Linux performance / Advanced: topology, observability, and controlled tuning
Linux performance β Advanced: topology, observability, and controlled tuning
Reason about NUMA, eBPF, profiles, network scaling, capacity, and reversible experiments.
What you will learn
- Design bounded tracing and profiling with explicit validity limits.
- Prepare capacity and tuning decisions with tested rejection and rollback criteria.
Reason about NUMA locality and page size
Connect CPU placement, memory-node allocation, first touch, huge pages, and topology before proposing locality changes.
Start learningDesign bounded eBPF observations
Use kernel event reasoning, filters, histograms, and lifecycle controls to plan an observability experiment without loading programs on the host.
Start learningInterpret call graphs and off-CPU time
Connect sampled execution, stack quality, blocked waits, and asynchronous request lifetimes without mistaking sample width for total latency.
Start learningTrace network scaling through queues and CPUs
Understand receive distribution, softirq work, socket queues, and transport constraints before changing network placement or buffers.
Start learningDesign capacity tests without misleading results
Specify demand generation, workload realism, safety bounds, and acceptance criteria before any isolated load experiment.
Start learningDeliver reversible, evidence-based tuning
Turn a measured mechanism into a controlled change, compare full outcomes, and retain a tested rollback instead of a collection of magic settings.
Start learning