Comprehensive Observability Platform

Project: Comprehensive Observability Platform

Problem Statement

You're the SRE lead at a fast-growing SaaS company running 50+ microservices. Your current monitoring setup is fragmented and provides incomplete visibility into your production systems:

  • Metrics scattered: No centralized metrics collection across services
  • Logs unsearchable: Logs scattered across different servers with no correlation
  • Debugging nightmares: Debugging production issues takes hours of SSH'ing to servers
  • No distributed tracing: Can't track requests across service boundaries
  • Alert fatigue: Poorly configured monitoring causes constant noise
  • No service maps: Can't visualize service dependencies and bottlenecks

The Challenge: Build a comprehensive observability platform that provides the "three pillars" of observability with correlation, dashboards, and proactive alerting.

Real-World Scenario:

 1# User reports: "Checkout is slow"
 2
 3# 1. Check metrics dashboard
 4$ curl http://observability:8080/metrics?service=checkout&timerange=1h
 5  - Request latency: P95 = 2.5s ⚠️
 6  - Error rate: 0.5% ⚠️
 7  - Database connections: 95/100 ⚠️
 8
 9# 2. View distributed trace
10$ curl http://observability:8080/traces?trace_id=abc123
11  Checkout Request
12    ├─ Auth Service
13    ├─ Inventory Service
14    ├─ Payment Service ⚠️ SLOW
15    │   ├─ Database Query ⚠️ CULPRIT
16    │   └─ External API
17    └─ Notification Service
18
19# 3. Search correlated logs
20$ curl http://observability:8080/logs?service=payment&level=error&time=last_hour&trace_id=abc123
21  [ERROR] Database connection pool exhausted
22  [ERROR] Slow query: SELECT * FROM transactions WHERE...
23  [INFO] Payment processing started for order #12345
24  [WARN] Using fallback payment processor
25
26# Root cause identified: Database connection pool exhaustion + slow query
27# Result: Automatic scaling triggered, alert sent, incident resolved

Requirements

Functional Requirements

Must Have:

  • ✅ Metrics Collection: Collect time-series metrics from all services with auto-discovery
  • ✅ Log Aggregation: Centralize logs from all services with structured parsing
  • ✅ Distributed Tracing: Track requests across service boundaries with OpenTelemetry
  • ✅ Correlation Engine: Link logs, metrics, and traces by trace ID and span ID
  • ✅ Querying: Search and filter logs, metrics, traces with complex queries
  • ✅ Visualization: Interactive dashboards with real-time updates and drill-down capabilities
  • ✅ Alerting: Intelligent alerting with anomaly detection, escalation policies
  • ✅ Service Maps: Automatic dependency discovery and visualization

Should Have:

  • ✅ Anomaly Detection: Machine learning-based anomaly detection for metrics
  • ✅ Log Pattern Analysis: Automatic log pattern extraction and anomaly detection
  • ✅ Custom Dashboard Builder: Drag-and-drop dashboard creation for teams
  • ✅ Query Builder: Visual query builder for non-technical users
  • ✅ SLI/SLO Management: Service Level Indicator/Objective tracking and reporting
  • ✅ Data Retention: Configurable retention policies for different data types
  • ✅ Multi-tenant: Team isolation with RBAC and resource quotas

Nice to Have:

  • Cost Analysis: Cloud resource cost tracking and optimization
  • Mobile App: Native mobile app for on-call engineers
  • AI Insights: Automated insights and root cause suggestions
  • Synthetic Monitoring: Active monitoring from multiple geographic locations

Non-Functional Requirements

  • Performance: Ingest 100k metrics/second, 50k log lines/second with <2s query latency
  • Scalability: Horizontal scaling for data collection and query processing
  • Availability: 99.9% uptime for data ingestion and querying
  • Retention: 30 days of high-resolution data, 1 year aggregated data
  • Security: Role-based access control, encryption at rest and in transit

