spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ovidiu-Cristian MARCU <>
Subject Re: ORC v/s Parquet for Spark 2.0
Date Tue, 26 Jul 2016 13:57:12 GMT
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] <>
[2] <>
[3] <>

> 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" < <>>
> 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 < <>>
>> 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.

View raw message