Actually I was referring to have a an external table in Oracle, which is used to export to CSV (insert into). Then you have a csv on the database server which needs to be moved to HDFS.
> On 11 Apr 2016, at 17:50, Michael Segel <msegel_had...@hotmail.com> wrote: > > Depending on the Oracle release… > > You could use webHDFS to gain access to the cluster and see the CSV file as > an external table. > > However, you would need to have an application that will read each block of > the file in parallel. This works for loading in to the RDBMS itself. > Actually you could use sqoop in reverse to push data to the RDBMS provided > that the block file is splittable. This is a classic M/R problem. > > But I don’t think this is what the OP wants to do. They want to pull data > from the RDBMs. If you could drop the table’s underlying file and can read > directly from it… you can do a very simple bulk load/unload process. However > you need to know the file’s format. > > Not sure what IBM or Oracle has done to tie their RDBMs to Big Data. > > As I and other posters to this thread have alluded to… this would be a block > bulk load/unload tool. > > >> On Apr 10, 2016, at 11:31 AM, Jörn Franke <jornfra...@gmail.com> wrote: >> >> >> I am not 100% sure, but you could export to CSV in Oracle using external >> tables. >> >> Oracle has also the Hadoop Loader, which seems to support Avro. However, I >> think you need to buy the Big Data solution. >> >>> On 10 Apr 2016, at 16:12, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: >>> >>> Yes I meant MR. >>> >>> Again one cannot beat the RDBMS export utility. I was specifically >>> referring to Oracle in above case that does not provide any specific text >>> bases export except the binary one Exp, data pump etc). >>> >>> In case of SAPO ASE, Sybase IQ, and MSSQL, one can use BCP (bulk copy) that >>> can be parallelised either through range partitioning or simple round robin >>> partitioning that can be used to get data out to file in parallel. Then >>> once get data into Hive table through import etc. >>> >>> In general if the source table is very large you can used either SAP >>> Replication Server (SRS) or Oracle Golden Gate to get data to Hive. Both >>> these replication tools provide connectors to Hive and they do a good job. >>> If one has something like Oracle in Prod then there is likely a Golden Gate >>> there. For bulk setting of Hive tables and data migration, replication >>> server is good option. >>> >>> HTH >>> >>> >>> Dr Mich Talebzadeh >>> >>> LinkedIn >>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> >>> http://talebzadehmich.wordpress.com >>> >>> >>>> On 10 April 2016 at 14:24, Michael Segel <msegel_had...@hotmail.com> wrote: >>>> Sqoop doesn’t use MapR… unless you meant to say M/R (Map Reduce) >>>> >>>> The largest problem with sqoop is that in order to gain parallelism you >>>> need to know how your underlying table is partitioned and to do multiple >>>> range queries. This may not be known, or your data may or may not be >>>> equally distributed across the ranges. >>>> >>>> If you’re bringing over the entire table, you may find dropping it and >>>> then moving it to HDFS and then doing a bulk load to be more efficient. >>>> (This is less flexible than sqoop, but also stresses the database servers >>>> less. ) >>>> >>>> Again, YMMV >>>> >>>> >>>>> On Apr 8, 2016, at 9:17 AM, Mich Talebzadeh <mich.talebza...@gmail.com> >>>>> wrote: >>>>> >>>>> Well unless you have plenty of memory, you are going to have certain >>>>> issues with Spark. >>>>> >>>>> I tried to load a billion rows table from oracle through spark using JDBC >>>>> and ended up with "Caused by: java.lang.OutOfMemoryError: Java heap >>>>> space" error. >>>>> >>>>> Sqoop uses MapR and does it in serial mode which takes time and you can >>>>> also tell it to create Hive table. However, it will import data into Hive >>>>> table. >>>>> >>>>> In any case the mechanism of data import is through JDBC, Spark uses >>>>> memory and DAG, whereas Sqoop relies on MapR. >>>>> >>>>> There is of course another alternative. >>>>> >>>>> Assuming that your Oracle table has a primary Key say "ID" (it would be >>>>> easier if it was a monotonically increasing number) or already >>>>> partitioned. >>>>> >>>>> You can create views based on the range of ID or for each partition. You >>>>> can then SELECT COLUMNS co1, col2, coln from view and spool it to a text >>>>> file on OS (locally say backup directory would be fastest). >>>>> bzip2 those files and scp them to a local directory in Hadoop >>>>> You can then use Spark/hive to load the target table from local files in >>>>> parallel >>>>> When creating views take care of NUMBER and CHAR columns in Oracle and >>>>> convert them to TO_CHAR(NUMBER_COLUMN) and varchar CAST(coln AS >>>>> VARCHAR2(n)) AS coln etc >>>>> >>>>> HTH >>>>> >>>>> >>>>> >>>>> Dr Mich Talebzadeh >>>>> >>>>> LinkedIn >>>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>> >>>>> http://talebzadehmich.wordpress.com >>>>> >>>>> >>>>>> On 8 April 2016 at 10:07, Gourav Sengupta <gourav.sengu...