Hi, we are migrating some jobs from Dstream to Structured Stream. Currently to handle aggregations we call map and reducebyKey on each RDD like rdd.map(event => (event._1, event)).reduceByKey((a, b) => merge(a, b))
The final output of each RDD is merged to the sink with support for aggregation at the sink( Like co-processor at HBase ). In the new DataSet API, I am not finding any suitable API to aggregate over the micro-batch. Most of the aggregation API uses state-store and provide global aggregations. ( with append mode it does not give the change in existing buckets ) Problems we are suspecting are : 1) state-store is tightly linked to the job definitions. while in our case we want may edit the job while keeping the older calculated aggregate as it is. The desired result can be achieved with below dataset APIs. dataset.groupByKey(a=>a._1).mapGroups( (key, valueItr) => merge(valueItr)) while on observing the physical plan it does not call any merge before sort. Anyone aware of API or other workarounds to get the desired result?
