Well I am not sure, but using a database as a storage, such as relational databases or certain nosql databases (eg MongoDB) for Spark is generally a bad idea - no data locality, it cannot handle real big data volumes for compute and you may potentially overload an operational database. And if your job fails for whatever reason (eg scheduling ) then you have to pull everything out again. Sqoop and HDFS seems to me the more elegant solution together with spark. These "assumption" on parallelism have to be anyway made with any solution. Of course you can always redo things, but why - what benefit do you expect? A real big data platform has to support anyway many different tools otherwise people doing analytics will be limited.
> On 06 Apr 2016, at 20:05, Michael Segel <msegel_had...@hotmail.com> wrote: > > I don’t think its necessarily a bad idea. > > Sqoop is an ugly tool and it requires you to make some assumptions as a way > to gain parallelism. (Not that most of the assumptions are not valid for most > of the use cases…) > > Depending on what you want to do… your data may not be persisted on HDFS. > There are use cases where your cluster is used for compute and not storage. > > I’d say that spending time re-inventing the wheel can be a good thing. > It would be a good idea for many to rethink their ingestion process so that > they can have a nice ‘data lake’ and not a ‘data sewer’. (Stealing that term > from Dean Wampler. ;-) > > Just saying. ;-) > > -Mike > >> On Apr 5, 2016, at 10:44 PM, Jörn Franke <jornfra...@gmail.com> wrote: >> >> I do not think you can be more resource efficient. In the end you have to >> store the data anyway on HDFS . You have a lot of development effort for >> doing something like sqoop. Especially with error handling. >> You may create a ticket with the Sqoop guys to support Spark as an execution >> engine and maybe it is less effort to plug it in there. >> Maybe if your cluster is loaded then you may want to add more machines or >> improve the existing programs. >> >>> On 06 Apr 2016, at 07:33, ayan guha <guha.a...@gmail.com> wrote: >>> >>> One of the reason in my mind is to avoid Map-Reduce application completely >>> during ingestion, if possible. Also, I can then use Spark stand alone >>> cluster to ingest, even if my hadoop cluster is heavily loaded. What you >>> guys think? >>> >>>> On Wed, Apr 6, 2016 at 3:13 PM, Jörn Franke <jornfra...@gmail.com> wrote: >>>> Why do you want to reimplement something which is already there? >>>> >>>>> On 06 Apr 2016, at 06:47, ayan guha <guha.a...@gmail.com> wrote: >>>>> >>>>> Hi >>>>> >>>>> Thanks for reply. My use case is query ~40 tables from Oracle (using >>>>> index and incremental only) and add data to existing Hive tables. Also, >>>>> it would be good to have an option to create Hive table, driven by job >>>>> specific configuration. >>>>> >>>>> What do you think? >>>>> >>>>> Best >>>>> Ayan >>>>> >>>>>> On Wed, Apr 6, 2016 at 2:30 PM, Takeshi Yamamuro <linguin....@gmail.com> >>>>>> wrote: >>>>>> Hi, >>>>>> >>>>>> It depends on your use case using sqoop. >>>>>> What's it like? >>>>>> >>>>>> // maropu >>>>>> >>>>>>> On Wed, Apr 6, 2016 at 1:26 PM, ayan guha <guha.a...@gmail.com> wrote: >>>>>>> Hi All >>>>>>> >>>>>>> Asking opinion: is it possible/advisable to use spark to replace what >>>>>>> sqoop does? Any existing project done in similar lines? >>>>>>> >>>>>>> -- >>>>>>> Best Regards, >>>>>>> Ayan Guha >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> --- >>>>>> Takeshi Yamamuro >>>>> >>>>> >>>>> >>>>> -- >>>>> Best Regards, >>>>> Ayan Guha >>> >>> >>> >>> -- >>> Best Regards, >>> Ayan Guha >
