Thread

1. Optimize planner memory consumption for huge arrays

   Andrei Lepikhov <a.lepikhov@postgrespro.ru> — 2023-09-04T05:25:44Z

   Hi, hackers,
   
   Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   
   It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   
   The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   
   [1] Report planning memory in EXPLAIN ANALYZE
   https://www.postgresql.org/message-id/flat/CAExHW5sZA=5LJ_ZPpRO-w09ck8z9p7eaYAqq3Ks9GDfhrxeWBw@mail.gmail.com
   
   --
   Regards,
   Andrey Lepikhov
   

2. Re: Optimize planner memory consumption for huge arrays

   Dilip Kumar <dilipbalaut@gmail.com> — 2023-09-04T08:37:03Z

   On Mon, Sep 4, 2023 at 11:58 AM Lepikhov Andrei
   <a.lepikhov@postgrespro.ru> wrote:
   >
   > Hi, hackers,
   >
   > Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   > The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   >
   > It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   >
   > The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   
   + Const *c = makeConst(nominal_element_type,
   + -1,
   + nominal_element_collation,
   + elmlen,
   + elem_values[i],
   + elem_nulls[i],
   + elmbyval);
   +
   + args = list_make2(leftop, c);
     if (is_join_clause)
     s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
       clause->inputcollid,
   @@ -1984,7 +1985,8 @@ scalararraysel(PlannerInfo *root,
       ObjectIdGetDatum(operator),
       PointerGetDatum(args),
       Int32GetDatum(varRelid)));
   -
   + list_free(args);
   + pfree(c);
   
   Maybe you can just use list_free_deep, instead of storing the constant
   in a separate variable.
   
   -- 
   Regards,
   Dilip Kumar
   EnterpriseDB: http://www.enterprisedb.com
   
   
   
   

3. Re: Optimize planner memory consumption for huge arrays

   Andrei Lepikhov <a.lepikhov@postgrespro.ru> — 2023-09-04T10:19:05Z

   
   On Mon, Sep 4, 2023, at 3:37 PM, Dilip Kumar wrote:
   > On Mon, Sep 4, 2023 at 11:58 AM Lepikhov Andrei
   > <a.lepikhov@postgrespro.ru> wrote:
   >>
   >> Hi, hackers,
   >>
   >> Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   >> The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   >>
   >> It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   >>
   >> The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   >
   > + Const *c = makeConst(nominal_element_type,
   > + -1,
   > + nominal_element_collation,
   > + elmlen,
   > + elem_values[i],
   > + elem_nulls[i],
   > + elmbyval);
   > +
   > + args = list_make2(leftop, c);
   >   if (is_join_clause)
   >   s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
   >     clause->inputcollid,
   > @@ -1984,7 +1985,8 @@ scalararraysel(PlannerInfo *root,
   >     ObjectIdGetDatum(operator),
   >     PointerGetDatum(args),
   >     Int32GetDatum(varRelid)));
   > -
   > + list_free(args);
   > + pfree(c);
   >
   > Maybe you can just use list_free_deep, instead of storing the constant
   > in a separate variable.
   As I see, the first element in the array is leftop, which is used on other iterations. So, we can't use list_free_deep here.
   
   -- 
   Regards,
   Andrei Lepikhov
   
   
   
   

4. Re: Optimize planner memory consumption for huge arrays

   Dilip Kumar <dilipbalaut@gmail.com> — 2023-09-04T10:59:01Z

   On Mon, Sep 4, 2023 at 3:49 PM Lepikhov Andrei
   <a.lepikhov@postgrespro.ru> wrote:
   >
   > > + Const *c = makeConst(nominal_element_type,
   > > + -1,
   > > + nominal_element_collation,
   > > + elmlen,
   > > + elem_values[i],
   > > + elem_nulls[i],
   > > + elmbyval);
   > > +
   > > + args = list_make2(leftop, c);
   > >   if (is_join_clause)
   > >   s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
   > >     clause->inputcollid,
   > > @@ -1984,7 +1985,8 @@ scalararraysel(PlannerInfo *root,
   > >     ObjectIdGetDatum(operator),
   > >     PointerGetDatum(args),
   > >     Int32GetDatum(varRelid)));
   > > -
   > > + list_free(args);
   > > + pfree(c);
   > >
   > > Maybe you can just use list_free_deep, instead of storing the constant
   > > in a separate variable.
   > As I see, the first element in the array is leftop, which is used on other iterations. So, we can't use list_free_deep here.
   
   Yeah you are right, thanks.
   
