Procpath 1.2.0

Procpath is a process tree analysis workbench

Procpath

Procpath is a process tree analysis command-line workbench. Its goal is to provide natural interface to the tree structure of processes running on a Linux system for inspection and later analysis.

Installation

pip install --user Procpath

For installing Procpath into a dedicated virtual environment pipx [1] is recommended.

pipx install Procpath

Quickstart

Get comma-separated PIDs of the process subtree (including the parent process pid=2610).

procpath query -d , '$..children[?(@.stat.pid == 2610)]..pid'

Get JSON document of said process subtree.

procpath query -i 2 '$..children[?(@.stat.pid == 2610)]'

Get total RSS in MiB of said process subtree (this is an example that query produces JSON that can be further processed outside of procpath, and below is a much easier way to calculate aggregates).

procpath query '$..children[?(@.stat.pid == 2610)]' \
  | jq '[.. | .stat? | objects | .rss] | add / 1024 * 4'

Get total RSS in MiB of said process subtree the easy way.

procpath query '$..children[?(@.stat.pid == 2610)]' \
  'SELECT SUM(stat_rss) / 1024.0 * 4 total FROM record'

Get total RSS in MiB of a docker-compose stack.

L=$(docker ps -f status=running -f name='^project_name' -q | xargs -I{} -- \
  docker inspect -f '{{.State.Pid}}' {} | tr '\n' ,)
procpath query "$..children[?(@.stat.pid in [$L])]" \
  'SELECT SUM(stat_rss) / 1024.0 * 4 total FROM record'

Record process trees of two Docker containers once a second, re-evaluating the containers’ root process PIDs once per 30 recordings. Then visualise RSS of each process (which is also just an example, that output SQLite database can be visualised in different ways, including exporting CSV, sqlite3 -csv ..., and doing it the old way, to using proper UI described in Visualisation section in the documentation).

procpath record \
  -e C1='docker inspect -f "{{.State.Pid}}" project_db_1' \
  -e C2='docker inspect -f "{{.State.Pid}}" project_app_1' \
  -i 1 -v 30 -d out.sqlite '$..children[?(@.stat.pid in [$C1, $C2])]'
# press Ctrl + C
sqlite3 out.sqlite \
  "SELECT stat_pid, group_concat(stat_rss / 1024.0 * 4) \
   FROM record \
   GROUP BY stat_pid" \
  | sed -z 's/\n/\n\n\n/g' | sed 's/|/\n/' | sed 's/,/\n/g' > special_fmt
gnuplot -p -e \
  "plot for [i=0:*] 'special_fmt' index i with lines title columnheader(1)"

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[1]

https://pypi.org/project/pipx/