Performance

Killercoda lab progress 0/4 completed

Interactive labs, not module completion

Understanding system performance to optimize Kubernetes workloads.

Overview

Performance analysis is more than running top. This section teaches systematic approaches to identifying bottlenecks, understanding resource consumption, and optimizing Linux systems—skills essential for Kubernetes operations.

Modules

#	Module	Description	Time
5.1	USE Method	Systematic performance analysis: Utilization, Saturation, Errors	80–110 min
5.2	CPU & Scheduling	CPU utilization, load average, CFS scheduler, priorities	70–100 min
5.3	Memory Management	Virtual memory, caching, swap, OOM killer	70–100 min
5.4	I/O Performance	Disk I/O, filesystems, block devices, storage tuning	70–100 min

These are planning estimates copied from the four module headers, not measured learner completion times. Their arithmetic gives an aggregate range of 290–410 minutes (about 4 hours 50 minutes–6 hours 50 minutes); individual setup, reading, and practice time will vary.

Why This Section Matters

Kubernetes resource management depends on Linux fundamentals:

Resource requests/limits — Based on actual CPU and memory usage
Node pressure — Understanding when nodes are overloaded
Pod eviction — Memory pressure triggers OOM kills
Performance debugging — Slow apps often have OS-level causes

Can’t set proper resource limits without understanding what they measure.

Prerequisites

System Essentials — Processes and filesystems
Container Primitives — cgroups and namespaces

Key Takeaways

After completing this section, you’ll understand:

How to systematically analyze performance issues
What CPU metrics actually mean
How Linux manages memory and when it fails
How to identify I/O bottlenecks