   -- 
   Regards,
   Dilip Kumar
   EnterpriseDB: http://www.enterprisedb.com
   
   
   
   

5. Re: Optimize planner memory consumption for huge arrays

   Ashutosh Bapat <ashutosh.bapat.oss@gmail.com> — 2023-09-06T13:09:35Z

   Hi Lepikhov,
   
   Thanks for using my patch and I am glad that you found it useful.
   
   On Mon, Sep 4, 2023 at 10:56 AM Lepikhov Andrei
   <a.lepikhov@postgrespro.ru> wrote:
   >
   > Hi, hackers,
   >
   > Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   > The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   
   I guess the numbers you mentioned in init.sql are total memory used by
   the planner (as reported by the patch in the thread) when planning
   that query and not memory consumed by Const nodes themselves. Am I
   right? I think the measurements need to be explained better and also
   the realistic scenario you are trying to oprimize.
   
   I guess, the reason you think that partitioning will increase the
   memory consumed is because each partition will have the clause
   translated for it. Selectivity estimation for each partition will
   create those many Const nodes and hence consume memory. Am I right?
   Can you please measure the memory consumed with and without your
   patch.
   
   >
   > It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   
   With vectorized operations becoming a norm these days, it's possible
   to have thousands of element in array of an ANY or IN clause. Also
   will be common to have thousands of partitions. But I think what we
   need to do here is to write a selectivity estimation function which
   takes an const array and return selectivity without requiring to
   create a Const node for each element.
   
   >
   > The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   >
   
   You might want to include your benchmarking results as well.
   
   -- 
   Best Wishes,
   Ashutosh Bapat
   
   
   
   

6. Re: Optimize planner memory consumption for huge arrays

   Andrei Lepikhov <a.lepikhov@postgrespro.ru> — 2023-09-08T05:11:31Z

   
   On Wed, Sep 6, 2023, at 8:09 PM, Ashutosh Bapat wrote:
   > Hi Lepikhov,
   >
   > Thanks for using my patch and I am glad that you found it useful.
   >
   > On Mon, Sep 4, 2023 at 10:56 AM Lepikhov Andrei
   > <a.lepikhov@postgrespro.ru> wrote:
   >>
   >> Hi, hackers,
   >>
   >> Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   >> The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   >
   > I guess the numbers you mentioned in init.sql are total memory used by
   > the planner (as reported by the patch in the thread) when planning
   > that query and not memory consumed by Const nodes themselves. Am I
   > right? I think the measurements need to be explained better and also
   > the realistic scenario you are trying to oprimize.
   
   Yes, it is the total memory consumed by the planner - I used the numbers generated by your patch [1]. I had been increasing the number of elements in the array to exclude the memory consumed by the planner for other purposes. As you can see, the array with 1 element consumes 12kB of memory, 1E4 elements - 2.6 MB. All of that memory increment is related to the only enlargement of this array. (2600-12)/10 = 260 bytes. So, I make a conclusion: each 4-byte element produces a consumption of 260 bytes of memory.
   This scenario I obtained from the user complaint - they had strict restrictions on memory usage and were stuck in this unusual memory usage case.
   
   > I guess, the reason you think that partitioning will increase the
   > memory consumed is because each partition will have the clause
   > translated for it. Selectivity estimation for each partition will
   > create those many Const nodes and hence consume memory. Am I right?
   
   Yes.
   
   > Can you please measure the memory consumed with and without your
   > patch.
   
   Done. See test case and results in 'init_parts.sql' in attachment. Short summary below. I varied a number of elements from 1 to 10000 and partitions from 1 to 100. As you can see, partitioning adds a lot of memory consumption by itself. But we see an effect from patch also.
   
   master:
   elems	1		1E1		1E2		1E3		1E4	
   parts
   1		28kB	50kB	0.3MB	2.5MB	25MB
   10		45kB	143kB	0.6MB	4.8MB	47MB
   100		208kB	125kB	3.3MB	27MB	274MB
   
   patched:
   elems	1		1E1		1E2		1E3		1E4
   parts
   1		28kB	48kB	0.25MB	2.2MB	22.8MB
   10		44kB	100kB	313kB	2.4MB	23.7MB
   100		208kB	101kB	0.9MB	3.7MB	32.4MB
   
   Just for comparison, without partitioning:
   elems	1		1E1		1E2		1E3		1E4	
   master:	12kB	14kB	37kB	266kB	2.5MB
   patched:	12kB	11.5kB	13kB	24kB	141kB
   
   >> It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   >
   > With vectorized operations becoming a norm these days, it's possible
   > to have thousands of element in array of an ANY or IN clause. Also
   > will be common to have thousands of partitions. But I think what we
   > need to do here is to write a selectivity estimation function which
   > takes an const array and return selectivity without requiring to
   > create a Const node for each element.
   