Constraints

  • Storage: ClickHouse for metrics/time-series, Elasticsearch for logs, Jaeger for traces
  • Standards: OpenTelemetry for instrumentation, Prometheus metrics format
  • Deployment: Kubernetes with Helm charts, multi-region support
  • API: RESTful API with GraphQL for complex queries
  • Frontend: React-based web application with real-time updates

Design Considerations

High-Level Architecture

Your observability platform should follow a layered architecture with clear separation of concerns:

Data Ingestion Layer:

  • Multiple collectors for different data types
  • Support for standard protocols
  • Data validation and normalization
  • High-throughput ingestion with buffering and batching

Processing & Storage Layer:

  • Distributed storage optimized for each data type:
    • ClickHouse for time-series metrics and analytics
    • Elasticsearch for full-text log search
    • Jaeger for distributed trace storage
    • Redis for caching and real-time data
    • Kafka for event streaming
  • Data retention and aggregation policies
  • Horizontal scalability for storage tiers

Query & API Layer:

  • Unified query engine supporting federated queries across all data stores
  • Correlation engine linking traces, metrics, and logs by trace/span IDs
  • Real-time stream processing capabilities
  • RESTful and GraphQL APIs with WebSocket support
  • Authentication and authorization

Frontend & Visualization Layer:

  • Interactive dashboard builder with drag-and-drop functionality
  • Real-time data visualization with auto-refresh
  • Service dependency graphs and traffic flow maps
  • Alert management with rule engine and escalation policies
  • Self-service analytics for teams

Technology Stack

Backend:

  • Go 1.24 with clean architecture and domain-driven design
  • Gin for high-performance HTTP API
  • GORM for PostgreSQL metadata storage
  • OpenTelemetry SDK for standardized instrumentation
  • Apache Kafka for event streaming

Frontend:

  • React 18 with TypeScript and modern hooks
  • Recharts for data visualization
  • Monaco Editor for custom query editing
  • WebSocket for real-time updates
  • Material-UI component library

Infrastructure:

  • Kubernetes for orchestration
  • Docker for containerization
  • Helm for deployment management
  • Prometheus for platform self-monitoring
  • Grafana for system monitoring dashboards

Key Design Patterns

Data Correlation:

  • Use trace ID and span ID as universal correlation keys
  • Implement bidirectional linking between logs, metrics, and traces
  • Support temporal correlation for events happening in similar timeframes
  • Apply pattern matching for automatic relationship discovery

Query Optimization:

  • Implement multi-tier caching
  • Use query result pagination and streaming for large datasets
  • Pre-aggregate common queries for dashboard performance
  • Apply time-based data downsampling for historical queries

Alerting Intelligence:

  • Combine threshold-based and ML-based anomaly detection
  • Implement alert grouping to reduce notification fatigue
  • Use escalation policies with different severity levels
  • Provide context-rich alerts with correlated data

Scalability Approach:

  • Horizontal scaling for all ingestion services
  • Shard storage by time ranges and service names
  • Implement read replicas for query workloads
  • Use distributed tracing for platform self-monitoring

Acceptance Criteria

Your observability platform is complete when it meets these criteria:

Data Ingestion:

  • ✅ Successfully ingests 100k+ metrics per second
  • ✅ Processes 50k+ log lines per second
  • ✅ Captures distributed traces across multiple services
  • ✅ Handles data validation and error recovery gracefully
  • ✅ Maintains data ingestion SLA of 99.9% uptime

Querying & Search:

  • ✅ Query response time under 2 seconds for standard queries
  • ✅ Supports complex filtering across all data types
  • ✅ Correlates traces, logs, and metrics automatically
  • ✅ Provides full-text search across all log data
  • ✅ Returns accurate aggregations and statistics

Visualization & Dashboards:

  • ✅ Real-time dashboard updates with sub-second latency
  • ✅ Drag-and-drop dashboard builder works intuitively
  • ✅ Service maps visualize dependencies accurately
  • ✅ Supports customizable time ranges and filters
  • ✅ Exports data in multiple formats

