Обсуждение: Parallel CREATE INDEX for GIN indexes

Hi,

In PG17 we shall have parallel CREATE INDEX for BRIN indexes, and back
when working on that I was thinking how difficult would it be to do
something similar to do that for other index types, like GIN. I even had
that on my list of ideas to pitch to potential contributors, as I was
fairly sure it's doable and reasonably isolated / well-defined.

However, I was not aware of any takers, so a couple days ago on a slow
weekend I took a stab at it. And yes, it's doable - attached is a fairly
complete, tested and polished version of the feature, I think. It turned
out to be a bit more complex than I expected, for reasons that I'll get
into when discussing the patches.

First, let's talk about the benefits - how much faster is that than the
single-process build we have for GIN indexes? I do have a table with the
archive of all our mailing lists - it's ~1.5M messages, table is ~21GB
(raw dump is about 28GB). This does include simple text data (message
body), JSONB (headers) and tsvector (full-text on message body).

If I do CREATE index with different number of workers (0 means serial
build), I get this timings (in seconds):

    workers   trgm   tsvector     jsonb    jsonb (hash)
   -----------------------------------------------------
    0         1240        378       104              57
    1          773        196        59              85
    2          548        163        51              78
    3          423        153        45              75
    4          362        142        43              75
    5          323        134        40              70
    6          295        130        39              73

Perhaps an easier to understand result is this table with relative
timing compared to serial build:

    workers   trgm   tsvector      jsonb   jsonb (hash)
   -----------------------------------------------------
    1          62%        52%        57%           149%
    2          44%        43%        49%           136%
    3          34%        40%        43%           132%
    4          29%        38%        41%           131%
    5          26%        35%        39%           123%
    6          24%        34%        38%           129%

This shows the benefits are pretty nice, depending on the opclass. For
most indexes it's maybe ~3-4x faster, which is nice, and I don't think
it's possible to do much better - the actual index inserts can happen
from a single process only, which is the main limit.

For some of the opclasses it can regress (like the jsonb_path_ops). I
don't think that's a major issue. Or more precisely, I'm not surprised
by it. It'd be nice to be able to disable the parallel builds in these
cases somehow, but I haven't thought about that.

I do plan to do some tests with btree_gin, but I don't expect that to
behave significantly differently.

There are small variations in the index size, when built in the serial
way and the parallel way. It's generally within ~5-10%, and I believe
it's due to the serial build adding the TIDs incrementally, while the
build adds them in much larger chunks (possibly even in one chunk with
all the TIDs for the key).  I believe the same size variation can happen
if the index gets built in a different way, e.g. by inserting the data
in a different order, etc. I did a number of tests to check if the index
produces the correct results, and I haven't found any issues. So I think
this is OK, and neither a problem nor an advantage of the patch.


Now, let's talk about the code - the series has 7 patches, with 6
non-trivial parts doing changes in focused and easier to understand
pieces (I hope so).


1) v20240502-0001-Allow-parallel-create-for-GIN-indexes.patch

This is the initial feature, adding the "basic" version, implemented as
pretty much 1:1 copy of the BRIN parallel build and minimal changes to
make it work for GIN (mostly about how to store intermediate results).

The basic idea is that the workers do the regular build, but instead of
flushing the data into the index after hitting the memory limit, it gets
written into a shared tuplesort and sorted by the index key. And the
leader then reads this sorted data, accumulates the TID for a given key
and inserts that into the index in one go.


2) v20240502-0002-Use-mergesort-in-the-leader-process.patch

The approach implemented by 0001 works, but there's a little bit of
issue - if there are many distinct keys (e.g. for trigrams that can
happen very easily), the workers will hit the memory limit with only
very short TID lists for most keys. For serial build that means merging
the data into a lot of random places, and in parallel build it means the
leader will have to merge a lot of tiny lists from many sorted rows.

Which can be quite annoying and expensive, because the leader does so
using qsort() in the serial part. It'd be better to ensure most of the
sorting happens in the workers, and the leader can do a mergesort. But
the mergesort must not happen too often - merging many small lists is
not cheaper than a single qsort (especially when the lists overlap).

So this patch changes the workers to process the data in two phases. The
first works as before, but the data is flushed into a local tuplesort.
And then each workers sorts the results it produced, and combines them
into results with much larger TID lists, and those results are written
to the shared tuplesort. So the leader only gets very few lists to
combine for a given key - usually just one list per worker.


3) v20240502-0003-Remove-the-explicit-pg_qsort-in-workers.patch

In 0002 the workers still do an explicit qsort() on the TID list before
writing the data into the shared tuplesort. But we can do better - the
workers can do a merge sort too. To help with this, we add the first TID
to the tuplesort tuple, and sort by that too - it helps the workers to
process the data in an order that allows simple concatenation instead of
the full mergesort.

Note: There's a non-obvious issue due to parallel scans always being
"sync scans", which may lead to very "wide" TID ranges when the scan
wraps around. More about that later.


4) v20240502-0004-Compress-TID-lists-before-writing-tuples-t.patch

The parallel build passes data between processes using temporary files,
which means it may need significant amount of disk space. For BRIN this
was not a major concern, because the summaries tend to be pretty small.

But for GIN that's not the case, and the two-phase processing introduced
by 0002 make it worse, because the worker essentially creates another
copy of the intermediate data. It does not need to copy the key, so
maybe it's not exactly 2x the space requirement, but in the worst case
it's not far from that.

But there's a simple way how to improve this - the TID lists tend to be
very compressible, and GIN already implements a very light-weight TID
compression, so this patch does just that - when building the tuple to
be written into the tuplesort, we just compress the TIDs.


5) v20240502-0005-Collect-and-print-compression-stats.patch

This patch simply collects some statistics about the compression, to
show how much it reduces the amounts of data in the various phases. The
data I've seen so far usually show ~75% compression in the first phase,
and ~30% compression in the second phase.

That is, in the first phase we save ~25% of space, in the second phase
we save ~70% of space. An example of the log messages from this patch,
for one worker (of two) in the trigram phase says:

LOG:  _gin_parallel_scan_and_build raw 10158870494 compressed 7519211584
      ratio 74.02%
LOG:  _gin_process_worker_data raw 4593563782 compressed 1314800758
      ratio 28.62%

Put differently, a single-phase version without compression (as in 0001)
would need ~10GB of disk space per worker. With compression, we need
only about ~8.8GB for both phases (or ~7.5GB for the first phase alone).

I do think these numbers look pretty good. The numbers are different for
other opclasses (trigrams are rather extreme in how much space they
need), but the overall behavior is the same.


6) v20240502-0006-Enforce-memory-limit-when-combining-tuples.patch

Until this part, there's no limit on memory used by combining results
for a single index key - it'll simply use as much memory as needed to
combine all the TID lists. Which may not be a huge issue because each
TID is only 6B, and we can accumulate a lot of those in a couple MB. And
a parallel CREATE INDEX usually runs with a fairly significant values of
maintenance_work_mem (in fact it requires it to even allow parallel).
But still, there should be some memory limit.

It however is not as simple as dumping current state into the index,
because the TID lists produced by the workers may overlap, so the tail
of the list may still receive TIDs from some future TID list. And that's
a problem because ginEntryInsert() expects to receive TIDs in order, and
if that's not the case it may fail with "could not split GIN page".

But we already have the first TID for each sort tuple (and we consider
it when sorting the data), and this is useful for deducing how far we
can flush the data, and keep just the minimal part of the TID list that
may change by merging.

So this patch implements that - it introduces the concept of "freezing"
the head of the TID list up to "first TID" from the next tuple, and uses
that to write data into index if needed because of memory limit.

We don't want to do that too often, so it only happens if we hit the
memory limit and there's at least a certain number (1024) of TIDs.


7) v20240502-0007-Detect-wrap-around-in-parallel-callback.patch

There's one more efficiency problem - the parallel scans are required to
be synchronized, i.e. the scan may start half-way through the table, and
then wrap around. Which however means the TID list will have a very wide
range of TID values, essentially the min and max of for the key.

Without 0006 this would cause frequent failures of the index build, with
the error I already mentioned:

  ERROR: could not split GIN page; all old items didn't fit

tracking the "safe" TID horizon addresses that. But there's still an
issue with efficiency - having such a wide TID list forces the mergesort
to actually walk the lists, because this wide list overlaps with every
other list produced by the worker. And that's much more expensive than
just simply concatenating them, which is what happens without the wrap
around (because in that case the worker produces non-overlapping lists).

One way to fix this would be to allow parallel scans to not be sync
scans, but that seems fairly tricky and I'm not sure if that can be
done. The BRIN parallel build had a similar issue, and it was just
simpler to deal with this in the build code.

So 0007 does something similar - it tracks if the TID value goes
backward in the callback, and if it does it dumps the state into the
tuplesort before processing the first tuple from the beginning of the
table. Which means we end up with two separate "narrow" TID list, not
one very wide one.



regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Thu, 2 May 2024 at 17:19, Tomas Vondra <tomas.vondra@enterprisedb.com> wrote:
>
> Hi,
>
> In PG17 we shall have parallel CREATE INDEX for BRIN indexes, and back
> when working on that I was thinking how difficult would it be to do
> something similar to do that for other index types, like GIN. I even had
> that on my list of ideas to pitch to potential contributors, as I was
> fairly sure it's doable and reasonably isolated / well-defined.
>
> However, I was not aware of any takers, so a couple days ago on a slow
> weekend I took a stab at it. And yes, it's doable - attached is a fairly
> complete, tested and polished version of the feature, I think. It turned
> out to be a bit more complex than I expected, for reasons that I'll get
> into when discussing the patches.

This is great. I've been thinking about approximately the same issue
recently, too, but haven't had time to discuss/implement any of this
yet. I think some solutions may even be portable to the btree parallel
build: it also has key deduplication (though to a much smaller degree)
and could benefit from deduplication during the scan/ssup load phase,
rather than only during insertion.

> First, let's talk about the benefits - how much faster is that than the
> single-process build we have for GIN indexes? I do have a table with the
> archive of all our mailing lists - it's ~1.5M messages, table is ~21GB
> (raw dump is about 28GB). This does include simple text data (message
> body), JSONB (headers) and tsvector (full-text on message body).

Sidenote: Did you include the tsvector in the table to reduce time
spent during index creation? I would have used an expression in the
index definition, rather than a direct column.

> If I do CREATE index with different number of workers (0 means serial
> build), I get this timings (in seconds):

[...]

> This shows the benefits are pretty nice, depending on the opclass. For
> most indexes it's maybe ~3-4x faster, which is nice, and I don't think
> it's possible to do much better - the actual index inserts can happen
> from a single process only, which is the main limit.

Can we really not insert with multiple processes? It seems to me that
GIN could be very suitable for that purpose, with its clear double
tree structure distinction that should result in few buffer conflicts
if different backends work on known-to-be-very-different keys.
We'd probably need multiple read heads on the shared tuplesort, and a
way to join the generated top-level subtrees, but I don't think that
is impossible. Maybe it's work for later effort though.

Have you tested and/or benchmarked this with multi-column GIN indexes?

> For some of the opclasses it can regress (like the jsonb_path_ops). I
> don't think that's a major issue. Or more precisely, I'm not surprised
> by it. It'd be nice to be able to disable the parallel builds in these
> cases somehow, but I haven't thought about that.

Do you know why it regresses?

> I do plan to do some tests with btree_gin, but I don't expect that to
> behave significantly differently.
>
> There are small variations in the index size, when built in the serial
> way and the parallel way. It's generally within ~5-10%, and I believe
> it's due to the serial build adding the TIDs incrementally, while the
> build adds them in much larger chunks (possibly even in one chunk with
> all the TIDs for the key).

I assume that was '[...] while the [parallel] build adds them [...]', right?

> I believe the same size variation can happen
> if the index gets built in a different way, e.g. by inserting the data
> in a different order, etc. I did a number of tests to check if the index
> produces the correct results, and I haven't found any issues. So I think
> this is OK, and neither a problem nor an advantage of the patch.
>
>
> Now, let's talk about the code - the series has 7 patches, with 6
> non-trivial parts doing changes in focused and easier to understand
> pieces (I hope so).

The following comments are generally based on the descriptions; I
haven't really looked much at the patches yet except to validate some
assumptions.

> 1) v20240502-0001-Allow-parallel-create-for-GIN-indexes.patch
>
> This is the initial feature, adding the "basic" version, implemented as
> pretty much 1:1 copy of the BRIN parallel build and minimal changes to
> make it work for GIN (mostly about how to store intermediate results).
>
> The basic idea is that the workers do the regular build, but instead of
> flushing the data into the index after hitting the memory limit, it gets
> written into a shared tuplesort and sorted by the index key. And the
> leader then reads this sorted data, accumulates the TID for a given key
> and inserts that into the index in one go.

In the code, GIN insertions are still basically single btree
insertions, all starting from the top (but with many same-valued
tuples at once). Now that we have a tuplesort with the full table's
data, couldn't the code be adapted to do more efficient btree loading,
such as that seen in the nbtree code, where the rightmost pages are
cached and filled sequentially without requiring repeated searches
down the tree? I suspect we can gain a lot of time there.

I don't need you to do that, but what's your opinion on this?

> 2) v20240502-0002-Use-mergesort-in-the-leader-process.patch
>
> The approach implemented by 0001 works, but there's a little bit of
> issue - if there are many distinct keys (e.g. for trigrams that can
> happen very easily), the workers will hit the memory limit with only
> very short TID lists for most keys. For serial build that means merging
> the data into a lot of random places, and in parallel build it means the
> leader will have to merge a lot of tiny lists from many sorted rows.
>
> Which can be quite annoying and expensive, because the leader does so
> using qsort() in the serial part. It'd be better to ensure most of the
> sorting happens in the workers, and the leader can do a mergesort. But
> the mergesort must not happen too often - merging many small lists is
> not cheaper than a single qsort (especially when the lists overlap).
>
> So this patch changes the workers to process the data in two phases. The
> first works as before, but the data is flushed into a local tuplesort.
> And then each workers sorts the results it produced, and combines them
> into results with much larger TID lists, and those results are written
> to the shared tuplesort. So the leader only gets very few lists to
> combine for a given key - usually just one list per worker.

Hmm, I was hoping we could implement the merging inside the tuplesort
itself during its own flush phase, as it could save significantly on
IO, and could help other users of tuplesort with deduplication, too.

> 3) v20240502-0003-Remove-the-explicit-pg_qsort-in-workers.patch
>
> In 0002 the workers still do an explicit qsort() on the TID list before
> writing the data into the shared tuplesort. But we can do better - the
> workers can do a merge sort too. To help with this, we add the first TID
> to the tuplesort tuple, and sort by that too - it helps the workers to
> process the data in an order that allows simple concatenation instead of
> the full mergesort.
>
> Note: There's a non-obvious issue due to parallel scans always being
> "sync scans", which may lead to very "wide" TID ranges when the scan
> wraps around. More about that later.

As this note seems to imply, this seems to have a strong assumption
that data received in parallel workers is always in TID order, with
one optional wraparound. Non-HEAP TAMs may break with this assumption,
so what's the plan on that?

