Mastering Kubernetes: 12 Critical Best Practices for Peak Container Platform Performance

Hey there! Congratulations on adopting containers and Kubernetes, one of the biggest modern innovations in application architecture and infrastructure management. As Kubernetes has quickly become the new norm for deploying and running containerized workloads in production, it opens up immense possibilities – but also some complexity.

Content Navigation show

You likely chose Kubernetes for critical reasons like scalability, portability, and automation. However, simply creating a few Pods and Services doesn‘t ensure you‘ll actually realize the full benefits. To truly maximize what Kubernetes has to offer – achieving factors like high availability, security, reduced costs, and operational efficiency – adopting Kubernetes best practices is an absolute must.

In this comprehensive guide, you‘ll learn 12 battle-tested best practices I‘ve curated from years of hands-on experience helping enterprises optimize their Kubernetes footprint. From small startups running a few critical workloads to large corporations managing thousands of nodes, these tips will ensure you extract the most value out of Kubernetes.

While some may seem basic, aspects like properly configuring resource limits, automation processes, and access controls make a world of difference when managing containers at scale. Other best practices around scaling, upgrades, monitoring, and security are critical as your usage expands across teams, applications, and infrastructure.

Think of this as your Kubernetes optimization checklist – let‘s dive in!

Why Kubernetes Best Practices Matter

92% of organizations using container technology rely on Kubernetes for orchestration, with adoption growth continuing to accelerate according to the CNCF Survey 2022. However, most struggle with aspects like configuring security, networking, storage/data management, and troubleshooting. Only 20% consider themselves experts in Kubernetes – highlighting the need to continue leveling up skills.

Much like coding best practices for software developers, Kubernetes best practices focus on ease of maintenance, reducing human error, improving stability and organization, and optimizing for performance and scale.

When operating Kubernetes clusters at scale, small inefficiencies or oversights get massively amplified – leading to cascading availability issues, security vulnerabilities, cost overruns, and more. Just as valuable application data needs safeguarding, the container management layer enabling those apps requires equal care and feeding.

Some of the top motivations behind adopting Kubernetes best practices include:

Availability – Well architected clusters with balanced resource partitions, redundant components, and graceful failovers minimize the blast radius of outages. Apps running on properly set up Kubernetes recover quickly.

Security – From hardening node access to RBAC controls, auditing, encryption, and network policies, proactive security steps reduce risk of breaches.

Performance & Efficiency – Optimizations for scheduling, right-sized resources, and automated scaling allow apps to utilize containers efficiently and scale seamlessly up or down based on demand.

Operational Excellence – Consolidated dashboarding, automated rollouts and rollbacks, and centralized logging transforms tasks like troubleshooting, upgrades, and compliance from reactive to proactive. Teams operate Kubernetes reliably and predictably.

Let‘s explore 12 Kubernetes best practices that will optimize your clusters along these critical dimensions…

#1: Configure Resource Requests and Limits

If you take away one tip for operating Kubernetes, it should be this – properly configure CPU and memory requests and limits on containers. Requiring users to specify requests and limits is in fact a hard requirement before using Kubernetes in production according to the Cloud Native Computing Foundation.

Why does something so basic matter so much? At scale, containers become black boxes competing for shared resources. Limits ensure no single container can monopolize resources and crash nodes due to excess usage. This helps isolate and contain errant containers.

Meanwhile, requests guarantee minimum resources required for each container to operate smoothly. Think of limits as a maximum cap and requests as the floor or baseline.

By combining limits and requests, Kubernetes can better schedule workloads and maintain stability across nodes. You prevent against both under and over provisioning. Here‘s an example configuration:

resources:
  requests: 
    cpu: 100m  
    memory: 100Mi
  limits:
    cpu: 200m
    memory: 200Mi

Now your app has reserved resources for smooth sailing, but can‘t drive nodes into the ground by exceeding allocated limits.

Using limits and requests is especially critical for multi-tenant clusters and on expensive instance types – it defs resource hogs from interfering with properly configured pods. One rogue container can bring down entire nodes without limits!

#2: Secure Access with RBAC Policy

Kubernetes ships with powerful controls for granting users and applications access to only components they absolutely need via Role-Based Access Control (RBAC). However, few actually implement and use RBAC restrictively.

This leads to granting overly permissive access and increases risk of breaches or data leaks when accounts are compromised.

The principle of least privilege certainly applies to Kubernetes access – create granular roles mapping to specific jobs and duties. For example:

Read-only analyst access to fleet statistics
Developer namespaced control of dev releases
Cluster admin authority only when needing to execute infrastructure changes

Segment access and customize visibility. Don‘t uniformly grant god mode!

