Hello,
On 05/15/15 08:29, Kyotaro HORIGUCHI wrote:
Hello,
Regarding the functional dependencies - you're right there's room
for improvement. For example it only works with dependencies
between pairs of columns, not multi-column dependencies. Is this
what you mean by incomplete?
No, It overruns dependencies->deps because build_mv_dependencies
stores many elements into dependencies->deps[n] although it
really has a room for only one element. I suppose that you paused
writing it when you noticed that the number of required elements
is unknown before finising walk through all pairs of
values. palloc'ing numattrs^2 is reasonable enough as POC code
for now. Am I looking wrong version of patch?
- dependencies = (MVDependencies)palloc0(sizeof(MVDependenciesData))
+ dependencies = (MVDependencies)palloc0(sizeof(MVDependenciesData) +
+ sizeof(MVDependency) * numattrs * numattrs);
Ah! That's clearly a bug. Thanks for noticing that, will fix in the next
version of the patch.
I mention this because I recently had a issue from strong
correlation between two columns in dbt3 benchmark. Two columns
in some table are in strong correlation but not in functional
dependencies, there are too many values and the distribution of
them is very uniform so MCV is no use for the table (histogram
has nothing to do with equal conditions). As the result, planner
estimates the number of rows largely wrong as expected
especially for joins.
I think the other statistics types (esp. histograms) might be more
useful here, but I assume you haven't tried that because of the
conflicts.
The current patch does not handle joins at all, though.
Well, that's one of the resons. But I understood that any
deterministic estimation cannot be applied for such distribution
when I saw what made the wrong estimation. eqsel and eqsel_join
finally relies on random match assumption on uniform distribution
when the value is not found in MCV list. And functional
dependencies stuff in your old patch (which works) (rightfully)
failed to find such relationship between the problematic
columns. So I tried ndistinct, which is not contained in your
patch to see how it works well.
Yes, that's certainly true. I think you're right that mv coefficient
might be quite useful in some cases.
With my patch:
alter table t add statistics (mcv) on (a,b,c);
...
Seq Scan on t (cost=0.00..22906.00 rows=9533 width=12)
Yes, your MV-MCV list should have one third of all possible (set
of) values so it works fine, I guess. But my original problem was
occurred on the condition that (the single column) MCVs contain
under 1% of possible values, MCV would not work for such cases,
but its very uniform distribution helps random assumption to
work.
Actually, I think the MCV list should contain all the items, as it
decides the sample contains all the values from the data. The usual 1D
MCV list uses the same logic. But you're right that on a data set with
more MCV items and mostly uniform distribution, this won't work.
$ perl gentbl.pl 200000 | psql postgres
<takes a while..>
posttres=# alter table t1 add statistics (mcv true) on (a, b);
postgres=# analyze t1;
postgres=# explain analyze select * from t1 where a = 1 and b = 2501;
Seq Scan on t1 (cost=0.00..124319.00 rows=1 width=8)
(actual time=0.051..1250.773 rows=8 loops=1)
The estimate "rows=1" is internally 2.4e-11, 3.33e+11 times
smaller than the real number. This will result in roughly the
same order of error for joins. This is because MV-MCV holds too
small part of the domain and then calculated using random
assumption. This won't be not saved by increasing
statistics_target to any sane amount.
Yes, the MCV lists don't do work well with data sets like this.
alter table t drop statistics all;
alter table t add statistics (histogram) on (a,b,c);
...
Seq Scan on t (cost=0.00..22906.00 rows=9667 width=12)
So both the MCV list and histogram do quite a good work here,
I understand how you calculate selectivity for equality clauses
using histogram. And it calculates the result rows as 2.3e-11,
which is almost same as MV-MCV, and this comes the same cause
with it then yields the same result for joins.
but there are certainly cases when that does not work and the
mvcoefficient works better.
+1
The condition mv-coef is effective where, as metioned above,
MV-MCV or MV-HISTO cannot hold sufficient part of the domain. The
appropriate combination of MV-MCV and mv-coef would be the same
as va_eq_(non_)const/eqjoinsel_inner for single column, which is,
applying mv-coef on the part of selectivity corresponding to
values not in MV-MCV. I have no idea to combinate it with
MV-HISTOGRAM right now.
The current patch does not handle joins, but it's one of the TODO
items.
Yes, but the result on the very large tables can be deduced from
the discussion above.
I think the result above shows that the multivariate coefficient
is significant to imporove estimates when correlated colums are
involved.
Yes, it looks interesting. I'm wondering what are the "failure cases"
when the coefficient approach does not work. It seems to me it relies
on an assumption of consistency for all the ndistinct values. For
example lets assume you have two columns - A and B, each with 1000
distinct values, and that each value in A has 100 matching values in
B, so the coefficient is ~10
1,000 * 1,000 / 100,000 = 10
Now, let's assume the distribution looks differently - with first 100
values in A matching all 1000 values of B, and the remaining 900
values just a single B value. Then
1,000 * 1,000 / (100,000 + 900) = ~9,9
So a very different distribution, but almost the same coefficient.
Are there any other assumptions like this?
I think no for now. Just like the current var_eq_(non_)const and
eqjoinsel_inner does, since no clue for *the true* distribution
available, we have no choice other than stand on the random (on
uniform dist) assumption. And it gives not so bad estimates for
not so extreme distributions. It's of course not perfect but good
enough.
Also, does the coefficient work only for equality conditions only?
The mvcoef is a parallel of ndistinct, (it is a bit wierd
expression though). So I guess it is appliable on the current
estimation codes where using ndistinct, almost of all of them
look to relate to equiality comparison.
ISTM the estimation of GROUP BY might benefit tremendously from this
statistics. That is, helping with cardinality estimation of analytical
queries, etc.
Also, we've only discussed 2-column coefficients. Would it be useful to
track those coefficients for large groups of columns? For example
ndistinct(A,B,C)
--------------------------------------------
ndistinct(A) * ndistinct(B) * ndistinct(C)
which might work better for queries like
SELECT a,b,c FROM t GROUP BY a,b,c;
Would you consider this in your patch? Otherwise I move on this
as a different project from yours if you don't mind. Except user
interface won't conflict with yours, I suppose. But finally they
should need some labor of consolidation.
I think it's a neat idea, and I think it might be added to the
patch. It would fit in quite nicely, actually - I already do have
other kinds of stats for addition, but I'm not going to work on
that in the near future. It will require changes in some parts of
the patch (selecting the stats for a list of clauses) and I'd like
to complete the current patch first, and then add features in
follow-up patches.
I see. Let's work on this for now.
Thanks!
--
Tomas Vondra http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services
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