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update(k8s): add storage and instance type

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This commit is contained in:
Aleksandr Tcitlionok
2024-12-05 08:05:15 +00:00
parent 55dd06584c
commit 0ec23d8122
4 changed files with 99 additions and 32 deletions
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12
app/extras/pseudographic.py
View File

@@ -87,19 +87,27 @@ def display_kubernetes_nodes():
table.add_column("Node Name", style="white")
table.add_column("CPU", justify="right", style="yellow")
table.add_column("Memory (MiB)", justify="right", style="cyan")
table.add_column("Pods Allocatable", justify="right", style="green")
table.add_column("Storage", style="green")
table.add_column("Instance Type", style="blue")
table.add_column("Pods Allocatable", justify="right", style="magenta")
nodes = v1.list_node()
for node in nodes.items:
storage = node.metadata.annotations.get("node.kubernetes.io/storage", "N/A")
instance_type = node.metadata.labels.get("beta.kubernetes.io/instance-type", "N/A")
table.add_row(
node.metadata.name,
node.status.capacity.get("cpu"),
f"{round(convert_memory_to_mib(node.status.capacity.get('memory')), 2)}",
storage,
instance_type,
node.status.allocatable.get("pods")
)
console.print(table)
def display_namespace_usage():
console = Console()
config.load_incluster_config()
@@ -143,5 +151,3 @@ if __name__ == "__main__":
display_virtual_machines()
display_kubernetes_nodes()
display_namespace_usage()
console.print("\n🚀 [bold cyan]Dashboard rendering complete![/bold cyan] 🚀")

5
app/routes/export.py
View File

@@ -85,17 +85,12 @@ def fetch_k8s_data_with_usage():
# Export endpoint
@router.get("/export")
def export_data(format: str = "yaml"):
"""
API endpoint to export data in YAML or JSON format.
"""
data = {
"metal_nodes": fetch_all("metal_nodes"),
"virtual_machines": fetch_all("virtual_machines"),
"kubernetes": fetch_k8s_data_with_usage(),
}
logging.info(f"Exporting data: {data}")
if format.lower() == "yaml":
yaml_data = yaml.safe_dump(data, sort_keys=False)
return Response(content=yaml_data, media_type="text/yaml")

33
app/routes/k8s.py
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@@ -9,12 +9,20 @@ def fetch_k8s_data_with_usage():
metrics_client = client.CustomObjectsApi()
# Fetch nodes
nodes = [{
nodes = []
for node in v1.list_node().items:
# Extract storage and instance type from labels or annotations
storage = node.metadata.annotations.get("node.kubernetes.io/storage", "N/A")
instance_type = node.metadata.labels.get("beta.kubernetes.io/instance-type", "N/A")
nodes.append({
"node_name": node.metadata.name,
"cpu": node.status.capacity.get("cpu"),
"memory": round(convert_memory_to_mib(node.status.capacity.get("memory")), 2), # Convert to MiB
"storage": storage,
"instance_type": instance_type,
"pods_allocatable": node.status.allocatable.get("pods"),
} for node in v1.list_node().items]
})
# Fetch namespaces
namespaces = [ns.metadata.name for ns in v1.list_namespace().items]
@@ -50,24 +58,15 @@ def fetch_k8s_data_with_usage():
def convert_cpu_to_cores(cpu):
"""
Convert CPU usage to cores for human-readable format.
Handles units: n (nano), u (micro), m (milli), or none (cores).
Returns float values for cores, rounded appropriately.
"""
if "n" in cpu: # Nanocores to cores
if "n" in cpu:
return round(int(cpu.replace("n", "")) / 1e9, 4)
elif "u" in cpu: # Microcores to cores
elif "u" in cpu:
return round(int(cpu.replace("u", "")) / 1e6, 4)
elif "m" in cpu: # Millicores to cores
elif "m" in cpu:
return round(int(cpu.replace("m", "")) / 1000, 4)
return float(cpu) # Already in cores
return float(cpu)
def convert_memory_to_mib(memory):
"""
Convert memory to MiB (mebibytes).
Handles units: Ki (kibibytes), Mi (mebibytes), Gi (gibibytes).
"""
if "Ki" in memory:
return int(memory.replace("Ki", "")) / 1024
elif "Mi" in memory:
@@ -78,8 +77,4 @@ def convert_memory_to_mib(memory):
@router.get("/k8s/data")
def get_k8s_data():
"""
API endpoint to fetch Kubernetes data including nodes, namespaces,
and namespace resource usage.
"""
return fetch_k8s_data_with_usage()

71
app/routes/think.py Normal file
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@@ -0,0 +1,71 @@
import os
from fastapi import APIRouter, HTTPException
from kubernetes import client, config
from typing import List, Dict
import requests
router = APIRouter()
def fetch_ai_summary(cluster_name: str, nodes: List[Dict], api_url: str, auth_token: str) -> str:
"""
Sends node data to an external AI API endpoint and retrieves a summary response.
"""
payload = {
"data": {
"cluster_name": cluster_name,
"nodes": nodes
}
}
headers = {
"Authorization": f"Bearer {auth_token}",
"Content-Type": "application/json"
}
try:
response = requests.post(api_url, json=payload, headers=headers)
if response.status_code == 200:
result = response.json()
if result.get("success"):
return result.get("result", "No result provided by the API.")
else:
return "API responded with success=false."
else:
return f"API request failed with status code {response.status_code}: {response.text}"
except requests.RequestException as e:
return f"An error occurred while contacting the API: {str(e)}"
@router.get("/think/k8s")
def think_k8s():
"""
Fetch Kubernetes data, send it to the AI API, and return the AI-generated summary.
"""
config.load_incluster_config()
v1 = client.CoreV1Api()
cluster_name = "k8s-cluster"
# Format node data
nodes = []
for node in v1.list_node().items:
node_data = {
"name": node.metadata.name,
"cpu": f"{node.status.capacity.get('cpu')} cores",
"memory": f"{round(int(node.status.capacity.get('memory').replace('Ki', '')) / 1024 / 1024, 2)} GB",
"storage": "N/A",
"type": "N/A",
"namespaces": [ns.metadata.name for ns in v1.list_namespace().items()]
}
nodes.append(node_data)
# Fetch token from environment
api_url = os.getenv("AI_API_URL")
auth_token = os.getenv("AI_API_TOKEN")
if not auth_token:
raise HTTPException(status_code=500, detail="AI API token is not set. Please set the AI_API_TOKEN environment variable.")
# Call AI API
summary = fetch_ai_summary(cluster_name, nodes, api_url, auth_token)
return {"summary": summary}