   Maybe you're right. Could you show any examples of vectorized usage of postgres to understand your idea more clearly?
   Here I propose only quick simple solution. I don't think it would change the way of development.
   
   >> The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   >>
   >
   > You might want to include your benchmarking results as well.
   
   Here is nothing interesting. pgbench TPS and planning time for the cases above doesn't change planning time.
   
   [1] Report planning memory in EXPLAIN ANALYZE
   
   -- 
   Regards,
   Andrei Lepikhov

7. Re: Optimize planner memory consumption for huge arrays

   Tomas Vondra <tomas.vondra@enterprisedb.com> — 2024-02-19T13:47:37Z

   
   On 9/8/23 07:11, Lepikhov Andrei wrote:
   > 
   > 
   > On Wed, Sep 6, 2023, at 8:09 PM, Ashutosh Bapat wrote:
   >> Hi Lepikhov,
   >>
   >> Thanks for using my patch and I am glad that you found it useful.
   >>
   >> On Mon, Sep 4, 2023 at 10:56 AM Lepikhov Andrei
   >> <a.lepikhov@postgrespro.ru> wrote:
   >>>
   >>> Hi, hackers,
   >>>
   >>> Looking at the planner behaviour with the memory consumption patch [1], I figured out that arrays increase memory consumption by the optimizer significantly. See init.sql in attachment.
   >>> The point here is that the planner does small memory allocations for each element during estimation. As a result, it looks like the planner consumes about 250 bytes for each integer element.
   >>
   >> I guess the numbers you mentioned in init.sql are total memory used by
   >> the planner (as reported by the patch in the thread) when planning
   >> that query and not memory consumed by Const nodes themselves. Am I
   >> right? I think the measurements need to be explained better and also
   >> the realistic scenario you are trying to oprimize.
   > 
   > Yes, it is the total memory consumed by the planner - I used the numbers generated by your patch [1]. I had been increasing the number of elements in the array to exclude the memory consumed by the planner for other purposes. As you can see, the array with 1 element consumes 12kB of memory, 1E4 elements - 2.6 MB. All of that memory increment is related to the only enlargement of this array. (2600-12)/10 = 260 bytes. So, I make a conclusion: each 4-byte element produces a consumption of 260 bytes of memory.
   > This scenario I obtained from the user complaint - they had strict restrictions on memory usage and were stuck in this unusual memory usage case.
   > 
   >> I guess, the reason you think that partitioning will increase the
   >> memory consumed is because each partition will have the clause
   >> translated for it. Selectivity estimation for each partition will
   >> create those many Const nodes and hence consume memory. Am I right?
   > 
   > Yes.
   > 
   >> Can you please measure the memory consumed with and without your
   >> patch.
   > 
   > Done. See test case and results in 'init_parts.sql' in attachment. Short summary below. I varied a number of elements from 1 to 10000 and partitions from 1 to 100. As you can see, partitioning adds a lot of memory consumption by itself. But we see an effect from patch also.
   > 
   > master:
   > elems	1		1E1		1E2		1E3		1E4	
   > parts
   > 1		28kB	50kB	0.3MB	2.5MB	25MB
   > 10		45kB	143kB	0.6MB	4.8MB	47MB
   > 100		208kB	125kB	3.3MB	27MB	274MB
   > 
   > patched:
   > elems	1		1E1		1E2		1E3		1E4
   > parts
   > 1		28kB	48kB	0.25MB	2.2MB	22.8MB
   > 10		44kB	100kB	313kB	2.4MB	23.7MB
   > 100		208kB	101kB	0.9MB	3.7MB	32.4MB
   > 
   > Just for comparison, without partitioning:
   > elems	1		1E1		1E2		1E3		1E4	
   > master:	12kB	14kB	37kB	266kB	2.5MB
   > patched:	12kB	11.5kB	13kB	24kB	141kB
   > 
   
   These improvements look pretty nice, considering how simple the patch
   seems to be. I can't even imagine how much memory we'd need with even
   more partitions (say, 1000) if 100 partitions means 274MB.
   
   BTW when releasing memory in scalararraysel, wouldn't it be good to also
   free the elem_values/elem_nulls? I haven't tried and maybe it's not that
   significant amount.
   
   
   Considering there are now multiple patches improving memory usage during
   planning with partitions, perhaps it's time to take a step back and
   think about how we manage (or rather not manage) memory during query
   planning, and see if we could improve that instead of an infinite
   sequence of ad hoc patches?
   
