{"id":20674,"date":"2026-08-15T15:03:53","date_gmt":"2026-08-15T13:03:53","guid":{"rendered":"https:\/\/webhosting.de\/ebpf-performance-analyse-linux-tracing-server-monitoring-observability\/"},"modified":"2026-08-15T15:03:53","modified_gmt":"2026-08-15T13:03:53","slug":"ebpf-performance-analysis-linux-tracing-server-monitoring-observability","status":"publish","type":"post","link":"https:\/\/webhosting.de\/en\/ebpf-performance-analyse-linux-tracing-server-monitoring-observability\/","title":{"rendered":"eBPF Performance Analysis: Efficient Linux Tracing for Modern Server Monitoring"},"content":{"rendered":"<p>I use eBPF Performance specifically to visualize latencies, system calls, and kernel paths directly at the source. This allows me to identify bottlenecks on Linux servers in real time, measure reliable metrics, and implement concrete measures for <strong>Server<\/strong>-Monitoring and error analysis.<\/p>\n\n<h2>Key points<\/h2>\n\n<ul>\n  <li><strong>Safe<\/strong> and dynamic: eBPF loads programs at runtime without requiring a restart.<\/li>\n  <li><strong>Deep<\/strong> In the kernel: tracing system calls, I\/O, networking, and the scheduler.<\/li>\n  <li><strong>Lower<\/strong> Overhead: Filter, select maps, keep data to a minimum.<\/li>\n  <li><strong>Tools<\/strong>: BCC, bpftrace, and dedicated tools for everyday scenarios.<\/li>\n  <li><strong>Integration<\/strong>: Integrate metrics into existing observability stacks.<\/li>\n<\/ul>\n\n<h2>Understanding eBPF: Fundamentals and Security Model<\/h2>\n\n<p>I use eBPF as <strong>Kernel VM<\/strong>, which links small programs to events, such as system calls, tracepoints, or scheduler signals. Before startup, the Verifier rigorously checks whether the code remains safe, contains no infinite loops, and performs memory accesses correctly. This allows me to load tracing and analysis logic at runtime without a reboot or risky kernel modules. This reduces risks on production hosts and preserves <strong>Availability<\/strong> in practice. If you want to delve deeper, you'll find practical examples in my notes on <a href=\"https:\/\/webhosting.de\/en\/ebpf-linux-analysis-tools-server-monitoring-insights\/\">Linux Analysis Tools<\/a>, which I use regularly at work.<\/p>\n\n<p>It is important to me to maintain a clear separation between data collection and analysis. eBPF programs extract only the most essential fields (e.g., duration, error code, PID, cgroup ID) and store them in maps. Aggregation into histograms or top lists takes place as close to the source as possible to keep the amount of data transferred to a minimum. This ensures that interactive analyses remain possible even at high event rates.<\/p>\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\/08\/serverperformance-analyse-7641.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Linux Tracing with Kprobes, Uprobes, and Tracepoints<\/h2>\n\n<p>For targeted tracing, I attach programs <strong>Kprobes<\/strong>, Uprobes, or tracepoints, depending on whether I\u2019m monitoring kernel functions, user-space libraries, or stable kernel events. Kprobes show me entry and exit points in the kernel, such as in the network or filesystem stack. Uprobes help me with application functions without modifying the source code, which significantly reduces diagnostic time. I use tracepoints when I need long-term stability of the interfaces and am planning updates. With stacked measurement points, I capture latencies along the path and identify <strong>Hotspots<\/strong> in seconds.