Linearizability
Linearizability is a consistency property in which each operation appears to take effect atomically at a point between invocation and completion while preserving real-time order.
Think Distributed Systems
CAP consistency is the safety guarantee that every successful read reflects the value of the most recent write, or the read returns a failure response.
- How it is used
- Defines the strongest consistency guarantee for reasoning about operations on replicated distributed objects.
- How deep it goes
- Formalizes sequential equivalence and real-time ordering, then stops short of implementation details by deferring consensus algorithms.
- Examples
- Uses read/write registers, queues, stacks, and a replicated increment-only counter to expose valid and stale histories.
- What you can do after
- Evaluate histories and systems by checking sequential equivalence, real-time ordering, and object-specific operation semantics.
Read how Think Distributed Systems explains linearizability →
Modern C, Third Edition
Linearizability is a concurrency correctness property under which each operation appears to take effect instantaneously at a point consistent with the ordering observed by threads.
- How it is used
- Coordinates reasoning about shared state accessed by multiple threads through atomic objects and mutex-protected critical sections.
- How deep it goes
- Defines indivisibility and observable ordering, then applies them to atomic fields and mutexes without formalizing happened-before.
- Examples
- Uses atomic simulation fields, a finished flag, cursor state, and a mutex-protected 3×3 cell-birth operation.
- What you can do after
- Evaluate concurrent updates by identifying atomic operations and mutex boundaries that establish indivisible, observable ordering.
Latency
Linearizability is a consistency property in which each operation appears to take effect atomically at a single point between its invocation and completion, preserving real-time order.
- How it is used
- Provides a strong consistency model for replicated key–value stores and other systems requiring single-copy data semantics.
- How deep it goes
- Defines operation scope, contrasts serializability, illustrates replica state changes, and notes implementation complexity and latency cost.
- Examples
- Uses put(A, 1), put(B, 2), and put(A, 3) across a three-replica key–value store.
- What you can do after
- Evaluate/understand when single-copy semantics justify linearizability’s latency cost and how replicated systems preserve acknowledged updates.
ScyllaDB in Action
Linearizability is a consistency property in which each operation appears to occur atomically in an order that respects real-time ordering.
- How it is used
- Explains how ScyllaDB reads can guarantee the latest value through SERIAL consistency.
- How deep it goes
- Defines linearizability through ordered latest-value reads, then defers detailed Paxos costs and operation.
- Examples
- Uses SERIAL read ordering, stale-read figures, and QUORUM reads and writes as concrete illustrations.
- What you can do after
- Apply ScyllaDB SERIAL or QUORUM consistency according to latest-value guarantees and Paxos cost.
Related concepts
- serializationcontrasts with · Serialization is the encoding of structured data or in-memory values into a representation suitable for storage or transmission and later reconstruction.
- consistencyequivalent to · Consistency is the property of related data, behavior, outputs, or states agreeing with one another or conforming to defined rules across relevant instances or operations.
- consistency modelkind of · A consistency model is a formal specification of how operations on state may be ordered and what values those operations can observe.
- safetykind of · Safety is the property of a system or process whereby specified harmful, hazardous, invalid, or otherwise forbidden outcomes are avoided under defined conditions.
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