Lesson 1When to kill, restart, or throttle a process: safe kill practices, systemctl restart, and using cgroups and nice/reniceKnow when to end, restart, or slow a process safely. Learn signal types, safe kill ways, systemctl restart, and cgroups with nice/renice to limit damage.
Choosing SIGTERM, SIGKILL, and othersUsing kill and pkill with safeguardsRestarting services with systemctlThrottling CPU with nice and reniceLimiting resources using cgroupsDocumenting and automating remediesLesson 2Analysing swap usage and OOM events: dmesg, kernel OOM killer logs, and /var/log/kern.logCheck swap use and Out Of Memory kills with free, dmesg, kernel logs, /var/log/kern.log. Spot thrashing, tune swappiness, decide on more RAM or limits.
Checking swap usage with free and /procRecognizing swap thrashing symptomsReading dmesg for OOM killer entriesParsing /var/log/kern.log detailsTuning swappiness and vm overcommitDeciding when to add RAM or adjust limitsLesson 3Identifying hot processes: ps, ps aux --sort, pgrep, pidstat and mapping PIDs to servicesQuickly find hot or naughty processes with ps, pgrep, pidstat, sorting. Link PIDs to services, units, containers to match resource use to culprits.
Sorting ps output by CPU and memoryUsing pgrep and pkill name filtersMonitoring per-process stats with pidstatMapping PIDs to systemd unitsRelating PIDs to containers or cgroupsTracking short-lived bursty processesLesson 4Identifying recurring resource spikes: inspecting cron, systemd timers, at jobs, and application schedulersFind repeating CPU, memory, I/O spikes by matching stats to scheduled jobs. Check cron, systemd timers, at, app schedulers for noisy or clashing tasks.
Listing and reading user and system crontabsInspecting systemd timers and calendar unitsReviewing at jobs and one-off schedulesTracing app-level schedulers and workersCorrelating spikes with job execution timesRefining or staggering noisy recurring jobsLesson 5Memory troubleshooting: free, /proc/meminfo, smem, pmap and checking for memory leaksTroubleshoot memory with free, /proc/meminfo, smem, pmap. Tell cache from real strain, per-process use, spot leaks or fragmentation patterns.
Interpreting free and available memoryReading /proc/meminfo key fieldsUsing smem for per-process breakdownsInspecting process maps with pmapSpotting memory leak growth patternsDifferentiating cache from real pressureLesson 6Integrating with monitoring data (Prometheus, Grafana) and using historical metrics to determine trendsMix local checks with Prometheus, Grafana data. Use past metrics, dashboards, alerts to spot trends, regressions, slow issues, check fix impacts.
Reviewing key CPU and load dashboardsInspecting memory, cache, and swap panelsAnalyzing disk and network latency graphsUsing PromQL to slice historical metricsCorrelating deploys with metric changesValidating fixes with before and after viewsLesson 7Load vs CPU saturation: uptime, load average interpretation and relation to CPU coresUnderstand load averages and CPU cores, run queues. Tell good high load from saturation, link to I/O wait, switches, latency.
Reading uptime and load averagesRelating load to CPU core countsSeparating runnable and blocked tasksIdentifying CPU-bound saturation casesRecognizing I/O wait driven loadUsing vmstat and mpstat to confirmLesson 8Collecting live system metrics: top, htop, vmstat, mpstat, iostat and how to interpret outputsGather live Linux stats with top, htop, vmstat, mpstat, iostat. Grasp CPU, memory, I/O views, key fields, rates, spot live bottlenecks.
Reading CPU usage in top and htopMonitoring memory and swap in topUsing vmstat for system-wide snapshotsAnalyzing CPU stats with mpstatChecking disk I/O patterns with iostatChoosing sampling intervals and filtersLesson 9Using perf, strace, and ltrace for deep process analysis and when to use eachKnow when to use perf, strace, ltrace for deep checks. Profile CPU hot spots, trace syscalls, library calls, keep overhead low for good diagnostics.
Profiling CPU hotspots with perf recordViewing perf reports and call graphsTracing syscalls with strace safelyFiltering noisy strace outputInspecting library calls using ltraceChoosing the right tool for each symptomLesson 10Using lightweight profiling and tracing tools (py-spy, gdb, flamegraphs) for Python appsLight profiling for Python apps with py-spy, gdb, flamegraphs. Grab stack samples live, find hot code, read flamegraphs without stopping services.
Sampling Python stacks with py-spyGenerating and reading flamegraphsAttaching gdb safely to live PythonHandling stripped or optimized buildsProfiling async and multithreaded codeReducing profiler overhead in production
