Thanks, that's easy enough to test. Didn't seem to help though.

test=# REINDEX index test_select_pkey;
REINDEX
test=# VACUUM ANALYZE test_select ;
VACUUM


(stopped postgres; reset O/S cache; started postgres)

test=# explain analyze SELECT * FROM test_select WHERE key1 >= 500000 ORDER
BY key1, key2, key3, id LIMIT 1;
                                                              QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.00..0.08 rows=1 width=21) (actual time=16.368..16.369
rows=1 loops=1)
   ->  Index Scan using my_key on test_select  (cost=0.00..41981.16
rows=501333 width=21) (actual time=16.366..16.366 rows=1 loops=1)
         Index Cond: (key1 >= 500000)
 Total runtime: 16.444 ms


(stopped postgres; reset O/S cache; started postgres)

test=# explain analyze SELECT * FROM test_select WHERE id = 500000;
                                                           QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------
 Index Scan using test_select_pkey on test_select  (cost=0.00..8.36 rows=1
width=21) (actual time=23.072..23.074 rows=1 loops=1)
   Index Cond: (id = 500000)
 Total runtime: 23.192 ms




On Mon, May 27, 2013 at 10:21 AM, Evgeny Shishkin <itparan...@gmail.com>wrote:

>
> On May 27, 2013, at 6:02 PM, John Mudd <johnbm...@gmail.com> wrote:
>
> > Postgres 9.1.2 on Ubuntu 12.04
> >
> > Any reason why a select by primary key would be slower than a select
> that includes an ORDER BY? I was really hoping using the primary key would
> give me a boost.
> >
>
> You created my_key after data loading, and PK was there all the time.
> If you REINDEX PK, i bet it will be as fast.
>
> > I stopped the server and cleared the O/S cache using "sync; echo 3 >
> /proc/sys/vm/drop_caches" between the runs.
> >
> >
> >
> > test=# VACUUM ANALYZE test_select;
> > VACUUM
> >
> > (stopped postgres; reset O/S cache; started postgres)
> >
> > test=# explain analyze SELECT * FROM test_select WHERE key1 >= 500000
> ORDER BY key1, key2, key3, id LIMIT 1;
> >                                                               QUERY PLAN
> >
> --------------------------------------------------------------------------------------------------------------------------------------
> >  Limit  (cost=0.00..0.08 rows=1 width=21) (actual time=12.599..12.600
> rows=1 loops=1)
> >    ->  Index Scan using my_key on test_select  (cost=0.00..41895.49
> rows=498724 width=21) (actual time=12.597..12.597 rows=1 loops=1)
> >          Index Cond: (key1 >= 500000)
> >  Total runtime: 12.678 ms
> >
> > (stopped postgres; reset O/S cache; started postgres)
> >
> > test=# explain analyze SELECT * FROM test_select WHERE id = 500000;
> >                                                            QUERY PLAN
> >
> ---------------------------------------------------------------------------------------------------------------------------------
> >  Index Scan using test_select_pkey on test_select  (cost=0.00..8.36
> rows=1 width=21) (actual time=31.396..31.398 rows=1 loops=1)
> >    Index Cond: (id = 500000)
> >  Total runtime: 31.504 ms
> >
> >
> >
> > Schema:
> >
> > test=# \d test_select
> >                             Table "public.test_select"
> >  Column |     Type     |                        Modifiers
> >
> --------+--------------+----------------------------------------------------------
> >  id     | integer      | not null default
> nextval('test_select_id_seq'::regclass)
> >  key1   | integer      |
> >  key2   | integer      |
> >  key3   | integer      |
> >  data   | character(4) |
> > Indexes:
> >     "test_select_pkey" PRIMARY KEY, btree (id)
> >     "my_key" btree (key1, key2, key3, id)
> >
> > test=#
> >
> >
> >
> > Sample data:
> >
> > test=# SELECT * FROM test_select LIMIT 10;
> >  id |  key1  |  key2  |  key3  | data
> > ----+--------+--------+--------+------
> >   1 | 984966 | 283954 | 772063 | x
> >   2 | 817668 | 393533 | 924888 | x
> >   3 | 751039 | 798753 | 454309 | x
> >   4 | 128505 | 329643 | 280553 | x
> >   5 | 105600 | 257225 | 710015 | x
> >   6 | 323891 | 615614 |  83206 | x
> >   7 | 194054 |  63506 | 353171 | x
> >   8 | 212068 | 881225 | 271804 | x
> >   9 | 644180 |  26693 | 200738 | x
> >  10 | 136586 | 498699 | 554417 | x
> > (10 rows)
> >
> >
> >
> >
> > Here's how I populated the table:
> >
> > import psycopg2
> >
> > conn = psycopg2.connect('dbname=test')
> >
> > cur = conn.cursor()
> >
> > def random_int():
> >     n = 1000000
> >     return random.randint(0,n)
> >
> > def random_key():
> >     return random_int(), random_int(), random_int()
> >
> > def create_table():
> >     cur.execute('''
> >             DROP TABLE IF EXISTS test_select;
> >
> >             CREATE TABLE test_select (
> >                 id                      SERIAL PRIMARY KEY,
> >                 key1                    INTEGER,
> >                 key2                    INTEGER,
> >                 key3                    INTEGER,
> >                 data                    char(4)
> >             );
> >         ''')
> >     conn.commit()
> >
> >     n = 1000000
> >     for i in range(n):
> >         cur.execute("INSERT INTO test_select(key1, key2, key3, data)
> VALUES(%s, %s, %s, 'x')", random_key())
> >     conn.commit()
> >
> >     cur.execute('CREATE INDEX my_key ON test_select(key1, key2, key3,
> id)')
> >     conn.commit()
> >
> > create_table()
> >
>
>

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