Help with bulk read performance

Hello

We have an application that needs to do bulk reads of ENTIRE Postgres tables very quickly (i.e. select * from table).
Wehave  
observed that such sequential scans run two orders of magnitude slower than observed raw disk reads (5 MB/s versus 100
MB/s). Part  
of this is due to the storage overhead we have observed in Postgres.  In the example below, it takes 1 GB to store 350
MBof nominal  
data.  However that suggests we would expect to get 35 MB/s bulk read rates.

Observations using iostat and top during these bulk reads suggest that the queries are CPU bound, not I/O bound.  In
fact,repeating  
the queries yields similar response times.  Presumably if it were an I/O issue the response times would be much shorter
thesecond  
time through with the benefit of caching.

We have tried these simple queries using psql, JDBC, pl/java stored procedures, and libpq.  In all cases the client
coderan on the  
same box as the server.
We have experimented with Postgres 8.1, 8.3 and 9.0.

We also tried playing around with some of the server tuning parameters such as shared_buffers to no avail.

Here is uname -a for a machine we have tested on:

Linux nevs-bdb1.fsl.noaa.gov 2.6.18-194.17.1.el5 #1 SMP Mon Sep 20 07:12:06 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux

A sample dataset that reproduces these results looks like the following (there are no indexes):

Table "bulk_performance.counts"
  Column |  Type   | Modifiers
--------+---------+-----------
  i1     | integer |
  i2     | integer |
  i3     | integer |
  i4     | integer |

There are 22 million rows in this case.

We HAVE observed that summation queries run considerably faster.  In this case,

select sum(i1), sum(i2), sum(i3), sum(i4) from bulk_performance.counts

runs at 35 MB/s.


Our business logic does operations on the resulting data such that the output is several orders of magnitude smaller
thanthe input.  
  So we had hoped that by putting our business logic into stored procedures (and thus drastically reducing the amount
ofdata  
flowing to the client) our throughput would go way up.  This did not happen.

So our questions are as follows:

Is there any way using stored procedures (maybe C code that calls SPI directly) or some other approach to get close to
theexpected  
35 MB/s doing these bulk reads?  Or is this the price we have to pay for using SQL instead of some NoSQL solution.  (We
actually 
tried Tokyo Cabinet and found it to perform quite well. However it does not measure up to Postgres in terms of
replication,data  
interrogation, community support, acceptance, etc).

Thanks

Dan Schaffer
Paul Hamer
Nick Matheson