Konsistenssitasojen

Konsistenssitasojen, or consistency levels, is a concept used in distributed computing to describe the guarantees a system provides about the visibility and ordering of updates across replicas. It characterizes how uniformly data appears after writes and during reads, balancing data correctness against latency and available performance. The term is common in databases, distributed caches, and cloud services.

Common models include:

- Strong consistency (linearizability): after a write completes, all reads reflect the new value, with a global order of operations that matches real time.

- Sequential consistency: operations appear in a single, global sequence that preserves each process’s order, but not necessarily real time.

- Causal consistency: causal relationships between operations are preserved; if one operation causally affects another, the effect is visible in the proper order.

- Eventual consistency: replicas converge over time, but reads may initially show stale data; there is no bound on staleness.

- Session or read-your-writes: within a session, a client’s reads reflect its own prior writes; often includes monotonic reads.

- Bounded staleness: reads are guaranteed to be within a defined freshness window or version.

Implementation and measurement considerations include replication strategies, quorum-based reads and writes, and the use of clocks or versioning (such as vector clocks) to detect conflicts. Consensus protocols (Paxos, Raft) underpin some strongly consistent systems, while weaker models leverage asynchronous replication for lower latency.

Trade-offs are central: stronger consistency typically increases latency or reduces availability during partitions, whereas weaker levels improve responsiveness but may allow stale or conflicting data. Konsistenssitasojen guide architectural choices in distributed databases (for example, Cassandra, DynamoDB, MongoDB, Spanner) and other multi-node systems, shaping data reliability and user experience.

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