Hi, I'm working on a project which uses Flink to compute hourly log statistics like top-K. The logs are fetched from Kafka by a FlinkKafkaProducer and packed into a DataStream.
The problem is, I find the computation quite challenging to express with Flink's DataStream API: 1. If I use something like `logs.timeWindow(Time.hours(1))`, suppose that the data volume is really high, e.g., billions of logs might be generated in one hour, will the window grow too large and can't be handled efficiently? 2. We have to create a `KeyedStream` before applying `timeWindow`. However, the distribution of some keys are skewed hence using them may compromise the performance due to unbalanced partition loads. (What I want is just rebalance the stream across all partitions.) 3. The top-K algorithm can be straightforwardly implemented with `DataSet`'s `mapPartition` and `reduceGroup` API as in [FLINK-2549](https://github.com/apache/flink/pull/1161/), but not so easy if taking the DataStream approach, even with the stateful operators. I still cannot figure out how to reunion streams once they are partitioned. 4. Is it possible to convert a DataStream into a DataSet? If yes, how can I make Flink analyze the data incrementally rather than aggregating the logs for one hour before starting to process?
