Обсуждение: Atomic access to large arrays

• It's posible to tune some TOAST parameters to get faster atomic access to large arrays?

• Using "EXTERNAL" strategy for storing TOAST-able columns could solve the problem?

• Atomic access will be faster if table stores vectors for data in the same parallel instead of matrices of global data?
  
  CREATE TABLE grid_f_data_i2 (
    //Specifies the variable and other features of data
    id_inventory integer REFERENCES grid_d_inventory(id_inventory),
    //A new grid is available each 3 hours since 5 years ago
    dh_date timestamp,
    // nu_parallel = y = Round((latitude+90) * 2
    smallint nu_parallel,
    //Values are scaled to be stored as signed integers of 2 bytes
    vl_parallel smallint[],
  CONSTRAINT meteo_f_gfs_tmp PRIMARY KEY
    (co_inventory, nu_parallel, dh_date)
  );
  
  
• There is another faster solution?
  

Victor,

Just wondering why do you use array ?

Oleg
On Wed, 22 Jul 2009, Victor de Buen (Bayes) wrote:

> Hi
>
> I'm storing historical meteorological gridded data from GFS (
> http://www.nco.ncep.noaa.gov/pmb/products/gfs/) into an array field in a
> table like this:
>
> CREATE TABLE grid_f_data_i2 (
>  //Specifies the variable and other features of data
>  id_inventory integer REFERENCES grid_d_inventory(id_inventory),
>  //A new grid is available each 3 hours since 5 years ago
>  dh_date timestamp,
>  //Values are scaled to be stored as signed integers of 2 bytes
>  vl_grid smallint[361][720],
> CONSTRAINT meteo_f_gfs_tmp PRIMARY KEY
>  (co_inventory, dh_date)
> );
>
> Dimensions of each value of field vl_grid are (lat:361 x lon:720 = 259920
> cells} for a grid of 0.5 degrees (about each 55 Km) around the world. So,
> vl_grid[y][x] stores the value at dh_date of a meteorological variable
> specified by id_inventory in the geodesic point
>
> latitude  = -90 + y*0.5
> longitude = x*0.5
>
> The reverse formula for the closest point in the grid of an arbitary
> geodesic point will be
>
> y = Round((latitude+90) * 2
> x = Round(longitude*2)
>
> Field vl_grid is stored in the TOAST table and has a good compression level.
> PostgreSql is the only one database that is able to store this huge amount
> of data in only 34 GB of disk. It's really great system. Queries returning
> big rectangular areas are very fast, but the target of almost all queries is
> to get historical series for a geodesic point
>
> SELECT  dh_date, vl_grid[123][152]
> FROM  grid_f_data_i2
> WHERE  id_inventory = 6
> ORDER BY dh_date
>
> In this case, atomic access to just a cell of each one of a only few
> thousands of rows becomes too slow.
>
> Please, could somebody answer some of these questions?
>
>   - It's posible to tune some TOAST parameters to get faster atomic access
>   to large arrays?
>
>
>   - Using "EXTERNAL" strategy for storing TOAST-able columns could solve
>   the problem?
>
>
>   - Atomic access will be faster if table stores vectors for data in the
>   same parallel instead of matrices of global data?
>   CREATE TABLE grid_f_data_i2 (
>     //Specifies the variable and other features of data
>     id_inventory integer REFERENCES grid_d_inventory(id_inventory),
>     //A new grid is available each 3 hours since 5 years ago
>     dh_date timestamp,
>     // nu_parallel = y = Round((latitude+90) * 2
>     smallint nu_parallel,
>     //Values are scaled to be stored as signed integers of 2 bytes
>     vl_parallel smallint[],
>   CONSTRAINT meteo_f_gfs_tmp PRIMARY KEY
>     (co_inventory, nu_parallel, dh_date)
>   );
>
>    - There is another faster solution?
>
> Thanks in advance and best regards
>
>

     Regards,
         Oleg
_____________________________________________________________
Oleg Bartunov, Research Scientist, Head of AstroNet (www.astronet.ru),
Sternberg Astronomical Institute, Moscow University, Russia
Internet: oleg@sai.msu.su, http://www.sai.msu.su/~megera/
phone: +007(495)939-16-83, +007(495)939-23-83

• It's posible to tune some TOAST parameters to get faster atomic access to large arrays?

