: it would be in production, but on your laptop.
Speaker 1: Nice.
Speaker 2: It preserves all your run time settings, makes port mapping
Speaker 2: easy and a really cool feature you can map local
Speaker 2: folders directly into the container.
Speaker 1: Oh so you can edit code locally and see the changes.
Speaker 2: Live instantly, make a code change, refresh your browser. It's reflected,
Speaker 2: super fast iteration. Plus you can even use it to
Speaker 2: sort of fake external cloud services, like how say your
Speaker 2: app needs to talk to AWSS three for object storage. Locally,
Speaker 2: you can just spin up a minio container using Docker compose.
Speaker 2: It behaves just like S three, so your app works
Speaker 2: without needing real cloud credentials or connectivity during development.
Speaker 1: That's incredibly useful for local dev loops. Okay, so we've
Speaker 1: built the image, tested it locally with compose. Now the
Speaker 1: moment of truth deploying to a real Kubernetes cluster. What
Speaker 1: does that actually involve?
Speaker 2: Right? Your main interaction point is going to be the
Speaker 2: command line tool quebectl, and you tell Kubernetes what you
Speaker 2: want by writing these declarative yamal configuration files.
Speaker 1: Yamil got it Eeryone's favorite.
Speaker 2: Huh Well, it gets the job done first, though you
Speaker 2: need a cluster. A really great option, especially when you're
Speaker 2: starting out, is a managed service think Google Kubernetes Engine GK,
Speaker 2: aws eks Azure AKS.
Speaker 1: Takes away some of the underlying management pain exactly.
Speaker 2: Gk's autopilot mode is particularly neat because it handles the
Speaker 2: node management for you. You just focus on your workloads.
Speaker 1: Okay, so clusteretty quebectl is set up and authenticated. Then
Speaker 1: what then?
Speaker 2: You need to get your container image somewhere kubernets can
Speaker 2: pull it from. That's a container registry like Docker Hub,
Speaker 2: Google Container Registry, AWSCR, et cetera. You push your image there,
Speaker 2: then you take your deployment yammo file, the one describing
Speaker 2: your application how many replicas you want which image to use,
Speaker 2: and you just run quebec to all apply dasheff your
Speaker 2: dash appoyment dot YAMO.
Speaker 1: And Kubernetes just makes that happen pretty much.
Speaker 2: It reads your desired state from the YAML and then
Speaker 2: works tirelessly in the background to make the cluster's actual
Speaker 2: state match that. It's constantly reconciling.
Speaker 1: Okay, but what if things go wrong? Because you know they.
Speaker 2: Sometimes do, oh for sure, troubleshooting is key. You'll see
Speaker 2: some common errors air image pull that usually means a
Speaker 2: typo in the image name in your Yamel, or maybe
Speaker 2: Kubernetes doesn't have permission to pull from your registry.
Speaker 1: Right authentication exactly.
Speaker 2: Or you might see stuck in pending that means Kubernetes
Speaker 2: wants to schedule your POD, but it can't find a
Speaker 2: node with enough resources cpu memory available.
Speaker 1: For it AH capacity issue.
Speaker 2: Yeah, and then there's the classic crash loop back off
Speaker 2: that tells you your container is starting crashing, restarting, crashing.
Speaker 1: Again, usually an application bug.
Speaker 2: Then often yeah, or maybe it's missing a configuration or
Speaker 2: can't connect to a database. It depends on something inside
Speaker 2: the container is failing repeatedly.
Speaker 1: So how do you debug that?
Speaker 2: Peek inside YEP, quebec dol exec is your friend. There.
Speaker 2: It lets you get a shell right inside the running containers.
Speaker 2: You can poke around, check files, run commands okay.
Speaker 1: And Quebeca logs FP is essential. It streams the logs
Speaker 1: from your container in real time, so you can see
Speaker 1: exactly what error messages your application is spitting out.
Speaker 2: Got it? So we deploy, maybe debug a bit. How
Speaker 2: do we actually make the application accessible? Like, give it
Speaker 2: an IP address.
Speaker 1: That's where the service object comes in. You create a
Speaker 1: service yammal. If you want external access from the Internet,
Speaker 1: you typically use a load balancer type service. The cloud
Speaker 1: provider will automatically provision a load balancer and give you
Speaker 1: an external IP okay.
Speaker 2: And for internal stuff like micro service is talking to
Speaker 2: each other.
Speaker 1: For that, you'd use a cluster IP type service. It
Speaker 1: gives you a stable internal IP address that other pods
Speaker 1: within the cluster can use to reach your application. The
Speaker 1: service uses labels, simple key value pairs you put on
Speaker 1: your pods to know which pods to send traffic to.
