Re: Query taking long time

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Hello,

I cannot do explain (analyze, buffers) since I am on 8.3 postgres version.
I am migrating to the new server and upgrading it.
Once it is ready again I will post the explain query here.
The new disk is SATA disk with 5TB, raid 0 or 1...
lspci | grep -i raid
00:1f.2 RAID bus controller: Intel Corporation C600/X79 series chipset SATA RAID Controller (rev 05)

All database is 200GB and the table entity2document2 is 

x=> select pg_size_pretty(pg_relation_size('entity2document2'));
 pg_size_pretty 
----------------
 11 GB
(1 row)

x=> select pg_size_pretty(pg_total_relation_size('entity2document2'));
 pg_size_pretty 
----------------
 29 GB
(1 row)

The index of the name column:
x=> select pg_size_pretty(pg_relation_size('entity2document2_name'));
 pg_size_pretty 
----------------
 2550 MB
(1 row)


I am tunning the new server with this parameters...
shared_buffers = 15000MB
work_mem = 1000MB
maintenance_work_mem = 2000MB

Any other parameter that should be modified?

Thank you for your help!
Andrés


El Mar 10, 2014, a las 9:22 PM, desmodemone escribió:

Hello Andres,
                       with enable_bitmapscan=off;   could you do :

explain ( analyze , buffers ) select * from entity2document2  where name='ranitidine' ;

I think it's interesting to understand how much it's clustered the table  entity2document2.
infact the query extract 13512 rows in 79945.362 ms around 4 ms for row, and I suspect the table is not well clustered on that column, so every time the
process is asking for a different page of the table or the i/o system have some problem.

Moreover, another point it's : how much it's big ? the rows are arounf 94M , but how much it's big ?  it's important the average row length


Have a nice day

2014-03-06 15:45 GMT+01:00 acanada <acanada@xxxxxxx>:
Hello Mat,

Setting enable_bitmapscan to off doesn't really helps. It gets worse...

x=> SET enable_bitmapscan=off; 
SET
x=> explain analyze select * from (select * from entity2document2  where name='ranitidine' ) as a  order by a.hepval;
                                                                           QUERY PLAN                                                                           
----------------------------------------------------------------------------------------------------------------------------------------------------------------
 Sort  (cost=18789.21..18800.70 rows=4595 width=131) (actual time=79965.282..79966.657 rows=13512 loops=1)
   Sort Key: entity2document2.hepval
   Sort Method:  quicksort  Memory: 2301kB
   ->  Index Scan using entity2document2_name on entity2document2  (cost=0.00..18509.70 rows=4595 width=131) (actual time=67.507..79945.362 rows=13512 loops=1)
         Index Cond: ((name)::text = 'ranitidine'::text)
 Total runtime: 79967.705 ms
(6 rows)

Any other idea? 

Thank you very much for your help. Regards,
Andrés

El Mar 6, 2014, a las 2:11 PM, desmodemone escribió:


