Imporve monitoring 6

2026-01-10 13:43:38 +01:00
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 # Code-Level Architecture Analysis: Notification & Subscription Endpoints
 **Date:** 2026-01-10
 **Analysis Method:** SigNoz Distributed Tracing + Deep Code Review
 **Status:** ARCHITECTURAL FLAWS IDENTIFIED
 ---
 ## 🎯 Executive Summary
 After deep code analysis, I've identified **SEVERE architectural problems** causing the 2.5s notification latency and 5.5s subscription latency. The issues are NOT simple missing indexes - they're **fundamental design flaws** in the auth/authorization chain.
 ### Critical Problems Found:
 1. **Gateway makes 5 SYNCHRONOUS external HTTP calls** for EVERY request
 2. **No caching layer** - same auth checks repeated millions of times
 3. **Decorators stacked incorrectly** - causing redundant checks
 4. **Header extraction overhead** - parsing on every request
 5. **Subscription data fetched from database** instead of being cached in JWT
 ---
 ## 🔍 Problem 1: Notification Endpoint Architecture (2.5s latency)
 ### Current Implementation
 **File:** `services/notification/app/api/notification_operations.py:46-56`
 ```python
@router.post(
    route_builder.build_base_route("send"),
    response_model=NotificationResponse,
    status_code=201
 )
@track_endpoint_metrics("notification_send")  # Decorator 1
 async def send_notification(
    notification_data: Dict[str, Any],
    tenant_id: UUID = Path(..., description="Tenant ID"),
    current_user: Dict[str, Any] = Depends(get_current_user_dep),  # Decorator 2 (hidden)
    notification_service: EnhancedNotificationService = Depends(get_enhanced_notification_service)
 ):
 ```
 ### The Authorization Chain
 When a request hits this endpoint, here's what happens:
 #### Step 1: `get_current_user_dep` (line 55)
 **File:** `shared/auth/decorators.py:448-510`
 ```python
 async def get_current_user_dep(request: Request) -> Dict[str, Any]:
    # Logs EVERY request (expensive string operations)
    logger.debug(
        "Authentication attempt",  # Line 452
        path=request.url.path,
        method=request.method,
        has_auth_header=bool(request.headers.get("authorization")),
        # ... 8 more header checks
    )
    # Try header extraction first
    try:
        user = get_current_user(request)  # Line 468 - CALL 1
    except HTTPException:
        # Fallback to JWT extraction
        auth_header = request.headers.get("authorization", "")
        if auth_header.startswith("Bearer "):
            user = extract_user_from_jwt(auth_header)  # Line 473 - CALL 2
 ```
 #### Step 2: `get_current_user()` extracts headers
 **File:** `shared/auth/decorators.py:320-333`
 ```python
 def get_current_user(request: Request) -> Dict[str, Any]:
    if hasattr(request.state, 'user') and request.state.user:
        return request.state.user
    # Fallback to headers (for dev/testing)
    user_info = extract_user_from_headers(request)  # CALL 3
    if not user_info:
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="User not authenticated"
        )
    return user_info
 ```
 #### Step 3: `extract_user_from_headers()` - THE BOTTLENECK
 **File:** `shared/auth/decorators.py:343-374`
 ```python
 def extract_user_from_headers(request: Request) -> Optional[Dict[str, Any]]:
    """Extract user information from forwarded headers"""
    user_id = request.headers.get("x-user-id")  # HTTP call to gateway?
    if not user_id:
        return None
    # Build user context from 15+ headers
    user_context = {
        "user_id": user_id,
        "email": request.headers.get("x-user-email", ""),  # Another header
        "role": request.headers.get("x-user-role", "user"),  # Another
        "tenant_id": request.headers.get("x-tenant-id"),  # Another
        "permissions": request.headers.get("X-User-Permissions", "").split(","),
        "full_name": request.headers.get("x-user-full-name", ""),
        "subscription_tier": request.headers.get("x-subscription-tier", ""),  # Gateway lookup!
        "is_demo": request.headers.get("x-is-demo", "").lower() == "true",
        "demo_session_id": request.headers.get("x-demo-session-id", ""),
        "demo_account_type": request.headers.get("x-demo-account-type", "")
    }
    return user_context
 ```
 ### 🔴 **ROOT CAUSE: Gateway Performs 5 Sequential Database/Service Calls**
 The trace shows that **BEFORE** the notification service is even called, the gateway makes these calls:
 ```
 Gateway Middleware Chain:
 1. GET /tenants/{tenant_id}/access/{user_id}     294ms  ← Verify user access
 2. GET /subscriptions/{tenant_id}/tier           110ms  ← Get subscription tier
 3. GET /tenants/{tenant_id}/access/{user_id}      12ms  ← DUPLICATE! Why?
 4. GET (unknown - maybe features?)                 2ms  ← Unknown call
 5. GET /subscriptions/{tenant_id}/status         102ms  ← Get subscription status
 ─────────────────────────────────────────────────────────
 TOTAL OVERHEAD: 520ms (43% of total request time!)
 ```
 ### Where This Happens (Hypothesis - needs gateway code)
 Based on the headers being injected, the gateway likely does:
 ```python
 # Gateway middleware (not in repo, but this is what's happening)
 async def inject_user_context_middleware(request, call_next):
    # Extract tenant_id and user_id from JWT
    token = extract_token(request)
    user_id = token.get("user_id")
    tenant_id = extract_tenant_from_path(request.url.path)
    # PROBLEM: Make external HTTP calls to get auth data
    # Call 1: Check if user has access to tenant (294ms)
    access = await tenant_service.check_access(tenant_id, user_id)
    # Call 2: Get subscription tier (110ms)
    subscription = await tenant_service.get_subscription_tier(tenant_id)
    # Call 3: DUPLICATE access check? (12ms)
    access2 = await tenant_service.check_access(tenant_id, user_id)  # WHY?
    # Call 4: Unknown (2ms)
    something = await tenant_service.get_something(tenant_id)
    # Call 5: Get subscription status (102ms)
    status = await tenant_service.get_subscription_status(tenant_id)
    # Inject into headers
    request.headers["x-user-role"] = access.role
    request.headers["x-subscription-tier"] = subscription.tier
    request.headers["x-subscription-status"] = status.status
    # Forward request
    return await call_next(request)
 ```
 ### Why This is BAD Architecture:
 1. ❌ **Service-to-Service HTTP calls** instead of shared cache
 2. ❌ **Sequential execution** (each waits for previous)
 3. ❌ **No caching** - every request makes ALL calls
 4. ❌ **Redundant checks** - access checked twice
 5. ❌ **Wrong layer** - auth data should be in JWT, not fetched per request
 ---
 ## 🔍 Problem 2: Subscription Tier Query (772ms!)
 ### Current Query (Hypothesis)
 **File:** `services/tenant/app/repositories/subscription_repository.py` (lines not shown, but likely exists)
 ```python
 async def get_subscription_by_tenant(tenant_id: str) -> Subscription:
    query = select(Subscription).where(
        Subscription.tenant_id == tenant_id,
        Subscription.status == 'active'
    )
    result = await self.session.execute(query)
    return result.scalar_one_or_none()
 ```
 ### Why It's Slow:
 **Missing Index!**
 ```sql
 -- Current situation: Full table scan
 EXPLAIN ANALYZE
 SELECT * FROM subscriptions
 WHERE tenant_id = 'uuid' AND status = 'active';
 -- Result: Seq Scan on subscriptions (cost=0.00..1234.56 rows=1)
 --         Planning Time: 0.5 ms
 --         Execution Time: 772.3 ms  ← SLOW!
 ```
 **Database Metrics Confirm:**
 ```
 Average Block Reads: 396 blocks/query
 Max Block Reads: 369,161 blocks (!!)
 Average Index Scans: 0.48 per query  ← Almost no indexes used!
 ```
 ### The Missing Indexes:
 ```sql
 -- Check existing indexes
 SELECT
    tablename,
    indexname,
    indexdef
 FROM pg_indexes
 WHERE tablename = 'subscriptions';
 -- Result: Probably only has PRIMARY KEY on `id`
 -- Missing:
 -- - Index on tenant_id
 -- - Composite index on (tenant_id, status)
 -- - Covering index including tier, status, valid_until
 ```
 ---
 ## 🔧 Architectural Solutions
 ### Solution 1: Move Auth Data Into JWT (BEST FIX)
 **Current (BAD):**
 ```
 User Request → Gateway → 5 HTTP calls to tenant-service → Inject headers → Forward
 ```
 **Better:**
 ```
 User Login → Generate JWT with ALL auth data → Gateway validates JWT → Forward
 ```
 **Implementation:**
 #### Step 1: Update JWT Payload
 **File:** Create `shared/auth/jwt_builder.py`
 ```python
 from datetime import datetime, timedelta
 import jwt
 def create_access_token(user_data: dict, subscription_data: dict) -> str:
    """
    Create JWT with ALL required auth data embedded
    No need for runtime lookups!
    """
    now = datetime.utcnow()
    payload = {
        # Standard JWT claims
        "sub": user_data["user_id"],
        "iat": now,
        "exp": now + timedelta(hours=24),
        "type": "access",
        # User data (already available at login)
        "user_id": user_data["user_id"],
        "email": user_data["email"],
        "role": user_data["role"],
        "full_name": user_data.get("full_name", ""),
        "tenant_id": user_data["tenant_id"],
        # Subscription data (fetch ONCE at login, cache in JWT)
        "subscription": {
            "tier": subscription_data["tier"],  # professional, enterprise
            "status": subscription_data["status"],  # active, cancelled
            "valid_until": subscription_data["valid_until"].isoformat(),
            "features": subscription_data["features"],  # list of enabled features
            "limits": {
                "max_users": subscription_data.get("max_users", -1),
                "max_products": subscription_data.get("max_products", -1),
                "max_locations": subscription_data.get("max_locations", -1)
            }
        },
        # Permissions (computed once at login)
        "permissions": compute_user_permissions(user_data, subscription_data)
    }
    return jwt.encode(payload, SECRET_KEY, algorithm="HS256")
 ```
 **Impact:**
 - Gateway calls: 5 → **0** (everything in JWT)
 - Latency: 520ms → **<1ms** (JWT decode)
 - Database load: **99% reduction**
 ---
 #### Step 2: Simplify Gateway Middleware
 **File:** Gateway middleware (Kong/nginx/custom)
 ```python
 # BEFORE: 520ms of HTTP calls
 async def auth_middleware(request):
    # 5 HTTP calls...
    pass
 # AFTER: <1ms JWT decode
 async def auth_middleware(request):
    # Extract JWT
    token = request.headers.get("Authorization", "").replace("Bearer ", "")
    # Decode (no verification needed if from trusted source)
    payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
    # Inject ALL data into headers at once
    request.headers["x-user-id"] = payload["user_id"]
    request.headers["x-user-email"] = payload["email"]
    request.headers["x-user-role"] = payload["role"]
    request.headers["x-tenant-id"] = payload["tenant_id"]
    request.headers["x-subscription-tier"] = payload["subscription"]["tier"]
    request.headers["x-subscription-status"] = payload["subscription"]["status"]
    request.headers["x-permissions"] = ",".join(payload.get("permissions", []))
    return await call_next(request)
 ```
 ---
 ### Solution 2: Add Database Indexes (Complementary)
 Even with JWT optimization, some endpoints still query subscriptions directly:
 ```sql
 -- Critical indexes for tenant service
 CREATE INDEX CONCURRENTLY idx_subscriptions_tenant_status
  ON subscriptions (tenant_id, status)
  WHERE status IN ('active', 'trial');
 -- Covering index (avoids table lookup)
 CREATE INDEX CONCURRENTLY idx_subscriptions_tenant_covering
  ON subscriptions (tenant_id)
  INCLUDE (tier, status, valid_until, features, max_users, max_products);
 -- Index for status checks
 CREATE INDEX CONCURRENTLY idx_subscriptions_status_valid
  ON subscriptions (status, valid_until DESC)
  WHERE status = 'active';
 ```
 **Expected Impact:**
 - Query time: 772ms → **5-10ms** (99% improvement)
 - Block reads: 369K → **<100 blocks**
 ---
 ### Solution 3: Add Redis Cache Layer (Defense in Depth)
 Even with JWT, cache critical data:
 ```python
 # shared/caching/subscription_cache.py
 import redis
 import json
 class SubscriptionCache:
    def __init__(self, redis_client):
        self.redis = redis_client
        self.TTL = 300  # 5 minutes
    async def get_subscription(self, tenant_id: str):
        """Get subscription from cache or database"""
        cache_key = f"subscription:{tenant_id}"
        # Try cache
        cached = await self.redis.get(cache_key)
        if cached:
            return json.loads(cached)
        # Fetch from database
        subscription = await self._fetch_from_db(tenant_id)
        # Cache it
        await self.redis.setex(
            cache_key,
            self.TTL,
            json.dumps(subscription)
        )
        return subscription
    async def invalidate(self, tenant_id: str):
        """Invalidate cache when subscription changes"""
        cache_key = f"subscription:{tenant_id}"
        await self.redis.delete(cache_key)
 ```
 **Usage:**
 ```python
 # services/tenant/app/api/subscription.py
@router.get("/api/v1/subscriptions/{tenant_id}/tier")
 async def get_subscription_tier(tenant_id: str):
    # Try cache first
    subscription = await subscription_cache.get_subscription(tenant_id)
    return {"tier": subscription["tier"]}
 ```
 ---
 ## 📈 Expected Performance Improvements
 | Component | Before | After (JWT) | After (JWT + Index + Cache) | Improvement |
 |-----------|--------|-------------|----------------------------|-------------|
 | **Gateway Auth Calls** | 520ms (5 calls) | <1ms (JWT decode) | <1ms | **99.8%** |
 | **Subscription Query** | 772ms | 772ms | 2ms (cache hit) | **99.7%** |
 | **Notification POST** | 2,500ms | 1,980ms (20% faster) | **50ms** | **98%** |
 | **Subscription GET** | 5,500ms | 4,780ms | **20ms** | **99.6%** |
 ### Overall Impact:
 **Notification endpoint:** 2.5s → **50ms** (98% improvement)
 **Subscription endpoint:** 5.5s → **20ms** (99.6% improvement)
 ---
 ## 🎯 Implementation Priority
 ### CRITICAL (Day 1-2): JWT Auth Data
 **Why:** Eliminates 520ms overhead on EVERY request across ALL services
 **Steps:**
 1. Update JWT payload to include subscription data
 2. Modify login endpoint to fetch subscription once
 3. Update gateway to use JWT data instead of HTTP calls
 4. Test with 1-2 endpoints first
 **Risk:** Low - JWT is already used, just adding more data
 **Impact:** **98% latency reduction** on auth-heavy endpoints
 ---
 ### HIGH (Day 3-4): Database Indexes
 **Why:** Fixes 772ms subscription queries
 **Steps:**
 1. Add indexes to subscriptions table
 2. Analyze `pg_stat_statements` for other slow queries
 3. Add covering indexes where needed
 4. Monitor query performance
 **Risk:** Low - indexes don't change logic
 **Impact:** **99% query time reduction**
 ---
 ### MEDIUM (Day 5-7): Redis Cache Layer
 **Why:** Defense in depth, handles JWT expiry edge cases
 **Steps:**
 1. Implement subscription cache service
 2. Add cache to subscription repository
 3. Add cache invalidation on updates
 4. Monitor cache hit rates
 **Risk:** Medium - cache invalidation can be tricky
 **Impact:** **Additional 50% improvement** for cache hits
 ---
 ## 🚨 Critical Architectural Lesson
 ### The Real Problem:
 **"Microservices without proper caching become a distributed monolith with network overhead"**
 Every request was:
 1. JWT decode (cheap)
 2. → 5 HTTP calls to tenant-service (expensive!)
 3. → 5 database queries in tenant-service (very expensive!)
 4. → Forward to actual service
 5. → Actual work finally happens
 **Solution:**
 - **Move static/slow-changing data into JWT** (subscription tier, role, permissions)
 - **Cache everything else** in Redis (user preferences, feature flags)
 - **Only query database** for truly dynamic data (current notifications, real-time stats)
 This is a **classic distributed systems anti-pattern** that's killing your performance!
 ---
 ## 📊 Monitoring After Fix
 ```sql
 -- Monitor gateway performance
 SELECT
    name,
    quantile(0.95)(durationNano) / 1000000 as p95_ms
 FROM signoz_traces.signoz_index_v3
 WHERE serviceName = 'gateway'
  AND timestamp >= now() - INTERVAL 1 DAY
 GROUP BY name
 ORDER BY p95_ms DESC;
 -- Target: All gateway calls < 10ms
 -- Current: 520ms average
 -- Monitor subscription queries
 SELECT
    query,
    calls,
    mean_exec_time,
    max_exec_time
 FROM pg_stat_statements
 WHERE query LIKE '%subscriptions%'
 ORDER BY mean_exec_time DESC;
 -- Target: < 5ms average
 -- Current: 772ms max
 ```
 ---
 ## 🚀 Conclusion
 The performance issues are caused by **architectural choices**, not missing indexes:
 1. **Auth data fetched via HTTP** instead of embedded in JWT
 2. **5 sequential database/HTTP calls** on every request
 3. **No caching layer** - same data fetched millions of times
 4. **Wrong separation of concerns** - gateway doing too much
 **The fix is NOT to add caching to the current architecture.**
 **The fix is to CHANGE the architecture to not need those calls.**
 Embedding auth data in JWT is the **industry standard** for exactly this reason - it eliminates the need for runtime authorization lookups!
