Although the ordering for each partition is kept, the order of messages fetched from all partitions is not guaranteed, as it does not necessarily reflect the order in which they were sent. Potentially, things become less precise when a consumer is consuming messages from multiple partitions. The consumer can then observe messages in the same order that they were committed to the broker. If you want a strict ordering of messages from one topic, the only option is to use one partition per topic. Kafka only provides ordering guarantees for messages in a single partition. If you want to read more about what each property does, see Kafka’s consumer configs. Note, however, that you should avoid using any properties that cause conflict with the properties or guarantees provided by your application. You will also want to ensure your configuration helps reduce delays caused by unnecessary rebalances of consumer groups. Minimizing the impact of rebalances to reduce downtimeĪs with producers, you will want to achieve a balance between throughput and latency that meets your needs.Handling of transactional messages from the producer and consumer side. ![]() Data loss or duplication when committing offsets or recovering from failure.Latency measured in the time it takes for messages to be fetched from the broker.Throughput measured in the number of messages processed over a specific period.Consumer scalability to accommodate increased throughput.We’ll look at how you can use a combination of these properties to regulate: We won’t cover all possible consumer configuration options here, but examine a curated set of properties that offer specific solutions to requirements that often need addressing: On top of our minimum configuration, there are a number of properties you can use to fine-tune your consumer configuration. A client id is advisable, as it can be used to identify the client as a source for requests in logs and metrics.īrvers = localhost:9092 key.deserializer = .serialization.StringDeserializer serializer = .serialization.StringDeserializer client.id = my-client Key properties It will also require deserializers to transform the message keys and values. Basic consumer configurationĪ basic consumer configuration must have a host:port bootstrap server address for connecting to a Kafka broker. If you want to read more about performance metrics for monitoring Kafka consumers, see Kafka’s Consumer Fetch Metrics. Think about the outcomes you expect from your consumers in terms of reliability and stability. As with producers, you will want to monitor the performance of your consumers before you start to make your adjustments. And you will also want to make sure, as far as possible, that you have scaled your consumer groups appropriately to handle the level of throughput you expect. When looking to optimize your consumers, you will certainly want to control what happens to messages in the event of failure. ![]() As we shall see in this post, some consumer configuration is actually dependent on complementary producer and Kafka configuration. Or at least not be an impediment after the improvements you’ve made. If you make your producer more efficient, you will want to calibrate your consumer to be able to accommodate those efficiencies. It is worthwhile considering both ends of the streaming pipeline. Here we’re going to examine commonly-used tuning options that optimize how messages are consumed by Kafka consumers. We recently gave a few pointers on how you can fine-tune Kafka producers to improve message publication to Kafka.
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