> 4) v20240502-0004-Compress-TID-lists-before-writing-tuples-t.patch
>
> The parallel build passes data between processes using temporary files,
> which means it may need significant amount of disk space. For BRIN this
> was not a major concern, because the summaries tend to be pretty small.
>
> But for GIN that's not the case, and the two-phase processing introduced
> by 0002 make it worse, because the worker essentially creates another
> copy of the intermediate data. It does not need to copy the key, so
> maybe it's not exactly 2x the space requirement, but in the worst case
> it's not far from that.
>
> But there's a simple way how to improve this - the TID lists tend to be
> very compressible, and GIN already implements a very light-weight TID
> compression, so this patch does just that - when building the tuple to
> be written into the tuplesort, we just compress the TIDs.

See note on 0002: Could we do this in the tuplesort writeback, rather
than by moving the data around multiple times?

[...]
> So 0007 does something similar - it tracks if the TID value goes
> backward in the callback, and if it does it dumps the state into the
> tuplesort before processing the first tuple from the beginning of the
> table. Which means we end up with two separate "narrow" TID list, not
> one very wide one.

See note above: We may still need a merge phase, just to make sure we
handle all TAM parallel scans correctly, even if that merge join phase
wouldn't get hit in vanilla PostgreSQL.

Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

On 5/2/24 19:12, Matthias van de Meent wrote:
> On Thu, 2 May 2024 at 17:19, Tomas Vondra <tomas.vondra@enterprisedb.com> wrote:
>>
>> Hi,
>>
>> In PG17 we shall have parallel CREATE INDEX for BRIN indexes, and back
>> when working on that I was thinking how difficult would it be to do
>> something similar to do that for other index types, like GIN. I even had
>> that on my list of ideas to pitch to potential contributors, as I was
>> fairly sure it's doable and reasonably isolated / well-defined.
>>
>> However, I was not aware of any takers, so a couple days ago on a slow
>> weekend I took a stab at it. And yes, it's doable - attached is a fairly
>> complete, tested and polished version of the feature, I think. It turned
>> out to be a bit more complex than I expected, for reasons that I'll get
>> into when discussing the patches.
> 
> This is great. I've been thinking about approximately the same issue
> recently, too, but haven't had time to discuss/implement any of this
> yet. I think some solutions may even be portable to the btree parallel
> build: it also has key deduplication (though to a much smaller degree)
> and could benefit from deduplication during the scan/ssup load phase,
> rather than only during insertion.
> 

Perhaps, although I'm not that familiar with the details of btree
builds, and I haven't thought about it when working on this over the
past couple days.

>> First, let's talk about the benefits - how much faster is that than the
>> single-process build we have for GIN indexes? I do have a table with the
>> archive of all our mailing lists - it's ~1.5M messages, table is ~21GB
>> (raw dump is about 28GB). This does include simple text data (message
>> body), JSONB (headers) and tsvector (full-text on message body).
> 
> Sidenote: Did you include the tsvector in the table to reduce time
> spent during index creation? I would have used an expression in the
> index definition, rather than a direct column.
> 

Yes, it's a materialized column, not computed during index creation.

>> If I do CREATE index with different number of workers (0 means serial
>> build), I get this timings (in seconds):
> 
> [...]
> 
>> This shows the benefits are pretty nice, depending on the opclass. For
>> most indexes it's maybe ~3-4x faster, which is nice, and I don't think
>> it's possible to do much better - the actual index inserts can happen
>> from a single process only, which is the main limit.
> 
> Can we really not insert with multiple processes? It seems to me that
> GIN could be very suitable for that purpose, with its clear double
> tree structure distinction that should result in few buffer conflicts
> if different backends work on known-to-be-very-different keys.
> We'd probably need multiple read heads on the shared tuplesort, and a
> way to join the generated top-level subtrees, but I don't think that
> is impossible. Maybe it's work for later effort though.
> 

Maybe, but I took it as a restriction and it seemed too difficult to
relax (or at least I assume that).

> Have you tested and/or benchmarked this with multi-column GIN indexes?
> 

I did test that, and I'm not aware of any bugs/issues. Performance-wise
it depends on which opclasses are used by the columns - if you take the
speedup for each of them independently, the speedup for the whole index
is roughly the average of that.

>> For some of the opclasses it can regress (like the jsonb_path_ops). I
>> don't think that's a major issue. Or more precisely, I'm not surprised
>> by it. It'd be nice to be able to disable the parallel builds in these
>> cases somehow, but I haven't thought about that.
> 
> Do you know why it regresses?
> 

No, but one thing that stands out is that the index is much smaller than
the other columns/opclasses, and the compression does not save much
(only about 5% for both phases). So I assume it's the overhead of
writing writing and reading a bunch of GB of data without really gaining
much from doing that.

>> I do plan to do some tests with btree_gin, but I don't expect that to
>> behave significantly differently.
>>
>> There are small variations in the index size, when built in the serial
>> way and the parallel way. It's generally within ~5-10%, and I believe
>> it's due to the serial build adding the TIDs incrementally, while the
>> build adds them in much larger chunks (possibly even in one chunk with
>> all the TIDs for the key).
> 
> I assume that was '[...] while the [parallel] build adds them [...]', right?
> 

Right. The parallel build adds them in larger chunks.

>> I believe the same size variation can happen
>> if the index gets built in a different way, e.g. by inserting the data
>> in a different order, etc. I did a number of tests to check if the index
>> produces the correct results, and I haven't found any issues. So I think
>> this is OK, and neither a problem nor an advantage of the patch.
>>
>>
>> Now, let's talk about the code - the series has 7 patches, with 6
>> non-trivial parts doing changes in focused and easier to understand
>> pieces (I hope so).
> 
> The following comments are generally based on the descriptions; I
> haven't really looked much at the patches yet except to validate some
> assumptions.
> 

OK

>> 1) v20240502-0001-Allow-parallel-create-for-GIN-indexes.patch
>>
>> This is the initial feature, adding the "basic" version, implemented as
>> pretty much 1:1 copy of the BRIN parallel build and minimal changes to
>> make it work for GIN (mostly about how to store intermediate results).
>>
>> The basic idea is that the workers do the regular build, but instead of
>> flushing the data into the index after hitting the memory limit, it gets
>> written into a shared tuplesort and sorted by the index key. And the
>> leader then reads this sorted data, accumulates the TID for a given key
>> and inserts that into the index in one go.
> 
> In the code, GIN insertions are still basically single btree
> insertions, all starting from the top (but with many same-valued
> tuples at once). Now that we have a tuplesort with the full table's
> data, couldn't the code be adapted to do more efficient btree loading,
> such as that seen in the nbtree code, where the rightmost pages are
> cached and filled sequentially without requiring repeated searches
> down the tree? I suspect we can gain a lot of time there.
> 
> I don't need you to do that, but what's your opinion on this?
> 

I have no idea. I started working on this with only very basic idea of
how GIN works / is structured, so I simply leveraged the existing
callback and massaged it to work in the parallel case too.

>> 2) v20240502-0002-Use-mergesort-in-the-leader-process.patch
>>
>> The approach implemented by 0001 works, but there's a little bit of
>> issue - if there are many distinct keys (e.g. for trigrams that can
>> happen very easily), the workers will hit the memory limit with only
>> very short TID lists for most keys. For serial build that means merging
>> the data into a lot of random places, and in parallel build it means the
>> leader will have to merge a lot of tiny lists from many sorted rows.
>>
>> Which can be quite annoying and expensive, because the leader does so
>> using qsort() in the serial part. It'd be better to ensure most of the
>> sorting happens in the workers, and the leader can do a mergesort. But
>> the mergesort must not happen too often - merging many small lists is
>> not cheaper than a single qsort (especially when the lists overlap).
>>
>> So this patch changes the workers to process the data in two phases. The
>> first works as before, but the data is flushed into a local tuplesort.
>> And then each workers sorts the results it produced, and combines them
>> into results with much larger TID lists, and those results are written
>> to the shared tuplesort. So the leader only gets very few lists to
>> combine for a given key - usually just one list per worker.
> 
> Hmm, I was hoping we could implement the merging inside the tuplesort
> itself during its own flush phase, as it could save significantly on
> IO, and could help other users of tuplesort with deduplication, too.
> 

Would that happen in the worker or leader process? Because my goal was
to do the expensive part in the worker, because that's what helps with
the parallelization.

>> 3) v20240502-0003-Remove-the-explicit-pg_qsort-in-workers.patch
>>
>> In 0002 the workers still do an explicit qsort() on the TID list before
>> writing the data into the shared tuplesort. But we can do better - the
>> workers can do a merge sort too. To help with this, we add the first TID
>> to the tuplesort tuple, and sort by that too - it helps the workers to
>> process the data in an order that allows simple concatenation instead of
>> the full mergesort.
>>
>> Note: There's a non-obvious issue due to parallel scans always being
>> "sync scans", which may lead to very "wide" TID ranges when the scan
>> wraps around. More about that later.
> 
> As this note seems to imply, this seems to have a strong assumption
> that data received in parallel workers is always in TID order, with
> one optional wraparound. Non-HEAP TAMs may break with this assumption,
> so what's the plan on that?
> 

Well, that would break the serial build too, right? Anyway, the way this
patch works can be extended to deal with that by actually sorting the
TIDs when serializing the tuplestore tuple. The consequence of that is
the combining will be more expensive, because it'll require a proper
mergesort, instead of just appending the lists.

>> 4) v20240502-0004-Compress-TID-lists-before-writing-tuples-t.patch
>>
>> The parallel build passes data between processes using temporary files,
>> which means it may need significant amount of disk space. For BRIN this
>> was not a major concern, because the summaries tend to be pretty small.
>>
>> But for GIN that's not the case, and the two-phase processing introduced
>> by 0002 make it worse, because the worker essentially creates another
>> copy of the intermediate data. It does not need to copy the key, so
>> maybe it's not exactly 2x the space requirement, but in the worst case
>> it's not far from that.
>>
>> But there's a simple way how to improve this - the TID lists tend to be
>> very compressible, and GIN already implements a very light-weight TID
>> compression, so this patch does just that - when building the tuple to
>> be written into the tuplesort, we just compress the TIDs.
> 
> See note on 0002: Could we do this in the tuplesort writeback, rather
> than by moving the data around multiple times?
> 

No idea, I've never done that ...

> [...]
>> So 0007 does something similar - it tracks if the TID value goes
>> backward in the callback, and if it does it dumps the state into the
>> tuplesort before processing the first tuple from the beginning of the
>> table. Which means we end up with two separate "narrow" TID list, not
>> one very wide one.
> 
> See note above: We may still need a merge phase, just to make sure we
> handle all TAM parallel scans correctly, even if that merge join phase
> wouldn't get hit in vanilla PostgreSQL.
> 

Well, yeah. But in fact the parallel code already does that, while the
existing serial code may fail with the "data don't fit" error.

The parallel code will do the mergesort correctly, and only emit TIDs
that we know are safe to write to the index (i.e. no future TIDs will go
before the "TID horizon").

But the serial build has nothing like that - it will sort the TIDs that
fit into the memory limit, but it also relies on not processing data out
of order (and disables sync scans to not have wrap around issues). But
if the TAM does something funny, this may break.



regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Hi,

Here's a slightly improved version, fixing a couple bugs in handling
byval/byref values, causing issues on 32-bit machines (but not only).
And also a couple compiler warnings about string formatting.

Other than that, no changes.

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Hello Tomas,

>>> 2) v20240502-0002-Use-mergesort-in-the-leader-process.patch
>>>
>>> The approach implemented by 0001 works, but there's a little bit of
>>> issue - if there are many distinct keys (e.g. for trigrams that can
>>> happen very easily), the workers will hit the memory limit with only
>>> very short TID lists for most keys. For serial build that means merging
>>> the data into a lot of random places, and in parallel build it means the
>>> leader will have to merge a lot of tiny lists from many sorted rows.
>>>
>>> Which can be quite annoying and expensive, because the leader does so
>>> using qsort() in the serial part. It'd be better to ensure most of the
>>> sorting happens in the workers, and the leader can do a mergesort. But
>>> the mergesort must not happen too often - merging many small lists is
>>> not cheaper than a single qsort (especially when the lists overlap).
>>>
>>> So this patch changes the workers to process the data in two phases. The
>>> first works as before, but the data is flushed into a local tuplesort.
>>> And then each workers sorts the results it produced, and combines them
>>> into results with much larger TID lists, and those results are written
>>> to the shared tuplesort. So the leader only gets very few lists to
>>> combine for a given key - usually just one list per worker.
>> 
>> Hmm, I was hoping we could implement the merging inside the tuplesort
>> itself during its own flush phase, as it could save significantly on
>> IO, and could help other users of tuplesort with deduplication, too.
>> 
>
> Would that happen in the worker or leader process? Because my goal was
> to do the expensive part in the worker, because that's what helps with
> the parallelization.

I guess both of you are talking about worker process, if here are
something in my mind:

*btbuild* also let the WORKER dump the tuples into Sharedsort struct
and let the LEADER merge them directly. I think this aim of this design
is it is potential to save a mergeruns. In the current patch, worker dump
to local tuplesort and mergeruns it and then leader run the merges
again. I admit the goal of this patch is reasonable, but I'm feeling we
need to adapt this way conditionally somehow. and if we find the way, we
can apply it to btbuild as well. 

-- 
Best Regards
Andy Fan

Tomas Vondra <tomas.vondra@enterprisedb.com> writes:

> 3) v20240502-0003-Remove-the-explicit-pg_qsort-in-workers.patch
>
> In 0002 the workers still do an explicit qsort() on the TID list before
> writing the data into the shared tuplesort. But we can do better - the
> workers can do a merge sort too. To help with this, we add the first TID
> to the tuplesort tuple, and sort by that too - it helps the workers to
> process the data in an order that allows simple concatenation instead of
> the full mergesort.
>
> Note: There's a non-obvious issue due to parallel scans always being
> "sync scans", which may lead to very "wide" TID ranges when the scan
> wraps around. More about that later.

This is really amazing.

> 7) v20240502-0007-Detect-wrap-around-in-parallel-callback.patch
>
> There's one more efficiency problem - the parallel scans are required to
> be synchronized, i.e. the scan may start half-way through the table, and
> then wrap around. Which however means the TID list will have a very wide
> range of TID values, essentially the min and max of for the key.
>
> Without 0006 this would cause frequent failures of the index build, with
> the error I already mentioned:
>
>   ERROR: could not split GIN page; all old items didn't fit

I have two questions here and both of them are generall gin index questions
rather than the patch here.

1. What does the "wrap around" mean in the "the scan may start half-way
through the table, and then wrap around".  Searching "wrap" in
gin/README gets nothing. 

2. I can't understand the below error.

>   ERROR: could not split GIN page; all old items didn't fit

When the posting list is too long, we have posting tree strategy. so in
which sistuation we could get this ERROR. 

> issue with efficiency - having such a wide TID list forces the mergesort
> to actually walk the lists, because this wide list overlaps with every
> other list produced by the worker.

If we split the blocks among worker 1-block by 1-block, we will have a
serious issue like here.  If we can have N-block by N-block, and N-block
is somehow fill the work_mem which makes the dedicated temp file, we
can make things much better, can we? 