--- a/docs/DATABASE_MONITORING.md
+++ b/docs/DATABASE_MONITORING.md
@@ -1,569 +0,0 @@
 # Database Monitoring with SigNoz
 This guide explains how to collect metrics and logs from PostgreSQL, Redis, and RabbitMQ databases and send them to SigNoz.
 ## Table of Contents
 1. [Overview](#overview)
 2. [PostgreSQL Monitoring](#postgresql-monitoring)
 3. [Redis Monitoring](#redis-monitoring)
 4. [RabbitMQ Monitoring](#rabbitmq-monitoring)
 5. [Database Logs Export](#database-logs-export)
 6. [Dashboard Examples](#dashboard-examples)
 ## Overview
 **Database monitoring provides:**
 - **Metrics**: Connection pools, query performance, cache hit rates, disk usage
 - **Logs**: Query logs, error logs, slow query logs
 - **Correlation**: Link database metrics with application traces
 **Three approaches for database monitoring:**
 1. **OpenTelemetry Collector Receivers** (Recommended)
   - Deploy OTel collector as sidecar or separate deployment
   - Scrape database metrics and forward to SigNoz
   - No code changes needed
 2. **Application-Level Instrumentation** (Already Implemented)
   - Use OpenTelemetry auto-instrumentation in your services
   - Captures database queries as spans in traces
   - Shows query duration, errors in application context
 3. **Database Exporters** (Advanced)
   - Dedicated exporters (postgres_exporter, redis_exporter)
   - More detailed database-specific metrics
   - Requires additional deployment
 ## PostgreSQL Monitoring
 ### Option 1: OpenTelemetry Collector with PostgreSQL Receiver (Recommended)
 Deploy an OpenTelemetry collector instance to scrape PostgreSQL metrics.
 #### Step 1: Create PostgreSQL Monitoring User
 ```sql
 -- Create monitoring user with read-only access
 CREATE USER otel_monitor WITH PASSWORD 'your-secure-password';
 GRANT pg_monitor TO otel_monitor;
 GRANT CONNECT ON DATABASE your_database TO otel_monitor;
 ```
 #### Step 2: Deploy OTel Collector for PostgreSQL
 Create a dedicated collector deployment:
 ```yaml
 # infrastructure/kubernetes/base/monitoring/postgres-otel-collector.yaml
 apiVersion: apps/v1
 kind: Deployment
 metadata:
  name: postgres-otel-collector
  namespace: bakery-ia
  labels:
    app: postgres-otel-collector
 spec:
  replicas: 1
  selector:
    matchLabels:
      app: postgres-otel-collector
  template:
    metadata:
      labels:
        app: postgres-otel-collector
    spec:
      containers:
      - name: otel-collector
        image: otel/opentelemetry-collector-contrib:latest
        ports:
        - containerPort: 4318
          name: otlp-http
        - containerPort: 4317
          name: otlp-grpc
        volumeMounts:
        - name: config
          mountPath: /etc/otel-collector
        command:
          - /otelcol-contrib
          - --config=/etc/otel-collector/config.yaml
      volumes:
      - name: config
        configMap:
          name: postgres-otel-collector-config
 ---
 apiVersion: v1
 kind: ConfigMap
 metadata:
  name: postgres-otel-collector-config
  namespace: bakery-ia
 data:
  config.yaml: |
    receivers:
      # PostgreSQL receiver for each database
      postgresql/auth:
        endpoint: auth-db-service:5432
        username: otel_monitor
        password: ${POSTGRES_MONITOR_PASSWORD}
        databases:
          - auth_db
        collection_interval: 30s
        metrics:
          postgresql.backends: true
          postgresql.bgwriter.buffers.allocated: true
          postgresql.bgwriter.buffers.writes: true
          postgresql.blocks_read: true
          postgresql.commits: true
          postgresql.connection.max: true
          postgresql.database.count: true
          postgresql.database.size: true
          postgresql.deadlocks: true
          postgresql.index.scans: true
          postgresql.index.size: true
          postgresql.operations: true
          postgresql.rollbacks: true
          postgresql.rows: true
          postgresql.table.count: true
          postgresql.table.size: true
          postgresql.temp_files: true
      postgresql/inventory:
        endpoint: inventory-db-service:5432
        username: otel_monitor
        password: ${POSTGRES_MONITOR_PASSWORD}
        databases:
          - inventory_db
        collection_interval: 30s
      # Add more PostgreSQL receivers for other databases...
    processors:
      batch:
        timeout: 10s
        send_batch_size: 1024
      memory_limiter:
        check_interval: 1s
        limit_mib: 512
      resourcedetection:
        detectors: [env, system]
      # Add database labels
      resource:
        attributes:
          - key: database.system
            value: postgresql
            action: insert
          - key: deployment.environment
            value: ${ENVIRONMENT}
            action: insert
    exporters:
      # Send to SigNoz
      otlphttp:
        endpoint: http://signoz-otel-collector.bakery-ia.svc.cluster.local:4318
        tls:
          insecure: true
      # Debug logging
      logging:
        loglevel: info
    service:
      pipelines:
        metrics:
          receivers: [postgresql/auth, postgresql/inventory]
          processors: [memory_limiter, resource, batch, resourcedetection]
          exporters: [otlphttp, logging]
 ```
 #### Step 3: Create Secrets
 ```bash
 # Create secret for monitoring user password
 kubectl create secret generic postgres-monitor-secrets \
  -n bakery-ia \
  --from-literal=POSTGRES_MONITOR_PASSWORD='your-secure-password'
 ```
 #### Step 4: Deploy
 ```bash
 kubectl apply -f infrastructure/kubernetes/base/monitoring/postgres-otel-collector.yaml
 ```
 ### Option 2: Application-Level Database Metrics (Already Implemented)
 Your services already collect database metrics via SQLAlchemy instrumentation:
 **Metrics automatically collected:**
 - `db.client.connections.usage` - Active database connections
 - `db.client.operation.duration` - Query duration (SELECT, INSERT, UPDATE, DELETE)
 - Query traces with SQL statements (in trace spans)
 **View in SigNoz:**
 1. Go to Traces → Select a service → Filter by `db.operation`
 2. See individual database queries with duration
 3. Identify slow queries causing latency
 ### PostgreSQL Metrics Reference
 | Metric | Description |
 |--------|-------------|
 | `postgresql.backends` | Number of active connections |
 | `postgresql.database.size` | Database size in bytes |
 | `postgresql.commits` | Transaction commits |
 | `postgresql.rollbacks` | Transaction rollbacks |
 | `postgresql.deadlocks` | Deadlock count |
 | `postgresql.blocks_read` | Blocks read from disk |
 | `postgresql.table.size` | Table size in bytes |
 | `postgresql.index.size` | Index size in bytes |
 | `postgresql.rows` | Rows inserted/updated/deleted |
 ## Redis Monitoring
 ### Option 1: OpenTelemetry Collector with Redis Receiver (Recommended)
 ```yaml
 # Add to postgres-otel-collector config or create separate collector
 receivers:
  redis:
    endpoint: redis-service.bakery-ia:6379
    password: ${REDIS_PASSWORD}
    collection_interval: 30s
    tls:
      insecure_skip_verify: false
      cert_file: /etc/redis-tls/redis-cert.pem
      key_file: /etc/redis-tls/redis-key.pem
      ca_file: /etc/redis-tls/ca-cert.pem
    metrics:
      redis.clients.connected: true
      redis.clients.blocked: true
      redis.commands.processed: true
      redis.commands.duration: true
      redis.db.keys: true
      redis.db.expires: true
      redis.keyspace.hits: true
      redis.keyspace.misses: true
      redis.memory.used: true
      redis.memory.peak: true
      redis.memory.fragmentation_ratio: true
      redis.cpu.time: true
      redis.replication.offset: true
 ```
 ### Option 2: Application-Level Redis Metrics (Already Implemented)
 Your services already collect Redis metrics via Redis instrumentation:
 **Metrics automatically collected:**
 - Redis command traces (GET, SET, etc.) in spans
 - Command duration
 - Command errors
 ### Redis Metrics Reference
 | Metric | Description |
 |--------|-------------|
 | `redis.clients.connected` | Connected clients |
 | `redis.commands.processed` | Total commands processed |
 | `redis.keyspace.hits` | Cache hit rate |
 | `redis.keyspace.misses` | Cache miss rate |
 | `redis.memory.used` | Memory usage in bytes |
 | `redis.memory.fragmentation_ratio` | Memory fragmentation |
 | `redis.db.keys` | Number of keys per database |
 ## RabbitMQ Monitoring
 ### Option 1: RabbitMQ Management Plugin + OpenTelemetry (Recommended)
 RabbitMQ exposes metrics via its management API.
 ```yaml
 receivers:
  rabbitmq:
    endpoint: http://rabbitmq-service.bakery-ia:15672
    username: ${RABBITMQ_USER}
    password: ${RABBITMQ_PASSWORD}
    collection_interval: 30s
    metrics:
      rabbitmq.consumer.count: true
      rabbitmq.message.current: true
      rabbitmq.message.acknowledged: true
      rabbitmq.message.delivered: true
      rabbitmq.message.published: true
      rabbitmq.queue.count: true
 ```
 ### RabbitMQ Metrics Reference
 | Metric | Description |
 |--------|-------------|
 | `rabbitmq.consumer.count` | Active consumers |
 | `rabbitmq.message.current` | Messages in queue |
 | `rabbitmq.message.acknowledged` | Messages acknowledged |
 | `rabbitmq.message.delivered` | Messages delivered |
 | `rabbitmq.message.published` | Messages published |
 | `rabbitmq.queue.count` | Number of queues |
 ## Database Logs Export
 ### PostgreSQL Logs
 #### Option 1: Configure PostgreSQL to Log to Stdout (Kubernetes-native)
 PostgreSQL logs should go to stdout/stderr, which Kubernetes automatically captures.
 **Update PostgreSQL configuration:**
 ```yaml
 # In your postgres deployment ConfigMap
 apiVersion: v1
 kind: ConfigMap
 metadata:
  name: postgres-config
  namespace: bakery-ia
 data:
  postgresql.conf: |
    # Logging
    logging_collector = off  # Use stdout/stderr instead
    log_destination = 'stderr'
    log_statement = 'all'  # Or 'ddl', 'mod', 'none'
    log_duration = on
    log_line_prefix = '%t [%p]: user=%u,db=%d,app=%a,client=%h '
    log_min_duration_statement = 100  # Log queries > 100ms
    log_checkpoints = on
    log_connections = on
    log_disconnections = on
    log_lock_waits = on
 ```
 #### Option 2: OpenTelemetry Filelog Receiver
 If PostgreSQL writes to files, use filelog receiver:
 ```yaml
 receivers:
  filelog/postgres:
    include:
      - /var/log/postgresql/*.log
    start_at: end
    operators:
      - type: regex_parser
        regex: '^(?P<timestamp>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}.\d+) \[(?P<pid>\d+)\]: user=(?P<user>[^,]+),db=(?P<database>[^,]+),app=(?P<application>[^,]+),client=(?P<client>[^ ]+) (?P<level>[A-Z]+):  (?P<message>.*)'
        timestamp:
          parse_from: attributes.timestamp
          layout: '%Y-%m-%d %H:%M:%S.%f'
      - type: move
        from: attributes.level
        to: severity
      - type: add
        field: attributes["database.system"]
        value: "postgresql"
 processors:
  resource/postgres:
    attributes:
      - key: database.system
        value: postgresql
        action: insert
      - key: service.name
        value: postgres-logs
        action: insert
 exporters:
  otlphttp/logs:
    endpoint: http://signoz-otel-collector.bakery-ia.svc.cluster.local:4318/v1/logs
 service:
  pipelines:
    logs/postgres:
      receivers: [filelog/postgres]
      processors: [resource/postgres, batch]
      exporters: [otlphttp/logs]
 ```
 ### Redis Logs
 Redis logs should go to stdout, which Kubernetes captures automatically. View them in SigNoz by:
 1. Ensuring Redis pods log to stdout
 2. No additional configuration needed - Kubernetes logs are available
 3. Optional: Use Kubernetes logs collection (see below)
 ### Kubernetes Logs Collection (All Pods)
 Deploy a DaemonSet to collect all Kubernetes pod logs:
 ```yaml
 # infrastructure/kubernetes/base/monitoring/logs-collector-daemonset.yaml
 apiVersion: apps/v1
 kind: DaemonSet
 metadata:
  name: otel-logs-collector
  namespace: bakery-ia
 spec:
  selector:
    matchLabels:
      name: otel-logs-collector
  template:
    metadata:
      labels:
        name: otel-logs-collector
    spec:
      serviceAccountName: otel-logs-collector
      containers:
      - name: otel-collector
        image: otel/opentelemetry-collector-contrib:latest
        volumeMounts:
        - name: varlog
          mountPath: /var/log
          readOnly: true
        - name: varlibdockercontainers
          mountPath: /var/lib/docker/containers
          readOnly: true
        - name: config
          mountPath: /etc/otel-collector
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers
      - name: config
        configMap:
          name: otel-logs-collector-config
 ---
 apiVersion: rbac.authorization.k8s.io/v1
 kind: ClusterRole
 metadata:
  name: otel-logs-collector
 rules:
 - apiGroups: [""]
  resources: ["pods", "namespaces"]
  verbs: ["get", "list", "watch"]
 ---
 apiVersion: rbac.authorization.k8s.io/v1
 kind: ClusterRoleBinding
 metadata:
  name: otel-logs-collector
 roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: otel-logs-collector
 subjects:
 - kind: ServiceAccount
  name: otel-logs-collector
  namespace: bakery-ia
 ---
 apiVersion: v1
 kind: ServiceAccount
 metadata:
  name: otel-logs-collector
  namespace: bakery-ia
 ```
 ## Dashboard Examples
 ### PostgreSQL Dashboard in SigNoz
 Create a custom dashboard with these panels:
 1. **Active Connections**
   - Query: `postgresql.backends`
   - Group by: `database.name`
 2. **Query Rate**
   - Query: `rate(postgresql.commits[5m])`
 3. **Database Size**
   - Query: `postgresql.database.size`
   - Group by: `database.name`
 4. **Slow Queries**
   - Go to Traces
   - Filter: `db.system="postgresql" AND duration > 1s`
   - See slow queries with full SQL
 5. **Connection Pool Usage**
   - Query: `db.client.connections.usage`
   - Group by: `service`
 ### Redis Dashboard
 1. **Hit Rate**
   - Query: `redis.keyspace.hits / (redis.keyspace.hits + redis.keyspace.misses)`
 2. **Memory Usage**
   - Query: `redis.memory.used`
 3. **Connected Clients**
   - Query: `redis.clients.connected`
 4. **Commands Per Second**
   - Query: `rate(redis.commands.processed[1m])`
 ## Quick Reference: What's Monitored
 | Database | Metrics | Logs | Traces |
 |----------|---------|------|--------|
 | **PostgreSQL** | ✅ Via receiver<br>✅ Via app instrumentation | ✅ Stdout/stderr<br>✅ Optional filelog | ✅ Query spans in traces |
 | **Redis** | ✅ Via receiver<br>✅ Via app instrumentation | ✅ Stdout/stderr | ✅ Command spans in traces |
 | **RabbitMQ** | ✅ Via receiver | ✅ Stdout/stderr | ✅ Publish/consume spans |
 ## Deployment Checklist
 - [ ] Deploy OpenTelemetry collector for database metrics
 - [ ] Create monitoring users in PostgreSQL
 - [ ] Configure database logging to stdout
 - [ ] Verify metrics appear in SigNoz
 - [ ] Create database dashboards
 - [ ] Set up alerts for connection limits, slow queries, high memory
 ## Troubleshooting
 ### No PostgreSQL metrics
 ```bash
 # Check collector logs
 kubectl logs -n bakery-ia deployment/postgres-otel-collector
 # Test connection to database
 kubectl exec -n bakery-ia deployment/postgres-otel-collector -- \
  psql -h auth-db-service -U otel_monitor -d auth_db -c "SELECT 1"
 ```
 ### No Redis metrics
 ```bash
 # Check Redis connection
 kubectl exec -n bakery-ia deployment/postgres-otel-collector -- \
  redis-cli -h redis-service -a PASSWORD ping
 ```
 ### Logs not appearing
 ```bash
 # Check if logs are going to stdout
 kubectl logs -n bakery-ia postgres-pod-name
 # Check logs collector
 kubectl logs -n bakery-ia daemonset/otel-logs-collector
 ```
 ## Best Practices
 1. **Use dedicated monitoring users** - Don't use application database users
 2. **Set appropriate collection intervals** - 30s-60s for metrics
 3. **Monitor connection pool saturation** - Alert before exhausting connections
 4. **Track slow queries** - Set `log_min_duration_statement` appropriately
 5. **Monitor disk usage** - PostgreSQL database size growth
 6. **Track cache hit rates** - Redis keyspace hits/misses ratio
 ## Additional Resources
 - [OpenTelemetry PostgreSQL Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/postgresqlreceiver)
 - [OpenTelemetry Redis Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/redisreceiver)
 - [SigNoz Database Monitoring](https://signoz.io/docs/userguide/metrics/)
--- a/docs/MONITORING_DOCUMENTATION.md
+++ b/docs/MONITORING_DOCUMENTATION.md
@@ -1,536 +0,0 @@
 # 📊 Bakery-ia Monitoring System Documentation
 ## 🎯 Overview
 The bakery-ia platform features a comprehensive, modern monitoring system built on **OpenTelemetry** and **SigNoz**. This documentation provides a complete guide to the monitoring architecture, setup, and usage.
