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From manish gupta <tomanishgupt...@gmail.com>
Subject Re: CarbonData Performance Optimization
Date Thu, 27 Sep 2018 06:17:00 GMT
+1

Regards
Manish Gupta

On Thu, 27 Sep 2018 at 11:36 AM, Kumar Vishal <kumarvishal1802@gmail.com>
wrote:

> +1
> Regards
> Kumar Vishal
>
> On Thu, Sep 27, 2018 at 8:57 AM Jacky Li <jacky.likun@qq.com> wrote:
>
> > +1
> >
> > > 在 2018年9月21日,上午10:20,Ravindra Pesala <ravi.pesala@gmail.com>
写道:
> > >
> > > Hi,
> > >
> > > In case of querying data using Spark  or Presto, carbondata is not well
> > > optimized for reading data and fill the vector. The major issues are as
> > > follows.
> > > 1. CarbonData has long method stack for reading and filling out the
> data
> > to
> > > vector.
> > > 2. Many conditions and checks before filling out the data to vector.
> > > 3. Maintaining intermediate copies of data leads more CPU utilization.
> > > Because of the above issues, there is a high chance of missing the CPU
> > > cache while processing the leads to poor performance.
> > >
> > > So here I am proposing the optimization to fill the vector without much
> > > method stack and condition checks and no intermediate copies to utilize
> > > more CPU cache.
> > >
> > > *Full Scan queries:*
> > >  After decompressing the page in our V3 reader we can immediately fill
> > the
> > > data to a vector without any condition checks inside loops. So here
> > > complete column page data is set to column vector in a single batch and
> > > gives back data to Spark/Presto.
> > > *Filter Queries:*
> > >  First, apply page level pruning using the min/max of each page and get
> > > the valid pages of blocklet.  Decompress only valid pages and fill the
> > > vector directly as mentioned in full scan query scenario.
> > >
> > > In this method, we can also get the advantage of avoiding two times
> > > filtering in Spark/Presto as they do the filtering again even though we
> > > return the filtered data.
> > >
> > > Please find the *TPCH performance report of updated carbon* as per the
> > > changes mentioned above. Please note that the changes I have done the
> > > changes in POC quality so it takes some time to stabilize it.
> > >
> > > *Configurations*
> > > Laptop with i7 processor and 16 GB RAM.
> > > TPCH Data Scale: 100 GB
> > > No Sort with no inverted index data.
> > > Total CarbonData Size : 32 GB
> > > Total Parquet Size :  31 GB
> > >
> > >
> > > Queries Parquet Carbon New Carbon Old Carbon Old vs Carbon New Carbon
> New
> > > Vs Parquet Carbon old Vs Parquet
> > > Q1 101 96 128 25.00% 4.95% -26.73%
> > > Q2 85 82 85 3.53% 3.53% 0.00%
> > > Q3 118 112 135 17.04% 5.08% -14.41%
> > > Q4 473 424 486 12.76% 10.36% -2.75%
> > > Q5 228 201 205 1.95% 11.84% 10.09%
> > > Q6 19.2 19.2 48 60.00% 0.00% -150.00%
> > > Q7 194 181 198 8.59% 6.70% -2.06%
> > > Q8 285 263 275 4.36% 7.72% 3.51%
> > > Q9 362 345 363 4.96% 4.70% -0.28%
> > > Q10 101 92 93 1.08% 8.91% 7.92%
> > > Q11 64 61 62 1.61% 4.69% 3.13%
> > > Q12 41.4 44 63 30.16% -6.28% -52.17%
> > > Q13 43.4 43.6 43.7 0.23% -0.46% -0.69%
> > > Q14 36.9 31.5 41 23.17% 14.63% -11.11%
> > > Q15 70 59 80 26.25% 15.71% -14.29%
> > > Q16 64 60 64 6.25% 6.25% 0.00%
> > > Q17 426 418 432 3.24% 1.88% -1.41%
> > > Q18 1015 921 1001 7.99% 9.26% 1.38%
> > > Q19 62 53 59 10.17% 14.52% 4.84%
> > > Q20 406 326 426 23.47% 19.70% -4.93%
> > > Full Scan Query* 140 116 164 29.27% 17.14% -17.14%
> > > *Full Scan Query means count of every coumn of lineitem, In this way we
> > can
> > > check the full scan query performance.
> > >
> > > The above optimization is not just limited to fileformat and Presto
> > > integration but also improves for CarbonSession integration.
> > > We can further optimize carbon by the tasks(Vishal is already working
> on
> > > it) like adaptive encoding for all types of columns and storing length
> > and
> > > values in separate pages in case of string datatype.Please refer
> > >
> >
> http://apache-carbondata-dev-mailing-list-archive.1130556.n5.nabble.com/Discussion-Carbondata-Store-size-optimization-td62283.html
> > > .
> > >
> > > --
> > > Thanks & Regards,
> > > Ravi
> > >
> >
> >
> >
> >
>

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