When Kubernetes first appeared on the scene, it was built around a simple but powerful idea: treat your applications as stateless. If a container died, Kubernetes would start a new one somewhere else in the cluster, and life would go on. This worked brilliantly for microservices that didn’t need to remember anything from one request to the next.
But then reality knocked on the cluster door. The business world runs on data: order histories, user profiles, financial transactions, product inventories, logs, analytics. These workloads aren’t stateless; they depend on keeping and accessing the same data over time. Suddenly, Kubernetes needed to figure out how to handle applications where “just restart it” could mean losing terabytes of critical information.
And this is where persistent storage enters the story. Without it, running stateful workloads in Kubernetes is like running a database on a temporary desk made of ice. You can write all you want, but the moment the temperature changes, everything melts.
Stateless vs. Stateful Workloads: The Divide That Changes Everything
The easiest way to understand the need for persistent storage is to look at the difference between stateless and stateful workloads in Kubernetes.
A stateless service is like a toll booth operator who doesn’t keep any records. Cars pass, they collect the toll, and the job is done. If the operator goes home and a replacement shows up, no history is lost. In Kubernetes terms, that is an HTTP API serving product listings, a rendering service for PDFs, or a lightweight event processor.
Stateful workloads, on the other hand, are more like a bank clerk. Every transaction needs to be recorded, stored, and accessible later. If the clerk disappears along with the records, the bank’s operations fall apart. In Kubernetes, that is your MySQL database, your Kafka brokers, your Elasticsearch cluster, or even Redis when running in persistence mode.
The technical reason for this divide lies in Kubernetes’ pod lifecycle: pods are ephemeral. They are not tied to specific hardware, and they can be deleted or rescheduled at any moment. This is great for scaling and resilience but terrible for anything that depends on local data being around tomorrow.
The Problem with Ephemeral Storage
Every pod in Kubernetes comes with some built-in storage, but it’s ephemeral, meaning it exists only as long as the pod exists. If the pod is destroyed, either because you deployed an update or because the node running it crashed, that storage is wiped clean.
In Kubernetes, you can use volumes like emptyDir for temporary storage. They are perfect for caches, temp files, or short-lived computation. But they are tied to the pod lifecycle. That means if your PostgreSQL pod is using emptyDir to store its database files, you might as well be storing them in /tmp once the pod is gone, so is your data.
This ephemeral nature also complicates recovery. Imagine a Kafka broker pod failing. Without persistent storage, when Kubernetes spins up a new broker, it is starting from scratch. The message offsets are gone, the partition replicas are gone, and the cluster has to rebuild state from other replicas if they exist at all.
Persistent Storage: Decoupling Data from Compute
The core idea behind persistent storage in Kubernetes is decoupling the data from the pod. Your compute resource (the pod) can come and go, but the data it uses lives independently on a storage system that Kubernetes can reattach when needed.
This model lets you:
- Survive node failures without losing data.
- Perform rolling updates without wiping application state.
- Scale stateful workloads across nodes without manual intervention.
- Maintain consistent application behavior, even across reschedules.
From an implementation perspective, Kubernetes gives us PersistentVolumes (PVs) and PersistentVolumeClaims (PVCs).
- A PV is the actual storage resource: this could be an AWS EBS volume, an Azure Managed Disk, a Google Persistent Disk, an NFS mount, or a Ceph RBD block device.
- A PVC is the contract between your application and that storage. Instead of hardcoding the storage details into your app configuration, you say, “I need 20GiB of ReadWriteOnce storage,” and Kubernetes figures out how to provision and attach it based on the available StorageClasses.
StatefulSets: Beyond Just Storage
While PersistentVolumes solve the storage problem, they don’t solve everything stateful workloads need. Many stateful applications rely on having stable network identities and ordered startup/shutdown sequences.
Take a database cluster with leader/follower nodes. You can’t just randomly start all pods at once and expect things to fall into place. Some nodes must start before others, and they need to keep the same name so that peers can find them.
That’s why Kubernetes introduced StatefulSets. Unlike Deployments, which treat pods as interchangeable cattle, StatefulSets treat pods more like named pets. Pod names are stable (app-0, app-1, etc.), and their associated PVCs are tied directly to those names.
