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Posted to user@spark.apache.org by El-Hassan Wanas <el...@gmail.com> on 2017/03/07 11:04:34 UTC
Spark JDBC reads
Hello,
There is, as usual, a big table lying on some JDBC data source. I am
doing some data processing on that data from Spark, however, in order to
speed up my analysis, I use reduced encodings and minimize the general
size of the data before processing.
Spark has been doing a great job at generating the proper workflows that
do that preprocessing for me, but it seems to generate those workflows
for execution on the Spark Cluster. The issue with that is the large
transfer cost is still incurred.
Is there any way to force Spark to run the preprocessing on the JDBC
data source and get the prepared output DataFrame instead?
Thanks,
Wanas
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Re: Spark JDBC reads
Posted by El-Hassan Wanas <el...@gmail.com>.
I was kind of hoping that I would use Spark in this instance to generate
that intermediate SQL as part of its workflow strategy. Sort of as a
database independent way of doing my preprocessing.
Is there any way that allows me to capture the generated SQL from catalyst?
If so I would just use JDBCRdd with that.
The other option being to generate that SQL in text format which isn't the
nicest thing to do.
On Mar 7, 2017 5:02 PM, "Subhash Sriram" <su...@gmail.com> wrote:
> Could you create a view of the table on your JDBC data source and just
> query that from Spark?
>
> Thanks,
> Subhash
>
> Sent from my iPhone
>
> > On Mar 7, 2017, at 6:37 AM, El-Hassan Wanas <el...@gmail.com>
> wrote:
> >
> > As an example, this is basically what I'm doing:
> >
> > val myDF = originalDataFrame.select(col(columnName).when(col(columnName)
> === "foobar", 0).when(col(columnName) === "foobarbaz", 1))
> >
> > Except there's much more columns and much more conditionals. The
> generated Spark workflow starts with an SQL that basically does:
> >
> > SELECT columnName, columnName2, etc. from table;
> >
> > Then the conditionals/transformations are evaluated on the cluster.
> >
> > Is there a way from the DataSet API to force the computation to happen
> on the SQL data source in this case? Or should I work with JDBCRDD and use
> createDataFrame on that?
> >
> >
> >> On 03/07/2017 02:19 PM, Jörn Franke wrote:
> >> Can you provide some source code? I am not sure I understood the
> problem .
> >> If you want to do a preprocessing at the JDBC datasource then you can
> write your own data source. Additionally you may want to modify the sql
> statement to extract the data in the right format and push some
> preprocessing to the database.
> >>
> >>> On 7 Mar 2017, at 12:04, El-Hassan Wanas <el...@gmail.com>
> wrote:
> >>>
> >>> Hello,
> >>>
> >>> There is, as usual, a big table lying on some JDBC data source. I am
> doing some data processing on that data from Spark, however, in order to
> speed up my analysis, I use reduced encodings and minimize the general size
> of the data before processing.
> >>>
> >>> Spark has been doing a great job at generating the proper workflows
> that do that preprocessing for me, but it seems to generate those workflows
> for execution on the Spark Cluster. The issue with that is the large
> transfer cost is still incurred.
> >>>
> >>> Is there any way to force Spark to run the preprocessing on the JDBC
> data source and get the prepared output DataFrame instead?
> >>>
> >>> Thanks,
> >>>
> >>> Wanas
> >>>
> >>>
> >>> ---------------------------------------------------------------------
> >>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
> >>>
> >
> >
> > ---------------------------------------------------------------------
> > To unsubscribe e-mail: user-unsubscribe@spark.apache.org
> >
>
Re: Spark JDBC reads
Posted by Subhash Sriram <su...@gmail.com>.
Could you create a view of the table on your JDBC data source and just query that from Spark?
Thanks,
Subhash
Sent from my iPhone
> On Mar 7, 2017, at 6:37 AM, El-Hassan Wanas <el...@gmail.com> wrote:
>
> As an example, this is basically what I'm doing:
>
> val myDF = originalDataFrame.select(col(columnName).when(col(columnName) === "foobar", 0).when(col(columnName) === "foobarbaz", 1))
>
> Except there's much more columns and much more conditionals. The generated Spark workflow starts with an SQL that basically does:
>
> SELECT columnName, columnName2, etc. from table;
>
> Then the conditionals/transformations are evaluated on the cluster.
