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 >>> >>> >> >
