ScyllaDB Anti-Entropy¶

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:

1. a down node;

2. a network partition;

3. dropped mutations;

4. process crashes (before a flush);

5. a replica that cannot write due to being out of resources;

6. 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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