we attach the Interceptor to the channel
To give some background: We are currently buffering monitoring data into Kafka, where each message in Kafka records several metrics at a point in time.
For each of the record, we need to perform some calculation based on the metrics in the record, append the results (multiple of them) to the record and send the resulting record into a data store (let’s call it DS1). All data required for the calculation are encapsulated in the record, essentially making this an embarrassingly parallel problem.
The formula for the calculation is stored in a different data store (let’s call it DS2), and can be changed (add/delete/modified by user). We are not required to react to the change immediately, but we should do so in reasonable time (e.g. 5 minutes).
Currently, we have prototyped an implementation which implements the data processing as described above in an Interceptor. We define the source as Kafka, the Sink as the sink for DS2, and we attach the Interceptor to the channel. As described above, the Interceptor will be reading the formula from DS1 regularly for any change, and will be responsible for processing the data as they come in from Kafka.
We are aware of other streaming processing frameworks such as Spark of Kafka. However, the implementation above is motivated by the fact that Flume has provided reliable streaming, and we want to reuse as much code as possible.
Is this usage of Flume a good idea in term of performance and scalability?
Hong Dai Thanh.