Cassandra Practice Test 2026 – Comprehensive All-in-One Guide to Exam Success!

Data compaction in Cassandra is primarily beneficial for achieving more optimal disk usage. During the compaction process, multiple SSTables (Sorted String Tables) are merged together, which helps to eliminate tombstones (markers for deleted data) and duplicate entries. This results in a more efficient organization of data on disk, which can lead to less wasted space and improved read performance, as more relevant data can be accessed more quickly.

Focusing on disk optimization helps ensure that the database can manage larger datasets without unnecessarily increasing storage costs or complexity. This is particularly important as applications scale and more data is ingested into the system regularly.

In contrast, while faster writes might be mistakenly thought to be a direct benefit of compaction, the process itself can momentarily slow down write operations due to the merging of data. Similarly, data integrity generally becomes more manageable with compaction rather than more complex, and compaction does not inherently increase memory pressure; in fact, it may help in reducing it by streamlining data storage and access patterns.