Open Source Linux Performance Monitoring Solutions

Source:https://chatgpt.com/share/67d0590d-9174-800d-9f3b-22e5faefa78a

Thanks for the details! I will research open-source Linux system performance monitoring solutions that include CPU, GPU, and memory monitoring, process and thread listing, and provide a web-based dashboard. I will prioritize lightweight options that allow saving data for later analysis and highlight those that use eBPF. I will update you once I have a thorough list.

Open-Source Linux Performance Monitoring Solutions

Monitoring system performance on Linux can be achieved with various open-source tools. Below are several lightweight solutions that provide web-based dashboards for CPU, GPU, memory, and process metrics. We also note which tools leverage eBPF for advanced low-overhead monitoring.

Netdata (Lightweight Web Dashboard, eBPF Integration)

Netdata is an open-source, real-time monitoring agent with a rich web UI. Written in C, it’s designed for efficiency (under 1% of a core) while capturing hundreds of system metrics per secondwww.linuxbabe.comwww.reddit.com. Netdata auto-detects and collects an extensive range of stats – CPU usage, memory, disks, network interfaces, file I/O, interrupts, containers, processes, and moregithub.com. It can even track hardware sensors and uses an eBPF plugin for kernel-level insights (e.g. syscall counts, file descriptor usage, process/thread creation)www.linuxbabe.comwww.netdata.cloud. Key features include:

  • Web Dashboard: Real-time, interactive charts accessible via a built-in web server (default port 19999)www.reddit.com. No configuration needed – metrics appear immediately after install.
  • Resource Efficiency: A single Netdata agent typically uses ~1% CPU and ~20-30 MB RAM by defaultwww.linuxbabe.com, thanks to its optimized C core and custom database engine.
  • Processes & Threads: Netdata highlights top processes by CPU, memory, etc., and with eBPF it can count new processes/threads and even detect zombieswww.netdata.cloud. This helps in spotting runaway processes or thread leaks.
  • GPU Monitoring: Netdata can monitor GPU stats via plugins (Nvidia GPUs through nvidia-smi, AMD GPUs via sysfs)blog.ronin.cloudwww.netdata.cloud, though these may require enabling optional modules.
  • Data Storage: By default, metrics are stored in-memory (ring buffer) for a timeframe (configurable retention). For longer storage, Netdata can stream metrics to external time-series databases or its cloud servicegithub.com. It supports archiving to Prometheus, Graphite, OpenTSDB, etc., or exporting metrics in real-time.
  • Alerts & Visualization: The web UI provides interactive graphs with 1-second resolution. Netdata comes with pre-configured health alarms and can notify on thresholds. The UI allows drilling down into specific metrics and correlation across subsystems in real time. eBPF usage: Netdata’s eBPF collector (available on modern kernels) runs sandboxed kernel probes to gather deep metrics with minimal overheadwww.netdata.cloudwww.linuxbabe.com. For example, it can measure I/O latency, track open vs. closed file descriptors, and monitor process forks at the kernel level in real time. This enhances observability without needing custom kernel modules. Overall, Netdata provides a comprehensive, low-impact monitoring dashboard suitable for both single servers and distributed systems.

Glances (Cross-Platform Monitor with Web Mode)

Glances is an open-source, cross-platform system monitor that can run in a terminal or web browser. Written in Python, it aggregates a wealth of system information on one screenwww.linode.comwww.linode.com. Glances focuses on being a single, at-a-glance dashboard for key metrics:

