On this page
The Evolution of Software Deployment
What is Kubernetes?
The Magic of Desired State
Behind the Scenes: The Core Architecture
The Control Plane Components
The Worker Node Components
The Smallest Building Block: Pods
Managing Network Traffic Internally
Automated Rollouts and Scaling
Handling Persistent Data
Putting It All Together
Conclusion
The Ultimate Kubernetes Guide for System Design
On This Page
The Evolution of Software Deployment
What is Kubernetes?
The Magic of Desired State
Behind the Scenes: The Core Architecture
The Control Plane Components
The Worker Node Components
The Smallest Building Block: Pods
Managing Network Traffic Internally
Automated Rollouts and Scaling
Handling Persistent Data
Putting It All Together
Conclusion
Software engineering teams face a massive hurdle when applications experience sudden spikes in network traffic.
A rapid influx of user requests quickly drains the memory and processing power of a web server.
When a physical server exhausts its available hardware resources, the software application crashes completely. Relying on manual human intervention to restart the failed services guarantees unacceptable system downtime.
Building highly reliable software requires a fully automated approach to deployment and scaling.
Let's get started.
The Evolution of Software Deployment
Historically, engineers deployed software directly onto physical hardware servers.
A single operating system shared its processing power among multiple different applications.
This created severe resource allocation conflicts.
If one application experienced a memory leak, it starved the other applications and caused system-wide crashes.
Engineers then introduced virtual machines to partition physical hardware into smaller units.
A virtual machine allows multiple independent operating systems to run on a single physical server.
This safely isolates applications from one another and prevents resource stealing. However, virtual machines are incredibly heavy and consume significant background memory.
Every virtual machine requires its own full operating system to function properly. This makes them slow to start and highly inefficient for rapid scaling. The software industry solved this efficiency problem by adopting containers.
A container is a standalone software package holding the application code and its required background libraries.
Unlike virtual machines, containers share the single underlying host operating system. This architectural difference makes containers incredibly lightweight and lightning fast.
Containers isolate applications perfectly from one another on the same physical machine. They guarantee that the code will execute identically in any computing environment.
However, moving to this new model created a massive logistical problem. A large microservice architecture might require thousands of running containers simultaneously.
These isolated pieces of software must communicate securely over an internal network. Managing thousands of individual containers manually is mathematically impossible for a human engineering team.
What is Kubernetes?
Let us look at how we solve this massive management problem.
Kubernetes is an open-source platform built specifically for container orchestration. It acts as the central management system for a massive network of computers.
The platform automates the deployment, scaling, and continuous monitoring of containerized applications.
Developers no longer need to start and stop individual software instances manually.
Instead, they provide a simple configuration file describing how the final system should look.
Kubernetes operates continuously in the background to make that configuration a reality. It monitors the underlying hardware constantly to ensure continuous software uptime.
The Magic of Desired State
To truly grasp how this technology works, we need to understand the difference between imperative and declarative systems. In an imperative setup, a developer writes exact sequential instructions to achieve a specific goal.
This requires writing massive amounts of complex logic to handle every possible error scenario.
Kubernetes completely abandons this approach and uses a strictly declarative model instead.
In a declarative system, we simply define the final required state of the application. For instance, a configuration file might state that five copies of a specific payment processing service must run continuously.
We do not write the detailed instructions explaining how to start them. We simply submit this desired state directly to the platform.
The system takes complete control from that exact moment forward. It continuously runs a monitoring loop to check the actual current state of the software. It mathematically compares the actual state against our declared desired state.
If a hardware failure causes two payment services to crash, the actual state drops to three.
The system immediately detects this sudden discrepancy in the numbers. It automatically provisions two brand new copies on a healthy server to restore the desired state of five.
This automated self-healing process ensures high availability without any human intervention. Developers can sleep peacefully knowing the system fixes itself.
Behind the Scenes: The Core Architecture
A complete Kubernetes environment is known as a Cluster.
A cluster is simply a unified collection of computing machines working together. We divide every cluster into two distinct structural sections.
These two sections are the Control Plane and the Worker Nodes.
The Control Plane acts as the central brain of the entire distributed system. The Worker Nodes act as the muscle by executing the actual application workloads. Understanding how these two sections communicate is absolutely vital for mastering modern system design. Let us break down the specific components inside each section.
The Control Plane Components
The Control Plane consists of several independent software components that manage the cluster. It makes all the global decisions regarding deployment and health monitoring.
