Обсуждение: More stable query plans via more predictable column statistics

"Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> This post summarizes a few weeks of research of ANALYZE statistics
> distribution on one of our bigger production databases with some real-world
> data and proposes a patch to rectify some of the oddities observed.

Please add this to the 2016-01 commitfest ...
        regards, tom lane

On Tue, Dec 1, 2015 at 10:21 AM, Shulgin, Oleksandr
<oleksandr.shulgin@zalando.de> wrote:
> Hi Hackers!
>
> This post summarizes a few weeks of research of ANALYZE statistics
> distribution on one of our bigger production databases with some real-world
> data and proposes a patch to rectify some of the oddities observed.
>
>
> Introduction
> ============
>
> We have observed that for certain data sets the distribution of samples
> between most_common_vals and histogram_bounds can be unstable: so that it
> may change dramatically with the next ANALYZE run, thus leading to radically
> different plans.
>
> I was revisiting the following performance thread and I've found some
> interesting details about statistics in our environment:
>
>
>
http://www.postgresql.org/message-id/flat/CAMkU=1zxyNMN11YL8G7AGF7k5u4ZHVJN0DqCc_ecO1qs49uJgA@mail.gmail.com#CAMkU=1zxyNMN11YL8G7AGF7k5u4ZHVJN0DqCc_ecO1qs49uJgA@mail.gmail.com
>
> My initial interest was in evaluation if distribution of samples could be
> made more predictable and less dependent on the factor of luck, thus leading
> to more stable execution plans.
>
>
> Unexpected findings
> ===================
>
> What I have found is that in a significant percentage of instances, when a
> duplicate sample value is *not* put into the MCV list, it does produce
> duplicates in the histogram_bounds, so it looks like the MCV cut-off happens
> too early, even though we have enough space for more values in the MCV list.
>
> In the extreme cases I've found completely empty MCV lists and histograms
> full of duplicates at the same time, with only about 20% of distinct values
> in the histogram (as it turns out, this happens due to high fraction of
> NULLs in the sample).

Wow, this is very interesting work.  Using values_cnt rather than
samplerows to compute avgcount seems like a clear improvement.  It
doesn't make any sense to raise the threshold for creating an MCV
based on the presence of additional nulls or too-wide values in the
table.  I bet compute_distinct_stats needs a similar fix.  But for
plan stability considerations, I'd say we should back-patch this all
the way, but those considerations might mitigate for a more restrained
approach.  Still, maybe we should try to sneak at least this much into
9.5 RSN, because I have to think this is going to help people with
mostly-NULL (or mostly-really-wide) columns.

As far as the rest of the fix, your code seems to remove the handling
for ndistinct < 0.  That seems unlikely to be correct, although it's
possible that I am missing something.  Aside from that, the rest of
this seems like a policy change, and I'm not totally sure off-hand
whether it's the right policy.  Having more MCVs can increase planning
time noticeably, and the point of the existing cutoff is to prevent us
from choosing MCVs that aren't actually "C".  I think this change
significantly undermines those protections.  It seems to me that it
might be useful to evaluate the effects of this part of the patch
separately from the samplerows -> values_cnt change.

-- 
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Robert Haas <robertmhaas@gmail.com> writes:
> Still, maybe we should try to sneak at least this much into
> 9.5 RSN, because I have to think this is going to help people with
> mostly-NULL (or mostly-really-wide) columns.

Please no.  We are trying to get to release, not destabilize things.

I think this is fine work for leisurely review and incorporation into
9.6.  It's not appropriate to rush it into 9.5 at the RC stage after
minimal review.
        regards, tom lane

On Fri, Dec 4, 2015 at 12:53 PM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
> Robert Haas <robertmhaas@gmail.com> writes:
>> Still, maybe we should try to sneak at least this much into
>> 9.5 RSN, because I have to think this is going to help people with
>> mostly-NULL (or mostly-really-wide) columns.
>
> Please no.  We are trying to get to release, not destabilize things.

Well, OK, but I don't really see how that particular bit is anything
other than a bug fix.

-- 
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Tom Lane wrote:
> "Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> > This post summarizes a few weeks of research of ANALYZE statistics
> > distribution on one of our bigger production databases with some real-world
> > data and proposes a patch to rectify some of the oddities observed.
> 
> Please add this to the 2016-01 commitfest ...

Tom, are you reviewing this for the current commitfest?

-- 
Álvaro Herrera                http://www.2ndQuadrant.com/
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Alvaro Herrera <alvherre@2ndquadrant.com> writes:
> Tom Lane wrote:
>> "Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
>>> This post summarizes a few weeks of research of ANALYZE statistics
>>> distribution on one of our bigger production databases with some real-world
>>> data and proposes a patch to rectify some of the oddities observed.

>> Please add this to the 2016-01 commitfest ...

> Tom, are you reviewing this for the current commitfest?

Um, I would like to review it, but I doubt I'll find time before the end
of the month.
        regards, tom lane

Hi,

On 01/20/2016 10:49 PM, Alvaro Herrera wrote:
> Tom Lane wrote:
>> "Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
>>> This post summarizes a few weeks of research of ANALYZE statistics
>>> distribution on one of our bigger production databases with some real-world
>>> data and proposes a patch to rectify some of the oddities observed.
>>
>> Please add this to the 2016-01 commitfest ...
>
> Tom, are you reviewing this for the current commitfest?

While I'm not the right Tom, I've been looking the the patch recently,
so let me post the review here ...

Firstly, I'd like to appreciate the level of detail of the analysis. I
may disagree with some of the conclusions, but I wish all my patch
submissions were of such high quality.

Regarding the patch itself, I think there's a few different points
raised, so let me discuss them one by one:


1) NULLs vs. MCV threshold
--------------------------

I agree that this seems like a bug, and that we should really compute
the threshold only using non-NULL values. I think the analysis rather
conclusively proves this, and I also think there are places where we do
the same mistake (more on that at the end of the review).


2) mincount = 1.25 * avgcount
-----------------------------

While I share the dislike of arbitrary constants (like the 1.25 here), I
do think we better keep this, otherwise we can just get rid of the
mincount entirely I think - the most frequent value will always be above
the (remaining) average count, making the threshold pointless.

It might have impact in the original code, but in the new one it's quite
useless (see the next point), unless I'm missing some detail.


3) modifying avgcount threshold inside the loop
-----------------------------------------------

The comment was extended with this statement:

  * We also decrease ndistinct in the process such that going forward
  * it refers to the number of distinct values left for the histogram.

and indeed, that's what's happening - at the end of each loop, we do this:

     /* Narrow our view of samples left for the histogram */
     sample_cnt -= track[i].count;
     ndistinct--;

but that immediately lowers the avgcount, as that's re-evaluated within
the same loop

     avgcount = (double) sample_cnt / (double) ndistinct;

which means it's continuously decreasing and lowering the threshold,
although that's partially mitigated by keeping the 1.25 coefficient.

It however makes reasoning about the algorithm much more complicated.


4) for (i = 0; /* i < num_mcv */; i++)
---------------------------------------

The way the loop is coded seems rather awkward, I guess. Not only
there's an unexpected comment in the "for" clause, but the condition
also says this

     /* Another way to say "while (i < num_mcv)" */
     if (i >= num_mcv)
         break;

Why not to write it as a while loop, then? Possibly including the
(num_hist >= 2) condition, like this:

     while ((i < num_mcv) && (num_hist >= 2))
     {
         ...
     }

In any case, the loop would deserve a comment explaining why we think
computing the thresholds like this makes sense.


Summary
-------

Overall, I think this is really about deciding when to cut-off the MCV,
so that it does not grow needlessly large - as Robert pointed out, the
larger the list, the more expensive the estimation (and thus planning).

