packets, 3

.woodpecker.yml

@@ -13,6 +13,42 @@ steps:
       dockerfile: Dockerfile
     when:
       - event: [push, tag]
+        
+  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:
+      - 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/requirements.txt

@@ -16,7 +16,6 @@ idna==3.10
 importlib_metadata==8.6.1
 itsdangerous==2.2.0
 Jinja2==3.1.5
-kaleido==1.0.0rc0
 loguru==0.7.3
 MarkupSafe==3.0.2
 msgspec==0.19.0

src/run.py

@@ -1,7 +1,6 @@
-from flask import Flask, session, g, render_template_string, Response
+from flask import Flask, session, g, render_template_string
 from flask_session import Session
 from flask_oidc import OpenIDConnect
-import kaleido
 from werkzeug.middleware.proxy_fix import ProxyFix
 from loguru import logger
 import redis
@@ -9,8 +8,6 @@ import json
 import os
 import plotly.express as px
 import plotly.graph_objects as po
-import plotly.io as pio
-import io
 import pandas as pd
 import psycopg
 import sqlalchemy
@@ -162,7 +159,7 @@ def ntpserver():
           select   time_bucket('5 minutes', time) as bucket,
             device,
             avg(cast(values->'rootdisp'->>'value'  as float)) as rootdisp,
-            avg(cast(values->'stratum'->>'value'  as int)) as stratum
+            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'
@@ -177,9 +174,12 @@ def ntpserver():
         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'
+                title='Root Dispersion',
+                ticksuffix=' ms'
             ),
             
             # Rechte Y-Achse
@@ -194,9 +194,58 @@ def ntpserver():
             legend=dict(x=0.05, y=1)  # Position der Legende
         )
 
-        img = fig.to_image(format='png')
-        return Response(img, mimetype='image/png')
+        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->'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: