DS201 Study Notes: Cassandra Read Repair

DS201 course study notes on Cassandra's Read Repair mechanism: how it detects and fixes data inconsistencies across replicas during reads.

Study notes from DS201: Foundations of Apache Cassandra™ and DataStax Enterprise.

Read Repair

In Cassandra, since data replicas are distributed across multiple nodes, maintaining data consistency across those replicas is important. Read Repair is a mechanism that automatically runs during read operations to fix data inconsistencies between replicas.

How It Works

When a client reads data, Cassandra collects data from multiple nodes (the replica set) that hold replicas of that data. Each replica has a timestamp indicating when the data was last updated.

Cassandra returns the data with the most recent timestamp to the client. At the same time, if the timestamps or contents differ between the collected replicas, Cassandra performs Read Repair. This is the process of updating replicas with stale data to the latest version, restoring data consistency.

read_repair_chance

read_repair_chance is a configuration value that specifies the probability of automatically performing Read Repair during read operations. This value is set in the range of 0 to 1 with the following meanings:

  • 0: Read Repair is not performed.
  • 1: Read Repair is always performed on every read operation.
  • Values between 0 and 1: Read Repair is performed with the specified probability.

Setting a higher read_repair_chance value means data consistency is checked more frequently and inconsistencies are resolved sooner, but read operation latency may increase. Conversely, setting a lower value improves read performance but may delay the resolution of inconsistencies.

Read Repair is an important feature that complements Cassandra’s “Eventual Consistency” model and enhances data consistency.