• Using "EXTERNAL" strategy for storing TOAST-able columns could solve the problem?

• Atomic access will be faster if table stores vectors for data in the same parallel instead of matrices of global data?
  
  CREATE TABLE grid_f_data_i2 (
    //Specifies the variable and other features of data
    id_inventory integer REFERENCES grid_d_inventory(id_inventory),
    //A new grid is available each 3 hours since 5 years ago
    dh_date timestamp,
    // nu_parallel = y = Round((latitude+90) * 2
    smallint nu_parallel,
    //Values are scaled to be stored as signed integers of 2 bytes
    vl_parallel smallint[],
  CONSTRAINT meteo_f_gfs_tmp PRIMARY KEY
    (co_inventory, nu_parallel, dh_date)
  );
  
  
• There is another faster solution?
  

"Victor de Buen (Bayes)" <vdebuen@bayesinf.com> writes:
> I'm storing historical meteorological gridded data from GFS (
> http://www.nco.ncep.noaa.gov/pmb/products/gfs/) into an array field in a
> table like this:

>   vl_grid smallint[361][720],

>    - It's posible to tune some TOAST parameters to get faster atomic access
>    to large arrays?

It might save a little bit to make the toast chunk size larger, but I'm
not sure you could gain much from that.

>    - Using "EXTERNAL" strategy for storing TOAST-able columns could solve
>    the problem?

Nope, wouldn't help --- AFAIR array access is not optimized for slice
access.  In any case, doing that would give up the compression savings
that you were so happy about.

If your normal access patterns involve "vertical" rather than
"horizontal" scans of the data, maybe you should rethink the choice
of table layout.  Or maybe the compression is enough to allow you
to consider storing the data twice, once in the current layout and
once in a "vertical" format.

            regards, tom lane

On Tue, Jul 21, 2009 at 7:43 PM, Victor de Buen
(Bayes)<vdebuen@bayesinf.com> wrote:
> Hi
>
> I'm storing historical meteorological gridded data from GFS
> (http://www.nco.ncep.noaa.gov/pmb/products/gfs/) into an array field in a
> table like this:
>
> CREATE TABLE grid_f_data_i2 (
>   //Specifies the variable and other features of data
>   id_inventory integer REFERENCES grid_d_inventory(id_inventory),
>   //A new grid is available each 3 hours since 5 years ago
>   dh_date timestamp,
>   //Values are scaled to be stored as signed integers of 2 bytes
>   vl_grid smallint[361][720],
> CONSTRAINT meteo_f_gfs_tmp PRIMARY KEY
>   (co_inventory, dh_date)
> );
>
> Dimensions of each value of field vl_grid are (lat:361 x lon:720 = 259920
> cells} for a grid of 0.5 degrees (about each 55 Km) around the world. So,
> vl_grid[y][x] stores the value at dh_date of a meteorological variable
> specified by id_inventory in the geodesic point
>
> latitude  = -90 + y*0.5
> longitude = x*0.5
>
> The reverse formula for the closest point in the grid of an arbitary
> geodesic point will be
>
> y = Round((latitude+90) * 2
> x = Round(longitude*2)
>
> Field vl_grid is stored in the TOAST table and has a good compression level.
> PostgreSql is the only one database that is able to store this huge amount
> of data in only 34 GB of disk. It's really great system. Queries returning
> big rectangular areas are very fast, but the target of almost all queries is
> to get historical series for a geodesic point
>
> SELECT  dh_date, vl_grid[123][152]
> FROM  grid_f_data_i2
> WHERE  id_inventory = 6
> ORDER BY dh_date
>
> In this case, atomic access to just a cell of each one of a only few
> thousands of rows becomes too slow.

That's a side effect of your use of arrays.  Arrays are very compact,
and ideal if you always want the whole block of data at once, but
asking for particular point is the down side of your trade off.  I
would suggest maybe experimenting with smaller grid sizes...maybe
divide your big grid into approximately 16 (4x4) separate subgrids.
This should still 'toast', and give decent compression, but mitigate
the impact of single point lookup somewhat.

merlin