<\/p>\n\n<table>\n  <thead>\n    <tr>\n      <th>Hook Type<\/th>\n      <th>Typical use<\/th>\n      <th>Strengths<\/th>\n    <\/tr>\n  <\/thead>\n  <tbody>\n    <tr>\n      <td>Kprobes<\/td>\n      <td>Kernel functions in the network, memory, or I\/O stack<\/td>\n      <td>High <strong>Flexibility<\/strong>, precise insights<\/td>\n    <\/tr>\n    <tr>\n      <td>Uprobes<\/td>\n      <td>User-Space Binaries and Libraries<\/td>\n      <td>No code changes needed, faster <strong>Use<\/strong><\/td>\n    <\/tr>\n    <tr>\n      <td>Tracepoints<\/td>\n      <td>Statically Defined Kernel Events<\/td>\n      <td>Stable interfaces, low <strong>Maintenance<\/strong><\/td>\n    <\/tr>\n  <\/tbody>\n<\/table>\n\n<p>Where available, I prefer to use <strong>fentry\/fexit<\/strong>-Hooks (BPF trampolines) instead of Kprobes, since they dock more stably and perform better at the limits of functionality. For user space, in addition to Uprobes, integration with statically defined <strong>USDT\/SDT Tests<\/strong> helpful features that I can use consistently without needing to know the symbols.<\/p>\n\n<h2>Everyday Tools: Using BCC and bpftrace Effectively<\/h2>\n\n<p>I often start my analyses with <strong>bpftrace<\/strong>, because one-liners give me meaningful histograms and top lists in minutes. For more extensive workflows, I use BCC, combine scripts, export metrics, and collect stack traces for hotpath profiling. This allows me to measure per-syscall latencies, error rates, and I\/O distribution per process without overloading the machine. I test typical hypotheses immediately: Is a new build causing more slow system calls, or is the file system the bottleneck? For more in-depth practical examples, see <a href=\"https:\/\/webhosting.de\/en\/bpftrace-detect-and-diagnose-hosting-server-issues-faster\/\">bpftrace in hosting<\/a>, which I often use for a quick diagnosis.<\/p>\n\n<p>In BCC and bpftrace, I deliberately decide whether to <strong>perf buffer<\/strong> or <strong>ringbuf<\/strong> Use: ringbuf is low-latency and efficient for continuous streams, while perf buffer remains a viable option for sporadic events with stack samples. I prefer to create histograms as log2 buckets so that <strong>Outliers<\/strong> and clearly identify broader distributions. If necessary, I sample periodically (e.g., 49\u201399 Hz) to keep profiling overhead to a minimum.<\/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\/08\/eBPF_performance_8862.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>eBPF for Comprehensive Server Monitoring<\/h2>\n\n<p>With eBPF, I measure metrics right where the work happens: in the <strong>Kernel<\/strong> and user-space interfaces. This allows me to correlate system calls, scheduler behavior, block I\/O, and network latencies along the entire path. I can identify whether context switches, locks, or disk wait times are limiting throughput. On web, database, and API servers, I can identify bottlenecks faster than with traditional agents. For packet-level analysis, I use <a href=\"https:\/\/webhosting.de\/en\/xdp-high-performance-packet-processing-kernel-speed\/\">XDP Message Processing<\/a> and track drops, retransmissions, and RTT distributions per socket or process in order to <strong>network paths<\/strong> to assess clearly.<\/p>\n\n<p>The breakdown by is particularly valuable <strong>Cgroups<\/strong> or containerization. This allows me to see exactly which service within a host is consuming CPU, I\/O, or sockets. In multi-tenant environments, this helps me verify fair limits and identify noisy neighbors without interfering with applications.<\/p>\n\n<h2>Understanding and Minimizing Overhead<\/h2>\n\n<p>When it comes to eBPF, I always make sure to only <strong>relevant<\/strong> Processing events and filtering them early on. Instead of entire payloads, I capture key metrics and select map types that match the access pattern\u2014such as LRU for frequently swapped keys. I optimize structures to maintain cache locality and avoid unnecessary memory accesses. Before rollout, I test on the staging environment and check event frequencies to handle peak loads smoothly. This keeps the additional effort to a minimum, while the <strong>Significance<\/strong> the data remains high.