  • Monitored Metrics: CPU utilization, load, memory usage, swap, disk I/O, network throughput, file system space, and more are shown continuouslygithub.com. It also reports sensor data (temperatures, fan speeds, etc.) and logged-in users, and can monitor Docker containers and their resource usegithub.com.
  • Processes and Threads: Glances includes a process list (like an interactive top). It shows running processes with their CPU%, memory, and other info, updating in real timegithub.com. The interface can highlight the most resource-intensive processes. (Threads are not listed separately by default, but a process’s thread count is shown and processes can be expanded in some views).
  • GPU Monitoring: Glances supports GPU stats for Nvidia and AMD GPUs. When the optional nvidia-ml-py library is installed (for NVIDIA) or on newer kernels for AMD, it will display GPU usage, memory utilization, and temperatureglances.readthedocs.io. A hotkey toggles a detailed per-GPU viewglances.readthedocs.io.
  • Web-Based UI: In Web mode, Glances runs a local web server to serve the dashboard (which is similar to the console view)github.com. This allows remote monitoring via a browser. The web interface is responsive and can be viewed on various devices.
  • Lightweight Design: Glances aims to be lightweight, though as a Python app its overhead is slightly higher than a native daemon. In practice it’s efficient for moderate update intervals – e.g. using a 3-5 second refresh to keep CPU usage low (it can be as low as a few percent CPU on idle systems). It dynamically adapts its layout to fit the browser or terminal size, showing more or less detail as space allowswww.linode.comwww.linode.com.
  • Extensibility and Data Export: Glances can be extended with plugins and has an API. It supports exporting data to various destinations for historical logging or visualization: CSV files, InfluxDB, Prometheus, Elasticsearch, etc.github.com. This means you can integrate Glances with Grafana or other tools by feeding its metrics outward, enabling later analysis of the captured data.
  • Alerts: Glances includes configurable thresholds. It will flag resources in color (e.g. yellow/red) when usage crosses caution or critical levels, giving a quick visual indication of issues. Note: Glances does not use eBPF; it relies on standard interfaces (like the Python psutil library) to gather stats. It prioritizes ease of setup and a broad overview. For a single host or small deployments where you want a quick, low-hassle status dashboard, Glances is very handygithub.com. Its ability to run in web mode makes it useful for remote monitoring of headless servers.

Prometheus + Node Exporter + Grafana (Metric Collection with Web Dashboards)

Another popular approach is to use Prometheus for metric collection, together with exporters on each Linux host and Grafana for visualization. This stack is more modular and suitable for monitoring multiple servers over time:

  • Node Exporter: An agent that runs on Linux systems to expose hardware and OS metrics in Prometheus format. It gathers CPU usage (per CPU core and aggregated), memory and swap stats, disk I/O and filesystem space, network traffic, etc.blog.cloudflare.com. It’s very lightweight (written in Go) – typically using a few MB of RAM and negligible CPU. The Node Exporter does not list individual processes, focusing on system-wide metricsblog.cloudflare.com.
  • Process and GPU Exporters: To get per-process or thread metrics, or GPU stats, additional exporters can be used. For example, cAdvisor can be deployed to collect container and process metrics – it breaks down CPU and memory usage by container (and can treat the whole host or systemd service as a “container”)blog.cloudflare.com. cAdvisor also supports NVIDIA GPU usage metrics (if a container uses GPU, it reports utilization via NVML)github.com. For bare-metal GPU monitoring, an NVIDIA exporter (using Nvidia’s DCGM or SMI) can be used. There are also Prometheus exporters that track specific processes or groups of processes by name, exposing their CPU/mem usage.
  • Prometheus Server: Prometheus scrapes metrics from Node Exporters (and other exporters) at intervals (e.g. every 5 or 15 seconds). It stores time-series data efficiently and allows setting alerts on metric conditions. Prometheus is heavier than the single-host tools above, but still reasonably efficient for moderate loads. It can easily handle tens of thousands of time-series on a single node. Resource use varies with retention period and scrape frequency; a typical small setup might use a few hundred MB of RAM. In exchange, you get a historical datastore of all metrics for analysis and graphingwww.redhat.comwww.redhat.com.
  • Grafana Dashboard: Grafana provides the web-based visualization. It connects to Prometheus (or other time-series DBs) and offers customizable dashboards with charts, gauges, and tables. There are many pre-built Grafana dashboards for Node Exporter and cAdvisor metrics, showing system load, CPU/memory trends, disk and network IO, etc., often with per-container or per-process breakdowns. The Grafana UI is interactive, allowing zooming in on timelines, adding ad-hoc queries, and correlating metrics. Users can create alerts in Grafana or leverage Prometheus alertmanager for notifications.
  • Data Storage and Analysis: This solution excels at long-term data storage and analysis. You can retain weeks or months of metrics, enabling after-the-fact analysis of performance issues. Grafana’s rich querying means you can compare across time ranges or hosts easilypcp.io. For example, you could investigate a spike in CPU from last week by looking at Grafana graphs, even if it’s long after the fact – something an in-memory tool like Netdata (without external storage) might not allow.
  • Lightweight Agents, Some Complexity: The node and process exporters themselves are lightweight, but the trade-off is the complexity of running multiple components (Prometheus server, possibly a Time-Series DB for long retention, and Grafana). This stack is best if you need a multi-host monitoring solution or plan to build a custom dashboard. In a small single-server scenario, it may be overkill, but it’s very powerful for infrastructure monitoring at scale. eBPF usage: By default, Node Exporter and cAdvisor rely on standard kernel interfaces (procfs, cgroups) rather than eBPF. However, eBPF can be introduced in this stack via custom exporters. For instance, Cloudflare’s ebpf_exporter allows running custom eBPF programs and exposing their metrics to Prometheusblog.cloudflare.comblog.cloudflare.com. This can uncover kernel-level metrics like scheduler latency or page cache hits that default exporters can’t see. Additionally, recent efforts integrate eBPF profiling data (e.g., continuous CPU profiler data) into Prometheus/Grafana for deeper analysis. These require more setup, but highlight that the Prometheus ecosystem can leverage eBPF when needed.

