Which action is a recommended alternative to immediately adding a queue for write throughput improvements?

Which action is a recommended alternative to immediately adding a queue for write throughput improvements?

• A. Increase CPU by doubling the number of cores.
• B. Add more queues with more topics.
• C. Batch writes, schema optimization, and connection pooling.
• D. Switch to a different messaging system.

Answer: (C)

Optimizing write throughput is often about reducing the work each write incurs and making better use of the resources you already have. Batching writes combines many small writes into a single operation, which lowers per-write overhead, improves network and IO efficiency, and typically raises throughput without extra hardware. Batch processing also helps amortize fixed costs like transaction handling and disk seeks. Schema optimization trims the amount of data you need to write and serialize, so each write is cheaper to process. This can mean removing unnecessary fields, using tighter data types, or restructuring data to be more compact, all of which directly boosts how much you can push through the system. Connection pooling is about avoiding the repeated cost of establishing and tearing down connections. Reusing connections reduces latency and frees up resources to handle more writes in parallel, improving sustained throughput. These strategies together focus on making the current setup faster and more scalable with less risk and disruption than immediate hardware or architectural changes. Increasing CPU cores can help if the processor is the bottleneck, but it doesn’t address IO or network overhead and can be less efficient. Adding more queues or topics adds complexity and resource usage without solving the underlying per-write costs. Switching to a different messaging system is a larger shift with greater risk and downtime.

Optimizing write throughput is often about reducing the work each write incurs and making better use of the resources you already have. Batching writes combines many small writes into a single operation, which lowers per-write overhead, improves network and IO efficiency, and typically raises throughput without extra hardware. Batch processing also helps amortize fixed costs like transaction handling and disk seeks.

Schema optimization trims the amount of data you need to write and serialize, so each write is cheaper to process. This can mean removing unnecessary fields, using tighter data types, or restructuring data to be more compact, all of which directly boosts how much you can push through the system.

Connection pooling is about avoiding the repeated cost of establishing and tearing down connections. Reusing connections reduces latency and frees up resources to handle more writes in parallel, improving sustained throughput.

These strategies together focus on making the current setup faster and more scalable with less risk and disruption than immediate hardware or architectural changes. Increasing CPU cores can help if the processor is the bottleneck, but it doesn’t address IO or network overhead and can be less efficient. Adding more queues or topics adds complexity and resource usage without solving the underlying per-write costs. Switching to a different messaging system is a larger shift with greater risk and downtime.