So even if we could fit the whole sample into the MCV list (i.e. we
believe we've seen all the values and we can fit them into the MCV
list), it may not make sense to do so. The ultimate goal is to estimate
conditions, and if we can do that reasonably even after cutting of the
least frequent values from the MCV list, then why not?

 From this point of view, the analysis concentrates deals just with the
ANALYZE part and does not discuss the estimation counter-part at all.


5) ndistinct estimation vs. NULLs
---------------------------------

While looking at the patch, I started realizing whether we're actually
handling NULLs correctly when estimating ndistinct. Because that part
also uses samplerows directly and entirely ignores NULLs, as it does this:

     numer = (double) samplerows *(double) d;

     denom = (double) (samplerows - f1) +
         (double) f1 *(double) samplerows / totalrows;

     ...
     if (stadistinct > totalrows)
         stadistinct = totalrows;

For tables with large fraction of NULLs, this seems to significantly
underestimate the ndistinct value - for example consider a trivial table
with 95% of NULL values and ~10k distinct values with skewed distribution:

     create table t (id int);

     insert into t
     select (case when random() < 0.05 then (10000 * random() * random())
                  else null end) from generate_series(1,1000000) s(i);

In practice, there are 8325 distinct values in my sample:

     test=# select count(distinct id) from t;
      count
     -------
       8325
     (1 row)

But after ANALYZE with default statistics target (100), ndistinct is
estimated to be ~1300, and with target=1000 the estimate increases to ~6000.

After fixing the estimator to consider fraction of NULLs, the estimates
look like this:

     statistics target |   master  |  patched
    ------------------------------------------
                   100 |     1302  |     5356
                  1000 |     6022  |     6791

So this seems to significantly improve the ndistinct estimate (patch
attached).

regards

--
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Hi,

On 02/08/2016 03:01 PM, Shulgin, Oleksandr wrote:>
...
>
> I've incorporated this fix into the v2 of my patch, I think it is
> related closely enough.  Also, added corresponding changes to
> compute_distinct_stats(), which doesn't produce a histogram.

I think it'd be useful not to have all the changes in one lump, but 
structure this as a patch series with related changes in separate 
chunks. I doubt we'd like to mix the changes in a single commit, and it 
makes the reviews and reasoning easier. So those NULL-handling fixes 
should be in one patch, the MCV patches in another one.

regards

--
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

On 3/2/16 11:10 AM, Shulgin, Oleksandr wrote:
> On Wed, Feb 24, 2016 at 12:30 AM, Tomas Vondra
> <tomas.vondra@2ndquadrant.com <mailto:tomas.vondra@2ndquadrant.com>> wrote:
>
>     I think it'd be useful not to have all the changes in one lump, but
>     structure this as a patch series with related changes in separate
>     chunks. I doubt we'd like to mix the changes in a single commit, and
>     it makes the reviews and reasoning easier. So those NULL-handling
>     fixes should be in one patch, the MCV patches in another one.
>
>
> OK, such a split would make sense to me.  Though, I'm a bit late as the
> commitfest is already closed to new patches, I guess asking the CF
> manager to split this might work (assuming I produce the patch files)?

If the patch is broken into two files that gives the review/committer
more options but I don't think it requires another CF entry.

--
-David
david@pgmasters.net

Shulgin, Oleksandr wrote:

> Alright.  I'm attaching the latest version of this patch split in two
> parts: the first one is NULLs-related bugfix and the second is the
> "improvement" part, which applies on top of the first one.

So is this null-related bugfix supposed to be backpatched?  (I assume
it's not because it's very likely to change existing plans).

-- 
Álvaro Herrera                http://www.2ndQuadrant.com/
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

On Thu, Mar 3, 2016 at 2:48 AM, Shulgin, Oleksandr
<oleksandr.shulgin@zalando.de> wrote:
> On Wed, Mar 2, 2016 at 7:33 PM, Alvaro Herrera <alvherre@2ndquadrant.com>
> wrote:
>> Shulgin, Oleksandr wrote:
>>
>> > Alright.  I'm attaching the latest version of this patch split in two
>> > parts: the first one is NULLs-related bugfix and the second is the
>> > "improvement" part, which applies on top of the first one.
>>
>> So is this null-related bugfix supposed to be backpatched?  (I assume
>> it's not because it's very likely to change existing plans).
>
> For the good, because cardinality estimations will be more accurate in these
> cases, so yes I would expect it to be back-patchable.

-1.  I think the cost of changing existing query plans in back
branches is too high.  The people who get a better plan never thank
us, but the people who (by bad luck) get a worse plan always complain.

-- 
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Hi,


On Mon, 2016-03-07 at 12:17 +0100, Shulgin, Oleksandr wrote:
> On Fri, Mar 4, 2016 at 7:27 PM, Robert Haas <robertmhaas@gmail.com>
> wrote:
>         On Thu, Mar 3, 2016 at 2:48 AM, Shulgin, Oleksandr
>         <oleksandr.shulgin@zalando.de> wrote:
>         > On Wed, Mar 2, 2016 at 7:33 PM, Alvaro Herrera
>         <alvherre@2ndquadrant.com>
>         > wrote:
>         >> Shulgin, Oleksandr wrote:
>         >>
>         >> > Alright.  I'm attaching the latest version of this patch
>         split in two
>         >> > parts: the first one is NULLs-related bugfix and the
>         second is the
>         >> > "improvement" part, which applies on top of the first
>         one.
>         >>
>         >> So is this null-related bugfix supposed to be backpatched?
>         (I assume
>         >> it's not because it's very likely to change existing
>         plans).
>         >
>         > For the good, because cardinality estimations will be more
>         accurate in these
>         > cases, so yes I would expect it to be back-patchable.
>         
>         -1.  I think the cost of changing existing query plans in back
>         branches is too high.  The people who get a better plan never
>         thank
>         us, but the people who (by bad luck) get a worse plan always
>         complain.
> 
> 
> They might get that different plan when they upgrade to the latest
> major version anyway.  Is it set somewhere that minor version upgrades
> should never affect the planner?  I doubt so.

Major versions are supposed to add features, which may easily result in
plan changes. Moreover people are expected to do more thorough testing
on major version upgrade, so they're more likely to spot them.

OTOH minor versions are bugfix-only relases, and sometimes the fixes are
security related and people are supposed to install them ASAP. So many
people simply upgrade them without much additional testing and while we
can't promise any of the fixes won't change the plans, we kinda try to
minimize such cases.

That being said, I don't have a clear opinion whether to backpatch this.
I think that it's clearly a bug (especially the first part dealing with
NULL values), and it'd be good to backpatch that. OTOH I can't really
quantify the risks of changing some plans to worse ones.

regards

-- 
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

On Mon, Mar 7, 2016 at 3:17 AM, Shulgin, Oleksandr
<oleksandr.shulgin@zalando.de> wrote:
> On Fri, Mar 4, 2016 at 7:27 PM, Robert Haas <robertmhaas@gmail.com> wrote:
>>
>> On Thu, Mar 3, 2016 at 2:48 AM, Shulgin, Oleksandr
>> <oleksandr.shulgin@zalando.de> wrote:
>> > On Wed, Mar 2, 2016 at 7:33 PM, Alvaro Herrera
>> > <alvherre@2ndquadrant.com>
>> > wrote:
>> >> Shulgin, Oleksandr wrote:
>> >>
>> >> > Alright.  I'm attaching the latest version of this patch split in two
>> >> > parts: the first one is NULLs-related bugfix and the second is the
>> >> > "improvement" part, which applies on top of the first one.
>> >>
>> >> So is this null-related bugfix supposed to be backpatched?  (I assume
>> >> it's not because it's very likely to change existing plans).
>> >
>> > For the good, because cardinality estimations will be more accurate in
>> > these
>> > cases, so yes I would expect it to be back-patchable.
>>
>> -1.  I think the cost of changing existing query plans in back
>> branches is too high.  The people who get a better plan never thank
>> us, but the people who (by bad luck) get a worse plan always complain.
>
>
> They might get that different plan when they upgrade to the latest major
> version anyway.  Is it set somewhere that minor version upgrades should
> never affect the planner?  I doubt so.

