I'm doing some performance profiling with a simple two-table query:

SELECT L."ProductID", sum(L."Amount")
FROM "drinv" H
JOIN "drinvln" L ON L."OrderNo" = H."OrderNo"
WHERE
("OrderDate" between '2003-01-01' AND '2003-04-30')
GROUP BY L."ProductID"

drinv and drinvln have about 100,000 and 3,500,000 rows respectively. Actual 
data size in the large table is 500-600MB. OrderNo is indexed in both tables, 
as is OrderDate.

The environment is PGSQL 8 on Win2k with 512MB RAM (results are similar to 7.3 
from Mammoth). I've tried tweaking various conf parameters, but apart from 
using up memory, nothing seems to have had a tangible effect - the Analyzer 
doesn't seem to take resources into account like some of the doco suggests.

The date selection represents about 5% of the range. Here's the plan summaries:

Three months (2003-01-01 to 2003-03-30) = 1 second

HashAggregate  (cost=119365.53..119368.74 rows=642 width=26)
  ->  Nested Loop  (cost=0.00..118791.66 rows=114774 width=26)
        ->  Index Scan using "drinv_OrderDate" on drinv h  (cost=0.00..200.27 
rows=3142 width=8)
              Index Cond: (("OrderDate" >= '2003-01-01'::date) AND ("OrderDate" 
<= '2003-03-30'::date))
        ->  Index Scan using "drinvln_OrderNo" on drinvln l  (cost=0.00..28.73 
rows=721 width=34)
              Index Cond: (l."OrderNo" = "outer"."OrderNo")


Four months (2003-01-01 to 2003-04-30) = 60 seconds

HashAggregate  (cost=126110.53..126113.74 rows=642 width=26)
  ->  Hash Join  (cost=277.55..125344.88 rows=153130 width=26)
        Hash Cond: ("outer"."OrderNo" = "inner"."OrderNo")
        ->  Seq Scan on drinvln l  (cost=0.00..106671.35 rows=3372935 width=34)
        ->  Hash  (cost=267.07..267.07 rows=4192 width=8)
              ->  Index Scan using "drinv_OrderDate" on drinv h  
(cost=0.00..267.07 rows=4192 width=8)
                    Index Cond: (("OrderDate" >= '2003-01-01'::date) AND 
("OrderDate" <= '2003-04-30'::date))


Four months (2003-01-01 to 2003-04-30) with Seq_scan disabled = 75 seconds


HashAggregate  (cost=130565.83..130569.04 rows=642 width=26)
  ->  Merge Join  (cost=519.29..129800.18 rows=153130 width=26)
        Merge Cond: ("outer"."OrderNo" = "inner"."OrderNo")
        ->  Sort  (cost=519.29..529.77 rows=4192 width=8)
              Sort Key: h."OrderNo"
              ->  Index Scan using "drinv_OrderDate" on drinv h  
(cost=0.00..267.07 rows=4192 width=8)
                    Index Cond: (("OrderDate" >= '2003-01-01'::date) AND 
("OrderDate" <= '2003-04-30'::date))
        ->  Index Scan using "drinvln_OrderNo" on drinvln l  
(cost=0.00..119296.29 rows=3372935 width=34)

Statistics were run on each table before query execution. The random page cost 
was lowered to 2, but as you can see, the estimated costs are wild anyway.

As a comparison, MS SQL Server took less than 15 seconds, or 4 times faster.

MySQL (InnoDB) took 2 seconds, which is 30 times faster.

The query looks straightforward to me (it might be clearer with a subselect), 
so what on earth is wrong?

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