-- 
Best Regards
Andy Fan

On 5/9/24 12:14, Andy Fan wrote:
> 
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> 
>> 3) v20240502-0003-Remove-the-explicit-pg_qsort-in-workers.patch
>>
>> In 0002 the workers still do an explicit qsort() on the TID list before
>> writing the data into the shared tuplesort. But we can do better - the
>> workers can do a merge sort too. To help with this, we add the first TID
>> to the tuplesort tuple, and sort by that too - it helps the workers to
>> process the data in an order that allows simple concatenation instead of
>> the full mergesort.
>>
>> Note: There's a non-obvious issue due to parallel scans always being
>> "sync scans", which may lead to very "wide" TID ranges when the scan
>> wraps around. More about that later.
> 
> This is really amazing.
> 
>> 7) v20240502-0007-Detect-wrap-around-in-parallel-callback.patch
>>
>> There's one more efficiency problem - the parallel scans are required to
>> be synchronized, i.e. the scan may start half-way through the table, and
>> then wrap around. Which however means the TID list will have a very wide
>> range of TID values, essentially the min and max of for the key.
>>
>> Without 0006 this would cause frequent failures of the index build, with
>> the error I already mentioned:
>>
>>   ERROR: could not split GIN page; all old items didn't fit
> 
> I have two questions here and both of them are generall gin index questions
> rather than the patch here.
> 
> 1. What does the "wrap around" mean in the "the scan may start half-way
> through the table, and then wrap around".  Searching "wrap" in
> gin/README gets nothing. 
> 

The "wrap around" is about the scan used to read data from the table
when building the index. A "sync scan" may start e.g. at TID (1000,0)
and read till the end of the table, and then wraps and returns the
remaining part at the beginning of the table for blocks 0-999.

This means the callback would not see a monotonically increasing
sequence of TIDs.

Which is why the serial build disables sync scans, allowing simply
appending values to the sorted list, and even with regular flushes of
data into the index we can simply append data to the posting lists.

> 2. I can't understand the below error.
> 
>>   ERROR: could not split GIN page; all old items didn't fit
> 
> When the posting list is too long, we have posting tree strategy. so in
> which sistuation we could get this ERROR. 
> 

AFAICS the index build relies on the assumption that we only append data
to the TID list on a leaf page, and when the page gets split, the "old"
part will always fit. Which may not be true, if there was a wrap around
and we're adding low TID values to the list on the leaf page.

FWIW the error in dataBeginPlaceToPageLeaf looks like this:

  if (!append || ItemPointerCompare(&maxOldItem, &remaining) >= 0)
    elog(ERROR, "could not split GIN page; all old items didn't fit");

It can fail simply because of the !append part.

I'm not sure why dataBeginPlaceToPageLeaf() relies on this assumption,
or with GIN details in general, and I haven't found any explanation. But
AFAIK this is why the serial build disables sync scans.

>> issue with efficiency - having such a wide TID list forces the mergesort
>> to actually walk the lists, because this wide list overlaps with every
>> other list produced by the worker.
> 
> If we split the blocks among worker 1-block by 1-block, we will have a
> serious issue like here.  If we can have N-block by N-block, and N-block
> is somehow fill the work_mem which makes the dedicated temp file, we
> can make things much better, can we? 
> 

I don't understand the question. The blocks are distributed to workers
by the parallel table scan, and it certainly does not do that block by
block. But even it it did, that's not a problem for this code.

The problem is that if the scan wraps around, then one of the TID lists
for a given worker will have the min TID and max TID, so it will overlap
with every other TID list for the same key in that worker. And when the
worker does the merging, this list will force a "full" merge sort for
all TID lists (for that key), which is very expensive.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On 5/9/24 11:44, Andy Fan wrote:
> 
> Hello Tomas,
> 
>>>> 2) v20240502-0002-Use-mergesort-in-the-leader-process.patch
>>>>
>>>> The approach implemented by 0001 works, but there's a little bit of
>>>> issue - if there are many distinct keys (e.g. for trigrams that can
>>>> happen very easily), the workers will hit the memory limit with only
>>>> very short TID lists for most keys. For serial build that means merging
>>>> the data into a lot of random places, and in parallel build it means the
>>>> leader will have to merge a lot of tiny lists from many sorted rows.
>>>>
>>>> Which can be quite annoying and expensive, because the leader does so
>>>> using qsort() in the serial part. It'd be better to ensure most of the
>>>> sorting happens in the workers, and the leader can do a mergesort. But
>>>> the mergesort must not happen too often - merging many small lists is
>>>> not cheaper than a single qsort (especially when the lists overlap).
>>>>
>>>> So this patch changes the workers to process the data in two phases. The
>>>> first works as before, but the data is flushed into a local tuplesort.
>>>> And then each workers sorts the results it produced, and combines them
>>>> into results with much larger TID lists, and those results are written
>>>> to the shared tuplesort. So the leader only gets very few lists to
>>>> combine for a given key - usually just one list per worker.
>>>
>>> Hmm, I was hoping we could implement the merging inside the tuplesort
>>> itself during its own flush phase, as it could save significantly on
>>> IO, and could help other users of tuplesort with deduplication, too.
>>>
>>
>> Would that happen in the worker or leader process? Because my goal was
>> to do the expensive part in the worker, because that's what helps with
>> the parallelization.
> 
> I guess both of you are talking about worker process, if here are
> something in my mind:
> 
> *btbuild* also let the WORKER dump the tuples into Sharedsort struct
> and let the LEADER merge them directly. I think this aim of this design
> is it is potential to save a mergeruns. In the current patch, worker dump
> to local tuplesort and mergeruns it and then leader run the merges
> again. I admit the goal of this patch is reasonable, but I'm feeling we
> need to adapt this way conditionally somehow. and if we find the way, we
> can apply it to btbuild as well. 
> 

I'm a bit confused about what you're proposing here, or how is that
related to what this patch is doing and/or to the what Matthias
mentioned in his e-mail from last week.

Let me explain the relevant part of the patch, and how I understand the
improvement suggested by Matthias. The patch does the work in three phases:


1) Worker gets data from table, split that into index items and add
those into a "private" tuplesort, and finally sorts that. So a worker
may see a key many times, with different TIDs, so the tuplesort may
contain many items for the same key, with distinct TID lists:

   key1: 1, 2, 3, 4
   key1: 5, 6, 7
   key1: 8, 9, 10
   key2: 1, 2, 3
   ...


2) Worker reads the sorted data, and combines TIDs for the same key into
larger TID lists, depending on work_mem etc. and writes the result into
a shared tuplesort. So the worker may write this:

   key1: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
   key2: 1, 2, 3


3) Leader reads this, combines TID lists from all workers (using a
higher memory limit, probably), and writes the result into the index.


The step (2) is optional - it would work without it. But it helps, as it
moves a potentially expensive sort into the workers (and thus the
parallel part of the build), and it also makes it cheaper, because in a
single worker the lists do not overlap and thus can be simply appended.
Which in the leader is not the case, forcing an expensive mergesort.

The trouble with (2) is that it "just copies" data from one tuplesort
into another, increasing the disk space requirements. In an extreme
case, when nothing can be combined, it pretty much doubles the amount of
disk space, and makes the build longer.

What I think Matthias is suggesting, is that this "TID list merging"
could be done directly as part of the tuplesort in step (1). So instead
of just moving the "sort tuples" from the appropriate runs, it could
also do an optional step of combining the tuples and writing this
combined tuple into the tuplesort result (for that worker).

Matthias also mentioned this might be useful when building btree indexes
with key deduplication.

AFAICS this might work, although it probably requires for the "combined"
tuple to be smaller than the sum of the combined tuples (in order to fit
into the space). But at least in the GIN build in the workers this is
likely true, because the TID lists do not overlap (and thus not hurting
the compressibility).

That being said, I still see this more as an optimization than something
required for the patch, and I don't think I'll have time to work on this
anytime soon. The patch is not extremely complex, but it's not trivial
either. But if someone wants to take a stab at extending tuplesort to
allow this, I won't object ...


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On 5/2/24 20:22, Tomas Vondra wrote:
>> 
>>> For some of the opclasses it can regress (like the jsonb_path_ops). I
>>> don't think that's a major issue. Or more precisely, I'm not surprised
>>> by it. It'd be nice to be able to disable the parallel builds in these
>>> cases somehow, but I haven't thought about that.
>>
>> Do you know why it regresses?
>>
> 
> No, but one thing that stands out is that the index is much smaller than
> the other columns/opclasses, and the compression does not save much
> (only about 5% for both phases). So I assume it's the overhead of
> writing writing and reading a bunch of GB of data without really gaining
> much from doing that.
> 

I finally got to look into this regression, but I think I must have done
something wrong before because I can't reproduce it. This is the timings
I get now, if I rerun the benchmark:

     workers       trgm   tsvector     jsonb  jsonb (hash)
    -------------------------------------------------------
           0       1225        404       104            56
           1        772        180        57            60
           2        549        143        47            52
           3        426        127        43            50
           4        364        116        40            48
           5        323        111        38            46
           6        292        111        37            45

and the speedup, relative to serial build:


     workers       trgm   tsvector      jsonb  jsonb (hash)
    --------------------------------------------------------
           1        63%        45%        54%          108%
           2        45%        35%        45%           94%
           3        35%        31%        41%           89%
           4        30%        29%        38%           86%
           5        26%        28%        37%           83%
           6        24%        28%        35%           81%

So there's a small regression for the jsonb_path_ops opclass, but only
with one worker. After that, it gets a bit faster than serial build.
While not a great speedup, it's far better than the earlier results that
showed maybe 40% regression.

I don't know what I did wrong before - maybe I had a build with an extra
debug info or something like that? No idea why would that affect only
one of the opclasses. But this time I made doubly sure the results are
correct etc.

Anyway, I'm fairly happy with these results. I don't think it's
surprising there are cases where parallel build does not help much.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Thu, 9 May 2024 at 15:13, Tomas Vondra <tomas.vondra@enterprisedb.com> wrote:
> Let me explain the relevant part of the patch, and how I understand the
> improvement suggested by Matthias. The patch does the work in three phases:
>
>
> 1) Worker gets data from table, split that into index items and add
> those into a "private" tuplesort, and finally sorts that. So a worker
> may see a key many times, with different TIDs, so the tuplesort may
> contain many items for the same key, with distinct TID lists:
>
>    key1: 1, 2, 3, 4
>    key1: 5, 6, 7
>    key1: 8, 9, 10
>    key2: 1, 2, 3
>    ...

This step is actually split in several components/phases, too.
As opposed to btree, which directly puts each tuple's data into the
tuplesort, this GIN approach actually buffers the tuples in memory to
generate these TID lists for data keys, and flushes these pairs of Key
+ TID list into the tuplesort when its own memory limit is exceeded.
That means we essentially double the memory used for this data: One
GIN deform buffer, and one in-memory sort buffer in the tuplesort.
This is fine for now, but feels duplicative, hence my "let's allow
tuplesort to merge the key+TID pairs into pairs of key+TID list"
comment.

> The trouble with (2) is that it "just copies" data from one tuplesort
> into another, increasing the disk space requirements. In an extreme
> case, when nothing can be combined, it pretty much doubles the amount of
> disk space, and makes the build longer.
>
> What I think Matthias is suggesting, is that this "TID list merging"
> could be done directly as part of the tuplesort in step (1). So instead
> of just moving the "sort tuples" from the appropriate runs, it could
> also do an optional step of combining the tuples and writing this
> combined tuple into the tuplesort result (for that worker).

Yes, but with a slightly more extensive approach than that even, see above.

> Matthias also mentioned this might be useful when building btree indexes
> with key deduplication.
>
> AFAICS this might work, although it probably requires for the "combined"
> tuple to be smaller than the sum of the combined tuples (in order to fit
> into the space).

*must not be larger than the sum; not "must be smaller than the sum" [^0].
For btree tuples with posting lists this is guaranteed to be true: The
added size of a btree tuple with a posting list (containing at least 2
values) vs one without is the maxaligned size of 2 TIDs, or 16 bytes
(12 on 32-bit systems). The smallest btree tuple with data is also 16
bytes (or 12 bytes on 32-bit systems), so this works out nicely.

> But at least in the GIN build in the workers this is
> likely true, because the TID lists do not overlap (and thus not hurting
> the compressibility).
>
> That being said, I still see this more as an optimization than something
> required for the patch,

Agreed.

> and I don't think I'll have time to work on this
> anytime soon. The patch is not extremely complex, but it's not trivial
> either. But if someone wants to take a stab at extending tuplesort to
> allow this, I won't object ...

Same here: While I do have some ideas on where and how to implement
this, I'm not planning on working on that soon.


Kind regards,

Matthias van de Meent

[^0] There's some overhead in the tuplesort serialization too, so
there is some leeway there, too.

On 5/9/24 17:51, Matthias van de Meent wrote:
> On Thu, 9 May 2024 at 15:13, Tomas Vondra <tomas.vondra@enterprisedb.com> wrote:
>> Let me explain the relevant part of the patch, and how I understand the
>> improvement suggested by Matthias. The patch does the work in three phases:
>>
>>
>> 1) Worker gets data from table, split that into index items and add
>> those into a "private" tuplesort, and finally sorts that. So a worker
>> may see a key many times, with different TIDs, so the tuplesort may
>> contain many items for the same key, with distinct TID lists:
>>
>>    key1: 1, 2, 3, 4
>>    key1: 5, 6, 7
>>    key1: 8, 9, 10
>>    key2: 1, 2, 3
>>    ...
> 
> This step is actually split in several components/phases, too.
> As opposed to btree, which directly puts each tuple's data into the
> tuplesort, this GIN approach actually buffers the tuples in memory to
> generate these TID lists for data keys, and flushes these pairs of Key
> + TID list into the tuplesort when its own memory limit is exceeded.
> That means we essentially double the memory used for this data: One
> GIN deform buffer, and one in-memory sort buffer in the tuplesort.
> This is fine for now, but feels duplicative, hence my "let's allow
> tuplesort to merge the key+TID pairs into pairs of key+TID list"
> comment.
> 

True, although the "GIN deform buffer" (flushed by the callback if using
too much memory) likely does most of the merging already. If it only
happened in the tuplesort merge, we'd likely have far more tuples and
overhead associated with that. So we certainly won't get rid of either
of these things.

You're right the memory limits are a bit unclear, and need more thought.
I certainly have not thought very much about not using more than the
specified maintenance_work_mem amount. This includes the planner code
determining the number of workers to use - right now it simply does the
same thing as for btree/brin, i.e. assumes each workers uses 32MB of
memory and checks how many workers fit into maintenance_work_mem.

That was a bit bogus even for BRIN, because BRIN sorts only summaries,
which is typically tiny - perhaps a couple kB, much less than 32MB. But
it's still just one sort, and some opclasses may be much larger (like
bloom, for example). So I just went with it.

But for GIN it's more complicated, because we have two tuplesorts (not
sure if both can use the memory at the same time) and the GIN deform
buffer. Which probably means we need to have a per-worker allowance
considering all these buffers.