Speaker 2: Labels and selectors right exactly.
Speaker 1: And the beauty of this declarative approach Updating your application
Speaker 1: is super simple. How so you just update the image
Speaker 1: tag in your deployment yammo file to point to your
Speaker 1: new container image version. Then you run a quebecle apply
Speaker 1: to f again with the updated file, and Kubernetes handles
Speaker 1: the rollout yep.
Speaker 2: It performs a rolling update by default, gradually replacing old
Speaker 2: pods with new ones. You can even watch the progress
Speaker 2: live using watchteled de quebectl get deployed. It's remarkably smooth.
Speaker 1: That's incredibly powerful, that automated reconciliation and rollout, But just
Speaker 1: getting it deployed isn't the whole story. How does Kubernetes
Speaker 1: make sure our apps actually stay up and running even
Speaker 1: if things go wrong or during those updates? Right?
Speaker 2: This is where automated operations and health checks become really critical.
Speaker 2: Kubernetes already gives you that basic self healing. We talked
Speaker 2: about restarting crash containers, rescheduling pods from dead nodes yea.
Speaker 2: But to be smarter than just basic restarts, Kubernetes needs
Speaker 2: signals from your application about its actual health. And that's
Speaker 2: where probes come in.
Speaker 1: Probes Okay, what kinds are there?
Speaker 2: There? Are two main types you'll use constantly, liveness probes
Speaker 2: and readiness probes.
Speaker 1: Liveness and readiness what's the difference.
Speaker 2: Aliveness probe tells Kubernetes, is this app location still alive
Speaker 2: and functioning? If the liveness probe fails, maybe your app
Speaker 2: is deadlocked or hung. Kubernetes nose it needs to restart
Speaker 2: that container to try and recover it.
Speaker 1: Okay, so liveness restart if broken exactly now.
Speaker 2: A readiness probe tells Kubernetes, is this application ready to
Speaker 2: actually serve traffic right now?
Speaker 1: Ah? So it might be alive but not quite ready,
Speaker 1: like still starting up precisely.
Speaker 2: Think about an application that needs to load data or
Speaker 2: warm up caches when it starts. It might be running,
Speaker 2: so the liveness probe passes, but it's not ready to
Speaker 2: handle user requests yet, right okay. If the readiness probe
Speaker 2: isn't passing, Kubernetes won't send any traffic to that pod.
Speaker 2: This is absolutely crucial for achieving zero downtime updates.
Speaker 1: Because it waits until the new pod is actually ready
Speaker 1: before sending users to it.
Speaker 2: You got it. No user ever hits an application that's
Speaker 2: still in the middle of booting up. Get your readiness
Speaker 2: probes right, and you unlock those seamless, zero downtime deployments.
Speaker 2: It's a massive win for user experience.
Speaker 1: That makes perfect sense. So assuming we've got those readiness
Speaker 1: checks nailed, ensuring no one sees a half baked app,
Speaker 1: what are the different ways we can actually roll out updates?
Speaker 1: You mentioned rolling updates, but are there other strategies?
Speaker 2: Yeah, there are a few main ones. Rolling update is
Speaker 2: the default, like I said, and honestly it's the best
Speaker 2: choice for most typical web services.
Speaker 1: How does it work again?
Speaker 2: It updates pods incrementally in batches. It ensures a certain
Speaker 2: number always available, brings up new ones, waits for them
Speaker 2: to be ready thanks to readiness probes, then terminates old ones.
Speaker 2: It's smooth, continuous.
Speaker 1: Okay, keeps the service up the whole time.
Speaker 2: What else, Well, there's the blue green strategy. This is
Speaker 2: quite different. You deploy the entire new version, the green
Speaker 2: version completely separate from the currently running blue versions.
Speaker 1: So both are running at the same time.
Speaker 2: For a period. Yes, once you're happy the green version
Speaker 2: is working perfectly, you just switch the load balancer or
Speaker 2: a router to point all traffic from blue to green.
Speaker 1: Instantly instant cutover zero downtime.
Speaker 2: There too, presume YEP, zero downtime. The big advantage is
Speaker 2: instant rollback. If Green has issues, just flip the switch
Speaker 2: back to Blue. The downside you need roughly double the
Speaker 2: infrastructure resources during the transition period, which can be costly.
Speaker 1: Right, running two full copies makes sense any other options.