People with meticulous standards are expected to re-validate their
application, including plans and performance, before doing major
version updates into production. They can continue to use a *fully
patched* server from a previous major release while they do that.

This is not the case for minor version updates.  We do not want to put
people in the position where getting a security or corruption-risk
update forces them to also accept changes which may destroy the
performance of their system.

I don't know if it is set out somewhere else, but there are many
examples in this list of us declining to back-patch performance bug
fixes which might negatively impact some users.  The only times we
have done it that I can think of are when there is almost no
conceivable way it could have a meaningful negative effect, or if the
bug was tied in with security or stability bugs that needed to be
fixed anyway and couldn't be separated.

Cheers,

Jeff

Hi Alex,

Thanks for excellent research.

I've ran your queries against Trustly's production database and I can
confirm your findings, the results are similar:

WITH ...
SELECT count(1),      min(hist_ratio)::real,      avg(hist_ratio)::real,      max(hist_ratio)::real,
stddev(hist_ratio)::realFROM stats2WHERE histogram_bounds IS NOT NULL; 

-[ RECORD 1 ]----
count  | 2814
min    | 0.193548
avg    | 0.927357
max    | 1
stddev | 0.164134


WHERE distinct_hist < num_hist
-[ RECORD 1 ]----
count  | 624
min    | 0.193548
avg    | 0.672407
max    | 0.990099
stddev | 0.194901


WITH ..
SELECT schemaname ||'.'|| tablename ||'.'|| attname || (CASE inherited
WHEN TRUE THEN ' (inherited)' ELSE '' END) AS columnname,      n_distinct, null_frac,      num_mcv, most_common_vals,
most_common_freqs,     mcv_frac, (mcv_frac / (1 - null_frac))::real AS nonnull_mcv_frac,      distinct_hist, num_hist,
hist_ratio,     histogram_bounds FROM stats2ORDER BY hist_ratioLIMIT 1; 
-[ RECORD 1
]-----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
columnname        | public.x.y
n_distinct        | 103
null_frac         | 0
num_mcv           | 10
most_common_vals  | {0,1,2,3,4,5,6,7,8,9}
most_common_freqs |
{0.4765,0.141733,0.1073,0.0830667,0.0559667,0.0373333,0.0251,0.0188,0.0141,0.0113667}
mcv_frac          | 0.971267
nonnull_mcv_frac  | 0.971267
distinct_hist     | 18
num_hist          | 93
hist_ratio        | 0.193548387096774
histogram_bounds  |

{10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,15,15,15,15,16,16,16,16,21,23,5074,5437,5830,6049,6496,7046,7784,14629,21285}



On Mon, Jan 18, 2016 at 4:46 PM, Shulgin, Oleksandr
<oleksandr.shulgin@zalando.de> wrote:
> On Wed, Dec 2, 2015 at 10:20 AM, Shulgin, Oleksandr
> <oleksandr.shulgin@zalando.de> wrote:
>>
>> On Tue, Dec 1, 2015 at 7:00 PM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>>>
>>> "Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
>>> > This post summarizes a few weeks of research of ANALYZE statistics
>>> > distribution on one of our bigger production databases with some
>>> > real-world
>>> > data and proposes a patch to rectify some of the oddities observed.
>>>
>>> Please add this to the 2016-01 commitfest ...
>>
>>
>> Added: https://commitfest.postgresql.org/8/434/
>
>
> It would be great if some folks could find a moment to run the queries I was
> showing on their data to confirm (or refute) my findings, or to contribute
> to the picture in general.
>
> As I was saying, the queries were designed in such a way that even
> unprivileged user can run them (the results will be limited to the stats
> data available to that user, obviously; and for custom-tailored statstarget
> one still needs superuser to join the pg_statistic table directly).  Also,
> on the scale of ~30k attribute statistics records, the queries take only a
> few seconds to finish.
>
> Cheers!
> --
> Alex
>



--
Joel Jacobson

Mobile: +46703603801
Trustly.com | Newsroom | LinkedIn | Twitter

On Wed, Jan 20, 2016 at 5:09 PM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
> Alvaro Herrera <alvherre@2ndquadrant.com> writes:
>> Tom Lane wrote:
>>> "Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
>>>> This post summarizes a few weeks of research of ANALYZE statistics
>>>> distribution on one of our bigger production databases with some real-world
>>>> data and proposes a patch to rectify some of the oddities observed.
>
>>> Please add this to the 2016-01 commitfest ...
>
>> Tom, are you reviewing this for the current commitfest?
>
> Um, I would like to review it, but I doubt I'll find time before the end
> of the month.

Tom, can you pick this up?

-- 
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Wed, Mar 9, 2016 at 1:25 AM, Shulgin, Oleksandr
<oleksandr.shulgin@zalando.de> wrote:
> Thank you for spending your time to run these :-)

n/p, it took like 30 seconds :-)

> I don't want to be asking for too much here, but is there a chance you could
> try the effects of the proposed patch on an offline copy of your database?

Yes, I think that should be possible.

> Do you envision or maybe have experienced problems with query plans
> referring to the columns that are near the top of the above hist_ratio
> report?  In other words: what are the practical implications for you with
> the values being duplicated rather badly throughout the histogram like in
> the example you shown?

I don't know much about the internals of query planner,
I just read the "57.1. Row Estimation Examples" to get a basic understanding.

If I understand it correctly, if the histogram_bounds contains a lot
of duplicated values,
then the row estimation will be inaccurate, which in turn will trick
the query planner
into a sub-optimal plan?

We've had some problems lately with the query planner, or actually we've always
had them but never noticed them nor cared about them, but now during peak times
we've had short periods where we haven't been able to fully cope up
with the traffic.

I tracked down the most self_time-consuming functions and quickly saw
how to optimize them.
Many of them where on the form:
SELECT .. FROM SomeBigTable WHERE Col1 = [some dynamic value] AND Col2
= [some constant value] AND Col3 = [some other constant value]
The number of rows matching the WHERE clause were very tiny, perfect
match for a partial index:
CREATE INDEX .. ON SomeBigTable USING btree (Col1) WHERE Col2 = [some
constant value] AND Col3 = [some other constant value];

Even though the new partial index matched the query perfectly, the
query planner didn't want to use it. Instead it continued to use some
other sub-optimal index.

The only way to force it to use the correct index was to use the
"+0"-trick which I recently learned from one of my colleagues:
SELECT .. FROM SomeBigTable WHERE Col1 = [some dynamic value] AND
Col2+0 = [some constant value] AND Col3+0 = [some other constant
value]
CREATE INDEX .. ON SomeBigTable USING btree (Col1) WHERE Col2+0 =
[some constant value] AND Col3+0 = [some other constant value];

By adding +0 to the columns, the query planner will as I understand it
be extremely motivated to use the correct index, as otherwise it would
have to do a seq scan on the entire big table, which would be very
costly.

I'm glad the trick worked, now the system is fast again.

We're still on 9.1, so maybe these problems will go away once we upgrade to 9.5.

I don't know if these problems I described can be fixed by your patch,
but I wanted to share this story since I know our systems (Trustly's
and Zalando's) are quite similar in design,
so maybe you have experienced something similar.

(Side note: My biggest wish would be some way to specify explicitly on
a per top-level function level a list of indexes the query planner is
allowed to consider or is NOT allowed to consider.)

Shulgin, Oleksandr wrote:

> Alright.  I'm attaching the latest version of this patch split in two
> parts: the first one is NULLs-related bugfix and the second is the
> "improvement" part, which applies on top of the first one.

