Eventually Consistent.

Vogels, W. (2009). Eventually Consistent. Communications of the ACM, 52(1), 40-44.

Type

Journal article

Intellectual base

Primary research

Year

2009

DOI

10.1145/1435417.1435432

Link

https://doi.org/10.1145/1435417.1435432

Cited from

cognitive_science, computer_science, logistics_supply_chain, systems_cybernetics

Cited by

7 citations across 7 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Bottleneck
  • Listed in the references but not attached to a specific claim.
• Concurrency
  • A common failure is designing for strong consistency without measuring the scalability cost, then being surprised by contention as load increases

    This sourceExplains eventual consistency and the consistency/availability trade-offs of replicated distributed data. SUPPORTS marker 060 (T4: scalability vs coherence — accepting eventual consistency at the cost of application complexity). DOI verified.

• Consistency Model
  • The management move is to match the guarantee to the operation rather than to the system as a whole — strong consistency for funds movement, eventual consistency for like-counts

    This sourceArticulates eventual consistency and the per-operation tiering of strong versus weak guarantees in large-scale systems.

• Eventual Consistency
  • The pattern compresses the design of a wide class of large-scale shared-state systems into four choices: which local writes are permitted without coordination; how changes propagate (push, pull, broadcast, gossip topology); how concurrent updates are merged (last-write-wins, version-vector, operational transformation, conflict-free replicated types); and what staleness bound the system advertises to its users.

    This sourceDefines eventual consistency, the staleness window, and the trade of consistency for availability and partition tolerance in large-scale distributed stores.

• Record-Reality Divergence
  • And cadence-to-stakes matching appears as the consistency-model choice: relaxed/eventual consistency tolerates staleness for speed where the stakes are low, while strong consistency forces near-synchronous reconciliation where correctness is critical.
• Scalability
  • A common failure is choosing replication factors based on capacity concerns rather than failure-mode analysis, leading to either over-replicated systems that are expensive and slow to update, or under-replicated systems that fail when a single node fails
• Shared Mental Model
  • And the distributed-database vocabulary of consistency models — strong consistency, eventual consistency, read-your-writes, monotonic reads — gives a precise language for shared-model update disciplines

    This sourceDefines and contrasts distributed-database consistency models (strong, eventual, read-your-writes, monotonic reads), supplying the vocabulary for shared-model update disciplines.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

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