{"id":21247,"date":"2026-09-01T18:19:15","date_gmt":"2026-09-01T16:19:15","guid":{"rendered":"https:\/\/webhosting.de\/linux-psi-pressure-stall-information-performanceanalyse-serverdruck\/"},"modified":"2026-09-01T18:19:15","modified_gmt":"2026-09-01T16:19:15","slug":"linux-psi-pressure-stall-information-performance-analysis-server-pressure","status":"publish","type":"post","link":"https:\/\/webhosting.de\/en\/linux-psi-pressure-stall-information-performanceanalyse-serverdruck\/","title":{"rendered":"Linux PSI for Precise Performance Analysis and Monitoring"},"content":{"rendered":"<p><strong>Linux PSI<\/strong> It provides me with metrics that show how long tasks wait for CPU, memory, or I\/O, thereby revealing actual bottlenecks. This allows me to pinpoint exactly when systems are blocked\u2014rather than just measuring utilization\u2014and use the pressure values to determine specific actions for performance analysis and monitoring.<\/p>\n\n<h2>Key points<\/h2>\n<ul>\n  <li><strong>some\/full<\/strong>: Early Warning Signal vs. Critical Blockage<\/li>\n  <li><strong>CPU\/Memory\/I\/O<\/strong>: Print costs clearly separated by resource<\/li>\n  <li><strong>avg10\/60\/300<\/strong>: Time Frame for Trend Analysis<\/li>\n  <li><strong>Cgroups<\/strong>: Identify the parties responsible and those affected<\/li>\n  <li><strong>Trigger<\/strong>: Respond automatically when a threshold is exceeded<\/li>\n<\/ul>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux-performance-monitoring-4826.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>What Linux PSI Measures and Why It Matters<\/h2>\n<p>I'm reading from <strong>Pressure<\/strong>-Metrics show how much actual processing time is lost due to a lack of CPU time, RAM, or I\/O. Traditional utilization metrics merely indicate how heavily resources are being used, while PSI reveals how often the system is actually at a standstill. This is precisely what highlights the difference between a short queue and a hard blockage. In dynamic setups with containers and dense deployments, this allows me to identify bottlenecks earlier and clearly assign them to a specific resource. This way, I can prioritize tuning measures effectively and avoid guessing about the actual <strong>Cause<\/strong>.<\/p>\n\n<h2>Enable and Verify PSI on Linux<\/h2>\n<p>First, I check whether PSI is running by looking at the files under <strong>\/proc\/pressure<\/strong> Read; if CPU, memory, and I\/O values are present there, everything is ready. If data is missing, I enable PSI using the kernel boot parameter `psi=1` or ensure that `CONFIG_PSI=y` is set in the kernel. This feature is available starting with kernel 4.20 and is often already enabled in current distributions. For quick checks, simple commands like `cat \/proc\/pressure\/cpu` suffice, which return `avg10`, `avg60`, `avg300`, and `total`. This way, I can tell within seconds whether my system is providing meaningful <strong>Metrics<\/strong> provides.<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux-performancemeeting-7283.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Understanding the Files in \/proc\/pressure<\/h2>\n<p>There are three files in \/proc\/pressure for <strong>CPU<\/strong>, memory, and io, each of which outputs two types of logs: \"some\" and \"full.\" \"Some\" indicates that at least one task had to wait, while \"full\" indicates that all non-idle tasks are stuck at the same time. In addition, I receive moving averages over 10, 60, and 300 seconds, as well as a cumulative total. Using these time windows, I distinguish between short-term spikes and persistent problems. This allows me to objectively assess whether only isolated spikes are occurring or whether there is a persistent <strong>Pressure<\/strong> is available.<\/p>\n\n<h2>\"some\" vs. \"full\" in practice<\/h2>\n<p>I consider \"some\" to be an early warning indicator and \"full\" to be a red flag, because \"full\" describes phases in which productive work is effectively at a standstill. If \"some\" increases for the CPU, I check scheduling, locks, and load balancing; optimizing threads or measuring the <a href=\"https:\/\/webhosting.de\/en\/measuring-and-optimizing-linux-scheduler-latency-to-improve-performance\/\">Measuring Scheduler Latency<\/a>. High memory-some values often indicate page reclaims, swapping, or resource-intensive allocations. If io-some increases, I check queues, priorities, and competing accesses. I don't make decisions based on gut feeling, but rather on clear <strong>signals<\/strong>.<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux-psi-performance-analysis-4723.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>System-wide vs. Cgroup-based Evaluation<\/h2>\n<p>First, I'll look at system-wide <strong>Values<\/strong>, ...to get the big picture, and then switch to Cgroups to identify the sources. With cgroup v2, I find separate pressure files for each service or container, which allows me to map them to pods, slices, or units. This approach separates symptoms from their sources, rather than attributing all loads to the host across the board. I then adjust quotas, CPU shares, or memory limits in a targeted manner. This way, I increase fairness and reduce mutual <strong>Influence<\/strong>.