Re: Hardware suggestions for maximum read performance

От: Mike McCann
Тема: Re: Hardware suggestions for maximum read performance
Дата: ,
Msg-id: 0D95463F-3004-446E-8F45-B8C6EAC59257@mbari.org
(см: обсуждение, исходный текст)
Ответ на: Re: Hardware suggestions for maximum read performance  (Jeff Janes)
Ответы: Re: Hardware suggestions for maximum read performance  (Scott Marlowe)
Список: pgsql-performance

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Hardware suggestions for maximum read performance  (Mike McCann, )
 Re: Hardware suggestions for maximum read performance  (Scott Marlowe, )
  Re: Hardware suggestions for maximum read performance  (Jeff Janes, )
   Re: Hardware suggestions for maximum read performance  (Mike McCann, )
    Re: Hardware suggestions for maximum read performance  (Jeff Janes, )
     Re: Hardware suggestions for maximum read performance  (Mike McCann, )
      Re: Hardware suggestions for maximum read performance  (Scott Marlowe, )
    Re: Hardware suggestions for maximum read performance  (Greg Smith, )
     Re: Hardware suggestions for maximum read performance  (Scott Marlowe, )
 Re: Hardware suggestions for maximum read performance  (Arjen van der Meijden, )
  Re: Hardware suggestions for maximum read performance  (Scott Marlowe, )
 Re: Hardware suggestions for maximum read performance  (Julien Cigar, )
 Re: Hardware suggestions for maximum read performance  ("Yuri Levinsky", )

On May 13, 2013, at 4:24 PM, Jeff Janes wrote:

On Mon, May 13, 2013 at 3:36 PM, Mike McCann <> wrote:

Increasing work_mem to 355 MB improves the performance by a factor of 2:

stoqs_march2013_s=# set work_mem='355MB';
SET
stoqs_march2013_s=# explain analyze select * from stoqs_measuredparameter order by datavalue;
                                                                QUERY PLAN                                                                
------------------------------------------------------------------------------------------------------------------------------------------
 Sort  (cost=422106.15..430560.68 rows=3381814 width=20) (actual time=2503.078..2937.130 rows=3381814 loops=1)
   Sort Key: datavalue
   Sort Method: quicksort  Memory: 362509kB
   ->  Seq Scan on stoqs_measuredparameter  (cost=0.00..55359.14 rows=3381814 width=20) (actual time=0.016..335.745 rows=3381814 loops=1)
 Total runtime: 3094.601 ms
(5 rows)

I tried changing random_page_cost to from 4 to 1 and saw no change.

I'm wondering now what changes might get this query to run in less than one second.  


I think you are worrying about the wrong thing here.  What is a web app going to do with 3,381,814 rows, once it obtains them?  Your current testing is not testing the time it takes to stream that data to the client, or for the client to do something meaningful with that data.

If you only plan to actually fetch a few dozen of those rows, then you probably need to incorporate that into your test, either by using a LIMIT, or by using a mock-up of the actual application to do some timings.

Also, what is the type and collation of the column you are sorting on?  non-'C' collations of text columns sort about 3 times slower than 'C' collation does.

 
If all the data is in memory, then will faster CPU and memory be the things that help?

Yes, those would help (it is not clear to me which of the two would help more), but I think you need to rethink your design of sending the entire database table to the application server for each page-view.


Cheers,

Jeff

Hi Jeff,

The datavalue column is double precision:

stoqs_march2013_s=# \d+ stoqs_measuredparameter
                                              Table "public.stoqs_measuredparameter"
     Column     |       Type       |                              Modifiers                               | Storage | Description 
----------------+------------------+----------------------------------------------------------------------+---------+-------------
 id             | integer          | not null default nextval('stoqs_measuredparameter_id_seq'::regclass) | plain   | 
 measurement_id | integer          | not null                                                             | plain   | 
 parameter_id   | integer          | not null                                                             | plain   | 
 datavalue      | double precision | not null                                                             | plain   | 
Indexes:
    "stoqs_measuredparameter_pkey" PRIMARY KEY, btree (id)
    "stoqs_measuredparameter_measurement_id_parameter_id_key" UNIQUE CONSTRAINT, btree (measurement_id, parameter_id)
    "stoqs_measuredparameter_datavalue" btree (datavalue)
    "stoqs_measuredparameter_measurement_id" btree (measurement_id)
    "stoqs_measuredparameter_parameter_id" btree (parameter_id)
Foreign-key constraints:
    "stoqs_measuredparameter_measurement_id_fkey" FOREIGN KEY (measurement_id) REFERENCES stoqs_measurement(id) DEFERRABLE INITIALLY DEFERRED
    "stoqs_measuredparameter_parameter_id_fkey" FOREIGN KEY (parameter_id) REFERENCES stoqs_parameter(id) DEFERRABLE INITIALLY DEFERRED
Has OIDs: no