>> The trouble with (2) is that it "just copies" data from one tuplesort
>> into another, increasing the disk space requirements. In an extreme
>> case, when nothing can be combined, it pretty much doubles the amount of
>> disk space, and makes the build longer.
>>
>> What I think Matthias is suggesting, is that this "TID list merging"
>> could be done directly as part of the tuplesort in step (1). So instead
>> of just moving the "sort tuples" from the appropriate runs, it could
>> also do an optional step of combining the tuples and writing this
>> combined tuple into the tuplesort result (for that worker).
> 
> Yes, but with a slightly more extensive approach than that even, see above.
> 
>> Matthias also mentioned this might be useful when building btree indexes
>> with key deduplication.
>>
>> AFAICS this might work, although it probably requires for the "combined"
>> tuple to be smaller than the sum of the combined tuples (in order to fit
>> into the space).
> 
> *must not be larger than the sum; not "must be smaller than the sum" [^0].

Yeah, I wrote that wrong.

> For btree tuples with posting lists this is guaranteed to be true: The
> added size of a btree tuple with a posting list (containing at least 2
> values) vs one without is the maxaligned size of 2 TIDs, or 16 bytes
> (12 on 32-bit systems). The smallest btree tuple with data is also 16
> bytes (or 12 bytes on 32-bit systems), so this works out nicely.
> 
>> But at least in the GIN build in the workers this is
>> likely true, because the TID lists do not overlap (and thus not hurting
>> the compressibility).
>>
>> That being said, I still see this more as an optimization than something
>> required for the patch,
> 
> Agreed.
> 

OK

>> and I don't think I'll have time to work on this
>> anytime soon. The patch is not extremely complex, but it's not trivial
>> either. But if someone wants to take a stab at extending tuplesort to
>> allow this, I won't object ...
> 
> Same here: While I do have some ideas on where and how to implement
> this, I'm not planning on working on that soon.
> 

Understood. I don't have a very good intuition on how significant the
benefit could be, which is one of the reasons why I have not prioritized
this very much.

I did a quick experiment, to measure how expensive it is to build the
second worker tuplesort - for the pg_trgm index build with 2 workers, it
takes ~30seconds. The index build takes ~550s in total, so 30s is ~5%.
If we eliminated all of this work we'd save this, but in reality some of
it will still be necessary.

Perhaps it's more significant for other indexes / slower storage, but it
does not seem like a *must have* for v1.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Tomas Vondra <tomas.vondra@enterprisedb.com> writes:

>> I guess both of you are talking about worker process, if here are
>> something in my mind:
>> 
>> *btbuild* also let the WORKER dump the tuples into Sharedsort struct
>> and let the LEADER merge them directly. I think this aim of this design
>> is it is potential to save a mergeruns. In the current patch, worker dump
>> to local tuplesort and mergeruns it and then leader run the merges
>> again. I admit the goal of this patch is reasonable, but I'm feeling we
>> need to adapt this way conditionally somehow. and if we find the way, we
>> can apply it to btbuild as well. 
>> 
>
> I'm a bit confused about what you're proposing here, or how is that
> related to what this patch is doing and/or to the what Matthias
> mentioned in his e-mail from last week.
>
> Let me explain the relevant part of the patch, and how I understand the
> improvement suggested by Matthias. The patch does the work in three phases:

What's in my mind is:

1. WORKER-1

Tempfile 1:

key1:  1
key3:  2
...

Tempfile 2:

key5:  3
key7:  4
...

2. WORKER-2

Tempfile 1:

Key2: 1
Key6: 2
...

Tempfile 2:
Key3: 3
Key6: 4
..

In the above example:  if we do the the merge in LEADER, only 1 mergerun
is needed. reading 4 tempfile 8 tuples in total and write 8 tuples.

If we adds another mergerun into WORKER, the result will be:

WORKER1:  reading 2 tempfile 4 tuples and write 1 tempfile (called X) 4
tuples. 
WORKER2:  reading 2 tempfile 4 tuples and write 1 tempfile (called Y) 4
tuples. 

LEADER:  reading 2 tempfiles (X & Y) including 8 tuples and write it
into final tempfile.

So the intermedia result X & Y requires some extra effort.  so I think
the "extra mergerun in worker" is *not always* a win, and my proposal is
if we need to distinguish the cases in which one we should add the
"extra mergerun in worker" step.

> The trouble with (2) is that it "just copies" data from one tuplesort
> into another, increasing the disk space requirements. In an extreme
> case, when nothing can be combined, it pretty much doubles the amount of
> disk space, and makes the build longer.

This sounds like the same question as I talk above, However my proposal
is to distinguish which cost is bigger between "the cost saving from
merging TIDs in WORKERS" and "cost paid because of the extra copy",
then we do that only when we are sure we can benefits from it, but I
know it is hard and not sure if it is doable. 

> What I think Matthias is suggesting, is that this "TID list merging"
> could be done directly as part of the tuplesort in step (1). So instead
> of just moving the "sort tuples" from the appropriate runs, it could
> also do an optional step of combining the tuples and writing this
> combined tuple into the tuplesort result (for that worker).

OK, I get it now. So we talked about lots of merge so far at different
stage and for different sets of tuples.

1. "GIN deform buffer" did the TIDs merge for the same key for the tuples
in one "deform buffer" batch, as what the current master is doing.

2. "in memory buffer sort" stage, currently there is no TID merge so
far and Matthias suggest that. 

3. Merge the TIDs for the same keys in LEADER vs in WORKER first +
LEADER then. this is what your 0002 commit does now and I raised some
concerns as above.

> Matthias also mentioned this might be useful when building btree indexes
> with key deduplication.

> AFAICS this might work, although it probably requires for the "combined"
> tuple to be smaller than the sum of the combined tuples (in order to fit
> into the space). But at least in the GIN build in the workers this is
> likely true, because the TID lists do not overlap (and thus not hurting
> the compressibility).
>
> That being said, I still see this more as an optimization than something
> required for the patch,

If GIN deform buffer is big enough (like greater than the in memory
buffer sort) shall we have any gain because of this, since the
scope is the tuples in in-memory-buffer-sort. 

> and I don't think I'll have time to work on this
> anytime soon. The patch is not extremely complex, but it's not trivial
> either. But if someone wants to take a stab at extending tuplesort to
> allow this, I won't object ...

Agree with this. I am more interested with understanding the whole
design and the scope to fix in this patch, and then I can do some code
review and testing, as for now, I still in the "understanding design and
scope" stage. If I'm too slow about this patch, please feel free to
commit it any time and I don't expect I can find any valueable
improvement and bugs.  I probably needs another 1 ~ 2 weeks to study
this patch.

-- 
Best Regards
Andy Fan

On 5/10/24 07:53, Andy Fan wrote:
> 
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> 
>>> I guess both of you are talking about worker process, if here are
>>> something in my mind:
>>>
>>> *btbuild* also let the WORKER dump the tuples into Sharedsort struct
>>> and let the LEADER merge them directly. I think this aim of this design
>>> is it is potential to save a mergeruns. In the current patch, worker dump
>>> to local tuplesort and mergeruns it and then leader run the merges
>>> again. I admit the goal of this patch is reasonable, but I'm feeling we
>>> need to adapt this way conditionally somehow. and if we find the way, we
>>> can apply it to btbuild as well. 
>>>
>>
>> I'm a bit confused about what you're proposing here, or how is that
>> related to what this patch is doing and/or to the what Matthias
>> mentioned in his e-mail from last week.
>>
>> Let me explain the relevant part of the patch, and how I understand the
>> improvement suggested by Matthias. The patch does the work in three phases:
> 
> What's in my mind is:
> 
> 1. WORKER-1
> 
> Tempfile 1:
> 
> key1:  1
> key3:  2
> ...
> 
> Tempfile 2:
> 
> key5:  3
> key7:  4
> ...
> 
> 2. WORKER-2
> 
> Tempfile 1:
> 
> Key2: 1
> Key6: 2
> ...
> 
> Tempfile 2:
> Key3: 3
> Key6: 4
> ..
> 
> In the above example:  if we do the the merge in LEADER, only 1 mergerun
> is needed. reading 4 tempfile 8 tuples in total and write 8 tuples.
> 
> If we adds another mergerun into WORKER, the result will be:
> 
> WORKER1:  reading 2 tempfile 4 tuples and write 1 tempfile (called X) 4
> tuples. 
> WORKER2:  reading 2 tempfile 4 tuples and write 1 tempfile (called Y) 4
> tuples. 
> 
> LEADER:  reading 2 tempfiles (X & Y) including 8 tuples and write it
> into final tempfile.
> 
> So the intermedia result X & Y requires some extra effort.  so I think
> the "extra mergerun in worker" is *not always* a win, and my proposal is
> if we need to distinguish the cases in which one we should add the
> "extra mergerun in worker" step.
> 

The thing you're forgetting is that the mergesort in the worker is
*always* a simple append, because the lists are guaranteed to be
non-overlapping, so it's very cheap. The lists from different workers
are however very likely to overlap, and hence a "full" mergesort is
needed, which is way more expensive.

And not only that - without the intermediate merge, there will be very
many of those lists the leader would have to merge.

If we do the append-only merges in the workers first, we still need to
merge them in the leader, of course, but we have few lists to merge
(only about one per worker).

Of course, this means extra I/O on the intermediate tuplesort, and it's
not difficult to imagine cases with no benefit, or perhaps even a
regression. For example, if the keys are unique, the in-worker merge
step can't really do anything. But that seems quite unlikely IMHO.

Also, if this overhead was really significant, we would not see the nice
speedups I measured during testing.

>> The trouble with (2) is that it "just copies" data from one tuplesort
>> into another, increasing the disk space requirements. In an extreme
>> case, when nothing can be combined, it pretty much doubles the amount of
>> disk space, and makes the build longer.
> 
> This sounds like the same question as I talk above, However my proposal
> is to distinguish which cost is bigger between "the cost saving from
> merging TIDs in WORKERS" and "cost paid because of the extra copy",
> then we do that only when we are sure we can benefits from it, but I
> know it is hard and not sure if it is doable. 
> 

Yeah. I'm not against picking the right execution strategy during the
index build, but it's going to be difficult, because we really don't
have the information to make a reliable decision.

We can't even use the per-column stats, because it does not say much
about the keys extracted by GIN, I think. And we need to do the decision
at the very beginning, before we write the first batch of data either to
the local or shared tuplesort.

But maybe we could wait until we need to flush the first batch of data
(in the callback), and make the decision then? In principle, if we only
flush once at the end, the intermediate sort is not needed at all (fairy
unlikely for large data sets, though).

Well, in principle, maybe we could even start writing into the local
tuplesort, and then "rethink" after a while and switch to the shared
one. We'd still need to copy data we've already written to the local
tuplesort, but hopefully that'd be just a fraction compared to doing
that for the whole table.


>> What I think Matthias is suggesting, is that this "TID list merging"
>> could be done directly as part of the tuplesort in step (1). So instead
>> of just moving the "sort tuples" from the appropriate runs, it could
>> also do an optional step of combining the tuples and writing this
>> combined tuple into the tuplesort result (for that worker).
> 
> OK, I get it now. So we talked about lots of merge so far at different
> stage and for different sets of tuples.
> 
> 1. "GIN deform buffer" did the TIDs merge for the same key for the tuples
> in one "deform buffer" batch, as what the current master is doing.
> 
> 2. "in memory buffer sort" stage, currently there is no TID merge so
> far and Matthias suggest that. 
> 
> 3. Merge the TIDs for the same keys in LEADER vs in WORKER first +
> LEADER then. this is what your 0002 commit does now and I raised some
> concerns as above.
> 

OK

>> Matthias also mentioned this might be useful when building btree indexes
>> with key deduplication.
> 
>> AFAICS this might work, although it probably requires for the "combined"
>> tuple to be smaller than the sum of the combined tuples (in order to fit
>> into the space). But at least in the GIN build in the workers this is
>> likely true, because the TID lists do not overlap (and thus not hurting
>> the compressibility).
>>
>> That being said, I still see this more as an optimization than something
>> required for the patch,
> 
> If GIN deform buffer is big enough (like greater than the in memory
> buffer sort) shall we have any gain because of this, since the
> scope is the tuples in in-memory-buffer-sort. 
> 

I don't think this is very likely. The only case when the GIN deform
tuple is "big enough" is when we don't need to flush in the callback,
but that is going to happen only for "small" tables. And for those we
should not really do parallel builds. And even if we do, the overhead
would be pretty insignificant.

>> and I don't think I'll have time to work on this
>> anytime soon. The patch is not extremely complex, but it's not trivial
>> either. But if someone wants to take a stab at extending tuplesort to
>> allow this, I won't object ...
> 
> Agree with this. I am more interested with understanding the whole
> design and the scope to fix in this patch, and then I can do some code
> review and testing, as for now, I still in the "understanding design and
> scope" stage. If I'm too slow about this patch, please feel free to
> commit it any time and I don't expect I can find any valueable
> improvement and bugs.  I probably needs another 1 ~ 2 weeks to study
> this patch.
> 

Sure, happy to discuss and answer questions.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Tomas Vondra <tomas.vondra@enterprisedb.com> writes:

>>> 7) v20240502-0007-Detect-wrap-around-in-parallel-callback.patch
>>>
>>> There's one more efficiency problem - the parallel scans are required to
>>> be synchronized, i.e. the scan may start half-way through the table, and
>>> then wrap around. Which however means the TID list will have a very wide
>>> range of TID values, essentially the min and max of for the key.
>> 
>> I have two questions here and both of them are generall gin index questions
>> rather than the patch here.
>> 
>> 1. What does the "wrap around" mean in the "the scan may start half-way
>> through the table, and then wrap around".  Searching "wrap" in
>> gin/README gets nothing. 
>> 
>
> The "wrap around" is about the scan used to read data from the table
> when building the index. A "sync scan" may start e.g. at TID (1000,0)
> and read till the end of the table, and then wraps and returns the
> remaining part at the beginning of the table for blocks 0-999.
>
> This means the callback would not see a monotonically increasing
> sequence of TIDs.
>
> Which is why the serial build disables sync scans, allowing simply
> appending values to the sorted list, and even with regular flushes of
> data into the index we can simply append data to the posting lists.

Thanks for the hints, I know the sync strategy comes from syncscan.c
now. 

>>> Without 0006 this would cause frequent failures of the index build, with
>>> the error I already mentioned:
>>>
>>>   ERROR: could not split GIN page; all old items didn't fit

>> 2. I can't understand the below error.
>> 
>>>   ERROR: could not split GIN page; all old items didn't fit

>   if (!append || ItemPointerCompare(&maxOldItem, &remaining) >= 0)
>     elog(ERROR, "could not split GIN page; all old items didn't fit");
>
> It can fail simply because of the !append part.

Got it, Thanks!

>> If we split the blocks among worker 1-block by 1-block, we will have a
>> serious issue like here.  If we can have N-block by N-block, and N-block
>> is somehow fill the work_mem which makes the dedicated temp file, we
>> can make things much better, can we? 

> I don't understand the question. The blocks are distributed to workers
> by the parallel table scan, and it certainly does not do that block by
> block. But even it it did, that's not a problem for this code.

OK, I get ParallelBlockTableScanWorkerData.phsw_chunk_size is designed
for this.

> The problem is that if the scan wraps around, then one of the TID lists
> for a given worker will have the min TID and max TID, so it will overlap
> with every other TID list for the same key in that worker. And when the
> worker does the merging, this list will force a "full" merge sort for
> all TID lists (for that key), which is very expensive.

OK.

Thanks for all the answers, they are pretty instructive!

