Thread: Tuning queries on large database
Hi,
I have some problem of performance on a PG database, and I don't
know how to improve. I Have two questions : one about the storage
of data, one about tuning queries. If possible !
My job is to compare Oracle and Postgres. All our operational databases
have been running under Oracle for about fifteen years. Now I try to replace
Oracle by Postgres.
I have a test platform under linux (Dell server, 4 Gb RAM, bi-processor,
Linux Red Hat 9 (2.4.20-31.9)) with 2 databases, 1 with Oracle
(V8i or V9i it's quite the same), 1 with PG (7.4.2). Both databases
have the same structure, same content, about 100 Gb each. I developped
some benches, representative of our use of databases. My problem
is that I have tables (relations) with more than 100 millions rows,
and each row has about 160 fields and an average size 256 bytes.
For Oracle I have a SGA size of 500 Mb.
For PG I have a postgresql.conf as :
max_connections = 1500
shared_buffers = 30000
sort_mem = 50000
effective_cache_size = 200000
and default value for other parameters.
I have a table named "data" which looks like this :
bench=> \d data
Table "public.data"
Column | Type | Modifiers
------------+-----------------------------+-----------
num_poste | numeric(9,0) | not null
dat | timestamp without time zone | not null
datrecu | timestamp without time zone | not null
rr1 | numeric(5,1) |
qrr1 | numeric(2,0) | ...
... all numeric fields
...
Indexes:
"pk_data" primary key, btree (num_poste, dat)
"i_data_dat" btree (dat)
It contains 1000 different values of "num_poste" and for each one
125000 different values of "dat" (1 row per hour, 15 years).
I run a vacuum analyze of the table.
bench=> select * from tailledb ;
schema | relfilenode | table | index | reltuples | size
--------+-------------+------------------+------------+-------------+----------
public | 125615917 | data | | 1.25113e+08 | 72312040
public | 251139049 | data | i_data_dat | 1.25113e+08 | 2744400
public | 250870177 | data | pk_data | 1.25113e+08 | 4395480
My first remark is that the table takes a lot of place on disk, about
70 Gb, instead of 35 Gb with oracle.
125 000 000 rows x 256 b = about 32 Gb. This calculation gives an idea
not so bad for oracle. What about for PG ? How data is stored ?
The different queries of the bench are "simple" queries (no join,
sub-query, ...) and are using indexes (I "explained" each one to
be sure) :
Q1 select_court : access to about 700 rows : 1 "num_poste" and 1 month
(using PK : num_poste=p1 and dat between p2 and p3)
Q2 select_moy : access to about 7000 rows : 10 "num_poste" and 1 month
(using PK : num_poste between p1 and p1+10 and dat between p2 and p3)
Q3 select_long : about 250 000 rows : 2 "num_poste"
(using PK : num_poste in (p1,p1+2))
Q4 select_tres_long : about 3 millions rows : 25 "num_poste"
(using PK : num_poste between p1 and p1 + 25)
The result is that for "short queries" (Q1 and Q2) it runs in a few
seconds on both Oracle and PG. The difference becomes important with
Q3 : 8 seconds with oracle
80 sec with PG
and too much with Q4 : 28s with oracle
17m20s with PG !
Of course when I run 100 or 1000 parallel queries such as Q3 or Q4,
it becomes a disaster !
I can't understand these results. The way to execute queries is the
same I think. I've read recommended articles on the PG site.
I tried with a table containing 30 millions rows, results are similar.
What can I do ?
Thanks for your help !
