Hi Amrit, Thanks a lot for your suggestion! I will investigate it later.
Best regards, Yang 2017-01-03 12:25 GMT+01:00 Amrit Jangid <amrit.jan...@goibibo.com>: > You can try out *debezium* : https://github.com/debezium. it reads data > from bin-logs, provides structure and stream into Kafka. > > Now Kafka can be your new source for streaming. > > On Tue, Jan 3, 2017 at 4:36 PM, Yuanzhe Yang <yyz1...@gmail.com> wrote: > >> Hi Hongdi, >> >> Thanks a lot for your suggestion. The data is truely immutable and the >> table is append-only. But actually there are different databases involved, >> so the only feature they share in common and I can depend on is jdbc... >> >> Best regards, >> Yang >> >> >> 2016-12-30 6:45 GMT+01:00 任弘迪 <ryan.hd....@gmail.com>: >> >>> why not sync binlog of mysql(hopefully the data is immutable and the >>> table is append-only), send the log through kafka and then consume it by >>> spark streaming? >>> >>> On Fri, Dec 30, 2016 at 9:01 AM, Michael Armbrust < >>> mich...@databricks.com> wrote: >>> >>>> We don't support this yet, but I've opened this JIRA as it sounds >>>> generally useful: https://issues.apache.org/jira/browse/SPARK-19031 >>>> >>>> In the mean time you could try implementing your own Source, but that >>>> is pretty low level and is not yet a stable API. >>>> >>>> On Thu, Dec 29, 2016 at 4:05 AM, "Yuanzhe Yang (杨远哲)" < >>>> yyz1...@gmail.com> wrote: >>>> >>>>> Hi all, >>>>> >>>>> Thanks a lot for your contributions to bring us new technologies. >>>>> >>>>> I don't want to waste your time, so before I write to you, I googled, >>>>> checked stackoverflow and mailing list archive with keywords "streaming" >>>>> and "jdbc". But I was not able to get any solution to my use case. I hope >>>>> I >>>>> can get some clarification from you. >>>>> >>>>> The use case is quite straightforward, I need to harvest a relational >>>>> database via jdbc, do something with data, and store result into Kafka. I >>>>> am stuck at the first step, and the difficulty is as follows: >>>>> >>>>> 1. The database is too large to ingest with one thread. >>>>> 2. The database is dynamic and time series data comes in constantly. >>>>> >>>>> Then an ideal workflow is that multiple workers process partitions of >>>>> data incrementally according to a time window. For example, the processing >>>>> starts from the earliest data with each batch containing data for one >>>>> hour. >>>>> If data ingestion speed is faster than data production speed, then >>>>> eventually the entire database will be harvested and those workers will >>>>> start to "tail" the database for new data streams and the processing >>>>> becomes real time. >>>>> >>>>> With Spark SQL I can ingest data from a JDBC source with partitions >>>>> divided by time windows, but how can I dynamically increment the time >>>>> windows during execution? Assume that there are two workers ingesting data >>>>> of 2017-01-01 and 2017-01-02, the one which finishes quicker gets next >>>>> task >>>>> for 2017-01-03. But I am not able to find out how to increment those >>>>> values >>>>> during execution. >>>>> >>>>> Then I looked into Structured Streaming. It looks much more promising >>>>> because window operations based on event time are considered during >>>>> streaming, which could be the solution to my use case. However, from >>>>> documentation and code example I did not find anything related to >>>>> streaming >>>>> data from a growing database. Is there anything I can read to achieve my >>>>> goal? >>>>> >>>>> Any suggestion is highly appreciated. Thank you very much and have a >>>>> nice day. >>>>> >>>>> Best regards, >>>>> Yang >>>>> --------------------------------------------------------------------- >>>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>>>> >>>>> >>>> >>> >> > > > -- > > Regards, > Amrit > Data Team >
