Re: [HACKERS] VOPS: vectorized executor for Postgres: how to speedupOLAP queries more than 10 times without changing anything in Postgresexecutor

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От Konstantin Knizhnik
Тема Re: [HACKERS] VOPS: vectorized executor for Postgres: how to speedupOLAP queries more than 10 times without changing anything in Postgresexecutor
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Msg-id cb182fa7-0cb2-ee5a-f25b-366757eea51c@postgrespro.ru
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Ответ на [HACKERS] VOPS: vectorized executor for Postgres: how to speedup OLAP queriesmore than 10 times without changing anything in Postgres executor  (Konstantin Knizhnik <k.knizhnik@postgrespro.ru>)
Ответы Re: [HACKERS] VOPS: vectorized executor for Postgres: how to speedupOLAP queries more than 10 times without changing anything in Postgres executor  (Thom Brown <thom@linux.com>)
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More progress in vectorized Postgres extension (VOPS). It is not 
required any more to use some special functions in queries.
You can use vector operators in query with standard SQL and still get 
ten times improvement on some queries.
VOPS extension now uses post parse analyze hook to transform query.
I really impressed by flexibility and extensibility of Postgres type 
system. User defined types&operatpors&casts do most of the work.

It is still responsibility of programmer or database administrator to 
create proper projections
of original table. This projections need to use tiles types for some 
attributes (vops_float4,...).
Then you can query this table using standard SQL. And this query will be 
executed using vector operations!

Example of such TPC-H queries:

Q1:
select    l_returnflag,    l_linestatus,    sum(l_quantity) as sum_qty,    sum(l_extendedprice) as sum_base_price,
sum(l_extendedprice*(1-l_discount))as sum_disc_price,    sum(l_extendedprice*(1-l_discount)*(1+l_tax)) as sum_charge,
avg(l_quantity) as avg_qty,    avg(l_extendedprice) as avg_price,    avg(l_discount) as avg_disc,    count(*) as
count_order
from    vops_lineitem_projection
where    l_shipdate <= '1998-12-01'::date
group by    l_returnflag,    l_linestatus
order by    l_returnflag,    l_linestatus;



Q6:
select    sum(l_extendedprice*l_discount) as revenue
from    lineitem_projection
where    l_shipdate between '1996-01-01'::date and '1997-01-01'::date    and l_discount between 0.08 and 0.1    and
l_quantity< 24;
 



On 13.02.2017 17:12, Konstantin Knizhnik wrote:
> Hello hackers,
>
> There were many discussions concerning  possible ways of speeding-up 
> Postgres. Different approaches were suggested:
>
> - JIT (now we have three different prototype implementations based on 
> LLVM)
> - Chunked (vectorized) executor
> - Replacing pull with push
> - Columnar store (cstore_fdw, IMCS)
> - Optimizing and improving current executor (reducing tuple deform 
> overhead, function call overhead,...)
>
> Obviously the best result can be achieved in case of combining all 
> this approaches. But actually them are more or less interchangeable: 
> vectorized execution is not eliminating interpretation overhead, but 
> it is divided by vector size and becomes less critical.
>
> I decided to write small prototype to estimate possible speed 
> improvement of vectorized executor. I created special types 
> representing "tile" and implement standard SQL operators for them. So 
> neither Postgres planer, nether Postgres executor, nether Postgres 
> heap manager are changed. But I was able to reach more than 10 times 
> speed improvement on TPC-H Q1/Q6 queries!
>
> Please find more information here: 
> https://cdn.rawgit.com/postgrespro/vops/ddcbfbe6/vops.html
> The sources of the project can be found here: 
> https://github.com/postgrespro/vops.git
>

-- 
Konstantin Knizhnik
Postgres Professional: http://www.postgrespro.com
The Russian Postgres Company




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