Monitorix (Classic Lightweight Monitoring with Web UI)

Monitorix is a veteran lightweight monitoring tool for Linux/UNIX systems. It runs a small web server that displays graphs of system performance metrics, refreshing periodically. Monitorix is designed to be simple and frugal – suitable for low-spec machines – while capturing a broad set of datawww.opensourceforu.com:

  • Features & Metrics: Despite its small footprint, Monitorix tracks many system resourceswww.opensourceforu.com. It logs system load averages, CPU usage (including per-core stats), memory and swap usage, and process data (active processes count, forks per second, etc.). It also charts disk usage and I/O, network traffic per interface, and even kernel metrics like context switches and entropy. Additional modules cover server software (Apache/Nginx requests, Mail server stats, MySQL queries, etc.)www.opensourceforu.comif enabled.
  • GPU and Sensors: Monitorix can gather hardware sensor data such as temperatures and fan speeds. It includes support for monitoring NVIDIA and AMD GPU statistics – reporting GPU utilization, memory usage, and temperature (using NVML for Nvidia and similar interfaces for AMD)www.monitorix.orgwww.monitorix.org. This is useful for workstations or servers with GPUs, providing at-a-glance GPU load graphs.
  • Web Interface: The interface consists of static graphs (usually generated via RRDTool). All metrics are displayed as time-series charts on a web page served by Monitorix’s built-in HTTP serverwww.opensourceforu.com. The charts update at a configured interval (e.g., every few minutes). It may not have the modern interactivity of Grafana, but it’s straightforward and accessible via any browser. Each graph typically shows the last few hours or days of data, and you can navigate to see different time spans.
  • Lightweight & Self-Contained: Monitorix is written in Perl and is very gentle on system resourceswww.opensourceforu.com. The collector daemon wakes at set intervals, reads system files (like /proc), updates RRD databases, and generates PNG graphs. CPU and memory overhead is minimal, making it suitable even on older hardware or routers. It’s essentially zero-maintenance once configured.
  • Data Retention: Because it uses round-robin databases (RRDs), Monitorix automatically retains historical data (with downsampling over time). This means you can look back at performance trends (CPU, memory, etc.) over days, weeks, or months as long as the tool has been running. The storage footprint stays small due to fixed-size RRD files.
  • Extensibility: While not as flexible as newer frameworks, Monitorix allows enabling/disabling specific metrics collection in its configuration. It also supports alerts for certain values. The focus, however, is on providing a quick visual health overview rather than deep exploration. Monitorix does not utilize eBPF – it sticks to traditional methods. Its strength is simplicity and low resource usewww.opensourceforu.com. It’s a good choice when you need a lightweight, all-in-one monitoring solution and can live with basic (but effective) graphs. For example, a small home server or VPS can run Monitorix to continuously log and display its health without the complexity of a full monitoring stack.