Alerting & Notifications:

  • ✅ Alert evaluation runs consistently at configured intervals
  • ✅ Anomaly detection identifies genuine issues
  • ✅ Notifications delivered via multiple channels
  • ✅ Escalation policies trigger correctly based on severity
  • ✅ Alert context includes correlated logs, metrics, and traces

System Reliability:

  • ✅ Platform self-monitoring detects component failures
  • ✅ Graceful degradation when individual components fail
  • ✅ Data retention policies enforce correctly
  • ✅ Horizontal scaling adds capacity without downtime
  • ✅ All components recover automatically from crashes

Security & Access Control:

  • ✅ Role-based access control enforced across all endpoints
  • ✅ Data encrypted at rest and in transit
  • ✅ Multi-tenant isolation prevents cross-tenant data leaks
  • ✅ Audit logging captures all administrative actions
  • ✅ API authentication supports multiple methods

Usage Examples

Example 1: Investigate Slow API Endpoint

 1# Step 1: Check metrics for the slow endpoint
 2curl -X POST http://observability:8080/api/query \
 3  -H "Content-Type: application/json" \
 4  -d '{
 5    "time_range": {"relative": "1h"},
 6    "filters": [
 7      {"field": "service_name", "operator": "eq", "value": "api-gateway"},
 8      {"field": "endpoint", "operator": "eq", "value": "/api/checkout"}
 9    ],
10    "aggregations": [
11      {"field": "latency", "function": "percentile_95"},
12      {"field": "error_rate", "function": "avg"}
13    ]
14  }'
15
16# Response shows P95 latency = 2.5s
17
18# Step 2: Find traces for slow requests
19curl -X POST http://observability:8080/api/traces/search \
20  -H "Content-Type: application/json" \
21  -d '{
22    "time_range": {"relative": "1h"},
23    "filters": [
24      {"field": "service_name", "operator": "eq", "value": "api-gateway"},
25      {"field": "duration", "operator": "gt", "value": 2000000}
26    ],
27    "limit": 10
28  }'
29
30# Response returns trace IDs for slow requests
31
32# Step 3: Get detailed trace with correlation
33curl http://observability:8080/api/traces/abc123?correlate=true
34
35# Response:
36{
37  "trace_id": "abc123",
38  "duration": 2300000,
39  "spans": [
40    {
41      "span_id": "span1",
42      "operation": "checkout",
43      "service": "api-gateway",
44      "duration": 2300000,
45      "children": [
46        {
47          "span_id": "span2",
48          "operation": "process_payment",
49          "service": "payment-service",
50          "duration": 2000000,
51          "tags": {"db.statement": "SELECT * FROM transactions..."}
52        }
53      ]
54    }
55  ],
56  "correlated_logs": [
57    {
58      "timestamp": "2024-01-15T10:30:45Z",
59      "level": "ERROR",
60      "service": "payment-service",
61      "message": "Database connection pool exhausted",
62      "trace_id": "abc123",
63      "span_id": "span2"
64    }
65  ],
66  "correlated_metrics": [
67    {
68      "metric": "db_connections_active",
69      "value": 95,
70      "max_value": 100,
71      "timestamp": "2024-01-15T10:30:45Z"
72    }
73  ]
74}
75
76# Root cause: Database connection pool exhaustion in payment service

