Vrops

vROps Cluster Dashboard Setup Instructions
1. Install Python
If you don’t have Python installed, follow these steps:

Download the latest version of Python from python.org.

Make sure to check the box that says Add Python to PATH before proceeding with the installation.

After installation, verify by running:

bash
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python –version
2. Install Required Python Libraries
Open a terminal (or command prompt) and run the following command to install the required libraries:

bash
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pip install dash pandas plotly requests
This will install the necessary libraries:

Dash: Web application framework.

Plotly: For creating interactive graphs.

Pandas: For handling data in DataFrame format.

Requests: For fetching data from the vROps API.

3. Set Up the Python Script
Create a Python file: Name the file vrops_dashboard.py (or any name you prefer).

Copy and paste the following code into vrops_dashboard.py:

python
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import dash
from dash import dcc, html, Input, Output
import plotly.express as px
import pandas as pd
import requests
from requests.auth import HTTPBasicAuth
import urllib3

# —- vROps Settings —-
vrops_url = “https://your-vrops-server”
username = “your-username”
password = “your-password”

# Disable SSL warnings (for lab/testing use only)
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

# —- Get Cluster Data —-
def get_cluster_data():
cluster_api = f”{vrops_url}/suite-api/api/resources?resourceKind=ClusterComputeResource”
clusters = requests.get(cluster_api, auth=HTTPBasicAuth(username, password), verify=False).json()[‘resourceList’]

data = []

for cluster in clusters:
cluster_id = cluster[‘identifier’]
name = cluster[‘resourceKey’][‘name’]

metric_url = f”{vrops_url}/suite-api/api/resources/{cluster_id}/stats/latest”
params = [(“statKey”, k) for k in metric_keys]
response = requests.get(metric_url, auth=HTTPBasicAuth(username, password), params=params, verify=False)
metrics = response.json().get(“values”, [])

m = {k[‘statKey’][‘key’]: float(k[‘data’][0]) for k in metrics if k[‘data’]}

data.append({
“Cluster”: name,
“CPU_Usage_MHz”: round(m.get(“cpu|usagemhz”, 0), 2),
“CPU_Capacity_MHz”: round(m.get(“cpu|capacitymhz”, 0), 2),
“Memory_Usage_GB”: round(m.get(“mem|usage”, 0) / 1024, 2),
“Memory_Capacity_GB”: round(m.get(“mem|capacity”, 0) / 1024, 2),
“Disk_Used_GB”: round(m.get(“diskspace|used”, 0) / 1024, 2),
“Disk_Capacity_GB”: round(m.get(“diskspace|capacity”, 0) / 1024, 2),
“VM_Count”: int(m.get(“summary|number_running_vms”, 0)),
“Host_Count”: int(m.get(“summary|total_number_hosts”, 0)),
“Health_Score”: int(m.get(“health|score”, 0)),
“Risk_Score”: int(m.get(“risk|score”, 0)),
“Efficiency_Score”: int(m.get(“efficiency|score”, 0)),
“Time_to_Exhaustion_Days”: int(m.get(“capacity|remainingtime”, 0)) if m.get(“capacity|remainingtime”) else None
})

df = pd.DataFrame(data)
df[“CPU_Usage_%”] = round((df[“CPU_Usage_MHz”] / df[“CPU_Capacity_MHz”] * 100).fillna(0), 1)
df[“Memory_Usage_%”] = round((df[“Memory_Usage_GB”] / df[“Memory_Capacity_GB”] * 100).fillna(0), 1)
df[“Disk_Usage_%”] = round((df[“Disk_Used_GB”] / df[“Disk_Capacity_GB”] * 100).fillna(0), 1)
return df