 ## 🚀 Monitoring Architecture
 ### Core Components
 ```mermaid
 graph TD
    A[Microservices] -->|OTLP| B[OpenTelemetry Collector]
    B -->|gRPC| C[SigNoz]
    C --> D[Traces Dashboard]
    C --> E[Metrics Dashboard]
    C --> F[Logs Dashboard]
    C --> G[Alerts]
 ```
 ### Technology Stack
 - **Instrumentation**: OpenTelemetry Python SDK
 - **Protocol**: OTLP (OpenTelemetry Protocol) over gRPC
 - **Backend**: SigNoz (open-source observability platform)
 - **Metrics**: Prometheus-compatible metrics via OTLP
 - **Traces**: Jaeger-compatible tracing via OTLP
 - **Logs**: Structured logging with trace correlation
 ## 📋 Monitoring Coverage
 ### Service Coverage (100%)
 | Service Category | Services | Monitoring Type | Status |
 |-----------------|----------|----------------|--------|
 | **Critical Services** | auth, orders, sales, external | Base Class | ✅ Monitored |
 | **AI Services** | ai-insights, training | Direct | ✅ Monitored |
 | **Data Services** | inventory, procurement, production, forecasting | Base Class | ✅ Monitored |
 | **Operational Services** | tenant, notification, distribution | Base Class | ✅ Monitored |
 | **Specialized Services** | suppliers, pos, recipes, orchestrator | Base Class | ✅ Monitored |
 | **Infrastructure** | gateway, alert-processor, demo-session | Direct | ✅ Monitored |
 **Total: 20 services with 100% monitoring coverage**
 ## 🔧 Monitoring Implementation
 ### Implementation Patterns
 #### 1. Base Class Pattern (16 services)
 Services using `StandardFastAPIService` inherit comprehensive monitoring:
 ```python
 from shared.service_base import StandardFastAPIService
 class MyService(StandardFastAPIService):
    def __init__(self):
        super().__init__(
            service_name="my-service",
            app_name="My Service",
            description="Service description",
            version="1.0.0",
            # Monitoring enabled by default
            enable_metrics=True,      # ✅ Metrics collection
            enable_tracing=True,      # ✅ Distributed tracing
            enable_health_checks=True # ✅ Health endpoints
        )
 ```
 #### 2. Direct Pattern (4 services)
 Critical services with custom monitoring needs:
 ```python
 # services/ai_insights/app/main.py
 from shared.monitoring.metrics import MetricsCollector, add_metrics_middleware
 from shared.monitoring.system_metrics import SystemMetricsCollector
 # Initialize metrics collectors
 metrics_collector = MetricsCollector("ai-insights")
 system_metrics = SystemMetricsCollector("ai-insights")
 # Add middleware
 add_metrics_middleware(app, metrics_collector)
 ```
 ### Monitoring Components
 #### OpenTelemetry Instrumentation
 ```python
 # Automatic instrumentation in base class
 FastAPIInstrumentor.instrument_app(app)      # HTTP requests
 HTTPXClientInstrumentor().instrument()       # Outgoing HTTP
 RedisInstrumentor().instrument()             # Redis operations
 SQLAlchemyInstrumentor().instrument()       # Database queries
 ```
 #### Metrics Collection
 ```python
 # Standard metrics automatically collected
 metrics_collector.register_counter("http_requests_total", "Total HTTP requests")
 metrics_collector.register_histogram("http_request_duration", "Request duration")
 metrics_collector.register_gauge("active_requests", "Active requests")
 # System metrics automatically collected
 system_metrics = SystemMetricsCollector("service-name")
 # → CPU, Memory, Disk I/O, Network I/O, Threads, File Descriptors
 ```
 #### Health Checks
 ```python
 # Automatic health check endpoints
 GET /health          # Overall service health
 GET /health/detailed # Detailed health with dependencies
 GET /health/ready    # Readiness probe
 GET /health/live     # Liveness probe
 ```
 ## 📊 Metrics Reference
 ### Standard Metrics (All Services)
 | Metric Type | Metric Name | Description | Labels |
 |-------------|------------|-------------|--------|
 | **HTTP Metrics** | `{service}_http_requests_total` | Total HTTP requests | method, endpoint, status_code |
 | **HTTP Metrics** | `{service}_http_request_duration_seconds` | Request duration histogram | method, endpoint, status_code |
 | **HTTP Metrics** | `{service}_active_requests` | Currently active requests | - |
 | **System Metrics** | `process.cpu.utilization` | Process CPU usage | - |
 | **System Metrics** | `process.memory.usage` | Process memory usage | - |
 | **System Metrics** | `system.cpu.utilization` | System CPU usage | - |
 | **System Metrics** | `system.memory.usage` | System memory usage | - |
 | **Database Metrics** | `db.query.duration` | Database query duration | operation, table |
 | **Cache Metrics** | `cache.operation.duration` | Cache operation duration | operation, key |
 ### Custom Metrics (Service-Specific)
 Examples of service-specific metrics:
 **Auth Service:**
 - `auth_registration_total` (by status)
 - `auth_login_success_total`
 - `auth_login_failure_total` (by reason)
 - `auth_registration_duration_seconds`
 **Orders Service:**
 - `orders_created_total`
 - `orders_processed_total` (by status)
 - `orders_processing_duration_seconds`
 **AI Insights Service:**
 - `ai_insights_generated_total`
 - `ai_model_inference_duration_seconds`
 - `ai_feedback_received_total`
 ## 🔍 Tracing Guide
 ### Trace Propagation
 Traces automatically flow across service boundaries:
 ```mermaid
 sequenceDiagram
    participant Client
    participant Gateway
    participant Auth
    participant Orders
    Client->>Gateway: HTTP Request (trace_id: abc123)
    Gateway->>Auth: Auth Check (trace_id: abc123)
    Auth-->>Gateway: Auth Response (trace_id: abc123)
    Gateway->>Orders: Create Order (trace_id: abc123)
    Orders-->>Gateway: Order Created (trace_id: abc123)
    Gateway-->>Client: Final Response (trace_id: abc123)
 ```
 ### Trace Context in Logs
 All logs include trace correlation:
 ```json
 {
    "level": "info",
    "message": "Processing order",
    "service": "orders-service",
    "trace_id": "abc123def456",
    "span_id": "789ghi",
    "order_id": "12345",
    "timestamp": "2024-01-08T19:00:00Z"
 }
 ```
 ### Manual Trace Enhancement
 Add custom trace attributes:
 ```python
 from shared.monitoring.tracing import add_trace_attributes, add_trace_event
 # Add custom attributes
 add_trace_attributes(
    user_id="123",
    tenant_id="abc",
    operation="order_creation"
 )
 # Add trace events
 add_trace_event("order_validation_started")
 # ... validation logic ...
 add_trace_event("order_validation_completed", status="success")
 ```
 ## 🚨 Alerting Guide
 ### Standard Alerts (Recommended)
 | Alert Name | Condition | Severity | Notification |
 |------------|-----------|----------|--------------|
 | **High Error Rate** | `error_rate > 5%` for 5m | High | PagerDuty + Slack |
 | **High Latency** | `p99_latency > 2s` for 5m | High | PagerDuty + Slack |
 | **Service Unavailable** | `up == 0` for 1m | Critical | PagerDuty + Slack + Email |
 | **High Memory Usage** | `memory_usage > 80%` for 10m | Medium | Slack |
 | **High CPU Usage** | `cpu_usage > 90%` for 5m | Medium | Slack |
 | **Database Connection Issues** | `db_connections < minimum_pool_size` | High | PagerDuty + Slack |
 | **Cache Hit Ratio Low** | `cache_hit_ratio < 70%` for 15m | Low | Slack |
 ### Creating Alerts in SigNoz
 1. **Navigate to Alerts**: SigNoz UI → Alerts → Create Alert
 2. **Select Metric**: Choose from available metrics
 3. **Set Condition**: Define threshold and duration
 4. **Configure Notifications**: Add notification channels
 5. **Set Severity**: Critical, High, Medium, Low
 6. **Add Description**: Explain alert purpose and resolution steps
 ### Example Alert Configuration (YAML)
 ```yaml
 # Example for Terraform/Kubernetes
 apiVersion: monitoring.coreos.com/v1
 kind: PrometheusRule
 metadata:
  name: bakery-ia-alerts
  namespace: monitoring
 spec:
  groups:
  - name: service-health
    rules:
    - alert: ServiceDown
      expr: up{service!~"signoz.*"} == 0
      for: 1m
      labels:
        severity: critical
      annotations:
        summary: "Service {{ $labels.service }} is down"
        description: "{{ $labels.service }} has been down for more than 1 minute"
        runbook: "https://github.com/yourorg/bakery-ia/blob/main/RUNBOOKS.md#service-down"
    - alert: HighErrorRate
      expr: rate(http_requests_total{status_code=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
      for: 5m
      labels:
        severity: high
      annotations:
        summary: "High error rate in {{ $labels.service }}"
        description: "Error rate is {{ $value }}% (threshold: 5%)"
        runbook: "https://github.com/yourorg/bakery-ia/blob/main/RUNBOOKS.md#high-error-rate"
 ```
 ## 📈 Dashboard Guide
 ### Recommended Dashboards
 #### 1. Service Overview Dashboard
 - HTTP Request Rate
 - Error Rate
 - Latency Percentiles (p50, p90, p99)
 - Active Requests
 - System Resource Usage
 #### 2. Performance Dashboard
 - Request Duration Histogram
 - Database Query Performance
 - Cache Performance
 - External API Call Performance
 #### 3. System Health Dashboard
 - CPU Usage (Process & System)
 - Memory Usage (Process & System)
 - Disk I/O
 - Network I/O
 - File Descriptors
 - Thread Count
 #### 4. Business Metrics Dashboard
 - User Registrations
 - Order Volume
 - AI Insights Generated
 - API Usage by Tenant
 ### Creating Dashboards in SigNoz
 1. **Navigate to Dashboards**: SigNoz UI → Dashboards → Create Dashboard
 2. **Add Panels**: Click "Add Panel" and select metric
 3. **Configure Visualization**: Choose chart type and settings
 4. **Set Time Range**: Default to last 1h, 6h, 24h, 7d
 5. **Add Variables**: For dynamic filtering (service, environment)
 6. **Save Dashboard**: Give it a descriptive name
 ## 🛠️ Troubleshooting Guide
 ### Common Issues & Solutions
 #### Issue: No Metrics Appearing in SigNoz
 **Checklist:**
 - ✅ OpenTelemetry Collector running? `kubectl get pods -n bakery-ia -l app.kubernetes.io/instance=signoz`
 - ✅ Service can reach collector? `telnet signoz-otel-collector.bakery-ia 4318`
 - ✅ OTLP endpoint configured correctly? Check `OTEL_EXPORTER_OTLP_ENDPOINT`
 - ✅ Service logs show OTLP export? Look for "Exporting metrics"
 - ✅ No network policies blocking? Check Kubernetes network policies
 **Debugging:**
 ```bash
 # Check OpenTelemetry Collector logs
 kubectl logs -n bakery-ia -l app=otel-collector
 # Check service logs for OTLP errors
 kubectl logs -l app=auth-service | grep -i otel
 # Test OTLP connectivity from service pod
 kubectl exec -it auth-service-pod -- curl -v http://signoz-otel-collector.bakery-ia:4318
 ```
 #### Issue: High Latency in Specific Service
 **Checklist:**
 - ✅ Database queries slow? Check `db.query.duration` metrics
 - ✅ External API calls slow? Check trace waterfall
 - ✅ High CPU usage? Check system metrics
 - ✅ Memory pressure? Check memory metrics
 - ✅ Too many active requests? Check concurrency
 **Debugging:**
 ```python
 # Add detailed tracing to suspicious code
 from shared.monitoring.tracing import add_trace_event
 add_trace_event("database_query_started", table="users")
 # ... database query ...
 add_trace_event("database_query_completed", duration_ms=45)
 ```
 #### Issue: High Error Rate
 **Checklist:**
 - ✅ Database connection issues? Check health endpoints
 - ✅ External API failures? Check dependency metrics
 - ✅ Authentication failures? Check auth service logs
 - ✅ Validation errors? Check application logs
 - ✅ Rate limiting? Check gateway metrics
 **Debugging:**
 ```bash
 # Check error logs with trace correlation
 kubectl logs -l app=auth-service | grep -i error | grep -i trace
 # Filter traces by error status
 # In SigNoz: Add filter http.status_code >= 400
 ```
 ## 📚 Runbook Reference
 See [RUNBOOKS.md](RUNBOOKS.md) for detailed troubleshooting procedures.
 ## 🔧 Development Guide
 ### Adding Custom Metrics
 ```python
 # In any service using direct monitoring
 self.metrics_collector.register_counter(
    "custom_metric_name",
    "Description of what this metric tracks",
    labels=["label1", "label2"]  # Optional labels
 )
 # Increment the counter
 self.metrics_collector.increment_counter(
    "custom_metric_name",
    value=1,
    labels={"label1": "value1", "label2": "value2"}
 )
 ```
 ### Adding Custom Trace Attributes
 ```python
 # Add context to current span
 from shared.monitoring.tracing import add_trace_attributes
 add_trace_attributes(
    user_id=user.id,
    tenant_id=tenant.id,
    operation="premium_feature_access",
    feature_name="advanced_forecasting"
 )
 ```
 ### Service-Specific Monitoring Setup
 For services needing custom monitoring beyond the base class:
 ```python
 # In your service's __init__ method
 from shared.monitoring.system_metrics import SystemMetricsCollector
 from shared.monitoring.metrics import MetricsCollector
 class MyService(StandardFastAPIService):
    def __init__(self):
        # Call parent constructor first
        super().__init__(...)
        # Add custom metrics collector
        self.custom_metrics = MetricsCollector("my-service")
        # Register custom metrics
        self.custom_metrics.register_counter(
            "business_specific_events",
            "Custom business event counter"
        )
        # Add system metrics if not using base class defaults
        self.system_metrics = SystemMetricsCollector("my-service")
 ```
 ## 📊 SigNoz Configuration
 ### Environment Variables
 ```env
 # OpenTelemetry Collector endpoint
 OTEL_EXPORTER_OTLP_ENDPOINT=http://signoz-otel-collector.bakery-ia:4318
 # Service-specific configuration
 OTEL_SERVICE_NAME=auth-service
 OTEL_RESOURCE_ATTRIBUTES=deployment.environment=production,k8s.namespace=bakery-ia
 # Metrics export interval (default: 60000ms = 60s)
 OTEL_METRIC_EXPORT_INTERVAL=60000
 # Batch span processor configuration
 OTEL_BSP_SCHEDULE_DELAY=5000
 OTEL_BSP_MAX_QUEUE_SIZE=2048
 OTEL_BSP_MAX_EXPORT_BATCH_SIZE=512
 ```
 ### Kubernetes Configuration
 ```yaml
 # Example deployment with monitoring sidecar
 apiVersion: apps/v1
 kind: Deployment
 metadata:
  name: auth-service
 spec:
  template:
    spec:
      containers:
      - name: auth-service
        image: auth-service:latest
        env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://signoz-otel-collector.bakery-ia:4318"
        - name: OTEL_SERVICE_NAME
          value: "auth-service"
        - name: ENVIRONMENT
          value: "production"
        resources:
          limits:
            cpu: "1"
            memory: "512Mi"
          requests:
            cpu: "200m"
            memory: "256Mi"
 ```
 ## 🎯 Best Practices
 ### Monitoring Best Practices
 1. **Use Consistent Naming**: Follow OpenTelemetry semantic conventions
 2. **Add Context to Traces**: Include user/tenant IDs in trace attributes
 3. **Monitor Dependencies**: Track external API and database performance
 4. **Set Appropriate Alerts**: Avoid alert fatigue with meaningful thresholds
 5. **Document Metrics**: Keep metrics documentation up to date
 6. **Review Regularly**: Update dashboards as services evolve
 7. **Test Alerts**: Ensure alerts fire correctly before production
 ### Performance Best Practices
 1. **Batch Metrics Export**: Use default 60s interval for most services
 2. **Sample Traces**: Consider sampling for high-volume services
 3. **Limit Custom Metrics**: Only track metrics that provide value
 4. **Use Histograms Wisely**: Histograms can be resource-intensive
 5. **Monitor Monitoring**: Track OTLP export success/failure rates
 ## 📞 Support
 ### Getting Help
 1. **Check Documentation**: This file and RUNBOOKS.md
 2. **Review SigNoz Docs**: https://signoz.io/docs/
 3. **OpenTelemetry Docs**: https://opentelemetry.io/docs/
 4. **Team Channel**: #monitoring in Slack
 5. **GitHub Issues**: https://github.com/yourorg/bakery-ia/issues
 ### Escalation Path
 1. **First Line**: Development team (service owners)
 2. **Second Line**: DevOps team (monitoring specialists)
 3. **Third Line**: SigNoz support (vendor support)
 ## 🎉 Summary
 The bakery-ia monitoring system provides:
 - **📊 100% Service Coverage**: All 20 services monitored
 - **🚀 Modern Architecture**: OpenTelemetry + SigNoz
 - **🔧 Comprehensive Metrics**: System, HTTP, database, cache
 - **🔍 Full Observability**: Traces, metrics, logs integrated
 - **✅ Production Ready**: Battle-tested and scalable
 **All services are fully instrumented and ready for production monitoring!** 🎉
--- a/docs/PILOT_LAUNCH_GUIDE.md
+++ b/docs/PILOT_LAUNCH_GUIDE.md
@@ -856,87 +856,227 @@ kubectl logs -n bakery-ia deployment/auth-service | grep -i "email\|smtp"
 ## Post-Deployment
-### Step 1: Access Monitoring Stack
+### Step 1: Access SigNoz Monitoring Stack
-Your production monitoring stack provides complete observability with multiple tools:
+Your production deployment includes **SigNoz**, a unified observability platform that provides complete visibility into your application:
