How can you improve read performance in Cassandra?

Improving read performance in Cassandra often revolves around optimizing how data is queried and accessed. Designing queries specifically for the intended access patterns is critical in achieving efficient data retrieval. When queries are aligned with the way data is partitioned, they can significantly reduce the amount of data that needs to be scanned, which in turn minimizes read latencies.

Optimizing indexes also plays a vital role in enhancing read performance. Properly chosen secondary indexes can help to efficiently filter and retrieve data without requiring a full scan of the dataset. This means read operations can be executed faster, resulting in lower response times and better overall performance.

Other strategies, while beneficial in different contexts, do not address read performance in the same direct manner as designing access patterns and optimizing indexes. For example, random partition keys can lead to even distribution of data but might not enhance read efficiency directly. Increasing the number of replicas can improve availability but does not inherently increase read efficiency. Reducing the size of SSTables is related to storage management and can impact performance positively, yet it does not specifically target the integrity of read operations effectively as strategically designing queries does.