- I'm receiving a batch of messages to a Kafka topic. Each message has a timestamp, however the messages can arrive / get processed out of order. I.e event 1's timestamp could've been a few seconds before event 2, and event 2 could still get processed before event 1.
- I know the number of messages that are sent per batch. - I need to process the messages in order. The messages are basically providing the history of an item. I need to be able to track the history accurately (i.e, if an event occurred 3 times, i need to accurately log the dates of the first, 2nd, and 3rd time it occurred). The approach I'm considering is: - Creating a cassandra table which is ordered by the timestamp of the messages. - Once a batch of messages has arrived, writing them all to cassandra, counting on them being ordered by the timestamp even if they are processed out of order. - Then iterating over the messages in the cassandra table, to process them in order. However, I'm concerned about Cassandra's eventual consistency. Could it be that even though I wrote the messages, they are not there when I try to read them (which would be almost immediately after they are written)? Should I enforce consistency = ALL to make sure the messages will be available immediately after being written? Is there a better way to handle this thru either Kafka streams or Cassandra?
