packets

.woodpecker.yml

@@ -13,12 +13,42 @@ steps:
       dockerfile: Dockerfile
     when:
       - event: [push, tag]
-  scan_image:
+        
-    image: aquasec/trivy
+  scan:
+    image: quay.io/wollud1969/woodpecker-helper:0.5.1
+    environment:
+      TRIVY_TOKEN:
+        from_secret: trivy_token
+      TRIVY_URL:
+        from_secret: trivy_url
+      DTRACK_API_KEY:
+        from_secret: dtrack_api_key
+      DTRACK_API_URL:
+        from_secret: dtrack_api_url
     commands:
-      - TRIVY_DISABLE_VEX_NOTICE=1 trivy image $FORGE_NAME/$CI_REPO:$CI_COMMIT_SHA --quiet --exit-code 1
+      - HOME=/home/`id -nu`
+      - TAG="${CI_COMMIT_TAG:-$CI_COMMIT_SHA}"
+      - |
+        trivy image \
+              --server $TRIVY_URL \
+              --token $TRIVY_TOKEN \
+              --format cyclonedx \
+              --scanners license \
+              --output /tmp/sbom.xml \
+              $FORGE_NAME/$CI_REPO:$TAG
+      - cat /tmp/sbom.xml
+      - |
+        curl -X "POST" \
+             -H "Content-Type: multipart/form-data" \
+             -H "X-Api-Key: $DTRACK_API_KEY" \
+             -F "autoCreate=true" \
+             -F "projectName=$CI_REPO" \
+             -F "projectVersion=$TAG" \
+             -F "bom=@/tmp/sbom.xml"\
+             "$DTRACK_API_URL/api/v1/bom"        
     when:
       - event: [push, tag]
+
   deploy:
     image: quay.io/wollud1969/k8s-admin-helper:0.2.1
     environment:

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.12-alpine3.21
+FROM python:3.11-alpine3.21
 
 ENV REDIS_URL=""
 ENV SECRET_KEY=""

src/run.py

@@ -7,6 +7,7 @@ import redis
 import json
 import os
 import plotly.express as px
+import plotly.graph_objects as po
 import pandas as pd
 import psycopg
 import sqlalchemy
@@ -123,15 +124,18 @@ def index():
                     {graph_html_3}
                     <div style="height:9vh; background-color:lightgrey; font-family: Courier, Consolas, monospace;">
                       <table style="border-collapse: collapse;">
-                        <!-- <style>table td {{ padding-right: 100px }}</style> -->
+                        <style>
+                          td.smallsep {{ padding-right: 10px }}
+                          td.largesep {{ padding-right: 30px }}
+                        </style> 
                         <tr>
-                          <td>Query 1:</td><td> {duration1:.3f} s</td><td>Graph 1:</td><td> {duration2:.3f} s</td>
+                          <td class="smallsep">Query 1:</td><td class="largesep"> {duration1:.3f} s</td><td class="smallsep">Graph 1:</td><td> {duration2:.3f} s</td>
                         </tr><tr>
-                          <td>Query 2:</td><td> {duration3:.3f} s</td><td>Graph 2:</td><td> {duration4:.3f} s</td>
+                          <td class="smallsep">Query 2:</td><td class="largesep"> {duration3:.3f} s</td><td class="smallsep">Graph 2:</td><td> {duration4:.3f} s</td>
                         </tr><tr>
-                          <td>Query 3:</td><td> {duration5:.3f} s</td><td>Graph 3:</td><td> {duration6:.3f} s</td>
+                          <td class="smallsep">Query 3:</td><td class="largesep"> {duration5:.3f} s</td><td class="smallsep">Graph 3:</td><td> {duration6:.3f} s</td>
                         </tr><tr>
-                          <td>Total:</td><td> {duration7:.3f} s</td><td></td><td></td>
+                          <td class="smallsep">Total:</td><td> {duration7:.3f} s</td><td></td><td></td>
                         </tr>
                       </table>   
                     </div>
@@ -145,6 +149,112 @@ def index():
             dbh.close()
 
 
+@app.route('/ntpserver')
+def ntpserver():
+    try:
+        dbh = psycopg.connect()
+        engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
+
+        query = """
+          select   time_bucket('5 minutes', time) as bucket,
+            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->'lan-in-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="",
+            title=f"Packets"
+        )
+        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->'local'->>'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()
+
+
+
+
 
 
 if __name__ == '__main__':