I have a Kafka cluster which receives messages from a source based on data changes in that source. In some cases the messages are meant to be processed in the future. So I have 2 options:<ol><li>Consume all messages and post messages that are meant for the future back to Kafka under a different topic (with the date in the topic name) and have a Storm topology that looks for topics with that date's name in it. This will ensure that messages are processed only on the day it's meant for.</li> <li>Store it in a separate DB and build a scheduler that reads messages and posts to Kafka only on that future date.</li> </ol>
Option 1 is easier to execute but my question is: Is Kafka a durable data store? And has anyone done this sort of eventing with Kafka? Are there any gaping holes in the design?Answer1:
You can configure the amount of time your messages stay in Kafka (log.retention.hours).
But keep in mind that Kafka is meant to be used as a "real-time buffer" between your producers and your consumers, not as durable data store. I don't think Kafka+Storm would be the appropriate tool for your use case. Why not just write your messages in some distributed file system, and schedule a job (MapReduce, Spark...) to process those events?