Performance Co-Pilot (PCP) with Grafana or Vector (Advanced Monitoring & Logging, eBPF Enabled)

Performance Co-Pilot (PCP) is a powerful toolkit for system performance analysis, geared towards both real-time monitoring and historical analysiswww.redhat.compcp.io. It consists of a collection of daemons and tools that work together:

  • Architecture: PCP uses a collector daemon (pmcd) with modular plugins called PMDAs (Performance Metrics Domain Agents) for different metric sourceswww.redhat.com. For example, one PMDA reads /proc for general stats, another might collect disk metrics, another can interface with databases, etc. This design is efficient and flexible, following a Unix philosophy of small componentswww.redhat.com. Collected metrics are identified by names and can be queried or logged.
  • Metrics & Processes: Out-of-the-box, PCP captures all typical system metrics (CPU, memory, disks, network, etc.) similarly to Node Exporter, but it also has a process PMDA that can monitor per-process details. This can list processes and their CPU, memory, I/O usage, and even thread counts. In fact, PCP includes an htop-like utility (pmchart/pcp-htop) that leverages these metricspcp.io. It’s capable of tracking hundreds of processes and tasks concurrently with low overhead.
  • Web Dashboard & Visualization: For visualization, PCP can integrate with Grafana using the Grafana-PCP plugingrafana.com. This allows you to build Grafana dashboards querying PCP’s archive or live data, similar to how one would use Prometheus. PCP also had a simpler built-in web UI called Vector for ad-hoc graphing. Additionally, PCP provides command-line tools to query and chart data (like pmrep for tabular reports or pmtime for timeline graphs). The key point is that PCP is data-source agnostic – you can view the data via Grafana or other frontends.
  • Historical Data and Logging: A standout feature of PCP is its logging capability. The pmlogger daemon can record metrics over time into archive fileswww.redhat.com. You can then replay or query these archives to analyze past performance, similar to how one would use sar logs. PCP’s archives, unlike fixed-size RRDs, can grow to retain detailed history. This makes PCP suitable for incident retrospectives – you can drill down into any past interval with the same resolution as live (depending on logging frequency). Comparing different time intervals or hosts is also supported nativelypcp.io.
  • Lightweight and Scalable: Despite its capabilities, PCP is designed to be efficient. It’s used in enterprise Linux (RHEL ships it as a monitoring solution). The collector and loggers are written in C and handle thousands of metrics with modest CPU/RAM. You can deploy PCP on many nodes and consolidate metrics centrally if needed. It’s routinely used to monitor large systems without significant impact on them.
  • eBPF Integration: PCP can harness eBPF for metrics that aren’t readily available from standard interfaces. For example, there are integrations where PCP runs BCC or bpftrace scripts via a PMDA, feeding the data into the PCP metric spacewww.redhat.comwww.redhat.com. Red Hat describes enabling “eBPF sourced metrics” in PCP on modern kernelswww.redhat.com. This means you could continuously log custom eBPF probe data (like kernel latency stats, function call counts, etc.) with PCP’s 24/7 logging ability. Essentially, PCP acts as a bridge to run eBPF tracing programs in the background and store those measurements historically – providing deep observability with low overhead. This is advanced usage, but highlights PCP’s extensibility.
  • Use Cases: PCP is well-suited for environments where you need detailed performance analysis and long-term data. For instance, on a production server, PCP could be collecting fine-grained metrics and if an incident occurs, you have a rich dataset to inspect (including per-process behavior, filesystems, CPU scheduler stats, etc.). Its integration with Grafana gives you a modern dashboard, while its CLI tools and logging provide forensic capabilities beyond most other tools. While PCP is more complex to set up than one-off tools like Netdata, it shines in depth and customizability. It’s a fully open-source solution used in industry for performance monitoring and can be as lightweight as needed by enabling only required metricspcp.io. Its support for eBPF and high-frequency logging make it a robust choice for power users.

Other Notable Tools and eBPF-Focused Solutions

Beyond the above, there are a few other tools and approaches worth mentioning:

  • Zabbix and CheckMK: These are comprehensive open-source monitoring platforms with web interfaces and long-term data storage. They can monitor Linux system metrics via agents and display graphs and dashboards. However, they are heavier solutions (requiring a server, database, and configuration of items to monitor). For example, Zabbix can be configured to record CPU, memory, and even top processes (via custom scripts) and will store data in an SQL databasewww.reddit.com. They are powerful for large-scale monitoring and alerting, but not as “lightweight” out-of-the-box as the other tools listed.
  • Cockpit: Cockpit is a web-based admin UI for Linux servers (from Red Hat). It provides a real-time view of system performance – CPU and memory graphs, disk I/O, network, and a process list with the ability to inspect and terminate processes. It is fairly lightweight and uses existing OS APIs (systemd, etc.) for metrics. Cockpit is easy to set up (it’s often available by default on CentOS/RHEL). However, it’s more of a management tool than a dedicated monitoring solution, and it doesn’t store historical data beyond the current session.
  • CLI Tools with Export/Logging: Traditional utilities like top/htop (for processes) or nvidia-smi (for GPUs) can be useful for on-demand checks but aren’t dashboards. That said, tools like sar (sysstat) and atop can log system performance at intervals. For instance, sar can record CPU, memory, etc. to daily files which can be reviewed later (or graphed via third-party tools). atop can run as a daemon (atop -w writing binary logs) to capture per-process resource usage over time, which can then be played back. While these aren’t web-based, one could combine them with scripts to visualize data later. They are extremely light on resources and good for historical forensics, but require manual effort to analyze (no built-in fancy UI).
  • eBPF Tracing Tools: If you need to go beyond standard metrics, eBPF itself can be used directly. Facebook’s bcc (BPF Compiler Collection) and the high-level bpftrace language let you write custom tracing programs for Linux. These can measure things like function latency, kernel events, per-process resource usage, etc., with minimal overheadwww.netdata.cloud. They are great for deep dives – for example, you could attach an eBPF probe to every process exit to count how many threads each process spawned, or measure disk I/O latency per process. However, these are command-line tools, not full-time monitoring daemons. One would typically run them for a short period to diagnose an issue. Some monitoring solutions (like PCP, as noted) can automate running eBPF scripts in the background to continuously gather such data.
  • Modern eBPF Observability Platforms: Recently, tools like Pixie (for Kubernetes) and Coroot have appeared, which use eBPF under the hood to automatically capture system and application telemetry. For example, Coroot is an open-source observability tool that requires no code changes – it uses eBPF to gather metrics, traces, logs, and profiles from the system and presents them in a coherent dashboardcoroot.comcoroot.com. It can profile applications and map services together using the data eBPF collects. These platforms often include their own web UIs and analysis engines (Coroot uses a UI with predefined dashboards and can do SLO-based alerting). They tend to be heavier than single-purpose monitors and target complex environments (like microservices or distributed apps), but they highlight the power of eBPF for system monitoring. If you are running cloud-native workloads, an eBPF-based tool like Pixie (which automatically monitors all syscalls, HTTP requests, etc. in a Kubernetes cluster) might be appealing. For a single Linux server, though, they may be overkill.
  • Continuous Profilers (Parca/Pyroscope): These are specialized tools focusing on profiling applications in production. Parca is an open-source continuous profiler that uses eBPF to sample CPU stacks at runtime, producing flame graphs of CPU usage over timeebpf.io. It comes with a web UI to visualize where CPU time is going inside applications. While not a general system monitor, it complements the above tools by drilling into why CPU is used (at the code level) rather than just how much. Parca and its frontend can be integrated with Grafana (Grafana acquired Pyroscope and can display profiles). If your monitoring needs include performance profiling, these eBPF profilers are worth exploring (they are reasonably efficient, designed to run always-on in production). Each of these solutions has its niche. For most users seeking a lightweight web-based dashboard of system health, tools like Netdata, Glances, or Monitorix are easy starting points. For those wanting a full-fledged metrics platform, Prometheus+Grafana or PCP provide more control and long-term insights. And for cutting-edge observability or debugging, eBPF-based tools offer unparalleled depth with minimal performance impactwww.netdata.cloudwww.redhat.com.

Conclusion: The landscape of Linux monitoring tools is rich. Open-source options cover needs from a simple single-server overview to enterprise-grade metric analytics. When choosing, consider the trade-offs: simplicity vs. flexibility, real-time vs. long-term data, and baseline metrics vs. deep instrumentation. Notably, eBPF is emerging in many of these tools as a way to gather granular data efficiently (Netdata, PCP, Coroot, Parca, etc. all harness eBPF). By selecting a tool or combination that fits your use case, you can monitor CPU, memory, GPU, and processes with minimal overhead – and have the data ready for analysis whenever you need it.Sources: