Monitoring - Glossary

Monitoring in the context of microservices is the process of collecting, processing, and analyzing data to track the health and performance of distributed systems. It provides visibility into how services interact, perform, and behave under various conditions.

What is Microservices Monitoring?

Unlike monolithic applications where monitoring focuses on a single application stack, microservices monitoring requires tracking dozens or hundreds of independent services, their interactions, dependencies, and the overall system health. This creates unique challenges around correlation, distributed debugging, and performance analysis.

The Three Pillars of Observability

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Metrics

Numerical data about system performance and behavior over time

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Logs

Discrete events and records of what happened in the system

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Traces

End-to-end journey of requests through distributed services

Observability Tools (Jaeger, Zipkin)

Observability is the ability to understand a system's internal state from its external outputs. In microservices, this means being able to trace requests as they flow through multiple services and understand the performance characteristics of each interaction.

Distributed Tracing

Distributed tracing tracks requests as they traverse multiple services, creating a complete picture of how a single user request is handled across the entire system. Each trace contains multiple spans representing individual operations.

Jaeger

An open-source, end-to-end distributed tracing system originally developed by Uber. It helps monitor and troubleshoot transactions in complex distributed systems.

• High scalability and performance
• Native Kubernetes support
• Multiple storage backends (Cassandra, Elasticsearch)
• Rich UI for trace visualization
• OpenTracing and OpenTelemetry compatible

Zipkin

A distributed tracing system originally developed by Twitter. It helps gather timing data needed to troubleshoot latency problems in microservice architectures.

• Lightweight and easy to deploy
• Multiple transport options (HTTP, Kafka)
• Simple storage requirements
• Active community and ecosystem
• Good for getting started with tracing

Example: When a user places an order, distributed tracing shows the request flowing through: API Gateway → Auth Service → Order Service → Payment Service → Inventory Service → Notification Service, with timing and status information for each step.

Centralized Logging (ELK Stack)

Centralized Logging is essential in microservices architectures because logs are scattered across multiple services and instances. It aggregates logs from all services into a single, searchable location, making debugging and analysis much more manageable.

Why Centralized Logging?

• Correlation: Connect logs from different services for a single request
• Search & Analysis: Query across all services simultaneously
• Debugging: Trace issues through the entire request flow
• Compliance: Centralized audit trails and retention policies
• Alerting: Set up alerts based on log patterns across services

The ELK Stack

The ELK Stack is a popular open-source solution for centralized logging, consisting of three main components that work together to collect, process, and visualize log data.

Elasticsearch

Search and analytics engine

• Distributed storage
• Full-text search
• Real-time indexing
• RESTful API

Logstash

Data processing pipeline

• Log parsing
• Data transformation
• Multiple input sources
• Filtering and enrichment

Kibana

Visualization and management

• Interactive dashboards
• Log exploration
• Custom visualizations
• Alerting and monitoring

Example: Netflix processes over 1 billion log events per day using centralized logging to quickly identify and resolve issues across thousands of microservices, reducing mean time to resolution from hours to minutes.

APM Solutions (Datadog, New Relic)

Application Performance Monitoring (APM) provides comprehensive monitoring, tracing, and analytics for applications and infrastructure. APM solutions offer a unified view of application performance, user experience, and business metrics.

What APM Provides

• End-to-end Visibility: Full request tracing across services
• Performance Metrics: Response times, throughput, error rates
• Infrastructure Monitoring: CPU, memory, disk, network
• User Experience Monitoring: Real user monitoring (RUM)

• Intelligent Alerting: ML-powered anomaly detection
• Service Maps: Visual representation of service dependencies
• Code-level Insights: Performance bottlenecks in code
• Business Intelligence: Correlation with business metrics

Datadog

A comprehensive monitoring and analytics platform that provides unified visibility across applications, infrastructure, and logs with powerful correlation capabilities.

• Unified monitoring platform
• 400+ integrations
• Machine learning insights
• Custom dashboards and alerting
• Strong Kubernetes support

New Relic

A full-stack observability platform that provides deep application insights, infrastructure monitoring, and digital experience monitoring with AI-powered analytics.

• AI-powered insights and alerting
• Full-stack observability
• Distributed tracing
• Code-level visibility
• Mobile and browser monitoring

Example: Shopify uses APM tools to monitor over 2,500 microservices during peak shopping events like Black Friday, automatically detecting performance degradations and scaling services before users are impacted.

Monitoring Best Practices

Implementation Best Practices

• Implement structured logging with consistent formats
• Use correlation IDs to trace requests across services
• Monitor both technical and business metrics
• Set up proactive alerting with proper thresholds
• Create service-level objectives (SLOs) and indicators (SLIs)

Common Challenges

• High cardinality metrics causing storage issues
• Alert fatigue from too many notifications
• Correlation of events across multiple time zones
• Performance impact of monitoring instrumentation
• Data retention and storage cost management