<\/p>\n\n<p>I use per-CPU maps to reduce false sharing, and tail calls to break down complex programs into small, reusable building blocks. Where appropriate, I use sampling or rate limits (e.g., only every nth event) to limit cardinality and memory footprint. When exporting, I use batching to prevent user-space readers from becoming a bottleneck.<\/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\/08\/ebpf-performance-analysis-linux-1764.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Practical Application: Step-by-Step Diagnosis with eBPF<\/h2>\n\n<p>I start every analysis with a clear <strong>Research Question<\/strong>: CPU overload, high latencies, I\/O bottlenecks, or network issues. I then select the appropriate tools, such as CPU profiling for hotpaths, I\/O latency traces for blocking devices, or socket analysis for TCP retransmissions. I formulate hypotheses, test them with single-line `bpftrace` commands, and refine measurement points if necessary. I convert the metrics I\u2019ve collected into time series, respond to trends, and compare configurations before and after changes. Based on the results, I derive concrete actions: adjusting limits, bundling threads, tuning caches, or simplifying code paths so that the <strong>Response times<\/strong> sink.<\/p>\n\n<p>Short, focused measurement windows (e.g., 60\u2013300 seconds) during peak load periods have proven effective. These snapshots are representative, easy to interpret, and minimize the impact on the system. For persistent issues, I switch to continuous, low-frequency sampling and correlate the data with deployments, cron jobs, or backup windows.<\/p>\n\n<h2>Integration into Observability Stacks<\/h2>\n\n<p>I export eBPF metrics as <strong>Counter<\/strong>, gauges, and distributions, and correlate them with logs and traces from applications. This allows me to specifically map kernel events to individual requests and identify timing patterns. In microservices environments, this correlation gives me a clear view of latency spikes across services. I route event streams to central systems and keep sampling rates under control so that dashboards remain meaningful. On this basis, I can configure alerts that detect real <strong>Causes<\/strong> instead of just reporting symptoms.<\/p>\n\n<p>I pay attention to <strong>cardinality<\/strong>: Process IDs, container labels, and sockets can cause the number of time series to skyrocket. Therefore, I normalize labels, limit key spaces (Top-N), and roll out details on demand as needed. I export distributions as buckets with consistent boundaries so that comparisons between hosts remain possible. Counters remain monotonic, and I clearly mark resets.<\/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\/08\/ebpf_monitoring_3721.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Typical eBPF Metrics That Really Help<\/h2>\n\n<p>I analyze per-syscall latencies and error rates in order to <strong>Outliers<\/strong> and quickly identify retry cascades. Top system calls per process show me where time is being wasted and which paths are worth pursuing. CPU profiles with stack traces highlight hot paths, which I prioritize for optimization. To assess memory pressure, I examine page-fault patterns and evaluate their impact on throughput and latency. For block I\/O, I use latency distributions per device or mount, while TCP metrics reveal retransmissions, drops, and RTT buckets per connection, and real <strong>network load<\/strong> quantify.<\/p>\n\n<p>When it comes to storage issues, I pay attention to <strong>Reclaim<\/strong>-Events, slab growth, and NUMA locality. For I\/O, I look at queue depths and merge rates; for the network, I focus on list backlogs, congestion signals, and Path MTU issues. These signals tell me whether I should optimize at the application or system level.