I went over patch 0001 and it seems pretty reasonable.  It's missing
some comment updates -- at least the large comments that talk about Duj1
should be modified to indicate why the code is now subtracting the null
count.  Also, I can't quite figure out why the "else" now in line 2131
is now "else if track_cnt != 0".  What happens if track_cnt is zero?
The comment above the "if" block doesn't provide any guidance.

-- 
Álvaro Herrera                http://www.2ndQuadrant.com/
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Robert Haas <robertmhaas@gmail.com> writes:
> On Wed, Jan 20, 2016 at 5:09 PM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>> Um, I would like to review it, but I doubt I'll find time before the end
>> of the month.

> Tom, can you pick this up?

Yes, now that I've gotten out from under the pathification thing,
I have cycles for patch review.  I'll take this one.
        regards, tom lane

Hi,

On Wed, 2016-03-09 at 10:58 +0100, Shulgin, Oleksandr wrote:
> On Tue, Mar 8, 2016 at 9:10 PM, Joel Jacobson <joel@trustly.com>
> wrote:
>         On Wed, Mar 9, 2016 at 1:25 AM, Shulgin, Oleksandr
>         <oleksandr.shulgin@zalando.de> wrote:
>         > Thank you for spending your time to run these :-)
>         
>         n/p, it took like 30 seconds :-)
> 
> 
> Great!  I'm glad to hear it was as easy to use as I hoped for :-)
> 
> 
>         > I don't want to be asking for too much here, but is there a
>         chance you could
>         > try the effects of the proposed patch on an offline copy of
>         your database?
>         
>         Yes, I think that should be possible.
>         
>         > Do you envision or maybe have experienced problems with
>         query plans
>         > referring to the columns that are near the top of the above
>         hist_ratio
>         > report?  In other words: what are the practical implications
>         for you with
>         > the values being duplicated rather badly throughout the
>         histogram like in
>         > the example you shown?
>         
>         I don't know much about the internals of query planner,
>         I just read the "57.1. Row Estimation Examples" to get a basic
>         understanding.
>         
>         If I understand it correctly, if the histogram_bounds contains
>         a lot
>         of duplicated values,
>         then the row estimation will be inaccurate, which in turn will
>         trick
>         the query planner
>         into a sub-optimal plan?
> 
> 
> Yes, basically it should matter the most for the equality comparison
> operator, such that a MCV entry would provide more accurate
> selectivity estimate (and the histogram is not used at all in this
> case anyway).  For the "less/greater-than" comparison both MCV list
> and histogram are used, so the drawback of having repeated values in
> the histogram, in my understanding is the same: less accurate
> selectivity estimates for the values that could fall precisely into a
> bin which didn't make it into the histogram.
> 
> 
>         We've had some problems lately with the query planner, or
>         actually we've always
>         had them but never noticed them nor cared about them, but now
>         during peak times
>         we've had short periods where we haven't been able to fully
>         cope up
>         with the traffic.
>         
>         I tracked down the most self_time-consuming functions and
>         quickly saw
>         how to optimize them.
>         Many of them where on the form:
>         SELECT .. FROM SomeBigTable WHERE Col1 = [some dynamic value]
>         AND Col2
>         = [some constant value] AND Col3 = [some other constant value]
>         The number of rows matching the WHERE clause were very tiny,
>         perfect
>         match for a partial index:
>         CREATE INDEX .. ON SomeBigTable USING btree (Col1) WHERE Col2
>         = [some
>         constant value] AND Col3 = [some other constant value];
>         
>         Even though the new partial index matched the query perfectly,
>         the
>         query planner didn't want to use it. Instead it continued to
>         use some
>         other sub-optimal index.
>         
>         The only way to force it to use the correct index was to use
>         the
>         "+0"-trick which I recently learned from one of my colleagues:
>         SELECT .. FROM SomeBigTable WHERE Col1 = [some dynamic value]
>         AND
>         Col2+0 = [some constant value] AND Col3+0 = [some other
>         constant
>         value]
>         CREATE INDEX .. ON SomeBigTable USING btree (Col1) WHERE Col2
>         +0 =
>         [some constant value] AND Col3+0 = [some other constant
>         value];
>         
>         By adding +0 to the columns, the query planner will as I
>         understand it
>         be extremely motivated to use the correct index, as otherwise
>         it would
>         have to do a seq scan on the entire big table, which would be
>         very
>         costly.
>         
>         I'm glad the trick worked, now the system is fast again.
>         
>         We're still on 9.1, so maybe these problems will go away once
>         we upgrade to 9.5.
> 
> 
> Hm... sounds like a planner bug to me.  I'm not exceptionally aware of
> the changes in partial index handling that were made after 9.1, though
> grepping the commit log for "partial index" produces a number of hits
> after the date of 9.1 release.

My first guess would be this is related to the costing bug addressed in 
   https://commitfest.postgresql.org/9/299/

I.e. the planner is not accounting for the index predicate correctly,
and ends up choosing the full index. It'd be interesting to see if the
patch makes the optimizer to choose the right index in your example.

The fact that +0 fixes the issue however seems a bit contradictory,
though. That forces the planner to use default selectivity estimates
(~5% for equality expressions, IIRC), not the per-column statistics.

Combined with the independence assumption for multiple AND clauses, this
may easily confuse the planner. I'd expect that would affect both
indexes equally, but perhaps not.

Would be useful to see explain analyze - it's not clear what you mean by
"the number of rows matching WHERE were tiny", whether an estimate or
the actual number.

regards

-- 
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Hi,

On Wed, 2016-03-09 at 11:23 +0100, Shulgin, Oleksandr wrote:
> On Tue, Mar 8, 2016 at 8:16 PM, Alvaro Herrera
> <alvherre@2ndquadrant.com> wrote:
>         Shulgin, Oleksandr wrote:
>         
>         > Alright.  I'm attaching the latest version of this patch
>         split in two
>         > parts: the first one is NULLs-related bugfix and the second
>         is the
>         > "improvement" part, which applies on top of the first one.
>         
>         I went over patch 0001 and it seems pretty reasonable.  It's
>         missing
>         some comment updates -- at least the large comments that talk
>         about Duj1
>         should be modified to indicate why the code is now subtracting
>         the null
>         count.
> 
> 
> Good point.
>  
> 
>         Also, I can't quite figure out why the "else" now in line 2131
>         is now "else if track_cnt != 0".  What happens if track_cnt is
>         zero?
>         The comment above the "if" block doesn't provide any guidance.
> 
> 
> It is there only to avoid potentially dividing zero by zero when
> calculating avgcount (which will not be used after that anyway).  I
> agree it deserves a comment.

That definitely deserves a comment. It's not immediately clear why
(track_cnt != 0) would prevent division by zero in the code. The only
way such error could happen is if ndistinct==0, because that's the
actual denominator. Which means this
   ndistinct = ndistinct * sample_cnt

would have to evaluate to 0. But ndistinct==0 can't happen as we're in
the (nonnull_cnt > 0) branch, and that guarantees (standistinct != 0).

Thus the only possibility seems to be (nonnull_cnt==toowide_cnt). Why
not to use this condition instead?

FWIW while looking at the code I noticed that we skip wide varlena
values but not cstrings. Seems a bit suspicious.

regards

-- 
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Tomas Vondra wrote:

> FWIW while looking at the code I noticed that we skip wide varlena
> values but not cstrings. Seems a bit suspicious.

Uh, can you actually have columns of cstring type?  I don't think you
can ...

-- 
Álvaro Herrera                http://www.2ndQuadrant.com/
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

On Wed, 2016-03-09 at 12:02 -0300, Alvaro Herrera wrote:
> Tomas Vondra wrote:
> 
> > FWIW while looking at the code I noticed that we skip wide varlena
> > values but not cstrings. Seems a bit suspicious.
> 
> Uh, can you actually have columns of cstring type?  I don't think you
> can ...

Yeah, but then why do we handle that in compute_scalar_stats?