# —- Dash App Setup —-
app = dash.Dash(__name__)
app.title = “vROps Cluster Dashboard”

app.layout = html.Div([
html.H1(“vROps Executive Cluster Overview”, style={“textAlign”: “center”}),

html.Div(id=’summary’, style={“textAlign”: “center”, “marginBottom”: “30px”}),

html.Div(id=’recommendations’, style={“textAlign”: “center”, “marginBottom”: “30px”}),

dcc.Dropdown(id=’cluster-dropdown’, multi=True, placeholder=’Filter by cluster…’, style={‘width’: ‘60%’, ‘margin’: ‘auto’}),

dcc.Loading(children=[
dcc.Graph(id=’cpu-graph’),
dcc.Graph(id=’memory-graph’),
dcc.Graph(id=’disk-graph’),
dcc.Graph(id=’health-graph’)
], type=”circle”),

dcc.Interval(id=’interval-refresh’, interval=5*60*1000, n_intervals=0)
])

@app.callback(
[
Output(‘cpu-graph’, ‘figure’),
Output(‘memory-graph’, ‘figure’),
Output(‘disk-graph’, ‘figure’),
Output(‘health-graph’, ‘figure’),
Output(‘summary’, ‘children’),
Output(‘recommendations’, ‘children’),
Output(‘cluster-dropdown’, ‘options’)
],
[Input(‘interval-refresh’, ‘n_intervals’), Input(‘cluster-dropdown’, ‘value’)]
)
def update_dashboard(_, selected_clusters):
df = get_cluster_data()

if selected_clusters:
df = df[df[‘Cluster’].isin(selected_clusters)]

cpu_fig = px.bar(df, x=”Cluster”, y=”CPU_Usage_%”, color=”CPU_Usage_%”, color_continuous_scale=[“green”, “yellow”, “red”],
title=”CPU Utilization % by Cluster”)

memory_fig = px.bar(df, x=”Cluster”, y=”Memory_Usage_%”, color=”Memory_Usage_%”, color_continuous_scale=[“green”, “yellow”, “red”],
title=”Memory Utilization % by Cluster”)

disk_fig = px.bar(df, x=”Cluster”, y=”Disk_Usage_%”, color=”Disk_Usage_%”, color_continuous_scale=[“green”, “yellow”, “red”],
title=”Disk Utilization % by Cluster”)

health_fig = px.bar(df, x=”Cluster”, y=[“Health_Score”, “Risk_Score”, “Efficiency_Score”],
barmode=”group”, title=”Cluster Health, Risk & Efficiency”)

summary = html.Div([
html.H4(f”Total Clusters: {len(df)}”),
html.H4(f”Avg CPU Usage: {df[‘CPU_Usage_%’].mean():.1f}%”),
html.H4(f”Avg Memory Usage: {df[‘Memory_Usage_%’].mean():.1f}%”),
html.H4(f”Avg Disk Usage: {df[‘Disk_Usage_%’].mean():.1f}%”),
html.H4(f”Avg Health Score: {df[‘Health_Score’].mean():.1f}%”)
])

# Simple recommendations
at_risk_clusters = df[df[‘CPU_Usage_%’] > 85]
recommendation_list = [html.Li(f”Consider adding capacity to {row[‘Cluster’]} (CPU usage at {row[‘CPU_Usage_%’]}%)”)
for _, row in at_risk_clusters.iterrows()]
if df[‘Time_to_Exhaustion_Days’].notnull().any():
for _, row in df[df[‘Time_to_Exhaustion_Days’] < 60].iterrows():
recommendation_list.append(html.Li(f”{row[‘Cluster’]} may run out of resources in {row[‘Time_to_Exhaustion_Days’]} days”))

recommendations = html.Div([
html.H3(“Recommendations”),
html.Ul(recommendation_list or [html.Li(“All clusters are operating within healthy limits.”)])
])

dropdown_options = [{‘label’: name, ‘value’: name} for name in df[‘Cluster’].unique()]

return cpu_fig, memory_fig, disk_fig, health_fig, summary, recommendations, dropdown_options

if __name__ == ‘__main__’:
app.run_server(debug=True)
4. Running the Dash App
Open a terminal and navigate to the folder where you saved vrops_dashboard.py.

Run the app:

bash
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python vrops_dashboard.py
Open your web browser and go to:

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http://127.0.0.1:8050/
You will see your live vROps Cluster Dashboard with real-time data, graphs, and recommendations.

Feel free to copy and paste this into a text editor or email! Let me know if you need further assistance or modifications.

Welcome to the Site!

Chris Tech Wizard

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