You could do something like that by using a derived table (subselect)
to pick the max date for each product sale and then joining that on
the products table again to pick up the amounts.
Note that you must use mysql 5.x to be able to use subselects.
Also this will return multiple rows for a given account if there are
more than one sale on the last day each item has been sold. (There
are several ways around that if it's a problem).
I just ran such a query on the table I'm working on right now to test
the idea... there are about 3000 rows in this table so the 0.52
second query time seems high, but there's only a primary key index.
I think indexing on the transaction date (date_xact) would probably
help in this example...
mysql> select t1a.account, maxdate, amount from (select account, max
(date_xact) maxdate from transactions t1 group by account) t1a left
join transactions t2 on t1a.account=t2.account and
maxdate=t2.date_xact order by t1a.account;
+---------+------------+-----------+
| account | maxdate | amount |
+---------+------------+-----------+
| 0 | 2005-08-17 | -15.06 |
| 1 | 2006-07-24 | 26790.00 |
| 2 | 2006-07-14 | 1500.00 |
| 2 | 2006-07-14 | 2040.00 |
| 2 | 2006-07-14 | 2520.00 |
| 3 | 2006-07-14 | -193.98 |
| 3 | 2006-07-14 | -328.98 |
| 3 | 2006-07-14 | -418.21 |
| 4 | 2006-07-14 | -186.00 |
| 4 | 2006-07-14 | -252.96 |
| 4 | 2006-07-14 | -312.48 |
| 5 | 2006-07-14 | -43.50 |
| 5 | 2006-07-14 | -59.16 |
| 5 | 2006-07-14 | -73.08 |
| 9 | 2006-06-27 | 60.06 |
| 9 | 2006-06-27 | 196.77 |
| 10 | 2006-03-27 | 60.04 |
| 11 | 2006-04-13 | 65.00 |
| 12 | 2006-06-23 | -272.03 |
| 13 | 2006-02-16 | 100.00 |
| 14 | 2006-07-14 | 114.75 |
| 14 | 2006-07-14 | 156.06 |
| 14 | 2006-07-14 | 192.78 |
| 15 | 2006-07-24 | -18240.00 |
| 15 | 2006-07-24 | -8550.00 |
| 16 | 2006-07-11 | -800.00 |
| 17 | 2004-07-07 | -51.87 |
| 17 | 2004-07-07 | -50.49 |
| 17 | 2004-07-07 | -27.31 |
| 18 | 2006-06-01 | 288.77 |
| 19 | 2006-05-11 | 175.00 |
| 20 | 2006-01-05 | 50.00 |
| 21 | 2006-07-15 | 152.90 |
| 22 | 2006-07-19 | -600.00 |
| 23 | 2006-05-31 | 10.00 |
| 24 | 2005-07-29 | -277.83 |
| 25 | 2005-11-08 | -178.00 |
| 26 | 2006-03-24 | 94.24 |
| 26 | 2006-03-24 | 74.40 |
| 26 | 2006-03-24 | 248.00 |
| 27 | 2006-03-24 | 22.04 |
| 27 | 2006-03-24 | 17.40 |
| 27 | 2006-03-24 | 58.00 |
| 28 | 2006-06-07 | 185.00 |
| 29 | 2006-03-27 | 136.00 |
| 30 | 2006-07-18 | 398.16 |
| 31 | 2006-02-04 | 500.00 |
| 32 | 2006-04-06 | 64.00 |
| 35 | 2006-04-15 | 1000.00 |
| 37 | 2005-12-23 | 200.00 |
| 38 | 2006-05-12 | -51.04 |
| 39 | 2005-07-31 | 1191.00 |
| 40 | 2006-05-29 | 65.00 |
+---------+------------+-----------+
53 rows in set (0.52 sec)
Douglas Sims
[EMAIL PROTECTED]
On Sep 27, 2006, at 11:36 PM, mos wrote:
This should be easy but I can't find a way of doing it in 1 step.
I have a Trans table like:
Product_Code: X(10)
Date_Sold: Date
Price_Sold: Float
Now there will be 1 row for each Product_Code, Date combination. So
over the past year a product_code could have over 300 rows, one row
for each day it was sold. There are thousands of products.
What I need to do is find the last price_sold for each
product_code. Not all products are sold each day so a product might
not have been sold for weeks.
The only solution I've found is to do:
drop table if exists CurrentPrices;
create table CurrentPrices select Prod_Code, cast(max(Date_Sold) as
Date), -1.0 Price_Sold from Trans group by Prod_Code;
alter table CurrentPrices add index ix_ProdCode (Prod_Code);
update CurrentPrices CP, Trans T set CP.Price_Sold=T.Price_Sold and
T.Date_Sold=CP.Date_Sold;
Is there a way to shorten this? It may take 2-3 minutes to execute.
I don't really need a new table as long as I get the Prod_Code and
the last Date_Sold.
TIA
Mike
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