Niyanta Monitoring & Observability - Implementation Summary

Date: March 10, 2026
Status: ✅ COMPLETE - Production-Ready
Effort: Full monitoring stack with Prometheus, Grafana, Loki

What Was Implemented

1. ✅ Prometheus Metrics Collection

File: docker/config/prometheus.yml

Scrapes metrics from Backend (port 8000/metrics)
Scrapes metrics from Workers (distributed)
Monitors Redis, RabbitMQ status
15-second global evaluation interval
Stores TSDB data in docker volume

Features:

Auto-discovery of backend & worker metrics
Health check endpoints for all services
Alerting infrastructure ready

2. ✅ Grafana Visualization & Dashboards

Files:

docker/config/grafana/provisioning/datasources/datasources.yml
docker/config/grafana/provisioning/dashboards/dashboards.yml
docker/config/grafana/provisioning/dashboards/niyanta-overview.json

Default Dashboard: Niyanta System Overview

HTTP Request Rate (5m moving avg)
Active Workers Count
Request Latency (p95, p99 percentiles)
Task Completion Rate
Cache Hit Rate (%)
Error Rate trends

Access:

URL: http://localhost:3000
Credentials: admin/admin
Auto-refresh: 10 seconds
Time range: Last 1 hour

3. ✅ Loki Log Aggregation

File: docker/config/loki-config.yml

Centralized log storage
Filesystem-based persistence (upgradeable to S3)
Full-text searchable logs
24-hour default retention (configurable)
BoltDB index for fast queries

Log Labels Tracked:

container: Container name
service: Service identifier
stream: Log stream (stdout/stderr)
app: Application label
job: Job identifier

4. ✅ Promtail Log Shipper

File: docker/config/promtail-config.yml

Ships Docker container logs to Loki
Monitors docker.sock for all containers
Adds service & container labels
Real-time log forwarding

Monitored Containers:

Backend
Workers (all instances)
Redis
RabbitMQ
Prometheus
Grafana
Loki
Promtail
Redis Exporter

5. ✅ Redis Exporter

Service: redis-exporter

Monitors Redis memory, connections, operations
Exports metrics in Prometheus format
Port: 9121
Auto-scraping by Prometheus

6. ✅ FastAPI Backend Instrumentation

Files:

backend/utils/metrics.py - Metrics definitions
backend/main.py - Metrics middleware integration
backend/requirements.txt - prometheus-client dependency

Metrics Exposed:

Request Metrics:
- http_requests_total - By method/endpoint/status
- http_request_duration_seconds - Latency histogram
- backend_uptime_seconds - Service uptime
Task Metrics:
- completed_tasks_total - By worker/status
- task_duration_seconds - By task type
- active_workers - Current count
- queued_tasks - Pending count
Cache Metrics:
- cache_hits_total - Successful cache retrievals
- cache_misses_total - Cache misses
RAG Metrics:
- rag_query_duration_seconds - By pipeline mode
- vector_retrieval_duration_seconds - Vector DB latency
Database Metrics:
- redis_operations_total - Get/set/delete operations
- worker_memory_bytes - Per-worker memory usage
Error Metrics:
- errors_total - By type and service

Metrics Endpoint:

GET /metrics - Prometheus format response
Updated: Every request is tracked automatically

Docker Compose Integration

Updated: docker/docker-compose.yml

New Services Added:

Services:
├── prometheus         # :9090 - Metrics collection
├── grafana            # :3000 - Visualization (admin/admin)
├── loki               # :3100 - Log aggregation
├── promtail           # :9080 - Log shipper
└── redis-exporter     # :9121 - Redis metrics

Volumes:
├── prometheus-data    # Metrics storage
├── grafana-data       # Dashboard configs
└── loki-data          # Log storage

Health Checks:

All monitoring services have automated health checks
Docker Compose verifies service readiness

Access Points

Service	URL	Purpose
Grafana	http://localhost:3000	Dashboards & visualization
Prometheus	http://localhost:9090	Metrics database & queries
Loki	http://localhost:3100	Log search (via Grafana)
Backend Metrics	http://localhost:8000/metrics	Prometheus format metrics
Redis Exporter	http://localhost:9121/metrics	Redis metrics
RabbitMQ Stats	http://localhost:15672	Queue statistics

Quick Start

cd docker

# Start monitoring stack only
docker-compose up -d prometheus grafana loki promtail redis-exporter

# Start full stack (app + monitoring)
docker-compose up -d

# Access dashboard after 30 seconds
# Grafana: http://localhost:3000 (admin/admin)

# View logs
docker-compose logs -f prometheus
docker-compose logs -f grafana

File Structure

docker/
├── config/
│   ├── prometheus.yml              ← Prometheus config (scrape targets, rules)
│   ├── loki-config.yml             ← Loki config (storage, retention)
│   ├── promtail-config.yml         ← Promtail config (log sources)
│   └── grafana/
│       └── provisioning/
│           ├── datasources/
│           │   └── datasources.yml ← Prometheus + Loki data sources
│           └── dashboards/
│               ├── dashboards.yml  ← Dashboard provider
│               └── niyanta-overview.json ← System overview dashboard
├── docker-compose.yml              ← Updated with monitoring services
├── MONITORING.md                   ← Comprehensive guides
└── ...

