To all,

Essentials: Running 7.4.1 on OSX on a loaded G5 with dual procs, 8GB memory, direct attached via fibre channel to a fully optioned 3.5TB XRaid (14 spindles, 2 sets of 7 in RAID 5) box running RAID 50.

Background: We are loading what are essentially xml based access logs from about 20+ webservers daily, about 6GB of raw data. We have a classic star schema. All the ETL tools are custom java code or standard *nix tools like sort, uniq etc...

The problem: We have about 46 million rows in a table with the following schema:

Table "public.d_referral"
      Column       |  Type   | Modifiers
id                 | integer | not null
referral_raw_url   | text    | not null
job_control_number | integer | not null
   "d_referral_pkey" primary key, btree (id)
   "idx_referral_url" btree (referral_raw_url)

This is one of our dimension tables. Part of the daily ETL process is to match all the new referral URL's against existing data in the d_referral table. Some of the values in referral_raw_url can be 5000 characters long :-( . The avg length is : 109.57 characters.

I sort and uniq all the incoming referrals and load them into a temp table.

Table "public.referral_temp"
Column | Type | Modifiers
url    | text | not null
   "referral_temp_pkey" primary key, btree (url)

I then do a left join

SELECT, t2.url FROM referral_temp t2 LEFT OUTER JOIN d_referral t1 ON t2.url = t1.referral_raw_url ORDER BY

This is the output from an explain analyze (Please note that I do a set enable_index_scan = false prior to issuing this because it takes forever using indexes.):

explain analyze SELECT, t2.url FROM referral_temp t2 LEFT OUTER JOIN d_referral t1 ON t2.url = t1.referral_raw_url ORDER BY;
Sort (cost=4012064.81..4013194.45 rows=451856 width=115) (actual time=1297320.823..1297739.813 rows=476176 loops=1)
Sort Key:
-> Hash Left Join (cost=1052345.95..3969623.10 rows=451856 width=115) (actual time=1146650.487..1290230.590 rows=476176 loops=1)
Hash Cond: ("outer".url = "inner".referral_raw_url)
-> Seq Scan on referral_temp t2 (cost=0.00..6645.56 rows=451856 width=111) (actual time=20.285..1449.634 rows=476176 loops=1)
-> Hash (cost=729338.16..729338.16 rows=46034716 width=124) (actual time=1146440.710..1146440.710 rows=0 loops=1)
-> Seq Scan on d_referral t1 (cost=0.00..729338.16 rows=46034716 width=124) (actual time=14.502..-1064277.123 rows=46034715 loops=1)
Total runtime: 1298153.193 ms
(8 rows)

What I would like to know is if there are better ways to do the join? I need to get all the rows back from the referral_temp table as they are used for assigning FK's for the fact table later in processing. When I iterate over the values that I get back those with = null I assign a new FK and push both into the d_referral table as new entries as well as a text file for later use. The matching records are written to a text file for later use.

If we cannot improve the join performance my question becomes are there better tools to match up the 46 million and growing at the rate of 1 million every 3 days, strings outside of postgresql? We don't want to have to invest in zillions of dollars worth of hardware but if we have to we will. I just want to make sure we have all the non hardware possibilities for improvement covered before we start investing in large disk arrays.



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TIP 9: the planner will ignore your desire to choose an index scan if your
     joining column's datatypes do not match

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