docker-stats-histo/display/generate_html.py

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#!/usr/bin/env python3
import argparse
import logging
import os
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import sqlite3
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from pathlib import Path
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import pandas as pd
import plotly.express as px
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log = logging.getLogger(__name__)
HERE = Path(os.path.dirname(os.path.realpath(__file__)))
TABLE_NAME = "dc_stats"
INTERVALS = [1] # on cron interval is 10min
def parse_args():
parser = argparse.ArgumentParser(description="Extract docker stats in a sqlite db")
parser.add_argument(
"sqlite",
nargs="?",
type=Path,
help="Path to sqlite file",
default=HERE / "stats.sqlite",
)
parser.add_argument(
"html",
nargs="?",
type=Path,
help="Path to sqlite file",
default=HERE / "stats.html",
)
parser.add_argument("--debug", "-d", action="store_true", help="Run in debug mode")
args = parser.parse_args()
return args.sqlite, args.html, args.debug
def generate_html_report(sqlite_fn: Path, html_fn: Path):
# Create your connection.
cnx = sqlite3.connect(sqlite_fn)
df = pd.read_sql_query(f"SELECT * FROM {TABLE_NAME}", cnx)
df = df.set_index("date")
log.info(f"Find {len(df)} items")
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# df["granu"] = 1
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dfs = pd.DataFrame()
for i in INTERVALS:
dfi = df[::i].copy()
breakpoint()
# dfi["granu"] = i
dfs = dfs.append(dfi)
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log.info(f"Find {len(dfs)} items")
fig = px.area(dfs)
fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color))
fig["layout"].pop("updatemenus") # optional, drop animation buttons
fig.update_layout(transition={"duration": 1e12})
fig.update_layout(
title="Utilisation de RAM pour les docker-compose de l'eunuque"
" (pour voir une stat, mettre la sourie sur le haut des courbes,"
" vers les points)",
xaxis_title="Temps",
yaxis_title="RAM",
)
fig.write_html(html_fn, include_plotlyjs="cdn")
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def main():
sqlite_fn, html_fn, debug = parse_args()
if debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
generate_html_report(sqlite_fn, html_fn)
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if __name__ == "__main__":
main()