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From Yuanzhe Yang <yyz1...@gmail.com>
Subject Re: [Spark Structured Streaming]: Is it possible to ingest data from a jdbc data source incrementally?
Date Tue, 03 Jan 2017 11:06:23 GMT
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.ren@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 <michael@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)" <yyz1989@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-unsubscribe@spark.apache.org
>>>
>>>
>>
>

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