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From Jinfeng Ni <...@apache.org>
Subject Re: Partitioning for parquet
Date Thu, 01 Jun 2017 05:05:03 GMT
You may want to check if query on the second table is slower because of
planning time or execution time. That could be determined by looking at the
query profile in web-UI.

Two factors might impact the planning time for second table having 11837:
1. Reading parquet metadata from those parquet files.  Parquet metadata
cache file might help for the cases of large number of small files.
2. Filter expression evaluation cost : query second would evaluate
expression 11837 times, vs just 410 times for first table.

In general, if you have 100M rows in 11837 files, ==> that's about 8500
rows per file. Performance-wise, this does not seem to be a good choice for
parquet format.



On Wed, May 31, 2017 at 9:33 PM, Padma Penumarthy <ppenumarthy@mapr.com>
wrote:

> Are you running same query on both tables ? What is the filter condition ?
> Since they are partitioned differently, same filter may prune the files
> differently.
> If possible, can you share query profiles ?
> You can check query profiles to see how many rows are being read from disk
> in both cases.
>
> Thanks,
> Padma
>
>
> > On May 31, 2017, at 6:15 PM, Raz Baluchi <raz.baluchi@gmail.com> wrote:
> >
> > As an experiment, I created an event file will 100 million entries
> spanning
> > 25 years. I then created tables both ways, one partitioned by year and
> > month and the other by date. The first table created 410 parquet files
> and
> > the second 11837.
> >
> > Querying the first table is consistently faster by a factor of 2x to 10x,
> >
> > Is this because drill is not very efficient at querying a large number of
> > small(ish) parquet files?
> >
> > On Wed, May 31, 2017 at 6:42 PM, rahul challapalli <
> > challapallirahul@gmail.com> wrote:
> >
> >> If most of your queries use date column in the filter condition, I would
> >> partition the data on the date column. Then you can simply say
> >>
> >> select * from events where `date` between '2016-11-11' and '2017-01-23';
> >>
> >> - Rahul
> >>
> >> On Wed, May 31, 2017 at 3:22 PM, Raz Baluchi <raz.baluchi@gmail.com>
> >> wrote:
> >>
> >>> So, if I understand you correctly, I would have to include the 'yr' and
> >>> 'mnth' columns in addition to the 'date' column in the query?
> >>>
> >>> e.g.
> >>>
> >>> select * from events where yr in (2016, 2017)  and mnth in (11,12,1)
> and
> >>> date between '2016-11-11' and '2017-01-23';
> >>>
> >>> Is that correct?
> >>>
> >>> On Wed, May 31, 2017 at 4:49 PM, rahul challapalli <
> >>> challapallirahul@gmail.com> wrote:
> >>>
> >>>> How to partition data is dependent on how you want to access your
> data.
> >>> If
> >>>> you can foresee that most of the queries use year and month, then
> >>> go-ahead
> >>>> and partition the data on those 2 columns. You can do that like below
> >>>>
> >>>> create table partitioned_data partition by (yr, mnth) as select
> >>>> extract(year from `date`) yr, extract(month from `date`) mnth, `date`,
> >>>> ........ from mydata;
> >>>>
> >>>> For partitioning to have any benefit, your queries should have filters
> >> on
> >>>> month and year columns.
> >>>>
> >>>> - Rahul
> >>>>
> >>>> On Wed, May 31, 2017 at 1:28 PM, Raz Baluchi <raz.baluchi@gmail.com>
> >>>> wrote:
> >>>>
> >>>>> Hi all,
> >>>>>
> >>>>> Trying to understand parquet partitioning works.
> >>>>>
> >>>>> What is the recommended partitioning scheme for event data that
will
> >> be
> >>>>> queried primarily by date. I assume that partitioning by year and
> >> month
> >>>>> would be optimal?
> >>>>>
> >>>>> Lets say I have data that looks like:
> >>>>>
> >>>>> application,status,date,message
> >>>>> kafka,down,2017-03023 04:53,zookeeper is not available
> >>>>>
> >>>>>
> >>>>> Would I have to create new columns for year and month?
> >>>>>
> >>>>> e.g.
> >>>>> application,status,date,message,year,month
> >>>>> kafka,down,2017-03023 04:53,zookeeper is not available,2017,03
> >>>>>
> >>>>> and then perform a CTAS using the year and month columns as the
> >>>> 'partition
> >>>>> by'?
> >>>>>
> >>>>> Thanks
> >>>>>
> >>>>
> >>>
> >>
>
>

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