1·lamport.azurewebsites.net
Time, Clocks, and the Ordering of Events in a Distributed System
Why I added thisBuild the happens-before relation and distinguish partial order from total order.
A CURATED READING LIST
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.
Curated by JakeUpdated Oct 10, 2026
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2·github.com
Dotted Version Vectors
Why I added thisUse the repository's worked examples to understand causality and false conflicts.
3·gsd.di.uminho.pt
Interval Tree Clocks: A Logical Clock for Dynamic Systems
Why I added thisExamine causality tracking when participants can be created and retired dynamically.
4·www.cs.cornell.edu
Consistent Global States of Distributed Systems: Fundamental Concepts and Mechanisms
Why I added thisConnect event ordering to meaningful global snapshots and consistent cuts.
5·www.cs.cornell.edu
Zab: High-performance broadcast for primary-backup systems
Why I added thisCompare primary-backup broadcast and recovery with consensus-based log replication.
KEEP EXPLORING
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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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Compare storage designs by starting with the workload and the guarantees each system must provide. GFS introduces a file-oriented design shaped by large data-processing jobs, and Bigtable builds a structured storage model for varied applications. Dynamo makes a useful contrast through its availability goals and treatment of conflicting versions. Megastore and Spanner bring transactional semantics into the comparison, while Mesa ends with the distinct needs of analytic data warehousing. Readers should already understand replication and basic consistency terminology. Build a comparison sheet covering partitioning, replication, failure recovery, and the responsibilities left to clients. The objective is to explain why a design fits its workload, rather than rank the papers as competing universal solutions. 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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