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Use psutil to collect host CPU utilization, memory availability, filesystem capacity, disk activity, and network traffic in Python. CPU utilization is sampled over time; disk and network activity APIs return cumulative counters that must be turned into rates by comparing successive samples. For historical queries and dashboards, you can optionally expose metrics with prometheus-client.
Install psutil and choose the scope of your monitor
Install psutil in the Python environment that will run the monitor:
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python -m pip install psutilpsutil provides both system-wide and process-level APIs. This example gathers host metrics. That distinction matters: a process metric describes the Python process, while a host metric describes the system visible to it. Operating-system support and individual fields vary; psutil documents support for Linux, Windows, macOS, BSD variants, Solaris, and AIX. Check the API reference for the version you install and validate the fields in your deployment environment.
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Keep collection separate from display or export. A snapshot can include CPU percentage, virtual memory, capacity for a selected filesystem path, disk I/O counters, and network counters by interface:
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import psutil
def sample_host(path="/"):
return {
"cpu_percent": psutil.cpu_percent(interval=None),
"memory": psutil.virtual_memory(),
"disk": psutil.disk_usage(path),
"disk_io": psutil.disk_io_counters(),
"network": psutil.net_io_counters(pernic=True),
}The returned objects contain byte counts and other numeric values. Keep those values unchanged during collection; convert bytes to units such as MiB only when formatting a display or preparing a report.
Sample CPU utilization over an interval
psutil.cpu_percent(interval=None) is nonblocking, but it needs a prior CPU-time sample for comparison. Discard the first result. The psutil FAQ explains that the first call has no earlier sample and returns a meaningless 0.0; prime the sampler before using its readings. Alternatively, passing a positive interval waits while psutil measures, which is simpler but blocks the calling thread for that interval.
import psutil
import time
psutil.cpu_percent(interval=None) # Prime it; discard the result.
time.sleep(1)
cpu_percent = psutil.cpu_percent(interval=None)
print(cpu_percent)For a monitor that also collects other metrics, use a sampling loop and allow time between nonblocking CPU calls. See the psutil FAQ and API reference for the documented behavior.
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psutil.virtual_memory() returns a structure with fields including total, available, free, used, and percent. For a cross-platform indication of memory that can be given to processes without swapping, psutil recommends available. The operating system may use reclaimable cache, so free can be much lower; the meaning of used also depends on the platform. The reported percentage is calculated as (total - available) / total * 100. Display available bytes and percentage together when readers need both a relative and absolute view.
Measure filesystem capacity separately from disk activity
Filesystem capacity
psutil.disk_usage(path) reports capacity for the filesystem containing that path. Choose a path that represents the volume you actually want to monitor; using "/" checks the filesystem containing the root directory. On UNIX systems, reserved space can make the reported free-space and percentage values differ from a simple calculation using only total and used.
Disk I/O activity
psutil.disk_io_counters() returns cumulative I/O counters, such as bytes read and written, rather than a current throughput rate. To calculate approximate bytes per second, subtract the earlier read_bytes or write_bytes value from the later one and divide by elapsed seconds. Use perdisk=True when you need attribution by device instead of host-wide counters. The psutil recipes demonstrate counter-delta sampling.
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Turn network counters into traffic rates
psutil.net_io_counters(pernic=True) returns cumulative counters per network interface, including bytes, packets, errors, and drops. To estimate traffic rates, take two samples, subtract the earlier byte count from the later one, and divide by elapsed seconds. The same method works for packets per second. Per-interface readings make it possible to distinguish traffic on different interfaces; aggregate counters are simpler when that attribution is unnecessary.
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Combine the metrics in a sampling loop
The following illustrative loop primes CPU sampling, gathers snapshots, and calculates network rates using elapsed monotonic time. It assumes the configured interface exists in both samples; production code should handle missing interfaces and deployment-specific fields explicitly.
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import time
import psutil
def sample_host(path="/"):
return {
"cpu_percent": psutil.cpu_percent(interval=None),
"memory": psutil.virtual_memory(),
"disk": psutil.disk_usage(path),
"disk_io": psutil.disk_io_counters(),
"network": psutil.net_io_counters(pernic=True),
}
psutil.cpu_percent(interval=None) # Prime and discard the initial reading.
time.sleep(1)
previous = sample_host()
previous_time = time.monotonic()
interface = "eth0" # Replace with a configured interface for this host.
while True:
time.sleep(1)
current = sample_host()
current_time = time.monotonic()
elapsed = current_time - previous_time
old_net = previous["network"].get(interface)
new_net = current["network"].get(interface)
if old_net is not None and new_net is not None and elapsed > 0:
sent_rate = (new_net.bytes_sent - old_net.bytes_sent) / elapsed
received_rate = (new_net.bytes_recv - old_net.bytes_recv) / elapsed
else:
sent_rate = received_rate = None
old_io = previous["disk_io"]
new_io = current["disk_io"]
if old_io is not None and new_io is not None and elapsed > 0:
read_rate = (new_io.read_bytes - old_io.read_bytes) / elapsed
write_rate = (new_io.write_bytes - old_io.write_bytes) / elapsed
else:
read_rate = write_rate = None
print({
"cpu_percent": current["cpu_percent"],
"memory_percent": current["memory"].percent,
"memory_available_bytes": current["memory"].available,
"filesystem_percent": current["disk"].percent,
"disk_read_bytes_per_second": read_rate,
"disk_write_bytes_per_second": write_rate,
"network_sent_bytes_per_second": sent_rate,
"network_received_bytes_per_second": received_rate,
})
previous, previous_time = current, current_timeDisk and network rates are estimates over the interval between samples. Their underlying counters are cumulative, so displaying an absolute counter as if it were a rate would be misleading. The example uses documented psutil APIs; it is not a benchmark or a claim about measurements on a particular server.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose between local output and Prometheus collection
| Approach | Useful when | What it provides | Key consideration |
|---|---|---|---|
| Local sampling loop | You need an immediate local display, log, or simple alerting input. | Whatever values your monitor collects and renders. | You must implement storage or forwarding yourself if you need historical queries or centralized dashboards. |
| prometheus-client | You want Prometheus to scrape an HTTP endpoint for time-series collection. | An application metrics exposition path; the official tutorial demonstrates an endpoint on port 8000. | Expose the psutil values you need; the default process collector is not a substitute for host-wide metrics. |
The client_python documentation describes installing the client and exposing metrics over HTTP. A Prometheus setup is useful when you need collection beyond a single running loop; the client provides exposition, while Prometheus handles scraping and time-series storage.
Keep process metrics distinct from host metrics
The default Python process collector reports metrics for the Python process, including process CPU, memory, file descriptors, and start time. According to the Prometheus collector documentation, it is available only on Linux and reads from /proc. Use psutil for the host metrics in this guide, and add process-level metrics separately when they answer a different monitoring question.
Quick Recap
Account for platform and deployment differences
- Confirm that the fields your monitor reads are available on the operating system and psutil version you deploy; individual APIs and values can differ across platforms.
- Choose filesystem paths, network interfaces, and disk-device detail deliberately. Containers, permissions, and deployment topology can affect what the monitoring process can see.
- Handle unavailable counters and changing interfaces or devices explicitly. Do not silently substitute zero for a missing measurement.
- Check the documentation for your installed psutil version. The current API reference notes breaking API changes in version 8.0, so examples and field assumptions should be checked against the version in use.
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