Choosing a Kubernetes Backup Strategy#

Kubernetes clusters contain two fundamentally different types of state: cluster state (the Kubernetes objects themselves – Deployments, Services, ConfigMaps, Secrets, CRDs) and application data (the contents of Persistent Volumes). A complete backup strategy must address both. Most backup failures happen because teams back up one but not the other, or because they never test the restore process.

What Needs Backing Up#

Before choosing tools, inventory what your cluster contains:

Choosing a Service Mesh#

A service mesh moves networking concerns – mutual TLS, traffic routing, retries, circuit breaking, observability – out of application code and into the infrastructure layer. Every pod gets a proxy that handles these concerns transparently. The control plane configures all proxies across the cluster.

The decision is not just “which mesh” but “do you need one at all.”

Do You Actually Need a Service Mesh?#

Many teams adopt a mesh prematurely. Before evaluating options, identify which specific problems you are trying to solve:

Choosing an Infrastructure as Code Tool#

Infrastructure as Code tools differ in language, state management, provider ecosystem, and operational model. The choice affects how your team writes, reviews, tests, and maintains infrastructure definitions for years. Switching IaC tools mid-project is possible but expensive – it typically means rewriting all definitions and carefully importing existing resources into the new tool’s state.

Decision Criteria#

Before comparing tools, establish what matters to your organization:

Choosing Kubernetes Storage#

Storage decisions in Kubernetes are harder to change than almost any other architectural choice. Migrating data between storage backends in production involves downtime, risk, and careful planning. Understand the tradeoffs before provisioning your first PersistentVolumeClaim.

The decision comes down to five criteria: performance (IOPS and latency), durability (can you survive node failure), portability (can you move the workload), cost, and access mode (single pod or shared).

Storage Categories#

Block Storage (ReadWriteOnce)#

Block storage provides a raw disk attached to a single node. Only one pod on that node can mount it at a time (ReadWriteOnce). This is the most common storage type for databases, caches, and any workload that needs fast, consistent disk I/O.

VPC Concepts#

A Virtual Private Cloud is an isolated virtual network inside a cloud provider. Every resource you launch – EC2 instances, RDS databases, Lambda functions with VPC access – lives inside a VPC. The VPC defines an IP address range using CIDR notation, and all resources within it get addresses from that range.

The most common mistake is giving every VPC a /16 (65,536 addresses). This wastes IP space and causes problems later when you need to peer VPCs – overlapping CIDR blocks cannot be peered. Plan your IP allocation before building anything.

Container Build Optimization#

A container build that takes eight minutes in CI is not just slow – it compounds across every push, every developer, every day. The difference between a naive Dockerfile and an optimized one is often the difference between a two-minute build and a twelve-minute build. The techniques here are not theoretical. They are the specific changes that eliminate wasted time.

BuildKit Over Legacy Builder#

BuildKit is the modern Docker build engine and the default since Docker 23.0. If you are running an older version, enable it explicitly with DOCKER_BUILDKIT=1. BuildKit provides several capabilities the legacy builder lacks.

Custom Resource Definitions (CRDs)#

CRDs extend the Kubernetes API with your own resource types. Once you create a CRD, you can kubectl get, kubectl apply, and kubectl delete instances of your custom type just like built-in resources. The custom resources are stored in etcd alongside native Kubernetes objects, benefit from the same RBAC, and participate in the same API machinery.

When to Use CRDs#

CRDs make sense when you need to represent application-specific concepts inside Kubernetes:

DaemonSets#

A DaemonSet ensures that a copy of a pod runs on every node in the cluster – or on a selected subset of nodes. When a new node joins the cluster, the DaemonSet controller automatically schedules a pod on it. When a node is removed, the pod is garbage collected.

This is the right abstraction for infrastructure that needs to run everywhere: log collectors, monitoring agents, network plugins, storage drivers, and security tooling.

Database Connection Pooling: PgBouncer, ProxySQL, and Application-Level Patterns#

Database connections are expensive resources. PostgreSQL forks a new OS process for every connection. MySQL creates a thread. Both allocate memory for session state, query buffers, and sort areas. When your application scales horizontally in Kubernetes – 10 pods, then 20, then 50 – the connection count multiplies, and most databases buckle long before your application pods do.

Connection pooling solves this by maintaining a smaller set of persistent connections to the database and sharing them across many application clients. Understanding pooling options, deployment patterns, and sizing is essential for any production database workload on Kubernetes.

How DNS Resolution Works#

When a client requests api.example.com, the resolution follows a chain of queries. The client asks its configured recursive resolver (often the ISP’s, or a public one like 8.8.8.8). The recursive resolver does the heavy lifting: it asks a root name server for .com, the .com TLD server for example.com, and the authoritative name server for example.com returns the answer for api.example.com. Each level caches the result according to the record’s TTL, so subsequent requests short-circuit the chain.