Speaker 2: There's also the simpler recreate strategy. This one just well,
Speaker 2: it kills all the old pods first and then it
Speaker 2: creates all the new ones.
Speaker 1: WHOA okay, so that definitely means.
Speaker 2: Downtime guaranteed downtime, yes, okay, but it's simple, and sometimes
Speaker 2: it's necessary if the old and new versions are incompatible
Speaker 2: and can't run side by side for some reason. But generally,
Speaker 2: rolling update is your go to workhoorse.
Speaker 1: Fantastic breakdown Okay. Beyond keeping things running, another huge concern
Speaker 1: is efficiency. How do we stop our apps from hogging resources,
Speaker 1: wasting money, or maybe even impacting other applications running on
Speaker 1: the same cluster.
Speaker 2: Resource management absolutely fundamental in Kubernetes, and you can figure
Speaker 2: this right in your pod specification using requests and limits
Speaker 2: for CPU and memory.
Speaker 1: Requests and limits, what do they each do think of.
Speaker 2: Requests as telling the Kubernetes scheduler, Okay, to run reliably,
Speaker 2: this pod needs at least this much CPU and this
Speaker 2: much memory. It's a guarantee. The scheduler uses this info
Speaker 2: to decide where to place your pod on a node
Speaker 2: that actually has those resources available, so.
Speaker 1: It ensures the pod gets what it needs to start exactly.
Speaker 2: Limits, on the other hand, define the absolute maximum resources
Speaker 2: a pod is allowed to consume. If a pod tries
Speaker 2: to use more memory than its limit, Kubernetes will likely
Speaker 2: kill it. Oh killed out of memory?
Speaker 1: Killed, okay, hard stop for memory? What about CPU?
Speaker 2: If it exceeds its CPU limit, it just gets throttled.
Speaker 2: It won't be killed, but its performance will be capped.
Speaker 1: So setting both gives you predictability.
Speaker 2: Precisely, it defines a quality of service or QoS class
Speaker 2: for your pod. Kubernetes uses these values constantly to manage
Speaker 2: resource contention on the nodes. It might even evict lower
Speaker 2: priority pods that are exceeding their request just to protect
Speaker 2: higher priority one.
Speaker 1: You can set priorities YEP, using something.
Speaker 2: Called priority class. So how do you figure out the
Speaker 2: right values? Best practices usually start a bit generous with
Speaker 2: your requests and limits.
Speaker 1: Okay, then use monitoring tools absolutely essential to observe how
Speaker 1: much CPU and memory your application actually uses under load,
Speaker 1: and then tune them down exactly. Fine tune those requests
Speaker 1: and limits to match reality, avoiding waste but still giving
Speaker 1: your app what it needs. Quick tip for web apps,
Speaker 1: you can often set the CPU limit higher than the
Speaker 1: CPU request.
Speaker 2: Oh why is that?
Speaker 1: It allows your app to burst to temporarily use more
Speaker 1: CPU if the node has spare capacity available. Good for
Speaker 1: handling short spikes and traffic without over provisioning the guaranteed
Speaker 1: request all the time.
Speaker 2: That's a really smart optimization balancing guarantees with burst potential. Okay,
Speaker 2: speaking of capacity and spikes, how do we handle that
Speaker 2: viral moment we talked about where traffic just explodes? Or conversely,
Speaker 2: how do we save money when things are quiet? How
Speaker 2: does Kubernety automate the scaling part?
Speaker 1: Right? Automatic scaling? Kubernates gives you two main tools here
Speaker 1: for scaling your application pods horizontally. There's the Horizontal Pod
Speaker 1: Autoscaler or HPA.
Speaker 2: HPA, what does it look at? The most common metric
Speaker 2: is CPU utilization? You can say, okay, if the average
Speaker 2: CPU across all my pods goes above sixty percent.
Speaker 1: Add more pods, simple enough.
Speaker 2: But it's way more flexible than just CPU. You can
Speaker 2: configure HPAs to scale based on memory usage or even
Speaker 2: custom metrics like request per second hitting your service, or
Speaker 2: maybe the number of messages sitting in a queue that
Speaker 2: your workers need to process, so.
Speaker 1: You can tie scaling directly to your application's actual load drivers.
Speaker 2: Exactly makes it much more responsive inefficient. Now that's scaling
Speaker 2: your pods. What about the underlying machines, the nodes.
Speaker 1: Yeah, if you add more pods, you might run at
Speaker 1: a node capacity right back to that pending state.
Speaker 2: Precisely, that's where the cluster autoscaler comes in. This is
Speaker 2: usually component provided by your cloud provider or installed separately.
