If you want to change the values, you can work out the optimal size of your buffers using a bandwidth-delay product calculation. Use .data.dir to specify the number of threads used for log loading at startup and flushing at shutdown. Adding more threads can improve throughput, but the number of CPU cores and disk bandwidth imposes a practical upper limit.Ī good starting point might be to start with the default of 8 multiplied by the number of disks. I/O threads ( num.io.threads) pick up requests from the request queue to process them. If there is 0% idle time, all resources are in use, which means that more threads could be beneficial. For example, metrics for the average time network threads are idle ( work:type=SocketServer,name=NetworkProcessorAvgIdlePercent) indicate the percentage of resources used. Kafka broker metrics can help with working out the number of threads required. To reduce congestion and regulate the request traffic, you can use to limit the number of requests allowed in the request queue before the network thread is blocked. Network threads ( ) handle requests to the Kafka cluster, such as produce and fetch requests from client applications.Īdjust the number of network threads to reflect the replication factor and the levels of activity from client producers and consumers interacting with the Kafka cluster. Improving request handling throughput by increasing I/O threads WARNING: Do not reduce these settings in production. This time you’re making sure that data is not accidentally lost, although you can temporarily enable the property to delete topics if circumstances demand it. Kafka users normally disable this property in production too. The property is enabled by default to allow topics to be deleted. In this case, you might want to set the replication factor to at least three replicas so that data is more durable by default. If you are using automatic topic creation, you can set the default number of partitions for topics using num.partitions. It’s usually disabled in production as Kafka users tend to prefer applying more control over topic creation. The property is enabled by default so that topics are created when needed by a producer or consumer. We’ll go into more detail about leaders, followers and in-sync replicas when discussing partition rebalancing for availability. Using replication, a failed broker can recover from in-sync replicas on other brokers. Topic replication is central to Kafka’s reliability and data durability. The importance of Kafka’s topic replication mechanism cannot be overstated. In which case, the defaults are applied to topics that do not have these properties set explicitly, including automatically-created topics. You can also set these properties at the broker level too. You can use Strimzi’s KafkaTopic to do this. When you configure topics, the number of partitions, minimum number of in-sync replicas, and partition replication factor are typically set at the topic level. These properties are ignored if they are added to the config specification. Some properties are managed directly by Strimzi, such as broker.id. Where example values are shown for properties, this is usually the default - adjust accordingly. In this post, we suggest what else you might add to optimize your Kafka brokers. In Strimzi, you configure these settings through the config property of the Kafka custom resource.
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