initial

app/netdata_DocRudi.py

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+#!/usr/bin/env python3
+# -*- coding: utf8 -*-
+import json
+from datetime import datetime
+from urllib import request, parse, error
+
+import plotly.graph_objs as go
+import plotly.utils
+from flask import Flask, render_template
+from plotly.subplots import make_subplots
+from waitress import serve
+import os
+import sys
+import socket
+
+netdata_host = os.environ.get('NETDATA_HOST', 'http://localhost:19999')
+netdata_query_seconds = int(os.environ.get('NETDATA_QUERY_SECONDS', '200000'))
+netdata_query_points = int(os.environ.get('NETDATA_QUERY_POINTS', '3000'))
+
+print(type(netdata_query_points), file=sys.stderr)
+site_refresh = int(os.environ.get('SITE_REFRESH', '0'))
+
+server_host = socket.gethostname()
+server_ip = socket.gethostbyname(server_host)
+server_port = os.environ.get('SERVER_PORT', '19998')
+
+
+app = Flask(__name__)
+
+def get_docker_data(q_context, q_dimension):
+    netdata_url = f"{netdata_host}/api/v2/data"
+    params = {
+        'group_by': 'instance',
+        'scope_contexts': q_context,
+        'dimensions': q_dimension,
+        'format': 'json',
+        'after': f'-{netdata_query_seconds}',
+        'points': f'{netdata_query_points}',
+        'group': 'average'
+    }
+    url = f"{netdata_url}?{parse.urlencode(params)}"
+    with request.urlopen(url) as response:
+        data = json.loads(response.read().decode())
+
+    return data['result']
+
+def process_label(label):
+    parts = label.split('@')[0].split('.')
+    if parts and parts[0].startswith('cgroup_'):
+        if parts[1] =='mem_usage':
+            return f'mem_{parts[0][7:]}'
+        else:
+            return f'{parts[1]}_{parts[0][7:]}'
+    return label
+
+def create_plot():
+    result_data_cpu = get_docker_data('cgroup.cpu','*')
+    result_data_mem_usage = get_docker_data('cgroup.mem_usage', 'ram')
+
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1,
+                        subplot_titles=('CPU-Nutzung der Docker-Container', 'Mem-Usage der Docker-Container'))
+
+    def plot_data_function(data, row):
+        if 'labels' in data and 'data' in data:
+            labels = data['labels']
+            plot_data = data['data']
+        elif isinstance(data, list) and len(data) > 1:
+            labels = data[0]
+            plot_data = data[1:]
+        else:
+            print(f"Unerwartete Datenstruktur für Reihe {row}")
+            return
+
+        for i, label in enumerate(labels[1:], start=1):  # Skip the first label (usually timestamp)
+            if 'slice_system-slice' in labels[i]:
+                continue
+            y_values = [round(float(row[i]), 2) if row[i] is not None else None for row in plot_data]
+            x_values = [datetime.fromtimestamp(row[0]) for row in plot_data]
+            processed_label = process_label(label)
+
+            trace = go.Scatter(
+                x=x_values,
+                y=y_values,
+                mode='lines',
+                name=processed_label
+            )
+            fig.add_trace(trace, row=row, col=1)
+
+    plot_data_function(result_data_cpu, row=1)
+    plot_data_function(result_data_mem_usage, row=2)
+
+    # Beschriftung für oberen Graphen wenn bei make_subplots shared_xaxes=false
+    # fig.update_xaxes(title_text="Zeit", tickformat='%Y-%m-%d %H:%M:%S', tickangle=0, row=1, col=1)
+    fig.update_xaxes(title_text="Zeit", tickformat='%Y-%m-%d\n%H:%M:%S', tickangle=0, row=2, col=1)
+    fig.update_yaxes(title_text="CPU-Nutzung (%)", tickformat='.2f', rangemode='tozero', row=1, col=1)
+    fig.update_yaxes(title_text="MemUsage (MiB)", tickformat='.0f', rangemode='tozero', row=2, col=1)
+
+    fig.update_layout(
+        height=1000,
+        legend_title="Container",
+        margin=dict(b=100),
+        showlegend=True,
+        paper_bgcolor='#a1bdd6',
+        plot_bgcolor='#687a8a'
+    )
+
+    return plotly.utils.PlotlyJSONEncoder().encode(fig)
+
+
+@app.route('/')
+def index():
+    plot = create_plot()
+    return render_template('index.html', plot=plot, netdata_host=netdata_host,
+                           site_refresh=site_refresh)
+
+def check_url(url, timeout=5):
+    try:
+        with request.urlopen(url, timeout=timeout):
+            print(f"Die Netdata-URL {url} ist erreichbar.", file=sys.stderr)
+    except error.URLError as e:
+        print(f"Fehler: Die URL {url} ist nicht erreichbar.", file=sys.stderr)
+        print(f"Fehlermeldung: {str(e)}", file=sys.stderr)
+        sys.exit(1)
+
+if __name__ == '__main__':
+    # app.run(host='0.0.0.0', port=19998, debug=True)
+    check_url(netdata_host)
+    print(f"Dashboard started at http://{server_host}:{server_port} | http://{server_ip}:{server_port}", file=sys.stderr)
+    serve(app, host="0.0.0.0", port=server_port)

app/requirements.txt

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+blinker==1.9.0
+click==8.1.7
+colorama==0.4.6
+Flask==3.1.0
+itsdangerous==2.2.0
+Jinja2==3.1.4
+MarkupSafe==3.0.2
+packaging==24.2
+plotly==5.24.1
+tenacity==9.0.0
+Werkzeug==3.1.3
+
+waitress~=3.0.2

app/templates/index.html

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+<!DOCTYPE html>
+<html lang="de">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    {% if site_refresh > 0 %}
+    <meta http-equiv="refresh" content="{{ site_refresh }}">
+    {% endif %}
+    <title>Docker Container CPU-Nutzung</title>
+    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+</head>
+<body style="background-color:#64a0d6">
+<h1>Docker Container CPU/MEM from netdata: <a href="{{ netdata_host }}" target="_blank">{{ netdata_host }}</a></h1>
+<div id="plot"></div>
+<script>
+    var graphs = {{ plot | safe }};
+    Plotly.plot('plot', graphs, {});
+</script>
+</body>
+</html>