Here is how you can define RBAC authorization policies:

# Developer access for test releases
kind: Role
metadata:   
  namespace: dev-test
  name: developer  
rules:
- apiGroups: [""]
  resources: ["pods", "services", "secrets"] 
  verbs: ["get", "list", "create", "update", "delete"]

Follow security best practices and guard your clusters closely via RBAC!

#3: Automate Deployments & Cluster Configuration

When you first setup a Kubernetes cluster, yaml files, kubectl and manual CLI flows likely sufficed. But as you start running more mission critical workloads, reduce operational risk by automating declarative configuration.

Tools like Kubernetes Operators, Helm charts and GitOps pipelines allow codifying and standardizing deployments, orchestration, scaling, and more for apps on Kubernetes.

For example, this Helm template allows packaging up all policies, configurations, resources, and policies needed to deploy a database:

apiVersion: v2
name: mysql 
description: Deploy a MySQL instance

# Values for configuring deployment
values:
  replicas: 1
  image: mysql
  resources:
    requests: 
      memory: 256Mi
      cpu: 100m

templates:
- mysql_deployment.yaml
- mysql_service.yaml 
- backup_cronjob.yaml

Rather than handfuls of piecemeal yaml files, keep Kubernetes configuration in modular, reusable definitions under source control. This is especially powerful when combined with GitOps – using git commits to trigger Kubernetes operations.

Tools like Flux listen for git changes and automatically sync the cluster state – enabling version control for desired infrastructure state. No more snowflake production clusters!

BETWEEN AUTOMATED INFRASTRUCTURE AND MANUAL SERVER ADMIN. WHY WOULD ANYONE PICK THE LATTER?

#4: Size Pod Containers Efficiently

There‘s an art to properly sizing Kubernetes pods and containers…

Too small – pods struggle with memory pressure and crash
Too large – resources are wasted and costs climb

Carefully analyze usage over time and right size requests and limits accordingly. Overestimate to be safe, but not excessively so.

You should also optimize container images themselves for minimal size. Multi-stage Docker builds are handy for leaving behind build dependencies:

# Build stage
FROM maven AS build
...

# Production image 
FROM openjdk:8-slim
COPY --from=build /app/target/*.jar /usr/app.jar

This final JVM slim container omits all the extra build tools needed just to compile your app. Smaller images equal faster deployment times and better infrastructure utilization.

#5: Install Updates Regularly

Like applying hourly critical software patches in your data center, staying current with the latest Kubernetes releases ensures you benefit from security fixes, feature upgrades, and performance improvements.

Mark cut over milestones for Kubernetes upgrades on your calendar to review release notes and test in dev. With the pace cloud native software evolves, what worked 6 months ago may already be outdated and missing capabilities today! Plan an upgrade testing regime.

Aim to upgrade within N months of each major Kubernetes release – such as 1.22 to 1.23. Upgrading one minor version increment at a time reduces risk of breaking changes. For air gapped on-prem cluster, schedule upgrades around your SRE team‘s bandwidth.

YOUR ONGOING KUBERNETES RELATIONSHIP SHOULDN‘T STAGNATE – TIME TO SPICE THINGS UP WITH A REFRESHING UPGRADE!

#6: Install Updates Regularly

YOUR ONGOING KUBERNETES RELATIONSHIP SHOULDN‘T STAGNATE – TIME TO SPICE THINGS UP WITH A REFRESHING UPGRADE!

#7: Monitor Resource Usage and Cluster Health

Would you drive 200 miles per hour blindfolded? Then why fly Kubernetes clusters blind without monitoring and observability?

Poor visibility into pod resource utilization, node capacity, controller manager latency, network traffic and other operational metrics makes preemptively catching issues impossible. You permanently operate in reactive firefighting mode.

Instead, pipe logs, metrics, and traces from Kubernetes components and workloads into an observability platform like Datadog, Prometheus, Grafana or Elastic Stack.

Analyze for trends, anomalies and insights to catch problems emerging. For example, Grafana provides out-of-the-box Kubernetes dashboards spanning:

Compute Resources – utilization by node, namespace and pod
Controllers – seconds taken to execute tasks
API Latency – responsiveness by endpoint
Storage Capacity – persistent volumes remaining
-…plus endless customizations!

This transforms cluster visibility from foggy to laser focused. Now bottlenecks, drained nodes, latency spikes and security events all trigger alerts before causing application outages.

YOU CAN‘T IMPROVE WHAT YOU DON‘T MEASURE – SO START MONITORING KUBERNETES PERFORMANCE RELIGIOUSLY.

#8: Use Namespaces to Partition Applications & Teams

As engineers, we love solving complex problems by dividing them into logical components. Namespaces allow compartmentalizing Kubernetes clusters to improve organization, access control, and resource sharing.