<\/p>\n\n<h2>PSI in Monitoring, Dashboards, and Kubernetes<\/h2>\n<p>I rarely collect PSI manually; instead, I use exporters to export the data as <strong>time series<\/strong> collect so that dashboards can show trends and correlations. In Kubernetes, I monitor PSI at the node, pod, and container levels, which provides a clear separation of consumption and bottlenecks per workload. This allows me to determine whether a single pod is increasing wait times for others or whether the issue is node-wide. I set alerts for full developments and for persistently high \u201csome\u201d values. This enables me to respond proactively before users experience wait times <strong>feel<\/strong>.<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux_performance_nacht_1234.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Typical Use Cases and Appropriate Thresholds<\/h2>\n<p>I use PSI during load testing to check whether response times increase due to CPU, memory, or I\/O pressure, and whether this increase is temporary or persistent. In capacity planning, I monitor avg300 to identify recurring patterns and scale resources or shift workloads in a timely manner. For autoscaling, I use triggers set close to the threshold at which the system reaches \u201cfull\u201d capacity so that I can react in a timely manner. In the event of a gradual decline in performance, I compare baselines before and after releases to make the effects transparent. This allows me to make fact-based decisions and invest where the most <strong>Effect<\/strong> arises.<\/p>\n\n<h2>Quick Reference Table of PSI Metrics<\/h2>\n<p>When reviewing PSI, I use a simple classification system to help me arrive at the correct hypothesis more quickly. The following table summarizes the interpretation of \u201csome\u201d and \u201cfull\u201d for each resource and provides initial courses of action. It is not a substitute for a more in-depth analysis, but it saves me valuable time during operations. It remains crucial to evaluate short-term peaks differently than longer phases. That\u2019s exactly why I use the moving averages avg10, avg60, and avg300 as <strong>Context<\/strong>.<\/p>\n<table>\n  <thead>\n    <tr>\n      <th>Resource<\/th>\n      <th>some-Signal<\/th>\n      <th>full signal<\/th>\n      <th>Common Causes<\/th>\n      <th>Possible Measures<\/th>\n    <\/tr>\n  <\/thead>\n  <tbody>\n    <tr>\n      <td>CPU<\/td>\n      <td>Occasional waiting periods<\/td>\n      <td>All tasks are blocked<\/td>\n      <td>Scheduler conflicts, locks, too many threads<\/td>\n      <td>Adjust thread pools, relax locks, and adjust CPU shares\/quotas<\/td>\n    <\/tr>\n    <tr>\n      <td>Memory<\/td>\n      <td>Reclaims, Page Faults, Allocation Bottlenecks<\/td>\n      <td>Strong Pressure, Swaps Dominate<\/td>\n      <td>Overcommitment, large heaps, cache pressure<\/td>\n      <td>Check limits, optimize allocations, reduce swapping<\/td>\n    <\/tr>\n    <tr>\n      <td>I\/O<\/td>\n      <td>Growing Lines<\/td>\n      <td>I\/O is a general-purpose term<\/td>\n      <td>Overloaded Disks\/Network, Competing Accesses<\/td>\n      <td>Priorities, Batching, Queue Tuning, Separate Volumes<\/td>\n    <\/tr>\n  <\/tbody>\n<\/table>\n\n<h2>Interpreting Storage Pressure Correctly<\/h2>\n<p>I analyze `memory.pressure` in conjunction with RSS, cache utilization, and swap usage, because only this combination provides meaningful insights. Often, a high `some` value is caused by a phase of intensive memory releases or an increase in page faults, which can be smoothed out with better allocation patterns. If \u201cfull\u201d appears, I stop experiments and first reduce the pressure by setting limits or using less aggressive caches. An in-depth introduction to the topic is provided by <a href=\"https:\/\/webhosting.de\/en\/memory-pressure-linux-kernel-hosting-systems-optimization-ram\/\">Memory Pressure<\/a> with practical tips on RAM tuning. This is how I prevent uncontrolled swapping from affecting response times <strong>dominates<\/strong>.<\/p>\n\n<h2>Identifying and Addressing I\/O Bottlenecks<\/h2>\n<p>I analyze io.pressure along with latencies, re-queue rates, and queue depths, because raw throughput figures can mask bottlenecks. A high \"some\" value under moderate load often indicates uneven access profiles, which can be smoothed out using batching or prioritization. For first-byte lags and a growing \u201cfull\u201d count, I rely on decoupling via asynchronous I\/O and separate volumes for hot paths. For detailed diagnostics, I use measurement series and the tried-and-true guide to <a href=\"https:\/\/webhosting.de\/en\/server-io-wait-analysis-iostat-vmstat-metrics-disk\/\">Analyze I\/O Wait<\/a>. This allows me to make well-informed decisions instead of <strong>Assumptions<\/strong>.