Kubernetes and databases, friends or foes?
Will the Future Belong to Kubernetes as an infrastructure provider for Databases?!
Alireza Kamrani
About existing challenges & Concerns to implementing Databases and Stateful products inside Kubernetes:
🗿Rethinking Databases and Stateful Workloads in Cloud-Native Environments
Kubernetes has become the de facto standard for container orchestration, largely because it helps organizations reduce infrastructure costs and simplify scalability and deployment. Many cloud providers have extended Kubernetes capabilities to support relational databases, often integrating them into hybrid or managed environments to improve cost efficiency and elasticity.
However, behind these services are specialized engineering teams fine-tuning complex infrastructure layers—particularly custom operators that handle database orchestration, replication, and recovery. For high-end databases like Oracle, only Oracle’s own cloud platform provides a truly stable and performant managed service, thanks to its specialized hardware stack. The same is true for systems like Kafka, where performance and resilience are extremely sensitive to latency and temporary downtime.
The Challenge of Stateful Workloads on Kubernetes
Running stateful workloads—especially databases—on Kubernetes is not inherently wrong, but it comes with deep technical implications.
Kubernetes was originally designed for stateless applications, where scaling and failover are simple. In contrast, databases rely on data consistency, low latency, and controlled failover, which require careful orchestration.
The main challenges typically arise from:
• Persistent Volumes and Storage Classes — The performance and stability of a database cluster depend heavily on the storage backend (e.g., Ceph, EBS, Longhorn, or OpenEBS).
• Network Complexity — Internal/external networking, service mesh configuration, and pod-to-pod latency can significantly impact query performance.
• Operator Maturity — A robust and customized Database Operator is essential to manage failover, replication, and consistency across nodes.
• Team Expertise — Database administrators and Kubernetes engineers must collaborate closely; operational silos often lead to instability in production.
Hybrid Architectures: A Practical Middle Ground
For most organizations, the best approach today is a hybrid architecture:
• Run stateless applications inside Kubernetes, benefiting from agility and automation.
• Keep mission-critical databases outside, in managed or dedicated environments that guarantee performance and reliability.
This model allows teams to mature their Kubernetes operations gradually, while avoiding risks associated with stateful workloads. Over time, as Kubernetes-native storage and operators evolve, more stateful workloads will safely migrate inside the cluster.
🏴☠️When Kubernetes Kills Database Performance?
Kubernetes can severely degrade database performance when certain architectural and operational pitfalls are ignored. Here’s where things usually go wrong:
Improper Storage Layer
Using slow or non-SSD storage backends (e.g., NFS or network volumes with high latency) causes transaction delays and poor IOPS for write-heavy databases.
→ Always validate your StorageClass and underlying disk throughput.
Over-Aggressive Pod Scheduling
When the scheduler relocates database pods frequently (due to node pressure or autoscaling), cache locality is lost and persistent volume reattachment delays occur.
→ Use node affinity, anti-affinity, and proper resource reservations.
Uncoordinated Scaling Events
Horizontal Pod Autoscalers (HPA) can’t easily understand transactional behavior. Blind scaling might increase concurrency but break performance consistency.
→ Combine autoscaling with workload-aware metrics and custom logic.
Unoptimized Network Path
Service Mesh or ingress routing adds overhead. Databases sensitive to latency (like Oracle and it's standby or PostgreSQL under heavy load) may experience jitter.
→ Keep DB pods close to clients and reduce unnecessary proxy layers.
Improper Resource Requests and Limits
CPU throttling and memory eviction due to misconfigured limits can silently destroy database performance under load.
→ Tune requests based on steady-state benchmarks, not theoretical peaks.
Lack of Observability
Without proper tracing, Prometheus metrics, or log correlation, root cause detection becomes guesswork.
→ Visibility is everything—monitor I/O, latency, and network hops continuously.
Finally, Kubernetes doesn’t kill performance — poor design does.
When engineered thoughtfully, databases can run stably inside clusters, especially for medium-priority or non-critical workloads.
