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From Jakub Wozniak <>
Subject Re: Custom datasource as a wrapper for existing ones?
Date Wed, 02 May 2018 20:17:19 GMT

Thanks a lot for your answers. 

We normally look for some stability so the use of internal APIs that are a subject to change
with no warning are somewhat questionable. 
As to the approach of putting this functionality on top of Spark instead of a datasource -
this works but poses a problem for Python. 
In Python we would like to reuse the code written in Java. An external lib in Java has to
proxy to Python and Spark proxies as well. 
This means passing over objects (like SparkSession) back and forth from one jvm to the other.
Not surprisingly this did not work for us in the past (although we did not push much hoping
for the datasource).
All in all if we don’t find another solution we might go for an external library that most
likely have to be reimplemented twice in Python… 
Or there might be a way to force our lib execution in the same JVM as Spark uses. To be seen…
Again the most elegant way would be the datasource.


> On 2 May 2018, at 21:07, Jörn Franke <> wrote:
> Some note on the internal API - it used to change with each release which was quiet annoying
because  other data sources (Avro, HadoopOffice etc) had to follow up in this. In the end
it is an internal API and thus does not guarantee to be stable. If you want to have something
stable you have to use the official data source APIs with some disadvantages.
>> On 2. May 2018, at 18:49, jwozniak <> wrote:
>> Hello,
>> At CERN we are developing a Big Data system called NXCALS that uses Spark as
>> Extraction API.
>> We have implemented a custom datasource that was wrapping 2 existing ones
>> (parquet and Hbase) in order to hide the implementation details (location of
>> the parquet files, hbase tables, etc) and to provide an abstraction layer to
>> our users. 
>> We have entered a stage where we execute some performance tests on our data
>> and we have noticed that this approach did not provide the expected
>> performance observed using pure Spark. In other words reading a parquet file
>> with some simple predicates behaves 15 times slower if the same code is
>> executed from within a custom datasource (that just uses Spark to read
>> parquet). 
>> After some investigation we've learnt that Spark did not apply the same
>> optimisations for both. 
>> We could see that in Spark 2.3.0 there was a new V2 version that abstracts
>> from SparkSession and focuses on low level Row API. 
>> Could you give us some suggestions of how to correctly implement our
>> datasource using the V2 API? 
>> Is this a correct way of doing it at all? 
>> What we want to achieve is to join existing datasources with some level of
>> additional abstraction on top. 
>> At the same time we want to profit from all catalyst & parquet optimisations
>> that exist for the original ones.
>> We also don't want to reimplement access to parquet files or Hbase at the
>> low level (like Row) but just profit from the Dataset API. 
>> We could have achieved the same by providing an external library on top of
>> Spark but the datasource approach looked like a more elegant solution. Only
>> the performance is still far from the desired one. 
>> Any help or direction in that matter would be greatly appreciated as we have
>> only started to build our Spark expertise yet.  
>> Best regards,
>> Jakub Wozniak
>> Software Engineer
>> --
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