 #### What is SigNoz?
 SigNoz is an **open-source, all-in-one observability platform** that provides:
 - **📊 Distributed Tracing** - See end-to-end request flows across all 18 microservices
 - **📈 Metrics Monitoring** - Application performance and infrastructure metrics
 - **📝 Log Management** - Centralized logs from all services with trace correlation
 - **🔍 Service Performance Monitoring (SPM)** - Automatic RED metrics (Rate, Error, Duration)
 - **🗄️ Database Monitoring** - All 18 PostgreSQL databases + Redis + RabbitMQ
 - **☸️ Kubernetes Monitoring** - Cluster, node, pod, and container metrics
 **Why SigNoz instead of Prometheus/Grafana?**
 - Single unified UI for traces, metrics, and logs (no context switching)
 - Automatic service dependency mapping
 - Built-in APM (Application Performance Monitoring)
 - Log-trace correlation with one click
 - Better query performance with ClickHouse backend
 - Modern UI designed for microservices
 #### Production Monitoring URLs
-Access via domain (recommended):
+Access via domain:
 ```
-https://monitoring.bakewise.ai/grafana       # Dashboards & visualization
+https://monitoring.bakewise.ai/signoz        # SigNoz - Main observability UI
-https://monitoring.bakewise.ai/prometheus    # Metrics & queries
+https://monitoring.bakewise.ai/alertmanager  # AlertManager - Alert management
 https://monitoring.bakewise.ai/signoz        # Unified observability platform (traces, metrics, logs)
 https://monitoring.bakewise.ai/alertmanager  # Alert management
 ```
 Or via port forwarding (if needed):
 ```bash
-# Grafana
+# SigNoz Frontend (Main UI)
-kubectl port-forward -n monitoring svc/grafana 3000:3000 &
+kubectl port-forward -n bakery-ia svc/signoz 8080:8080 &
 # Open: http://localhost:8080
-# Prometheus
+# SigNoz AlertManager
-kubectl port-forward -n monitoring svc/prometheus-external 9090:9090 &
+kubectl port-forward -n bakery-ia svc/signoz-alertmanager 9093:9093 &
 # Open: http://localhost:9093
-# SigNoz
+# OTel Collector (for debugging)
-kubectl port-forward -n monitoring svc/signoz-frontend 3301:3301 &
+kubectl port-forward -n bakery-ia svc/signoz-otel-collector 4317:4317 &  # gRPC
-
+kubectl port-forward -n bakery-ia svc/signoz-otel-collector 4318:4318 &  # HTTP
 # AlertManager
 kubectl port-forward -n monitoring svc/alertmanager-external 9093:9093 &
 ```
-#### Available Dashboards
+#### Key SigNoz Features to Explore
-Login to Grafana (admin / your-password) and explore:
+Once you open SigNoz (https://monitoring.bakewise.ai/signoz), explore these tabs:
-**Main Dashboards:**
+**1. Services Tab - Application Performance**
-1. **Gateway Metrics** - HTTP request rates, latencies, error rates
+- View all 18 microservices with live metrics
-2. **Services Overview** - Multi-service health and performance
+- See request rate, error rate, and latency (P50/P90/P99)
-3. **Circuit Breakers** - Reliability metrics
+- Click on any service to drill down into operations
 - Identify slow endpoints and error-prone operations
-**Extended Dashboards:**
+**2. Traces Tab - Request Flow Visualization**
-4. **Service Performance Monitoring (SPM)** - RED metrics from distributed traces
+- See complete request journeys across services
-5. **PostgreSQL Database** - Database health, connections, query performance
+- Identify bottlenecks (slow database queries, API calls)
-6. **Node Exporter Infrastructure** - CPU, memory, disk, network per node
+- Debug errors with full stack traces
-7. **AlertManager Monitoring** - Alert tracking and notification status
+- Correlate with logs for complete context
-8. **Business Metrics & KPIs** - Tenant activity, ML jobs, forecasts
+
 **3. Dashboards Tab - Infrastructure & Database Metrics**
 - **PostgreSQL** - Monitor all 18 databases (connections, queries, cache hit ratio)
 - **Redis** - Cache performance (memory, hit rate, commands/sec)
 - **RabbitMQ** - Message queue health (depth, rates, consumers)
 - **Kubernetes** - Cluster metrics (nodes, pods, containers)
 **4. Logs Tab - Centralized Log Management**
 - Search and filter logs from all services
 - Click on trace ID in logs to see related request trace
 - Auto-enriched with Kubernetes metadata (pod, namespace, container)
 - Identify patterns and anomalies
 **5. Alerts Tab - Proactive Monitoring**
 - Configure alerts on metrics, traces, or logs
 - Email/Slack/Webhook notifications
 - View firing alerts and alert history
 #### Quick Health Check
 ```bash
-# Verify all monitoring pods are running
+# Verify SigNoz components are running
-kubectl get pods -n monitoring
+kubectl get pods -n bakery-ia -l app.kubernetes.io/instance=signoz
-# Check Prometheus targets (all should be UP)
+# Expected output:
-kubectl port-forward -n monitoring svc/prometheus-external 9090:9090
+# signoz-0                              READY 1/1
-# Open: http://localhost:9090/targets
+# signoz-otel-collector-xxx             READY 1/1
 # signoz-alertmanager-xxx               READY 1/1
 # signoz-clickhouse-xxx                 READY 1/1
 # signoz-zookeeper-xxx                  READY 1/1
-# View active alerts
+# Check OTel Collector health
-kubectl port-forward -n monitoring svc/prometheus-external 9090:9090
+kubectl exec -n bakery-ia deployment/signoz-otel-collector -- wget -qO- http://localhost:13133
-# Open: http://localhost:9090/alerts
+
 # View recent telemetry in OTel Collector logs
 kubectl logs -n bakery-ia deployment/signoz-otel-collector --tail=50 | grep -i "traces\|metrics\|logs"
 ```
 #### Verify Telemetry is Working
 1. **Check Services are Reporting:**
   ```bash
   # Open SigNoz and navigate to Services tab
   # You should see all 18 microservices listed
   # If services are missing, check if they're sending telemetry:
   kubectl logs -n bakery-ia deployment/auth-service | grep -i "telemetry\|otel"
   ```
 2. **Check Database Metrics:**
   ```bash
   # Navigate to Dashboards → PostgreSQL in SigNoz
   # You should see metrics from all 18 databases
   # Verify OTel Collector is scraping databases:
   kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep postgresql
   ```
 3. **Check Traces are Being Collected:**
   ```bash
   # Make a test API request
   curl https://bakewise.ai/api/v1/health
   # Navigate to Traces tab in SigNoz
   # Search for "gateway" service
   # You should see the trace for your request
   ```
 4. **Check Logs are Being Collected:**
   ```bash
   # Navigate to Logs tab in SigNoz
   # Filter by namespace: bakery-ia
   # You should see logs from all pods
   # Verify filelog receiver is working:
   kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep filelog
   ```
 ### Step 2: Configure Alerting
-Update AlertManager with your notification email addresses:
+SigNoz includes integrated alerting with AlertManager. Configure it for your team:
 #### Update Email Notification Settings
 The alerting configuration is in the SigNoz Helm values. To update:
 ```bash
-# Edit alertmanager configuration
+# For production, edit the values file:
-kubectl edit configmap -n monitoring alertmanager-config
+nano infrastructure/helm/signoz-values-prod.yaml
-# Update recipient emails in the routes section:
+# Update the alertmanager.config section:
-# - alerts@bakewise.ai (general alerts)
+# 1. Update SMTP settings:
-# - critical-alerts@bakewise.ai (critical issues)
+#    - smtp_from: 'your-alerts@bakewise.ai'
-# - oncall@bakewise.ai (on-call rotation)
+#    - smtp_auth_username: 'your-alerts@bakewise.ai'
 #    - smtp_auth_password: (use Kubernetes secret)
 #
 # 2. Update receivers:
 #    - critical-alerts email: critical-alerts@bakewise.ai
 #    - warning-alerts email: oncall@bakewise.ai
 #
 # 3. (Optional) Add Slack webhook for critical alerts
 # Apply the updated configuration:
 helm upgrade signoz signoz/signoz \
  -n bakery-ia \
  -f infrastructure/helm/signoz-values-prod.yaml
 ```
-Test alert delivery:
+#### Create Alerts in SigNoz UI
 1. **Open SigNoz Alerts Tab:**
   ```
   https://monitoring.bakewise.ai/signoz → Alerts
   ```
 2. **Create Common Alerts:**
   **Alert 1: High Error Rate**
   - Name: `HighErrorRate`
   - Query: `error_rate > 5` for `5 minutes`
   - Severity: `critical`
   - Description: "Service {{service_name}} has error rate >5%"
   **Alert 2: High Latency**
   - Name: `HighLatency`
   - Query: `P99_latency > 3000ms` for `5 minutes`
   - Severity: `warning`
   - Description: "Service {{service_name}} P99 latency >3s"
   **Alert 3: Service Down**
   - Name: `ServiceDown`
   - Query: `request_rate == 0` for `2 minutes`
   - Severity: `critical`
   - Description: "Service {{service_name}} not receiving requests"
   **Alert 4: Database Connection Issues**
   - Name: `DatabaseConnectionsHigh`
   - Query: `pg_active_connections > 80` for `5 minutes`
   - Severity: `warning`
   - Description: "Database {{database}} connection count >80%"
   **Alert 5: High Memory Usage**
   - Name: `HighMemoryUsage`
   - Query: `container_memory_percent > 85` for `5 minutes`
   - Severity: `warning`
   - Description: "Pod {{pod_name}} using >85% memory"
 #### Test Alert Delivery
 ```bash
-# Fire a test alert
+# Method 1: Create a test alert in SigNoz UI
 # Go to Alerts → New Alert → Set a test condition that will fire
 # Method 2: Fire a test alert via stress test
 kubectl run memory-test --image=polinux/stress --restart=Never \
  --namespace=bakery-ia -- stress --vm 1 --vm-bytes 600M --timeout 300s
-# Check alert appears in AlertManager
+# Check alert appears in SigNoz Alerts tab
 # https://monitoring.bakewise.ai/signoz → Alerts
 # Also check AlertManager
 # https://monitoring.bakewise.ai/alertmanager
 # Verify email notification received
@@ -945,6 +1085,26 @@ kubectl run memory-test --image=polinux/stress --restart=Never \
 kubectl delete pod memory-test -n bakery-ia
 ```
 #### Configure Notification Channels
 In SigNoz Alerts tab, configure channels:
 1. **Email Channel:**
   - Already configured via AlertManager
   - Emails sent to addresses in signoz-values-prod.yaml
 2. **Slack Channel (Optional):**
   ```bash
   # Add Slack webhook URL to signoz-values-prod.yaml
   # Under alertmanager.config.receivers.critical-alerts.slack_configs:
   #   - api_url: 'https://hooks.slack.com/services/YOUR/WEBHOOK/URL'
   #     channel: '#alerts-critical'
   ```
 3. **Webhook Channel (Optional):**
   - Configure custom webhook for integration with PagerDuty, OpsGenie, etc.
   - Add to alertmanager.config.receivers
 ### Step 3: Configure Backups
 ```bash
@@ -992,26 +1152,61 @@ kubectl edit configmap -n monitoring alertmanager-config
 # Update recipient emails in the routes section
 ```
-### Step 4: Verify Monitoring is Working
+### Step 4: Verify SigNoz Monitoring is Working
 Before proceeding, ensure all monitoring components are operational:
 ```bash
-# 1. Check Prometheus targets
+# 1. Verify SigNoz pods are running
-# Open: https://monitoring.bakewise.ai/prometheus/targets
+kubectl get pods -n bakery-ia -l app.kubernetes.io/instance=signoz
 # All targets should show "UP" status
-# 2. Verify Grafana dashboards load data
+# Expected pods (all should be Running/Ready):
-# Open: https://monitoring.bakewise.ai/grafana
+# - signoz-0 (or signoz-1, signoz-2 for HA)
-# Navigate to any dashboard and verify metrics are displaying
+# - signoz-otel-collector-xxx
 # - signoz-alertmanager-xxx
 # - signoz-clickhouse-xxx
 # - signoz-zookeeper-xxx
-# 3. Check SigNoz is receiving traces
+# 2. Check SigNoz UI is accessible
-# Open: https://monitoring.bakewise.ai/signoz
+curl -I https://monitoring.bakewise.ai/signoz
-# Search for traces from "gateway" service
+# Should return: HTTP/2 200 OK
-# 4. Verify AlertManager cluster
+# 3. Verify OTel Collector is receiving data
-# Open: https://monitoring.bakewise.ai/alertmanager
+kubectl logs -n bakery-ia deployment/signoz-otel-collector --tail=100 | grep -i "received"
-# Check that all 3 AlertManager instances are connected
+# Should show: "Traces received: X" "Metrics received: Y" "Logs received: Z"
 # 4. Check ClickHouse database is healthy
 kubectl exec -n bakery-ia deployment/signoz-clickhouse -- clickhouse-client --query="SELECT count() FROM system.tables WHERE database LIKE 'signoz_%'"
 # Should return a number > 0 (tables exist)
 ```
 **Complete Verification Checklist:**
 - [ ] **SigNoz UI loads** at https://monitoring.bakewise.ai/signoz
 - [ ] **Services tab shows all 18 microservices** with metrics
 - [ ] **Traces tab has sample traces** from gateway and other services
 - [ ] **Dashboards tab shows PostgreSQL metrics** from all 18 databases
 - [ ] **Dashboards tab shows Redis metrics** (memory, commands, etc.)
 - [ ] **Dashboards tab shows RabbitMQ metrics** (queues, messages)
 - [ ] **Dashboards tab shows Kubernetes metrics** (nodes, pods)
 - [ ] **Logs tab displays logs** from all services in bakery-ia namespace
 - [ ] **Alerts tab is accessible** and can create new alerts
 - [ ] **AlertManager** is reachable at https://monitoring.bakewise.ai/alertmanager
 **If any checks fail, troubleshoot:**
 ```bash
 # Check OTel Collector configuration
 kubectl describe configmap -n bakery-ia signoz-otel-collector
 # Check for errors in OTel Collector
 kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep -i error
 # Check ClickHouse is accepting writes
 kubectl logs -n bakery-ia deployment/signoz-clickhouse | grep -i error
 # Restart OTel Collector if needed
 kubectl rollout restart deployment/signoz-otel-collector -n bakery-ia
 ```
 ### Step 5: Document Everything
@@ -1033,41 +1228,113 @@ Create a secure runbook with all credentials and procedures:
 ### Step 6: Train Your Team
-Conduct a training session covering:
+Conduct a training session covering SigNoz and operational procedures:
- [ ] **Access monitoring dashboards**
+#### Part 1: SigNoz Navigation (30 minutes)
  - Show how to login to https://monitoring.bakewise.ai/grafana
  - Walk through key dashboards (Services Overview, Database, Infrastructure)
  - Explain how to interpret metrics and identify issues
- [ ] **Check application logs**
+- [ ] **Login and Overview**
  - Show how to access https://monitoring.bakewise.ai/signoz
  - Navigate through main tabs: Services, Traces, Dashboards, Logs, Alerts
  - Explain the unified nature of SigNoz (all-in-one platform)
 - [ ] **Services Tab - Application Performance Monitoring**
  - Show all 18 microservices
  - Explain RED metrics (Request rate, Error rate, Duration/latency)
  - Demo: Click on a service → Operations → See endpoint breakdown
  - Demo: Identify slow endpoints and high error rates
 - [ ] **Traces Tab - Request Flow Debugging**
  - Show how to search for traces by service, operation, or time
  - Demo: Click on a trace → See full waterfall (service → database → cache)
  - Demo: Find slow database queries in trace spans
  - Demo: Click "View Logs" to correlate trace with logs
 - [ ] **Dashboards Tab - Infrastructure Monitoring**
  - Navigate to PostgreSQL dashboard → Show all 18 databases
  - Navigate to Redis dashboard → Show cache metrics
  - Navigate to Kubernetes dashboard → Show node/pod metrics
  - Explain what metrics indicate issues (connection %, memory %, etc.)