>
> Is there a way from the DataSet API to force the computation to happen on the SQL data source in this case? Or should I work with JDBCRDD and use createDataFrame on that?
>
>
>> On 03/07/2017 02:19 PM, Jörn Franke wrote:
>> Can you provide some source code? I am not sure I understood the problem .
>> If you want to do a preprocessing at the JDBC datasource then you can write your own data source. Additionally you may want to modify the sql statement to extract the data in the right format and push some preprocessing to the database.
>>
>>> On 7 Mar 2017, at 12:04, El-Hassan Wanas <el...@gmail.com> wrote:
>>>
>>> Hello,
>>>
>>> There is, as usual, a big table lying on some JDBC data source. I am doing some data processing on that data from Spark, however, in order to speed up my analysis, I use reduced encodings and minimize the general size of the data before processing.
>>>
>>> Spark has been doing a great job at generating the proper workflows that do that preprocessing for me, but it seems to generate those workflows for execution on the Spark Cluster. The issue with that is the large transfer cost is still incurred.
>>>
>>> Is there any way to force Spark to run the preprocessing on the JDBC data source and get the prepared output DataFrame instead?
>>>
>>> Thanks,
>>>
>>> Wanas
>>>
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>>>
>
>
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> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>
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Re: Spark JDBC reads
Posted by El-Hassan Wanas <el...@gmail.com>.
As an example, this is basically what I'm doing:
val myDF =
originalDataFrame.select(col(columnName).when(col(columnName) ===
"foobar", 0).when(col(columnName) === "foobarbaz", 1))
Except there's much more columns and much more conditionals. The
generated Spark workflow starts with an SQL that basically does:
SELECT columnName, columnName2, etc. from table;
Then the conditionals/transformations are evaluated on the cluster.
Is there a way from the DataSet API to force the computation to happen
on the SQL data source in this case? Or should I work with JDBCRDD and
use createDataFrame on that?
On 03/07/2017 02:19 PM, Jrn Franke wrote:
> Can you provide some source code? I am not sure I understood the problem .
> If you want to do a preprocessing at the JDBC datasource then you can write your own data source. Additionally you may want to modify the sql statement to extract the data in the right format and push some preprocessing to the database.
>
>> On 7 Mar 2017, at 12:04, El-Hassan Wanas <el...@gmail.com> wrote:
>>
>> Hello,
>>
>> There is, as usual, a big table lying on some JDBC data source. I am doing some data processing on that data from Spark, however, in order to speed up my analysis, I use reduced encodings and minimize the general size of the data before processing.
>>
>> Spark has been doing a great job at generating the proper workflows that do that preprocessing for me, but it seems to generate those workflows for execution on the Spark Cluster. The issue with that is the large transfer cost is still incurred.
>>
>> Is there any way to force Spark to run the preprocessing on the JDBC data source and get the prepared output DataFrame instead?
>>
>> Thanks,
>>
>> Wanas
>>
>>
>> ---------------------------------------------------------------------
>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>>
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Re: Spark JDBC reads
Posted by Jörn Franke <jo...@gmail.com>.
Can you provide some source code? I am not sure I understood the problem .
If you want to do a preprocessing at the JDBC datasource then you can write your own data source. Additionally you may want to modify the sql statement to extract the data in the right format and push some preprocessing to the database.
> On 7 Mar 2017, at 12:04, El-Hassan Wanas <el...@gmail.com> wrote:
>
> Hello,
>
> There is, as usual, a big table lying on some JDBC data source. I am doing some data processing on that data from Spark, however, in order to speed up my analysis, I use reduced encodings and minimize the general size of the data before processing.
>
> Spark has been doing a great job at generating the proper workflows that do that preprocessing for me, but it seems to generate those workflows for execution on the Spark Cluster. The issue with that is the large transfer cost is still incurred.
>
> Is there any way to force Spark to run the preprocessing on the JDBC data source and get the prepared output DataFrame instead?
>
> Thanks,
>
> Wanas
>
>
> ---------------------------------------------------------------------
> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>
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