Speaker 2: Watches for pods that are stuck and pending because of
Speaker 2: resource constraints, and it automatically adds more nodes to the
Speaker 2: cluster to accommodate them nice and just as importantly, if
Speaker 2: nodes become underutilized for a while, it will consolidate the
Speaker 2: pods onto fewer nodes and then terminate the unnecessary ones
Speaker 2: to save.
Speaker 1: Costs, So it scales the infrastructure up and down too,
Speaker 1: very cool.
Speaker 2: What about really fast scaling needs like almost instant?
Speaker 1: Ah? Yeah, for super rapid scaling, there's a clever technique
Speaker 1: using placeholder pods. You deploy these special pods with a
Speaker 1: very low priority. They basically just sit there consuming resources
Speaker 1: in occupying space. Okay. Why because when your real application
Speaker 1: needs to scale up quickly due to a sudden spike,
Speaker 1: its new higher priority pods can immediately preempt those low
Speaker 1: priority placeholder pods they get kicked out, freeing up resources
Speaker 1: instantly for your critical application. It gives you immediate headroom
Speaker 1: without waiting for new nodes to spin up.
Speaker 2: That's a neat trick. So underlying all this scaling tech,
Speaker 2: what's the most important design pile for building an application
Speaker 2: that can actually scale like this?
Speaker 1: Oh? Hands down, the single most important thing is avoiding
Speaker 1: local state. Your application replicas need to be stateless.
Speaker 2: Meaning any replica can handle any incoming request without needing
Speaker 2: specific data stored only on that particular instance or data
Speaker 2: from another specific instance. All the necessary state should be
Speaker 2: external in a database, a cache, object, storage, whatever.
Speaker 1: Because if any replica can handle any request, Kubernetes can
Speaker 1: just add or remove replicas without worrying about losing data
Speaker 1: or breaking user sessions exactly.
Speaker 2: It makes scaling effortless. Statelessness is foundational.
Speaker 1: Okay, critical point, But as you said, many apps do
Speaker 1: have state, especially databases. Any specific gotchas to watch out
Speaker 1: for when scaling those?
Speaker 2: Oh yeah, databases bring their own challenges at scale. Watch
Speaker 2: out for things like the n plus one query problem.
Speaker 2: That's where say, fetching a list of one hundred items
Speaker 2: accidentally triggers under and one database queries instead of just
Speaker 2: one or two kills performance seen that one before. Common
Speaker 2: strategies are using database read replicas. Send all your select
Speaker 2: queries your reads to replicas to take the load off
Speaker 2: your primary right database. Also think about primary keys at
Speaker 2: massive scale, simple auto incrementing integers can become a bottleneck
Speaker 2: because every insert wants the next ID, causing lock contention.
Speaker 1: So what's the alternative.
Speaker 2: Using u abyss's universally unique identifiers. They're random, so inserts
Speaker 2: are spread out, reducing contention. And while it's a bigger
Speaker 2: architectural decision, splitting your application into smaller micro services can
Speaker 2: sometimes help. How so it lets different parts of your
Speaker 2: system scale independently. Maybe your user authentication service needs way
Speaker 2: more replicas than your reporting service. Micro services allow that
Speaker 2: plus independent development cycles, but it definitely adds complexity, especially
Speaker 2: around debugging and interservice communication. It's a tradeoff.
Speaker 1: Not a magic bullet makes sense. Okay, we've covered building scalable,
Speaker 1: resilient apps. Let's shift gears a bit to more complex
Speaker 1: setups like those micro services and then securing everything. How
Speaker 1: does Kubernetes actually handle the networking between all these different
Speaker 1: services inside the cluster and how do they find each other?
Speaker 2: Good question. Kubernetes has really robust internal services and load balancing.
Speaker 2: First off, every single pod gets its own unique IP
Speaker 2: address within the cluster, and if a pod has multiple containers,
Speaker 2: they all share that same pod IP address.
Speaker 1: Okay, so pods have ips, how do they find the
Speaker 1: right IP for the service they need to talk to,
Speaker 1: especially since POD's coming go.
Speaker 2: That's where the service object comes in again, specifically the
Speaker 2: cluster IP type we mentioned for internal communication the service
Speaker 2: it's a stable virtual IP address. When a POD sends
Speaker 2: traffic to that service IP, Kubernetes automatically load balances it
Speaker 2: across all the healthy pods backing that service, the ones
Speaker 2: with the matching labels, so.
Speaker 1: The service IP stays the same even if the pod
Speaker 1: ips change underneath exactly.