-- 
Best Regards
Andy Fan

On 5/13/24 10:19, Andy Fan wrote:
> 
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> 
>> ...
>>
>> I don't understand the question. The blocks are distributed to workers
>> by the parallel table scan, and it certainly does not do that block by
>> block. But even it it did, that's not a problem for this code.
> 
> OK, I get ParallelBlockTableScanWorkerData.phsw_chunk_size is designed
> for this.
> 
>> The problem is that if the scan wraps around, then one of the TID lists
>> for a given worker will have the min TID and max TID, so it will overlap
>> with every other TID list for the same key in that worker. And when the
>> worker does the merging, this list will force a "full" merge sort for
>> all TID lists (for that key), which is very expensive.
> 
> OK.
> 
> Thanks for all the answers, they are pretty instructive!
> 

Thanks for the questions, it forces me to articulate the arguments more
clearly. I guess it'd be good to put some of this into a README or at
least a comment at the beginning of gininsert.c or somewhere close.

regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Hi Tomas,

I have completed my first round of review, generally it looks good to
me, more testing need to be done in the next days. Here are some tiny
comments from my side, just FYI. 

1. Comments about GinBuildState.bs_leader looks not good for me. 
   
    /*
     * bs_leader is only present when a parallel index build is performed, and
     * only in the leader process. (Actually, only the leader process has a
     * GinBuildState.)
     */
    GinLeader  *bs_leader;

In the worker function _gin_parallel_build_main:
initGinState(&buildstate.ginstate, indexRel); is called, and the
following members in workers at least: buildstate.funcCtx,
buildstate.accum and so on.  So is the comment "only the leader process
has a GinBuildState" correct?

2. progress argument is not used?
_gin_parallel_scan_and_build(GinBuildState *state,
                             GinShared *ginshared, Sharedsort *sharedsort,
                             Relation heap, Relation index,
                             int sortmem, bool progress)


3. In function tuplesort_begin_index_gin, comments about nKeys takes me
some time to think about why 1 is correct(rather than
IndexRelationGetNumberOfKeyAttributes) and what does the "only the index
key" means. 

base->nKeys = 1;            /* Only the index key */

finally I think it is because gin index stores each attribute value into
an individual index entry for a multi-column index, so each index entry
has only 1 key.  So we can comment it as the following? 

"Gin Index stores the value of each attribute into different index entry
for mutli-column index, so each index entry has only 1 key all the
time." This probably makes it easier to understand.


4. GinBuffer: The comment "Similar purpose to BuildAccumulator, but much
simpler." makes me think why do we need a simpler but
similar structure, After some thoughts, they are similar at accumulating
TIDs only. GinBuffer is designed for "same key value" (hence
GinBufferCanAddKey). so IMO, the first comment is good enough and the 2 
comments introduce confuses for green hand and is potential to remove
it. 

/*
 * State used to combine accumulate TIDs from multiple GinTuples for the same
 * key value.
 *
 * XXX Similar purpose to BuildAccumulator, but much simpler.
 */
typedef struct GinBuffer


5. GinBuffer: ginMergeItemPointers always allocate new memory for the
new items and hence we have to pfree old memory each time. However it is
not necessary in some places, for example the new items can be appended
to Buffer->items (and this should be a common case). So could we
pre-allocate some spaces for items and reduce the number of pfree/palloc
and save some TID items copy in the desired case?

6. GinTuple.ItemPointerData first;        /* first TID in the array */

is ItemPointerData.ip_blkid good enough for its purpose?  If so, we can
save the memory for OffsetNumber for each GinTuple.

Item 5) and 6) needs some coding and testing. If it is OK to do, I'd
like to take it as an exercise in this area. (also including the item
1~4.)


-- 
Best Regards
Andy Fan

Hi Andy,

Thanks for the review. Here's an updated patch series, addressing most
of the points you've raised - I've kept them in "fixup" patches for now,
should be merged into 0001.

More detailed responses below.

On 5/28/24 11:29, Andy Fan wrote:
> 
> Hi Tomas,
> 
> I have completed my first round of review, generally it looks good to
> me, more testing need to be done in the next days. Here are some tiny
> comments from my side, just FYI. 
> 
> 1. Comments about GinBuildState.bs_leader looks not good for me. 
>    
>     /*
>      * bs_leader is only present when a parallel index build is performed, and
>      * only in the leader process. (Actually, only the leader process has a
>      * GinBuildState.)
>      */
>     GinLeader  *bs_leader;
> 
> In the worker function _gin_parallel_build_main:
> initGinState(&buildstate.ginstate, indexRel); is called, and the
> following members in workers at least: buildstate.funcCtx,
> buildstate.accum and so on.  So is the comment "only the leader process
> has a GinBuildState" correct?
> 

Yeah, this is misleading. I don't remember what exactly was my reasoning
for this wording, I've removed the comment.

> 2. progress argument is not used?
> _gin_parallel_scan_and_build(GinBuildState *state,
>                              GinShared *ginshared, Sharedsort *sharedsort,
>                              Relation heap, Relation index,
>                              int sortmem, bool progress)
> 

I've modified the code to use the progress flag, but now that I look at
it I'm a bit unsure I understand the purpose of this. I've modeled this
after what the btree does, and I see that there are two places calling
_bt_parallel_scan_and_sort:

1) _bt_leader_participate_as_worker: progress=true

2) _bt_parallel_build_main: progress=false

Isn't that a bit weird? AFAIU the progress will be updated only by the
leader, but will that progress be correct? And doesn't that means the if
the leader does not participate as a worker, the progress won't be updated?

FWIW The parallel BRIN code has the same issue - it's not using the
progress flag in _brin_parallel_scan_and_build.

> 
> 3. In function tuplesort_begin_index_gin, comments about nKeys takes me
> some time to think about why 1 is correct(rather than
> IndexRelationGetNumberOfKeyAttributes) and what does the "only the index
> key" means. 
> 
> base->nKeys = 1;            /* Only the index key */
> 
> finally I think it is because gin index stores each attribute value into
> an individual index entry for a multi-column index, so each index entry
> has only 1 key.  So we can comment it as the following? 
> 
> "Gin Index stores the value of each attribute into different index entry
> for mutli-column index, so each index entry has only 1 key all the
> time." This probably makes it easier to understand.
> 

OK, I see what you mean. The other tuplesort_begin_ functions nearby
have similar comments, but you're right GIN is a bit special in that it
"splits" multi-column indexes into individual index entries. I've added
a comment (hopefully) clarifying this.

> 
> 4. GinBuffer: The comment "Similar purpose to BuildAccumulator, but much
> simpler." makes me think why do we need a simpler but
> similar structure, After some thoughts, they are similar at accumulating
> TIDs only. GinBuffer is designed for "same key value" (hence
> GinBufferCanAddKey). so IMO, the first comment is good enough and the 2 
> comments introduce confuses for green hand and is potential to remove
> it. 
> 
> /*
>  * State used to combine accumulate TIDs from multiple GinTuples for the same
>  * key value.
>  *
>  * XXX Similar purpose to BuildAccumulator, but much simpler.
>  */
> typedef struct GinBuffer
> 

I've updated the comment explaining the differences a bit clearer.

> 
> 5. GinBuffer: ginMergeItemPointers always allocate new memory for the
> new items and hence we have to pfree old memory each time. However it is
> not necessary in some places, for example the new items can be appended
> to Buffer->items (and this should be a common case). So could we
> pre-allocate some spaces for items and reduce the number of pfree/palloc
> and save some TID items copy in the desired case?
> 

Perhaps, but that seems rather independent of this patch.

Also, I'm not sure how much would this optimization matter in practice.
The merge should happens fairly rarely, when we decide to store the TIDs
into the index. And then it's also subject to the caching built into the
memory contexts, limiting the malloc costs. We'll still pay for the
memcpy, of course.

Anyway, it's an optimization that would affect existing callers of
ginMergeItemPointers. I don't plan to tweak this in this patch.


> 6. GinTuple.ItemPointerData first;        /* first TID in the array */
> 
> is ItemPointerData.ip_blkid good enough for its purpose?  If so, we can
> save the memory for OffsetNumber for each GinTuple.
> 
> Item 5) and 6) needs some coding and testing. If it is OK to do, I'd
> like to take it as an exercise in this area. (also including the item
> 1~4.)
> 

It might save 2 bytes in the struct, but that's negligible compared to
the memory usage overall (we only keep one GinTuple, but many TIDs and
so on), and we allocate the space in power-of-2 pattern anyway (which
means the 2B won't matter).

Moreover, using just the block number would make it harder to compare
the TIDs (now we can just call ItemPointerCompare).


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Here's a cleaned up patch series, merging the fixup patches into 0001.

I've also removed the memset() from ginInsertBAEntry(). This was meant
to fix valgrind reports, but I believe this was just a symptom of
incorrect handling of byref data types, which was fixed in 2024/05/02
patch version.

The other thing I did is cleanup of FIXME and XXX comments. There were a
couple stale/obsolete comments, discussing issues that have been already
fixed (like the scan wrapping around).

A couple things to fix remain, but all of them are minor. And there's
also a couple XXX comments, often describing thing that is then done in
one of the following patches.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Here's a bit more cleaned up version, clarifying a lot of comments,
removing a bunch of obsolete comments, or comments speculating about
possible solutions, that sort of thing. I've also removed couple more
XXX comments, etc.

The main change however is that the sorting no longer relies on memcmp()
to compare the values. I did that because it was enough for the initial
WIP patches, and it worked till now - but the comments explained this
may not be a good idea if the data type allows the same value to have
multiple binary representations, or something like that.

I don't have a practical example to show an issue, but I guess if using
memcmp() was safe we'd be doing it in a bunch of places already, and
AFAIK we're not. And even if it happened to be OK, this is a probably
not the place where to start doing it.

So I've switched this to use the regular data-type comparisons, with
SortSupport etc. There's a bit more cleanup remaining and testing
needed, but I'm not aware of any bugs.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

Tomas Vondra <tomas.vondra@enterprisedb.com> writes:

Hi Tomas,

I am in a incompleted review process but I probably doesn't have much
time on this because of my internal tasks. So I just shared what I
did and the non-good-result patch.

What I tried to do is:
1) remove all the "sort" effort for the state->bs_sort_state tuples since
its input comes from state->bs_worker_state which is sorted already.

2). remove *partial* "sort" operations between accum.rbtree to
state->bs_worker_state because the tuple in accum.rbtree is sorted
already. 

Both of them can depend on the same API changes. 

1. 
struct Tuplesortstate
{
..
+ bool input_presorted; /*  the tuples are presorted. */
+ new_tapes;  // writes the tuples in memory into a new 'run'. 
}

and user can set it during tuplesort_begin_xx(, presorte=true);


2. in tuplesort_puttuple, if memory is full but presorted is
true, we can (a) avoid the sort. (b) resuse the existing 'runs'
to reduce the effort of 'mergeruns' unless new_tapes is set to
true. once it switch to a new tapes, the set state->new_tapes to false
and wait 3) to change it to true again.

3. tuplesort_dumptuples(..);  // dump the tuples in memory and set
new_tapes=true to tell the *this batch of input is presorted but they
are done, the next batch is just presort in its own batch*.

In the gin-parallel-build case,  for the case 1), we can just use

for tuple in bs_worker_sort: 
    tuplesort_putgintuple(state->bs_sortstate, ..);
tuplesort_dumptuples(..);

At last we can get a). only 1 run in the worker so that the leader can
have merge less runs in mergeruns.  b). reduce the sort both in
perform_sort_tuplesort and in sortstate_puttuple_common.

for the case 2). we can have:

   for tuple in RBTree.tuples:
         tuplesort_puttuples(tuple) ;  
      // this may cause a dumptuples internally when the memory is full,
      // but it is OK.
    tuplesort_dumptuples(..);

we can just remove the "sort" into sortstate_puttuple_common but
probably increase the 'runs' in sortstate which will increase the effort
of mergeruns at last.

But the test result is not good, maybe the 'sort' is not a key factor of
this. I do missed the perf step before doing this. or maybe my test data
is too small. 

Here is the patch I used for the above activity. and I used the
following sql to test. 

CREATE TABLE t(a int[], b numeric[]);

-- generate 1000 * 1000 rows.
insert into t select i, n
from normal_rand_array(1000, 90, 1::int4, 10000::int4) i,
normal_rand_array(1000, 90, '1.00233234'::numeric, '8.239241989134'::numeric) n;

alter table t set (parallel_workers=4);
set debug_parallel_query to on;
set max_parallel_maintenance_workers to 4;

create index on t using gin(a);
create index on t using gin(b);

I found normal_rand_array is handy to use in this case and I
register it into https://commitfest.postgresql.org/48/5061/.

Besides the above stuff, I didn't find anything wrong in the currrent
patch, and the above stuff can be categoried into "furture improvement"
even it is worthy to. 

-- 
Best Regards
Andy Fan

On 7/2/24 02:07, Andy Fan wrote:
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> 
> Hi Tomas,
> 
> I am in a incompleted review process but I probably doesn't have much
> time on this because of my internal tasks. So I just shared what I
> did and the non-good-result patch.
> 
> What I tried to do is:
> 1) remove all the "sort" effort for the state->bs_sort_state tuples since
> its input comes from state->bs_worker_state which is sorted already.
> 
> 2). remove *partial* "sort" operations between accum.rbtree to
> state->bs_worker_state because the tuple in accum.rbtree is sorted
> already. 
> 
> Both of them can depend on the same API changes. 
> 
> 1. 
> struct Tuplesortstate
> {
> ..
> + bool input_presorted; /*  the tuples are presorted. */
> + new_tapes;  // writes the tuples in memory into a new 'run'. 
> }
> 
> and user can set it during tuplesort_begin_xx(, presorte=true);
> 
> 
> 2. in tuplesort_puttuple, if memory is full but presorted is
> true, we can (a) avoid the sort. (b) resuse the existing 'runs'
> to reduce the effort of 'mergeruns' unless new_tapes is set to
> true. once it switch to a new tapes, the set state->new_tapes to false
> and wait 3) to change it to true again.
> 
> 3. tuplesort_dumptuples(..);  // dump the tuples in memory and set
> new_tapes=true to tell the *this batch of input is presorted but they
> are done, the next batch is just presort in its own batch*.
> 
> In the gin-parallel-build case,  for the case 1), we can just use
> 
> for tuple in bs_worker_sort: 
>     tuplesort_putgintuple(state->bs_sortstate, ..);
> tuplesort_dumptuples(..);
> 
> At last we can get a). only 1 run in the worker so that the leader can
> have merge less runs in mergeruns.  b). reduce the sort both in
> perform_sort_tuplesort and in sortstate_puttuple_common.
> 
> for the case 2). we can have:
> 
>    for tuple in RBTree.tuples:
>          tuplesort_puttuples(tuple) ;  
>       // this may cause a dumptuples internally when the memory is full,
>       // but it is OK.
>     tuplesort_dumptuples(..);
> 
> we can just remove the "sort" into sortstate_puttuple_common but
> probably increase the 'runs' in sortstate which will increase the effort
> of mergeruns at last.
> 
> But the test result is not good, maybe the 'sort' is not a key factor of
> this. I do missed the perf step before doing this. or maybe my test data
> is too small. 
> 

If I understand the idea correctly, you're saying that we write the data
from BuildAccumulator already sorted, so if we do that only once, it's
already sorted and we don't actually need the in-worker tuplesort.