 - [ ] **Logs Tab - Log Search and Analysis**
  - Show how to filter by service, severity, time range
  - Demo: Search for "error" in last hour
  - Demo: Click on trace_id in log → Jump to related trace
  - Show Kubernetes metadata (pod, namespace, container)
 - [ ] **Alerts Tab - Proactive Monitoring**
  - Show how to create alerts on metrics
  - Review pre-configured alerts
  - Show alert history and firing alerts
  - Explain how to acknowledge/silence alerts
 #### Part 2: Operational Tasks (30 minutes)
 - [ ] **Check application logs** (multiple ways)
  ```bash
-  # View logs for a service
+  # Method 1: Via kubectl (for immediate debugging)
  kubectl logs -n bakery-ia deployment/orders-service --tail=100 -f
-  # Search for errors
+  # Method 2: Via SigNoz Logs tab (for analysis and correlation)
-  kubectl logs -n bakery-ia deployment/gateway | grep ERROR
+  # 1. Open https://monitoring.bakewise.ai/signoz → Logs
  # 2. Filter by k8s_deployment_name: orders-service
  # 3. Click on trace_id to see related request flow
  ```
 - [ ] **Restart services when needed**
  ```bash
  # Restart a service (rolling update, no downtime)
  kubectl rollout restart deployment/orders-service -n bakery-ia
  # Verify restart in SigNoz:
  # 1. Check Services tab → orders-service → Should show brief dip then recovery
  # 2. Check Logs tab → Filter by orders-service → See restart logs
  ```
 - [ ] **Investigate performance issues**
  ```bash
  # Scenario: "Orders API is slow"
  # 1. SigNoz → Services → orders-service → Check P99 latency
  # 2. SigNoz → Traces → Filter service:orders-service, duration:>1s
  # 3. Click on slow trace → Identify bottleneck (DB query? External API?)
  # 4. SigNoz → Dashboards → PostgreSQL → Check orders_db connections/queries
  # 5. Fix identified issue (add index, optimize query, scale service)
  ```
 - [ ] **Respond to alerts**
-  - Show how to access AlertManager at https://monitoring.bakewise.ai/alertmanager
+  - Show how to access alerts in SigNoz → Alerts tab
  - Show AlertManager UI at https://monitoring.bakewise.ai/alertmanager
  - Review common alerts and their resolution steps
  - Reference the [Production Operations Guide](./PRODUCTION_OPERATIONS_GUIDE.md)
 #### Part 3: Documentation and Resources (10 minutes)
 - [ ] **Share documentation**
-  - [PILOT_LAUNCH_GUIDE.md](./PILOT_LAUNCH_GUIDE.md) - This guide
+  - [PILOT_LAUNCH_GUIDE.md](./PILOT_LAUNCH_GUIDE.md) - This guide (deployment)
-  - [PRODUCTION_OPERATIONS_GUIDE.md](./PRODUCTION_OPERATIONS_GUIDE.md) - Daily operations
+  - [PRODUCTION_OPERATIONS_GUIDE.md](./PRODUCTION_OPERATIONS_GUIDE.md) - Daily operations with SigNoz
  - [security-checklist.md](./security-checklist.md) - Security procedures
 - [ ] **Bookmark key URLs**
  - SigNoz: https://monitoring.bakewise.ai/signoz
  - AlertManager: https://monitoring.bakewise.ai/alertmanager
  - Production app: https://bakewise.ai
 - [ ] **Setup on-call rotation** (if applicable)
-  - Configure in AlertManager
+  - Configure rotation schedule in AlertManager
  - Document escalation procedures
  - Test alert delivery to on-call phone/email
 #### Part 4: Hands-On Exercise (15 minutes)
 **Exercise: Investigate a Simulated Issue**
 1. Create a load test to generate traffic
 2. Use SigNoz to find the slowest endpoint
 3. Identify the root cause using traces
 4. Correlate with logs to confirm
 5. Check infrastructure metrics (DB, memory, CPU)
 6. Propose a fix based on findings
 This trains the team to use SigNoz effectively for real incidents.
 ---
@@ -1204,17 +1471,33 @@ kubectl scale deployment monitoring -n bakery-ia --replicas=0
 - **RBAC Implementation:** [rbac-implementation.md](./rbac-implementation.md) - Access control
 **Monitoring Access:**
- **Grafana:** https://monitoring.bakewise.ai/grafana (admin / your-password)
+- **SigNoz (Primary):** https://monitoring.bakewise.ai/signoz - All-in-one observability
- **Prometheus:** https://monitoring.bakewise.ai/prometheus
+  - Services: Application performance monitoring (APM)
- **SigNoz:** https://monitoring.bakewise.ai/signoz
+  - Traces: Distributed tracing across all services
- **AlertManager:** https://monitoring.bakewise.ai/alertmanager
+  - Dashboards: PostgreSQL, Redis, RabbitMQ, Kubernetes metrics
  - Logs: Centralized log management with trace correlation
  - Alerts: Alert configuration and management
 - **AlertManager:** https://monitoring.bakewise.ai/alertmanager - Alert routing and notifications
 **External Resources:**
 - **MicroK8s Docs:** https://microk8s.io/docs
 - **Kubernetes Docs:** https://kubernetes.io/docs
 - **Let's Encrypt:** https://letsencrypt.org/docs
 - **Cloudflare DNS:** https://developers.cloudflare.com/dns
- **Monitoring Stack README:** infrastructure/kubernetes/base/components/monitoring/README.md
+- **SigNoz Documentation:** https://signoz.io/docs/
 - **OpenTelemetry Documentation:** https://opentelemetry.io/docs/
 **Monitoring Architecture:**
 - **OpenTelemetry:** Industry-standard instrumentation framework
  - Auto-instruments FastAPI, HTTPX, SQLAlchemy, Redis
  - Collects traces, metrics, and logs from all services
  - Exports to SigNoz via OTLP protocol (gRPC port 4317, HTTP port 4318)
 - **SigNoz Components:**
  - **Frontend:** Web UI for visualization and analysis
  - **OTel Collector:** Receives and processes telemetry data
  - **ClickHouse:** Time-series database for fast queries
  - **AlertManager:** Alert routing and notification delivery
  - **Zookeeper:** Coordination service for ClickHouse cluster
 ---
--- a/docs/PRODUCTION_OPERATIONS_GUIDE.md
+++ b/docs/PRODUCTION_OPERATIONS_GUIDE.md
@@ -60,84 +60,129 @@
 **Production URLs:**
 ```
-https://monitoring.bakewise.ai/grafana       # Dashboards & visualization
+https://monitoring.bakewise.ai/signoz        # SigNoz - Unified observability (PRIMARY)
-https://monitoring.bakewise.ai/prometheus    # Metrics & alerts
+https://monitoring.bakewise.ai/alertmanager  # AlertManager - Alert management
 https://monitoring.bakewise.ai/alertmanager  # Alert management
 https://monitoring.bakewise.ai/signoz        # Unified observability platform (traces, metrics, logs)
 ```
 **What is SigNoz?**
 SigNoz is a comprehensive, open-source observability platform that provides:
 - **Distributed Tracing** - End-to-end request tracking across all microservices
 - **Metrics Monitoring** - Application and infrastructure metrics
 - **Log Management** - Centralized log aggregation with trace correlation
 - **Service Performance Monitoring (SPM)** - RED metrics (Rate, Error, Duration) from traces
 - **Database Monitoring** - All 18 PostgreSQL databases + Redis + RabbitMQ
 - **Kubernetes Monitoring** - Cluster, node, pod, and container metrics
 **Port Forwarding (if ingress not available):**
 ```bash
-# Grafana
+# SigNoz Frontend (Main UI)
-kubectl port-forward -n monitoring svc/grafana 3000:3000
+kubectl port-forward -n bakery-ia svc/signoz 8080:8080
-# Prometheus
+# SigNoz AlertManager
-kubectl port-forward -n monitoring svc/prometheus-external 9090:9090
+kubectl port-forward -n bakery-ia svc/signoz-alertmanager 9093:9093
-# AlertManager
+# OTel Collector (for debugging)
-kubectl port-forward -n monitoring svc/alertmanager-external 9093:9093
+kubectl port-forward -n bakery-ia svc/signoz-otel-collector 4317:4317  # gRPC
-
+kubectl port-forward -n bakery-ia svc/signoz-otel-collector 4318:4318  # HTTP
 # SigNoz
 kubectl port-forward -n monitoring svc/signoz-frontend 3301:3301
 ```
-### Key Dashboards
+### Key SigNoz Dashboards and Features
-#### 1. Services Overview Dashboard
+#### 1. Services Tab - APM Overview
 **What to Monitor:**
- Request rate per service
+- **Service List** - All 18 microservices with health status
- Error rate (aim: <1%)
+- **Request Rate** - Requests per second per service
- P95/P99 latency (aim: <2s)
+- **Error Rate** - Percentage of failed requests (aim: <1%)
- Active connections
+- **P50/P90/P99 Latency** - Response time percentiles (aim: P99 <2s)
- Pod health status
+- **Operations** - Breakdown by endpoint/operation
 **Red Flags:**
- ❌ Error rate >5%
+- ❌ Error rate >5% sustained
- ❌ P95 latency >3s
+- ❌ P99 latency >3s
- ❌ Any service showing 0 requests (might be down)
+- ❌ Sudden drop in request rate (service might be down)
- ❌ Pod restarts >3 in last hour
+- ❌ High latency on specific endpoints
-#### 2. Database Dashboard (PostgreSQL)
+**How to Access:**
 - Navigate to `Services` tab in SigNoz
 - Click on any service for detailed metrics
 - Use "Traces" tab to see sample requests
 #### 2. Traces Tab - Distributed Tracing
 **What to Monitor:**
- Active connections per database
+- **End-to-end request flows** across microservices
- Cache hit ratio (aim: >90%)
+- **Span duration** - Time spent in each service
- Query duration (P95)
+- **Database query performance** - Auto-captured from SQLAlchemy
- Transaction rate
+- **External API calls** - Auto-captured from HTTPX
- Replication lag (if applicable)
+- **Error traces** - Requests that failed with stack traces
 **Features:**
 - Filter by service, operation, status code, duration
 - Search by trace ID or span ID
 - Correlate traces with logs
 - Identify slow database queries and N+1 problems
 **Red Flags:**
- ❌ Connection count >80% of max
+- ❌ Traces showing >10 database queries per request (N+1 issue)
- ❌ Cache hit ratio <80%
+- ❌ External API calls taking >1s
- ❌ Slow queries >1s frequently
+- ❌ Services with >500ms internal processing time
- ❌ Locks increasing
+- ❌ Error spans with exceptions
-#### 3. Node Exporter (Infrastructure)
+#### 3. Dashboards Tab - Infrastructure Metrics
-**What to Monitor:**
+**Pre-built Dashboards:**
- CPU usage per node
+- **PostgreSQL Monitoring** - All 18 databases
- Memory usage and swap
+  - Active connections, transactions/sec, cache hit ratio
- Disk I/O and latency
+  - Slow queries, lock waits, replication lag
- Network throughput
+  - Database size, disk I/O
- Disk space remaining
+- **Redis Monitoring** - Cache performance
  - Memory usage, hit rate, evictions
  - Commands/sec, latency
 - **RabbitMQ Monitoring** - Message queue health
  - Queue depth, message rates
  - Consumer status, connections
 - **Kubernetes Cluster** - Node and pod metrics
  - CPU, memory, disk, network per node
  - Pod resource utilization
  - Container restarts and OOM kills
 **Red Flags:**
- ❌ CPU usage >85% sustained
+- ❌ PostgreSQL: Cache hit ratio <80%, active connections >80% of max
- ❌ Memory usage >90%
+- ❌ Redis: Memory >90%, evictions increasing
- ❌ Swap usage >0 (indicates memory pressure)
+- ❌ RabbitMQ: Queue depth growing, no consumers
- ❌ Disk space <20% remaining
+- ❌ Kubernetes: CPU >85%, memory >90%, disk <20% free
- ❌ Disk I/O latency >100ms
+
 #### 4. Logs Tab - Centralized Logging
 **Features:**
 - **Unified logs** from all 18 microservices + databases
 - **Trace correlation** - Click on trace ID to see related logs
 - **Kubernetes metadata** - Auto-tagged with pod, namespace, container
 - **Search and filter** - By service, severity, time range, content
 - **Log patterns** - Automatically detect common patterns
 #### 4. Business Metrics Dashboard
 **What to Monitor:**
- Active tenants
+- Error and warning logs across all services
- ML training jobs (success/failure rate)
+- Database connection errors
- Forecast requests per hour
+- Authentication failures
- Alert volume
+- API request/response logs
 - API health score
 **Red Flags:**
- ❌ Training failure rate >10%
+- ❌ Increasing error logs
- ❌ No forecast requests (might indicate issue)
+- ❌ Repeated "connection refused" or "timeout" messages
- ❌ Alert volume spike (investigate cause)
+- ❌ Authentication failures (potential security issue)
 - ❌ Out of memory errors
 #### 5. Alerts Tab - Alert Management
 **Features:**
 - Create alerts based on metrics, traces, or logs
 - Configure notification channels (email, Slack, webhook)
 - View firing alerts and alert history
 - Alert silencing and acknowledgment
 **Pre-configured Alerts (see SigNoz):**
 - High error rate (>5% for 5 minutes)
 - High latency (P99 >3s for 5 minutes)
 - Service down (no requests for 2 minutes)
 - Database connection errors
 - High memory/CPU usage
 ### Alert Severity Levels
@@ -195,7 +240,35 @@ Response:
 3. See "Certificate Rotation" section below
 ```
-### Metrics to Track Daily
+### Daily Monitoring Workflow with SigNoz
 #### Morning Health Check (5 minutes)
 1. **Open SigNoz Dashboard**
   ```
   https://monitoring.bakewise.ai/signoz
   ```
 2. **Check Services Tab:**
   - Verify all 18 services are reporting metrics
   - Check error rate <1% for all services
   - Check P99 latency <2s for critical services
 3. **Check Alerts Tab:**
   - Review any firing alerts
   - Check for patterns (repeated alerts on same service)
   - Acknowledge or resolve as needed
 4. **Quick Infrastructure Check:**
   - Navigate to Dashboards → PostgreSQL
     - Verify all 18 databases are up
     - Check connection counts are healthy
   - Navigate to Dashboards → Redis
     - Check memory usage <80%
   - Navigate to Dashboards → Kubernetes
     - Verify node health, no OOM kills
 #### Command-Line Health Check (Alternative)
 ```bash
 # Quick health check command
@@ -211,19 +284,19 @@ echo ""
 echo "2. Resource Usage:"
 kubectl top nodes
 kubectl top pods -n bakery-ia --sort-by=memory | head -10
 echo ""
-echo "3. Database Connections:"
+echo "3. SigNoz Components:"
-kubectl exec -n bakery-ia deployment/auth-db -- psql -U postgres -c \
+kubectl get pods -n bakery-ia -l app.kubernetes.io/instance=signoz
  "SELECT count(*) as connections FROM pg_stat_activity;"
 echo ""
-echo "4. Recent Alerts:"
+echo "4. Recent Alerts (from SigNoz AlertManager):"
-curl -s http://localhost:9090/api/v1/alerts | jq '.data.alerts[] | {alert: .labels.alertname, state: .state}' | head -10
+curl -s http://localhost:9093/api/v1/alerts 2>/dev/null | jq '.data[] | select(.status.state=="firing") | {alert: .labels.alertname, severity: .labels.severity}' | head -10
 echo ""
-echo "5. Disk Usage:"
+echo "5. OTel Collector Health:"
-kubectl exec -n bakery-ia deployment/auth-db -- df -h /var/lib/postgresql/data
+kubectl exec -n bakery-ia deployment/signoz-otel-collector -- wget -qO- http://localhost:13133 2>/dev/null || echo "✅ Health check endpoint responding"
 echo ""
 echo "=== End Health Check ==="
@@ -233,6 +306,38 @@ chmod +x ~/health-check.sh
 ./health-check.sh
 ```
 #### Troubleshooting Common Issues
 **Issue: Service not showing in SigNoz**
 ```bash
 # Check if service is sending telemetry
 kubectl logs -n bakery-ia deployment/SERVICE_NAME | grep -i "telemetry\|otel\|signoz"
 # Check OTel Collector is receiving data
 kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep SERVICE_NAME
 # Verify service has proper OTEL endpoints configured
 kubectl exec -n bakery-ia deployment/SERVICE_NAME -- env | grep OTEL
 ```
 **Issue: No traces appearing**
 ```bash
 # Check tracing is enabled in service
 kubectl exec -n bakery-ia deployment/SERVICE_NAME -- env | grep ENABLE_TRACING
 # Verify OTel Collector gRPC endpoint is reachable
 kubectl exec -n bakery-ia deployment/SERVICE_NAME -- nc -zv signoz-otel-collector 4317
 ```
 **Issue: Logs not appearing**
 ```bash
 # Check filelog receiver is working
 kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep filelog
 # Check k8sattributes processor
 kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep k8sattributes
 ```
 ---
 ## Security Operations
--- a/docs/SIGNOZ_COMPLETE_CONFIGURATION_GUIDE.md
+++ b/docs/SIGNOZ_COMPLETE_CONFIGURATION_GUIDE.md
@@ -1,518 +0,0 @@
 # SigNoz Complete Configuration Guide
 ## Root Cause Analysis and Solutions
 This document provides a comprehensive analysis of the SigNoz telemetry collection issues and the proper configuration for all receivers.
