Friends,
I run Postgresql 12.3, on Windows. I have just discovered a pretty significant problem with Postgresql and my data. I have a large table, 500M rows, 50 columns. It is split in 3 partitions by Year. In addition to the primary key, one of the columns is indexed, and I do lookups on this.
Select * from bigtable b where b.instrument_ref in (x,y,z,...)
limit 1000
It responded well with sub-second response, and it uses the index of the column. However, when I changed it to:
Select * from bigtable b where b.instrument_ref in (x,y,z,)
limit 10000 -- (notice 10K now)
The planner decided to do a full table scan on the entire 500M row table! And that did not work very well. First I had no clue as to why it did so, and when I disabled sequential scan the query immediately returned. But I should not have to do so.
I got my first hint of why this problem occurs when I looked at the statistics. For the column in question, "instrument_ref" the statistics claimed it to be:
The default_statistics_target=500, and analyze has been run.
select * from pg_stats where attname like 'instr%_ref'; -- Result: 40.000
select count(distinct instrumentid_ref) from bigtable -- Result: 33 385 922 (!!)
That is an astonishing difference of almost a 1000X.
When the planner only thinks there are 40K different values, then it makes sense to switch to table scan in order to fill the limit=10.000. But it is wrong, very wrong, an the query returns in 100s of seconds instead of a few.
I have tried to increase the statistics target to 5000, and it helps, but it reduces the error to 100X. Still crazy high.
I understand that this is a known problem. I have read previous posts about it, still I have never seen anyone reach such a high difference factor.
I have considered these fixes:
- hardcode the statistics to a particular ratio of the total number of rows
- randomize the rows more, so that it does not suffer from page clustering. However, this has probably other implications
Feel free to comment :)