Il 05/mar/2014 00:36 "Venkata Balaji Nagothi" <vbnpgc@xxxxxxxxx> ha scritto:
>
> After looking at the distinct values, yes the composite Index on "name" and "hepval" is not recommended. That would worsen - its expected.
>
> We need to look for other possible work around. Please drop off the above Index. Let me see if i can drill further into this.
>
> Meanwhile - can you help us know the memory parameters (work_mem, temp_buffers etc) set ?
>
> Do you have any other processes effecting this query's performance ?
>
> Any info about your Disk, RAM, CPU would also help.
>
> Regards,
> Venkata Balaji N
>
> Fujitsu Australia
>
>
>
>
> Venkata Balaji N
>
> Sr. Database Administrator
> Fujitsu Australia
>
>
> On Tue, Mar 4, 2014 at 10:23 PM, acanada <acanada@xxxxxxx> wrote:
>>
>> Hello,
>>
>> I don't know if this helps to figure out what is the problem but after adding the multicolumn index on name and hepval, the performance is even worse (¿?).  Ten times worse...
>>
>> explain analyze select * from (select * from entity_compounddict2document  where name='progesterone') as a order by a.hepval;
>>                                                                          QUERY PLAN                                                                          
>> -------------------------------------------------------------------------------------------------------------------------------------------------------------
>>  Sort  (cost=422746.18..423143.94 rows=159104 width=133) (actual time=95769.674..95797.943 rows=138165 loops=1)
>>    Sort Key: entity_compounddict2document.hepval
>>    Sort Method:  quicksort  Memory: 25622kB
>>    ->  Bitmap Heap Scan on entity_compounddict2document  (cost=3501.01..408999.90 rows=159104 width=133) (actual time=70.789..95519.258 rows=138165 loops=1)
>>          Recheck Cond: ((name)::text = 'progesterone'::text)
>>          ->  Bitmap Index Scan on entity_compound2document_name  (cost=0.00..3461.23 rows=159104 width=0) (actual time=35.174..35.174 rows=138165 loops=1)
>>                Index Cond: ((name)::text = 'progesterone'::text)
>>  Total runtime: 95811.838 ms
>> (8 rows)
>>
>> Any ideas please?
>>
>> Thank you 
>> Andrés.
>>
>>
>>
>> El Mar 4, 2014, a las 12:28 AM, Venkata Balaji Nagothi escribió:
>>
>>> On Mon, Mar 3, 2014 at 9:17 PM, acanada <acanada@xxxxxxx> wrote:
>>>>
>>>> Hello,
>>>>
>>>> Thankyou for your answer.
>>>> I have made more changes than a simple re-indexing recently. I have moved the sorting field to the table in order to avoid the join clause. Now the schema is very simple. The query only implies one table:
>>>>
>>>> x=> \d+ entity_compounddict2document;
>>>>                       Table "public.entity_compounddict2document"
>>>>       Column      |              Type              | Modifiers | Storage  | Description 
>>>> ------------------+--------------------------------+-----------+----------+-------------
>>>>  id               | integer                        | not null  | plain    | 
>>>>  document_id      | integer                        |           | plain    | 
>>>>  name             | character varying(255)         |           | extended | 
>>>>  qualifier        | character varying(255)         |           | extended | 
>>>>  tagMethod        | character varying(255)         |           | extended | 
>>>>  created          | timestamp(0) without time zone |           | plain    | 
>>>>  updated          | timestamp(0) without time zone |           | plain    | 
>>>>  curation         | integer                        |           | plain    | 
>>>>  hepval           | double precision               |           | plain    | 
>>>>  cardval          | double precision               |           | plain    | 
>>>>  nephval          | double precision               |           | plain    | 
>>>>  phosval          | double precision               |           | plain    | 
>>>>  patternCount     | double precision               |           | plain    | 
>>>>  ruleScore        | double precision               |           | plain    | 
>>>>  hepTermNormScore | double precision               |           | plain    | 
>>>>  hepTermVarScore  | double precision               |           | plain    | 
>>>> Indexes:
>>>>     "entity_compounddict2document_pkey" PRIMARY KEY, btree (id)
>>>>     "entity_compound2document_cardval" btree (cardval)
>>>>     "entity_compound2document_heptermnormscore" btree ("hepTermNormScore")
>>>>     "entity_compound2document_heptermvarscore" btree ("hepTermVarScore")
>>>>     "entity_compound2document_hepval" btree (hepval)
>>>>     "entity_compound2document_name" btree (name)
>>>>     "entity_compound2document_nephval" btree (nephval)
>>>>     "entity_compound2document_patterncount" btree ("patternCount")
>>>>     "entity_compound2document_phosval" btree (phosval)
>>>>     "entity_compound2document_rulescore" btree ("ruleScore")
>>>> Has OIDs: no
>>>>
>>>>            tablename            |                   indexname                                              |  num_rows    | table_size  | index_size | unique | number_of_scans | tuples_read | tuples_fetched 