For example, segment by:

Environments – dev, test, prod for clear separation
Applications – app1, app2, app3 for independent teams
Versions – v1, v2, canary for upgrade staging

This separates concerns so developers can manage daily builds without jeopardizing production stability. It also allows scoping CPU/memory quotas and policies. The principle subdivide and conquer applies perfectly to Kubernetes namespaces.

Here‘s an example namespace for orchestrating rollouts:

apiVersion: v1
kind: Namespace
metadata:
  name: canary-test

Now you can test upgrades and configurations in canary-test before impacting mainstream workloads. Namespaces introduce order amongst the chaos!

#9: Store Secrets Securely

Hardcoding credentials and keys directly in Kubernetes YAML files makes kitten cry. 😿 Such sensitive data gets committed to source control and logs for the world to see!

Instead, take advantage of Secret objects – encrypted configuration objects decoded runtime by pods needing access. Here‘s an example Secret definition:

apiVersion: v1
kind: Secret 
type: Opaque
metadata:
  name: db-secrets
data:
  DB_USERNAME: YWRtaW4= # base64 encoded 
  DB_PASSWORD: c3Ryb25ncGFzc3dvcmQ=

The base64 values get decoded into admin and strongpassword enviroment variables for database connection handling at runtime.

Secrets prevent exposing API keys, usernames, passwords and other confidential data. Use them liberally to keep your clusters secure!

For bonus points, don‘t persist the base64 secrets files themselves. Fetch from a secrets management system like HashiCorp Vault instead during deploy.

#10: Distribute Critical Services Across Zones

Distributed, resilient architecture is fundamental to Kubernetes DNA. Replicas automatically recover from failures thanks to replicated state.

You can take this a step further by distributing critical services and backups across availability zones, data centers or cloud regions. This adds geographic redundancy against zone-wide failures.

For example, when creating a StatefulSet, you can configure anti-affinity rules to spread replicas cross zone:

spec:
  selector:    
    matchLabels:      
      app: pg-cluster
  replicas: 3 
  template:
    spec:
      affinity:
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100 
            podAffinityTerm:
              labelSelector:
                matchExpressions:
                  - key: app 
                    operator: In
                    values: 
                    - pg-cluster          
              topologyKey: failure-domain.beta.kubernetes.io/zone

This StatefulSet will attempt to schedule each of the 3 replicas in different zones. Now database writes stay available even if an entire zone goes down!

#11: Follow Principle of Least Privilege

Earlier I covered locking down overly permissive access with RBAC policies. This reflects a broader security best practice known as the Principle of Least Privilege.

This philosophy recommends granting the minimum set of permissions needed rather than broad universal privileges. Apply this model throughout Kubernetes:

Node Access – Avoid root and use read only mounts / drop capabilities to prevent breakout attacks
Network Policies – Restrict communication narrowly between pods rather than wide open ingress/egress
Admission Control – Limit privileged pod creation or mounting host volumes even for admin users
Auditing – Log detailed events to identify suspicious activities

By default deny all, explicitly allow specific permissions that follow from defined responsibilities. Tell me again why cluster-admin needs access to production financial data? 🤔

#12: Practice Graceful Draining & Upgrades

Fly fast, but avoid uncontrolled crashes! The velocity and iteration speed enabled by containers and Kubernetes creates unease around upgrades and infrastructure changes.

Rather than anxiety inducing emergency maintenance windows, implement graceful draining to carefully reschedule workloads prior to node maintenance or cluster upgrades.

For example, when draining nodes, respect PodDisruptionBudgets to maintain minimum service availability and evict/reschedule with care:

kubectl drain node-1 --ignore-daemonsets --delete-emptydir-data

This safely drains node-1 without disrupting daemonsets and minimizes workload disruption. Controlled maintenance transitions beat scrambling any day!

Conclusion: Level Up Your Kubernetes Game

Hopefully this guide has shown that Kubernetes best practices cover quite a spectrum – from fundamentals like resource allocation and RBAC to hardening clusters, DevSecOps integration, graceful upgrades, and mastering observability.

While no single checklist fully captures the intricacies of cloud native operations, treating this guide as your Kubernetes optimization blueprint sets you firmly down the right path.

Whether managing massive distributed applications or just getting started on your cloud journey, keep these tips top of mind. Following Kubernetes best practices separates the hobbyist dabblers from sophisticated enterprise implementers.

Now you‘re fully equipped to build resilient, efficient and bulletproof Kubernetes clusters. Here‘s to architecting a secure containerized future!

Have you deployed any other winning Kubernetes patterns? Planning a migration and need expertise guidance? Post your experiences managing Kubernetes at scale below!