Speaker 2: It provides that stable endpoint for actually finding that service.
Speaker 2: IP pods typically use Kubernetes built in DNS service. They
Speaker 2: can just resolve a predictable name like my backendservice dot,
Speaker 2: my namespace, dot SVC, dot cluster, dot local, and DNS
Speaker 2: gives them the right cluster IP so DNAs.
Speaker 1: For discovery yeh neat. And what about getting traffic into
Speaker 1: the cluster from the outside world, especially if you have
Speaker 1: multiple services you want to expose.
Speaker 2: That's the job of ingress. Think of ingress as an
Speaker 2: intelligent HTTP and HTTPS load balancer or reverse proxy sitting
Speaker 2: at the edge of your cluster.
Speaker 1: What makes it intelligent It allows you.
Speaker 2: To expose multiple internal services through a single external IP address.
Speaker 2: You can define rules to route traffic based on the
Speaker 2: requested path like goes to your front end service as
Speaker 2: EP goes to your back end API service, or you
Speaker 2: can route based on the host name requested, so.
Speaker 1: One load balance er IP can serve multiple applications or
Speaker 1: parts of applications correct.
Speaker 2: And a really significant benefit of using ingress is TLS termination.
Speaker 1: Meaning it handles the HTTPS encryption exactly.
Speaker 2: The Ingress controller handles the SSLTLS handshake with the client,
Speaker 2: decrups the traffic, and then forwards PLANYHGTP traffic to your
Speaker 2: internal services. If you want, this offloads that CPU intensive
Speaker 2: encryption work from your application pods, letting them focus purely
Speaker 2: on their job.
Speaker 1: That's a huge win for efficiency in simplifying application code. Okay,
Speaker 1: this routing is powerful, but what if you have different
Speaker 1: kinds of nodes in your cluster. Maybe some have GPUs,
Speaker 1: some have faster disks, some are arm architecture. How do
Speaker 1: you make sure the right workloads land on the right hardware.
Speaker 2: Yeah, that's super common. You use node feature selection. Kubernetes
Speaker 2: nodes can have labels applied to them, just like pods.
Speaker 2: These labels describe their characteristics, things like kubernetest ioor Dot
Speaker 2: AMD sixty four or cloud dot, Google dot Com for
Speaker 2: GKSH Accelerator, dot Vidia Dash, Tesla DASHT four.
Speaker 1: The labels identify node capabilities. How do you use them?
Speaker 2: You use node selectors or the more powerful node affinity
Speaker 2: rules in your POD specification. A node selector is simple,
Speaker 2: only schedule this pod on nodes with the labeled disc
Speaker 2: type SSD. Node affinity is more expressive. You can say
Speaker 2: things like, I prefer nodes with GPUs, but it's okay
Speaker 2: if none are available, or I require a node that's
Speaker 2: in availability zone A central one.
Speaker 1: A gives you fine grained control over placement. What about
Speaker 1: the opposite, keeping certain workloads off specific nodes.
Speaker 2: For that, you use taints and tolerations. You apply a
Speaker 2: taint to a node, for example, tainting all your GPU
Speaker 2: nodes by default, No pods will be scheduled onto a
Speaker 2: tainted node unless that pod has a matching toleration in
Speaker 2: its spec ah.
Speaker 1: So it's like a repellent unless the pod has the antidote.
Speaker 2: Good way to put it. It's perfect for isolating workloads
Speaker 2: onto dedicated hardware, or maybe keeping general workloads off nodes
Speaker 2: reserved for special.
Speaker 1: Tasks, and for spreading things out for high availability, making
Speaker 1: sure a single node failure doesn't hurt too much.
Speaker 2: For that, you'd use topology spread constraints. These let you
Speaker 2: tell Kubernetes try to spread the pods for this deployment
Speaker 2: evenly across different nodes, or even better, spread them evenly
Speaker 2: across different availability zones. It significantly improves resilience against localized failures.
Speaker 1: Wow lots of control over placement and resilience. There. Yeah, okay,
Speaker 1: let's tackle the big one again. State. We know stateless
Speaker 1: is ideal for scaling, but many apps need state like databases.
Speaker 1: How does Kubernetes handle persistent data storage reliably?
Speaker 2: Kubernetes is actually very well equipped for stateful applications thanks
Speaker 2: to its volume system. We mentioned empty for temporary scratch
Speaker 2: based on the node and can fig maps for mounting
Speaker 2: configuration files right. But for persistent data, the core concepts
Speaker 2: are persistent volumes pvs and persistent volume claims PVCs.
Speaker 1: PVS and PVCs explain those think of it like this.
Speaker 2: A persistent volume PV represents an actual piece of storage
Speaker 2: in your cluster, like a network disc provided by your
Speaker 2: cloud provider. A persistent volume claim PVC is a request
Speaker 2: for storage made by a POD, or rather by you
Speaker 2: on behalf of the pod. The POD says I need
Speaker 2: ten give of storage with these characteristics via the PVC.
Speaker 1: So the PVC is the request, the PV is the
Speaker 1: actual storage, fulfilling.
Speaker 2: It exactly, and Kupernetes binds a suitable PV to the PVC.
Speaker 2: This decouples the POD's need for storage from the specific
Speaker 2: underlying storage implementation. Your pod just asks for what it needs.
Speaker 1: How does the underlying storage actually get created? Does someone
Speaker 1: have to manually create those cloud discs?
Speaker 2: You can do it manually, but usually you use storage
Speaker 2: classes for dynamic provisioning. A storage class defines a type
Speaker 2: of storage like fast SSD or standard HDD, and tells
Speaker 2: Kubinetes how to automatically provision a new PV and its
Speaker 2: underlying cloud disc whenever a PVC.
Speaker 1: Requests that class automation.
Speaker 2: Again, Yes, and crucially, when you define your storage class
Speaker 2: you can set its reclaim policy. Setting it to retain
Speaker 2: is highly recommended for production data.
Speaker 1: Why retain it.
Speaker 2: Means even if you delete the PVC and PV objects
Speaker 2: inside Kupernetes, the actual underlying cloud disc with your data
Speaker 2: will not be deleted. It protects your precious data from
Speaker 2: accidental quebectyl delete commands. You'd have to manually delete the
Speaker 2: disc later.
Speaker 1: That's a critical safety net. Okay, so that works for
Speaker 1: single pods needing storage. What about clustered state apps like
Speaker 1: a multnode database.
Speaker 2: For those, Kubernetes provides the stateful set workload controller. It's
Speaker 2: specifically designed for applications that need stable unique network identifiers
Speaker 2: for each replica and stable persistent storage tied to each
Speaker 2: specific replicaxable. How each pod in a stateful set gets
Speaker 2: a predictable ordinal host name like my dB zero, my
Speaker 2: dB one, my dB two, and it automatically creates a
Speaker 2: dedicated PVC for each replica based on a template you provide,
Speaker 2: So my dB zero always gets the same persistent disc
Speaker 2: even if the pod gets rescheduled.
Speaker 1: Perfect for things like database clusters or zoo keeper nodes.
Speaker 2: Exactly what it's built for. And one more volume type
Speaker 2: worth mentioning generic ephemeral volumes. These are useful for large
Speaker 2: amounts of temporary data needed during a POD's lifetime, maybe
Speaker 2: for some complex calculation. They provide mounted disk space, but
Speaker 2: it's automatically cleaned up when the pod is deleted, faster
Speaker 2: than empty eder for large amounts of data.
Speaker 1: Sometimes, got it. So lots of options for state Now
Speaker 1: what about all those background tasks things that aren't directly
Speaker 1: serving user requests but are critical batge processing, image resizing,
Speaker 1: sending newsletters.
Speaker 2: Yeah, Kubernetes has excellent support for various kinds of background processing.
Speaker 2: The approach depends on the type of task. Okay, like what, Well,
Speaker 2: if you have a task que that needs to run continuously,
Speaker 2: maybe processing jobs offloaded from your web front, and you
Speaker 2: typically just use a standard deployment, you'd have worker pods
Speaker 2: running your processing code constantly pulling tasks from an external
Speaker 2: qu system like rehttis or Rabbit MQ.
Speaker 1: So a deployment for continuous background work. What about tasks
Speaker 1: that just run once and finish?
Speaker 2: For those one off tasks like running a database migration
Speaker 2: script or a specific batch analysis, the dedicated job object
Speaker 2: is perfect. You define a job, Kubernetes runs the pod
Speaker 2: associated with it until they complete successfully, and then the
Speaker 2: job is marked as.
Speaker 1: Finished and sure as it runs to completion. Can you
Speaker 1: schedule these jobs like run a report every night?
Speaker 2: Absolutely, That's what cron job is for. You define a
Speaker 2: job template and provide a schedule using the standard Unix
Speaker 2: Kron format like zero two for two am every day.
Speaker 2: Kubernetes takes care of creating a new job based on
Speaker 2: that schedule.
Speaker 1: Very handy. Can jobs handle like processing a fixed list
Speaker 1: of items in parallel?
