Cassandra Practice Test

Which of the following strategies helps in avoiding large partitions in Cassandra?

1. Using wide rows

2. Implementing effective data modeling

3. Storing data as blobs

4. Disabling compaction

The correct answer is: Implementing effective data modeling

Implementing effective data modeling is crucial for avoiding large partitions in Cassandra. In Cassandra, partitions are segments of data that are stored together on disk, and their size can significantly affect both performance and storage efficiency. When data modeling is done correctly, it ensures that partitions are kept at a manageable size, leading to better performance during read and write operations, as well as improved overall system stability. Effective data modeling involves understanding the access patterns of your queries and designing tables and partition keys that distribute data evenly across nodes. This helps prevent situations where one partition ends up containing too much data, which could lead to slow queries and increased resource consumption. While using wide rows can sometimes seem like a viable strategy, it generally leads to larger partitions, especially if too much data is stored within a single partition. Storing data as blobs doesn’t inherently resolve partition size issues, and actually, it may complicate data retrieval. Disabling compaction is counterproductive, as compaction is designed to manage and optimize data storage; without it, table performance and efficiency may decline. Thus, effective data modeling stands out as the best strategy for avoiding large partitions in Cassandra.