Example 2: Create Custom Dashboard

 1# Create a new dashboard for monitoring payment service
 2curl -X POST http://observability:8080/api/dashboards \
 3  -H "Content-Type: application/json" \
 4  -d '{
 5    "name": "Payment Service Health",
 6    "description": "Monitor payment service performance and errors",
 7    "panels": [
 8      {
 9        "title": "Request Rate",
10        "type": "time_series",
11        "query": {
12          "metrics": ["payment_requests_total"],
13          "aggregation": "rate",
14          "group_by": ["status"]
15        },
16        "position": {"x": 0, "y": 0, "w": 6, "h": 4}
17      },
18      {
19        "title": "Error Rate",
20        "type": "gauge",
21        "query": {
22          "metrics": ["payment_errors_total"],
23          "aggregation": "rate"
24        },
25        "thresholds": [
26          {"value": 0.01, "color": "green"},
27          {"value": 0.05, "color": "yellow"},
28          {"value": 0.1, "color": "red"}
29        ],
30        "position": {"x": 6, "y": 0, "w": 3, "h": 4}
31      },
32      {
33        "title": "Recent Errors",
34        "type": "logs",
35        "query": {
36          "filters": [
37            {"field": "service", "operator": "eq", "value": "payment-service"},
38            {"field": "level", "operator": "eq", "value": "ERROR"}
39          ],
40          "limit": 50
41        },
42        "position": {"x": 0, "y": 4, "w": 12, "h": 6}
43      }
44    ],
45    "refresh_interval": "30s"
46  }'
47
48# Response:
49{
50  "id": "dashboard-123",
51  "name": "Payment Service Health",
52  "url": "http://observability:8080/dashboards/dashboard-123"
53}

Example 3: Configure Alert Rule

 1# Create an alert rule for high error rate
 2curl -X POST http://observability:8080/api/alerts/rules \
 3  -H "Content-Type: application/json" \
 4  -d '{
 5    "name": "High Error Rate - Payment Service",
 6    "description": "Alert when payment service error rate exceeds 5%",
 7    "query": {
 8      "metrics": ["payment_errors_total", "payment_requests_total"],
 9      "aggregation": "rate",
10      "formula": "(payment_errors_total / payment_requests_total) * 100"
11    },
12    "condition": "gt",
13    "threshold": 5.0,
14    "duration": "5m",
15    "severity": "critical",
16    "labels": {
17      "service": "payment-service",
18      "team": "payments"
19    },
20    "notifications": [
21      {
22        "channel": "slack",
23        "config": {
24          "channel": "#alerts-payments",
25          "message_template": "🚨 Payment service error rate is {{value}}%"
26        }
27      },
28      {
29        "channel": "pagerduty",
30        "config": {
31          "severity": "critical",
32          "escalation_policy": "payments-oncall"
33        }
34      }
35    ],
36    "annotations": {
37      "runbook_url": "https://wiki.company.com/runbooks/payment-errors",
38      "dashboard_url": "http://observability:8080/dashboards/payment-health"
39    }
40  }'
41
42# Alert will trigger when error rate > 5% for 5 minutes

Example 4: Query with Correlation

 1# Find all data related to a specific user session
 2curl -X POST http://observability:8080/api/query/correlate \
 3  -H "Content-Type: application/json" \
 4  -d '{
 5    "trace_id": "session-xyz789",
 6    "time_range": {"relative": "24h"},
 7    "include": ["traces", "logs", "metrics", "events"],
 8    "correlation_depth": 2
 9  }'
10
11# Response includes all correlated data:
12{
13  "trace_id": "session-xyz789",
14  "time_range": {"start": "...", "end": "..."},
15  "traces": [
16    {
17      "trace_id": "session-xyz789",
18      "spans": [...],
19      "total_duration": 3500000
20    }
21  ],
22  "logs": [
23    {
24      "timestamp": "...",
25      "service": "auth-service",
26      "message": "User login successful",
27      "trace_id": "session-xyz789"
28    },
29    {
30      "timestamp": "...",
31      "service": "cart-service",
32      "message": "Added item to cart",
33      "trace_id": "session-xyz789"
34    }
35  ],
36  "metrics": [
37    {
38      "timestamp": "...",
39      "metric": "user_session_duration",
40      "value": 3500,
41      "labels": {"trace_id": "session-xyz789"}
42    }
43  ],
44  "events": [
45    {
46      "timestamp": "...",
47      "type": "user_action",
48      "title": "Checkout completed",
49      "trace_id": "session-xyz789"
50    }
51  ],
52  "correlation_graph": {
53    "nodes": [...],
54    "edges": [...]
55  }
56}

