Description

What this gets into:
Distributed systems trade-offs are among the most consequential design decisions in system architecture and among the most commonly made without adequate understanding of what is being traded. This module builds the reasoning framework behind those trade-offs rather than the vocabulary for describing them.

Technical territory covered:
– CAP theorem in practice: how the theoretical constraints of consistency, availability, and partition tolerance translate into actual design decisions — which consistency models different distributed databases and storage systems implement, what that means for the applications built on them, and how to select storage systems based on the consistency requirements of the specific workload rather than general reputation
– Replication strategies and their implications: synchronous versus asynchronous replication, quorum-based approaches, how each affects write latency, read staleness, and failure behavior, and how to reason about the right trade-off for a given system’s tolerance for stale reads versus write availability under node failure
– Failure detection and system behavior under partition: how distributed systems detect node failure, how they behave during network partitions, and how to design for the failure modes that distributed systems produce rather than the ideal-case behavior they exhibit under normal conditions

Estimated hours: +/- 4

Engineering outcome:
A reasoning framework for distributed system trade-offs that applies to every subsequent design decision involving distributed storage, replication, or consistency — grounded in understanding what is actually being traded rather than which option is most commonly recommended.