Thanks for the suggestion and advice to examine the web app performance.  We've actually taken quite a few steps to optimize how the web app works. The example query I provided is a simple worst-case one that we can use to help us decide on the proper hardware.  An actual query performed by the web app is:

stoqs_march2013_s=# explain analyze SELECT stoqs_measuredparameter.id,
stoqs_march2013_s-#        stoqs_parameter.name AS parameter__name,
stoqs_march2013_s-#        stoqs_parameter.standard_name AS parameter__standard_name,
stoqs_march2013_s-#        stoqs_measurement.depth AS measurement__depth,
stoqs_march2013_s-#        stoqs_measurement.geom AS measurement__geom,
stoqs_march2013_s-#        stoqs_instantpoint.timevalue AS measurement__instantpoint__timevalue,
stoqs_march2013_s-#        stoqs_platform.name AS measurement__instantpoint__activity__platform__name,
stoqs_march2013_s-#        stoqs_measuredparameter.datavalue AS datavalue,
stoqs_march2013_s-#        stoqs_parameter.units AS parameter__units
stoqs_march2013_s-# FROM stoqs_parameter p1,
stoqs_march2013_s-#      stoqs_measuredparameter
stoqs_march2013_s-#      INNER JOIN stoqs_measurement ON (stoqs_measuredparameter.measurement_id = stoqs_measurement.id)
stoqs_march2013_s-#      INNER JOIN stoqs_instantpoint ON (stoqs_measurement.instantpoint_id = stoqs_instantpoint.id)
stoqs_march2013_s-#      INNER JOIN stoqs_parameter ON (stoqs_measuredparameter.parameter_id = stoqs_parameter.id)
stoqs_march2013_s-#      INNER JOIN stoqs_activity ON (stoqs_instantpoint.activity_id = stoqs_activity.id)
stoqs_march2013_s-#      INNER JOIN stoqs_platform ON (stoqs_activity.platform_id = stoqs_platform.id)
stoqs_march2013_s-#      INNER JOIN stoqs_measuredparameter mp1 ON mp1.measurement_id = stoqs_measuredparameter.measurement_id
stoqs_march2013_s-# WHERE (p1.name = 'sea_water_sigma_t')
stoqs_march2013_s-#       AND (mp1.datavalue > 25.19)
stoqs_march2013_s-#       AND (mp1.datavalue < 26.01)
stoqs_march2013_s-#       AND (mp1.parameter_id = p1.id)
stoqs_march2013_s-#       AND (stoqs_instantpoint.timevalue <= '2013-03-17 19:05:06'
stoqs_march2013_s(#       AND stoqs_instantpoint.timevalue >= '2013-03-17 15:35:13'
stoqs_march2013_s(#       AND stoqs_parameter.name IN ('fl700_uncorr')
stoqs_march2013_s(#       AND stoqs_measurement.depth >= -1.88
stoqs_march2013_s(#       AND stoqs_platform.name IN ('dorado')
stoqs_march2013_s(#       AND stoqs_measurement.depth <= 83.57)
stoqs_march2013_s-# ORDER BY stoqs_activity.name ASC, stoqs_instantpoint.timevalue ASC;
                                                                                                QUERY PLAN                                                                       
                          