 ---
 ## Problem 1: OpAMP Configuration Corruption
 ### Root Cause
 **What is OpAMP?**
 [OpAMP (Open Agent Management Protocol)](https://signoz.io/docs/operate/configuration/) is a protocol for remote configuration management in OpenTelemetry Collectors. In SigNoz, OpAMP runs a server that dynamically configures log pipelines in the SigNoz OTel collector.
 **The Issue:**
 - OpAMP was successfully connecting to the SigNoz backend and receiving remote configuration
 - The remote configuration contained only `nop` (no-operation) receivers and exporters
 - This overwrote the local collector configuration at runtime
 - Result: The collector appeared healthy but couldn't receive or export any data
 **Why This Happened:**
 1. The SigNoz backend's OpAMP server was pushing an invalid/incomplete configuration
 2. The collector's `--manager-config` flag pointed to OpAMP configuration
 3. OpAMP's `--copy-path=/var/tmp/collector-config.yaml` overwrote the good config
 ### Solution Options
 #### Option 1: Disable OpAMP (Current Solution)
 Since OpAMP is pushing bad configuration and we have a working static configuration, we disabled it:
 ```bash
 kubectl patch deployment -n bakery-ia signoz-otel-collector --type=json -p='[
  {
    "op": "replace",
    "path": "/spec/template/spec/containers/0/args",
    "value": [
      "--config=/conf/otel-collector-config.yaml",
      "--feature-gates=-pkg.translator.prometheus.NormalizeName"
    ]
  }
 ]'
 ```
 **Important:** This patch must be applied after every `helm install` or `helm upgrade` because the Helm chart doesn't support disabling OpAMP via values.
 #### Option 2: Fix OpAMP Configuration (Recommended for Production)
 To properly use OpAMP:
 1. **Check SigNoz Backend Configuration:**
   - Verify the SigNoz service is properly configured to serve OpAMP
   - Check logs: `kubectl logs -n bakery-ia statefulset/signoz`
   - Look for OpAMP-related errors
 2. **Configure OpAMP Server Settings:**
   According to [SigNoz configuration documentation](https://signoz.io/docs/operate/configuration/), set these environment variables in the SigNoz statefulset:
   ```yaml
   signoz:
     env:
       OPAMP_ENABLED: "true"
       OPAMP_SERVER_ENDPOINT: "ws://signoz:4320/v1/opamp"
   ```
 3. **Verify OpAMP Configuration File:**
   ```bash
   kubectl get configmap -n bakery-ia signoz-otel-collector -o yaml
   ```
   Should contain:
   ```yaml
   otel-collector-opamp-config.yaml: |
     server_endpoint: "ws://signoz:4320/v1/opamp"
   ```
 4. **Monitor OpAMP Status:**
   ```bash
   kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep opamp
   ```
 ### References
 - [SigNoz Architecture](https://signoz.io/docs/architecture/)
 - [OpenTelemetry Collector Configuration](https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/configuration/)
 - [SigNoz Helm Chart](https://github.com/SigNoz/charts)
 ---
 ## Problem 2: Database and Infrastructure Receivers Configuration
 ### Overview
 You have the following infrastructure requiring monitoring:
 - **21 PostgreSQL databases** (auth, inventory, orders, forecasting, production, etc.)
 - **1 Redis instance** (caching layer)
 - **1 RabbitMQ instance** (message queue)
 All receivers were disabled because they lacked proper credentials and configuration.
 ---
 ## PostgreSQL Receiver Configuration
 ### Prerequisites
 Based on [SigNoz PostgreSQL Integration Guide](https://signoz.io/docs/integrations/postgresql/), each PostgreSQL instance needs a monitoring user with proper permissions.
 ### Step 1: Create Monitoring Users
 For each PostgreSQL database, create a dedicated monitoring user:
 **For PostgreSQL 10 and newer:**
 ```sql
 CREATE USER monitoring WITH PASSWORD 'your_secure_password';
 GRANT pg_monitor TO monitoring;
 GRANT SELECT ON pg_stat_database TO monitoring;
 ```
 **For PostgreSQL 9.6 to 9.x:**
 ```sql
 CREATE USER monitoring WITH PASSWORD 'your_secure_password';
 GRANT SELECT ON pg_stat_database TO monitoring;
 ```
 ### Step 2: Create Monitoring User for All Databases
 Run this script to create monitoring users in all PostgreSQL databases:
 ```bash
 #!/bin/bash
 # File: infrastructure/scripts/create-pg-monitoring-users.sh
 DATABASES=(
  "auth-db"
  "inventory-db"
  "orders-db"
  "ai-insights-db"
  "alert-processor-db"
  "demo-session-db"
  "distribution-db"
  "external-db"
  "forecasting-db"
  "notification-db"
  "orchestrator-db"
  "pos-db"
  "procurement-db"
  "production-db"
  "recipes-db"
  "sales-db"
  "suppliers-db"
  "tenant-db"
  "training-db"
 )
 MONITORING_PASSWORD="monitoring_secure_pass_$(openssl rand -hex 16)"
 echo "Creating monitoring users with password: $MONITORING_PASSWORD"
 echo "Save this password for your SigNoz configuration!"
 for db in "${DATABASES[@]}"; do
  echo "Processing $db..."
  kubectl exec -n bakery-ia deployment/$db -- psql -U postgres -c "
    CREATE USER monitoring WITH PASSWORD '$MONITORING_PASSWORD';
    GRANT pg_monitor TO monitoring;
    GRANT SELECT ON pg_stat_database TO monitoring;
  " 2>&1 | grep -v "already exists" || true
 done
 echo ""
 echo "Monitoring users created!"
 echo "Password: $MONITORING_PASSWORD"
 ```
 ### Step 3: Store Credentials in Kubernetes Secret
 ```bash
 kubectl create secret generic -n bakery-ia postgres-monitoring-secrets \
  --from-literal=POSTGRES_MONITOR_USER=monitoring \
  --from-literal=POSTGRES_MONITOR_PASSWORD=<password-from-script>
 ```
 ### Step 4: Configure PostgreSQL Receivers in SigNoz
 Update `infrastructure/helm/signoz-values-dev.yaml`:
 ```yaml
 otelCollector:
  config:
    receivers:
      # PostgreSQL receivers for database metrics
      postgresql/auth:
        endpoint: auth-db-service.bakery-ia:5432
        username: ${env:POSTGRES_MONITOR_USER}
        password: ${env:POSTGRES_MONITOR_PASSWORD}
        databases:
          - auth_db
        collection_interval: 60s
        tls:
          insecure: true  # Set to false if using TLS
      postgresql/inventory:
        endpoint: inventory-db-service.bakery-ia:5432
        username: ${env:POSTGRES_MONITOR_USER}
        password: ${env:POSTGRES_MONITOR_PASSWORD}
        databases:
          - inventory_db
        collection_interval: 60s
        tls:
          insecure: true
      # Add all other databases...
      postgresql/orders:
        endpoint: orders-db-service.bakery-ia:5432
        username: ${env:POSTGRES_MONITOR_USER}
        password: ${env:POSTGRES_MONITOR_PASSWORD}
        databases:
          - orders_db
        collection_interval: 60s
        tls:
          insecure: true
    # Update metrics pipeline
    service:
      pipelines:
        metrics:
          receivers:
            - otlp
            - postgresql/auth
            - postgresql/inventory
            - postgresql/orders
            # Add all PostgreSQL receivers
          processors: [memory_limiter, batch, resourcedetection]
          exporters: [signozclickhousemetrics]
 ```
 ### Step 5: Add Environment Variables to OTel Collector Deployment
 The Helm chart needs to inject these environment variables. Modify your Helm values:
 ```yaml
 otelCollector:
  env:
    - name: POSTGRES_MONITOR_USER
      valueFrom:
        secretKeyRef:
          name: postgres-monitoring-secrets
          key: POSTGRES_MONITOR_USER
    - name: POSTGRES_MONITOR_PASSWORD
      valueFrom:
        secretKeyRef:
          name: postgres-monitoring-secrets
          key: POSTGRES_MONITOR_PASSWORD
 ```
 ### References
 - [PostgreSQL Monitoring with OpenTelemetry | SigNoz](https://signoz.io/blog/opentelemetry-postgresql-metrics-monitoring/)
 - [PostgreSQL Integration | SigNoz](https://signoz.io/docs/integrations/postgresql/)
 ---
 ## Redis Receiver Configuration
 ### Current Infrastructure
 - **Service**: `redis-service.bakery-ia:6379`
 - **Password**: Available in secret `redis-secrets`
 - **TLS**: Currently not configured
 ### Step 1: Check if Redis Requires TLS
 ```bash
 kubectl exec -n bakery-ia deployment/redis -- redis-cli CONFIG GET tls-port
 ```
 If TLS is not configured (tls-port is 0 or empty), you can use `insecure: true`.
 ### Step 2: Configure Redis Receiver
 Update `infrastructure/helm/signoz-values-dev.yaml`:
 ```yaml
 otelCollector:
  config:
    receivers:
      # Redis receiver for cache metrics
      redis:
        endpoint: redis-service.bakery-ia:6379
        password: ${env:REDIS_PASSWORD}
        collection_interval: 60s
        transport: tcp
        tls:
          insecure: true  # Change to false if using TLS
        metrics:
          redis.maxmemory:
            enabled: true
          redis.cmd.latency:
            enabled: true
  env:
    - name: REDIS_PASSWORD
      valueFrom:
        secretKeyRef:
          name: redis-secrets
          key: REDIS_PASSWORD
    service:
      pipelines:
        metrics:
          receivers: [otlp, redis, ...]
 ```
 ### Optional: Configure TLS for Redis
 If you want to enable TLS for Redis (recommended for production):
 1. **Generate TLS Certificates:**
 ```bash
 # Create CA
 openssl genrsa -out ca-key.pem 4096
 openssl req -new -x509 -days 3650 -key ca-key.pem -out ca-cert.pem
 # Create Redis server certificate
 openssl genrsa -out redis-key.pem 4096
 openssl req -new -key redis-key.pem -out redis.csr
 openssl x509 -req -days 3650 -in redis.csr -CA ca-cert.pem -CAkey ca-key.pem -CAcreateserial -out redis-cert.pem
 # Create Kubernetes secret
 kubectl create secret generic -n bakery-ia redis-tls \
  --from-file=ca-cert.pem=ca-cert.pem \
  --from-file=redis-cert.pem=redis-cert.pem \
  --from-file=redis-key.pem=redis-key.pem
 ```
 2. **Mount Certificates in OTel Collector:**
 ```yaml
 otelCollector:
  volumes:
    - name: redis-tls
      secret:
        secretName: redis-tls
  volumeMounts:
    - name: redis-tls
      mountPath: /etc/redis-tls
      readOnly: true
  config:
    receivers:
      redis:
        tls:
          insecure: false
          cert_file: /etc/redis-tls/redis-cert.pem
          key_file: /etc/redis-tls/redis-key.pem
          ca_file: /etc/redis-tls/ca-cert.pem
 ```
 ### References
 - [Redis Monitoring with OpenTelemetry | SigNoz](https://signoz.io/blog/redis-opentelemetry/)
 - [Redis Monitoring 101 | SigNoz](https://signoz.io/blog/redis-monitoring/)
 ---
 ## RabbitMQ Receiver Configuration
 ### Current Infrastructure
 - **Service**: `rabbitmq-service.bakery-ia`
  - Port 5672: AMQP protocol
  - Port 15672: Management API (required for metrics)
 - **Credentials**:
  - Username: `bakery`
  - Password: Available in secret `rabbitmq-secrets`
 ### Step 1: Enable RabbitMQ Management Plugin
 ```bash
 kubectl exec -n bakery-ia deployment/rabbitmq -- rabbitmq-plugins enable rabbitmq_management
 ```
 ### Step 2: Verify Management API Access
 ```bash
 kubectl port-forward -n bakery-ia svc/rabbitmq-service 15672:15672
 # In browser: http://localhost:15672
 # Login with: bakery / <password>
 ```
 ### Step 3: Configure RabbitMQ Receiver
 Update `infrastructure/helm/signoz-values-dev.yaml`:
 ```yaml
 otelCollector:
  config:
    receivers:
      # RabbitMQ receiver via management API
      rabbitmq:
        endpoint: http://rabbitmq-service.bakery-ia:15672
        username: ${env:RABBITMQ_USER}
        password: ${env:RABBITMQ_PASSWORD}
        collection_interval: 30s
  env:
    - name: RABBITMQ_USER
      valueFrom:
        secretKeyRef:
          name: rabbitmq-secrets
          key: RABBITMQ_USER
    - name: RABBITMQ_PASSWORD
      valueFrom:
        secretKeyRef:
          name: rabbitmq-secrets
          key: RABBITMQ_PASSWORD
    service:
      pipelines:
        metrics:
          receivers: [otlp, rabbitmq, ...]
 ```
 ### References
 - [RabbitMQ Monitoring with OpenTelemetry | SigNoz](https://signoz.io/blog/opentelemetry-rabbitmq-metrics-monitoring/)
 - [OpenTelemetry Receivers | SigNoz](https://signoz.io/docs/userguide/otel-metrics-receivers/)
 ---
 ## Complete Implementation Plan
 ### Phase 1: Enable Basic Infrastructure Monitoring (No TLS)
 1. **Create PostgreSQL monitoring users** (all 21 databases)
 2. **Create Kubernetes secrets** for credentials
 3. **Update Helm values** with receiver configurations
 4. **Configure environment variables** in OTel Collector
 5. **Apply Helm upgrade** and OpAMP patch
 6. **Verify metrics collection**
 ### Phase 2: Enable TLS (Optional, Production-Ready)
 1. **Generate TLS certificates** for Redis
 2. **Configure Redis TLS** in deployment
 3. **Update Redis receiver** with TLS settings
 4. **Configure PostgreSQL TLS** if required
 5. **Test and verify** secure connections
 ### Phase 3: Enable OpAMP (Optional, Advanced)
 1. **Fix SigNoz OpAMP server configuration**
 2. **Test remote configuration** in dev environment
 3. **Gradually enable** OpAMP after validation
 4. **Monitor** for configuration corruption
 ---
 ## Verification Commands
 ### Check Collector Metrics
 ```bash
 kubectl port-forward -n bakery-ia svc/signoz-otel-collector 8888:8888
 curl http://localhost:8888/metrics | grep "otelcol_receiver_accepted"
 ```
 ### Check Database Connectivity
 ```bash
 kubectl exec -n bakery-ia deployment/signoz-otel-collector -- \
  /bin/sh -c "nc -zv auth-db-service 5432"
 ```
 ### Check RabbitMQ Management API
 ```bash
 kubectl exec -n bakery-ia deployment/signoz-otel-collector -- \
  /bin/sh -c "wget -O- http://rabbitmq-service:15672/api/overview"
 ```
 ### Check Redis Connectivity
 ```bash
 kubectl exec -n bakery-ia deployment/signoz-otel-collector -- \
  /bin/sh -c "nc -zv redis-service 6379"
 ```
 ---
 ## Troubleshooting
 ### PostgreSQL Connection Refused
 - Verify monitoring user exists: `kubectl exec deployment/auth-db -- psql -U postgres -c "\du"`
 - Check user permissions: `kubectl exec deployment/auth-db -- psql -U monitoring -c "SELECT 1"`
 ### Redis Authentication Failed
 - Verify password: `kubectl get secret redis-secrets -o jsonpath='{.data.REDIS_PASSWORD}' | base64 -d`
 - Test connection: `kubectl exec deployment/redis -- redis-cli -a <password> PING`
 ### RabbitMQ Management API Not Available
 - Check plugin status: `kubectl exec deployment/rabbitmq -- rabbitmq-plugins list`
 - Enable plugin: `kubectl exec deployment/rabbitmq -- rabbitmq-plugins enable rabbitmq_management`
 ---
 ## Summary
 **Current Status:**
 - ✅ OTel Collector receiving traces (97+ spans)
 - ✅ ClickHouse authentication fixed
 - ✅ OpAMP disabled (preventing config corruption)
 - ❌ PostgreSQL receivers not configured (no monitoring users)
 - ❌ Redis receiver not configured (missing in pipeline)
 - ❌ RabbitMQ receiver not configured (missing in pipeline)
 **Next Steps:**
 1. Create PostgreSQL monitoring users across all 21 databases
 2. Configure Redis receiver with existing credentials
 3. Configure RabbitMQ receiver with existing credentials
 4. Test and verify all metrics are flowing
 5. Optionally enable TLS for production
 6. Optionally fix and re-enable OpAMP for dynamic configuration
--- a/docs/SIGNOZ_ROOT_CAUSE_ANALYSIS.md
+++ b/docs/SIGNOZ_ROOT_CAUSE_ANALYSIS.md
@@ -1,289 +0,0 @@
 # SigNoz OpenAMP Root Cause Analysis & Resolution
 ## Problem Statement
 Services were getting `StatusCode.UNAVAILABLE` errors when trying to send traces to the SigNoz OTel Collector at port 4317. The OTel Collector was continuously restarting due to OpenAMP trying to apply invalid remote configurations.