The API Server serves as the central communication hub. It acts as the primary gateway for the entire architecture.
Whenever we want to deploy new software, we send a network request directly to the API Server. It validates our request and securely processes the configuration data. The etcd component acts as the permanent memory of the system. It is a highly reliable database storing the complete configuration and current hardware status.
All cluster data lives permanently inside etcd.
If the system experiences a catastrophic failure, it can recover completely by reading the data saved here.
The Scheduler is the component responsible for assigning computational workloads. When a new software instance needs to run, the Scheduler evaluates the specific memory requirements.
It examines all available servers to find a machine with enough free processing power. Once it finds an optimal server, it assigns the application to execute there.
The Controller Manager is a continuous background monitoring program. It runs multiple internal loop processes constantly to check system health.
These loops check the actual state of the system against the desired state stored in etcd.
If a mismatch exists, the Controller Manager instructs the cluster to take corrective action immediately. This continuous monitoring ensures the cluster remains highly reliable at all times.
The Worker Node Components
The Worker Nodes are the individual physical or virtual servers that execute our software.
A typical production environment might contain hundreds of these worker servers. Each node runs specific background programs to integrate seamlessly with the central brain.
The Kubelet is a tiny software agent running on every single Worker Node.
It registers the server with the central control system and continuously listens for instructions. When it receives a new assignment, it ensures the requested software starts correctly.
If an application crashes, the Kubelet reports this critical failure back to the central brain. The Container Runtime is the actual underlying software responsible for running the code.
While the Kubelet receives the orders, it hands the execution over to the Container Runtime. This runtime handles downloading the software packages and running them on the local operating system.
The Kube-proxy handles all internal and external network routing. It is a vital networking program that runs on every worker server.
It maintains a strict list of network forwarding rules. These rules ensure that internal network communication finds the correct destination application seamlessly.
Without these precise routing rules, the isolated containers would be unable to communicate with each other.
The Smallest Building Block: Pods
The most crucial concept for beginners to learn is that this platform does not run containers directly. It wraps containers inside a higher-level structure called a Pod.
A Pod is the smallest deployable computing unit we can create and manage in this ecosystem.
A Pod typically contains a single running software container.
However, tightly coupled containers can share a single Pod if they require the exact same resources.
All containers inside the same Pod share the exact same internal network address. They also share the same local storage directories to pass data back and forth.
When a system needs to scale up, it does not add more containers to an existing Pod.
Adding containers to a Pod does not actually increase the available processing power.
Instead, the system creates entirely new replica Pods to handle the additional workload. These identical replica Pods are distributed evenly across multiple different worker servers.
This distribution ensures the workload is balanced properly.
Managing Network Traffic Internally
Applications are frequently destroyed and recreated on different physical servers. Because of this constant movement, individual Pods are completely ephemeral. Ephemeral simply means they are temporary and short-lived.
When a Pod is deleted and recreated, it receives a completely different internal network address.
Relying on direct network addresses to communicate with applications is impossible in this highly dynamic environment.
To solve this complex routing problem, we use a core construct called a Service.
A Service provides a single permanent network address for a group of temporary Pods. When our web application needs to communicate with our database, it sends data to the permanent Service address.
The Service then acts as an automatic internal load balancer. It looks at all the currently active Pods associated with that specific database. It distributes the incoming network traffic evenly across all the healthy Pods.
This prevents any single Pod from becoming overwhelmed with too many network requests.
Automated Rollouts and Scaling
Updating a live software application is traditionally a very risky process. Deploying new code often requires taking the old system offline.
Kubernetes solves this problem through a mechanism called an automated rolling update. When we submit a new version of our application code, the system does not shut down the old version immediately.
Shutting down the old version would instantly drop all active user connections.
Instead, the platform slowly creates new Pods running the updated software version. It waits patiently for the new Pods to become fully healthy and ready to accept traffic.
Once verified, the system safely terminates a few of the older Pods.
It continues this careful replacement process sequentially until every Pod runs the new version.
If the new code crashes upon launch, the system detects the error instantly. It automatically halts the rollout process and reverts to the previous working version.
This guarantees zero downtime and protects users from faulty software releases.
Modern systems also require dynamic scaling based on real-time usage metrics. Kubernetes utilizes an autoscaler component to achieve this automatically. This autoscaler continuously monitors the processing power and memory consumption of our running Pods.
