Distributed Systems Fundamentals
Master the constraints and trade-offs that shape all distributed architecture decisions
Why Fundamentals Matter
Distributed systems differ fundamentally from single-machine applications. They operate under constraints imposed by the network, time, and the possibility of failures. Understanding these constraints prevents building systems that fail silently or behave unexpectedly under stress.
This section covers five critical areas:
The Five Pillars
1. Fallacies of Distributed Computing
Every engineer begins with assumptions that are false in distributed systems. Network latency is not zero, bandwidth is not infinite, and the network is not always reliable. These eight fallacies undermine countless systems.
What You'll Learn:
- The eight false assumptions and why they're wrong
- Real-world consequences of believing each fallacy
- How to design systems that don't depend on these false assumptions
2. CAP & PACELC Theorems
The CAP theorem proves you cannot simultaneously have Consistency, Availability, and Partition tolerance. PACELC refines this for networks that are working normally. These theorems guide every architectural decision.
What You'll Learn:
- What the CAP theorem actually says (and doesn't say)
- How to analyze your system's position in CAP space
- PACELC and the consistency-latency trade-off in normal conditions
- How to make intentional trade-offs rather than accidental ones
3. Consistency Models
Consistency spans a spectrum from strongly consistent (but slow) to eventually consistent (but complex). Choosing the right model prevents both performance disasters and data anomalies.
What You'll Learn:
- Strong, causal, and eventual consistency models
- When each model is appropriate
- Hybrid approaches and per-operation consistency levels
- Detecting and handling inconsistencies
4. Partition Tolerance and Failure Modes
Partitions happen. Network segments become isolated, services become unavailable, and cascades of timeouts ripple through your system. Designing for partition tolerance means accepting failure as a given.
What You'll Learn:
- Types of partitions and how they cascade
- Network partition detection strategies
- The relationship between timeouts and partition detection
- Designing for graceful degradation
5. Idempotency
Retries are essential in distributed systems, but they create the risk of duplicate processing. Idempotency allows safe retries without worrying about side effects.
What You'll Learn:
- Why idempotency matters for reliability
- Idempotent vs non-idempotent operations
- Implementing idempotency with tokens and versioning
- Patterns for safely retrying failed operations
Learning Path
Total Time: 45 minutes
- Start Here (7 min): Fallacies of Distributed Computing - Understand what you're up against
- Theory (10 min): CAP & PACELC Theorems - Learn the fundamental constraints
- Strategy (9 min): Consistency Models - Choose your approach
- Reliability (8 min): Partition Tolerance - Prepare for failures
- Practice (6 min): Idempotency - Make retries safe
Key Concepts Quick Reference
| Concept | Definition | Why It Matters |
|---|---|---|
| Network Partition | A break in communication between parts of a distributed system | You must choose between consistency and availability |
| Eventual Consistency | All nodes eventually converge to the same state | Enables high availability and partition tolerance |
| Strong Consistency | All reads reflect all completed writes | Simplifies application logic but reduces availability |
| Idempotent Operation | Producing the same result whether executed once or multiple times | Allows safe retries without duplicate side effects |
| Fallacy | A false assumption about how distributed systems work | Each one has cost if you design based on it |
Before You Move On
You should understand:
- Why the network is not reliable, latency is not zero, and bandwidth is not infinite
- The three properties of CAP and why you must choose two
- The spectrum of consistency models and their trade-offs
- How partitions affect your system and how to prepare for them
- Why idempotency enables safe retries
Next Section
Once you understand the constraints, explore Communication Patterns to see how services can interact effectively within these constraints.
📄️ Fallacies of Distributed Computing
Understand the eight false assumptions that undermine distributed systems and learn to design systems that don't depend on them.
📄️ CAP & PACELC Theorems
Understand the fundamental trade-offs: Consistency, Availability, Partition tolerance, and how PACELC refines these choices for modern systems.
📄️ Consistency Models and Trade-offs
Understand the spectrum of consistency models from strong to eventual, and how to choose the right model for your data and operations.
📄️ Partition Tolerance and Failure Modes
Understand how network partitions cascade through systems, failure detection strategies, and how to design for graceful degradation.
📄️ Idempotency
Master idempotent operations to enable safe retries and deliver reliable message processing without duplicates or missing data.
References
- Brewer, E. A. (2000). "Towards Robust Distributed Systems". PODC Keynote.
- Gilbert, S., & Lynch, N. A. (2002). "Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services". ACM SIGACT News.
- Maheshwari, S., & Mylesand, L. (2019). "CAP Twelve Years Later". IEEE Computer Magazine.
- Papadimitriou, C. H., & Deutsch, D. (2021). "A Different Kind of Time". Communications of the ACM.
- Coulouris, G., Dollimore, J., Kindberg, T., & Blair, G. (2011). "Distributed Systems: Concepts and Design" (5th ed.).