Example 5: Service Dependency Map

 1# Generate service dependency map for last 24 hours
 2curl -X GET "http://observability:8080/api/service-map?time_range=24h"
 3
 4# Response:
 5{
 6  "services": [
 7    {
 8      "name": "api-gateway",
 9      "type": "service",
10      "metrics": {
11        "request_rate": 1000.5,
12        "error_rate": 0.02,
13        "avg_latency": 150
14      }
15    },
16    {
17      "name": "auth-service",
18      "type": "service",
19      "metrics": {...}
20    },
21    {
22      "name": "payment-service",
23      "type": "service",
24      "metrics": {...}
25    }
26  ],
27  "dependencies": [
28    {
29      "source": "api-gateway",
30      "target": "auth-service",
31      "request_rate": 500.2,
32      "error_rate": 0.01,
33      "avg_latency": 50
34    },
35    {
36      "source": "api-gateway",
37      "target": "payment-service",
38      "request_rate": 200.1,
39      "error_rate": 0.05,
40      "avg_latency": 300
41    }
42  ]
43}

Key Takeaways

By completing this observability platform project, you will have mastered:

Observability Fundamentals

  • Three Pillars Implementation: Understand how logs, metrics, and traces work together to provide complete system visibility
  • Correlation Techniques: Learn advanced methods for linking different types of observability data using trace IDs, span IDs, and temporal patterns
  • Data Modeling: Design efficient schemas for time-series data, logs, and traces that balance query performance with storage costs
  • Retention Strategies: Implement intelligent data lifecycle management with aggregation and archival policies

High-Performance Data Systems

  • Stream Processing: Build real-time data ingestion pipelines that handle 100k+ events per second with low latency
  • Time-Series Optimization: Master ClickHouse query patterns, partitioning strategies, and compression techniques
  • Search Engine Mastery: Optimize Elasticsearch for log search with proper indexing, sharding, and query design
  • Caching Layers: Implement multi-tier caching using Redis and application-level caches for query performance

Distributed Systems Architecture

  • Service Integration: Design service discovery, communication patterns, and integration points for microservices
  • Fault Tolerance: Implement circuit breakers, retries with exponential backoff, and graceful degradation
  • Horizontal Scalability: Build systems that scale by adding more instances without downtime or data loss
  • Data Consistency: Handle eventual consistency in distributed data stores while maintaining query correctness

Modern Web Development

  • Real-Time Applications: Build WebSocket-based real-time updates and server-sent events for live dashboards
  • Data Visualization: Create interactive charts, graphs, and service maps using Recharts and D3.js
  • User Experience: Design responsive, intuitive interfaces for complex technical data
  • API Design: Implement RESTful and GraphQL APIs with proper pagination, filtering, and error handling

DevOps & Production Engineering

  • Container Orchestration: Deploy and manage applications on Kubernetes with Helm charts and operators
  • Platform Monitoring: Apply observability practices to monitor the observability platform itself
  • CI/CD Pipelines: Automate testing, building, and deployment with comprehensive test coverage
  • Security Best Practices: Implement authentication, authorization, encryption, and audit logging

Machine Learning Integration

  • Anomaly Detection: Apply statistical and ML-based methods to detect unusual patterns in metrics
  • Pattern Recognition: Use clustering and classification to identify log patterns and anomalies
  • Predictive Alerting: Build models that predict issues before they become critical incidents
  • Root Cause Analysis: Implement algorithms that suggest likely root causes based on correlated data

Next Steps

Extend the Platform

Once your core platform is working, consider these enhancements:

Advanced Features:

  • Cost Attribution: Track cloud resource costs and attribute them to services and teams
  • Synthetic Monitoring: Add active monitoring from multiple geographic locations
  • AI-Powered Insights: Implement automated root cause analysis using ML models
  • Mobile Application: Build native mobile apps for on-call engineers
  • Capacity Planning: Add forecasting models to predict future resource needs

