Optimize AutoMQ’s broker performance with memory and network tuning, leveraging cloud-native scalability and Kafka compatibility for efficient resource allocation.
AutoMQ enhances server performance through methods like read/write efficiency optimization and resource isolation, ensuring stable and efficient operation.
Users should adjust parameters based on their hardware environment and business scenarios.
Control the size of the Block Cache; it influences the maximum size of data cached during Catch-up Read (catch-up read, cold read). In Catch-up Read scenarios, consider appropriately increasing this value.
When the memory allocation strategy is POOLED_DIRECT, the sum of s3.wal.cache.size and s3.block.cache.size should be about 1/2 of MaxDirectMemorySize (if MaxDirectMemorySize is large, it can be slightly increased).
The baseline for assessing local network bandwidth should be the network’s stable capability to achieve both outbound and inbound bandwidth.Since the server internally performs operations such as writing the WAL, S3, and Compaction, each data write from a client translates into three network writes by the server. By considering the business’s read/write ratio, you can ultimately determine the maximum write load a single machine can manage.