<\/p>\n\n<h2>Assessing Opportunities and Limitations Realistically<\/h2>\n\n<p>With eBPF, I can gain deep insights into the system without kernel patches or reboots, which makes operations <strong>reliable<\/strong> . The flexible programming covers many use cases, from debugging to tuning. I run into limitations when missing hooks fail to map certain paths, or when the verifier enforces very strict rules. A lack of expertise also hinders success, which is why I invest in training and small-scale experiments. The bottom line is that I gain valuable transparency, as long as I adhere to safety mechanisms and the <strong>Complexity<\/strong> keep track of the programs.<\/p>\n\n<p>Another practical aspect is the <strong>Kernel Compatibility<\/strong>: Functions and structures vary across distributions and versions. A clean abstraction (e.g., using tracepoints whenever possible) and portability techniques help me ensure that tools remain maintainable over the long term.<\/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\/08\/entwickler_schreibtisch_4312.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>A Practical Checklist for Getting Started<\/h2>\n\n<p>First, I'll define the <strong>Goal<\/strong> of the measurement to maintain focus and avoid unnecessary data collection. After that, I activate the appropriate hooks, check event rates, and reduce noise using filters. I collect only metrics that support or refute my hypothesis, and I keep the test duration short to minimize interference. I document results immediately, compare them with previous values, and share them with the team to ensure that next steps remain clear. Finally, I define actions, plan a follow-up check, and convert useful scripts into <strong>Reuse<\/strong> for future analysis.<\/p>\n\n<p>I also maintain standard thresholds (e.g., acceptable percentiles per service class) and link them to playbooks. This allows alerts to be translated directly into diagnostic steps and accelerators (e.g., adjusting cgroup limits, calibrating thread pools) to be tested without delay.<\/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\/08\/ebpf-server-monitoring-8472.png\" alt=\"\" width=\"1536\" height=\"1024\"\/>\n<\/figure>\n\n\n<h2>Portability with CO-RE and BTF<\/h2>\n\n<p>To ensure that tools remain stable across kernel versions, I rely on <strong>CO-RE<\/strong> (Compile Once \u2013 Run Everywhere) and <strong>BTF<\/strong>-Type information. libbpf adapts field accesses at runtime to the specific kernel structure. I generate a vmlinux.h file and use bpf_core_read() to safely resolve offsets. This reduces maintenance effort, prevents breakages after updates, and makes tools more robust across distributions.<\/p>\n\n<p>When CO-RE isn't available, I fall back on tracepoints or stable symbols and deliberately accept less depth in favor of stability. I strike this balance depending on the system's criticality.<\/p>\n\n<h2>Container and Kubernetes environments<\/h2>\n\n<p>In clusters, I run eBPF-Collector as <strong>DaemonSet<\/strong> and isolate visibility using namespaces and cgroups. I measure metrics per pod\/namespace and link them to workloads without instrumenting inside containers. For production, I carefully plan permissions: modern kernels allow CAP_BPF\/CAP_PERFMON, while older ones sometimes require CAP_SYS_ADMIN. I follow security guidelines and set only the minimum necessary privileges.<\/p>\n\n<p>For network paths, I choose between the following options depending on the destination: <strong>XDP<\/strong> (early, high-performance dropping\/accounting) and <strong>tc<\/strong>-Hooks (closely tied to the traffic-shaping logic). On multi-tenant hosts, I make sure to use strict filters so that only relevant container events are captured.<\/p>\n\n<h2>Resource and Safety Limits in Production<\/h2>\n\n<p>I size maps conservatively, test worst-case event rates, and set strict limits. I explicitly plan for eBPF map memory (adjusting memlock\/rlimits as needed) and verify that reader processes can keep up under load. I enable audit logs for load errors so that authorization issues and verifier rejections are immediately visible. I ensure data protection by avoiding payloads, masking PII, and collecting only metadata.