>>>>  entity_compounddict2document   | entity_compound2document_cardval               | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_heptermnormscore      | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_heptermvarscore       | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_hepval                | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_name                  | 5.42452e+07 | 6763 MB    | 1505 MB    | Y      |              24 |      178680 |              0
>>>>  entity_compounddict2document   | entity_compound2document_nephval               | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_patterncount          | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_phosval               | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compound2document_rulescore             | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>  entity_compounddict2document   | entity_compounddict2document_pkey              | 5.42452e+07 | 6763 MB    | 1162 MB    | Y      |               0 |           0 |              0
>>>>
>>>> The table has aprox. 54,000,000 rows
>>>> There are no NULLs in hepval field and pg_settings haven't changed. I also have done "analyze" to this table.
>>>>
>>>> I have simplified the query and added the last advise that you told me:
>>>>
>>>> Query: 
>>>>
>>>>  explain analyze select * from (select * from entity_compounddict2document  where name='ranitidine') as a order by a.hepval;
>>>>                                                                       QUERY PLAN                                                                      
>>>> ------------------------------------------------------------------------------------------------------------------------------------------------------
>>>>  Sort  (cost=11060.50..11067.55 rows=2822 width=133) (actual time=32715.097..32716.488 rows=13512 loops=1)
>>>>    Sort Key: entity_compounddict2document.hepval
>>>>    Sort Method:  quicksort  Memory: 2301kB
>>>>    ->  Bitmap Heap Scan on entity_compounddict2document  (cost=73.82..10898.76 rows=2822 width=133) (actual time=6.034..32695.483 rows=13512 loops=1)
>>>>          Recheck Cond: ((name)::text = 'ranitidine'::text)
>>>>          ->  Bitmap Index Scan on entity_compound2document_name  (cost=0.00..73.12 rows=2822 width=0) (actual time=3.221..3.221 rows=13512 loops=1)
>>>>                Index Cond: ((name)::text = 'ranitidine'::text)
>>>>  Total runtime: 32717.548 ms
>>>>
>>>> Another query:
>>>> explain analyze select * from (select * from entity_compounddict2document  where name='progesterone' ) as a  order by a.hepval;
>>>>
>>>> QUERY PLAN
>>>> ------------------------------------------------------------------------------------------------------------------------------------------------------------
>>>>  Sort  (cost=367879.25..368209.24 rows=131997 width=133) (actual time=9262.887..9287.046 rows=138165 loops=1)
>>>>    Sort Key: entity_compounddict2document.hepval
>>>>    Sort Method:  quicksort  Memory: 25622kB
>>>>    ->  Bitmap Heap Scan on entity_compounddict2document  (cost=2906.93..356652.81 rows=131997 width=133) (actual time=76.316..9038.485 rows=138165 loops=1)
>>>>          Recheck Cond: ((name)::text = 'progesterone'::text)
>>>>          ->  Bitmap Index Scan on entity_compound2document_name  (cost=0.00..2873.93 rows=131997 width=0) (actual time=40.913..40.913 rows=138165 loops=1)
>>>>                Index Cond: ((name)::text = 'progesterone'::text)
>>>>  Total runtime: 9296.815 ms
>>>>
>>>>
>>>> It has improved (I supose because of the lack of the join table) but still taking a lot of time... Anything I can do??
>>>>
>>>> Any help would be very appreciated. Thank you very much.
>>>
>>>
>>>
>>> Good to know performance has increased.
>>>
>>> "entity_compounddict2document" table goes through high INSERTS ?
>>>
>>> Can you help us know if the "helpval" column and "name" column have high duplicate values ? "n_distinct" value from pg_stats table would have that info. 
>>>
>>> Below could be a possible workaround -
>>>
>>> As mentioned earlier in this email, a composite Index on name and hepval column might help. If the table does not go through lot of INSERTS, then consider performing a CLUSTER on the table using the same INDEX.
>>>
>>> Other recommendations -
>>>
>>> Please drop off all the Non-primary key Indexes which have 0 scans / hits. This would harm the DB and the DB server whilst maintenance and DML operations.
>>>
>>> Regards,
>>> Venkata Balaji N
>>>
>>> Fujitsu Australia
>>
>>
>>
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>>
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>>
>


Hi I think the problem is th heap scan of the table , that the backend have to do because the btree to bitmap conversion becomes lossy. Try to disable the enable_bitmapscan for the current session and rerun the query.

Mat Dba




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