General performance questions about postgres on Apple hardware... - Mailing list pgsql-performance

To all,

This is a 2 question email.  First is asking about general tuning of the
Apple hardware/postgres combination.  The second is whether is is
possible to speed up a particular query.

PART 1

Hardware:  Apple G5 dual 2.0 with 8GB memory attached via dual fibre
channel to a fully loaded 3.5TB XRaid.  The XRaid is configured as two 7
disk hardware based RAID5 sets software striped to form a RAID50 set.
The DB, WALS, etc are all on that file set.  Running OSX journaled file
system  Running postgres 7.4.1.  OSX Server 10.3.2  Postgres is compiled
locally with  '--enable-recode' '--enable-multibyte=UNICODE'
'CFLAGS=-mcpu=970 -mtune=970 -mpowerpc64 -O3'

Config stuff that we have changed:

tcpip_socket = true
max_connections = 100

# - Memory -

shared_buffers = 16000          # min 16, at least max_connections*2,
8KB each
sort_mem = 256000               # min 64, size in KB
vacuum_mem = 64000              # min 1024, size in KB
fsync = true                    # turns forced synchronization on or off
wal_sync_method = open_sync     # the default varies across platforms:
                                # fsync, fdatasync, open_sync, or
open_datasync
wal_buffers = 64                # min 4, 8KB each
checkpoint_segments = 300       # in logfile segments, min 1, 16MB each
checkpoint_timeout = 30 # range 30-3600, in seconds
effective_cache_size = 400000   # typically 8KB each
random_page_cost = 1            # units are one sequential page fetch cost
default_statistics_target = 1000        # range 1-1000


We are generally getting poor performance out of the RAID set, they
claim 200/MB/sec per channel, the best we can get with straight OS based
data transfers is 143MB/sec. :-( (we have a call into apple about this)
When I execute the following, d_url is a big table,

create table temp_url as select * from d_url ;

I would expect to bound by IO but via iostat we are seeing only about
30mb/sec with bursts of 100+ when the WAL is written.  sy is high as
well and the tps seems low.

Can anyone shed some light on what we might do to improve performance
for postgres on this platform?  Also, is there a test that is available
that would we could run to show the maximum postgres can do on this
platform?  This is a data warehouse system so generally we only have 1-3
queries running at anytime.  More often only 1.  We are obviously
working with very large tables so we are interested in maximizing our IO
throughput.

          disk1           disk2           disk0       cpu
  KB/t tps  MB/s   KB/t tps  MB/s   KB/t tps  MB/s  us sy id
 17.04 961 15.99  17.16 957 16.03   8.83   6  0.05  12 32 56
 22.75 580 12.89  22.79 578 12.87   0.00   0  0.00  10 34 56
 24.71 586 14.14  24.67 587 14.14   0.00   0  0.00  12 40 48
 21.98 648 13.91  21.97 648 13.91   0.00   0  0.00  16 27 56
 22.07 608 13.10  22.09 607 13.09   0.00   0  0.00  14 29 57
 26.54 570 14.77  26.37 575 14.80   0.00   0  0.00  12 34 54
 18.91 646 11.93  18.90 646 11.93   0.00   0  0.00   9 33 58
 15.12 636  9.38  15.12 636  9.38   0.00   0  0.00  14 22 64
 16.22 612  9.69  16.23 611  9.68   0.00   0  0.00  20 27 54
 15.02 573  8.41  15.01 574  8.41   0.00   0  0.00  14 29 57
 15.54 593  9.00  15.52 595  9.02   0.00   0  0.00  13 28 59
 22.35 596 13.01  22.42 593 12.99   0.00   0  0.00   9 32 58
 61.57 887 53.33  60.73 901 53.43   4.00   1  0.00   8 48 44
 11.13 2173 23.62  11.13 2167 23.54   0.00   0  0.00  10 68 22
 10.07 2402 23.63  10.20 2368 23.58   4.00   1  0.00  10 72 18
 14.75 1110 15.99  14.74 1116 16.06   8.92   6  0.05  12 42 46
 22.79 510 11.36  22.79 510 11.36   0.00   0  0.00  16 28 56
 23.65 519 11.99  23.50 522 11.98   0.00   0  0.00  13 42 46
 22.45 592 12.98  22.45 592 12.98   0.00   0  0.00  14 27 58
 25.38 579 14.35  25.37 579 14.35   0.00   0  0.00   8 36 56


PART 2

Trying to understand if there is a faster way to do this?  This is part
of our nightly bulk load of a data warehouse.  We are reading in new
data, pulling out the relevant bits, and then need to check to see if
they already exist in the dimension tables.  Use to do this via separate
lookups for each value, not very fast.  Trying to do this all in the DB now.

The query is

SELECT t1.id, t2.md5, t2.url FROM referral_temp t2 LEFT OUTER JOIN
d_referral t1 ON t2.md5 = t1.referral_md5;


\d d_referral
 id                 | integer | not null
 referral_md5       | text    | not null
 referral_raw_url   | text    | not null
 referral_host      | text    |
 referral_path      | text    |
 referral_query     | text    |
 job_control_number | integer | not null


\d referral_temp
 md5    | text |
 url    | text |

Actual row count in the temp table:

select count(*) from referral_temp ;
  502347

Actual row count in d_referral table:

select count(*) from d_referral ;
  27908024


Note: that an analyze had not been performed on the referral_temp table
prior to the explain analyze run.

explain analyze  SELECT t1.id, t2.md5, t2.url from referral_temp t2 LEFT
OUTER JOIN d_referral t1 ON t2.md5 = t1.referral_md5

Nested Loop Left Join  (cost=0.00..3046.00 rows=1001 width=68) (actual
time=136.513..6440616.541 rows=502347 loops=1)
   ->  Seq Scan on referral_temp t2  (cost=0.00..20.00 rows=1000
width=64) (actual time=21.730..10552.421 rows=502347 loops=1)
   ->  Index Scan using d_referral_referral_md5_key on d_referral t1
(cost=0.00..3.01 rows=1 width=40) (actual time=12.768..14.022 rows=1
loops=502347)
         Index Cond: ("outer".md5 = t1.referral_md5)


Thanks.

--sean
 Total runtime: 6441969.698 ms
(5 rows)


Here is an explain analyze after the analyze was done.  Unfortunately I
think a lot of the data was still in cache when I did this again :-(

explain analyze  SELECT t1.id, t2.md5, t2.url from referral_temp t2 LEFT
OUTER JOIN d_referral t1 ON t2.md5 = t1.referral_md5;

Nested Loop Left Join  (cost=0.00..1468759.69 rows=480082 width=149)
(actual time=69.576..3226854.850 rows=502347 loops=1)
   ->  Seq Scan on referral_temp t2  (cost=0.00..16034.81 rows=480081
width=145) (actual time=11.206..4003.521 rows=502347 loops=1)
   ->  Index Scan using d_referral_referral_md5_key on d_referral t1
(cost=0.00..3.01 rows=1 width=40) (actual time=6.396..6.402 rows=1
loops=502347)
         Index Cond: ("outer".md5 = t1.referral_md5)
 Total runtime: 3227830.752 ms