   Our traditional attitude is to not manage memory, and rely on the memory
   context to not be very long-lived. And that used to be fine, but
   partitioning clearly changed the equation, increasing the amount of
   allocated memory etc.
   
   I don't think we want to stop relying on memory contexts for planning in
   general - memory contexts are obviously very convenient etc. But maybe
   we could identify "stages" in the planning and release the memory more
   aggressively in those?
   
   For example, I don't think we expect selectivity functions to allocate
   long-lived objects, right? So maybe we could run them in a dedicated
   memory context, and reset it aggressively (after each call).
   
   Ofc, I'm not suggesting this patch should be responsible for doing this.
   
   
   >>> It is maybe not a problem most of the time. However, in the case of partitions, memory consumption multiplies by each partition. Such a corner case looks weird, but the fix is simple. So, why not?
   >>
   >> With vectorized operations becoming a norm these days, it's possible
   >> to have thousands of element in array of an ANY or IN clause. Also
   >> will be common to have thousands of partitions. But I think what we
   >> need to do here is to write a selectivity estimation function which
   >> takes an const array and return selectivity without requiring to
   >> create a Const node for each element.
   > 
   > Maybe you're right. Could you show any examples of vectorized usage of postgres to understand your idea more clearly?
   > Here I propose only quick simple solution. I don't think it would change the way of development.
   > 
   
   I'm a big fan of SIMD and vectorization, but I don't think there's a
   chance to achieve that without major reworks to how we evaluate
   expressions. It's pretty fundamentally incompatible with how we handle
   with user-defined functions, FunctionCall etc.
   
   >>> The diff in the attachment is proof of concept showing how to reduce wasting of memory. Having benchmarked a bit, I didn't find any overhead.
   >>>
   >>
   >> You might want to include your benchmarking results as well.
   > 
   > Here is nothing interesting. pgbench TPS and planning time for the cases above doesn't change planning time.
   > 
   
   Yeah, I don't think we'd expect regressions from this patch. It pretty
   much just pfree-s a list + Const node.
   
   
   regards
   
   -- 
   Tomas Vondra
   EnterpriseDB: http://www.enterprisedb.com
   The Enterprise PostgreSQL Company
   
   
   
   

8. Re: Optimize planner memory consumption for huge arrays

   Tom Lane <tgl@sss.pgh.pa.us> — 2024-02-19T15:45:12Z

   Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
   > Considering there are now multiple patches improving memory usage during
   > planning with partitions, perhaps it's time to take a step back and
   > think about how we manage (or rather not manage) memory during query
   > planning, and see if we could improve that instead of an infinite
   > sequence of ad hoc patches?
   
   +1, I've been getting an itchy feeling about that too.  I don't have
   any concrete proposals ATM, but I quite like your idea here:
   
   > For example, I don't think we expect selectivity functions to allocate
   > long-lived objects, right? So maybe we could run them in a dedicated
   > memory context, and reset it aggressively (after each call).
   
   That could eliminate a whole lot of potential leaks.  I'm not sure
   though how much it moves the needle in terms of overall planner memory
   consumption.  I've always supposed that the big problem was data
   structures associated with rejected Paths, but I might be wrong.
   Is there some simple way we could get a handle on where the most
   memory goes while planning?
   
   			regards, tom lane
   
   
   
   

9. Re: Optimize planner memory consumption for huge arrays

   Tomas Vondra <tomas.vondra@enterprisedb.com> — 2024-02-19T17:37:49Z

   On 2/19/24 16:45, Tom Lane wrote:
   > Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
   >> Considering there are now multiple patches improving memory usage during
   >> planning with partitions, perhaps it's time to take a step back and
   >> think about how we manage (or rather not manage) memory during query
   >> planning, and see if we could improve that instead of an infinite
   >> sequence of ad hoc patches?
   > 
   > +1, I've been getting an itchy feeling about that too.  I don't have
   > any concrete proposals ATM, but I quite like your idea here:
   > 
   >> For example, I don't think we expect selectivity functions to allocate
   >> long-lived objects, right? So maybe we could run them in a dedicated
   >> memory context, and reset it aggressively (after each call).
   > 
   > That could eliminate a whole lot of potential leaks.  I'm not sure 
   > though how much it moves the needle in terms of overall planner
   > memory consumption.
   
   I'm not sure about that either, maybe not much - for example it would
   not help with the two other memory usage patches (which are related to
   SpecialJoinInfo and RestrictInfo, outside selectivity functions).
   
   It was an ad hoc thought, inspired by the issue at hand. Maybe it would
   be possible to find similar "boundaries" in other parts of the planner.
   