I think that's a good idea in principle, but maybe the simplest way to
handle this is by remembering if we already flushed any data, and if we
do that for the first time at the very end of the scan, we can write
stuff directly to the shared  tuplesort. That seems much simpler than
doing this inside the tuplesort code.

Or did I get the idea wrong?

FWIW I'm not sure how much this will help in practice. We only really
want to do parallel index build for fairly large tables, which makes it
less likely the data will fit into the buffer (and if we flush during
the scan, that disables the optimization).

> Here is the patch I used for the above activity. and I used the
> following sql to test. 
> 
> CREATE TABLE t(a int[], b numeric[]);
> 
> -- generate 1000 * 1000 rows.
> insert into t select i, n
> from normal_rand_array(1000, 90, 1::int4, 10000::int4) i,
> normal_rand_array(1000, 90, '1.00233234'::numeric, '8.239241989134'::numeric) n;
> 
> alter table t set (parallel_workers=4);
> set debug_parallel_query to on;

I don't think this forces parallel index builds - this GUC only affects
queries that go through the regular planner, but index build does not do
that, it just scans the table directly.

So maybe your testing did not actually do any parallel index builds?
That might explain why you didn't see any improvements.

Maybe try this to "force" parallel index builds:

set min_parallel_table_scan = '64kB';
set maintenance_work_mem = '256MB';

> set max_parallel_maintenance_workers to 4;
> 
> create index on t using gin(a);
> create index on t using gin(b);
> 
> I found normal_rand_array is handy to use in this case and I
> register it into https://commitfest.postgresql.org/48/5061/.
> 
> Besides the above stuff, I didn't find anything wrong in the currrent
> patch, and the above stuff can be categoried into "furture improvement"
> even it is worthy to. 
> 

Thanks for the review!

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
<tomas.vondra@enterprisedb.com> wrote:
>
> Here's a bit more cleaned up version, clarifying a lot of comments,
> removing a bunch of obsolete comments, or comments speculating about
> possible solutions, that sort of thing. I've also removed couple more
> XXX comments, etc.
>
> The main change however is that the sorting no longer relies on memcmp()
> to compare the values. I did that because it was enough for the initial
> WIP patches, and it worked till now - but the comments explained this
> may not be a good idea if the data type allows the same value to have
> multiple binary representations, or something like that.
>
> I don't have a practical example to show an issue, but I guess if using
> memcmp() was safe we'd be doing it in a bunch of places already, and
> AFAIK we're not. And even if it happened to be OK, this is a probably
> not the place where to start doing it.

I think one such example would be the values '5.00'::jsonb and
'5'::jsonb when indexed using GIN's jsonb_ops, though I'm not sure if
they're treated as having the same value inside the opclass' ordering.

> So I've switched this to use the regular data-type comparisons, with
> SortSupport etc. There's a bit more cleanup remaining and testing
> needed, but I'm not aware of any bugs.

A review of patch 0001:

---

> src/backend/access/gin/gininsert.c         | 1449 +++++++++++++++++++-

The nbtree code has `nbtsort.c` for its sort- and (parallel) build
state handling, which is exclusively used during index creation. As
the changes here seem to be largely related to bulk insertion, how
much effort would it be to split the bulk insertion code path into a
separate file?

I noticed that new fields in GinBuildState do get to have a
bs_*-prefix, but none of the other added or previous fields of the
modified structs in gininsert.c have such prefixes. Could this be
unified?

> +/* Magic numbers for parallel state sharing */
> +#define PARALLEL_KEY_GIN_SHARED            UINT64CONST(0xB000000000000001)
> ...

These overlap with BRIN's keys; can we make them unique while we're at it?

> +      * mutex protects all fields before heapdesc.

I can't find the field that this `heapdesc` might refer to.

> +_gin_begin_parallel(GinBuildState *buildstate, Relation heap, Relation index,
> ...
> +     if (!isconcurrent)
> +        snapshot = SnapshotAny;
> +    else
> +        snapshot = RegisterSnapshot(GetTransactionSnapshot());

grumble: I know this is required from the index with the current APIs,
but I'm kind of annoyed that each index AM has to construct the table
scan and snapshot in their own code. I mean, this shouldn't be
meaningfully different across AMs, so every AM implementing this same
code makes me feel like we've got the wrong abstraction.

I'm not asking you to change this, but it's one more case where I'm
annoyed by the state of the system, but not quite enough yet to change
it.

---
> +++ b/src/backend/utils/sort/tuplesortvariants.c

I was thinking some more about merging tuples inside the tuplesort. I
realized that this could be implemented by allowing buffering of tuple
writes in writetup. This would require adding a flush operation at the
end of mergeonerun to store the final unflushed tuple on the tape, but
that shouldn't be too expensive. This buffering, when implemented
through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
merge the TID lists of same-valued GIN tuples while they're getting
stored and re-sorted, thus reducing the temporary space usage of the
tuplesort by some amount with limited overhead for other
non-deduplicating tuplesorts.

I've not yet spent the time to get this to work though, but I'm fairly
sure it'd use less temporary space than the current approach with the
2 tuplesorts, and could have lower overall CPU overhead as well
because the number of sortable items gets reduced much earlier in the
process.

---

> +++ b/src/include/access/gin_tuple.h
> + typedef struct GinTuple

I think this needs some more care: currently, each GinTuple is at
least 36 bytes in size on 64-bit systems. By using int instead of Size
(no normal indexable tuple can be larger than MaxAllocSize), and
packing the fields better we can shave off 10 bytes; or 12 bytes if
GinTuple.keylen is further adjusted to (u)int16: a key needs to fit on
a page, so we can probably safely assume that the key size fits in
(u)int16.

Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

On Wed, 3 Jul 2024 at 20:36, Matthias van de Meent
<boekewurm+postgres@gmail.com> wrote:
>
> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
> <tomas.vondra@enterprisedb.com> wrote:
> > So I've switched this to use the regular data-type comparisons, with
> > SortSupport etc. There's a bit more cleanup remaining and testing
> > needed, but I'm not aware of any bugs.

I've hit assertion failures in my testing of the combined patches, in
AssertCheckItemPointers: it assumes it's never called when the buffer
is empty and uninitialized, but that's wrong: we don't initialize the
items array until the first tuple, which will cause the assertion to
fire. By updating the first 2 assertions of AssertCheckItemPointers, I
could get it working.

> ---
> > +++ b/src/backend/utils/sort/tuplesortvariants.c
>
> I was thinking some more about merging tuples inside the tuplesort. I
> realized that this could be implemented by allowing buffering of tuple
> writes in writetup. This would require adding a flush operation at the
> end of mergeonerun to store the final unflushed tuple on the tape, but
> that shouldn't be too expensive. This buffering, when implemented
> through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
> merge the TID lists of same-valued GIN tuples while they're getting
> stored and re-sorted, thus reducing the temporary space usage of the
> tuplesort by some amount with limited overhead for other
> non-deduplicating tuplesorts.
>
> I've not yet spent the time to get this to work though, but I'm fairly
> sure it'd use less temporary space than the current approach with the
> 2 tuplesorts, and could have lower overall CPU overhead as well
> because the number of sortable items gets reduced much earlier in the
> process.

I've now spent some time on this. Attached the original patchset, plus
2 incremental patches, the first of which implement the above design
(patch no. 8).

Local tests show it's significantly faster: for the below test case
I've seen reindex time reduced from 777455ms to 626217ms, or ~20%
improvement.

After applying the 'reduce the size of GinTuple' patch, index creation
time is down to 551514ms, or about 29% faster total. This all was
tested with a fresh stock postgres configuration.

"""
CREATE UNLOGGED TABLE testdata
AS SELECT sha256(i::text::bytea)::text
    FROM generate_series(1, 15000000) i;
CREATE INDEX ON testdata USING gin (sha256 gin_trgm_ops);
"""

Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

On 7/3/24 20:36, Matthias van de Meent wrote:
> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
> <tomas.vondra@enterprisedb.com> wrote:
>>
>> Here's a bit more cleaned up version, clarifying a lot of comments,
>> removing a bunch of obsolete comments, or comments speculating about
>> possible solutions, that sort of thing. I've also removed couple more
>> XXX comments, etc.
>>
>> The main change however is that the sorting no longer relies on memcmp()
>> to compare the values. I did that because it was enough for the initial
>> WIP patches, and it worked till now - but the comments explained this
>> may not be a good idea if the data type allows the same value to have
>> multiple binary representations, or something like that.
>>
>> I don't have a practical example to show an issue, but I guess if using
>> memcmp() was safe we'd be doing it in a bunch of places already, and
>> AFAIK we're not. And even if it happened to be OK, this is a probably
>> not the place where to start doing it.
> 
> I think one such example would be the values '5.00'::jsonb and
> '5'::jsonb when indexed using GIN's jsonb_ops, though I'm not sure if
> they're treated as having the same value inside the opclass' ordering.
> 

Yeah, possibly. But doing the comparison the "proper" way seems to be
working pretty well, so I don't plan to investigate this further.

>> So I've switched this to use the regular data-type comparisons, with
>> SortSupport etc. There's a bit more cleanup remaining and testing
>> needed, but I'm not aware of any bugs.
> 
> A review of patch 0001:
> 
> ---
> 
>> src/backend/access/gin/gininsert.c         | 1449 +++++++++++++++++++-
> 
> The nbtree code has `nbtsort.c` for its sort- and (parallel) build
> state handling, which is exclusively used during index creation. As
> the changes here seem to be largely related to bulk insertion, how
> much effort would it be to split the bulk insertion code path into a
> separate file?
> 

Hmmm. I haven't tried doing that, but I guess it's doable. I assume we'd
want to do the move first, because it involves pre-existing code, and
then do the patch on top of that.

But what would be the benefit of doing that? I'm not sure doing it just
to make it look more like btree code is really worth it. Do you expect
the result to be clearer?

> I noticed that new fields in GinBuildState do get to have a
> bs_*-prefix, but none of the other added or previous fields of the
> modified structs in gininsert.c have such prefixes. Could this be
> unified?
> 

Yeah, these are inconsistencies from copying the infrastructure code to
make the parallel builds work, etc.

>> +/* Magic numbers for parallel state sharing */
>> +#define PARALLEL_KEY_GIN_SHARED            UINT64CONST(0xB000000000000001)
>> ...
> 
> These overlap with BRIN's keys; can we make them unique while we're at it?
> 

We could, and I recall we had a similar discussion in the parallel BRIN
thread, right?. But I'm somewhat unsure why would we actually want/need
these keys to be unique. Surely, we don't need to mix those keys in the
single shm segment, right? So it seems more like an aesthetic thing. Or
is there some policy to have unique values for these keys?

>> +      * mutex protects all fields before heapdesc.
> 
> I can't find the field that this `heapdesc` might refer to.
> 

Yeah, likely a leftover from copying a bunch of code and then removing
it without updating the comment. Will fix.

>> +_gin_begin_parallel(GinBuildState *buildstate, Relation heap, Relation index,
>> ...
>> +     if (!isconcurrent)
>> +        snapshot = SnapshotAny;
>> +    else
>> +        snapshot = RegisterSnapshot(GetTransactionSnapshot());
> 
> grumble: I know this is required from the index with the current APIs,
> but I'm kind of annoyed that each index AM has to construct the table
> scan and snapshot in their own code. I mean, this shouldn't be
> meaningfully different across AMs, so every AM implementing this same
> code makes me feel like we've got the wrong abstraction.
> 
> I'm not asking you to change this, but it's one more case where I'm
> annoyed by the state of the system, but not quite enough yet to change
> it.
> 

Yeah, it's not great, but not something I intend to rework.

> ---
>> +++ b/src/backend/utils/sort/tuplesortvariants.c
> 
> I was thinking some more about merging tuples inside the tuplesort. I
> realized that this could be implemented by allowing buffering of tuple
> writes in writetup. This would require adding a flush operation at the
> end of mergeonerun to store the final unflushed tuple on the tape, but
> that shouldn't be too expensive. This buffering, when implemented
> through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
> merge the TID lists of same-valued GIN tuples while they're getting
> stored and re-sorted, thus reducing the temporary space usage of the
> tuplesort by some amount with limited overhead for other
> non-deduplicating tuplesorts.
> 
> I've not yet spent the time to get this to work though, but I'm fairly
> sure it'd use less temporary space than the current approach with the
> 2 tuplesorts, and could have lower overall CPU overhead as well
> because the number of sortable items gets reduced much earlier in the
> process.
> 

Will respond to your later message about this.

> ---
> 
>> +++ b/src/include/access/gin_tuple.h
>> + typedef struct GinTuple
> 
> I think this needs some more care: currently, each GinTuple is at
> least 36 bytes in size on 64-bit systems. By using int instead of Size
> (no normal indexable tuple can be larger than MaxAllocSize), and
> packing the fields better we can shave off 10 bytes; or 12 bytes if
> GinTuple.keylen is further adjusted to (u)int16: a key needs to fit on
> a page, so we can probably safely assume that the key size fits in
> (u)int16.
> 

Yeah, I guess using int64 is a bit excessive - you're right about that.
I'm not sure this is necessarily about "indexable tuples" (GinTuple is
not indexed, it's more an intermediate representation). But if we can
make it smaller, that probably can't hurt.

I don't have a great intuition on how beneficial this might be. For
cases with many TIDs per index key, it probably won't matter much. But
if there's many keys (so that GinTuples store only very few TIDs), it
might make a difference.

I'll try to measure the impact on the same "realistic" cases I used for
the earlier steps.

regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On 7/5/24 21:45, Matthias van de Meent wrote:
> On Wed, 3 Jul 2024 at 20:36, Matthias van de Meent
> <boekewurm+postgres@gmail.com> wrote:
>>
>> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
>> <tomas.vondra@enterprisedb.com> wrote:
>>> So I've switched this to use the regular data-type comparisons, with
>>> SortSupport etc. There's a bit more cleanup remaining and testing
>>> needed, but I'm not aware of any bugs.
> 
> I've hit assertion failures in my testing of the combined patches, in
> AssertCheckItemPointers: it assumes it's never called when the buffer
> is empty and uninitialized, but that's wrong: we don't initialize the
> items array until the first tuple, which will cause the assertion to
> fire. By updating the first 2 assertions of AssertCheckItemPointers, I
> could get it working.
> 

Yeah, sorry. I think I broke this assert while doing the recent
cleanups. The assert used to be called only when doing a sort, and then
it certainly can't be empty. But then I moved it to be called from the
generic GinTuple assert function, and that broke this assumption.

