Vectorised Query Execution extension

[Mich Talebzadeh <mi...@gmail.com>]

As I understand from the manual:

Vectorized query execution is a Hive feature that greatly reduces the CPU
usage for typical query operations like scans, filters, aggregates, and
joins. A standard query execution system processes one row at a time. This
involves long code ...... Vectorized query execution streamlines operations
by processing a block of 1024 rows at a time. Within the block, each column
is stored as a vector (an array of a primitive data type).

As fart as I can see Vectorized query execution (VQE) can be applied to
most columns and sql operations. Is it therefore possible to extend it
beyond 1024 rows to include the whole column in table?

VQE would be very useful especially with ORC as it basically means that one
can process the whole column separately thus improving performance of the
query.

HTH

Dr Mich Talebzadeh



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Re: Vectorised Query Execution extension

[Gopal Vijayaraghavan <go...@apache.org>]

> Vectorized query execution streamlines operations by processing a block
>of 1024 rows at a time.

The real win of vectorization + columnar is that you get to take advantage
of them at the same time.

We get to execute the function once per 1024 rows when things are
repeating - particularly true when the repetition naturally clusters
together (like a Date field).

And what's the possibility that no-row has nulls in a file - the current
mode of operation prevents small fractions of nulls from hurting the whole
runtime.

Making it 1024 also was because of the way Java -XX:+UseNUMA allocates
pages. If you look at LLAP startup options, you'll notice that it does
most of the allocations off the TLAB, to restrict the allocations to the
same NUMA zone as the thread.

No such easy solutions exist for larger allocations.

so 

1) we'd lose isRepeating=true when you increase the block size
2) we get slower memory with NUMA interleaving when you increase the size
of allocations.
3) we lose low-pause GC effects when allocating from the Humongous section
of the G1GC

The illusion of Java is that allocations are free - imagine a count(1)
that allocates a huge array before returning versus a reader which reuses
the same memory to read chunks and operate, which one would pause more
often for the GC?

> VQE would be very useful especially with ORC as it basically means that
>one can process the whole column separately thus improving performance of
>the query.

Why and How? 

The columnar layout already process the whole column separately because of
how chunks are read out.

There's more to be done there for pure performance, of course - we could
run the pre-exec filters on the String dictionaries and then only run pure
int:int comparisons for the offsets, we could execute deterministic UDFs
once per dictionary offset to make the isRepeating model operate across a
whole ORC stripe.

Cheers,
Gopal

Re: Vectorised Query Execution extension

[Jörn Franke <jo...@gmail.com>]

Even if it is possible it does only make sense to a certain limit given by your CPU and CPU caches.

> On 04 Aug 2016, at 22:57, Mich Talebzadeh <mi...@gmail.com> wrote:
> 
> As I understand from the manual:
> 
> Vectorized query execution is a Hive feature that greatly reduces the CPU usage for typical query operations like scans, filters, aggregates, and joins. A standard query execution system processes one row at a time. This involves long code ...... Vectorized query execution streamlines operations by processing a block of 1024 rows at a time. Within the block, each column is stored as a vector (an array of a primitive data type).
> 
> As fart as I can see Vectorized query execution (VQE) can be applied to most columns and sql operations. Is it therefore possible to extend it beyond 1024 rows to include the whole column in table?
> 
> VQE would be very useful especially with ORC as it basically means that one can process the whole column separately thus improving performance of the query.
> 
> HTH
> 
> Dr Mich Talebzadeh
>  
> LinkedIn  https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>  
> http://talebzadehmich.wordpress.com
> 
> Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction.
>