-- 
Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Tomas Vondra <tomas.vondra@2ndquadrant.com> writes:
> On Wed, 2016-03-09 at 12:02 -0300, Alvaro Herrera wrote:
>> Tomas Vondra wrote:
>>> FWIW while looking at the code I noticed that we skip wide varlena
>>> values but not cstrings. Seems a bit suspicious.

>> Uh, can you actually have columns of cstring type?  I don't think you
>> can ...

> Yeah, but then why do we handle that in compute_scalar_stats?

If you're looking at what I think you're looking at, we aren't bothering
because we assume that cstrings won't be very wide.  Since they're not
toastable or compressable, they certainly won't exceed BLCKSZ.
        regards, tom lane

"Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> Yes, I now recall that my actual concern was that sample_cnt may calculate
> to 0 due to the latest condition above, but that also implies track_cnt ==
> 0, and then we have a for loop there which will not run at all due to this,
> so I figured we can avoid calculating avgcount and running the loop
> altogether with that check.  I'm not opposed to changing the condition if
> that makes the code easier to understand (or dropping it altogether if
> calculating 0/0 is believed to be harmless anyway).

Avoiding intentional zero divides is good.  It might happen to work
conveniently on your machine, but I wouldn't think it's portable.
        regards, tom lane

"Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> I've just seen that this patch doesn't have a reviewer assigned anymore...

I took my name off it because I was busy with other things and didn't
want to discourage other people from reviewing it meanwhile.  I do hope
to get to it eventually but there's a lot of stuff on my to-do list.

> I would welcome any review.

Me too.
        regards, tom lane

"Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> Alright.  I'm attaching the latest version of this patch split in two
> parts: the first one is NULLs-related bugfix and the second is the
> "improvement" part, which applies on top of the first one.

I've applied the first of these patches, broken into two parts first
because it seemed like there were two issues and second because Tomas
deserved primary credit for one part, ie realizing we were using the
Haas-Stokes formula wrong.

As for the other part, I committed it with one non-cosmetic change:
I do not think it is right to omit "too wide" values when considering
the threshold for MCVs.  As submitted, the patch was inconsistent on
that point anyway since it did it differently in compute_distinct_stats
and compute_scalar_stats.  But the larger picture here is that we define
the MCV population to exclude nulls, so it's reasonable to consider a
value as an MCV even if it's greatly outnumbered by nulls.  There is
no such exclusion for "too wide" values; those things are just an
implementation limitation in analyze.c, not something that is part of
the pg_statistic definition.  If there are a lot of "too wide" values
in the sample, we don't know whether any of them are duplicates, but
we do know that the frequencies of the normal-width values have to be
discounted appropriately.

Haven't looked at 0002 yet.
        regards, tom lane

<p dir="ltr">On Apr 1, 2016 23:14, "Tom Lane" <<a href="mailto:tgl@sss.pgh.pa.us">tgl@sss.pgh.pa.us</a>>
wrote:<br/> ><br /> > "Shulgin, Oleksandr" <<a
href="mailto:oleksandr.shulgin@zalando.de">oleksandr.shulgin@zalando.de</a>>writes:<br /> > > Alright.  I'm
attachingthe latest version of this patch split in two<br /> > > parts: the first one is NULLs-related bugfix and
thesecond is the<br /> > > "improvement" part, which applies on top of the first one.<br /> ><br /> > I've
appliedthe first of these patches,<p dir="ltr">Great news, thank you!<p dir="ltr">> broken into two parts first<br
/>> because it seemed like there were two issues and second because Tomas<br /> > deserved primary credit for one
part,ie realizing we were using the<br /> > Haas-Stokes formula wrong.<br /> ><br /> > As for the other part,
Icommitted it with one non-cosmetic change:<br /> > I do not think it is right to omit "too wide" values when
considering<br/> > the threshold for MCVs.  As submitted, the patch was inconsistent on<br /> > that point anyway
sinceit did it differently in compute_distinct_stats<br /> > and compute_scalar_stats.  But the larger picture here
isthat we define<br /> > the MCV population to exclude nulls, so it's reasonable to consider a<br /> > value as
anMCV even if it's greatly outnumbered by nulls.  There is<br /> > no such exclusion for "too wide" values; those
thingsare just an<br /> > implementation limitation in analyze.c, not something that is part of<br /> > the
pg_statisticdefinition.  If there are a lot of "too wide" values<br /> > in the sample, we don't know whether any of
themare duplicates, but<br /> > we do know that the frequencies of the normal-width values have to be<br /> >
discountedappropriately.<p dir="ltr">Okay.<p dir="ltr">> Haven't looked at 0002 yet.<p dir="ltr">[crosses fingers]
hopeyou'll have a chance to do that before feature freeze for 9.6…<p dir="ltr">--<br /> Alex<br /> 

"Shulgin, Oleksandr" <oleksandr.shulgin@zalando.de> writes:
> On Apr 1, 2016 23:14, "Tom Lane" <tgl@sss.pgh.pa.us> wrote:
>> Haven't looked at 0002 yet.

> [crosses fingers] hope you'll have a chance to do that before feature
> freeze for 9.6

I studied this patch for awhile after rebasing it onto yesterday's
commits.  I did not like the fact that the compute_scalar_stats logic
would allow absolutely anything into the MCV list once num_hist falls
below 2.  I think it's important that we continue to reject values that
are only seen once in the sample, because there's no very good reason to
think that they are MCVs and not just infrequent values that by luck
appeared in the sample.  However, after I rearranged the tests there so
that "if (num_hist >= 2)" only controlled whether to apply the 1/K limit,
one of the regression tests started to fail: there's a place in
rowsecurity.sql that expects that if a column contains nothing but several
instances of a single value, that value will be recorded as a lone MCV.
Now this isn't a particularly essential thing for that test, but it still
seems like a good property for ANALYZE to have.  The reason it's failing,
of course, is that the test as written cannot possibly accept the last
(or only) value.

Before I noticed the regression failure, I'd been thinking that maybe it'd
be better if the decision rule were not "at least 100+x% of the average
frequency of this value and later ones", but "at least 100+x% of the
average frequency of values after this one".  With that formulation, we're
not constrained as to the range of x.  Now, if there are *no* values after
this one, then this way needs an explicit special case in order not to
compute 0/0; but the preceding para shows that we need a special case for
the last value anyway.

So, attached is a patch rewritten along these lines.  I used 50% rather
than 25% as the new cutoff percentage --- obviously it should be higher
in this formulation than before, but I have no idea if that particular
number is good or we should use something else.  Also, the rule for the
last value is "at least 1% of the non-null samples".  That's a pure guess
as well.

I do not have any good corpuses of data to try this on.  Can folks who
have been following this thread try it on their data and see how it
does?  Also please try some other multipliers besides 1.5, so we can
get a feeling for where that cutoff should be placed.

            regards, tom lane

diff --git a/src/backend/commands/analyze.c b/src/backend/commands/analyze.c
index 44a4b3f..a2c606b 100644
*** a/src/backend/commands/analyze.c
--- b/src/backend/commands/analyze.c
*************** compute_distinct_stats(VacAttrStatsP sta
*** 2120,2128 ****
           * we are able to generate a complete MCV list (all the values in the
           * sample will fit, and we think these are all the ones in the table),
           * then do so.  Otherwise, store only those values that are
!          * significantly more common than the (estimated) average. We set the
!          * threshold rather arbitrarily at 25% more than average, with at
!          * least 2 instances in the sample.
           */
          if (track_cnt < track_max && toowide_cnt == 0 &&
              stats->stadistinct > 0 &&
--- 2120,2138 ----
           * we are able to generate a complete MCV list (all the values in the
           * sample will fit, and we think these are all the ones in the table),
           * then do so.  Otherwise, store only those values that are
!          * significantly more common than the ones we omit.  We determine that
!          * by considering the values in frequency order, and accepting each
!          * one if it is at least 50% more common than the average among the
!          * values after it.  The 50% threshold is somewhat arbitrary.
!          *
!          * Note that the 50% rule will never accept a value with count 1,
!          * since all the values have count at least 1; this is a property we
!          * desire, since there's no very good reason to assume that a
!          * single-occurrence value is an MCV and not just a random non-MCV.
!          *
!          * We need a special rule for the very last value.  If we get to it,
!          * we'll accept it if it's at least 1% of the non-null samples and has
!          * count more than 1.
           */
          if (track_cnt < track_max && toowide_cnt == 0 &&
              stats->stadistinct > 0 &&
*************** compute_distinct_stats(VacAttrStatsP sta
*** 2133,2153 ****
          }
          else
          {
!             /* d here is the same as d in the Haas-Stokes formula */
              int            d = nonnull_cnt - summultiple + nmultiple;
!             double        avgcount,
!                         mincount;