   I keep thinking about how compilers/optimizers typically have separate
   optimizations passes, maybe that's something we might leverage ...
   
   > I've always supposed that the big problem was data structures
   > associated with rejected Paths, but I might be wrong. Is there some
   > simple way we could get a handle on where the most memory goes while
   > planning?
   > 
   
   I suspect this might have changed thanks to partitioning - it's not a
   coincidence most of the recent memory usage improvements address cases
   with many partitions.
   
   As for how to analyze the memory usage - maybe there's a simpler way,
   but what I did recently was adding simple instrumentation into memory
   contexts, recording pointer/size/backtrace for each request, and then a
   script that aggregated that into a "currently allocated" report with
   information about "source" of the allocation.
   
   
   regards
   
   -- 
   Tomas Vondra
   EnterpriseDB: http://www.enterprisedb.com
   The Enterprise PostgreSQL Company
   
   
   
   

10. Re: Optimize planner memory consumption for huge arrays

    Tom Lane <tgl@sss.pgh.pa.us> — 2024-02-19T21:51:47Z

    Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    > On 2/19/24 16:45, Tom Lane wrote:
    >> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>> For example, I don't think we expect selectivity functions to allocate
    >>> long-lived objects, right? So maybe we could run them in a dedicated
    >>> memory context, and reset it aggressively (after each call).
    
    >> That could eliminate a whole lot of potential leaks.  I'm not sure 
    >> though how much it moves the needle in terms of overall planner
    >> memory consumption.
    
    > I'm not sure about that either, maybe not much - for example it would
    > not help with the two other memory usage patches (which are related to
    > SpecialJoinInfo and RestrictInfo, outside selectivity functions).
    
    > It was an ad hoc thought, inspired by the issue at hand. Maybe it would
    > be possible to find similar "boundaries" in other parts of the planner.
    
    Here's a quick and probably-incomplete implementation of that idea.
    I've not tried to study its effects on memory consumption, just made
    sure it passes check-world.
    
    The main hazard here is that something invoked inside clause
    selectivity might try to cache a data structure for later use.
    However, there are already places that do that kind of thing,
    and they already explicitly switch into the planner_cxt, because
    otherwise they fail under GEQO.  (If we do find places that need
    fixing for this, they were probably busted under GEQO already.)
    Perhaps it's worth updating the comments at those places, but
    I didn't bother in this first cut.
    
    			regards, tom lane
    
    

11. Re: Optimize planner memory consumption for huge arrays

    Andrei Lepikhov <a.lepikhov@postgrespro.ru> — 2024-02-20T04:17:31Z

    On 19/2/2024 20:47, Tomas Vondra wrote:
    > On 9/8/23 07:11, Lepikhov Andrei wrote:
    >> Just for comparison, without partitioning:
    >> elems	1		1E1		1E2		1E3		1E4	
    >> master:	12kB	14kB	37kB	266kB	2.5MB
    >> patched:	12kB	11.5kB	13kB	24kB	141kB
    >>
    > 
    > These improvements look pretty nice, considering how simple the patch
    > seems to be. I can't even imagine how much memory we'd need with even
    > more partitions (say, 1000) if 100 partitions means 274MB.
    > 
    > BTW when releasing memory in scalararraysel, wouldn't it be good to also
    > free the elem_values/elem_nulls? I haven't tried and maybe it's not that
    > significant amount.
    Agree. Added into the next version of the patch.
    Moreover, I see a slight planning speedup. Looking into the reason for 
    that, I discovered that it is because sometimes the planner utilizes the 
    same memory piece for the next array element. It finds this piece more 
    quickly than before that optimization.
    
    -- 
    regards,
    Andrei Lepikhov
    Postgres Professional
    

12. Re: Optimize planner memory consumption for huge arrays

    Andrei Lepikhov <a.lepikhov@postgrespro.ru> — 2024-02-20T04:41:17Z

    On 20/2/2024 04:51, Tom Lane wrote:
    > Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >> On 2/19/24 16:45, Tom Lane wrote:
    >>> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>>> For example, I don't think we expect selectivity functions to allocate
    >>>> long-lived objects, right? So maybe we could run them in a dedicated
    >>>> memory context, and reset it aggressively (after each call).
    > Here's a quick and probably-incomplete implementation of that idea.
    > I've not tried to study its effects on memory consumption, just made
    > sure it passes check-world.
    Thanks for the sketch. The trick with the planner_tmp_cxt_depth 
    especially looks interesting.
    I think we should design small memory contexts - one per scalable 
    direction of memory utilization, like selectivity or partitioning 
    (appending ?).
    My coding experience shows that short-lived GEQO memory context forces 
    people to learn on Postgres internals more precisely :).
    
