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MICROSERVICES DISTRIBUTED SYSTEMS OBSERVABILITY LEARNING PROJECTS

Microservices & Distributed Systems Observability: Learning Through Building

Great topic choice! Observability is one of those areas where theoretical knowledge only gets you so far—you truly understand it when you’ve built the components yourself and seen why certain design decisions were made.

Core Concept Analysis

Observability answers the question: “What is happening inside my distributed system right now, and why?” It breaks down into these fundamental building blocks:

The Three Pillars

  1. Metrics - Numerical measurements over time (CPU usage, request count, latency percentiles)
  2. Logs - Discrete events with context (structured records of what happened)
  3. Traces - Request paths across service boundaries (showing causality and timing)

Key Internal Mechanisms

  • Context Propagation - How trace IDs flow between services via headers (W3C TraceContext)
  • Time-Series Storage - How metrics databases compress and index temporal data
  • Span Assembly - How distributed spans are collected and reconstructed into traces
  • Cardinality Management - Why label combinations matter for performance
  • Sampling Strategies - How to reduce data volume without losing signal

Project 1: Mini Distributed Tracer

  • File: MICROSERVICES_DISTRIBUTED_SYSTEMS_OBSERVABILITY_LEARNING_PROJECTS.md
  • Programming Language: C
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Observability / Distributed Systems
  • Software or Tool: OpenTelemetry / Jaeger
  • Main Book: “Distributed Systems Observability” by Cindy Sridharan

What you’ll build: A distributed tracing system from scratch that instruments HTTP services, propagates context, and visualizes request flows.

Why it teaches observability: This forces you to implement the exact mechanisms that OpenTelemetry and Jaeger use internally—you’ll understand why trace context must be propagated, how spans are linked parent-to-child, and how a collector reassembles the full picture.

Core challenges you’ll face:

  • Implementing W3C TraceContext header parsing and injection (maps to context propagation)
  • Designing span data structures with timing, tags, and parent references (maps to trace assembly)
  • Building a collector that receives spans from multiple services and reconstructs traces
  • Creating a visualization that shows the waterfall view of a distributed request

Key Concepts:

Difficulty: Intermediate Time estimate: 1-2 weeks Prerequisites: HTTP basics, any backend language, basic understanding of microservices

Real world outcome: You’ll have 3-4 microservices that you can curl, and see a visual trace in your browser showing:

  • The full request path across all services
  • Timing breakdown for each service (like Jaeger’s waterfall view)
  • Error highlighting when a service fails

Learning milestones:

  1. After implementing context injection/extraction - you’ll understand how traceparent headers work
  2. After building the collector - you’ll see why central collection is necessary
  3. After visualization - you’ll internalize why span timing and parent IDs matter for debugging

Project 2: Mini Time-Series Database

  • File: MICROSERVICES_DISTRIBUTED_SYSTEMS_OBSERVABILITY_LEARNING_PROJECTS.md
  • Programming Language: C
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 4: Expert
  • Knowledge Area: Databases / Observability
  • Software or Tool: Prometheus / TSDB
  • Main Book: “Prometheus: Up & Running” by Brazil & Ruck

What you’ll build: A simplified Prometheus-style TSDB that ingests metrics, stores them efficiently, and supports basic queries.

Why it teaches observability: Prometheus TSDB uses ingenious techniques—WAL, head blocks, compaction, inverted indexes—that you’ll only truly understand by implementing. This project reveals why metrics storage is fundamentally different from regular databases.

Core challenges you’ll face:

  • Implementing a Write-Ahead Log for crash recovery (maps to durability)
  • Building an inverted index for label-based lookups (maps to query performance)
  • Designing chunk-based storage with delta compression (maps to storage efficiency)
  • Implementing basic PromQL-like range queries

Resources for key challenges:

Key Concepts:

  • Write-Ahead Logging: “Designing Data-Intensive Applications” by Martin Kleppmann (Ch. 3) - Durability patterns
  • Inverted Indexes: “Prometheus and its storage” by Palark - Label indexing
  • Delta Compression: Prometheus Storage docs - Compression techniques

Difficulty: Advanced Time estimate: 2-3 weeks Prerequisites: Understanding of file I/O, data structures (B-trees, hash maps), basic compression concepts

Real world outcome: You’ll have a running database where you can:

  • Push metrics from your applications via HTTP
  • Query with a simple PromQL-like syntax: http_requests_total{status="500"}[5m]
  • See the actual storage files and understand their structure

Learning milestones:

  1. After WAL implementation - you’ll understand crash recovery
  2. After inverted index - you’ll see why label cardinality is such a big deal
  3. After compression - you’ll appreciate how Prometheus handles millions of samples

Project 3: OpenTelemetry Collector Clone

  • File: MICROSERVICES_DISTRIBUTED_SYSTEMS_OBSERVABILITY_LEARNING_PROJECTS.md
  • Programming Language: C
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Observability / Data Pipelines
  • Software or Tool: OpenTelemetry Collector
  • Main Book: “Learning OpenTelemetry” by Ted Young

What you’ll build: A telemetry pipeline that receives metrics/traces/logs, processes them (filtering, sampling, enriching), and exports to multiple backends.

Why it teaches observability: The OpenTelemetry Collector is the heart of modern observability pipelines. Building one teaches you the receiver→processor→exporter pattern and why vendor-neutral telemetry matters.

Core challenges you’ll face:

  • Implementing OTLP (OpenTelemetry Protocol) receivers for multiple signal types
  • Building a processor chain (sampling, attribute filtering, batching)
  • Creating exporters for different backends (Jaeger, Prometheus, stdout)
  • Managing backpressure when backends are slow

Key Concepts:

Difficulty: Intermediate-Advanced Time estimate: 2 weeks Prerequisites: Protocol buffers/gRPC basics, concurrent programming

Real world outcome: Your collector will:

  • Accept telemetry from instrumented applications
  • Show a live stream of data flowing through the pipeline
  • Export to multiple destinations simultaneously (terminal + file + HTTP endpoint)

Learning milestones:

  1. After receivers work - you’ll understand OTLP and signal types
  2. After processors - you’ll see why sampling decisions are complex
  3. After exporters - you’ll appreciate vendor-neutral telemetry

Project 4: Log Aggregation System

  • File: MICROSERVICES_DISTRIBUTED_SYSTEMS_OBSERVABILITY_LEARNING_PROJECTS.md
  • Programming Language: C
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Logging / Indexing
  • Software or Tool: ELK Stack / Loki
  • Main Book: “Logging and Log Management” by Anton Chuvakin

What you’ll build: A centralized logging system that collects structured logs from multiple services, indexes them, and provides fast search.

Why it teaches observability: Logs are the most detailed signal. Building an aggregator teaches you structured logging, efficient indexing (similar to Elasticsearch), and correlation with traces.

Core challenges you’ll face:

  • Designing a structured log format with trace correlation fields
  • Building an ingestion pipeline that handles high throughput
  • Implementing full-text indexing for fast search
  • Creating a query interface for filtering by time, service, and content

Key Concepts:

  • Structured Logging: “Distributed Systems Observability” by Cindy Sridharan (Ch. 3) - Why structure matters
  • Inverted Indexes for Text: “Designing Data-Intensive Applications” by Martin Kleppmann (Ch. 3) - Full-text search basics
  • Log Correlation: Coralogix Distributed Tracing Guide - Linking logs to traces

Difficulty: Intermediate Time estimate: 1-2 weeks Prerequisites: Basic data structures, file I/O, text processing

Real world outcome: A web interface where you can:

  • See logs streaming in real-time from your services
  • Search logs with queries like service:api AND level:error AND trace_id:abc123
  • Click a trace ID to see all logs from that request across services

Learning milestones:

  1. After structured format - you’ll see why JSON logs beat plain text
  2. After indexing - you’ll understand how ELK Stack searches billions of logs
  3. After trace correlation - you’ll appreciate unified observability

Project 5: Metrics Dashboard with Alerting

  • File: MICROSERVICES_DISTRIBUTED_SYSTEMS_OBSERVABILITY_LEARNING_PROJECTS.md
  • Programming Language: C
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Visualization / Alerting
  • Software or Tool: Grafana / Alertmanager
  • Main Book: “Site Reliability Engineering” by Google

What you’ll build: A Grafana-like dashboard that queries your TSDB, displays charts, and triggers alerts based on thresholds.

Why it teaches observability: Dashboards and alerts are how humans interact with observability data. Building one teaches you PromQL semantics, threshold-based alerting, and SLI/SLO concepts.

