Tunable consistency

Some databases let you choose the consistency level per query. Use strong levels for money and inventory, weak ones for feeds and analytics, and accept the latency difference consciously.

Where it shows up in real systems

  • Wide-column store

    Every node accepts writes and each row is stored on RF replicas. The consistency level decides how many replicas must answer: ONE is fast and stays available when nodes fail but can return stale data; QUORUM on both reads and writes guarantees you read your latest write, at the cost of latency and availability. This is PACELC in practice.

    In production: Apache Cassandra, ScyllaDB, Amazon Keyspaces, Azure Managed Instance for Cassandra

See it break, then fix it

Build an architecture, send it a day of traffic and outages, and learn why it holds or fails. Free to start.