timing

.woodpecker.yml

@@ -20,7 +20,7 @@ steps:
     when:
       - event: [push, tag]
   deploy:
-    image: quay.io/wollud1969/k8s-admin-helper:0.1.3
+    image: quay.io/wollud1969/k8s-admin-helper:0.2.1
     environment:
       KUBE_CONFIG_CONTENT:
         from_secret: kube_config

deployment/deploy.sh

@@ -19,10 +19,11 @@ kubectl create namespace $NAMESPACE \
   -o yaml | \
   kubectl -f - apply
 
-SECRETS_FILE=`mktemp`
+# SECRETS_FILE=`mktemp`
-gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --homedir /tmp/.gnupg --output $SECRETS_FILE secrets.asc
+# gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --homedir /tmp/.gnupg --output $SECRETS_FILE secrets.asc
-. $SECRETS_FILE
+# . $SECRETS_FILE
-rm $SECRETS_FILE
+# rm $SECRETS_FILE
+eval "`cat secrets.asc | /usr/local/bin/decrypt-secrets.sh`"
 
 kubectl create secret generic pv-stats \
   --dry-run=client \

src/run.py

@@ -10,6 +10,7 @@ import plotly.express as px
 import pandas as pd
 import psycopg
 import sqlalchemy
+import time
 
 try:
   redis_url = os.environ['REDIS_URL']
@@ -50,10 +51,15 @@ def index():
         dbh = psycopg.connect()
         engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
 
+        start_time = time.time()
         df = pd.read_sql("SELECT month, cast(year AS varchar), current_energy AS value FROM pv_energy_by_month", con=engine)
+        step1_time = time.time()
+        duration1 = step1_time - start_time
         fig_1 = px.bar(df, x='month', y='value', color='year', barmode='group')
+        step2_time = time.time()
+        duration2 = step2_time - start_time
         fig_1.update_layout(
-            title="Jahreswerte Exportierte Energie",
+            title=f"Jahreswerte Exportierte Energie {duration1:.3f}, {duration2:.3f}",
             xaxis_title="",
             yaxis_title="",
             legend_title="Jahr",
@@ -66,22 +72,32 @@ def index():
         )
         graph_html_1 = fig_1.to_html(full_html=False, default_height='33%')
 
+        start_time = time.time()
         df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) - '1 day'::interval AND time < date_trunc('day', now()) GROUP BY bucket ORDER BY bucket", con=engine)
+        step1_time = time.time()
+        duration1 = step1_time - start_time
         fig_2 = px.line(df, x='bucket', y='avg_power')
+        step2_time = time.time()
+        duration2 = step2_time - start_time
         fig_2.update_layout(
             xaxis_title="",
             yaxis_title="",
-            title="Export gestern",
+            title=f"Export gestern {duration1:.3f}, {duration2:.3f}",
             yaxis=dict(ticksuffix=" W")
         )
         graph_html_2 = fig_2.to_html(full_html=False, default_height='33%')
 
+        start_time = time.time()
         df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval GROUP BY bucket ORDER BY bucket", con=engine)
+        step1_time = time.time()
+        duration1 = step1_time - start_time
         fig_3 = px.line(df, x='bucket', y='avg_power')
+        step2_time = time.time()
+        duration2 = step2_time - start_time
         fig_3.update_layout(
             xaxis_title="",
             yaxis_title="",
-            title="Export heute",
+            title=f"Export heute {duration1:.3f}, {duration2:.3f}",
             yaxis=dict(ticksuffix=" W")
         )
         graph_html_3 = fig_3.to_html(full_html=False, default_height='33%')