If an application suddenly experiences high user traffic, processor usage will spike significantly.
The autoscaler detects this elevated metric instantly. It automatically updates the configuration to request more running copies of the application. The system responds by deploying additional Pods to handle the heavy load safely. When the traffic subsides, the autoscaler removes the unnecessary resources to save computing power.
Handling Persistent Data
Containers are completely temporary by design. When a container crashes, any data saved inside its internal file system is permanently destroyed.
This behavior works perfectly for stateless applications that only process incoming network requests. However, databases require permanent data retention.
They cannot lose user records simply because a temporary container restarts. The system handles this strict requirement through the use of Volumes.
A Volume is a dedicated storage directory that exists completely independent of any specific container. The system attaches this persistent storage directory directly to a Pod.
When the database writes data, it is stored safely on a separate physical storage drive.
If the container crashes and a replacement is created, the new container automatically connects to the exact same Volume. The application resumes operating perfectly with its permanent data fully intact. This ensures absolute data safety during hardware failures.
Putting It All Together
Let us trace exactly what happens during a standard software deployment.
This technical walkthrough illustrates the entire system design in action. We will see how the components collaborate to maintain absolute stability.
First, we create a declarative configuration file specifying that three copies of a web server must run.
We submit this file over the network to the API Server.
The API Server validates our data and saves this new desired state directly into the etcd database. At this precise moment, zero copies are actually running.
The Controller Manager notices this specific mismatch during its continuous background loop.
To fix this, it generates three new pending Pod creation requests.
The Scheduler notices these three pending Pods and evaluates their strict memory requirements. It selects the three most optimal Worker Nodes and updates the API Server with these assignments.
![][image4]
On the selected Worker Nodes, the local Kubelet processes notice their new assignments.
The Kubelet instructs the local Container Runtime to download the application code and start the containers.
Once running, the Kubelet performs a health check and reports back to the API Server.
The actual state of the cluster now perfectly matches the desired state stored in the database. The system has successfully deployed the software completely automatically.
Conclusion
Understanding this container orchestration platform fundamentally changes how engineers design distributed systems. It abstracts the highly unreliable hardware layer away from the software application completely.
This allows architects to focus purely on the structural logic of their code.
Here are the key takeaways from this guide:
-
Automated Orchestration: Manual configuration of massive software systems is highly prone to failure. Automated platforms ensure continuous availability without constant human intervention.
-
Declarative Configuration: We declare the final desired state of an application. The automated system forces the actual reality to match that exact state continuously.
-
Decoupled Architecture: The system uses a central Control Plane to make global decisions. Worker Nodes execute the actual application code securely.
-
Container Encapsulation: The system manages computing resources in abstract units called Pods. A Pod securely holds one or more tightly coupled application containers.
-
Stable Internal Networking: Permanent network Services act as reliable load balancers. They route traffic safely to computing units that are constantly destroyed and replaced.
-
Continuous Self Healing: Background monitoring loops automatically detect unexpected hardware failures. They instantly replace crashed applications on healthy machines to prevent downtime.
What our users say
Eric
I've completed my first pass of "grokking the System Design Interview" and I can say this was an excellent use of money and time. I've grown as a developer and now know the secrets of how to build these really giant internet systems.
Roger Cruz
The world gets better inch by inch when you help someone else. If you haven't tried Grokking The Coding Interview, check it out, it's a great resource!
MO JAFRI
The courses which have "grokking" before them, are exceptionally well put together! These courses magically condense 3 years of CS in short bite-size courses and lectures (I have tried System Design, OODI, and Coding patterns). The Grokking courses are godsent, to be honest.
Designgurus on Substack
Deep dives, systems design teardowns, and interview tactics delivered daily.
Access to 50+ courses
New content added monthly
Certificate of completion
$24.92
/month
Billed Annually
Recommended Course
Grokking the System Design Interview
164,733+ students
4.7
Grokking the System Design Interview is a comprehensive course for system design interview. It provides a step-by-step guide to answering system design questions.
View CourseRead More
Master Your System Design Interview: In-Depth Guide to Cache Invalidation Strategies
Arslan Ahmad
Consistency Patterns in Distributed Systems: A Complete Guide
Arslan Ahmad
5 Best Leader Election Algorithms for System Design
Arslan Ahmad
Grokking Scalability in System Design: Techniques, Principles, and Best Practices
Arslan Ahmad