!             /* estimate # occurrences in sample of a typical nonnull value */
!             avgcount = (double) nonnull_cnt / (double) d;
!             /* set minimum threshold count to store a value */
!             mincount = avgcount * 1.25;
!             if (mincount < 2)
!                 mincount = 2;
              if (num_mcv > track_cnt)
                  num_mcv = track_cnt;
              for (i = 0; i < num_mcv; i++)
              {
                  if (track[i].count < mincount)
                  {
                      num_mcv = i;
--- 2143,2181 ----
          }
          else
          {
!             /* d here is initially the same as d in the Haas-Stokes formula */
              int            d = nonnull_cnt - summultiple + nmultiple;
!             int            remaining_samples = nonnull_cnt;

!             /* can't store more MCVs than we tracked ... */
              if (num_mcv > track_cnt)
                  num_mcv = track_cnt;
+             /* locate first value we're not going to store as an MCV */
              for (i = 0; i < num_mcv; i++)
              {
+                 double        avgcount,
+                             mincount;
+
+                 /* remove current value from remaining_samples and d */
+                 remaining_samples -= track[i].count;
+                 d--;
+                 if (d > 0)
+                 {
+                     /* get avg # occurrences of distinct values after this */
+                     avgcount = (double) remaining_samples / (double) d;
+                     /* set minimum count to accept a value (is surely > 1) */
+                     mincount = avgcount * 1.50;
+                 }
+                 else
+                 {
+                     /* last value, use 1% rule */
+                     mincount = nonnull_cnt * 0.01;
+
+                     /* here, we need a clamp to avoid accepting count 1 */
+                     if (mincount < 2)
+                         mincount = 2;
+                 }
+                 /* if this value falls below threshold, we're done */
                  if (track[i].count < mincount)
                  {
                      num_mcv = i;
*************** compute_scalar_stats(VacAttrStatsP stats
*** 2375,2381 ****
                          /*
                           * Found a new item for the mcv list; find its
                           * position, bubbling down old items if needed. Loop
!                          * invariant is that j points at an empty/ replaceable
                           * slot.
                           */
                          int            j;
--- 2403,2409 ----
                          /*
                           * Found a new item for the mcv list; find its
                           * position, bubbling down old items if needed. Loop
!                          * invariant is that j points at an empty/replaceable
                           * slot.
                           */
                          int            j;
*************** compute_scalar_stats(VacAttrStatsP stats
*** 2475,2488 ****
           * we are able to generate a complete MCV list (all the values in the
           * sample will fit, and we think these are all the ones in the table),
           * then do so.  Otherwise, store only those values that are
!          * significantly more common than the (estimated) average. We set the
!          * threshold rather arbitrarily at 25% more than average, with at
!          * least 2 instances in the sample.  Also, we won't suppress values
!          * that have a frequency of at least 1/K where K is the intended
!          * number of histogram bins; such values might otherwise cause us to
!          * emit duplicate histogram bin boundaries.  (We might end up with
!          * duplicate histogram entries anyway, if the distribution is skewed;
!          * but we prefer to treat such values as MCVs if at all possible.)
           */
          if (track_cnt == ndistinct && toowide_cnt == 0 &&
              stats->stadistinct > 0 &&
--- 2503,2532 ----
           * we are able to generate a complete MCV list (all the values in the
           * sample will fit, and we think these are all the ones in the table),
           * then do so.  Otherwise, store only those values that are
!          * significantly more common than the ones we omit.  We determine that
!          * by considering the values in frequency order, and accepting each
!          * one if it is at least 50% more common than the average among the
!          * values after it.  The 50% threshold is somewhat arbitrary.
!          *
!          * We need a special rule for the very last value.  If we get to it,
!          * we'll accept it if it's at least 1% of the non-null samples and has
!          * count more than 1.
!          *
!          * Also, we will treat values as MCVs if they have a frequency of at
!          * least 1/K where K is the intended number of histogram entries.
!          * Relegating such values to the histogram might cause us to emit
!          * duplicate histogram entries.  (We might get duplicate histogram
!          * entries anyway, if the distribution is skewed; but we prefer to
!          * treat such values as MCVs if at all possible.)  For this purpose,
!          * measure the frequency with respect to the population represented by
!          * the histogram; so in the loop below, cur_remaining_samples/num_hist
!          * is the correct calculation.
!          *
!          * In any case, unless we believe we have a complete MCV list, we will
!          * not accept an MCV value with count 1, since there's no very good
!          * reason to assume that a single-occurrence value is an MCV and not
!          * just a random non-MCV.  This is automatic with the 50% rule but
!          * needs enforcement with the other ones.
           */
          if (track_cnt == ndistinct && toowide_cnt == 0 &&
              stats->stadistinct > 0 &&
*************** compute_scalar_stats(VacAttrStatsP stats
*** 2490,2523 ****
          {
              /* Track list includes all values seen, and all will fit */
              num_mcv = track_cnt;
          }
          else
          {
!             /* d here is the same as d in the Haas-Stokes formula */
              int            d = ndistinct + toowide_cnt;
!             double        avgcount,
!                         mincount,
!                         maxmincount;

!             /* estimate # occurrences in sample of a typical nonnull value */
!             avgcount = (double) values_cnt / (double) d;
!             /* set minimum threshold count to store a value */
!             mincount = avgcount * 1.25;
!             if (mincount < 2)
!                 mincount = 2;
!             /* don't let threshold exceed 1/K, however */
!             maxmincount = (double) values_cnt / (double) num_bins;
!             if (mincount > maxmincount)
!                 mincount = maxmincount;
              if (num_mcv > track_cnt)
                  num_mcv = track_cnt;
              for (i = 0; i < num_mcv; i++)
              {
                  if (track[i].count < mincount)
                  {
                      num_mcv = i;
                      break;
                  }
              }
          }

--- 2534,2605 ----
          {
              /* Track list includes all values seen, and all will fit */
              num_mcv = track_cnt;
+             /* Nothing left for the histogram */
+             num_hist = 0;
          }
          else
          {
!             /* d here is initially the same as d in the Haas-Stokes formula */
              int            d = ndistinct + toowide_cnt;
!             int            remaining_samples = values_cnt;