    -- 
    regards,
    Andrei Lepikhov
    Postgres Professional
    
    
    
    
    

13. Re: Optimize planner memory consumption for huge arrays

    Alena Rybakina <lena.ribackina@yandex.ru> — 2024-02-22T18:50:03Z

    Hi!
    
    On 20.02.2024 07:41, Andrei Lepikhov wrote:
    > On 20/2/2024 04:51, Tom Lane wrote:
    >> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>> On 2/19/24 16:45, Tom Lane wrote:
    >>>> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>>>> For example, I don't think we expect selectivity functions to 
    >>>>> allocate
    >>>>> long-lived objects, right? So maybe we could run them in a dedicated
    >>>>> memory context, and reset it aggressively (after each call).
    >> Here's a quick and probably-incomplete implementation of that idea.
    >> I've not tried to study its effects on memory consumption, just made
    >> sure it passes check-world.
    > Thanks for the sketch. The trick with the planner_tmp_cxt_depth 
    > especially looks interesting.
    > I think we should design small memory contexts - one per scalable 
    > direction of memory utilization, like selectivity or partitioning 
    > (appending ?).
    > My coding experience shows that short-lived GEQO memory context forces 
    > people to learn on Postgres internals more precisely :).
    >
    I think there was a problem in your patch when you freed up the memory 
    of a variable in the eqsel_internal function, because we have a case 
    where the variable was deleted by reference in the 
    eval_const_expressions_mutator function (it is only for T_SubPlan and 
    T_AlternativeSubPlan type of nodes.
    
    This query just causes an error in your case:
    
    create table a (id bigint, c1 bigint, primary key(id));
    create table b (a_id bigint, b_id bigint, b2 bigint, primary key(a_id, 
    b_id));
    explain select id
         from a, b
         where id = a_id
           and b2 = (select  min(b2)
                     from    b
                     where   id = a_id);
    drop table a;
    drop table b;
    
    We can return a copy of the variable or not release the memory of this 
    variable.
    
    I attached two patch: the first one is removing your memory cleanup and 
    another one returns the copy of variable.
    
    The author of the corrections is not only me, but also Daniil Anisimov.
    
    -- 
    Regards,
    Alena Rybakina
    Postgres Professional: http://www.postgrespro.com
    The Russian Postgres Company
    

14. Re: Optimize planner memory consumption for huge arrays

    Tom Lane <tgl@sss.pgh.pa.us> — 2024-02-24T23:07:39Z

    I wrote:
    > Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >> On 2/19/24 16:45, Tom Lane wrote:
    >>> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>>> For example, I don't think we expect selectivity functions to allocate
    >>>> long-lived objects, right? So maybe we could run them in a dedicated
    >>>> memory context, and reset it aggressively (after each call).
    
    >>> That could eliminate a whole lot of potential leaks.  I'm not sure 
    >>> though how much it moves the needle in terms of overall planner
    >>> memory consumption.
    
    >> It was an ad hoc thought, inspired by the issue at hand. Maybe it would
    >> be possible to find similar "boundaries" in other parts of the planner.
    
    > Here's a quick and probably-incomplete implementation of that idea.
    > I've not tried to study its effects on memory consumption, just made
    > sure it passes check-world.
    
    I spent a bit more time on this patch.  One thing I was concerned
    about was whether it causes any noticeable slowdown, and it seems that
    it does: testing with "pgbench -S" I observe perhaps 1% slowdown.
    However, we don't necessarily need to reset the temp context after
    every single usage.  I experimented with resetting it every tenth
    time, and that got me from 1% slower than HEAD to 1% faster.  Of
    course "every tenth time" is very ad hoc.  I wondered if we could
    make it somehow conditional on how much memory had been consumed
    in the temp context, but there doesn't seem to be any cheap way
    to get that.  Applying something like MemoryContextMemConsumed
    would surely be a loser.  I'm not sure if it'd be worth extending
    the mcxt.c API to provide something like "MemoryContextResetIfBig",
    with some internal rule that would be cheap to apply like "reset
    if we have any non-keeper blocks".
    
    I also looked into whether it really does reduce overall memory
    consumption noticeably, by collecting stats about planner memory
    consumption during the core regression tests.  The answer is that
    it barely helps.  I see the average used space across all planner
    invocations drop from 23344 bytes to 23220, and the worst-case
    numbers hardly move at all.  So that's a little discouraging.
    But of course the regression tests prefer not to deal in very
    large/expensive test cases, so maybe it's not surprising that
    I don't see much win in this test.
    