Speaker 2: Yes, they can do that too. You can configure a
Speaker 2: job for a static work queue using parameters like completions,
Speaker 2: the total number of tasks to complete and parallelism how
Speaker 2: many plots should run simultaneously. Each pod gets an environment
Speaker 2: variable job completion index, which you can use to figure
Speaker 2: out which specific item from the list it's supposed to process.
Speaker 1: Lever and do these background tasks need health checks too?
Speaker 2: Like liveness probes, they definitely can and often should. Even
Speaker 2: a background worker can get stuck or hang. You could,
Speaker 2: for example, have a command based liveness probe that checks
Speaker 2: the timestamp on a file the worker is supposed to
Speaker 2: update periodically. If the file gets too old, the probe
Speaker 2: fails and Kubernetes restarts the worker, ensuring your background processing
Speaker 2: doesn't just silently.
Speaker 1: Stop reliability everywhere. This is amazing, but it also sounds
Speaker 1: like a lot of yammal files, deployments, services, jobs, cron jobs, PVCs.
Speaker 1: How do people manage all this configuration consistently, especially across
Speaker 1: different environments like staging in production, and keep it secure.
Speaker 2: That is a huge challenge, and it leads us directly
Speaker 2: to a really important practice called git ops get ups.
Speaker 1: So using Git for operations.
Speaker 2: Essentially, yes, it means treating your Kubernetes configuration all those
Speaker 2: yammo files as code. You store them in a Git repository.
Speaker 1: Ah, okay, what are the benefits?
Speaker 2: Massive benefits. First, you get version control for your infrastructure configuration.
Speaker 2: You have a history of every change. You can do
Speaker 2: code reviews for infrastructure changes, just like application code Collaboration
Speaker 2: becomes much easier.
Speaker 1: Makes sense, audit trail rollbacks.
Speaker 2: Exactly, and it makes spinning up multiple environments super easy.
Speaker 2: You can have branches or directories in your Git repo
Speaker 2: for production, staging, development, etc. Perhaps using tools like customize
Speaker 2: or helm to manage the differences, but pulling from the
Speaker 2: same core.
Speaker 1: Figuration consistency across environments. How does the deployment part work
Speaker 1: with gitups?
Speaker 2: Typically, a Git push to the repository triggers an automated pipeline,
Speaker 2: maybe using Jenkins get lab ci GitHub actions that applies
Speaker 2: the changes to the target Kubernetes cluster using Quebec del play,
Speaker 2: or even better, you can use a dedicated gitups operator
Speaker 2: like flux or rgocd running inside your cluster.
Speaker 1: How's that work?
Speaker 2: The operator continuously monitors your Git repository and automatically reconciles
Speaker 2: the cluster's state to match whatever is defined and get.
Speaker 2: So Git becomes the single source of truth you change
Speaker 2: the YAML and GIT, the operator sees it and updates
Speaker 2: the cluster. No manual quebectal commands needed for deployment.
Speaker 1: That sounds very powerful and declarative. But what about secrets,
Speaker 1: database passwords, API keys. Yeah, you don't commit those to.
Speaker 2: Get right, Absolutely not. Sensitive information like that should be
Speaker 2: stored in Kubernetes's secret objects. These are separate from your
Speaker 2: regular yamlcinfigs. You create the secret securely with Kubernetes, often
Speaker 2: populated from a secure vault system, and then you reference
Speaker 2: that secret in your deployment yamol telling Kubernetes to mount
Speaker 2: the secret data as files or environment variables into your container.
Speaker 2: It keeps secrets out of GIT but accessible to your application.
Speaker 1: Okay, crucial distinction there. So get ops from managing the
Speaker 1: can fig now tying everything together security. How do we
Speaker 1: ensure this whole Kubernetes platform, from the cluster infrastructure itself
Speaker 1: right down to our individual applications is secure. This seems
Speaker 1: like a huge topic.
Speaker 2: It is huge, and it's multi layer. There's no single
Speaker 2: silver bullet. First, the basics. Keep things updated. That means
Speaker 2: regular cluster updates. Managed services like gk Offer release channels
Speaker 2: that help automate patching the Kubernetes control.
Speaker 1: Plane in nodes see Foundation secure.
Speaker 2: YEP, and just as important, diligently updating your container images
Speaker 2: regularly rebuild your application images using up to date secure
Speaker 2: base images. Minimal images like distrolists are fantastic because they
Speaker 2: can contain only your application and its runtime dependencies, no shell,
Speaker 2: no package manager, drastically reducing the a pack surface if
Speaker 2: someone did manage to break into the container.