Core challenges you’ll face:

  • Implementing a PromQL-like query engine with aggregations (rate, sum, avg)
  • Building real-time chart rendering with time-range selection
  • Designing an alerting engine with configurable rules and notifications
  • Creating alert state management (pending → firing → resolved)

Key Concepts:

Difficulty: Intermediate Time estimate: 1-2 weeks Prerequisites: Basic frontend knowledge (or terminal UI), understanding of time-series data

Real world outcome: A dashboard where you can:

  • Create charts like “request rate over last hour” with live updates
  • Set alerts: “Notify me when error rate > 5% for 2 minutes”
  • See alert history and current firing alerts

Learning milestones:

  1. After query engine - you’ll understand why PromQL is so powerful
  2. After alerting engine - you’ll see how monitoring tools detect problems
  3. After SLO integration - you’ll appreciate error budget concepts

Project Comparison Table

Project Difficulty Time Depth of Understanding Fun Factor
Mini Distributed Tracer Intermediate 1-2 weeks Deep insight into tracing ⭐⭐⭐⭐⭐ (visual output!)
Mini Time-Series DB Advanced 2-3 weeks Core storage concepts ⭐⭐⭐⭐ (systems-level)
OTel Collector Clone Intermediate-Advanced 2 weeks Pipeline architecture ⭐⭐⭐⭐ (real patterns)
Log Aggregation System Intermediate 1-2 weeks Search & correlation ⭐⭐⭐⭐ (searchable logs)
Metrics Dashboard Intermediate 1-2 weeks User-facing observability ⭐⭐⭐⭐⭐ (charts & alerts)

My Recommendation

Start with Project 1: Mini Distributed Tracer

Here’s why:

  1. Immediate visual feedback - You’ll see traces render in your browser, which is deeply satisfying
  2. Core concept density - Context propagation and span assembly are the hardest concepts to internalize without building
  3. Foundation for everything else - Once you understand traces, metrics and logs are easier to place in context
  4. Real debugging use - You can actually use your tracer to debug issues in demo microservices

After completing the tracer, do Project 2 (Mini TSDB) to understand the storage layer, then Project 5 (Dashboard) to tie it all together with a user interface.

Final Overall Project: Full Observability Platform

What you’ll build: A complete, integrated observability platform for a microservices e-commerce demo (5-7 services) that includes your tracer, TSDB, log aggregator, and dashboard—all working together.

Why it teaches the full picture: Real observability isn’t three separate tools—it’s a unified system where you can jump from an alert → to the spike in metrics → to the trace that caused it → to the error log with the stack trace. Building this integration teaches you what commercial tools like Datadog, Honeycomb, and Grafana Cloud actually do.

What you’ll integrate:

  • Your distributed tracer capturing request flows
  • Your TSDB storing metrics scraped from services
  • Your log aggregator indexing structured logs
  • Your dashboard showing correlated views

Core challenges you’ll face:

  • Correlating metrics spikes with traces (exemplars)
  • Linking logs to traces via trace_id
  • Building a unified query interface across signals
  • Implementing RED metrics (Rate, Errors, Duration) derived from traces

Key Concepts:

  • Signal Correlation: OpenTelemetry Deep Dive - Linking the three pillars
  • Exemplars: Prometheus Exemplars - Connecting metrics to traces
  • RED Method: “Distributed Systems Observability” by Cindy Sridharan (Ch. 2) - Essential service metrics

Difficulty: Advanced Time estimate: 1 month+ Prerequisites: Completed at least 3 of the above projects

Real world outcome: A platform where you can:

  • See a dashboard showing service health (latency, error rate, throughput)
  • Click an anomaly to drill down to the specific traces
  • Click a trace to see all logs from that request
  • Receive alerts when SLOs are breached
  • Demo video: Record yourself debugging a real issue using your platform

Learning milestones:

  1. After basic integration - you’ll understand why unified observability matters
  2. After exemplars work - you’ll see how metrics connect to traces
  3. After full workflow - you’ll have internalized production debugging patterns

Essential Reading List

For the theory behind what you’re building:

Book What It Teaches
“Distributed Systems Observability” by Cindy Sridharan The conceptual foundation—read first
“Designing Data-Intensive Applications” by Martin Kleppmann Storage internals (Ch. 3, 5 for TSDB concepts)
“Site Reliability Engineering” by Google SLIs, SLOs, and alerting philosophy
“Building Microservices” by Sam Newman Why distributed systems need observability

Sources