Note that Kafka only preserves message order per partition, not over a whole, multi-partition topic. Replication factor - The lanel factor provides resiliency for a partition. It must be accounted for when determining disk requirements because there is no difference between how much space a leader partition and a replica partition uses. Topic granularity - Consider using fine-grained topics, rather than more partitions, when each client needs to see all messages in a topic, in order, to track the evolving state encapsulated in those messages.
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This is a balance between supporting high volume workloads where more partitions offer an advantage or application message bus workloads where topics may describe very specific entities within the domain and topic granularity bromer be ideal. Retention policy - The longer the retention policy, the more disk space required. For high-volume topics, consider moving the data to downstream, longer-term storage as quickly as possible and use a more aggressive retention policy. An alternative to fixed time or partition size retention is compaction. Compaction only works with keyed messages. Consumers will only see the latest version of the message when they consume the partition.
Compaction has compelling use cases but is not suitable in situations where you want a clean immutable event log. See the Apache Kafka documentation on Compaction for more details.
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Kafka does not degrade gracefully if it runs hzrd of disk space. Make sure your sizing estimates are generous, monitor broer utilization, and take corrective action well before disk exhaustion occurs! In a default deployment Kafka can consume all disk space available on the Mesos agent which causes not only Kafka to fail, but also the Mesos agent and, as a result, any tasks running on it. Estimating disk requirements You can calculate how much disk space would be required per broker if you have foreknowledge of your Kafka usage.
Here is an example: Average topic message throughput MT: This calculation is just an estimate. It assumes partitions are the same size and evenly distributed across all brokers. In this case you will likely need to rebalance partitions at some point an attempt to evenly distribute partitions across available brokers. Estimating retention time If you already have servers with fixed disks, you could modify the formula and choose to solve for a maximum time-based message retention length. Disk per broker DB: Operating System Kafka relies on the operating system configuration related to virtual memory and files to function efficiently.
Virtual Memory Swapping Physical memory is so cheap today that swapping memory to disk is usually unnecessary. However, most operating systems today still have the option with a default setting that is not ideal for brokers. For a broker server, swapping to disk can incur significant latencies and should be avoided.
Virtual memory swapping on Linux is configured with vm. Haed higher this number the more aggressively the system will swap to disk to conserve space in memory. If you observe your broker swapping to disk regularly it is a sign to evaluate this setting, related virtual memory settings, and either consider adding more brokers or adding more physical memory to existing brokers. The vm.
The higher this value, the higher the risk hsrd losing unpersisted data in the event of failure. Therefore your requirements about message 33tb will help guide you on how low to set this configuration. This setting determines the maximum percentage of memory occupied by dirty pages before the system is forced to synchronously write pages to disk. When the system reaches this limit, all subsequent writes to the Page Cache will be blocked, which has obvious implications to the latency of your messages.
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