Hi Stephan: I totally agree with you, this discussion covers too many topics, so we can cut it into a series of sub-discussions proposed by you, firstly we can focus on phrase-1: “What Flink API Stack Should be for a Unified Engine”. Best, Feng Wang
On Dec 3, 2018, at 19:36, Stephan Ewen <se...@apache.org<mailto:se...@apache.org>> wrote: Hi all! This is a great discussion to start and I agree with the idea behind it. We should get started designing what the Flink stack should look like in the future. This discussion is very big, though, and from past experiences if the scope is too big, the discussions and up falling apart when everyone goes into different details. So my suggestion would be to stage this discussion and take this aspect after aspect, starting with what we want to expose to users and then going into the internal details. *Discussion (1) What should the API stack look like* - Relationship of DataStream, DataSet, and Table API - Where do we want automatic optimization, and where not - Future of DataSet (subsumed in data stream, or remains independent) - What happens with iterations - What happens with the collection execution mode *Discussion (2) What should the abstractions look like.* - This is based on the outcome of (1) - Operator DAG - Operator Interface - what is sent to the REST API when a job is submitted, etc. - modules and dependency structure *Discussion (3) what is special for Batch* - I would like to follow the philosophy that "batch allows us to activate additional optimizations" made possible by the bounded nature of the inputs. - special case scheduling - additional runtime algorithms (like hybrid hash joins) - no watermarks / late data / etc. - Special casing in failover (or possibly not, could be still the same core mechanism What do you think? Best, Stephan On Mon, Dec 3, 2018 at 12:17 PM Haibo Sun <sunhaib...@163.com<mailto:sunhaib...@163.com>> wrote: Thanks, zhijiang. For the optimization, such as cost-based estimation, we still want to keep it in the data set layer, but your suggestion is also a thought that can be considered. As I know, currently these batch scenarios have been contained in DataSet, such as the sort-merge join algorithm. So I think that the unification should consider such features as input selection at reading. Best, Haibo At 2018-12-03 16:38:13, "zhijiang" <wangzhijiang...@aliyun.com.INVALID<mailto:wangzhijiang...@aliyun.com.INVALID>> wrote: Hi haibo, Thanks for bringing this discussion! I reviewd the google doc and really like the idea of unifying the stream and batch in all stacks. Currently only network runtime stack is unified for both stream and batch jobs, but the compilation, operator and runtime task stacks are all separate. The stream stack developed frequently and behaved dominantly these years, but the batch stack was touched less. If they are unified into one stack, the batch jobs can also get benefits from all the improvements. I think it is a very big work but worth doing, left some concerns: 1. The current job graph generation for batch covers complicated optimization such as cost-based estimate, plan etc. Would this part also be considered retaining during integrating with stream graph generation? 2. I saw some other special improvements for batch scenarios in the doc, such as input selection while reading. I acknowledge these roles for special batch scenarios, but they seem not the blocker for unification motivation, because current batch jobs can also work without these improvements. So the further improvments can be separated into individual topics after we reaching the unification of stream and batch firstly. Best, Zhijiang ------------------------------------------------------------------ 发件人：孙海波 <sunhaib...@163.com<mailto:sunhaib...@163.com>> 发送时间：2018年12月3日(星期一) 10:52 收件人：dev <dev@flink.apache.org<mailto:dev@flink.apache.org>> 主 题：[DISCUSS] Unified Core API for Streaming and Batch Hi all, This post proposes unified core API for Streaming and Batch. Currently DataStream and DataSet adopt separated compilation processes, execution tasks and basic programming models in the runtime layer, which complicates the system implementation. We think that batch jobs can be processed in the same way as streaming jobs, thus we can unify the execution stack of DataSet into that of DataStream. After the unification the DataSet API will also be built on top of StreamTransformation, and its basic programming model will be changed from "UDF on Driver" to "UDF on StreamOperator". Although the DataSet operators will need to implement the interface StreamOperator instead after the unification, user jobs do not need to change since DataSet uses the same UDF interfaces as DataStream. The unification has at least three benefits: 1. The system will be greatly simplified with the same execution stack for both streaming and batch jobs. 2. It is no longer necessary to implement two sets of Driver(s) (operator strategies) for batch, namely chained and non-chained. 3. The unified programming model enables streaming and batch jobs to share the same operator implementation. The following is the design draft. Any feedback is highly appreciated. https://docs.google.com/document/d/1G0NUIaaNJvT6CMrNCP6dRXGv88xNhDQqZFrQEuJ0rVU/edit?usp=sharing Best, Haibo