!             /*
!              * num_hist is the planned histogram size; it's limited by the
!              * number of distinct sample vals not absorbed into the MCV list.
!              * Start with assumption that nothing is absorbed into MCV list.
!              */
!             num_hist = Min(ndistinct, num_bins + 1);
!
!             /* can't store more MCVs than we tracked ... */
              if (num_mcv > track_cnt)
                  num_mcv = track_cnt;
+             /* locate first value we're not going to store as an MCV */
              for (i = 0; i < num_mcv; i++)
              {
+                 int            cur_remaining_samples = remaining_samples;
+                 double        avgcount,
+                             mincount;
+
+                 /* remove current value from remaining_samples and d */
+                 remaining_samples -= track[i].count;
+                 d--;
+                 if (d > 0)
+                 {
+                     /* get avg # occurrences of distinct values after this */
+                     avgcount = (double) remaining_samples / (double) d;
+                     /* set minimum count to accept a value (is surely > 1) */
+                     mincount = avgcount * 1.50;
+                 }
+                 else
+                 {
+                     /* last value, use 1% rule */
+                     mincount = values_cnt * 0.01;
+
+                     /* here, we need a clamp to avoid accepting count 1 */
+                     if (mincount < 2)
+                         mincount = 2;
+                 }
+
+                 /* don't let threshold exceed 1/K, however */
+                 if (num_hist >= 2)        /* else we won't make a histogram */
+                 {
+                     double        hfreq;
+
+                     hfreq = (double) cur_remaining_samples / (double) num_hist;
+                     if (mincount > hfreq)
+                         mincount = hfreq;
+                     /* hfreq could be 1, so clamp to avoid accepting count 1 */
+                     if (mincount < 2)
+                         mincount = 2;
+                 }
+                 /* if this value falls below threshold, we're done */
                  if (track[i].count < mincount)
                  {
                      num_mcv = i;
                      break;
                  }
+                 /* update planned histogram size, removing this value */
+                 num_hist = Min(ndistinct - (i + 1), num_bins + 1);
              }
          }

*************** compute_scalar_stats(VacAttrStatsP stats
*** 2560,2568 ****
           * values not accounted for in the MCV list.  (This ensures the
           * histogram won't collapse to empty or a singleton.)
           */
-         num_hist = ndistinct - num_mcv;
-         if (num_hist > num_bins)
-             num_hist = num_bins + 1;
          if (num_hist >= 2)
          {
              MemoryContext old_context;
--- 2642,2647 ----

<p dir="ltr">On Apr 2, 2016 18:38, "Tom Lane" <<a href="mailto:tgl@sss.pgh.pa.us">tgl@sss.pgh.pa.us</a>>
wrote:<br/> ><br /> > "Shulgin, Oleksandr" <<a
href="mailto:oleksandr.shulgin@zalando.de">oleksandr.shulgin@zalando.de</a>>writes:<br /> > > On Apr 1, 2016
23:14,"Tom Lane" <<a href="mailto:tgl@sss.pgh.pa.us">tgl@sss.pgh.pa.us</a>> wrote:<br /> > >> Haven't
lookedat 0002 yet.<br /> ><br /> > > [crosses fingers] hope you'll have a chance to do that before feature<br
/>> > freeze for 9.6<br /> ><br /> > I studied this patch for awhile after rebasing it onto yesterday's<br
/>> commits.<p dir="ltr">Fantastic! I could not hope for a better reply :-) <p dir="ltr">> I did not like the
factthat the compute_scalar_stats logic<br /> > would allow absolutely anything into the MCV list once num_hist
falls<br/> > below 2. I think it's important that we continue to reject values that<br /> > are only seen once in
thesample, because there's no very good reason to<br /> > think that they are MCVs and not just infrequent values
thatby luck<br /> > appeared in the sample.<p dir="ltr">In my understanding we only put a value in the track list if
we'veseen it at least twice, no?<p dir="ltr">> However, after I rearranged the tests there so<br /> > that "if
(num_hist>= 2)" only controlled whether to apply the 1/K limit,<br /> > one of the regression tests started to
fail:<pdir="ltr">Uh-oh.<p dir="ltr">> there's a place in<br /> > rowsecurity.sql that expects that if a column
containsnothing but several<br /> > instances of a single value, that value will be recorded as a lone MCV.<br />
>Now this isn't a particularly essential thing for that test, but it still<br /> > seems like a good property for
ANALYZEto have.<p dir="ltr">No objection here.<p dir="ltr">> The reason it's failing,<br /> > of course, is that
thetest as written cannot possibly accept the last<br /> > (or only) value.<p dir="ltr">Yeah, this I would expect
fromsuch a change.<p dir="ltr">> Before I noticed the regression failure, I'd been thinking that maybe it'd<br />
>be better if the decision rule were not "at least 100+x% of the average<br /> > frequency of this value and
laterones", but "at least 100+x% of the<br /> > average frequency of values after this one".<p dir="ltr">Hm, sounds
prettysimilar to what I wanted to achieve, but better formalized.<p dir="ltr">> With that formulation, we're<br />
>not constrained as to the range of x.  Now, if there are *no* values after<br /> > this one, then this way needs
anexplicit special case in order not to<br /> > compute 0/0; but the preceding para shows that we need a special
casefor<br /> > the last value anyway.<br /> ><br /> > So, attached is a patch rewritten along these lines.  I
used50% rather<br /> > than 25% as the new cutoff percentage --- obviously it should be higher<br /> > in this
formulationthan before, but I have no idea if that particular<br /> > number is good or we should use something
else. Also, the rule for the<br /> > last value is "at least 1% of the non-null samples".  That's a pure guess<br />
>as well.<br /> ><br /> > I do not have any good corpuses of data to try this on.  Can folks who<br /> >
havebeen following this thread try it on their data and see how it<br /> > does?  Also please try some other
multipliersbesides 1.5, so we can<br /> > get a feeling for where that cutoff should be placed.<p dir="ltr">Expect
meto run it on my pet db early next week. :-) <p dir="ltr">Many thanks!<br /> --<br /> Alex<br /> 

Alex Shulgin <alex.shulgin@gmail.com> writes:
> On Sun, Apr 3, 2016 at 3:43 AM, Alex Shulgin <alex.shulgin@gmail.com> wrote:
>> I'm not sure yet about the 1% rule for the last value, but would also love
>> to see if we can avoid the arbitrary limit here.  What happens with a last
>> value which is less than 1% popular in the current code anyway?

> Now that I think about it, I don't really believe this arbitrary heuristic
> is any good either, sorry.

Yeah, it was just a placeholder to produce a working patch.

Maybe we could base this cutoff on the stats target for the column?
That is, "1%" would be the right number if stats target is 100,
otherwise scale appropriately.

> What was your motivation to introduce some limit at the bottom anyway?

Well, we have to do *something* with the last (possibly only) value.
Neither "include always" nor "omit always" seem sane to me.  What other
decision rule do you want there?
        regards, tom lane

Alex Shulgin <alex.shulgin@gmail.com> writes:
> On Sun, Apr 3, 2016 at 7:18 AM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>> Well, we have to do *something* with the last (possibly only) value.
>> Neither "include always" nor "omit always" seem sane to me.  What other
>> decision rule do you want there?

> Well, what implies that the last value is somehow special?  I would think
> we should just do with it whatever we do with the rest of the candidate
> MCVs.

Sure, but both of the proposed decision rules break down when there are no
values after the one under consideration.  We need to do something sane
there.

> For "the only value" case: we cannot build a histogram out of a single
> value, so omitting it from MCVs is not a good strategy, ISTM.
> From my point of view that amounts to "include always".

If there is only one value, it will have 100% of the samples, so it would
get included under just about any decision rule (other than "more than
100% of this value plus following values").  I don't think making sure
this case works is sufficient to get us to a reasonable rule --- it's
a necessary case, but not a sufficient case.
        regards, tom lane

Alex Shulgin <alex.shulgin@gmail.com> writes:
> On Sun, Apr 3, 2016 at 7:49 AM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>> If there is only one value, it will have 100% of the samples, so it would
>> get included under just about any decision rule (other than "more than
>> 100% of this value plus following values").  I don't think making sure
>> this case works is sufficient to get us to a reasonable rule --- it's
>> a necessary case, but not a sufficient case.

> Well, if it's the only value it will be accepted simply because we are
> checking that special case already and don't even bother to loop through
> the track list.

That was demonstrably not the case in the failing regression test.
I forget what aspect of the test case allowed it to get past the short
circuit, but it definitely got into the scan-the-track-list code.
        regards, tom lane

Alex Shulgin <alex.shulgin@gmail.com> writes:
> This recalled observation can now also explain to me why in the regression
> you've seen, the short path was not followed: my bet is that stadistinct
> appeared negative.

