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Caution
You're viewing documentation for a previous version of ScyllaDB Open Source. Switch to the latest stable version.
ScyllaDB replicates data according to eventual consistency. This means that, in ScyllaDB, when considering the CAP Theorem, availability and partition tolerance are considered a higher priority over consistency. Although ScyllaDB’s tunable consistency allows users to make a tradeoff between availability and consistency, ScyllaDB’s consistency level is tunable per query.
However, over time, there can be a number of reasons for data inconsistencies, including:
a down node;
a network partition;
dropped mutations;
process crashes (before a flush);
a replica that cannot write due to being out of resources;
file corruption.
To mitigate entropy, or data inconsistency, ScyllaDB uses a few different processes. The goal of ScyllaDB anti-entropy - based on that of Apache Cassandra - is to compare data on all replicas, synchronize data between all replicas, and, finally, ensure each replica has the most recent data.
Anti-entropy measures include write-time changes such as hinted handoff, read-time changes such as read repair, and finally, periodic maintenance via repair.
ScyllaDB Hinted Handoff - High-Level view of ScyllaDB Hinted Handoff
ScyllaDB Read Repair - High-Level view of ScyllaDB Read Repair
ScyllaDB Repair - Description of ScyllaDB Repair
Also learn more in the Cluster Management, Repair and ScyllaDB Manager lesson on ScyllaDB University.
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