Kafka Best Practices
This section summarizes best practices of Distributed Message Service (DMS) for Kafka in common scenarios. Each practice is given a description and procedure.
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Best Practice |
Description |
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This document provides producers and consumers with message suggestions, improving the efficiency and reliability of message sending and consumption. |
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This document describes how to optimize consumer polling in scenarios where real-time message consumption is not required, saving resources when there are few or no messages. |
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Interconnecting Logstash to Kafka to Produce and Consume Messages |
Kafka instances are available as the input and output sources of Logstash. This document describes how to connect Logstash to Kafka instances for message production and consumption. |
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MirrorMaker can mirror data from a source cluster to a target cluster. This document describes how to use MirrorMaker to synchronize data between two Kafka instances unidirectionally or bidirectionally. |
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This document describes the causes of message stacking and the handling measures. |
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This document describes the causes of high CPU usage and full disk space and the handling measures. |
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This document describes the causes of unbalanced service data and the handling measures. |
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This document describes how to generate an alarm when the number of stacked messages exceeds a specified threshold. In this way, you can be aware of the service running status in time by SMS or email. |
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