********************************************************************
* Les points de vue exprimes sont strictement personnels et *
* n'engagent pas la responsabilite de METEO-FRANCE. *
********************************************************************
* Valerie SCHNEIDER Tel : +33 (0)5 61 07 81 91 *
* METEO-FRANCE / DSI/DEV Fax : +33 (0)5 61 07 81 09 *
* 42, avenue G. Coriolis Email : Valerie.Schneider@meteo.fr *
* 31057 TOULOUSE Cedex - FRANCE http://www.meteo.fr *
********************************************************************
> sort_mem = 50000 That is way, way too large. Try more like 5000 or lower. > num_poste | numeric(9,0) | not null For starters numerics are really, really slow compared to integers. Why aren't you using an integer for this field since youhave '0' decimal places. > schema | relfilenode | table | index | reltuples | size > --------+-------------+------------------+------------+-------------+---------- > public | 125615917 | data | | 1.25113e+08 | 72312040 > public | 251139049 | data | i_data_dat | 1.25113e+08 | 2744400 > public | 250870177 | data | pk_data | 1.25113e+08 | 4395480 > > My first remark is that the table takes a lot of place on disk, about > 70 Gb, instead of 35 Gb with oracle. Integers will take a lot less space than numerics. > The different queries of the bench are "simple" queries (no join, > sub-query, ...) and are using indexes (I "explained" each one to > be sure) : > Q1 select_court : access to about 700 rows : 1 "num_poste" and 1 month > (using PK : num_poste=p1 and dat between p2 and p3) > Q2 select_moy : access to about 7000 rows : 10 "num_poste" and 1 month > (using PK : num_poste between p1 and p1+10 and dat between p2 and p3) > Q3 select_long : about 250 000 rows : 2 "num_poste" > (using PK : num_poste in (p1,p1+2)) > Q4 select_tres_long : about 3 millions rows : 25 "num_poste" > (using PK : num_poste between p1 and p1 + 25) > > The result is that for "short queries" (Q1 and Q2) it runs in a few > seconds on both Oracle and PG. The difference becomes important with > Q3 : 8 seconds with oracle > 80 sec with PG > and too much with Q4 : 28s with oracle > 17m20s with PG ! > > Of course when I run 100 or 1000 parallel queries such as Q3 or Q4, > it becomes a disaster ! Please reply with the EXPLAIN ANALYZE output of these queries so we can have some idea of how to help you. Chris
On Wed, 2004-08-04 at 08:44, Valerie Schneider DSI/DEV wrote:
> Hi,
>
> I have some problem of performance on a PG database, and I don't
> know how to improve. I Have two questions : one about the storage
> of data, one about tuning queries. If possible !
>
> My job is to compare Oracle and Postgres. All our operational databases
> have been running under Oracle for about fifteen years. Now I try to replace
> Oracle by Postgres.
You may assume some additional hardware may be required -- this would be
purchased out of the Oracle License budget :)
> My first remark is that the table takes a lot of place on disk, about
> 70 Gb, instead of 35 Gb with oracle.
> 125 000 000 rows x 256 b = about 32 Gb. This calculation gives an idea
> not so bad for oracle. What about for PG ? How data is stored ?
This is due to the datatype you've selected. PostgreSQL does not convert
NUMERIC into a more appropriate integer format behind the scenes, nor
will it use the faster routines for the math when it is an integer.
Currently it makes the assumption that if you've asked for numeric
rather than integer or float that you are dealing with either large
numbers or require high precision math.
Changing most of your columns to integer + Check constraint (where
necessary) will give you a large speed boost and reduce disk
requirements a little.
> The different queries of the bench are "simple" queries (no join,
> sub-query, ...) and are using indexes (I "explained" each one to
> be sure) :
Care to send us the EXPLAIN ANALYZE output for each of the 4 queries
after you've improved the datatype selection?
--
Rod Taylor <rbt [at] rbt [dot] ca>
Build A Brighter Lamp :: Linux Apache {middleware} PostgreSQL
PGP Key: http://www.rbt.ca/signature.asc
Attachment
>X-Original-To: pgsql-performance-postgresql.org@localhost.postgresql.org
>X-Authentication-Warning: houston.familyhealth.com.au: chriskl owned process
doing -bs
>Date: Wed, 4 Aug 2004 21:21:51 +0800 (WST)
>From: Christopher Kings-Lynne <chriskl@familyhealth.com.au>
>To: Valerie Schneider DSI/DEV <Valerie.Schneider@meteo.fr>
>Cc: pgsql-performance@postgresql.org, <pgsql-general@postgresql.org>
>Subject: Re: [PERFORM] Tuning queries on large database
>MIME-Version: 1.0
>X-Virus-Scanned: by amavisd-new at hub.org
>X-Spam-Status: No, hits=0.0 tagged_above=0.0 required=5.0 tests=
>X-Spam-Level:
>X-Mailing-List: pgsql-performance
>
>> sort_mem = 50000
>
>That is way, way too large. Try more like 5000 or lower.
