1·mwhittaker.github.io
An Illustrated Proof of the CAP Theorem
Why I added thisWork through a concrete partition scenario and the consistency and availability requirements.
A CURATED READING LIST
Understanding CAP Theorem and Consistency
CAP is useful only when its definitions are clear. This sequence begins with a small illustrated argument, then adds Brewer's discussion of partition handling and recovery. The middle readings distinguish eventual consistency from stronger guarantees expressed through operation histories. The final paper asks what availability and convergence permit beyond the usual CAP slogan. Engineers comparing replicated databases should use the list to turn vague requirements into specific observations a client must be able to make. After each reading, sketch a two-replica example with delayed messages and state which outcomes are allowed. The progression moves from accessible explanation to formal research; the last resource deserves a slower reading than the first. Curated from collections by Dan Creswell (https://dancres.github.io/Pages/) and Fred Hébert (https://ferd.ca/a-distributed-systems-reading-list.html); commentary and reading order are original.
Curated by JakeUpdated Oct 10, 2026
Start hereAn Illustrated Proof of the CAP Theorem ↗
Read at your own pace. Save this list to keep track of your progress.
2·www.infoq.com
CAP Twelve Years Later: How the "Rules" Have Changed
Why I added thisRefine the familiar CAP shorthand by considering partition handling and recovery.
3·www.allthingsdistributed.com
Eventually Consistent
Why I added thisDistinguish consistency guarantees that applications can observe after a write.
4·aphyr.com
Strong consistency models
Why I added thisCompare operation histories rather than treating strong consistency as a single guarantee.
5·www.cs.utexas.edu
Consistency, Availability, and Convergence
Why I added thisFinish with formal limits connecting availability, causal consistency, and convergence.
KEEP EXPLORING
More from Jake
7 resources
Advanced Distributed Systems Research Papers
Use this collection after the fundamentals, consensus, and storage paths to explore design choices that cross those boundaries. Gray's economics paper and the data-engineering rules establish a cost-oriented way to reason about placement and caching; their numerical examples are historical, not present-day purchasing advice. The next papers examine ordering throughput and wide-area replication. Eventually consistent counters provide a focused replication problem, followed by Photon and Mesa as larger systems with demanding data-processing requirements. For each resource, identify the bottleneck, the mechanism introduced to address it, and the new constraint that mechanism creates. The sequence works well for a reading group because the papers invite concrete comparisons of assumptions and evaluation methods rather than agreement on one preferred architecture. Curated from collections by Dan Creswell (https://dancres.github.io/Pages/) and Fred Hébert (https://ferd.ca/a-distributed-systems-reading-list.html); commentary and reading order are original.
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Logical Clocks and Ordering in Distributed Systems
Event ordering is a prerequisite for understanding replication conflicts, snapshots, and recovery. Lamport's paper establishes happens-before and explains how logical timestamps can support a total ordering. The dotted version vector examples then make causality concrete in a replicated key-value setting. Interval tree clocks extend the discussion to changing populations of participants, and the global-state survey connects local observations to consistent snapshots. Zab closes the sequence with ordering requirements in a primary-backup service. These resources belong together because they show different uses of ordering rather than presenting timestamps as a universal solution. Draw small executions with concurrent events and delayed messages, and ask what information each mechanism preserves or intentionally discards. Curated from collections by Dan Creswell (https://dancres.github.io/Pages/) and Fred Hébert (https://ferd.ca/a-distributed-systems-reading-list.html); commentary and reading order are original.
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Distributed Transactions and Isolation
Transaction guarantees are easiest to discuss when their scope and observable behavior are explicit. Start with an everyday service interaction that motivates asynchronous work and compensation. The PostgreSQL chapter provides concrete isolation terminology, but it is deliberately identified as historical version documentation. Strong consistency models helps distinguish ordering guarantees from transaction isolation before the HAT paper analyzes which transactional properties can coexist with high availability. Megastore and Spanner then provide two system-level case studies. This sequence suits engineers designing workflows that span records, services, or datacenters. Write a short concurrent execution for every guarantee you intend to require, and separate the atomicity of a business workflow from the semantics of an individual database transaction. Curated from collections by Dan Creswell (https://dancres.github.io/Pages/) and Fred Hébert (https://ferd.ca/a-distributed-systems-reading-list.html); commentary and reading order are original.
By Jake
ReadingTrackA little more read. A little more remembered.