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Use top in batch mode to capture repeated process samples, convert the fields you need into a timestamped numeric data file, then plot that file with gnuplot. The key is to inspect and normalize top output first: its display is not a universal CSV format, and CPU and memory values describe different things.
Choose the monitoring scope and sampling interval
For a known process, filter by its PID. To discover busy tasks, capture the process table instead. Set a deliberate refresh delay and iteration count: shorter delays produce more samples and may add collection overhead. The Linux top(1) manual describes top as providing “a dynamic real-time view of a running system.”
This example captures 30 updates, two seconds apart, for one PID:
top -b -d 2 -n 30 -p "$PID" > top.logIn this command, -b selects batch mode, -d 2 sets the update delay, -n 30 limits the number of updates, and -p selects the PID. Check top -h or the installed top(1) manual for the exact options supported by your local implementation. The command saves raw output; it does not create a gnuplot-ready data file.
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Record context and inspect the output
A time-series plot needs a usable x-axis. Preserve timestamps explicitly where possible, or extract a timestamp or uptime from each record only after confirming how your version of top prints it. If you need elapsed seconds, create that coordinate during normalization rather than assuming raw output contains a consistent timestamp field.
Keep enough capture context to interpret the values and reproduce the collection:
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- Host and relevant distribution or kernel context
topversion and exact command line- Sampling delay and capture start/end time
- Whether the data represents one PID, a set of processes, or a host-level summary
Choose fields intentionally. For a process plot, common fields are PID, command, %CPU, RES, and %MEM; VIRT may be useful when virtual address-space size is relevant. Use field management or supported configuration options to make selection repeatable. Then inspect the actual headers and sample rows before writing a parser. Batch output is useful for downstream processing, but its headers, alignment, and columns are not a stable CSV contract across implementations and configurations.
Interpret process CPU and memory columns correctly
The meaning of each field determines what the plotted line can tell you. The top(1) manual defines the relevant measures as follows:
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| Field | Meaning | How to label or interpret it |
|---|---|---|
%CPU |
The task’s share of elapsed CPU time since the last screen update. | Label it as process CPU usage in percent and retain the sampling interval and machine context. It is interval-oriented, not a host-wide CPU summary. |
RES |
Resident physical memory for the task. | Plot with a memory unit, such as KiB, after confirming the unit shown by your local output. |
%MEM |
Resident memory as a proportion of physical memory. | This is a percentage representation of resident memory, not an independent byte count. |
VIRT |
Virtual address-space size, including code, data, shared libraries, swapped pages, and mapped but unused pages. | Do not treat it as physical RAM currently resident in memory. |
The system summary area in top reports host-level CPU states and memory separately from per-process rows. Do not label a system summary as the usage of one process. Also avoid summing process RES values and presenting the result as unique host memory: shared pages can be counted in more than one process. The manual describes PSS as a proportional attribution of shared resident pages, while noting collection cost and privilege implications.
Normalize samples into a plotting data file
Convert the selected records into a simple whitespace-delimited or otherwise clearly specified table, for example:
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# seconds cpu_percent resident_kib
0 1.2 18432
2 3.8 18944
4 2.1 19008These rows illustrate a schema, not measured output from the capture command. The first column is elapsed seconds; the second is process CPU percent; the third is resident memory in KiB. Ensure the memory unit matches the source values before assigning the column name. Remove headers and nonnumeric lines or handle them explicitly in the parser.
Account for process lifecycle changes while normalizing:
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- If the process exits before capture finishes, later samples may be absent.
- If a PID is reused, a later row can refer to a different process. Where identity matters, record a process label or other identity context alongside the PID.
- For multiple processes, decide how to represent missing samples and keep process identity in the data, rather than silently merging rows into one series.
- Check for truncated lines, changed columns, and formatting differences before trusting the resulting values.
Plot CPU and memory with gnuplot
gnuplot’s official plot documentation supports plotting data-file columns. With the normalized three-column example, save a script such as process.gnuplot:
set xlabel "Elapsed time (seconds)"
set ylabel "CPU usage (%)"
set y2label "Resident memory (KiB)"
set y2tics
set key left top
plot "process.dat" using 1:2 with lines title "%CPU",
"process.dat" using 1:3 axes x1y2 with lines title "RES (KiB)"Run it from the directory containing process.dat with gnuplot process.gnuplot. The first series reads columns 1 and 2; the second reads columns 1 and 3 against the secondary y-axis. This is appropriate when you want both measures in one chart, but the two axes have different units and scales. Separate panels are often easier to compare without implying that a CPU percentage and a memory quantity are directly comparable.
Know what sampled monitoring can miss
This workflow produces periodic samples, not a continuous trace. A process that starts and exits between refreshes may never appear. A process CPU reading reflects usage over the interval since the refresh, so the chosen delay affects how the graph should be read. Host load or host-level CPU states are not interchangeable with a single process’s %CPU. Parsers can also fail when output columns change, headers differ, or text is truncated.
When a cgroup monitor fits better
If the question concerns a service or group of processes managed together, the process list may be the wrong unit of observation. The systemd-cgtop(1) manual describes monitoring control groups, ordering them by CPU, memory, or disk I/O, and batch operation suitable for files or programs. Use it when the service’s aggregate cgroup resources are the target; use top when you need individual process rows or a known PID. Compare the scope and output you need before changing tools; neither is a universal winner.
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