ntp graphs

debug-build-run.sh

@@ -7,22 +7,7 @@ IMAGE_NAME=numberimage
 docker build --progress=plain -t $IMAGE_NAME .
 
 
-SECRETS=`mktemp`
+. load-debug-env
-gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --output $SECRETS ./deployment/secrets.asc
-. $SECRETS
-rm $SECRETS
-
-DB_NAMESPACE=database1
-DB_DEPLOYNAME=database
-
-REDIS_NAMESPACE=redis
-REDIS_SERVICE_NAME=redis
-
-PGHOST=`kubectl get services $DB_DEPLOYNAME -n $DB_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
-REDISHOST=`kubectl get services $REDIS_SERVICE_NAME -n $REDIS_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
-
-REDIS_URL=redis://$REDISHOST:6379/4
-
 
 docker run \
   -it \

load-debug-env

@@ -0,0 +1,15 @@
+SECRETS=`mktemp`
+gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --output $SECRETS ./deployment/secrets.asc
+. $SECRETS
+rm $SECRETS
+
+DB_NAMESPACE=database1
+DB_DEPLOYNAME=database
+
+REDIS_NAMESPACE=redis
+REDIS_SERVICE_NAME=redis
+
+PGHOST=`kubectl get services $DB_DEPLOYNAME -n $DB_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
+REDISHOST=`kubectl get services $REDIS_SERVICE_NAME -n $REDIS_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
+
+REDIS_URL=redis://$REDISHOST:6379/4

src/ntp_routes.py

@@ -1,157 +1,69 @@
-from flask import Flask, session, g, render_template_string
+from flask import Flask, session, g, render_template_string, Response
 from loguru import logger
 import json
-import plotly.express as px
-import plotly.graph_objects as po
 import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.ticker import ScalarFormatter
 import pandas as pd
 import psycopg
 import sqlalchemy
 import time
+import io
 
 from app import app
 from app import oidc
 
 
 
-@app.route('/ntpserver')
-def ntpserver():
-    try:
-        dbh = psycopg.connect()
-        engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
 
-        query = """
+@app.route('/ntp/stratum-rootdisp.png')
-          select   time_bucket('5 minutes', time) as bucket,
+def stratum_rootdisp_png():
-            device,
-            avg(cast(values->'rootdisp'->>'value'  as float)) as rootdisp,
-            max(cast(values->'stratum'->>'value'  as int)) as stratum
-          from measurements
-          where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-            application = 'TSM' and attributes->>'Label' = 'david'
-          group by bucket, device
-          order by bucket, device
-        """      
-
-        df = pd.read_sql(query, con=engine)
-
-        fig = po.Figure()
-        fig.add_trace(po.Scatter(x=df['bucket'], y=df['rootdisp'], mode='lines', name='Root Dispersion', yaxis='y1', line=dict(color='red')))
-        fig.add_trace(po.Scatter(x=df['bucket'], y=df['stratum'], mode='lines', name='Stratum', yaxis='y2', line=dict(color='blue')))
-
-        fig.update_layout(
-            title='NTP Server Numbers',
-            
-            # Linke Y-Achse
-            yaxis=dict(
-                title='Root Dispersion',
-                ticksuffix=' ms'
-            ),
-            
-            # Rechte Y-Achse
-            yaxis2=dict(
-                title='Stratum',
-                overlaying='y',  # Legt die zweite Y-Achse über die erste
-                side='right',  # Setzt sie auf die rechte Seite
-                tickmode='linear',  # Stellt sicher, dass die Ticks in festen Intervallen sind
-                dtick=1,  # Zeigt nur ganzzahlige Ticks
-            ),
-            
-            legend=dict(x=0.05, y=1)  # Position der Legende
-        )
-
-        graph_html_1 = fig.to_html(full_html=False, default_height='30%')
-
-        query = """
-            select   time_bucket('5 minutes', time) as bucket,
-              device,
-              avg(cast(values->'time-req-pkts'->>'value'  as float)) as packets
-            from measurements
-            where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-              application = 'SNMP' and attributes->>'Label' = 'david'
-            group by bucket, device
-            order by bucket, device
-        """
-        df = pd.read_sql(query, con=engine)
-        fig_2 = px.line(df, x='bucket', y='packets')
-        fig_2.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            yaxis_ticksuffix="p/s",
-            title=f"Time Requests"
-        )
-        graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
-
-        query = """
-            select   time_bucket('5 minutes', time) as bucket,
-              device,
-              avg(cast(values->'load1'->>'value'  as float)) as loadaverage1min
-            from measurements
-            where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-              application = 'SNMP' and attributes->>'Label' = 'david'
-            group by bucket, device
-            order by bucket, device
-        """
-        df = pd.read_sql(query, con=engine)
-        fig_3 = px.line(df, x='bucket', y='loadaverage1min')
-        fig_3.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            title=f"CPU Load"
-        )
-        graph_html_3 = fig_3.to_html(full_html=False, default_height='30%')
-
-        return render_template_string(f"""
-            <html>
-                <head>
-                    <title>NTP Server Numbers</title>
-                </head>
-                <body>
-                    {graph_html_1}
-                    {graph_html_2}
-                    {graph_html_3}
-                </body>
-            </html>
-        """)
-    except Exception as e:
-        raise Exception(f"Error when querying NTP server values: {e}")
-    finally:
-        if dbh is not None:
-            dbh.close()
-
-
-def get_dataframe():
-
-@app.route('/plot.png')
-def plot_png():
     dbh = psycopg.connect()
     engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
     query = """
       select   time_bucket('5 minutes', time) as bucket,
-        device,
+        attributes->>'Label' as device,
         avg(cast(values->'rootdisp'->>'value'  as float)) as rootdisp,
         max(cast(values->'stratum'->>'value'  as int)) as stratum
       from measurements
       where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
-        application = 'TSM' and attributes->>'Label' = 'david'
+        application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
-      group by bucket, device
+      group by bucket, attributes->>'Label'
-      order by bucket, device
+      order by bucket, attributes->>'Label'
     """
     df = pd.read_sql(query, con=engine)
     