>> ---
>>> +++ b/src/backend/utils/sort/tuplesortvariants.c
>>
>> I was thinking some more about merging tuples inside the tuplesort. I
>> realized that this could be implemented by allowing buffering of tuple
>> writes in writetup. This would require adding a flush operation at the
>> end of mergeonerun to store the final unflushed tuple on the tape, but
>> that shouldn't be too expensive. This buffering, when implemented
>> through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
>> merge the TID lists of same-valued GIN tuples while they're getting
>> stored and re-sorted, thus reducing the temporary space usage of the
>> tuplesort by some amount with limited overhead for other
>> non-deduplicating tuplesorts.
>>
>> I've not yet spent the time to get this to work though, but I'm fairly
>> sure it'd use less temporary space than the current approach with the
>> 2 tuplesorts, and could have lower overall CPU overhead as well
>> because the number of sortable items gets reduced much earlier in the
>> process.
> 
> I've now spent some time on this. Attached the original patchset, plus
> 2 incremental patches, the first of which implement the above design
> (patch no. 8).
> 
> Local tests show it's significantly faster: for the below test case
> I've seen reindex time reduced from 777455ms to 626217ms, or ~20%
> improvement.
> 
> After applying the 'reduce the size of GinTuple' patch, index creation
> time is down to 551514ms, or about 29% faster total. This all was
> tested with a fresh stock postgres configuration.
> 
> """
> CREATE UNLOGGED TABLE testdata
> AS SELECT sha256(i::text::bytea)::text
>     FROM generate_series(1, 15000000) i;
> CREATE INDEX ON testdata USING gin (sha256 gin_trgm_ops);
> """
> 

Those results look really promising. I certainly would not have expected
such improvements - 20-30% speedup on top of the already parallel run
seems great. I'll do a bit more testing to see how much this helps for
the "more realistic" data sets.

I can't say I 100% understand how the new stuff in tuplesortvariants.c
works, but that's mostly because my knowledge of tuplesort internals is
fairly limited (and I managed to survive without that until now).

What makes me a bit unsure/uneasy is that this seems to "inject" custom
code fairly deep into the tuplesort logic. I'm not quite sure if this is
a good idea ...


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Sun, 7 Jul 2024, 18:11 Tomas Vondra, <tomas.vondra@enterprisedb.com> wrote:
>
> On 7/3/24 20:36, Matthias van de Meent wrote:
>> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
>> <tomas.vondra@enterprisedb.com> wrote:
>>> So I've switched this to use the regular data-type comparisons, with
>>> SortSupport etc. There's a bit more cleanup remaining and testing
>>> needed, but I'm not aware of any bugs.
>>
>> A review of patch 0001:
>>
>> ---
>>
>>> src/backend/access/gin/gininsert.c         | 1449 +++++++++++++++++++-
>>
>> The nbtree code has `nbtsort.c` for its sort- and (parallel) build
>> state handling, which is exclusively used during index creation. As
>> the changes here seem to be largely related to bulk insertion, how
>> much effort would it be to split the bulk insertion code path into a
>> separate file?
>>
>
> Hmmm. I haven't tried doing that, but I guess it's doable. I assume we'd
> want to do the move first, because it involves pre-existing code, and
> then do the patch on top of that.
>
> But what would be the benefit of doing that? I'm not sure doing it just
> to make it look more like btree code is really worth it. Do you expect
> the result to be clearer?

It was mostly a consideration of file size and separation of concerns.
The sorted build path is quite different from the unsorted build,
after all.

>> I noticed that new fields in GinBuildState do get to have a
>> bs_*-prefix, but none of the other added or previous fields of the
>> modified structs in gininsert.c have such prefixes. Could this be
>> unified?
>>
>
> Yeah, these are inconsistencies from copying the infrastructure code to
> make the parallel builds work, etc.
>
>>> +/* Magic numbers for parallel state sharing */
>>> +#define PARALLEL_KEY_GIN_SHARED            UINT64CONST(0xB000000000000001)
>>> ...
>>
>> These overlap with BRIN's keys; can we make them unique while we're at it?
>>
>
> We could, and I recall we had a similar discussion in the parallel BRIN
> thread, right?. But I'm somewhat unsure why would we actually want/need
> these keys to be unique. Surely, we don't need to mix those keys in the
> single shm segment, right? So it seems more like an aesthetic thing. Or
> is there some policy to have unique values for these keys?

Uniqueness would be mostly useful for debugging shared memory issues,
but indeed, in a correctly working system we wouldn't have to worry
about parallel state key type confusion.

>> ---
>>
>>> +++ b/src/include/access/gin_tuple.h
>>> + typedef struct GinTuple
>>
>> I think this needs some more care: currently, each GinTuple is at
>> least 36 bytes in size on 64-bit systems. By using int instead of Size
>> (no normal indexable tuple can be larger than MaxAllocSize), and
>> packing the fields better we can shave off 10 bytes; or 12 bytes if
>> GinTuple.keylen is further adjusted to (u)int16: a key needs to fit on
>> a page, so we can probably safely assume that the key size fits in
>> (u)int16.
>
> Yeah, I guess using int64 is a bit excessive - you're right about that.
> I'm not sure this is necessarily about "indexable tuples" (GinTuple is
> not indexed, it's more an intermediate representation).

Yes, but even if the GinTuple itself isn't stored on disk in the
index, a GinTuple's key *is* part of the the primary GIN btree's keys
somewhere down the line, and thus must fit on a page somewhere. That's
what I was referring to.

> But if we can make it smaller, that probably can't hurt.
>
> I don't have a great intuition on how beneficial this might be. For
> cases with many TIDs per index key, it probably won't matter much. But
> if there's many keys (so that GinTuples store only very few TIDs), it
> might make a difference.

This will indeed matter most when small TID lists are common. I
suspect it's not uncommon to find us such a in situation when users
have a low maintenance_work_mem (and thus don't have much space to
buffer and combine index tuples before they're flushed), or when the
generated index keys can't be store in the available memory (such as
in my example; it didn't tune m_w_m at all, and the table I tested had
~15GB of data).

> I'll try to measure the impact on the same "realistic" cases I used for
> the earlier steps.

That would be greatly appreciated, thanks!


Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

On Sun, 7 Jul 2024, 18:26 Tomas Vondra, <tomas.vondra@enterprisedb.com> wrote:
>
> On 7/5/24 21:45, Matthias van de Meent wrote:
>> On Wed, 3 Jul 2024 at 20:36, Matthias van de Meent
>> <boekewurm+postgres@gmail.com> wrote:
>>>
>>> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
>>> <tomas.vondra@enterprisedb.com> wrote:
>>>> So I've switched this to use the regular data-type comparisons, with
>>>> SortSupport etc. There's a bit more cleanup remaining and testing
>>>> needed, but I'm not aware of any bugs.
>>> ---
>>>> +++ b/src/backend/utils/sort/tuplesortvariants.c
>>>
>>> I was thinking some more about merging tuples inside the tuplesort. I
>>> realized that this could be implemented by allowing buffering of tuple
>>> writes in writetup. This would require adding a flush operation at the
>>> end of mergeonerun to store the final unflushed tuple on the tape, but
>>> that shouldn't be too expensive. This buffering, when implemented
>>> through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
>>> merge the TID lists of same-valued GIN tuples while they're getting
>>> stored and re-sorted, thus reducing the temporary space usage of the
>>> tuplesort by some amount with limited overhead for other
>>> non-deduplicating tuplesorts.
>>>
>>> I've not yet spent the time to get this to work though, but I'm fairly
>>> sure it'd use less temporary space than the current approach with the
>>> 2 tuplesorts, and could have lower overall CPU overhead as well
>>> because the number of sortable items gets reduced much earlier in the
>>> process.
>>
>> I've now spent some time on this. Attached the original patchset, plus
>> 2 incremental patches, the first of which implement the above design
>> (patch no. 8).
>>
>> Local tests show it's significantly faster: for the below test case
>> I've seen reindex time reduced from 777455ms to 626217ms, or ~20%
>> improvement.
>>
>> After applying the 'reduce the size of GinTuple' patch, index creation
>> time is down to 551514ms, or about 29% faster total. This all was
>> tested with a fresh stock postgres configuration.
>>
>> """
>> CREATE UNLOGGED TABLE testdata
>> AS SELECT sha256(i::text::bytea)::text
>>     FROM generate_series(1, 15000000) i;
>> CREATE INDEX ON testdata USING gin (sha256 gin_trgm_ops);
>> """
>>
>
> Those results look really promising. I certainly would not have expected
> such improvements - 20-30% speedup on top of the already parallel run
> seems great. I'll do a bit more testing to see how much this helps for
> the "more realistic" data sets.
>
> I can't say I 100% understand how the new stuff in tuplesortvariants.c
> works, but that's mostly because my knowledge of tuplesort internals is
> fairly limited (and I managed to survive without that until now).
>
> What makes me a bit unsure/uneasy is that this seems to "inject" custom
> code fairly deep into the tuplesort logic. I'm not quite sure if this is
> a good idea ...

I thought it was still fairly high-level: it adds (what seems to me) a
natural extension to the pre-existing "write a tuple to the tape" API,
allowing the Tuplesort (TS) implementation to further optimize its
ordered tape writes through buffering (and combining) of tuple writes.
While it does remove the current 1:1 relation of TS tape writes to
tape reads for the GIN case, there is AFAIK no code in TS that relies
on that 1:1 relation.

As to the GIN TS code itself; yes it's more complicated, mainly
because it uses several optimizations to reduce unnecessary
allocations and (de)serializations of GinTuples, and I'm aware of even
more such optimizations that can be added at some point.

As an example: I suspect the use of GinBuffer in writetup_index_gin to
be a significant resource drain in my patch because it lacks
"freezing" in the tuplesort buffer. When no duplicate key values are
encountered, the code is nearly optimal (except for a full tuple copy
to get the data into the GinBuffer's memory context), but if more than
one GinTuple has the same key in the merge phase we deserialize both
tuple's posting lists and merge the two. I suspect that merge to be
more expensive than operating on the compressed posting lists of the
GinTuples themselves, so that's something I think could be improved. I
suspect/guess it could save another 10% in select cases, and will
definitely reduce the memory footprint of the buffer.
Another thing that can be optimized is the current approach of
inserting data into the index: I think it's kind of wasteful to
decompress and later re-compress the posting lists once we start
storing the tuples on disk.

Kind regards,

Matthias van de Meent

On 7/8/24 11:45, Matthias van de Meent wrote:
> On Sun, 7 Jul 2024, 18:26 Tomas Vondra, <tomas.vondra@enterprisedb.com> wrote:
>>
>> On 7/5/24 21:45, Matthias van de Meent wrote:
>>> On Wed, 3 Jul 2024 at 20:36, Matthias van de Meent
>>> <boekewurm+postgres@gmail.com> wrote:
>>>>
>>>> On Mon, 24 Jun 2024 at 02:58, Tomas Vondra
>>>> <tomas.vondra@enterprisedb.com> wrote:
>>>>> So I've switched this to use the regular data-type comparisons, with
>>>>> SortSupport etc. There's a bit more cleanup remaining and testing
>>>>> needed, but I'm not aware of any bugs.
>>>> ---
>>>>> +++ b/src/backend/utils/sort/tuplesortvariants.c
>>>>
>>>> I was thinking some more about merging tuples inside the tuplesort. I
>>>> realized that this could be implemented by allowing buffering of tuple
>>>> writes in writetup. This would require adding a flush operation at the
>>>> end of mergeonerun to store the final unflushed tuple on the tape, but
>>>> that shouldn't be too expensive. This buffering, when implemented
>>>> through e.g. a GinBuffer in TuplesortPublic->arg, could allow us to
>>>> merge the TID lists of same-valued GIN tuples while they're getting
>>>> stored and re-sorted, thus reducing the temporary space usage of the
>>>> tuplesort by some amount with limited overhead for other
>>>> non-deduplicating tuplesorts.
>>>>
>>>> I've not yet spent the time to get this to work though, but I'm fairly
>>>> sure it'd use less temporary space than the current approach with the
>>>> 2 tuplesorts, and could have lower overall CPU overhead as well
>>>> because the number of sortable items gets reduced much earlier in the
>>>> process.
>>>
>>> I've now spent some time on this. Attached the original patchset, plus
>>> 2 incremental patches, the first of which implement the above design
>>> (patch no. 8).
>>>
>>> Local tests show it's significantly faster: for the below test case
>>> I've seen reindex time reduced from 777455ms to 626217ms, or ~20%
>>> improvement.
>>>
>>> After applying the 'reduce the size of GinTuple' patch, index creation
>>> time is down to 551514ms, or about 29% faster total. This all was
>>> tested with a fresh stock postgres configuration.
>>>
>>> """
>>> CREATE UNLOGGED TABLE testdata
>>> AS SELECT sha256(i::text::bytea)::text
>>>     FROM generate_series(1, 15000000) i;
>>> CREATE INDEX ON testdata USING gin (sha256 gin_trgm_ops);
>>> """
>>>
>>
>> Those results look really promising. I certainly would not have expected
>> such improvements - 20-30% speedup on top of the already parallel run
>> seems great. I'll do a bit more testing to see how much this helps for
>> the "more realistic" data sets.
>>
>> I can't say I 100% understand how the new stuff in tuplesortvariants.c
>> works, but that's mostly because my knowledge of tuplesort internals is
>> fairly limited (and I managed to survive without that until now).
>>
>> What makes me a bit unsure/uneasy is that this seems to "inject" custom
>> code fairly deep into the tuplesort logic. I'm not quite sure if this is
>> a good idea ...
> 
> I thought it was still fairly high-level: it adds (what seems to me) a
> natural extension to the pre-existing "write a tuple to the tape" API,
> allowing the Tuplesort (TS) implementation to further optimize its
> ordered tape writes through buffering (and combining) of tuple writes.
> While it does remove the current 1:1 relation of TS tape writes to
> tape reads for the GIN case, there is AFAIK no code in TS that relies
> on that 1:1 relation.
> 
> As to the GIN TS code itself; yes it's more complicated, mainly
> because it uses several optimizations to reduce unnecessary
> allocations and (de)serializations of GinTuples, and I'm aware of even
> more such optimizations that can be added at some point.
> 
> As an example: I suspect the use of GinBuffer in writetup_index_gin to
> be a significant resource drain in my patch because it lacks
> "freezing" in the tuplesort buffer. When no duplicate key values are
> encountered, the code is nearly optimal (except for a full tuple copy
> to get the data into the GinBuffer's memory context), but if more than
> one GinTuple has the same key in the merge phase we deserialize both
> tuple's posting lists and merge the two. I suspect that merge to be
> more expensive than operating on the compressed posting lists of the
> GinTuples themselves, so that's something I think could be improved. I
> suspect/guess it could save another 10% in select cases, and will
> definitely reduce the memory footprint of the buffer.
> Another thing that can be optimized is the current approach of
> inserting data into the index: I think it's kind of wasteful to
> decompress and later re-compress the posting lists once we start
> storing the tuples on disk.
> 

I need to experiment with this a bit more, to better understand the
behavior and pros/cons. But one thing that's not clear to me is why
would this be better than simply increasing the amount of memory for the
initial BuildAccumulator buffer ...

Wouldn't that have pretty much the same effect?