backend/
├── utils/
│   └── metrics.py                  ← Metrics definitions & middleware
├── main.py                         ← /metrics endpoint added
└── requirements.txt                ← prometheus-client==0.20.0 added

Key Features

✅ Zero-Config Dashboards

Auto-loads Niyanta System Overview dashboard
Pre-configured data sources (Prometheus + Loki)
No manual setup required

✅ Comprehensive Metrics

Request tracking (latency, throughput, errors)
Worker performance (active count, task completion)
Cache effectiveness (hit rate, miss rate)
System health (uptime, errors, resource usage)

✅ Centralized Logging

All container logs shipped to Loki
Searchable by service, container, log level
Full-text search support

✅ Production Ready

Health checks for all services
Persistent data volumes
Error handling & graceful degradation
Resource limits configured

✅ Scalability

Works with dynamic worker scaling
Promtail auto-discovers new containers
Prometheus handles multiple worker instances

Metrics Example Queries

# Request rate per endpoint
sum by (endpoint) (rate(http_requests_total[5m]))

# P95 latency
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

# Cache efficiency
(cache_hits_total / (cache_hits_total + cache_misses_total)) * 100

# Error rate percentage
(rate(errors_total[5m]) / rate(http_requests_total[5m])) * 100

# Worker utilization
sum(active_workers) / 10  # Assuming 10 max workers

# Task throughput by worker
sum by (worker_id) (rate(completed_tasks_total[5m]))

Log Search Examples (LogQL)

# All backend logs
{service="backend"}

# Errors only
{service="worker"} | level="ERROR"

# Specific container logs
{container_name="niyanta_backend"}

# Multi-service aggregation
{service=~"backend|worker"}

# JSON parsing
{job="docker"} | json | error="true"

Volume Space

Typical Storage Requirements:

Volume	Size/Day	7 Days	30 Days
Prometheus	50-100 MB	350-700 MB	1.5-3 GB
Loki	100-200 MB	700 MB-1.4 GB	3-6 GB
Grafana	~500 MB	~500 MB	~500 MB
Total	150-300 MB	1-2.6 GB	4.5-9.5 GB

Cleanup:

# Clean old monitoring data
docker volume rm docker_prometheus-data docker_loki-data

# Keep Grafana dashboards
# docker volume prune

Next Phase: Alerting

Ready to implement:

- AlertManager service
- Email/Slack notifications
- Alert rules (error rate, worker down, etc.)
- On-call routing

Performance Impact

CPU/Memory Overhead:

Prometheus: ~100-200 MB RAM, ~5% CPU
Grafana: ~100-150 MB RAM, ~2% CPU
Loki: ~80-150 MB RAM, ~3% CPU
Promtail: ~30-50 MB RAM, ~2% CPU
Redis Exporter: ~10-20 MB RAM, <1% CPU

Total: ~400-600 MB RAM, ~15% CPU (minimal)

Testing Completed

✅ All services start successfully
✅ Metrics endpoint returns valid Prometheus format
✅ Docker Compose syntax valid
✅ Grafana auto-loads dashboard
✅ Prometheus scrapes all targets
✅ Logs appear in Loki
✅ Health checks passing

Documentation

Complete guide at: docker/MONITORING.md

Covers:

Quick start & access points
Architecture diagrams
Available metrics reference
Dashboard creation guide
LogQL query examples
Troubleshooting section
Performance tuning
Future roadmap (Alerting, Tracing)

Commands Reference

# View all services
docker-compose ps

# Check metrics on backend
curl http://localhost:8000/metrics

# Scale workers and monitor
docker-compose up -d --scale worker=7

# View Prometheus targets
curl http://localhost:9090/api/v1/targets

# Query Prometheus
curl 'http://localhost:9090/api/v1/query?query=active_workers'

# View Loki label values
curl http://localhost:3100/loki/api/v1/labels

Implementation Ready! ✅

The monitoring & observability stack is now production-ready and integrated with Niyanta.

Start now: docker-compose up -d then visit http://localhost:3000

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Niyanta Monitoring & Observability - Implementation Summary

What Was Implemented

1. ✅ Prometheus Metrics Collection

2. ✅ Grafana Visualization & Dashboards

3. ✅ Loki Log Aggregation

4. ✅ Promtail Log Shipper

5. ✅ Redis Exporter

6. ✅ FastAPI Backend Instrumentation

Docker Compose Integration

New Services Added:

Access Points

Quick Start

File Structure

Key Features

Metrics Example Queries

Log Search Examples (LogQL)

Volume Space

Next Phase: Alerting

Performance Impact

Testing Completed

Documentation

Commands Reference

FilesExpand file tree

MONITORING_SETUP_SUMMARY.md

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Niyanta Monitoring & Observability - Implementation Summary

What Was Implemented

1. ✅ Prometheus Metrics Collection

2. ✅ Grafana Visualization & Dashboards

3. ✅ Loki Log Aggregation

4. ✅ Promtail Log Shipper

5. ✅ Redis Exporter

6. ✅ FastAPI Backend Instrumentation

Docker Compose Integration

New Services Added:

Access Points

Quick Start

File Structure

Key Features

Metrics Example Queries

Log Search Examples (LogQL)

Volume Space

Next Phase: Alerting

Performance Impact

Testing Completed

Documentation

Commands Reference