On 11/3/2010 10:52 AM, Nick Matheson wrote:
Hello We have an application that needs to do bulk reads of ENTIRE Postgres tables very quickly (i.e. select * from table). We have 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 MB of 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 the second 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 code ran 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 than the input. So we had hoped that by putting our business logic into stored procedures (and thus drastically reducing the amount of data 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 the expected 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
I have no idea if this would be helpful or not, never tried it, but when you fire off "select * from bigtable" pg will create the entire resultset in memory (and maybe swap?) and then send it all to the client in one big lump. You might try a cursor and fetch 100-1000 at a time from the cursor. No idea if it would be faster or slower.
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