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From prosp4300 <>
Subject Re:Re: ORC v/s Parquet for Spark 2.0
Date Wed, 27 Jul 2016 07:34:19 GMT
Thanks for this immediate correction :)

在 2016-07-27 15:17:54,"Gourav Sengupta" <> 写道:


in my email above I was referring to KUDU, and there is goes how can KUDU be right if it is
mentioned in forums first with a wrong spelling. Its got a difficult beginning where people
were trying to figure out its name.

Gourav Sengupta

On Wed, Jul 27, 2016 at 8:15 AM, Gourav Sengupta <> wrote:


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 :)


On Tue, Jul 26, 2016 at 11:19 PM, Sudhir Babu Pothineni <> wrote:

Just correction:

ORC Java libraries from Hive are forked into Apache ORC. Vectorization default. 

Do not know If Spark leveraging this new repo?


Sent from my iPhone
On Jul 26, 2016, at 4:50 PM, Koert Kuipers <> 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 <>

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?


On 26 Jul 2016, at 15:19, Koert Kuipers <> 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.

On Jul 26, 2016 5:09 AM, "Jörn Franke" <> 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 <> 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
This seems like biased.

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