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From Jörn Franke <jornfra...@gmail.com>
Subject Re: ORC v/s Parquet for Spark 2.0
Date Wed, 27 Jul 2016 20:30:24 GMT
Kudu has been from my impression be designed to offer somethings between hbase and parquet
for write intensive loads - it is not faster for warehouse type of querying compared to parquet
(merely slower, because that is not its use case).   I assume this is still the strategy of
it.

For some scenarios it could make sense together with parquet and Orc. However I am not sure
what the advantage towards using hbase + parquet and Orc.

> On 27 Jul 2016, at 11:47, "Uwe@Moosheimer.com" <Uwe@Moosheimer.com> wrote:
> 
> Hi Gourav,
> 
> Kudu (if you mean Apache Kuda, the Cloudera originated project) is a in memory db with
data storage while Parquet is "only" a columnar storage format.
> 
> As I understand, Kudu is a BI db to compete with Exasol or Hana (ok ... that's more a
wish :-).
> 
> Regards,
> Uwe
> 
> Mit freundlichen Grüßen / best regards
> Kay-Uwe Moosheimer
> 
>> Am 27.07.2016 um 09:15 schrieb Gourav Sengupta <gourav.sengupta@gmail.com>:
>> 
>> Gosh,
>> 
>> whether ORC came from this or that, it runs queries in HIVE with TEZ at a speed that
is better than SPARK.
>> 
>> Has anyone heard of KUDA? Its better than Parquet. But I think that someone might
just start saying that KUDA has difficult lineage as well. After all dynastic rules dictate.
>> 
>> Personally I feel that if something stores my data compressed and makes me access
it faster I do not care where it comes from or how difficult the child birth was :)
>> 
>> 
>> Regards,
>> Gourav
>> 
>>> On Tue, Jul 26, 2016 at 11:19 PM, Sudhir Babu Pothineni <sbpothineni@gmail.com>
wrote:
>>> Just correction:
>>> 
>>> ORC Java libraries from Hive are forked into Apache ORC. Vectorization default.

>>> 
>>> Do not know If Spark leveraging this new repo?
>>> 
>>> <dependency>
>>>  <groupId>org.apache.orc</groupId>
>>>     <artifactId>orc</artifactId>
>>>     <version>1.1.2</version>
>>>     <type>pom</type>
>>> </dependency>
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> Sent from my iPhone
>>>> On Jul 26, 2016, at 4:50 PM, Koert Kuipers <koert@tresata.com> wrote:
>>>> 
>>> 
>>>> parquet was inspired by dremel but written from the ground up as a library
with support for a variety of big data systems (hive, pig, impala, cascading, etc.). it is
also easy to add new support, since its a proper library.
>>>> 
>>>> orc bas been enhanced while deployed at facebook in hive and at yahoo in
hive. just hive. it didn't really exist by itself. it was part of the big java soup that is
called hive, without an easy way to extract it. hive does not expose proper java apis. it
never cared for that.
>>>> 
>>>>> On Tue, Jul 26, 2016 at 9:57 AM, Ovidiu-Cristian MARCU <ovidiu-cristian.marcu@inria.fr>
wrote:
>>>>> Interesting opinion, thank you
>>>>> 
>>>>> Still, on the website parquet is basically inspired by Dremel (Google)
[1] and part of orc has been enhanced while deployed for Facebook, Yahoo [2].
>>>>> 
>>>>> Other than this presentation [3], do you guys know any other benchmark?
>>>>> 
>>>>> [1]https://parquet.apache.org/documentation/latest/
>>>>> [2]https://orc.apache.org/docs/
>>>>> [3] http://www.slideshare.net/oom65/file-format-benchmarks-avro-json-orc-parquet
>>>>> 
>>>>>> On 26 Jul 2016, at 15:19, Koert Kuipers <koert@tresata.com>
wrote:
>>>>>> 
>>>>>> when parquet came out it was developed by a community of companies,
and was designed as a library to be supported by multiple big data projects. nice
>>>>>> 
>>>>>> orc on the other hand initially only supported hive. it wasn't even
designed as a library that can be re-used. even today it brings in the kitchen sink of transitive
dependencies. yikes
>>>>>> 
>>>>>> 
>>>>>>> On Jul 26, 2016 5:09 AM, "Jörn Franke" <jornfranke@gmail.com>
wrote:
>>>>>>> I think both are very similar, but with slightly different goals.
While they work transparently for each Hadoop application you need to enable specific support
in the application for predicate push down. 
>>>>>>> In the end you have to check which application you are using
and do some tests (with correct predicate push down configuration). Keep in mind that both
formats work best if they are sorted on filter columns (which is your responsibility) and
if their optimatizations are correctly configured (min max index, bloom filter, compression
etc) . 
>>>>>>> 
>>>>>>> If you need to ingest sensor data you may want to store it first
in hbase and then batch process it in large files in Orc or parquet format.
>>>>>>> 
>>>>>>>> On 26 Jul 2016, at 04:09, janardhan shetty <janardhanp22@gmail.com>
wrote:
>>>>>>>> 
>>>>>>>> Just wondering advantages and disadvantages to convert data
into ORC or Parquet. 
>>>>>>>> 
>>>>>>>> In the documentation of Spark there are numerous examples
of Parquet format. 
>>>>>>>> 
>>>>>>>> Any strong reasons to chose Parquet over ORC file format
?
>>>>>>>> 
>>>>>>>> Also : current data compression is bzip2
>>>>>>>> 
>>>>>>>> http://stackoverflow.com/questions/32373460/parquet-vs-orc-vs-orc-with-snappy

>>>>>>>> This seems like biased.
>> 

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