Integration Ecosystem:

  • Cloud Providers: Integrate with AWS CloudWatch, GCP Operations, Azure Monitor
  • CI/CD Systems: Connect with Jenkins, GitHub Actions, GitLab CI for deployment tracking
  • Issue Trackers: Auto-create tickets in Jira, GitHub Issues when alerts fire
  • Communication Tools: Expand notifications to Microsoft Teams, Discord, custom webhooks
  • Security Tools: Integrate with security scanners and SIEM systems

Performance Optimization:

  • Query Caching: Implement intelligent query result caching with invalidation
  • Data Tiering: Move cold data to cheaper storage with query federation
  • Compression: Add custom compression for logs and traces to reduce storage costs
  • Index Optimization: Fine-tune database indexes based on actual query patterns

Deepen your expertise in adjacent areas:

Observability Ecosystem:

  • Study Prometheus in depth
  • Learn Grafana advanced features
  • Explore OpenTelemetry collector configuration and pipelines
  • Understand Loki for log aggregation as an alternative to Elasticsearch

Data Engineering:

  • Master Apache Kafka for high-throughput event streaming
  • Learn Apache Flink or Spark for stream processing
  • Study data warehousing concepts and dimensional modeling
  • Explore column-oriented databases

Site Reliability Engineering:

  • Read Google's SRE books on monitoring and alerting
  • Study SLI/SLO/SLA frameworks and error budgets
  • Learn chaos engineering principles with tools like Chaos Mesh
  • Understand capacity planning and performance modeling

Machine Learning:

  • Study time-series forecasting
  • Learn anomaly detection algorithms
  • Explore clustering algorithms for log pattern analysis
  • Understand reinforcement learning for automated remediation

Production Deployment

Prepare your platform for production use:

Reliability:

  • Set up multi-region deployments for high availability
  • Implement automated failover between regions
  • Add comprehensive health checks and readiness probes
  • Build disaster recovery procedures with backup/restore

Security Hardening:

  • Conduct security audit and penetration testing
  • Implement network policies and service mesh
  • Add secrets management with Vault or cloud KMS
  • Set up security scanning in CI/CD pipeline

Operational Excellence:

  • Create comprehensive runbooks for common scenarios
  • Build automated remediation for known issues
  • Set up on-call rotation and escalation policies
  • Implement change management and deployment safeguards

Documentation:

  • Write detailed architecture and design documentation
  • Create user guides for different personas
  • Document API with OpenAPI/Swagger specifications
  • Build internal training materials and workshops

Career Development

This project demonstrates skills valuable for these roles:

  • Site Reliability Engineer: Platform design, monitoring, incident response
  • Platform Engineer: Building internal developer platforms and tooling
  • DevOps Engineer: Infrastructure automation, CI/CD, monitoring
  • Data Engineer: Stream processing, data pipelines, time-series databases
  • Backend Engineer: Distributed systems, API design, scalability
  • Cloud Architect: Multi-cloud design, Kubernetes, cloud-native patterns

Download Complete Solution

📦 Download Complete Solution

Get the full implementation with all source code, configurations, and deployment files:

⬇️ Download Solution

Package includes:

  • Complete Go backend implementation
  • React frontend with TypeScript and all components
  • Kubernetes manifests and Helm charts for deployment
  • Docker Compose configuration for local development
  • Database schemas and migration scripts
  • Sample dashboards and alert configurations
  • Comprehensive README with setup instructions and architecture guide
  • Integration tests and performance benchmarks

💡 Note: The README in the solution package contains detailed implementation guides, architecture diagrams, deployment instructions, and troubleshooting tips. Start there for the complete development workflow.

Congratulations! You've built a production-ready comprehensive observability platform with unified logging, metrics, and tracing, advanced correlation, intelligent alerting, and beautiful visualizations. This project demonstrates advanced Go system design, distributed systems patterns, and modern web development practices that you can apply to any production monitoring scenario.