Why shouldn't Cassandra batches be used to improve write throughput?

It's a common misconception carried over from relational databases that batching writes always improves throughput. In Cassandra, batching across multiple partitions usually does the opposite.

• A multi-partition logged batch has to write to the batchlog first, then fan out each individual statement to its own set of replicas — that's more total work than sending the same statements independently, not less.
• The coordinator handling the batch becomes a bottleneck: instead of spreading writes evenly across the cluster, it has to orchestrate every statement in the batch itself.
• Large batches increase the risk of hitting batch_size_warn_threshold / batch_size_fail_threshold, and can cause coordinator-side memory pressure.

In Cassandra, throughput comes from spreading independent writes across many coordinators and replicas in parallel — typically via async driver calls — not from bundling them together. Batches should be reserved for genuine atomicity needs on a small number of statements, ideally within a single partition, rather than treated as a bulk-loading or performance optimization.

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