>
>> num_poste | numeric(9,0) | not null
>
>For starters numerics are really, really slow compared to integers. Why
>aren't you using an integer for this field since youhave '0' decimal
>places.
>
>> schema | relfilenode | table | index | reltuples | size
>>
--------+-------------+------------------+------------+-------------+----------
>> public | 125615917 | data | | 1.25113e+08 |
72312040
>> public | 251139049 | data | i_data_dat | 1.25113e+08 |
2744400
>> public | 250870177 | data | pk_data | 1.25113e+08 |
4395480
>>
>> My first remark is that the table takes a lot of place on disk, about
>> 70 Gb, instead of 35 Gb with oracle.
>
>Integers will take a lot less space than numerics.
>
>> The different queries of the bench are "simple" queries (no join,
>> sub-query, ...) and are using indexes (I "explained" each one to
>> be sure) :
>> Q1 select_court : access to about 700 rows : 1 "num_poste" and 1 month
>> (using PK : num_poste=p1 and dat between p2 and p3)
>> Q2 select_moy : access to about 7000 rows : 10 "num_poste" and 1 month
>> (using PK : num_poste between p1 and p1+10 and dat between p2 and p3)
>> Q3 select_long : about 250 000 rows : 2 "num_poste"
>> (using PK : num_poste in (p1,p1+2))
>> Q4 select_tres_long : about 3 millions rows : 25 "num_poste"
>> (using PK : num_poste between p1 and p1 + 25)
>>
>> The result is that for "short queries" (Q1 and Q2) it runs in a few
>> seconds on both Oracle and PG. The difference becomes important with
>> Q3 : 8 seconds with oracle
>> 80 sec with PG
>> and too much with Q4 : 28s with oracle
>> 17m20s with PG !
>>
>> Of course when I run 100 or 1000 parallel queries such as Q3 or Q4,
>> it becomes a disaster !
>
>Please reply with the EXPLAIN ANALYZE output of these queries so we can
>have some idea of how to help you.
>
>Chris
>
>
>
>---------------------------(end of broadcast)---------------------------
>TIP 4: Don't 'kill -9' the postmaster
Q1 :
bench=> explain analyze select 'Q1',min(td),max(u) from data where
num_poste=1000 and dat between
(date_trunc('month',to_timestamp('31012004','ddmmyyyy')-interval '2000
days'))::timestamp and
(date_trunc('month',to_timestamp('31012004','ddmmyyyy')-interval '2000 days') +
interval '1 month' - interval '1 hour')::timestamp;
QUERY PLAN
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
----------------------------------------------------------------------
Aggregate (cost=2501.90..2501.90 rows=1 width=21) (actual
time=581.460..581.461 rows=1 loops=1)
-> Index Scan using pk_data on data (cost=0.00..2498.80 rows=619 width=21)
(actual time=92.986..579.089 rows=744 loops=1)
Index Cond: ((num_poste = 1000::numeric) AND (dat >=
(date_trunc('month'::text, (to_timestamp('31012004'::text, 'ddmmyyyy'::text) -
'2000 days'::interval)))::timestamp without time zone) AND (dat <=
(((date_trunc('month'::text, (to_timestamp('31012004'::text, 'ddmmyyyy'::text) -
'2000 days'::interval)) + '1 mon'::interval) - '01:00:00'::interval))::timestamp
without time zone))
Total runtime: 609.149 ms
(4 rows)
Q2 :
bench=> explain analyze select 'Q2',count(*) from data where num_poste between
100 and 100+10 and dat between
(date_trunc('month',to_timestamp('31012004','ddmmyyyy')-interval '3000
days'))::timestamp and
(date_trunc('month',to_timestamp('31012004','ddmmyyyy')-interval '3000 days') +
interval '1 month' - interval '1 hour')::timestamp;
QUERY PLAN
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
----------------------
Aggregate (cost=23232.05..23232.05 rows=1 width=0) (actual
time=5678.849..5678.850 rows=1 loops=1)
-> Index Scan using pk_data on data (cost=0.00..23217.68 rows=5747 width=0)
