Re: Help refining/eliminating recursive selects

What about something like this:

select * from test order by tx_date desc limit 1

Yudie

----- Original Message -----
From: "Edmund Bacon" <ebacon(at)onesystem(dot)com>
To: <pgsql-sql(at)postgresql(dot)org>
Sent: Thursday, February 19, 2004 5:09 PM
Subject: [SQL] Help refining/eliminating recursive selects

I have the following table:

create table test (
id serial primary key,
product integer,
tx_date date,
quantity integer)

with the following data:
id | product | tx_date | quantity
----+---------+------------+----------
1 | 1 | 2004-01-01 | 10
2 | 2 | 2004-01-01 | 8
3 | 3 | 2004-01-01 | 7
4 | 4 | 2004-01-01 | 12
5 | 1 | 2004-01-15 | 9
6 | 2 | 2004-01-15 | 12
7 | 3 | 2004-01-15 | 8
8 | 5 | 2004-01-07 | 15

what I want to do is to find the most recent record for each product in
the table.

The only ways I seem to be able to achieve this is by one of the
following

1) A self join:

SELECT * FROM test
JOIN (SELECT product, max(tx_date) AS tx_date
FROM test
GROUP BY product) x
USING(product, tx_date);

2) A correlated subquery:

SELECT * FROM test t
WHERE tx_date =
( SELECT max(tx_date) FROM test
WHERE product = t.product);

or

3) a two-part select:

SELECT product, max(tx_date) AS tx_date
INTO TEMP TABLE t_prod_date
FROM test
GROUP BY product;

SELECT * FROM test
JOIN t_prod_date
USING(product, tx_date);

I can't help but feel like I'm missing something simple that would do
what I want and not mean I need to scan the table multiple times.
Is there a better way?

In trying to answer some questions in advance:

The two-part select _IS_ comparitively slow on the above dataset. In my
actual situation, I have about 300 possible products and over 20,00
records to sort through. In that case the overhead of creating the temp
table is easily overcome by reducing the search space. In my case the
two-part select runs in about 2/3 the time the self-join. Note that we
are currently talking about .5 and .3 seconds, but the dataset is
growing.

The correlated-subquery on the large dataset is horribly slow,
comparitively speaking, at about 8 seconds. I'm trying various index
approaches, (ANALYZING after adding/dropping an index) with no luck,
yet. Maybe I just haven't hit on the right combination of fields to
index on?

If anybody can either
(a) point me in a better direction, or
(b) confirm my approach,
I would greatly appreciate it

Thanks very much.

--
Edmund Bacon <ebacon(at)onesystem(dot)com>

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