<\/p>\n\n<h2>Verifier Troubleshooting and Common Pitfalls<\/h2>\n\n<p>When the verifier rejects programs, it\u2019s often due to potentially unsafe paths: unsecured pointers, call stacks that are too deep, prohibited helpers, or unbound loops. I mitigate this by using explicit bounds checks, smaller helper functions, conservative loops, and permitted helpers. For more in-depth analysis, I generate verifier logs, compile with debug information, and narrow down the problematic section step by step. I also pay attention to program limits (instruction and stack limits) and split logic using tail calls when necessary.<\/p>\n\n<h2>Automation, Reuse, and Runbooks<\/h2>\n\n<p>I pin tried-and-true scripts in <strong>bpffs<\/strong>, so that they can be used by multiple processes. I version profiles, assign clear names, and provide default filters (e.g., cgroup IDs). Nightly jobs collect basic metrics at a low rate, while on-demand profiles dig deeper. I document the results directly in the ticket\/incident, including the configuration, time period, and kernel version\u2014this ensures that the measurements remain reproducible.<\/p>\n\n<h2>Measurement Quality and Statistics in Practice<\/h2>\n\n<p>I make a strict distinction between <strong>Waiting times<\/strong> (I\/O, locks) and <strong>CPU time<\/strong> and take cache warm-up phases into account. I use percentiles (P50\/P90\/P99) consistently across services so that optimizations remain comparable. When latencies fluctuate significantly, I use logarithmic buckets. I check time sources (ktime) for monotonicity and resolution to avoid smoothing out short spikes. Before-and-after comparisons are run under identical load so that I can measure real progress.<\/p>\n\n<h2>Practical examples from everyday life<\/h2>\n\n<ul>\n  <li>Web server: P99 latency is increasing \u2192 Trace for accept\/connect\/sendfile shows retransmissions; Solution: Tune the TCP stack, adjust the send buffer, and warm up the CDN cache.<\/li>\n  <li>Database: Long syscall times during fsync \u2192 Block I\/O distribution reveals queue saturation; Solution: Adjust writeback settings; move the journal to faster storage.<\/li>\n  <li>Microservice: Outliers in RPC \u2192 Scheduler traces show runqueue spikes; Solution: Adjust CPU affinity\/quotas, calibrate goroutine pools.<\/li>\n  <li>Batch Job: Throughput fluctuates \u2192 Page fault analysis reveals reclaim waves; Solution: Reduce memory pressure; use HugePages strategically.<\/li>\n<\/ul>\n\n<h2>Outlook and summary<\/h2>\n\n<p>I see eBPF as <strong>key<\/strong> for modern Linux tracing, because it allows me to measure causes rather than symptoms. The combination of secure hooks, flexible tools, and low overhead provides quick answers to tough performance questions. By proceeding step by step, thoroughly testing hypotheses, and keeping measurements focused, you can achieve more reliable services and shorter downtime. I integrate the metrics I\u2019ve gathered into existing observability landscapes and use them to make clear decisions about configuration, hardware, and code. This ensures that server monitoring isn\u2019t based on gut feelings but is data-driven\u2014with noticeable <strong>Benefit<\/strong> for users and operations.<\/p>","protected":false},"excerpt":{"rendered":"<p>Discover how to use eBPF performance analysis and Linux tracing to optimize your server monitoring. Focus: eBPF performance and best practices for admins.