![]() ![]() This option is intended for a production environment for moving data scalably and reliably across systems.įor outdated JSON-format-only message download documentation, please refer to this resource on downloading selected topic messages. You can use source connectors to move data from your system into Kafka clusters and sink connectors to move data from Kafka clusters out to your system. Kafka Connect allows you to copy data between Kafka and other systems seamlessly: JDBC, JMS, Elasticsearch, and Amazon S3, just to name a few. If you want to move data around in a production fashion, please check out Kafka Connect. We advise against downloading messages for more than what you see on the Control Center UI (10–50 messages). Instead, it should be used for one-off downloads. It is crucial to note that you should not use this feature for production purposes. For example, download a handful of messages from within Control Center for manually inspecting the messages produced by KafkaProducer, for manually inspecting new schemas being applied to messages, or for aiding troubleshooting any Kafka message-related errors in Control Center. This is a simple yet effective tool if you are working with KafkaProducer, Confluent Schema Registry, or Control Center. Beyond downloading messages from the UI: Learn about other ways to move messages aroundĬontrol Center 6.2.0 enhances the existing download functionality in the “Topics” page to support message export in JSON, CSV (recently added in 6.2.0), or both formats.How to find messages: Learn about how to better navigate through “Messages” page.How to download messages from the UI: Step-by-step tutorial on how to download messages.What ”exporting Kafka messages” is: A description of the feature and its intended audience.Now that you are ready, let’s delve into the second feature here in part 2: exporting Kafka messages via Control Center. Having a running Control Center instance at hand helps you explore the features discussed in this blog series better. If you are not too familiar with Control Center, you can always refer to the Control Center overview first. Keep in mind that some features discussed in this series are only available in Confluent Platform. ![]() If you prefer setting up a Confluent Cloud environment, please refer to the Cloud quick start. An Linux machine with access to your Kafka clusterįor the purpose of the demo we will use a docker container called kafka-tools which has kafkacat and Kafka binary installed.To set up a simple Confluent Platform environment, including a Control Center instance, please refer to the quick start.An working Kafka cluster with some producers and consumers.In this article we will see a few more options to monitor Kafka consumers. Managed Kafka services like Confluent Kafka, Amazon MSK come with consumer lag monitoring built using Control Center, Cloud Watch respectively. An efficient streaming application requires consumer lag to be minimal, a higher consumer lag indicates performance problems with the consumers. If the rate of production is higher than the rate of consumption then consumer groups will exhibit lag. Kafka consumer lag gives the offset difference between last produced message and the last consumed message. ![]()
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