de-p1st-monitor

Research

See ./research.

• HDD temp:
  • Modern hard drives will throttle their read and write speeds when the drive reaches a critical pre-set temperature (usually around 60°C)
  • 20-50°C (short-term)
  • 20-40°C (long-term usage)
• SSD temp:
  • Most SSDs implement thermal throttling as a safety feature if a drive gets too hot. As the driver approaches the 70ºC limit that most manufacturers set, the more likely it is that the drive will start to slow itself down to prevent failure.
  • 30-50°C

Keep it simple!

Lines of code including docstrings and comments:

find ./src -name '*.py' | xargs wc -l
#=> 1518 total

Configuration

See cfg/yodaTux.ini for a configuration file covering all config options.

Installation

Install dependencies on Ubuntu

sudo apt-get install python3-pip

# Ubuntu 18.04 and below
sudo apt-get install python3-setuptools
sudo apt-get install python3-wheel

sudo apt-get install python3-psutil

# Ubuntu 18.04 and below: psutil < 5.6.2
sudo apt-get install python3-dev
sudo apt-get install build-essential
# Ubuntu 20.04 and below: psutil < 5.6.2
sudo python3 -m pip install psutil --upgrade

Install:

• On Arch Linux

make

• On Ubuntu

make install-pip

Usage

Command line interface

usage: de-p1st-monitor [-h] [--config CONFIG] [--export]

Iterates over all config sections. For each section the current sensor data is
read and logged to a .csv file.

options:
  -h, --help            show this help message and exit
  --config CONFIG, -c CONFIG
                        Path to .ini configuration file.
  --export, -e          If `True`, export .csv files and print their paths to
                        stdout. No sensor data is logged during this.

Periodic logging

Add a cron entry executing this e.g. every 3 Minutes:

de-p1st-monitor

Example log files

ssh nas 'tail -n 1 /var/log/de-p1st-monitor/*'

==> /var/log/de-p1st-monitor/cpu_15min.csv <==
20230315T103001,0.10400390625

==> /var/log/de-p1st-monitor/cpu_1min.csv <==
20230315T103001,0.03076171875

==> /var/log/de-p1st-monitor/cpu_5min.csv <==
20230315T103001,0.0301513671875

==> /var/log/de-p1st-monitor/drive_20d86155-30d4-404c-95e8-c701cfb16ca5.csv <==
20230315T103001,24

==> /var/log/de-p1st-monitor/drive_4651c3f1-e4b8-45aa-a823-df762530a307.csv <==
20230315T103001,21

==> /var/log/de-p1st-monitor/drive_68c349e8-5118-4773-9fd5-5dbad9acee4e.csv <==
20230315T103001,29

==> /var/log/de-p1st-monitor/drive_b8ef1da9-d76d-44b4-86d4-71c82c888b6f.csv <==
20230315T103001,28

==> /var/log/de-p1st-monitor/filesystem_3CBA-B4EA.csv <==
20230315T103001,0.228

==> /var/log/de-p1st-monitor/filesystem_a454430b-dee3-4b6b-8325-f7bdb9435ed1.csv <==
20230314T231501,0.762

==> /var/log/de-p1st-monitor/filesystem_b8ef1da9-d76d-44b4-86d4-71c82c888b6f.csv <==
20230315T103001,0.034

==> /var/log/de-p1st-monitor/filesystem_c385a436-0288-486f-a2b9-c64c2db667e7.csv <==
20230315T103001,0.374

==> /var/log/de-p1st-monitor/memory.csv <==
20230315T103001,4127,15329

==> /var/log/de-p1st-monitor/net_enp0s31f6.csv <==
20230315T103001,69366974632,58725303985,20230304T173014

==> /var/log/de-p1st-monitor/swap.csv <==
20230315T103001,25,4095

==> /var/log/de-p1st-monitor/temp_coretemp_Core 0.csv <==
20230315T103001,26.0

==> /var/log/de-p1st-monitor/temp_coretemp_Core 1.csv <==
20230315T103001,34.0

==> /var/log/de-p1st-monitor/temp_coretemp_Package id 0.csv <==
20230315T103001,35.0

Plots

Creating plots with graph-cli

1. Export and fetch data

ssh_target=rootnas
dst=~/de-p1st-monitor-"${ssh_target}"
files="${dst}".files

# Export .csv files on SSH target and save list of exported files to $files.
ssh "${ssh_target}" 'de-p1st-monitor --export' > "${files}"

rm -rf "${dst}"
mkdir -p "${dst}"

rsync --checksum --archive --progress --human-readable --delete \
  --files-from="${files}" "${ssh_target}":/ "${dst}"
mv "${dst}"/var/log/de-p1st-monitor/* "${dst}"
rm -r "${dst}"/var "${files}"

cd "${dst}"

2. Install (Python) graph-cli

python -m venv ~/de-p1st-monitor.venv
source ~/de-p1st-monitor.venv/bin/activate
pip install graph-cli

3. Create plots

Create one plot for each .csv file:

sample_duration=4H

for file in *.csv; do
  graph "${file}" -x 1 --resample "${sample_duration}" --figsize 1600x1000 -o "${file}".resample-"${sample_duration}"-mean.png || {
    echo "Error while processing ${file}"
  }
done

for file in {swap,memory}.csv {temp_,drive_,net_,cpu_,filesystem_}*.csv; do
  graph "${file}" -x 1 --resample "${sample_duration}" --resample-action max --figsize 1600x1000 -o "${file}".resample-"${sample_duration}"-max.png || {
    echo "Error while processing ${file}"
  }
done

4. Optionally, create more plots

Some self-explaining examples:

# x and y axis by column name
graph cpu_1min.csv -x 'datetime#Date' -y 'float#LoadAverage1min' --resample 1H -o cpu_1min_resample-1H.png
# x and y axis by column number
graph cpu_1min.csv -x 1 -y 2 --resample 1H -o cpu_1min_resample-1H.png
# specify x axis; use all other axes for y
graph cpu_1min.csv -x 1 --resample 1H -o cpu_1min_resample-1H.png
# increased plot size
graph cpu_1min.csv -x 1 --resample 1H --figsize 1600x1000 -o cpu_1min_resample-1H.png

# resample using sum
graph net_enp0s31f6.csv.exported.csv -x 1 --resample 1H --resample-action sum --figsize 1600x1000 -o net_enp0s31f6.csv.exported_resample-1H-sum.png

# resample using max
graph cpu_1min.csv -x 1 --resample 1H --resample-action max --figsize 1600x1000 -o cpu_1min_resample-1H-max.png

Example plots