    Anyway, 0001 attached is a cleaned-up patch with the every-tenth-
    time rule, and 0002 (not meant for commit) is the quick and
    dirty instrumentation patch I used for collecting usage stats.
    
    Even though this seems of only edge-case value, I'd much prefer
    to do this than the sort of ad-hoc patching initially proposed
    in this thread.
    
    			regards, tom lane
    
    

15. Re: Optimize planner memory consumption for huge arrays

    Tomas Vondra <tomas.vondra@enterprisedb.com> — 2024-02-25T13:52:47Z

    On 2/25/24 00:07, Tom Lane wrote:
    > I wrote:
    >> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>> On 2/19/24 16:45, Tom Lane wrote:
    >>>> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >>>>> For example, I don't think we expect selectivity functions to allocate
    >>>>> long-lived objects, right? So maybe we could run them in a dedicated
    >>>>> memory context, and reset it aggressively (after each call).
    > 
    >>>> That could eliminate a whole lot of potential leaks.  I'm not sure 
    >>>> though how much it moves the needle in terms of overall planner
    >>>> memory consumption.
    > 
    >>> It was an ad hoc thought, inspired by the issue at hand. Maybe it would
    >>> be possible to find similar "boundaries" in other parts of the planner.
    > 
    >> Here's a quick and probably-incomplete implementation of that idea.
    >> I've not tried to study its effects on memory consumption, just made
    >> sure it passes check-world.
    > 
    > I spent a bit more time on this patch.  One thing I was concerned
    > about was whether it causes any noticeable slowdown, and it seems that
    > it does: testing with "pgbench -S" I observe perhaps 1% slowdown.
    > However, we don't necessarily need to reset the temp context after
    > every single usage.  I experimented with resetting it every tenth
    > time, and that got me from 1% slower than HEAD to 1% faster.
    
    Isn't 1% well within the usual noise and/or the differences that can be
    caused simply by slightly different alignment of the binary? I'd treat
    this as "same performance" ...
    
    > Of course "every tenth time" is very ad hoc.  I wondered if we could
    > make it somehow conditional on how much memory had been consumed
    > in the temp context, but there doesn't seem to be any cheap way
    > to get that.  Applying something like MemoryContextMemConsumed
    > would surely be a loser.  I'm not sure if it'd be worth extending
    > the mcxt.c API to provide something like "MemoryContextResetIfBig",
    > with some internal rule that would be cheap to apply like "reset
    > if we have any non-keeper blocks".
    
    Wouldn't it be sufficient to look simply at MemoryContextMemAllocated?
    That's certainly way cheaper than MemoryContextStatsInternal, especially
    if the context tree is shallow (which I think we certainly expect here).
    
    I think MemoryContextResetIfBig is an interesting idea - I think a good
    way to define "big" would be "has multiple blocks", because that's the
    only case where we can actually reclaim some memory.
    
    > 
    > I also looked into whether it really does reduce overall memory
    > consumption noticeably, by collecting stats about planner memory
    > consumption during the core regression tests.  The answer is that
    > it barely helps.  I see the average used space across all planner
    > invocations drop from 23344 bytes to 23220, and the worst-case
    > numbers hardly move at all.  So that's a little discouraging.
    > But of course the regression tests prefer not to deal in very
    > large/expensive test cases, so maybe it's not surprising that
    > I don't see much win in this test.
    > 
    
    I'm not really surprised by this - I think you're right most of our
    selectivity functions either doesn't do memory-expensive stuff, or we
    don't have such corner cases in our regression tests. Or at least not to
    the extent to move the overall average, so we'd need to look at
    individual cases allocating quite a bit of memory.
    
    But I think that's fine - I see this as a safety measure, not something
    that'd improve the "good" cases.
    
    > Anyway, 0001 attached is a cleaned-up patch with the every-tenth-
    > time rule, and 0002 (not meant for commit) is the quick and
    > dirty instrumentation patch I used for collecting usage stats.
    > 
    > Even though this seems of only edge-case value, I'd much prefer
    > to do this than the sort of ad-hoc patching initially proposed
    > in this thread.
    > 
    
    +1 to that, it seems like a more principled approach.
    
    
    regards
    
    -- 
    Tomas Vondra
    EnterpriseDB: http://www.enterprisedb.com
    The Enterprise PostgreSQL Company
    
    
    
    

16. Re: Optimize planner memory consumption for huge arrays

    Tom Lane <tgl@sss.pgh.pa.us> — 2024-02-25T16:29:17Z

    Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    > On 2/25/24 00:07, Tom Lane wrote:
    >> ...  I'm not sure if it'd be worth extending
    >> the mcxt.c API to provide something like "MemoryContextResetIfBig",
    >> with some internal rule that would be cheap to apply like "reset
    >> if we have any non-keeper blocks".
    