(actual time=44.408..5669.387 rows=7920 loops=1)
Index Cond: ((num_poste >= 100::numeric) AND (num_poste <=
110::numeric) AND (dat >= (date_trunc('month'::text,
(to_timestamp('31012004'::text, 'ddmmyyyy'::text) - '3000
days'::interval)))::timestamp without time zone) AND (dat <=
(((date_trunc('month'::text, (to_timestamp('31012004'::text, 'ddmmyyyy'::text) -
'3000 days'::interval)) + '1 mon'::interval) - '01:00:00'::interval))::timestamp
without time zone))
Total runtime: 5679.059 ms
(4 rows)
Q3 :
bench=> explain analyze select 'Q3',sum(rr1),count(ff) from data where num_poste
in (50,50+2);
QUERY PLAN
--------------------------------------------------------------------------------
------------------------------------------------------------------
Aggregate (cost=986770.56..986770.56 rows=1 width=17) (actual
time=75401.030..75401.031 rows=1 loops=1)
-> Index Scan using pk_data, pk_data on data (cost=0.00..985534.43
rows=247225 width=17) (actual time=35.823..74885.689 rows=250226 loops=1)
Index Cond: ((num_poste = 50::numeric) OR (num_poste = 52::numeric))
Total runtime: 75405.666 ms
(4 rows)
Q4 :
bench=> explain analyze select 'Q4',count(*) from data where num_poste between
600 and 625;
QUERY PLAN
--------------------------------------------------------------------------------
--------------------------------------------------------------
Aggregate (cost=12166763.62..12166763.62 rows=1 width=0) (actual
time=1162090.302..1162090.303 rows=1 loops=1)
-> Index Scan using pk_data on data (cost=0.00..12159021.19 rows=3096971
width=0) (actual time=94.679..1158266.561 rows=3252938 loops=1)
Index Cond: ((num_poste >= 600::numeric) AND (num_poste <=
625::numeric))
Total runtime: 1162102.217 ms
(4 rows)
Now I'm going to recreate my table with integer and real datatype,
and to decrease sort_mem to 5000.
Then I'll try these queries again.
Thanks.
********************************************************************
* Les points de vue exprimes sont strictement personnels et *
* n'engagent pas la responsabilite de METEO-FRANCE. *
********************************************************************
* Valerie SCHNEIDER Tel : +33 (0)5 61 07 81 91 *
* METEO-FRANCE / DSI/DEV Fax : +33 (0)5 61 07 81 09 *
* 42, avenue G. Coriolis Email : Valerie.Schneider@meteo.fr *
* 31057 TOULOUSE Cedex - FRANCE http://www.meteo.fr *
********************************************************************
>> not so bad for oracle. What about for PG ? How data is stored
I agree with the datatype issue. Smallint, bigint, integer... add a
constraint...
Also the way order of the records in the database is very important. As
you seem to have a very large static population in your table, you should
insert it, ordered by your favourite selection index (looks like it's
poste).
Also, you have a lot of static data which pollutes your table. Why not
create two tables, one for the current year, and one for all the past
years. Use a view to present a merged view.
You often make sums. Why not use separate tables to cache these sums by
month, by poste, by whatever ?
Rule on insert on the big table updates the cache tables.
Valerie Schneider DSI/DEV wrote: > Hi, > > I have some problem of performance on a PG database, and I don't > know how to improve. I Have two questions : one about the storage > of data, one about tuning queries. If possible ! > > My job is to compare Oracle and Postgres. All our operational databases > have been running under Oracle for about fifteen years. Now I try to replace > Oracle by Postgres. Show us the explain analyze on your queries. Regards Gaetano Mendola