Обсуждение: Why creating GIN table index is so slow than inserting data into empty table with the same index?

example:

select version();
                                          version
--------------------------------------------------------------------------------------------
 PostgreSQL 8.3.6 on i486-pc-linux-gnu, compiled by GCC gcc-4.3.real (Debian 4.3.3-3) 4.3.3

show maintenance_work_mem ;
 maintenance_work_mem
----------------------
 128MB

create table a (i1 int, i2 int, i3 int, i4 int, i5 int, i6 int);

insert into a select n, n, n, n, n, n from generate_series(1, 100000) as n;
INSERT 0 100000
Время: 570,110 мс

create index arr_gin on a using gin ( (array[i1, i2, i3, i4, i5, i6]) );
CREATE INDEX
Время: 203068,314 мс

truncate a;
drop index arr_gin ;

create index arr_gin on a using gin ( (array[i1, i2, i3, i4, i5, i6]) );
CREATE INDEX
Время: 3,246 мс

insert into a select n, n, n, n, n, n from generate_series(1, 100000) as n;
INSERT 0 100000
Время: 2405,481 мс

select pg_size_pretty(pg_total_relation_size('a')) as total,
       pg_size_pretty(pg_relation_size('a')) as table;
  total  |  table
---------+---------
 9792 kB | 5096 kB


203068.314 ms VS 2405.481 ms, is this behaviour normal ?

Thanks !

--
Sergey Burladyan

Sergey Burladyan <eshkinkot@gmail.com> writes:
> show maintenance_work_mem ;
>  maintenance_work_mem
> ----------------------
>  128MB

> create table a (i1 int, i2 int, i3 int, i4 int, i5 int, i6 int);
> insert into a select n, n, n, n, n, n from generate_series(1, 100000) as n;
> create index arr_gin on a using gin ( (array[i1, i2, i3, i4, i5, i6]) );

[ takes forever ]

Seems the reason this is so awful is that the incoming data is exactly
presorted, meaning that the tree structure that ginInsertEntry() is
trying to build degenerates to a linear list (ie, every new element
becomes the right child of the prior one).  So the processing becomes
O(N^2) up till you reach maintenance_work_mem and flush the tree.  With
a large setting for maintenance_work_mem it gets spectacularly slow.

I think a reasonable solution for this might be to keep an eye on
maxdepth and force a flush if that gets too large (more than a few
hundred, perhaps?).  Something like this:

    /* If we've maxed out our available memory, dump everything to the index */
+   /* Also dump if the tree seems to be getting too unbalanced */
-   if (buildstate->accum.allocatedMemory >= maintenance_work_mem * 1024L)
+   if (buildstate->accum.allocatedMemory >= maintenance_work_mem * 1024L ||
+       buildstate->accum.maxdepth > DEPTH_LIMIT)
    {

The new fast-insert code likely will need a similar defense.

Comments?

            regards, tom lane

Tom Lane wrote:
> Sergey Burladyan <eshkinkot@gmail.com> writes:
>> show maintenance_work_mem ;
>>  maintenance_work_mem
>> ----------------------
>>  128MB
>
>> create table a (i1 int, i2 int, i3 int, i4 int, i5 int, i6 int);
>> insert into a select n, n, n, n, n, n from generate_series(1, 100000) as n;
>> create index arr_gin on a using gin ( (array[i1, i2, i3, i4, i5, i6]) );
>
> [ takes forever ]
>
> Seems the reason this is so awful is that the incoming data is exactly
> presorted, meaning that the tree structure that ginInsertEntry() is
> trying to build degenerates to a linear list (ie, every new element
> becomes the right child of the prior one).  So the processing becomes
> O(N^2) up till you reach maintenance_work_mem and flush the tree.  With
> a large setting for maintenance_work_mem it gets spectacularly slow.

Yes, this is probably the same issue I bumped into a while ago:

http://archives.postgresql.org/message-id/49350A13.3020105@enterprisedb.com

> I think a reasonable solution for this might be to keep an eye on
> maxdepth and force a flush if that gets too large (more than a few
> hundred, perhaps?).  Something like this:
>
>     /* If we've maxed out our available memory, dump everything to the index */
> +   /* Also dump if the tree seems to be getting too unbalanced */
> -   if (buildstate->accum.allocatedMemory >= maintenance_work_mem * 1024L)
> +   if (buildstate->accum.allocatedMemory >= maintenance_work_mem * 1024L ||
> +       buildstate->accum.maxdepth > DEPTH_LIMIT)
>     {
>
> The new fast-insert code likely will need a similar defense.

I fooled around with a balanced tree, which solved the problem but
unfortunately made the unsorted case slower. Limiting the depth like
that should avoid that so it's worth trying.

--
   Heikki Linnakangas
   EnterpriseDB   http://www.enterprisedb.com

Heikki Linnakangas <heikki.linnakangas@enterprisedb.com> writes:
> Tom Lane wrote:
>> I think a reasonable solution for this might be to keep an eye on
>> maxdepth and force a flush if that gets too large (more than a few
>> hundred, perhaps?).  Something like this:

> I fooled around with a balanced tree, which solved the problem but
> unfortunately made the unsorted case slower.

Yeah, rebalancing the search tree would fix that, but every balanced
tree algorithm I know about is complicated, slow, and needs extra
memory.  It's really unclear that it'd be worth the trouble for a
transient data structure like this one.

            regards, tom lane