    > I think MemoryContextResetIfBig is an interesting idea - I think a good
    > way to define "big" would be "has multiple blocks", because that's the
    > only case where we can actually reclaim some memory.
    
    Yeah.  Also: once we had such an idea, it'd be very tempting to apply
    it to other frequently-reset contexts like the executor's per-tuple
    evaluation contexts.  I'm not quite prepared to argue that
    MemoryContextReset should just act that way all the time ... but
    it's sure interesting to think about.
    
    Another question is whether this wouldn't hurt debugging, in that
    dangling-pointer bugs would become harder to catch.  We'd certainly
    want to turn off the optimization in USE_VALGRIND builds, and maybe
    we just shouldn't do it at all if USE_ASSERT_CHECKING.
    
    			regards, tom lane
    
    
    
    

17. Re: Optimize planner memory consumption for huge arrays

    Tomas Vondra <tomas.vondra@enterprisedb.com> — 2024-02-26T09:01:31Z

    
    On 2/25/24 17:29, Tom Lane wrote:
    > Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    >> On 2/25/24 00:07, Tom Lane wrote:
    >>> ...  I'm not sure if it'd be worth extending
    >>> the mcxt.c API to provide something like "MemoryContextResetIfBig",
    >>> with some internal rule that would be cheap to apply like "reset
    >>> if we have any non-keeper blocks".
    > 
    >> I think MemoryContextResetIfBig is an interesting idea - I think a good
    >> way to define "big" would be "has multiple blocks", because that's the
    >> only case where we can actually reclaim some memory.
    > 
    > Yeah.  Also: once we had such an idea, it'd be very tempting to apply
    > it to other frequently-reset contexts like the executor's per-tuple
    > evaluation contexts.  I'm not quite prepared to argue that
    > MemoryContextReset should just act that way all the time ... but
    > it's sure interesting to think about.
    > 
    
    Do the context resets consume enough time to make this measurable? I may
    be wrong, but I'd guess it's not measurable. In which case, what would
    be the benefit?
    
    > Another question is whether this wouldn't hurt debugging, in that
    > dangling-pointer bugs would become harder to catch.  We'd certainly
    > want to turn off the optimization in USE_VALGRIND builds, and maybe
    > we just shouldn't do it at all if USE_ASSERT_CHECKING.
    > 
    > 			regards, tom lane
    
    +1 to disable this optimization in assert-enabled builds. I guess we'd
    invent a new constant to disable it, and tie it to USE_ASSERT_CHECKING
    (similar to CLOBBER_FREED_MEMORY, for example).
    
    Thinking about CLOBBER_FREED_MEMORY, could it be useful to still clobber
    the memory, even if we don't actually reset the context?
    
    
    regards
    
    -- 
    Tomas Vondra
    EnterpriseDB: http://www.enterprisedb.com
    The Enterprise PostgreSQL Company
    
    
    
    

18. Re: Optimize planner memory consumption for huge arrays

    Tom Lane <tgl@sss.pgh.pa.us> — 2024-02-26T15:20:58Z

    Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
    > On 2/25/24 17:29, Tom Lane wrote:
    >> Yeah.  Also: once we had such an idea, it'd be very tempting to apply
    >> it to other frequently-reset contexts like the executor's per-tuple
    >> evaluation contexts.  I'm not quite prepared to argue that
    >> MemoryContextReset should just act that way all the time ... but
    >> it's sure interesting to think about.
    
    > Do the context resets consume enough time to make this measurable?
    
    I think they do.  We previously invented the "isReset" mechanism to
    eliminate work in the case of exactly zero allocations since the
    last reset, and that made a very measurable difference at the time,
    even though you'd think the amount of work saved would be negligible.
    This idea seems like it might be able to supersede that one and win
    in a larger fraction of cases.
    
    > +1 to disable this optimization in assert-enabled builds. I guess we'd
    > invent a new constant to disable it, and tie it to USE_ASSERT_CHECKING
    > (similar to CLOBBER_FREED_MEMORY, for example).
    
    > Thinking about CLOBBER_FREED_MEMORY, could it be useful to still clobber
    > the memory, even if we don't actually reset the context?
    
    I think in any case where we were trying to support debugging, we'd
    just disable the optimization, so that reset always resets.
    
    			regards, tom lane