-    fig, ax1 = plt.subplots()
+    df['rootdisp'] = df['rootdisp'] / 1e6
 
-    ax1.plot(df['bucket'], df['rootdisp'], 'r-', label='Root Dispersion')
+    
+    # Extract date for title
+    plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
+    
+    # Create figure with two side-by-side subplots
+    fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
+    
+    for i, device in enumerate(['harrison', 'david']):
+        ax1 = axes[i]
+        ax2 = ax1.twinx()
+        
+        device_df = df[df['device'] == device]
+        
+        ax1.plot(device_df['bucket'], device_df['rootdisp'], 'r-', label='Root Dispersion')
         ax1.set_xlabel('Time')
         ax1.set_ylabel('Root Dispersion (ms)', color='r')
         ax1.tick_params(axis='y', labelcolor='r')
         
-    ax2 = ax1.twinx()
+        ax2.plot(device_df['bucket'], device_df['stratum'], 'b-', label='Stratum')
-    ax2.plot(df['bucket'], df['stratum'], 'b-', label='Stratum')
         ax2.set_ylabel('Stratum', color='b')
         ax2.tick_params(axis='y', labelcolor='b')
-    ax2.set_yticks(range(int(df['stratum'].min()), int(df['stratum'].max()) + 1))
+        ax2.set_yticks(range(int(device_df['stratum'].min()), int(device_df['stratum'].max()) + 1))
         
-    fig.suptitle('NTP Server Numbers')
+        ax1.set_title(f'{device.capitalize()}')
+        
+        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+        fig.autofmt_xdate(rotation=45)
+    
+    fig.suptitle(f'Stratum and Root Dispersion - {plot_date}')
     fig.tight_layout()
     
     img_io = io.BytesIO()
@@ -161,3 +73,57 @@ def plot_png():
 
     return Response(img_io, mimetype='image/png')
 
+
+@app.route('/ntp/packets-load.png')
+def packets_load_png():
+    dbh = psycopg.connect()
+    engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
+    query = """
+      select   time_bucket('5 minutes', time) as bucket,
+        attributes->>'Label' as device,
+        avg(cast(values->'load1'->>'value'  as float)) as load,
+        avg(cast(values->'processed-pkts'->>'value'  as int)) as packets
+      from measurements
+      where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
+        application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
+      group by bucket, attributes->>'Label'
+      order by bucket, attributes->>'Label'
+    """
+    df = pd.read_sql(query, con=engine)
+    
+    
+    # Extract date for title
+    plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
+    
+    # Create figure with two side-by-side subplots
+    fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
+    
+    for i, device in enumerate(['harrison', 'david']):
+        ax1 = axes[i]
+        ax2 = ax1.twinx()
+        
+        device_df = df[df['device'] == device]
+        
+        ax1.plot(device_df['bucket'], device_df['load'], 'r-', label='CPU Load')
+        ax1.set_xlabel('Time')
+        ax1.set_ylabel('Load', color='r')
+        ax1.tick_params(axis='y', labelcolor='r')
+        
+        ax2.plot(device_df['bucket'], device_df['packets'], 'b-', label='Processed Packets')
+        ax2.set_ylabel('Packets', color='b')
+        ax2.tick_params(axis='y', labelcolor='b')
+        
+        ax1.set_title(f'{device.capitalize()}')
+        
+        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+        fig.autofmt_xdate(rotation=45)
+    
+    fig.suptitle(f'CPU Load and Processed Packets - {plot_date}')
+    fig.tight_layout()
+    
+    img_io = io.BytesIO()
+    plt.savefig(img_io, format='png')
+    img_io.seek(0)
+    plt.close(fig)
+
+    return Response(img_io, mimetype='image/png')

src/requirements.txt

@@ -39,3 +39,5 @@ urllib3==2.3.0
 Werkzeug==3.1.3
 zipp==3.21.0
 pillow==11.1.0
+matplotlib==3.10.1
+

src/run.py

@@ -10,7 +10,7 @@ import ntp_routes
 
 
 if __name__ == '__main__':
-    app.run(port=8080)
+    app.run(host='0.0.0.0', port=8080)
 else:
     exposed_app = ProxyFix(app, x_for=1, x_host=1)