This means that if mysql-0 dies, Kubernetes will recreate it as mysql-0 with the exact same PVC still attached regardless of which node it lands on. The application can resume operation without losing track of its data.
The Real-World Challenges of Persistent Storage in Kubernetes
Even with PVs, PVCs, and StatefulSets, storage in Kubernetes isn’t “plug and play” for every scenario.
- Performance tuning: Some workloads are highly sensitive to I/O latency. Choosing the wrong StorageClass or backend can bottleneck your entire system.
- Availability across zones: Many block storage systems are bound to a single availability zone, complicating HA deployments.
- Backup and DR: Persistent volumes aren’t the same as backups—if the underlying storage fails or is deleted, you still need recovery mechanisms like snapshots or replication.
- Multi-writer complexity: Workloads needing ReadWriteMany access require careful coordination to avoid corruption, often using shared file systems or distributed storage.
And there’s a deeper reason this all feels hard: most traditional external storage isn’t Kubernetes-native. Because it sits outside the Kubernetes control plane with its own scheduler, failure domains, and data-service model Kubernetes can’t naturally coordinate attach/detach, failover, or policies, so reschedules become brittle and operations feel bolted on.
Container-Native Storage: The Modern Answer
Persistent storage in Kubernetes isn’t just about having a disk that survives pod restarts. It’s about having storage that understands and speaks Kubernetes’ language. Traditional storage systems were designed long before containers became mainstream. They often treat Kubernetes as just another client, bolting themselves onto the cluster from the outside. This works in theory, but in practice it creates friction: manual provisioning, complex integration steps, mismatched scaling patterns, and poor automation.
Container-Native Storage (CNS) turns that model inside out. Instead of being an external system that Kubernetes has to talk to, CNS is deployed inside Kubernetes as a set of microservices, just like your applications. The storage layer becomes a citizen of the same environment – scheduled, scaled, and managed using the same Kubernetes primitives as everything else.
This shift matters for persistent storage because it solves the two big challenges we’ve been circling in this blog:
- Ensuring data truly outlives the pod in a way that is reliable and predictable during failovers.
- Making persistence as dynamic and automated as the rest of Kubernetes, so you don’t have to treat stateful workloads like special snowflakes.
With CNS, persistent volumes aren’t provisioned manually by a storage admin in advance; they are created dynamically when a PersistentVolumeClaim is made. The moment your application says, “I need 50GiB of ReadWriteOnce storage,” the CNS layer automatically provisions a volume, integrates it with Kubernetes’ PersistentVolume subsystem, and binds it to your workload.
Because CNS is distributed across the cluster:
- Data can be replicated across nodes for high availability, so the loss of a node doesn’t mean the loss of your storage.
- Failover is native: If a pod moves to another node, the storage moves with it (or an identical replica is already there).
- Storage performance scales with the cluster: Adding nodes doesn’t just give you more compute; it gives you more storage capacity and throughput as well.
- Data services like snapshots, thin provisioning, etc. are built right into the same environment, without requiring external management tools.
In other words, CNS doesn’t just give Kubernetes persistent storage—it gives it “Kubernetes-native persistent storage”. The persistence layer no longer lags behind the compute layer in automation, resilience, and scale. This is what finally makes it possible to treat stateful workloads with the same operational confidence as stateless ones.
How DataCore Can Help
Choosing and running the right persistent storage strategy in Kubernetes isn’t just about picking a technology. It’s about aligning that technology with your application’s performance profile, availability needs, and growth plans. This is where DataCore can make a difference.
DataCore’s expertise lies in building software-defined, container-native storage solutions that are designed to integrate seamlessly with Kubernetes. By combining enterprise-grade data services—like high availability, replication, snapshots, and backup integration—with a Kubernetes-native operational model, DataCore helps organizations run even their most demanding stateful workloads with confidence.
Whether you are modernizing existing applications, deploying cloud-native databases, or building new stateful services from the ground up, DataCore provides the tooling, architecture guidance, and operational support to ensure your storage layer is as agile, resilient, and automated as Kubernetes itself. The result: a platform where both stateless and stateful workloads can thrive side by side, without compromise.
Ready to make your Kubernetes persistent storage layer production-grade? Contact us to discuss how DataCore can help you run stateful workloads with enterprise-grade reliability and performance.
