Distributed Systems Overview

Overview

A distributed system is a collection of independent computers that appears to its users as a single coherent system. These computers communicate and coordinate their actions by passing messages over a network. Distributed systems enable scalability, fault tolerance, and geographic distribution, but introduce fundamental challenges around consistency, coordination, and failure handling.

Why Distributed Systems?

graph TB
    REASONS[Why Distribute?] --> SCALE[Scalability<br/>Handle more load]
    REASONS --> FAULT[Fault Tolerance<br/>No single point of failure]
    REASONS --> LATENCY[Low Latency<br/>Servers closer to users]
    REASONS --> AVAIL[Availability<br/>24/7 service]

Fundamental Challenges

graph TB
    CHALLENGES[Distributed System Challenges] --> TIME[Time & Ordering<br/>No global clock]
    CHALLENGES --> CONSENSUS[Agreement<br/>Getting nodes to agree]
    CHALLENGES --> FAILURE[Failure Detection<br/>Is it slow or dead?]
    CHALLENGES --> CONSISTENCY[Consistency<br/>Keeping data in sync]
    CHALLENGES --> PARTITION[Network Partitions<br/>Messages can be lost/delayed]

The Eight Fallacies of Distributed Computing

Peter Deutsch’s fallacies (1994):

1. The network is reliable — It isn’t. Messages get lost, connections drop.
2. Latency is zero — It isn’t. Cross-datacenter communication takes milliseconds.
3. Bandwidth is infinite — It isn’t. Network congestion is real.
4. The network is secure — It isn’t. Every communication can be intercepted.
5. Topology doesn’t change — It does. Nodes join and leave constantly.
6. There is one administrator — There isn’t. Multiple teams manage different parts.
7. Transport cost is zero — It isn’t. Serialization, encryption, and routing cost CPU and time.
8. The network is homogeneous — It isn’t. Different hardware, protocols, and configurations.

Topics in This Section

Real-World Distributed Systems

Interview Focus

• Explain the CAP theorem and its real-world implications
• Describe the difference between consistency models
• Explain why distributed consensus is hard
• Describe how vector clocks capture causality
• Give examples of distributed systems you use daily

Cross References

• CAP Theorem
• Consistency Models
• Consensus
• Replication
• Cloud Overview