On 01/10/2016 01:08 AM, Peter Geoghegan wrote:
On Sat, Jan 9, 2016 at 11:02 AM, Tomas Vondra
So, this seems to bring reasonable speedup, as long as the selectivity is
below 50%, and the data set is sufficiently large.
What about semijoins? Apparently they can use bloom filters
particularly effectively. Have you considered them as a special case?
You mean to handle them in a special way in the code, or just to perform
benchmark semijoins (and not just regular joins)?
Also, have you considered Hash join conditions with multiple
attributes as a special case? I'm thinking of cases like this:
regression=# set enable_mergejoin = off;
regression=# explain analyze select * from tenk1 o join tenk2 t on
o.twenty = t.twenty and t.hundred = o.hundred;
Hash Join (cost=595.00..4103.00 rows=50000 width=488) (actual
time=12.086..1026.194 rows=1000000 loops=1)
Hash Cond: ((o.twenty = t.twenty) AND (o.hundred = t.hundred))
-> Seq Scan on tenk1 o (cost=0.00..458.00 rows=10000 width=244)
(actual time=0.017..4.212 rows=10000 loops=1)
-> Hash (cost=445.00..445.00 rows=10000 width=244) (actual
time=12.023..12.023 rows=10000 loops=1)
Buckets: 16384 Batches: 1 Memory Usage: 2824kB
-> Seq Scan on tenk2 t (cost=0.00..445.00 rows=10000
width=244) (actual time=0.006..3.453 rows=10000 loops=1)
Planning time: 0.567 ms
Execution time: 1116.094 ms
(Note that while the optimizer has a slight preference for a merge
join in this case, the plan I show here is a bit faster on my
The patch I posted actually does not build bloom filter on the values
directly, but on the hashvalue we already use. So it handles hashjoins
with arbitrary number of attributes just fine.
Or perhaps you're thinking about some special optimization that might
improve such cases (I can't think of any)?
Tomas Vondra http://www.2ndQuadrant.com
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