Distributed Systems: Complete Course

From Fundamentals to Production Systems

A comprehensive 47-part course covering distributed systems from absolute basics to advanced production patterns. Designed for backend engineers, FAANG interview preparation, and engineers building high-scale systems. Written in a narrative book style that explains concepts thoroughly with real-world context.

Course Structure

Part 1: Foundations (Modules 1-6)

Module	Topic	Key Concepts
1	Introduction to Distributed Systems	Why distributed, types, fallacies, partial failures
2	Network Fundamentals	TCP/UDP, HTTP/2/3, gRPC, network partitions
3	Serialization and Data Exchange	JSON, Protobuf, Avro, schema evolution
4	Time, Clocks, and Ordering	Lamport clocks, vector clocks, HLC
5	Consistency Models	Linearizability, eventual, causal consistency
6	CAP Theorem and Beyond	CAP, PACELC, consistency-availability trade-offs

Part 2: Data Management (Modules 7-12)

Module	Topic	Key Concepts
7	Replication Strategies	Leader-follower, multi-leader, leaderless
8	Partitioning and Sharding	Hash/range partitioning, rebalancing, hotspots
9	Consensus Algorithms	Paxos, Raft, ZAB, leader election
10	Distributed Transactions	2PC, 3PC, distributed ACID
11	Kafka Deep Dive	Partitions, consumer groups, exactly-once
12	Conflict Resolution	LWW, vector clocks, CRDTs

Part 3: Scaling Patterns (Modules 13-17)

Module	Topic	Key Concepts
13	Horizontal vs Vertical Scaling	When to use which, auto-scaling
14	Load Balancing	L4/L7, algorithms, health checks
15	Caching Strategies	Write-through, write-behind, cache invalidation
16	CDN and Edge Computing	Global distribution, edge functions
17	Multi-Region Architecture	Active-active, active-passive, data sovereignty

Part 4: Messaging and Streaming (Modules 18-22)

Module	Topic	Key Concepts
18	Message Queues	RabbitMQ, SQS, at-least-once delivery
19	Event Sourcing	Event stores, projections, snapshots
20	CQRS Pattern	Read/write separation, materialized views
21	Stream Processing	Kafka Streams, Flink, windowing
22	Failure Detection	Heartbeats, phi accrual, SWIM protocol

Part 5: Fault Tolerance (Modules 23-27)

Module	Topic	Key Concepts
23	Circuit Breakers	States, fallbacks, half-open recovery
24	Retry Strategies	Exponential backoff, jitter, idempotency
25	Bulkhead Pattern	Resource isolation, thread pools, semaphores
26	Rate Limiting	Token bucket, sliding window, distributed limiting
27	Load Balancing Deep Dive	Round robin, least connections, consistent hashing

Part 6: Modern Infrastructure (Modules 28-30)

Module	Topic	Key Concepts
28	Service Mesh	Sidecars, mTLS, traffic management
29	Distributed Tracing	Spans, context propagation, sampling
30	Production-Ready Systems	Complete service template, middleware, health checks

Part 7: Advanced Patterns (Modules 31-37)

Module	Topic	Key Concepts
31	Saga Pattern	Choreography, orchestration, compensating transactions
32	Chaos Engineering	Failure injection, game days, building confidence
33	Distributed Locks	Redlock, fencing tokens, ZooKeeper, etcd
34	CRDTs	G-Counter, PN-Counter, OR-Set, convergence
35	Gossip Protocols	Epidemic broadcast, SWIM, membership
36	Consistent Hashing	Hash rings, virtual nodes, minimal disruption
37	Bloom Filters	Probabilistic membership, false positives, applications

Part 8: Production Excellence (Modules 38-41)

Module	Topic	Key Concepts
38	Deployment Strategies	Blue-green, canary, feature flags, GitOps
39	Incident Response	Detection, roles, mitigation, postmortems
40	Performance Tuning	Profiling, bottlenecks, caching, optimization
41	Security	mTLS, authorization, secrets, encryption

Part 9: Observability & Operations (Modules 44-47)

Module	Topic	Key Concepts
44	Logging and Log Aggregation	Structured logging, ELK, Loki, correlation IDs
45	Metrics and Monitoring	Prometheus, Grafana, SLOs, alerting
46	Service Discovery	DNS, Consul, etcd, Kubernetes discovery
47	Configuration Management	Feature flags, secrets, dynamic config

Part 10: Mastery (Modules 42-43)

Module	Topic	Content
42	System Design Case Studies	URL shortener, cache, queue, search, payments
43	Interview Preparation	100 questions, learning roadmap, resources

What Makes This Course Different

This course is written as a book, not a reference manual. Each chapter:

Explains the "why" before the "how"
Tells stories that illustrate concepts
Connects ideas to previous chapters
Discusses tradeoffs rather than prescribing solutions
Prepares you for real-world decisions, not just interviews

Early chapters (1-30) include Go code examples for practical implementation. Advanced chapters (31-43) focus on conceptual depth with narrative explanations. The new observability modules (44-47) combine deep technical content with comprehensive Go implementations.

Prerequisites

Basic programming knowledge (examples in Go, concepts are language-agnostic)
Understanding of basic data structures
Familiarity with REST APIs
Basic SQL knowledge

How to Use This Course

Sequential Learning: Modules build on each other; follow the order
Active Reading: Pause to consider how concepts apply to systems you know
Implement Early Chapters: Build the code examples to internalize patterns
Discuss Later Chapters: The advanced topics benefit from conversation and debate
Use for Interview Prep: Chapter 43 provides 100 practice questions

Scaling Milestones Reference

Users	RPS	Data	Architecture
1K	10	1GB	Single server
10K	100	10GB	Read replicas, CDN
100K	1K	100GB	Sharding, caching
1M	10K	1TB	Microservices, Kafka
10M	100K	10TB	Multi-region, edge
100M	1M	100TB	Global distribution
1B	10M	1PB	Custom infrastructure

The Journey Ahead

Distributed systems are among the most challenging and rewarding areas of software engineering. They power everything from social networks to financial systems, from search engines to autonomous vehicles.

This course won't make you an expert overnight nothing can. But it will give you the conceptual foundation to understand distributed systems deeply, the patterns to design them effectively, and the vocabulary to discuss them clearly.

Welcome to the journey.

Begin with Module 1: Introduction to Distributed Systems