 ## Root Cause Analysis
 ### Primary Issue: Missing `signozmeter` Connector Pipeline
 **Error Message:**
 ```
 connector "signozmeter" used as receiver in [metrics/meter] pipeline
 but not used in any supported exporter pipeline
 ```
 **Root Cause:**
 The OpenAMP server was pushing a remote configuration that included:
 1. A `metrics/meter` pipeline that uses `signozmeter` as a receiver
 2. However, no pipeline was exporting TO the `signozmeter` connector
 **Technical Explanation:**
 - **Connectors** in OpenTelemetry are special components that act as BOTH exporters AND receivers
 - They bridge between pipelines (e.g., traces → metrics)
 - The `signozmeter` connector generates usage/meter metrics from trace data
 - For a connector to work, it must be:
  1. Used as an **exporter** in one pipeline (the source)
  2. Used as a **receiver** in another pipeline (the destination)
 **What Was Missing:**
 Our configuration had:
 - ✅ `signozmeter` connector defined
 - ✅ `metrics/meter` pipeline receiving from `signozmeter`
 - ❌ **No pipeline exporting TO `signozmeter`**
 The traces pipeline needed to export to `signozmeter`:
 ```yaml
 traces:
  receivers: [otlp]
  processors: [...]
  exporters: [clickhousetraces, metadataexporter, signozmeter]  # <-- signozmeter was missing
 ```
 ### Secondary Issue: gRPC Endpoint Format
 **Problem:** Services had `http://` prefix in gRPC endpoints
 **Solution:** Removed `http://` prefix (gRPC doesn't use HTTP protocol prefix)
 **Before:**
 ```yaml
 OTEL_EXPORTER_OTLP_ENDPOINT: "http://signoz-otel-collector.bakery-ia.svc.cluster.local:4317"
 ```
 **After:**
 ```yaml
 OTEL_EXPORTER_OTLP_ENDPOINT: "signoz-otel-collector.bakery-ia.svc.cluster.local:4317"
 ```
 ### Tertiary Issue: Hardcoded Endpoints
 **Problem:** Each service manifest had hardcoded OTEL endpoints instead of referencing ConfigMap
 **Solution:** Updated all 18 services to use `valueFrom: configMapKeyRef`
 ## Solution Implemented
 ### 1. Added Complete Meter Pipeline Configuration
 **Added Connector:**
 ```yaml
 connectors:
  signozmeter:
    dimensions:
      - name: service.name
      - name: deployment.environment
      - name: host.name
    metrics_flush_interval: 1h
 ```
 **Added Batch Processor:**
 ```yaml
 processors:
  batch/meter:
    timeout: 1s
    send_batch_size: 20000
    send_batch_max_size: 25000
 ```
 **Added Exporters:**
 ```yaml
 exporters:
  # Meter exporter
  signozclickhousemeter:
    dsn: "tcp://admin:PASSWORD@signoz-clickhouse:9000/signoz_meter"
    timeout: 45s
    sending_queue:
      enabled: false
  # Metadata exporter
  metadataexporter:
    dsn: "tcp://admin:PASSWORD@signoz-clickhouse:9000/signoz_metadata"
    timeout: 10s
    cache:
      provider: in_memory
 ```
 **Updated Traces Pipeline:**
 ```yaml
 traces:
  receivers: [otlp]
  processors: [memory_limiter, batch, signozspanmetrics/delta, resourcedetection]
  exporters: [clickhousetraces, metadataexporter, signozmeter]  # Added signozmeter
 ```
 **Added Meter Pipeline:**
 ```yaml
 metrics/meter:
  receivers: [signozmeter]
  processors: [batch/meter]
  exporters: [signozclickhousemeter]
 ```
 ### 2. Fixed gRPC Endpoint Configuration
 Updated ConfigMaps:
 - `infrastructure/kubernetes/base/configmap.yaml`
 - `infrastructure/kubernetes/overlays/prod/prod-configmap.yaml`
 ### 3. Centralized OTEL Configuration
 Created script: `infrastructure/kubernetes/fix-otel-endpoints.sh`
 Updated 18 service manifests to use ConfigMap reference instead of hardcoded values.
 ## Results
 ### Before Fix
 - ❌ OTel Collector continuously restarting
 - ❌ Services unable to export traces (StatusCode.UNAVAILABLE)
 - ❌ Error: `connector "signozmeter" used as receiver but not used in any supported exporter pipeline`
 - ❌ OpenAMP constantly trying to reload bad config
 ### After Fix
 - ✅ OTel Collector stable and running
 - ✅ Message: `"Everything is ready. Begin running and processing data."`
 - ✅ No more signozmeter connector errors
 - ✅ OpenAMP errors are now just warnings (remote server issues, not local config)
 - ⚠️  Service connectivity still showing transient errors (separate investigation needed)
 ## OpenAMP Behavior
 **What is OpenAMP?**
 - OpenTelemetry Agent Management Protocol
 - Allows remote management and configuration of collectors
 - SigNoz uses it for central configuration management
 **Current State:**
 - OpenAMP continues to show errors, but they're now **non-fatal**
 - The errors are from the remote OpAMP server (signoz:4320), not local config
 - Local configuration is valid and working
 - Collector is stable and processing data
 **OpenAMP Error Pattern:**
 ```
 [ERROR] opamp/server_client.go:146
 Server returned an error response
 ```
 This is a **warning** that the remote OpAMP server has configuration issues, but it doesn't affect the locally-configured collector.
 ## Files Modified
 ### Helm Values
 1. `infrastructure/helm/signoz-values-dev.yaml`
   - Added connectors section
   - Added batch/meter processor
   - Added signozclickhousemeter exporter
   - Added metadataexporter
   - Updated traces pipeline to export to signozmeter
   - Added metrics/meter pipeline
 2. `infrastructure/helm/signoz-values-prod.yaml`
   - Same changes as dev
 ### ConfigMaps
 3. `infrastructure/kubernetes/base/configmap.yaml`
   - Fixed OTEL_EXPORTER_OTLP_ENDPOINT (removed http://)
 4. `infrastructure/kubernetes/overlays/prod/prod-configmap.yaml`
   - Fixed OTEL_EXPORTER_OTLP_ENDPOINT (removed http://)
 ### Service Manifests (18 files)
 All services in `infrastructure/kubernetes/base/components/*/` changed from:
 ```yaml
 - name: OTEL_EXPORTER_OTLP_ENDPOINT
  value: "http://..."
 ```
 To:
 ```yaml
 - name: OTEL_EXPORTER_OTLP_ENDPOINT
  valueFrom:
    configMapKeyRef:
      name: bakery-config
      key: OTEL_EXPORTER_OTLP_ENDPOINT
 ```
 ## Verification Commands
 ```bash
 # 1. Check OTel Collector is stable
 kubectl get pods -n bakery-ia | grep otel-collector
 # Should show: 1/1 Running
 # 2. Check for configuration errors
 kubectl logs -n bakery-ia deployment/signoz-otel-collector --tail=50 | grep -E "failed to apply config|signozmeter"
 # Should show: NO errors about signozmeter
 # 3. Verify collector is ready
 kubectl logs -n bakery-ia deployment/signoz-otel-collector | grep "Everything is ready"
 # Should show: "Everything is ready. Begin running and processing data."
 # 4. Check service configuration
 kubectl get configmap bakery-config -n bakery-ia -o jsonpath='{.data.OTEL_EXPORTER_OTLP_ENDPOINT}'
 # Should show: signoz-otel-collector.bakery-ia.svc.cluster.local:4317 (no http://)
 # 5. Verify service is using ConfigMap
 kubectl get deployment gateway -n bakery-ia -o yaml | grep -A 5 "OTEL_EXPORTER"
 # Should show: valueFrom / configMapKeyRef
 # 6. Run verification script
 ./infrastructure/helm/verify-signoz-telemetry.sh
 ```
 ## Next Steps
 ### Immediate
 1. ✅ OTel Collector is stable with OpenAMP enabled
 2. ⏭️ Investigate remaining service connectivity issues
 3. ⏭️ Generate test traffic and verify data collection
 4. ⏭️ Check ClickHouse for traces/metrics/logs
 ### Short-term
 1. Monitor OpenAMP errors - they're warnings, not blocking
 2. Consider contacting SigNoz about OpAMP server configuration
 3. Set up SigNoz dashboards and alerts
 4. Document common queries
 ### Long-term
 1. Evaluate if OpAMP remote management is needed
 2. Consider HTTP exporter as alternative to gRPC
 3. Implement service mesh if connectivity issues persist
 4. Set up proper TLS for production
 ## Key Learnings
 ### About OpenTelemetry Connectors
 - Connectors must be used in BOTH directions
 - Source pipeline must export TO the connector
 - Destination pipeline must receive FROM the connector
 - Missing either direction causes pipeline build failures
 ### About OpenAMP
 - OpenAMP can push remote configurations
 - Local config takes precedence
 - Remote server errors don't prevent local operation
 - Collector continues with last known good config
 ### About gRPC Configuration
 - gRPC endpoints don't use `http://` or `https://` prefixes
 - Only use `hostname:port` format
 - HTTP/REST endpoints DO need the protocol prefix
 ### About Configuration Management
 - Centralize configuration in ConfigMaps
 - Use `valueFrom: configMapKeyRef` pattern
 - Single source of truth prevents drift
 - Makes updates easier across all services
 ## References
 - [SigNoz Helm Charts](https://github.com/SigNoz/charts)
 - [OpenTelemetry Connectors](https://opentelemetry.io/docs/collector/configuration/#connectors)
 - [OpAMP Specification](https://github.com/open-telemetry/opamp-spec)
 - [SigNoz OTel Collector](https://github.com/SigNoz/signoz-otel-collector)
 ---
 **Resolution Date:** 2026-01-09
 **Status:** ✅ Resolved - OTel Collector stable, OpenAMP functional
 **Remaining:** Service connectivity investigation ongoing
--- a/docs/SIGNOZ_VERIFICATION_GUIDE.md
+++ b/docs/SIGNOZ_VERIFICATION_GUIDE.md
@@ -1,435 +0,0 @@
 # SigNoz Telemetry Verification Guide
 ## Overview
 This guide explains how to verify that your services are correctly sending metrics, logs, and traces to SigNoz, and that SigNoz is collecting them properly.
 ## Current Configuration
 ### SigNoz Components
 - **Version**: v0.106.0
 - **OTel Collector**: v0.129.12
 - **Namespace**: `bakery-ia`
 - **Ingress URL**: https://monitoring.bakery-ia.local
 ### Telemetry Endpoints
 The OTel Collector exposes the following endpoints:
 | Protocol | Port | Purpose |
 |----------|------|---------|
 | OTLP gRPC | 4317 | Traces, Metrics, Logs (gRPC) |
 | OTLP HTTP | 4318 | Traces, Metrics, Logs (HTTP) |
 | Jaeger gRPC | 14250 | Jaeger traces (gRPC) |
 | Jaeger HTTP | 14268 | Jaeger traces (HTTP) |
 | Metrics | 8888 | Prometheus metrics from collector |
 | Health Check | 13133 | Collector health status |
 ### Service Configuration
 Services are configured via the `bakery-config` ConfigMap:
 ```yaml
 # Observability enabled
 ENABLE_TRACING: "true"
 ENABLE_METRICS: "true"
 ENABLE_LOGS: "true"
 # OTel Collector endpoint
 OTEL_EXPORTER_OTLP_ENDPOINT: "http://signoz-otel-collector.bakery-ia.svc.cluster.local:4317"
 OTEL_EXPORTER_OTLP_PROTOCOL: "grpc"
 ```
 ### Shared Tracing Library
 Services use `shared/monitoring/tracing.py` which:
 - Auto-instruments FastAPI endpoints
 - Auto-instruments HTTPX (inter-service calls)
 - Auto-instruments Redis operations
 - Auto-instruments SQLAlchemy (PostgreSQL)
 - Uses OTLP exporter to send traces to SigNoz
 **Default endpoint**: `http://signoz-otel-collector.bakery-ia:4318` (HTTP)
 ## Verification Steps
 ### 1. Quick Verification Script
 Run the automated verification script:
 ```bash
 ./infrastructure/helm/verify-signoz-telemetry.sh
 ```
 This script checks:
 - ✅ SigNoz components are running
 - ✅ OTel Collector endpoints are exposed
 - ✅ Configuration is correct
 - ✅ Health checks pass
 - ✅ Data is being collected in ClickHouse
 ### 2. Manual Verification
 #### Check SigNoz Components Status
 ```bash
 kubectl get pods -n bakery-ia | grep signoz
 ```
 Expected output:
 ```
 signoz-0                                      1/1     Running
 signoz-otel-collector-xxxxx                   1/1     Running
 chi-signoz-clickhouse-cluster-0-0-0           1/1     Running
 signoz-zookeeper-0                            1/1     Running
 signoz-clickhouse-operator-xxxxx              2/2     Running
 ```
 #### Check OTel Collector Logs
 ```bash
 kubectl logs -n bakery-ia -l app.kubernetes.io/component=otel-collector --tail=50
 ```
 Look for:
 - `"msg":"Everything is ready. Begin running and processing data."`
 - No error messages about invalid processors
 - Evidence of data reception (traces/metrics/logs)
 #### Check Service Logs for Tracing
 ```bash
 # Check a specific service (e.g., gateway)
 kubectl logs -n bakery-ia -l app=gateway --tail=100 | grep -i "tracing\|otel"
 ```
 Expected output:
 ```
 Distributed tracing configured
 service=gateway-service
 otel_endpoint=http://signoz-otel-collector.bakery-ia:4318
 ```
 ### 3. Generate Test Traffic
 Run the traffic generation script:
 ```bash
 ./infrastructure/helm/generate-test-traffic.sh
 ```
 This script:
 1. Makes API calls to various service endpoints
 2. Checks service logs for telemetry
 3. Waits for data processing (30 seconds)
 ### 4. Verify Data in ClickHouse
 ```bash
 # Get ClickHouse password
 CH_PASSWORD=$(kubectl get secret -n bakery-ia signoz-clickhouse -o jsonpath='{.data.admin-password}' 2>/dev/null | base64 -d)
 # Get ClickHouse pod
 CH_POD=$(kubectl get pods -n bakery-ia -l clickhouse.altinity.com/chi=signoz-clickhouse -o jsonpath='{.items[0].metadata.name}')
 # Check traces
 kubectl exec -n bakery-ia $CH_POD -- clickhouse-client --user=admin --password=$CH_PASSWORD --query="
 SELECT
    serviceName,
    COUNT() as trace_count,
    min(timestamp) as first_trace,
    max(timestamp) as last_trace
 FROM signoz_traces.signoz_index_v2
 WHERE timestamp >= now() - INTERVAL 1 HOUR
 GROUP BY serviceName
 ORDER BY trace_count DESC
 "
 # Check metrics
 kubectl exec -n bakery-ia $CH_POD -- clickhouse-client --user=admin --password=$CH_PASSWORD --query="
 SELECT
    metric_name,
    COUNT() as sample_count
 FROM signoz_metrics.samples_v4
 WHERE unix_milli >= toUnixTimestamp(now() - INTERVAL 1 HOUR) * 1000
 GROUP BY metric_name
 ORDER BY sample_count DESC
 LIMIT 10
 "
 # Check logs
 kubectl exec -n bakery-ia $CH_POD -- clickhouse-client --user=admin --password=$CH_PASSWORD --query="
 SELECT
    COUNT() as log_count,
    min(timestamp) as first_log,
    max(timestamp) as last_log
 FROM signoz_logs.logs
 WHERE timestamp >= now() - INTERVAL 1 HOUR
 "
 ```
 ### 5. Access SigNoz UI
 #### Via Ingress (Recommended)
 1. Add to `/etc/hosts`:
   ```
   127.0.0.1 monitoring.bakery-ia.local
   ```
 2. Access: https://monitoring.bakery-ia.local
 #### Via Port-Forward
 ```bash
 kubectl port-forward -n bakery-ia svc/signoz 3301:8080
 ```
 Then access: http://localhost:3301
 ### 6. Explore Telemetry Data in SigNoz UI
 1. **Traces**:
   - Go to "Services" tab
   - You should see your services listed (gateway, auth-service, inventory-service, etc.)
   - Click on a service to see its traces
   - Click on individual traces to see span details
 2. **Metrics**:
   - Go to "Dashboards" or "Metrics" tab
   - Should see infrastructure metrics (PostgreSQL, Redis, RabbitMQ)
   - Should see service metrics (request rate, latency, errors)
 3. **Logs**:
   - Go to "Logs" tab
   - Should see logs from your services
   - Can filter by service name, log level, etc.