Yes, I think that's right.  The table under consideration had just a few
live rows (I think 3), so that even though there was only one value in
the sample, the "if (stats->stadistinct > 0.1 * totalrows)" condition
succeeded.

> Given that we change the logic in the complex path substantially, the
> assumptions that lead to the "Take all MVCs" condition above might no
> longer hold, and I see it as a pretty compelling argument to remove the
> extra checks, thus keeping the only one: track_cnt == ndistinct.  This
> should also bring the patch's effect more close to the thread's topic,
> which is "More stable query plans".

The reason for checking toowide_cnt is that if it's greater than zero,
then in fact the track list does NOT include all values seen, and it's
flat-out wrong to claim that it is an exhaustive set of values.

The reason for the track_cnt <= num_mcv condition is that if that's not
true, the track list has to be trimmed to meet the statistics target.
Again, that's not optional.

I think the reasoning for having the stats->stadistinct > 0 test in there
was that if we'd set it negative, then we think that the set of distinct
values will grow --- which again implies that the set of values actually
seen should not be considered exhaustive.  Of course, with a table as
small as that regression-test example, we have little evidence to support
either that conclusion or its opposite.

It's possible that what we should do to eliminate the sudden change
of behaviors is to drop the "track list includes all values seen, and all
will fit" code path entirely, and always go through the track list
one-at-a-time.

If we do, though, the currently-proposed filter rules aren't going to
be too satisfactory: if we have a relatively small group of roughly
equally common MCVs, this logic would reject all of them, which is
surely not what we want.

The point of the original logic was to try to decide whether the
values in the sample are significantly more common than typical values
in the whole table population.  I think we may have broken that with
3d3bf62f3: you can't make any such determination if you consider only
what's in the sample without trying to estimate what is not in the
sample.
        regards, tom lane

Alex Shulgin <alex.shulgin@gmail.com> writes:
> On Sun, Apr 3, 2016 at 10:53 PM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>> The reason for checking toowide_cnt is that if it's greater than zero,
>> then in fact the track list does NOT include all values seen, and it's
>> flat-out wrong to claim that it is an exhaustive set of values.

> But do we really state that with the short path?

Well, yes: the point of the short path is that we're hypothesizing that
the track list contains all values in the table, and that they should all
be considered MCVs.  Another way to think about it is that if we didn't
have the width-limit implementation restriction, those values would appear
in the track list, almost certainly with count 1, and so we would not
have taken the short path anyway.

Now you can argue that the long path would have accepted all the real
track-list entries as MCVs, and have rejected all these hypothetical
count-1 entries for too-wide values, and so the end result would be the
same.  But that gets back to the fact that that's not necessarily how
the long path behaves, either today or with the proposed patch.

The design intention was that the short path would handle columns
with a finite, small set of values (think booleans or enums) where the
ideal thing is that the table population is completely represented by
the MCV list.  As soon as we have good reason to think that the MCV
list won't represent the table contents fully, we should switch over
to a different approach where we're trying to identify which sample
values are common enough to justify putting in the MCV list.  In that
situation there are good reasons to not blindly fill the MCV list all
the way to the stats-target limit, but to try to cut it off at the
point of diminishing returns, so that the planner isn't saddled with
a huge MCV list that doesn't really contain a lot of useful information.

So that's the logic behind there being two code paths with discontinuous
behavior.  I'm not sure whether we need to try to narrow the discontinuity
or whether it's OK to act that way and we just need to refine the decision
rule about which path to take.  But anyway, comparisons of frequencies
of candidate MCVs seem to me to make sense in a large-ndistinct scenario
(where we have to be selective) but not a small-ndistinct scenario
(where we should just take 'em all).

>> The point of the original logic was to try to decide whether the
>> values in the sample are significantly more common than typical values
>> in the whole table population.  I think we may have broken that with
>> 3d3bf62f3: you can't make any such determination if you consider only
>> what's in the sample without trying to estimate what is not in the
>> sample.

> Speaking of rabbit holes...
> I'm out of ideas, unfortunately.  We badly need more eyes/brainpower on
> this, which is why I have submitted a talk proposal on this topic to
> PGDay.ru this summer in St. Petersburg, fearing that it might be too late
> to commit a satisfactory version during the current dev cycle for 9.6, and
> in hope to draw at least some attention to it.

If you're thinking it's too late to get more done for 9.6, I'm inclined to
revert the aspect of 3d3bf62f3 that made us work from "d" (the observed
number of distinct values in the sample) rather than stadistinct (the
extrapolated estimate for the table).  On reflection I think that that's
inconsistent with the theory behind the old MCV-cutoff rule.  It wouldn't
matter if we were going to replace the cutoff rule with something else,
but it's beginning to sound like that won't happen for 9.6.
        regards, tom lane

Alex Shulgin <alex.shulgin@gmail.com> writes:
> On Mon, Apr 4, 2016 at 1:06 AM, Tom Lane <tgl@sss.pgh.pa.us> wrote:
>> I'm inclined to
>> revert the aspect of 3d3bf62f3 that made us work from "d" (the observed
>> number of distinct values in the sample) rather than stadistinct (the
>> extrapolated estimate for the table).  On reflection I think that that's
>> inconsistent with the theory behind the old MCV-cutoff rule.  It wouldn't
>> matter if we were going to replace the cutoff rule with something else,
>> but it's beginning to sound like that won't happen for 9.6.

> Please feel free to do what you think is in the best interest of the people
> preparing 9.6 for the freeze.  I'm not all that familiar with the process,
> but I guess reverting this early might save some head-scratching if some
> interesting interactions of this change combined with some others are going
> to show up.

I've reverted that bit; so we still have the improvements associated with
ignoring nulls, but nothing else at the moment.  I'll set this commitfest
item back to Waiting on Author, just in case you are able to make some
more progress before the end of the week.
        regards, tom lane

<p dir="ltr">On Apr 5, 2016 00:31, "Tom Lane" <<a href="mailto:tgl@sss.pgh.pa.us">tgl@sss.pgh.pa.us</a>>
wrote:<br/> ><br /> > Alex Shulgin <<a href="mailto:alex.shulgin@gmail.com">alex.shulgin@gmail.com</a>>
writes:<br/> > > On Mon, Apr 4, 2016 at 1:06 AM, Tom Lane <<a
href="mailto:tgl@sss.pgh.pa.us">tgl@sss.pgh.pa.us</a>>wrote:<br /> > >> I'm inclined to<br /> > >>
revertthe aspect of 3d3bf62f3 that made us work from "d" (the observed<br /> > >> number of distinct values in
thesample) rather than stadistinct (the<br /> > >> extrapolated estimate for the table).  On reflection I
thinkthat that's<br /> > >> inconsistent with the theory behind the old MCV-cutoff rule.  It wouldn't<br />
>>> matter if we were going to replace the cutoff rule with something else,<br /> > >> but it's
beginningto sound like that won't happen for 9.6.<br /> ><br /> > > Please feel free to do what you think is
inthe best interest of the people<br /> > > preparing 9.6 for the freeze.  I'm not all that familiar with the
process,<br/> > > but I guess reverting this early might save some head-scratching if some<br /> > >
interestinginteractions of this change combined with some others are going<br /> > > to show up.<br /> ><br />
>I've reverted that bit; so we still have the improvements associated with<br /> > ignoring nulls, but nothing
elseat the moment.  I'll set this commitfest<br /> > item back to Waiting on Author, just in case you are able to
makesome<br /> > more progress before the end of the week.<p dir="ltr">OK, though it's unlikely that I'll get
productiveagain before next week, but maybe someone who has also been following this thread wants to step in?<p
dir="ltr">Thanks.<br/> --<br /> Alex<br />