<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux-performanceanalyse-1928.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>PSI vs. Load Average and Traditional Metrics<\/h2>\n<p>I deliberately compare PSI with load average, CPU utilization, iowait, and memory utilization to bridge the gaps between these perspectives. A high load with low cpu.pressure often simply indicates to me that many tasks are actively processing data\u2014without system-wide bottlenecks. Conversely, rising cpu.pressure with moderate utilization is an indication of scheduler conflicts or lock contention. When it comes to I\/O, iowait alone doesn\u2019t tell me how much the entire system is suffering; io.pressure quantifies how much productive time is being lost. It is precisely this translation of \u201cutilization\u201d into \u201clost time\u201d that makes my decisions significantly more reliable.<\/p>\n\n<h2>Read the AVG window with total precision<\/h2>\n<p>I view the avg10\/60\/300 values as percentages of the time during which tasks were blocked. An avg10 of 2.50 means that 2.5% of potential processing time was lost in the last 10 seconds. The total value accumulates stall time since boot (in fine-grained time units) and thus shows me the <strong>Area under the curve<\/strong>. For capacity planning, I look at the slope of the total vs. daily profiles: If the line becomes significantly steeper during peak periods, I plan to reduce the load. For operational signals, I evaluate patterns: a brief spike in avg10 worries me less than a simultaneous increase in avg60 and avg300, which indicates structural pressure.<\/p>\n\n<h2>Cgroups in Practice: Structure, Paths, and Permissions<\/h2>\n<p>I work with cgroup v2 using the pressure files directly in the respective service, slice, or pod directories. This allows me to determine for each unit, pod, or container whether pressure is generated locally or merely passed on. Systemd units, Kubernetes pods, and user-defined groups can be clearly distinguished from one another in this way. Once the assignment is successful, I apply targeted throttling: tighter CPU quotas, fairer CPU shares, and realistic memory limits. In practice, I make sure to perform the measurement where it has an effect\u2014in the exact Cgroup that sets the limits. This prevents me from addressing symptoms in one place while leaving the actual source untouched.<\/p>\n\n<h2>Alerting Strategies Without an Overload of Alerts<\/h2>\n<p>I define alarms in a way that takes trends and persistence into account. For early detection, I set thresholds to \"some,\" combine them with observation windows and hysteresis, and check whether avg10 <em>and<\/em> avg60 remains elevated. For immediate intervention, I link \u201cfull\u201d to short windows and automatic responses (scaling, prioritization, throttling). To avoid false positives, I only trigger an action once a condition has been confirmed multiple times, and I only revert the settings once the values drop significantly below the return threshold. I link alerts to service SLOs: If p95 latencies rise and pressure increases at the same time, the finding is reliable\u2014load alone isn\u2019t sufficient for me.<\/p>\n\n<h2>Real-World Examples: Patterns I Recognize Immediately<\/h2>\n<p>I like to collect recurring patterns because they help me make decisions faster:<\/p>\n<ul>\n  <li><strong>CPU: Lock Contention Instead of \u201cNot Enough Cores\u201d<\/strong> \u2013 cpu.some is rising even though CPU utilization isn't at its limit. I'm investigating hotlocks, reducing thread affinity, and smoothing out spikes with backpressure. This often yields better results than adding more cores.<\/li>\n  <li><strong>Memory: Reclaim Spiral<\/strong> \u2013 memory.some spikes and fluctuates along with page faults as swapping kicks in. I lower cache aggressiveness, reduce heap spikes (e.g., batch sizes), adjust limits, and thereby prevent memory.full from even appearing.<\/li>\n  <li><strong>I\/O: Unbalanced Accesses<\/strong> \u2013 io.some increases while throughput remains normal. I decouple read\/write paths, bundle small I\/Os into batches, and distribute hot paths across separate volumes. This allows me to reduce wait times without necessarily increasing raw throughput.<\/li>\n<\/ul>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/developer_desk_9502.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Limitations and Obstacles in Interpretation<\/h2>\n<p>I keep in mind that PSI measures wait time\u2014not absolute utilization. A CPU-bound batch job can show high utilization without increasing cpu.pressure, as long as enough cores are available. Conversely, low throughput with high io.pressure can indicate a clear bottleneck. In virtualized environments, I also check whether limits or affinities are causing local bottlenecks: A container pinned to only a few cores can show high cpu.pressure even though the host has free resources. It\u2019s also important to compare the system-wide view with the cgroup-local view\u2014this is the only way I can tell if I\u2019m solving the problem in the right place.