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
--------------------------
 Sort  (cost=10741.41..10741.42 rows=1 width=1282) (actual time=770.211..770.211 rows=0 loops=1)
   Sort Key: stoqs_activity.name, stoqs_instantpoint.timevalue
   Sort Method: quicksort  Memory: 25kB
   ->  Hash Join  (cost=3002.89..10741.40 rows=1 width=1282) (actual time=770.200..770.200 rows=0 loops=1)
         Hash Cond: (stoqs_instantpoint.activity_id = stoqs_activity.id)
         ->  Nested Loop  (cost=2983.69..10722.19 rows=3 width=954) (actual time=770.036..770.036 rows=0 loops=1)
               ->  Nested Loop  (cost=2983.69..9617.36 rows=191 width=946) (actual time=91.369..680.072 rows=20170 loops=1)
                     ->  Hash Join  (cost=2983.69..8499.07 rows=193 width=842) (actual time=91.346..577.633 rows=20170 loops=1)
                           Hash Cond: (stoqs_measuredparameter.parameter_id = stoqs_parameter.id)
                           ->  Nested Loop  (cost=2982.38..8478.47 rows=4628 width=24) (actual time=91.280..531.408 rows=197746 loops=1)
                                 ->  Nested Loop  (cost=2982.38..4862.37 rows=512 width=4) (actual time=91.202..116.140 rows=20170 loops=1)
                                       ->  Seq Scan on stoqs_parameter p1  (cost=0.00..1.30 rows=1 width=4) (actual time=0.002..0.011 rows=1 loops=1)
                                             Filter: ((name)::text = 'sea_water_sigma_t'::text)
                                       ->  Bitmap Heap Scan on stoqs_measuredparameter mp1  (cost=2982.38..4854.40 rows=534 width=8) (actual time=91.194..109.846 rows=20170 loop
s=1)
                                             Recheck Cond: ((datavalue > 25.19::double precision) AND (datavalue < 26.01::double precision) AND (parameter_id = p1.id))
                                             ->  BitmapAnd  (cost=2982.38..2982.38 rows=534 width=0) (actual time=90.794..90.794 rows=0 loops=1)
                                                   ->  Bitmap Index Scan on stoqs_measuredparameter_datavalue  (cost=0.00..259.54 rows=12292 width=0) (actual time=62.769..62.769
 rows=23641 loops=1)
                                                         Index Cond: ((datavalue > 25.19::double precision) AND (datavalue < 26.01::double precision))
                                                   ->  Bitmap Index Scan on stoqs_measuredparameter_parameter_id  (cost=0.00..2719.38 rows=147035 width=0) (actual time=27.412..2
7.412 rows=34750 loops=1)
                                                         Index Cond: (parameter_id = p1.id)
                                 ->  Index Scan using stoqs_measuredparameter_measurement_id on stoqs_measuredparameter  (cost=0.00..6.98 rows=7 width=20) (actual time=0.008..0.
017 rows=10 loops=20170)
                                       Index Cond: (measurement_id = mp1.measurement_id)
                           ->  Hash  (cost=1.30..1.30 rows=1 width=826) (actual time=0.012..0.012 rows=1 loops=1)
                                 Buckets: 1024  Batches: 1  Memory Usage: 1kB
                                 ->  Seq Scan on stoqs_parameter  (cost=0.00..1.30 rows=1 width=826) (actual time=0.007..0.010 rows=1 loops=1)
                                       Filter: ((name)::text = 'fl700_uncorr'::text)
                     ->  Index Scan using stoqs_measurement_pkey on stoqs_measurement  (cost=0.00..5.78 rows=1 width=116) (actual time=0.004..0.004 rows=1 loops=20170)
                           Index Cond: (id = stoqs_measuredparameter.measurement_id)
                           Filter: ((depth >= (-1.88)::double precision) AND (depth <= 83.57::double precision))
               ->  Index Scan using stoqs_instantpoint_pkey on stoqs_instantpoint  (cost=0.00..5.77 rows=1 width=16) (actual time=0.004..0.004 rows=0 loops=20170)
                     Index Cond: (id = stoqs_measurement.instantpoint_id)
                     Filter: ((timevalue <= '2013-03-17 19:05:06-07'::timestamp with time zone) AND (timevalue >= '2013-03-17 15:35:13-07'::timestamp with time zone))
         ->  Hash  (cost=18.82..18.82 rows=30 width=336) (actual time=0.151..0.151 rows=7 loops=1)
               Buckets: 1024  Batches: 1  Memory Usage: 1kB
               ->  Hash Join  (cost=1.09..18.82 rows=30 width=336) (actual time=0.035..0.145 rows=7 loops=1)
                     Hash Cond: (stoqs_activity.platform_id = stoqs_platform.id)
                     ->  Seq Scan on stoqs_activity  (cost=0.00..16.77 rows=177 width=66) (actual time=0.005..0.069 rows=177 loops=1)
                     ->  Hash  (cost=1.07..1.07 rows=1 width=278) (actual time=0.014..0.014 rows=1 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 1kB
                           ->  Seq Scan on stoqs_platform  (cost=0.00..1.07 rows=1 width=278) (actual time=0.008..0.012 rows=1 loops=1)
                                 Filter: ((name)::text = 'dorado'::text)
 Total runtime: 770.445 ms
(42 rows)


We assume that steps taken to improve the worst-case query scenario will also improve these kind of queries.  If anything above pops out as needing better planning please let us know that too!

Thanks,
Mike

--
Mike McCann
Software Engineer
Monterey Bay Aquarium Research Institute
7700 Sandholdt Road
Moss Landing, CA 95039-9644
Voice: 831.775.1769  Fax: 831.775.1736 http://www.mbari.org

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