Speaker 1: Smaller target makes sense. What about protecting running applications during maintenance,
Speaker 1: like those node updates.
Speaker 2: That's where pod disruption budgets or pdbs come in. A
Speaker 2: PDB tells Kubernetes for this application, like my front end deployment,
Speaker 2: I need at least x pods or y percent of
Speaker 2: pods to always remain available during voluntary.
Speaker 1: Disruptions voluntary like node upgrades or.
Speaker 2: Drains Exactly so, if an admin drains and node for maintenance,
Speaker 2: Kubernetes will respect the PDB and ensure it doesn't take
Speaker 2: down too many pods of that application at once, preventing
Speaker 2: an outage.
Speaker 1: Okay, service availability during maintenance. What about securing the pods
Speaker 1: themselves inside the container?
Speaker 2: Big one here Pod Security context. This lets you define
Speaker 2: granular security settings for your containers. The absolute most important
Speaker 2: one is configuring your container to run as a non
Speaker 2: route user.
Speaker 1: Why is running as non roots so critical?
Speaker 2: If an attacker compromises a process running as root inside
Speaker 2: a container, they potentially have much broader privileges, making it
Speaker 2: easier to escalate attacks or break out of the container.
Speaker 2: Running as a dedicated, unprivileged user significantly limits the blast
Speaker 2: radius of a compromise. It's a fundamental security best practice.
Speaker 1: Got it non route by default? Can you enforce these
Speaker 1: kinds of security settings across the cluster?
Speaker 2: Yes, using admission controllers, specifically the built in POD Security
Speaker 2: Admission Controller standard. Since Kubernetes View one point twenty five
Speaker 2: is key, You can apply policies at the namespace level,
Speaker 2: like the baseline or stricter restricted profile. These policies automatically
Speaker 2: check incoming POD configurations and will either mutate them to
Speaker 2: be compliant if possible, or reject them entirely if they
Speaker 2: violate the rules like trying to run as route when
Speaker 2: the policy forbids it.
Speaker 1: So automatic enforcement is security standards exactly.
Speaker 2: It prevents insecure configurations from ever getting into the cluster,
Speaker 2: and finally controlling who can do what in them cluster
Speaker 2: user permissions right that's managed by role based access control
Speaker 2: or RBAC. RBAC less cluster administrators define rolls which specify
Speaker 2: permissions like can get pods or can create deployments, and
Speaker 2: role bindings which assign those roles to specific users, groups
Speaker 2: or service accounts, often scoped to particular name spaces, so
Speaker 2: you can.
Speaker 1: Get developers permissions only in their own development name space,
Speaker 1: but not let them touch production precisely.
Speaker 2: Fine green control limit privileges to the minimum necessary prevent
Speaker 2: accidental or malicious changes. It's absolutely essential for securing a
Speaker 2: multi tenant or production cluster. Security really has to be
Speaker 2: built in at every layer.
Speaker 1: Wow. Okay, so we've really covered a huge amount of
Speaker 1: ground here. We've journeyed through the absolute core of Kubernetes,
Speaker 1: from packaging that first app in a container.
Speaker 2: Right through to scaling it globally, handling that tricky persistent state,
Speaker 2: running background jobs reliably.
Speaker 1: And finally locking it all down with robust multi layered
Speaker 1: security practic is in managing configuration with GitOps, you've really
Speaker 1: shown how to build systems that don't just survive going viral,
Speaker 1: but are actually engineered to thrive on that kind of success. Yeah.
Speaker 2: I mean, what we've really unpacked is how Kubernetes transforms
Speaker 2: raw compute power, raw infrastructure into this intelligent, self managing,
Speaker 2: self healing platform. It lets you the developer or operator,
Speaker 2: focus much more on your application's logic and delivering value,
Speaker 2: rather than getting bogged down in the endless data day
Speaker 2: fiddling with infrastructure details.
Speaker 1: Abstracts ways so much complexity will still giving you that
Speaker 1: control when you need it exactly.
Speaker 2: So, maybe a final thought to leave everyone with, Considering
Speaker 2: everything we've dug into today, the automation, the scaling, the resilience,
Speaker 2: the security. If your application genuinely hit the headlines tomorrow
Speaker 2: went massively viral, how quickly could you scale it to
Speaker 2: the moon. How confident are you right now that every
Speaker 2: piece of your architecture, from the container build to the
Speaker 2: deployment strategy to the auto scaling rule is truly ready
Speaker 2: for that ride