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Following is a crisp, battle-tested playbook for running databases on Kubernetes—what to do, what to avoid, and how to keep them safe, fast, and recoverable.

Before you start

• Default to managed DBs if possible (RDS/Aurora/Cloud SQL/AlloyDB/Atlas). Run on K8s only when you need: portability, custom extensions, tight sidecar/tooling, or cost control with commodity nodes.
• Use an Operator, not raw manifests. Prefer mature operators (e.g., Crunchy/Percona for Postgres & MySQL, Vitess for MySQL sharding, PXC/MongoDB Enterprise/StackGres, RabbitMQ Operator for queues). Operators give sane HA, backups, upgrades, and day-2 ops.

Core architecture

• StatefulSets + Headless Services for stable identities and volumes.

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# airbnb_mcp.py
from textwrap import dedent

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.mcp import MCPTools
from agno.tools.reasoning import ReasoningTools
from agno.os import AgentOS

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switch to instavote namespace

kubectl config set-context --current --namespace=instavote

helm uninstall -n dev instavote 
kubectl delete deploy vote redis db result worker  -n instavote 
kubectl delete svc vote redis db result -n instavote 

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---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: vote
  namespace: instavote
spec:
  ingressClassName: nginx
  rules:
  - host: vote.example.com

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Event Driven Auto Scaling with KEDA

Configure Prometheus

Install Prometheus with Grafana with helm

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

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Create a namespace

kubectl create namespace instavote 
kubectl config set-context --current --namespace=instavote

git clone https://github.com/schoolofdevops/instavote-kustomize.git

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apiVersion: apps/v1
kind: Deployment
metadata:
  name: vote
  namespace: default
spec:
  replicas: 2
  selector:
    matchLabels:
      app: vote

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rag/build_index.py

# rag/build_index.py
import argparse, json
from pathlib import Path
from typing import Dict, Any, Iterable, Tuple, List

# ---------- common snippet renderers ----------

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What’s happening

• That log line:

  Overriding ... dispatch key: AutocastCPU ... new kernel: ... ipex-cpu ...
  INFO ... Automatically detected platform cpu.
  

means IPEX’s autocast kernels replaced the default ones. With --dtype=float16 on CPU, PyTorch/ipex either upcasts or hits slow/non-vectorized code paths and can “hang” at model load/compile.

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{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,