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From Shivram Mani <shivram.m...@gmail.com>
Subject Re: [Propose] More data skipping technology for IO intensive performance enhancement
Date Tue, 05 Jul 2016 17:15:39 GMT
+1 on any form of predicate pushdown. Query planning/optimizer will have to
be modified with the modified cost of plans with the reduced data.

On Tue, Jul 5, 2016 at 9:37 AM, Kavinder Dhaliwal <kdhaliwal@pivotal.io>
wrote:

> This is an excellent idea that bring HAWQ up to speed with comparable
> databases (Presto, Impala). In addition to taking advantage of the stats
> available in file formats like ORC, HAWQ should also transition to
> vectorized reading of files as this also provides a performance boost. The
> newer Apache ORC library only supports vectorized reading so HAWQ should
> also adopt these new methods.
>
> Kavinder
>
> On Mon, Jul 4, 2016 at 2:32 AM, Lei Chang <lei_chang@apache.org> wrote:
>
> > Good idea. I think it can potentially increase the performance of IO
> bound
> > workload.
> >
> > Cheers
> > Lei
> >
> >
> > On Sat, Jul 2, 2016 at 11:19 PM, Ming Li <mli@pivotal.io> wrote:
> >
> > > Data skipping technology can extremely avoiding unnecessary IO,  so it
> > can
> > > extremely enhance performance for IO intensive query. Including
> > eliminating
> > > query on unnecessary table partition according to the partition key
> > range ,
> > > I think more options are available now:
> > >
> > > (1) Parquet / ORC format introduce a lightweight meta data info like
> > > Min/Max/Bloom filter for each block, such meta data can be exploited
> when
> > > predicate/filter info can be fetched before executing scan.
> > >
> > > However now in HAWQ, all data in parquet need to be scanned into memory
> > > before processing predicate/filter. We don't generate the meta info
> when
> > > INSERT into parquet table, the scan executor doesn't utilize the meta
> > info
> > > neither. Maybe some scan API need to be refactored so that we can get
> > > predicate/filter
> > > info before executing base relation scan.
> > >
> > > (2) Base on (1) technology,  especially with Bloom filter, more
> optimizer
> > > technology can be explored furthur. E.g. Impala implemented Runtime
> > > filtering(*
> > >
> >
> https://www.cloudera.com/documentation/enterprise/latest/topics/impala_runtime_filtering.html
> > > <
> > >
> >
> https://www.cloudera.com/documentation/enterprise/latest/topics/impala_runtime_filtering.html
> > > >*
> > > ),  which can be used at
> > > - dynamic partition pruning
> > > - converting join predicate to base relation predicate
> > >
> > > It tell the executor to wait for one moment(the interval time can be
> set
> > in
> > > guc) before executing base relation scan, if the interested values(e.g.
> > the
> > > column in join predicate only have very small set) arrived in time, it
> > can
> > > use these value to filter this scan, if doesn't arrived in time, it
> scan
> > > without this filter, which doesn't impact result correctness.
> > >
> > > Unlike (1) technology, this technology cannot be used in any case, it
> > only
> > > outperform in some cases. So it just add some more query plan
> > > choices/paths, and the optimizer need based on statistics info to
> > calculate
> > > the cost, and apply it when cost down.
> > >
> > > All in one, maybe more similar technology can be adoptable for HAWQ
> now,
> > > let's start to think about performance related technology, moreover we
> > need
> > > to instigate how these technology can be implemented in HAWQ.
> > >
> > > Any ideas or suggestions are welcomed? Thanks.
> > >
> >
>



-- 
shivram mani

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