You should also try LLAP. With ORC or text, it will cache the hot columns
and partitions in memory. I can't seem to find the slides yet, but the
Comcast team had good results with LLAP:

https://dataworkssummit.com/san-jose-2017/sessions/hadoop-query-performance-smackdown/

https://twitter.com/thejasn/status/875065727056715776

Now that ORC has a C++ reader (and soon a writer), someone could write a
patch for Impala to support ORC. You'd need to talk to the Impala project
though.

.. Owen

On Tue, Jun 20, 2017 at 1:00 AM, Furcy Pin <furcy....@flaminem.com> wrote:

> Another option would be to try Facebook's Presto https://prestodb.io/
>
> Like Impala, Presto is designed for fast interactive querying over Hive
> tables, but it is also capable of querying data from many other SQL sources
> (mySQL, postgreSQL, Kafka, Cassandra, ... https://prestodb.io/docs/
> current/connector.html)
>
> In terms of performances on small queries, it seems to be as fast as
> Impala, a league over Spark-SQL, and of course two leagues over Hive.
>
> Unlike Impala, Presto is also able to read ORC file format, and make the
> most of it (e.g. read pre-aggregated values from ORC headers).
>
> It can also make use of Hive's bucketing feature, while Impala still
> cannot:
> https://github.com/prestodb/presto/issues/6666
> https://issues.apache.org/jira/browse/IMPALA-3118
>
> Regards,
>
> Furcy
>
>
>
>
>
> On Tue, Jun 20, 2017 at 5:36 AM, Sruthi Kumar Annamneedu <
> sruthikumar...@gmail.com> wrote:
>
>> Try using Parquet with Snappy compression and Impala will work with this
>> combination.
>>
>> On Sun, Jun 18, 2017 at 3:35 AM, rakesh sharma <
>> rakeshsharm...@hotmail.com> wrote:
>>
>>> We are facing an issue of format. We would like to do bi style queries
>>> from hive using impala and that supports parquet but we would like the data
>>> to be compressed to the best ratio like orc. But impala cannot query orc
>>> formats. What can be a design consideration for this. Any help
>>>
>>> Thanks
>>> Rakesh
>>>
>>> Get Outlook for Android <https://aka.ms/ghei36>
>>>
>>>
>>
>

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