Hi Henry, 1) I don't recommend this method very much, but you said that you expect to convert mysql table to stream and then to flink table. Under this premise, I said that you can do this by joining two stream tables. But as you know, this join depends on the time period in which the state is saved. To make it equivalent to a dimension table, you must permanently save the state of the stream table that is defined as a "dimension table." I just said that modifying the relevant configuration in Flink can do this, Not for a single table.
2) Imagine that there are one million records in two tables. The records in both tables are just beginning to stream into flink, and the records as dimension tables are not fully arrived. Therefore, your matching results may not be as accurate as directly querying Mysql. In fact, the current stream & stream join is not very mature, there are some problems in semantics, I personally recommend that you return to stream/batch (mysql) join. For more principle content, I recommend you read a book, referred to as 《DDIA》. Thanks, vino. 徐涛 <happydexu...@gmail.com> 于2018年9月25日周二 下午5:48写道： > Hi Vino, > I do not quite understand in some sentences below, would you please help > explain it a bit more detailedly? > 1. “*such as setting the state retention time of one of the tables to be > permanent*” , as I know, the state retention time is a global config, I > can not set this property per table. > 2. "*you may not be able to match the results, because the data belonging > to the mysql table is just beginning to play as a stream*” Why it is not > able to match the results? > > Best > Henry > > 在 2018年9月25日，下午5:29，vino yang <yanghua1...@gmail.com> 写道： > > Hi Henry, > > If you have converted the mysql table to a flink stream table. In flink > table/sql, streams and stream joins can also do this, such as setting the > state retention time of one of the tables to be permanent. But when the job > is just running, you may not be able to match the results, because the data > belonging to the mysql table is just beginning to play as a stream. > > Thanks, vino. > > 徐涛 <happydexu...@gmail.com> 于2018年9月25日周二 下午5:10写道： > >> Hi Vino & Hequn, >> I am now using the table/sql API, if I import the mysql table as a stream >> then convert it into a table, it seems that it can also be a workaround for >> batch/streaming joining. May I ask what is the difference between the UDTF >> method? Does this implementation has some defects? >> Best >> Henry >> >> 在 2018年9月22日，上午10:28，Hequn Cheng <chenghe...@gmail.com> 写道： >> >> Hi >> >> +1 for vino's answer. >> Also, this kind of join will be supported in FLINK-9712 >> <https://issues.apache.org/jira/browse/FLINK-9712>. You can check more >> details in the jira. >> >> Best, Hequn >> >> On Fri, Sep 21, 2018 at 4:51 PM vino yang <yanghua1...@gmail.com> wrote: >> >>> Hi Henry, >>> >>> There are three ways I can think of: >>> >>> 1) use DataStream API, implement a flatmap UDF to access dimension table; >>> 2) use table/sql API, implement a UDTF to access dimension table; >>> 3) customize the table/sql join API/statement's implementation (and >>> change the physical plan) >>> >>> Thanks, vino. >>> >>> 徐涛 <happydexu...@gmail.com> 于2018年9月21日周五 下午4:43写道： >>> >>>> Hi All, >>>> Sometimes some “dimension table” need to be joined from the >>>> "fact table", if data are not joined before sent to Kafka. >>>> So if the data are joined in Flink, does the “dimension table” >>>> have to be import as a stream, or there are some other ways can achieve it? >>>> Thanks a lot! >>>> >>>> Best >>>> Henry >>> >>> >> >