<\/p>","protected":false},"author":1,"featured_media":20667,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[922],"tags":[],"class_list":["post-20674","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technologie"],"acf":[],"_wp_attached_file":null,"_wp_attachment_metadata":null,"litespeed-optimize-size":null,"litespeed-optimize-set":null,"_elementor_source_image_hash":null,"_wp_attachment_image_alt":null,"stockpack_author_name":null,"stockpack_author_url":null,"stockpack_provider":null,"stockpack_image_url":null,"stockpack_license":null,"stockpack_license_url":null,"stockpack_modification":null,"color":null,"original_id":null,"original_url":null,"original_link":null,"unsplash_location":null,"unsplash_sponsor":null,"unsplash_exif":null,"unsplash_attachment_metadata":null,"_elementor_is_screenshot":null,"surfer_file_name":null,"surfer_file_original_url":null,"envato_tk_source_kit":null,"envato_tk_source_index":null,"envato_tk_manifest":null,"envato_tk_folder_name":null,"envato_tk_builder":null,"envato_elements_download_event":null,"_menu_item_type":null,"_menu_item_menu_item_parent":null,"_menu_item_object_id":null,"_menu_item_object":null,"_menu_item_target":null,"_menu_item_classes":null,"_menu_item_xfn":null,"_menu_item_url":null,"_trp_menu_languages":null,"rank_math_primary_category":null,"rank_math_title":null,"inline_featured_image":null,"_yoast_wpseo_primary_category":null,"rank_math_schema_blogposting":null,"rank_math_schema_videoobject":null,"_oembed_049c719bc4a9f89deaead66a7da9fddc":null,"_oembed_time_049c719bc4a9f89deaead66a7da9fddc":null,"_yoast_wpseo_focuskw":null,"_yoast_wpseo_linkdex":null,"_oembed_27e3473bf8bec795fbeb3a9d38489348":null,"_oembed_c3b0f6959478faf92a1f343d8f96b19e":null,"_trp_translated_slug_en_us":null,"_wp_desired_post_slug":null,"_yoast_wpseo_title":null,"tldname":null,"tldpreis":null,"tldrubrik":null,"tldpolicylink":null,"tldsize":null,"tldregistrierungsdauer":null,"tldtransfer":null,"tldwhoisprivacy":null,"tldregistrarchange":null,"tldregistrantchange":null,"tldwhoisupdate":null,"tldnameserverupdate":null,"tlddeletesofort":null,"tlddeleteexpire":null,"tldumlaute":null,"tldrestore":null,"tldsubcategory":null,"tldbildname":null,"tldbildurl":null,"tldclean":null,"tldcategory":null,"tldpolicy":null,"tldbesonderheiten":null,"tld_bedeutung":null,"_oembed_d167040d816d8f94c072940c8009f5f8":null,"_oembed_b0a0fa59ef14f8870da2c63f2027d064":null,"_oembed_4792fa4dfb2a8f09ab950a73b7f313ba":null,"_oembed_33ceb1fe54a8ab775d9410abf699878d":null,"_oembed_fd7014d14d919b45ec004937c0db9335":null,"_oembed_21a029d076783ec3e8042698c351bd7e":null,"_oembed_be5ea8a0c7b18e658f08cc571a909452":null,"_oembed_a9ca7a298b19f9b48ec5914e010294d2":null,"_oembed_f8db6b27d08a2bb1f920e7647808899a":null,"_oembed_168ebde5096e77d8a89326519af9e022":null,"_oembed_cdb76f1b345b42743edfe25481b6f98f":null,"_oembed_87b0613611ae54e86e8864265404b0a1":null,"_oembed_27aa0e5cf3f1bb4bc416a4641a5ac273":null,"_oembed_time_27aa0e5cf3f1bb4bc416a4641a5ac273":null,"_tldname":null,"_tldclean":null,"_tldpreis":null,"_tldcategory":null,"_tldsubcategory":null,"_tldpolicy":null,"_tldpolicylink":null,"_tldsize":null,"_tldregistrierungsdauer":null,"_tldtransfer":null,"_tldwhoisprivacy":null,"_tldregistrarchange":null,"_tldregistrantchange":null,"_tldwhoisupdate":null,"_tldnameserverupdate":null,"_tlddeletesofort":null,"_tlddeleteexpire":null,"_tldumlaute":null,"_tldrestore":null,"_tldbildname":null,"_tldbildurl":null,"_tld_bedeutung":null,"_tldbesonderheiten":null,"_oembed_ad96e4112edb9f8ffa35731d4098bc6b":null,"_oembed_8357e2b8a2575c74ed5978f262a10126":null,"_oembed_3d5fea5103dd0d22ec5d6a33eff7f863":null,"_eael_widget_elements":null,"_oembed_0d8a206f09633e3d62b95a15a4dd0487":null,"_oembed_time_0d8a206f09633e3d62b95a15a4dd0487":null,"_aioseo_description":null,"_eb_attr":null,"_eb_data_table":null,"_oembed_819a879e7da16dd629cfd15a97334c8a":null,"_oembed_time_819a879e7da16dd629cfd15a97334c8a":null,"_acf_changed":null,"_wpcode_auto_insert":null,"_edit_last":null,"_edit_lock":null,"_oembed_e7b913c6c84084ed9702cb4feb012ddd":null,"_oembed_bfde9e10f59a17b85fc8917fa7edf782":null,"_oembed_time_bfde9e10f59a17b85fc8917fa7edf782":null,"_oembed_03514b67990db061d7c4672de26dc514":null,"_oembed_time