<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\">\n  <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/webhosting.de\/wp-content\/uploads\/2026\/09\/linux-performanceanalyse-1928.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Operational Guidelines: Sampling, Overhead, and Visualization<\/h2>\n<p>I keep the sampling simple: an interval of 1\u20135 seconds is sufficient for me to make operational decisions, because the average windows already smooth out the data. I consider the overhead from PSI to be negligible, especially since I keep the measurement close to the system and capture only a few, well-placed time series. For visualization, I arrange panels side by side for each resource (some\/full, avg10\/60\/300, total) and correlate them with latency and error rates. In post-mortems, I plot the slope of \u201ctotal\u201d against deployments, releases, or configuration changes\u2014this makes it clear which measures actually reduce pressure.<\/p>\n\n<h2>Targeted Countermeasures for Each Resource<\/h2>\n<p>I use the patterns to identify specific steps without automatically adding more hardware:<\/p>\n<ul>\n  <li><strong>CPU<\/strong>: Limit thread pools and concurrency guards; mitigate hotlocks (granularity\/locking strategy); distribute the load fairly (shares\/quotas); account for topology (NUMA, affinity). Only when local load balancing fails do I scale horizontally or vertically.<\/li>\n  <li><strong>Memory<\/strong>: Stabilize allocations (batching, buffers), control caches, set realistic limits, flatten heap spikes, and reduce the impact of swapping. I take specific measurements before and after changes, because memory.some is sensitive to allocation patterns.<\/li>\n  <li><strong>I\/O<\/strong>: Smooth out access profiles (batching, asynchronous I\/O), decouple hot paths, set priorities, choose appropriate queue depths, and disentangle competing workloads. I measure success by a decrease in io.pressure and shorter P99 latencies.<\/li>\n<\/ul>\n\n<h2>PSI in Everyday Team Life: Communication and Ownership<\/h2>\n<p>I also use PSI as a common language between platform and product teams. Instead of talking abstractly about \u201cslow,\u201d I specify the resource and the pattern: \u201cio.some avg60 has been above 4% for 20 minutes on Service X\u201d or \u201cmemory.full is triggering in cgroup Y.\u201d This level of precision makes prioritization easier because it\u2019s clear which owners need to take action and which allocation of resources (time, resources) promises the greatest impact. Using defined baselines, I establish quality goals that are both technically sound and understandable to stakeholders.<\/p>\n\n<h2>Triggers, Baselines, and Phased Rollout<\/h2>\n<p>I use PSI triggers with thresholds and monitoring windows so that a daemon reacts automatically when pressure remains elevated. To ensure reliable results, I establish a baseline based on typical load phases before making any changes, which I later compare with new data sets. I define alerts conservatively: \u201csome\u201d for persistently elevated levels gives me time, while \u201cfull\u201d triggers countermeasures. In large fleets, I roll out PSI-based alerts in phases to avoid false positives and fine-tune tolerances. This way, my monitoring remains <strong>clear<\/strong> and reliable, without overwhelming teams with unnecessary notifications.<\/p>\n\n<h2>Benefits for Hosting, Virtualization, and Multi-Tenancy<\/h2>\n<p>I use PSI to see if individual workloads are slowing down others, if hardware reserves are sufficient, and where limits need to be adjusted. In shared environments, I identify persistent CPU, memory, or I\/O pressure from individual accounts and plan for resource reallocation in a timely manner. Cgroup-based metrics show me which services are affected and where I can specifically throttle or prioritize them. This allows me to maintain reliable response times and ensure fair resource utilization even under heavy load. This reduces costs, prevents escalations, and significantly improves <strong>Quality<\/strong>.<\/p>\n\n<h2>Conclusion: Key Metrics Lead to Decisions<\/h2>\n<p>I use Linux PSI because it quantifies latency and thus bridges the gap between system utilization and user experience. With \"some,\" I identify early warning signs; with \"full,\" I respond to actual bottlenecks; and with Cgroups, I pinpoint the exact causes. Dashboards, triggers, and baselines transform this insight into concrete actions: optimized limits, better load balancing, and clean I\/O paths. Those who actively use PSI reduce the time it takes to identify the root cause and avoid many rounds of trial-and-error tuning. This turns monitoring data into clear <strong>Decisions<\/strong>, which make the systems noticeably faster.<\/p>","protected":false},"excerpt":{"rendered":"<p>Linux PSI (Pressure Stall Information) shows you how much the CPU, memory, and I\/O are slowing down your system. 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