Summary
The video discusses how network issues and failures can cause nodes to go out of sync in a Cassandra cluster, leading to the need for repair. It explains the trade-off between consistency and availability, as dictated by the CAP theorem, when handling database queries during network partitions. Timestamps play a crucial role in ensuring data consistency across replicas, with the coordinator node identifying the most recent data and updating out-of-date nodes accordingly. Apache Cassandra employs read repair probabilistically to maintain data consistency, while caution should be taken when performing full repairs to prevent clustering issues. Refreshing nodes periodically is also emphasized to maintain cluster health.
Repair in Apache Cassandra
Nodes can get out of sync due to network issues or failures, leading to the need for repair. Consistency versus availability must be considered when querying the database. CAP theorem plays a role in deciding whether to prioritize consistency or availability during a network partition.
Request Processing in Cassandra Cluster
Explains the process of handling requests in a Cassandra cluster with nodes storing replicas of data. Coordinator node optimizes by requiring a checksum of data before returning it to the client. Timestamps are used to ensure data consistency in replicas.
Data Consistency and Timestamps
Discusses data consistency issues during network partitions with varying timestamps on replicas. Coordinator node identifies the most recent data and sends updates to out-of-date nodes. Consistency levels in queries can impact data consistency in the cluster.
Read Repair in Apache Cassandra
Apache Cassandra performs read repair probabilistically with dclocal_read_repair_chance asynchronously. Full repairs should be done cautiously to avoid clustering issues. Emphasizes the importance of occasional node refresh for nodes that are not frequently read from.
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