_03514b67990db061d7c4672de26dc514":null,"rank_math_news_sitemap_robots":null,"rank_math_robots":null,"_eael_post_view_count":"170","_trp_automatically_translated_slug_ru_ru":null,"_trp_automatically_translated_slug_et":null,"_trp_automatically_translated_slug_lv":null,"_trp_automatically_translated_slug_fr_fr":null,"_trp_automatically_translated_slug_en_us":null,"_wp_old_slug":null,"_trp_automatically_translated_slug_da_dk":null,"_trp_automatically_translated_slug_pl_pl":null,"_trp_automatically_translated_slug_es_es":null,"_trp_automatically_translated_slug_hu_hu":null,"_trp_automatically_translated_slug_fi":null,"_trp_automatically_translated_slug_ja":null,"_trp_automatically_translated_slug_lt_lt":null,"_elementor_edit_mode":null,"_elementor_template_type":null,"_elementor_version":null,"_elementor_pro_version":null,"_wp_page_template":null,"_elementor_page_settings":null,"_elementor_data":null,"_elementor_css":null,"_elementor_conditions":null,"_happyaddons_elements_cache":null,"_oembed_75446120c39305f0da0ccd147f6de9cb":null,"_oembed_time_75446120c39305f0da0ccd147f6de9cb":null,"_oembed_3efb2c3e76a18143e7207993a2a6939a":null,"_oembed_time_3efb2c3e76a18143e7207993a2a6939a":null,"_oembed_59808117857ddf57e478a31d79f76e4d":null,"_oembed_time_59808117857ddf57e478a31d79f76e4d":null,"_oembed_965c5b49aa8d22ce37dfb3bde0268600":null,"_oembed_time_965c5b49aa8d22ce37dfb3bde0268600":null,"_oembed_81002f7ee3604f645db4ebcfd1912acf":null,"_oembed_time_81002f7ee3604f645db4ebcfd1912acf":null,"_elementor_screenshot":null,"_oembed_7ea3429961cf98fa85da9747683af827":null,"_oembed_time_7ea3429961cf98fa85da9747683af827":null,"_elementor_controls_usage":null,"_elementor_page_assets":[],"_elementor_screenshot_failed":null,"theplus_transient_widgets":null,"_eael_custom_js":null,"_wp_old_date":null,"_trp_automatically_translated_slug_it_it":null,"_trp_automatically_translated_slug_pt_pt":null,"_trp_automatically_translated_slug_zh_cn":null,"_trp_automatically_translated_slug_nl_nl":null,"_trp_automatically_translated_slug_pt_br":null,"_trp_automatically_translated_slug_sv_se":null,"rank_math_analytic_object_id":null,"rank_math_internal_links_processed":"1","_trp_automatically_translated_slug_ro_ro":null,"_trp_automatically_translated_slug_sk_sk":null,"_trp_automatically_translated_slug_bg_bg":null,"_trp_automatically_translated_slug_sl_si":null,"litespeed_vpi_list":null,"litespeed_vpi_list_mobile":null,"rank_math_seo_score":null,"rank_math_contentai_score":null,"ilj_limitincominglinks":null,"ilj_maxincominglinks":null,"ilj_limitoutgoinglinks":null,"ilj_maxoutgoinglinks":null,"ilj_limitlinksperparagraph":null,"ilj_linksperparagraph":null,"ilj_blacklistdefinition":null,"ilj_linkdefinition":null,"_eb_reusable_block_ids":null,"rank_math_focus_keyword":"eBPF Performance","rank_math_og_content_image":null,"_yoast_wpseo_metadesc":null,"_yoast_wpseo_content_score":null,"_yoast_wpseo_focuskeywords":null,"_yoast_wpseo_keywordsynonyms":null,"_yoast_wpseo_estimated-reading-time-minutes":null,"rank_math_description":null,"surfer_last_post_update":null,"surfer_last_post_update_direction":null,"surfer_keywords":null,"surfer_location":null,"surfer_draft_id":null,"surfer_permalink_hash":null,"surfer_scrape_ready":null,"_thumbnail_id":"20667","footnotes":null,"_links":{"self":[{"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/posts\/20674","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/comments?post=20674"}],"version-history":[{"count":0,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/posts\/20674\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/media\/20667"}],"wp:attachment":[{"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/media?parent=20674"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/categories?post=20674"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/webhosting.de\/en\/wp-json\/wp\/v2\/tags?post=20674"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}