 ## Troubleshooting
 ### Services Can't Connect to OTel Collector
 **Symptoms**:
 ```
 [ERROR] opentelemetry.exporter.otlp.proto.grpc.exporter: Failed to export traces
 error code: StatusCode.UNAVAILABLE
 ```
 **Solutions**:
 1. **Check OTel Collector is running**:
   ```bash
   kubectl get pods -n bakery-ia -l app.kubernetes.io/component=otel-collector
   ```
 2. **Verify service can reach collector**:
   ```bash
   # From a service pod
   kubectl exec -it -n bakery-ia <service-pod> -- curl -v http://signoz-otel-collector.bakery-ia.svc.cluster.local:4318
   ```
 3. **Check endpoint configuration**:
   - gRPC endpoint should NOT have `http://` prefix
   - HTTP endpoint should have `http://` prefix
   Update your service's tracing setup:
   ```python
   # For gRPC (recommended)
   setup_tracing(app, "my-service", otel_endpoint="signoz-otel-collector.bakery-ia.svc.cluster.local:4317")
   # For HTTP
   setup_tracing(app, "my-service", otel_endpoint="http://signoz-otel-collector.bakery-ia.svc.cluster.local:4318")
   ```
 4. **Restart services after config changes**:
   ```bash
   kubectl rollout restart deployment/<service-name> -n bakery-ia
   ```
 ### No Data in SigNoz
 **Possible causes**:
 1. **Services haven't been called yet**
   - Solution: Generate traffic using the test script
 2. **Tracing not initialized**
   - Check service logs for tracing initialization messages
   - Verify `ENABLE_TRACING=true` in ConfigMap
 3. **Wrong OTel endpoint**
   - Verify `OTEL_EXPORTER_OTLP_ENDPOINT` in ConfigMap
   - Should be: `http://signoz-otel-collector.bakery-ia.svc.cluster.local:4317`
 4. **Service not using tracing library**
   - Check if service imports and calls `setup_tracing()` in main.py
   ```python
   from shared.monitoring.tracing import setup_tracing
   app = FastAPI(title="My Service")
   setup_tracing(app, "my-service")
   ```
 ### OTel Collector Errors
 **Check collector logs**:
 ```bash
 kubectl logs -n bakery-ia -l app.kubernetes.io/component=otel-collector --tail=100
 ```
 **Common errors**:
 1. **Invalid processor error**:
   - Check `signoz-values-dev.yaml` has `signozspanmetrics/delta` (not `spanmetrics`)
   - Already fixed in your configuration
 2. **ClickHouse connection error**:
   - Verify ClickHouse is running
   - Check ClickHouse service is accessible
 3. **Configuration validation error**:
   - Validate YAML syntax in `signoz-values-dev.yaml`
   - Check all processors used in pipelines are defined
 ## Infrastructure Metrics
 SigNoz automatically collects metrics from your infrastructure:
 ### PostgreSQL Databases
 - **Receivers configured for**:
  - auth_db (auth-db-service:5432)
  - inventory_db (inventory-db-service:5432)
  - orders_db (orders-db-service:5432)
 - **Metrics collected**:
  - Connection counts
  - Query performance
  - Database size
  - Table statistics
 ### Redis
 - **Endpoint**: redis-service:6379
 - **Metrics collected**:
  - Memory usage
  - Keys count
  - Hit/miss ratio
  - Command stats
 ### RabbitMQ
 - **Endpoint**: rabbitmq-service:15672 (management API)
 - **Metrics collected**:
  - Queue lengths
  - Message rates
  - Connection counts
  - Consumer activity
 ## Best Practices
 ### 1. Service Implementation
 Always initialize tracing in your service's `main.py`:
 ```python
 from fastapi import FastAPI
 from shared.monitoring.tracing import setup_tracing
 import os
 app = FastAPI(title="My Service")
 # Initialize tracing
 otel_endpoint = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://signoz-otel-collector.bakery-ia:4318")
 setup_tracing(
    app,
    service_name="my-service",
    service_version=os.getenv("SERVICE_VERSION", "1.0.0"),
    otel_endpoint=otel_endpoint
 )
 ```
 ### 2. Custom Spans
 Add custom spans for important operations:
 ```python
 from opentelemetry import trace
 tracer = trace.get_tracer(__name__)
@app.post("/process")
 async def process_data(data: dict):
    with tracer.start_as_current_span("process_data") as span:
        span.set_attribute("data.size", len(data))
        span.set_attribute("data.type", data.get("type"))
        # Your processing logic
        result = process(data)
        span.set_attribute("result.status", "success")
        return result
 ```
 ### 3. Error Tracking
 Record exceptions in spans:
 ```python
 from shared.monitoring.tracing import record_exception
 try:
    result = risky_operation()
 except Exception as e:
    record_exception(e)
    raise
 ```
 ### 4. Correlation
 Use trace IDs in logs for correlation:
 ```python
 from shared.monitoring.tracing import get_current_trace_id
 trace_id = get_current_trace_id()
 logger.info("Processing request", trace_id=trace_id)
 ```
 ## Next Steps
 1. ✅ **Verify SigNoz is running** - Run verification script
 2. ✅ **Generate test traffic** - Run traffic generation script
 3. ✅ **Check data collection** - Query ClickHouse or use UI
 4. ✅ **Access SigNoz UI** - Visualize traces, metrics, and logs
 5. ⏭️ **Set up dashboards** - Create custom dashboards for your use cases
 6. ⏭️ **Configure alerts** - Set up alerts for critical metrics
 7. ⏭️ **Document** - Document common queries and dashboard configurations
 ## Useful Commands
 ```bash
 # Quick status check
 kubectl get pods -n bakery-ia | grep signoz
 # View OTel Collector metrics
 kubectl port-forward -n bakery-ia svc/signoz-otel-collector 8888:8888
 # Then visit: http://localhost:8888/metrics
 # Restart OTel Collector
 kubectl rollout restart deployment/signoz-otel-collector -n bakery-ia
 # View all services with telemetry
 kubectl get pods -n bakery-ia -l tier!=infrastructure
 # Check specific service logs
 kubectl logs -n bakery-ia -l app=<service-name> --tail=100 -f
 # Port-forward to SigNoz UI
 kubectl port-forward -n bakery-ia svc/signoz 3301:8080
 ```
 ## Resources
 - [SigNoz Documentation](https://signoz.io/docs/)
 - [OpenTelemetry Python](https://opentelemetry.io/docs/languages/python/)
 - [SigNoz GitHub](https://github.com/SigNoz/signoz)
 - [Helm Chart Values](infrastructure/helm/signoz-values-dev.yaml)
 - [Verification Script](infrastructure/helm/verify-signoz-telemetry.sh)
 - [Traffic Generation Script](infrastructure/helm/generate-test-traffic.sh)
--- a/docs/TECHNICAL-DOCUMENTATION-SUMMARY.md
+++ b/docs/TECHNICAL-DOCUMENTATION-SUMMARY.md
@@ -38,7 +38,8 @@ Bakery-IA is an **AI-powered SaaS platform** designed specifically for the Spani
 **Infrastructure:**
 - Docker containers, Kubernetes orchestration
 - PostgreSQL 17, Redis 7.4, RabbitMQ 4.1
- Prometheus + Grafana monitoring
+- **SigNoz unified observability platform** - Traces, metrics, logs
 - OpenTelemetry instrumentation across all services
 - HTTPS with automatic certificate renewal
 ---
@@ -711,6 +712,14 @@ Data Collection → Feature Engineering → Prophet Training
 - Service decoupling
 - Asynchronous processing
 **4. Distributed Tracing (OpenTelemetry)**
 - End-to-end request tracking across all 18 microservices
 - Automatic instrumentation for FastAPI, HTTPX, SQLAlchemy, Redis
 - Performance bottleneck identification
 - Database query performance analysis
 - External API call monitoring
 - Error tracking with full context
 ### Scalability & Performance
 **1. Microservices Architecture**
@@ -731,6 +740,16 @@ Data Collection → Feature Engineering → Prophet Training
 - 1,000+ req/sec per gateway instance
 - 10,000+ concurrent connections
 **4. Observability & Monitoring**
 - **SigNoz Platform**: Unified traces, metrics, and logs
 - **Auto-Instrumentation**: Zero-code instrumentation via OpenTelemetry
 - **Application Monitoring**: All 18 services reporting metrics
 - **Infrastructure Monitoring**: 18 PostgreSQL databases, Redis, RabbitMQ
 - **Kubernetes Monitoring**: Node, pod, container metrics
 - **Log Aggregation**: Centralized logs with trace correlation
 - **Real-Time Alerting**: Email and Slack notifications
 - **Query Performance**: ClickHouse backend for fast analytics
 ---
 ## Security & Compliance
@@ -786,8 +805,13 @@ Data Collection → Feature Engineering → Prophet Training
 - **Orchestration**: Kubernetes
 - **Ingress**: NGINX Ingress Controller
 - **Certificates**: Let's Encrypt (auto-renewal)
- **Monitoring**: Prometheus + Grafana
+- **Observability**: SigNoz (unified traces, metrics, logs)
- **Logging**: ELK Stack (planned)
+  - **Distributed Tracing**: OpenTelemetry auto-instrumentation (FastAPI, HTTPX, SQLAlchemy, Redis)
  - **Application Metrics**: RED metrics (Rate, Error, Duration) from all 18 services
  - **Infrastructure Metrics**: PostgreSQL (18 databases), Redis, RabbitMQ, Kubernetes cluster
  - **Log Management**: Centralized logs with trace correlation and Kubernetes metadata
  - **Alerting**: Multi-channel notifications (email, Slack) via AlertManager
 - **Telemetry Backend**: ClickHouse for high-performance time-series storage
 ### CI/CD Pipeline
 1. Code push to GitHub
@@ -834,11 +858,14 @@ Data Collection → Feature Engineering → Prophet Training
 - Stripe integration
 - Automated billing
-### 5. Real-Time Operations
+### 5. Real-Time Operations & Observability
 - SSE for instant alerts
 - WebSocket for live updates
 - Sub-second dashboard refresh
 - Always up-to-date data
 - **Full-stack observability** with SigNoz
 - Distributed tracing for performance debugging
 - Real-time metrics from all layers (app, DB, cache, queue, cluster)
 ### 6. Developer-Friendly
 - RESTful APIs
--- a/infrastructure/helm/signoz-values-dev.yaml
+++ b/infrastructure/helm/signoz-values-dev.yaml
@@ -779,33 +779,63 @@ otelCollector:
          processors: [memory_limiter, batch, resourcedetection, k8sattributes]
          exporters: [clickhouselogsexporter]
  # ClusterRole configuration for Kubernetes monitoring
  # CRITICAL: Required for k8s_cluster receiver to access Kubernetes API
  # Without these permissions, k8s metrics will not appear in SigNoz UI
  clusterRole:
    create: true
    name: "signoz-otel-collector-bakery-ia"
    annotations: {}
    # Complete RBAC rules required by k8sclusterreceiver
    # Based on OpenTelemetry and SigNoz official documentation
    rules:
      # Core API group - fundamental Kubernetes resources
      - apiGroups: [""]
        resources:
          - "events"
          - "namespaces"
          - "nodes"
          - "nodes/proxy"
          - "nodes/metrics"
          - "nodes/spec"
          - "pods"
          - "pods/status"
          - "replicationcontrollers"
          - "replicationcontrollers/status"
          - "resourcequotas"
          - "services"
          - "endpoints"
        verbs: ["get", "list", "watch"]
      # Apps API group - modern workload controllers
      - apiGroups: ["apps"]
        resources: ["deployments", "daemonsets", "statefulsets", "replicasets"]
        verbs: ["get", "list", "watch"]
      # Batch API group - job management
      - apiGroups: ["batch"]
        resources: ["jobs", "cronjobs"]
        verbs: ["get", "list", "watch"]
      # Autoscaling API group - HPA metrics (CRITICAL)
      - apiGroups: ["autoscaling"]
        resources: ["horizontalpodautoscalers"]
        verbs: ["get", "list", "watch"]
      # Extensions API group - legacy support
      - apiGroups: ["extensions"]
        resources: ["deployments", "daemonsets", "replicasets"]
        verbs: ["get", "list", "watch"]
      # Metrics API group - resource metrics
      - apiGroups: ["metrics.k8s.io"]
        resources: ["nodes", "pods"]
        verbs: ["get", "list", "watch"]
    clusterRoleBinding:
      annotations: {}
      name: "signoz-otel-collector-bakery-ia"
 # Additional Configuration
 serviceAccount:
  create: true
  annotations: {}
  name: "signoz-otel-collector"
 # RBAC Configuration for Kubernetes monitoring
 # Required for k8s_cluster and kubeletstats receivers to access Kubernetes API
 rbac:
  create: true
  rules:
    - apiGroups: [""]
      resources: ["nodes", "nodes/proxy", "nodes/metrics", "pods", "services", "endpoints", "namespaces"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["apps"]
      resources: ["deployments", "daemonsets", "statefulsets", "replicasets"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["batch"]
      resources: ["jobs", "cronjobs"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["extensions"]
      resources: ["deployments", "daemonsets", "replicasets"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["metrics.k8s.io"]
      resources: ["nodes", "pods"]
      verbs: ["get", "list", "watch"]
 # Security Context
 securityContext:
  runAsNonRoot: true
--- a/infrastructure/helm/signoz-values-prod.yaml
+++ b/infrastructure/helm/signoz-values-prod.yaml
@@ -893,6 +893,57 @@ otelCollector:
    targetCPUUtilizationPercentage: 70
    targetMemoryUtilizationPercentage: 80
  # ClusterRole configuration for Kubernetes monitoring
  # CRITICAL: Required for k8s_cluster receiver to access Kubernetes API
  # Without these permissions, k8s metrics will not appear in SigNoz UI
  clusterRole:
    create: true
    name: "signoz-otel-collector-bakery-ia"
    annotations: {}
    # Complete RBAC rules required by k8sclusterreceiver
    # Based on OpenTelemetry and SigNoz official documentation
    rules:
      # Core API group - fundamental Kubernetes resources
      - apiGroups: [""]
        resources:
          - "events"
          - "namespaces"
          - "nodes"
          - "nodes/proxy"
          - "nodes/metrics"
          - "nodes/spec"
          - "pods"
          - "pods/status"
          - "replicationcontrollers"
          - "replicationcontrollers/status"
          - "resourcequotas"
          - "services"
          - "endpoints"
        verbs: ["get", "list", "watch"]
      # Apps API group - modern workload controllers
      - apiGroups: ["apps"]
        resources: ["deployments", "daemonsets", "statefulsets", "replicasets"]
        verbs: ["get", "list", "watch"]
      # Batch API group - job management
      - apiGroups: ["batch"]
        resources: ["jobs", "cronjobs"]
        verbs: ["get", "list", "watch"]
      # Autoscaling API group - HPA metrics (CRITICAL)
      - apiGroups: ["autoscaling"]
        resources: ["horizontalpodautoscalers"]
        verbs: ["get", "list", "watch"]
      # Extensions API group - legacy support
      - apiGroups: ["extensions"]
        resources: ["deployments", "daemonsets", "replicasets"]
        verbs: ["get", "list", "watch"]
      # Metrics API group - resource metrics
      - apiGroups: ["metrics.k8s.io"]
        resources: ["nodes", "pods"]
        verbs: ["get", "list", "watch"]
    clusterRoleBinding:
      annotations: {}
      name: "signoz-otel-collector-bakery-ia"
 # Schema Migrator - Manages ClickHouse schema migrations
 schemaMigrator:
  enabled: true
@@ -911,27 +962,6 @@ serviceAccount:
  annotations: {}
  name: "signoz"
 # RBAC Configuration for Kubernetes monitoring
 # Required for k8s_cluster receiver to access Kubernetes API
 rbac:
  create: true
  rules:
    - apiGroups: [""]
      resources: ["nodes", "nodes/proxy", "nodes/metrics", "pods", "services", "endpoints", "namespaces"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["apps"]
      resources: ["deployments", "daemonsets", "statefulsets", "replicasets"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["batch"]
      resources: ["jobs", "cronjobs"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["extensions"]
      resources: ["deployments", "daemonsets", "replicasets"]
      verbs: ["get", "list", "watch"]
    - apiGroups: ["metrics.k8s.io"]
      resources: ["nodes", "pods"]
      verbs: ["get", "list", "watch"]
 # Security Context
 securityContext:
  runAsNonRoot: true
--- a/infrastructure/signoz/dashboards/infrastructure-monitoring.json
+++ b/infrastructure/signoz/dashboards/infrastructure-monitoring.json
@@ -99,10 +99,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -156,10 +158,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -220,10 +224,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -240,9 +246,10 @@
              "orderBy": [],
              "groupBy": [
                {
                  "id": "k8s.pod.name--string--tag--false",
                  "key": "k8s.pod.name",
                  "dataType": "string",
-                  "type": "resource",
+                  "type": "tag",
                  "isColumn": false
                }
              ],
@@ -293,9 +300,10 @@
              "orderBy": [],
              "groupBy": [
                {
                  "id": "k8s.node.name--string--tag--false",
                  "key": "k8s.node.name",
                  "dataType": "string",
-                  "type": "resource",
+                  "type": "tag",
                  "isColumn": false
                }
              ],
@@ -337,10 +345,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -357,9 +367,10 @@
              "orderBy": [],
              "groupBy": [
                {
                  "id": "k8s.deployment.name--string--tag--false",
                  "key": "k8s.deployment.name",
                  "dataType": "string",
-                  "type": "resource",
+                  "type": "tag",
                  "isColumn": false
                }
              ],
@@ -382,10 +393,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -402,9 +415,10 @@
              "orderBy": [],
              "groupBy": [
                {
                  "id": "k8s.deployment.name--string--tag--false",
                  "key": "k8s.deployment.name",
                  "dataType": "string",
-                  "type": "resource",
+                  "type": "tag",
                  "isColumn": false
                }
              ],
--- a/infrastructure/signoz/dashboards/system-health.json
+++ b/infrastructure/signoz/dashboards/system-health.json
@@ -90,10 +90,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -147,10 +149,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -204,10 +208,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",
@@ -261,10 +267,12 @@
              "filters": {
                "items": [
                  {
                    "id": "filter-k8s-namespace",
                    "key": {
                      "id": "k8s.namespace.name--string--tag--false",
                      "key": "k8s.namespace.name",
                      "dataType": "string",
-                      "type": "resource",
+                      "type": "tag",
                      "isColumn": false
                    },
                    "op": "=",