@gmail.com> >>>>>> wrote: >>>>>> Hi, >>>>>> >>>>>> Some metrics thrown around the discussion: >>>>>> >>>>>> SQOOP: extract 500 million rows (in single thread) 20 mins (data size 21 >>>>>> GB) >>>>>> SPARK: load the data into memory (15 mins) >>>>>> >>>>>> SPARK: use JDBC (and similar to SQOOP difficult parallelization) to load >>>>>> 500 million records - manually killed after 8 hours. >>>>>> >>>>>> (both the above studies were done in a system of same capacity, with 32 >>>>>> GB RAM and dual hexacore Xeon processors and SSD. SPARK was running >>>>>> locally, and SQOOP ran on HADOOP2 and extracted data to local file >>>>>> system) >>>>>> >>>>>> In case any one needs to know what needs to be done to access both the >>>>>> CSV and JDBC modules in SPARK Local Server mode, please let me know. >>>>>> >>>>>> >>>>>> Regards, >>>>>> Gourav Sengupta >>>>>> >>>>>>> On Thu, Apr 7, 2016 at 12:26 AM, Yong Zhang <java8...@hotmail.com> >>>>>>> wrote: >>>>>>> Good to know that. >>>>>>> >>>>>>> That is why Sqoop has this "direct" mode, to utilize the vendor >>>>>>> specific feature. >>>>>>> >>>>>>> But for MPP, I still think it makes sense that vendor provide some kind >>>>>>> of InputFormat, or data source in Spark, so Hadoop eco-system can >>>>>>> integrate with them more natively. >>>>>>> >>>>>>> Yong >>>>>>> >>>>>>> Date: Wed, 6 Apr 2016 16:12:30 -0700 >>>>>>> Subject: Re: Sqoop on Spark >>>>>>> From: mohaj...@gmail.com >>>>>>> To: java8...@hotmail.com >>>>>>> CC: mich.talebza...@gmail.com; jornfra...@gmail.com; >>>>>>> msegel_had...@hotmail.com; guha.a...@gmail.com; linguin....@gmail.com; >>>>>>> user@spark.apache.org >>>>>>> >>>>>>> >>>>>>> It is using JDBC driver, i know that's the case for Teradata: >>>>>>> http://developer.teradata.com/connectivity/articles/teradata-connector-for-hadoop-now-available >>>>>>> >>>>>>> Teradata Connector (which is used by Cloudera and Hortonworks) for >>>>>>> doing Sqoop is parallelized and works with ORC and probably other >>>>>>> formats as well. It is using JDBC for each connection between >>>>>>> data-nodes and their AMP (compute) nodes. There is an additional layer >>>>>>> that coordinates all of it. >>>>>>> I know Oracle has a similar technology I've used it and had to supply >>>>>>> the JDBC driver. >>>>>>> >>>>>>> Teradata Connector is for batch data copy, QueryGrid is for interactive >>>>>>> data movement. >>>>>>> >>>>>>> On Wed, Apr 6, 2016 at 4:05 PM, Yong Zhang <java8...@hotmail.com> wrote: >>>>>>> If they do that, they must provide a customized input format, instead >>>>>>> of through JDBC. >>>>>>> >>>>>>> Yong >>>>>>> >>>>>>> Date: Wed, 6 Apr 2016 23:56:54 +0100 >>>>>>> Subject: Re: Sqoop on Spark >>>>>>> From: mich.talebza...@gmail.com >>>>>>> To: mohaj...@gmail.com >>>>>>> CC: jornfra...@gmail.com; msegel_had...@hotmail.com; >>>>>>> guha.a...@gmail.com; linguin....@gmail.com; user@spark.apache.org >>>>>>> >>>>>>> >>>>>>> SAP Sybase IQ does that and I believe SAP Hana as well. >>>>>>> >>>>>>> Dr Mich Talebzadeh >>>>>>> >>>>>>> LinkedIn >>>>>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>>> >>>>>>> http://talebzadehmich.wordpress.com >>>>>>> >>>>>>> >>>>>>> >>>>>>> On 6 April 2016 at 23:49, Peyman Mohajerian <mohaj...@gmail.com> wrote: >>>>>>> For some MPP relational stores (not operational) it maybe feasible to >>>>>>> run Spark jobs and also have data locality. I know QueryGrid (Teradata) >>>>>>> and PolyBase (microsoft) use data locality to move data between their >>>>>>> MPP and Hadoop. >>>>>>> I would guess (have no idea) someone like IBM already is doing that for >>>>>>> Spark, maybe a bit off topic! >>>>>>> >>>>>>> On Wed, Apr 6, 2016 at 3:29 PM, Jörn Franke <jornfra...@gmail.com> >>>>>>> wrote: >>>>>>> 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 >