🧑💻Troubleshooting and Proof of Performance
How DBA and SRE Teams Can Isolate “Slow States”
One of the toughest challenges for DBA and SRE teams in Kubernetes environments is proving that a slowdown didn’t originate on their side. Application teams often point to “the database” when latency spikes appear, but validating this requires a structured troubleshooting approach.
Recommended steps to isolate issues:
1. Establish Baselines
Continuously record baseline metrics: query latency, IOPS, CPU, and memory per database pod.
Use time-series analysis to compare “slow periods” against normal operation.
2. Correlate Metrics Across Layers
Combine database logs, Kubernetes metrics (via kubectl top pods or Prometheus), and application traces.
If latency increases without CPU/I/O pressure on DB pods, the issue may lie in the network or app layer.
3. Check Storage and Volume Attach Events
Review kubectl describe pods and node events for PersistentVolume reattachments. Even brief delays can create performance cliffs.
4. Inspect Node and Network Health
Verify if kubelet restarts, CNI latency, or DNS lookup delays occurred around the same timestamp.
5. Validate Application Query Behavior
Use query plans and APM tracing to confirm that slow queries weren’t caused by schema changes or unindexed joins.
6. Prove Negative Evidence Clearly
Build an evidence bundle: Grafana dashboards + database slow logs + system metrics.
This documentation helps SRE/DBA teams demonstrate that the database layer remained stable.
By following this methodology, teams can defend against false blame while improving collaboration between DBAs, SREs, and developers.
It’s not just about finding the root cause — it’s about building trust through transparency and data correlation.
The Role of AI and Intelligent Operators
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to transform how Kubernetes operators are designed and optimized.
While most database operators today handle replication, failover, and backup reliably, the next generation will integrate AI-driven intelligence to automate complex decisions and minimize human intervention.
Key emerging directions include:
Predictive Scaling & Auto-Tuning — Operators that analyze workload patterns and dynamically adjust replicas or resources based on CPU, I/O, and latency trends.
Anomaly Detection — Identifying performance degradation or abnormal database behavior and automatically triggering recovery actions.
Intelligent Failover Decisions — Choosing the best failover node by evaluating historical health, latency, and resource utilization rather than static rules.
Self-Healing Configurations — Continuously optimizing tuning parameters based on real-time metrics and feedback loops.
Some existing operators already provide a strong foundation for this vision:
- Crunchy PostgreSQL Operator (by CrunchyData)
- Percona Kubernetes Operator (for MySQL and MongoDB)
- Oracle Cloud Native Database Operator
- Operator Framework / Operator SDK for building custom AI-enabled operators.
The combination of these tools with ML pipelines and observability platforms like Prometheus, Grafana, and Cortex opens the door to truly autonomous and self-optimizing Kubernetes database management in the near future.
Best Practices and Recommendations
If you’re exploring Kubernetes for database workloads, consider the following:
• Start small — Begin with development or sandbox environments before touching production.
• Choose your storage backend wisely — Latency, throughput, and availability vary dramatically between storage solutions.
• Use StatefulSets, not Deployments — StatefulSets ensure stable network identities and persistent volumes for database pods.
• Invest in a strong operator — For databases like PostgreSQL, MongoDB, or MySQL, mature operators (such as CrunchyData or Percona) can dramatically improve resilience.
• Monitor everything — Use Prometheus and Grafana for metrics, and enable audit logging for database operations.
Looking Ahead:
The future does belong to Kubernetes — but not all at once, and not for every workload.
Stateless services will continue to dominate Kubernetes clusters, while stateful workloads will transition more slowly as the ecosystem matures. With advancements in storage abstraction, distributed volume systems, and intelligent operators, the boundary between “in-cluster” and “external” databases will blur over the next few years.
Until then, success depends less on tools and more on team expertise, observability, and iterative learning.
Start small, learn fast, and scale with confidence.
Alireza Kamrani
Database infrastructure manager
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