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Mon, 8 Jul 2024, 13:38 Tomas Vondra, <tomas.vondra@enterprisedb.com> wrote:
>
> On 7/8/24 11:45, Matthias van de Meent wrote:
> > As to the GIN TS code itself; yes it's more complicated, mainly
> > because it uses several optimizations to reduce unnecessary
> > allocations and (de)serializations of GinTuples, and I'm aware of even
> > more such optimizations that can be added at some point.
> >
> > As an example: I suspect the use of GinBuffer in writetup_index_gin to
> > be a significant resource drain in my patch because it lacks
> > "freezing" in the tuplesort buffer. When no duplicate key values are
> > encountered, the code is nearly optimal (except for a full tuple copy
> > to get the data into the GinBuffer's memory context), but if more than
> > one GinTuple has the same key in the merge phase we deserialize both
> > tuple's posting lists and merge the two. I suspect that merge to be
> > more expensive than operating on the compressed posting lists of the
> > GinTuples themselves, so that's something I think could be improved. I
> > suspect/guess it could save another 10% in select cases, and will
> > definitely reduce the memory footprint of the buffer.
> > Another thing that can be optimized is the current approach of
> > inserting data into the index: I think it's kind of wasteful to
> > decompress and later re-compress the posting lists once we start
> > storing the tuples on disk.
> >
>
> I need to experiment with this a bit more, to better understand the
> behavior and pros/cons. But one thing that's not clear to me is why
> would this be better than simply increasing the amount of memory for the
> initial BuildAccumulator buffer ...
>
> Wouldn't that have pretty much the same effect?

I don't think so:

The BuildAccumulator buffer will probably never be guaranteed to have
space for all index entries, though it does use memory more
efficiently than Tuplesort. Therefore, it will likely have to flush
keys multiple times into sorted runs, with likely duplicate keys
existing in the tuplesort.

My patch 0008 targets the reduction of IO and CPU once
BuildAccumulator has exceeded its memory limits. It reduces the IO and
computational requirement of Tuplesort's sorted-run merging by merging
the tuples in those sorted runs in that merge process, reducing the
number of tuples, bytes stored, and number of compares required in
later operations. It enables some BuildAccumulator-like behaviour
inside the tuplesort, without needing to assign huge amounts of memory
to the BuildAccumulator by allowing efficient spilling to disk of the
incomplete index data. And, during merging, it can work with
O(n_merge_tapes * tupsz) of memory, rather than O(n_distinct_keys *
tupsz). This doesn't make BuildAccumulator totally useless, but it
does reduce the relative impact of assigning more memory.

One significant difference between the modified Tuplesort and
BuildAccumulator is that the modified Tuplesort only merges the
entries once they're getting written, i.e. flushed from the in-memory
structure; while BuildAccumulator merges entries as they're being
added to the in-memory structure.

Note that this difference causes BuildAccumulator to use memory more
efficiently during in-memory workloads (it doesn't duplicate keys in
memory), but as BuildAccumulator doesn't have spilling it doesn't
handle full indexes' worth of data (it does duplciate keys on disk).

I hope this clarifies things a bit. I'd be thrilled if we'd be able to
put BuildAccumulator-like behaviour into the in-memory portion of
Tuplesort, but that'd require a significantly deeper understanding of
the Tuplesort internals than what I currently have, especially in the
area of its memory management.


Kind regards

Matthias van de Meent
Neon (https://neon.tech)

Andy Fan <zhihuifan1213@163.com> writes:

I just realize all my replies is replied to sender only recently,
probably because I upgraded the email cient and the short-cut changed
sliently, resent the lastest one only.... 

>>> Suppose RBTree's output is:
>>> 
>>> batch-1 at RBTree:
>>> 1  [tid1, tid8, tid100]
>>> 2  [tid1, tid9, tid800]
>>> ...
>>> 78 [tid23, tid99, tid800]
>>> 
>>> batch-2 at RBTree
>>> 1  [tid1001, tid1203, tid1991]
>>> ...
>>> ...
>>> 97 [tid1023, tid1099, tid1800]
>>> 
>>> Since all the tuples in each batch (1, 2, .. 78) are sorted already, we
>>> can just flush them into tuplesort as a 'run' *without any sorts*,
>>> however within this way, it is possible to produce more 'runs' than what
>>> you did in your patch.
>>> 
>>
>> Oh! Now I think I understand what you were proposing - you're saying
>> that when dumping the RBTree to the tuplesort, we could tell the
>> tuplesort this batch of tuples is already sorted, and tuplesort might
>> skip some of the work when doing the sort later.
>>
>> I guess that's true, and maybe it'd be useful elsewhere, I still think
>> this could be left as a future improvement. Allowing it seems far from
>> trivial, and it's not quite clear if it'd be a win (it might interfere
>> with the existing sort code in unexpected ways).
>
> Yes, and I agree that can be done later and I'm thinking Matthias's
> proposal is more promising now. 
>
>>> new way: the No. of batch depends on size of RBTree's batch size.
>>> existing way: the No. of batch depends on size of work_mem in tuplesort.
>>> Usually the new way would cause more no. of runs which is harmful for
>>> mergeruns.  so I can't say it is an improve of not and not include it in
>>> my previous patch. 
>>> 
>>> however case 1 sounds a good canidiates for this method.
>>> 
>>> Tuples from state->bs_worker_state after the perform_sort and ctid
>>> merge: 
>>> 
>>> 1 [tid1, tid8, tid100, tid1001, tid1203, tid1991]
>>> 2 [tid1, tid9, tid800]
>>> 78 [tid23, tid99, tid800]
>>> 97 [tid1023, tid1099, tid1800]
>>> 
>>> then when we move tuples to bs_sort_state, a). we don't need to sort at
>>> all. b). we can merge all of them into 1 run which is good for mergerun
>>> on leader as well. That's the thing I did in the previous patch.
>>> 
>>
>> I'm sorry, I don't understand what you're proposing. Could you maybe
>> elaborate in more detail?
>
> After we called "tuplesort_performsort(state->bs_worker_sort);" in
> _gin_process_worker_data, all the tuples in bs_worker_sort are sorted
> already, and in the same function _gin_process_worker_data, we have
> code:
>
> while ((tup = tuplesort_getgintuple(worker_sort, &tuplen, true)) != NULL)
> {
>
>   ....(1)
>   
>   tuplesort_putgintuple(state->bs_sortstate, ntup, ntuplen);
>
> }
>
> and later we called 'tuplesort_performsort(state->bs_sortstate);'.  Even
> we have some CTID merges activity in  '....(1)', the tuples are still
> ordered, so the sort (in both tuplesort_putgintuple and
> 'tuplesort_performsort) are not necessary, what's more, in the each of
> 'flush-memory-to-disk' in tuplesort, it create a 'sorted-run', and in
> this case, acutally we only need 1 run only since all the input tuples
> in the worker is sorted. The reduction of 'sort-runs' in worker will be
> helpful to leader's final mergeruns.  the 'sorted-run' benefit doesn't
> exist for the case-1 (RBTree -> worker_state). 
>
> If Matthias's proposal is adopted, my optimization will not be useful
> anymore and Matthias's porposal looks like a more natural and effecient
> way.

-- 
Best Regards
Andy Fan

Hi,

I got to do the detailed benchmarking on the latest version of the patch
series, so here's the results. My goal was to better understand the
impact of each patch individually - especially the two parts introduced
by Matthias, but not only - so I ran the test on a build with each fo
the 0001-0009 patches.

This is the same test I did at the very beginning, but the basic details
are that I have a 22GB table with archives of our mailing lists (1.6M
messages, roughly), and I build a couple different GIN indexes on that:

create index trgm on messages using gin (msg_body gin_trgm_ops);
create index tsvector on messages using gin (msg_body_tsvector);
create index jsonb on messages using gin (msg_headers);
create index jsonb_hash on messages using gin (msg_headers jsonb_path_ops);

The indexes are 700MB-3GB, so not huge, but also not tiny. I did the
test with a varying number of parallel workers for each patch, measuring
the execution time and a couple more metrics (using pg_stat_statements).
See the attached scripts for details, and also conf/results from the two
machines I use for these tests.

Attached is also a PDF with a summary of the tests - there are four
sections with results in total, two for each machine with different
work_mem values (more on this later).

For each configuration, there are tables/charts for three metrics:

- total CREATE INDEX duration
- relative CREATE INDEX duration (relative to serial build)
- amount of temporary files written

Hopefully it's easy to understand/interpret, but feel free to ask.
There's also CSVs with raw results, in case you choose to do your own
analysis (there's more metrics than presented here).

While doing these tests, I realized there's a bug in how the patches
handle collations - it simply grabbed the value for the indexed column,
but if that's missing (e.g. for tsvector), it fell over. Instead the
patch needs to use the default collation, so that's fixed in 0001.

The other thing I realized while working on this is that it's probably
wrong to tie parallel callback to work_mem - both conceptually, but also
for performance reasons. I did the first run with the default work_mem
(4MB), and that showed some serious regressions with the 0002 patch
(where it took ~3.5x longer than serial build). It seemed to be due to a
lot of merges of small TID lists, so I tried re-running the tests with
work_mem=32MB, and the regression pretty much disappeared.

Also, with 4MB there were almost no benefits of parallelism on the
smaller indexes (jsonb and jsonb_hash) - that's probably not unexpected,
but 32MB did improve that a little bit (still not great, though).

In practice this would not be a huge issue, because the later patches
make the regression go away - so unless we commit only the first couple
patches, the users would not be affected by this. But it's annoying, and
more importantly it's a bit bogus to use work_mem here - why should that
be appropriate? It was more a temporary hack because I didn't have a
better idea, and the comment in ginBuildCallbackParallel() questions
this too, after all.

My plan is to derive this from maintenance_work_mem, or rather the
fraction we "allocate" for each worker. The planner logic caps the
number of workers to maintenance_work_mem / 32MB, which means each
worker has >=32MB of maintenance_work_mem at it's disposal. The worker
needs to do the BuildAccumulator thing, and also the tuplesort. So it
seems reasonable to use 1/2 of the budget (>=16MB) for each of those.
Which seems good enough, IMHO. It's significantly more than 4MB, and the
32MB I used for the second round was rather arbitrary.

So for further discussion, let's focus on results in the two sections
for 32MB ...

And let's talk about the improvement by Matthias, namely:

* 0008 Use a single GIN tuplesort
* 0009 Reduce the size of GinTuple by 12 bytes

I haven't really seen any impact on duration - it seems more or less
within noise. Maybe it would be different on machines with less RAM, but
on my two systems it didn't really make a difference.

It did significantly reduce the amount of temporary data written, by
~40% or so. This is pretty nicely visible on the "trgm" case, which
generates the most temp files of the four indexes. An example from the
i5/32MB section looks like this:

label    0000 0001  0002  0003  0004  0005  0006  0007  0008  0009  0010
------------------------------------------------------------------------
trgm / 3    0 2635  3690  3715  1177  1177  1179  1179   696   682  1016

So we start with patches producing 2.6GB - 3.7GB of temp files. Then the
compression of TID lists cuts that down to ~1.2GB, and the 0008 patch
cuts that to just 700MB. That's pretty nice, even if it doesn't speed
things up. The 0009 (GinTuple reduction) improves that a little bit, but
the difference is smaller.

I'm still a bit unsure about the tuplesort changes, but producing less
temporary files seems like a good thing.


Now, what's the 0010 patch about?

For some indexes (e.g. trgm), the parallel builds help a lot, because
they produce a lot of temporary data and the parallel sort is a
substantial part of the work. But for other indexes (especially the
"smaller" indexes on jsonb headers), it's not that great. For example
for "jsonb", having 3 workers shaves off only ~25% of the time, not 75%.

Clearly, this happens because a lot of time is spent outside the sort,
actually inserting data into the index. So I was wondering if we might
parallelize that too, and how much time would it save - 0010 is an
experimental patch doing that. It splits the processing into 3 phases:

1. workers feeding data into tuplesort
2. leader finishes sort and "repartitions" the data
3. workers inserting their partition into index

The patch is far from perfect (more a PoC) - it implements these phases
by introducing a barrier to coordinate the processes. Workers feed the
data into the tuplesort as now, but instead of terminating they wait on
a barrier.

The leader reads data from the tuplesort, and partitions them evenly
into the a SharedFileSet with one file per worker. And then wakes up the
workers through the barrier again, and they do the inserts.

This does help a little bit, reducing the duration by ~15-25%. I wonder
if this might be improved by partitioning the data differently - not by
shuffling everything from the tuplesort into fileset (it increases the
amount of temporary data in the charts). And also by by distributing the
data differently - right now it's a bit of a round robin, because it
wasn't clear we know how many entries are there.


regards

-- 
Tomas Vondra
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company

On Tue, 9 Jul 2024 at 03:18, Andy Fan <zhihuifan1213@163.com> wrote:
>> and later we called 'tuplesort_performsort(state->bs_sortstate);'.  Even
>> we have some CTID merges activity in  '....(1)', the tuples are still
>> ordered, so the sort (in both tuplesort_putgintuple and
>> 'tuplesort_performsort) are not necessary, what's more, in the each of
>> 'flush-memory-to-disk' in tuplesort, it create a 'sorted-run', and in
>> this case, acutally we only need 1 run only since all the input tuples
>> in the worker is sorted. The reduction of 'sort-runs' in worker will be
>> helpful to leader's final mergeruns.  the 'sorted-run' benefit doesn't
>> exist for the case-1 (RBTree -> worker_state).
>>
>> If Matthias's proposal is adopted, my optimization will not be useful
>> anymore and Matthias's porposal looks like a more natural and effecient
>> way.

I think they might be complementary. I don't think it's reasonable to
expect GIN's BuildAccumulator to buffer all the index tuples at the
same time (as I mentioned upthread: we are or should be limited by
work memory), but the BuildAccumulator will do a much better job at
combining tuples than the in-memory sort + merge-write done by
Tuplesort (because BA will use (much?) less memory for the same number
of stored values). So, the idea of making BuildAccumulator responsible
for providing the initial sorted runs does resonate with me, and can
also be worth pursuing.

I think it would indeed save time otherwise spent comparing if tuples
can be merged before they're first spilled to disk, when we already
have knowledge about which tuples are a sorted run. Afterwards, only
the phases where we merge sorted runs from disk would require my
buffered write approach that merges Gin tuples.

Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

Tomas Vondra <tomas.vondra@enterprisedb.com> writes:

> Hi,
>
> I got to do the detailed benchmarking on the latest version of the patch
> series, so here's the results. My goal was to better understand the
> impact of each patch individually - especially the two parts introduced
> by Matthias, but not only - so I ran the test on a build with each fo
> the 0001-0009 patches.
>
> This is the same test I did at the very beginning, but the basic details
> are that I have a 22GB table with archives of our mailing lists (1.6M
> messages, roughly), and I build a couple different GIN indexes on
> that:
..
>

Very impresive testing!

> And let's talk about the improvement by Matthias, namely:
>
> * 0008 Use a single GIN tuplesort
> * 0009 Reduce the size of GinTuple by 12 bytes
>
> I haven't really seen any impact on duration - it seems more or less
> within noise. Maybe it would be different on machines with less RAM, but
> on my two systems it didn't really make a difference.
>
> It did significantly reduce the amount of temporary data written, by
> ~40% or so. This is pretty nicely visible on the "trgm" case, which
> generates the most temp files of the four indexes. An example from the
> i5/32MB section looks like this:
>
> label    0000 0001  0002  0003  0004  0005  0006  0007  0008  0009  0010
> ------------------------------------------------------------------------
> trgm / 3    0 2635  3690  3715  1177  1177  1179  1179   696   682
> 1016

After seeing the above data, I want to know where the time is spent and
why the ~40% IO doesn't make a measurable duration improvement. then I
did the following test.

create table gin_t (a int[]);
insert into gin_t select * from rand_array(30000000, 0, 100, 0, 50); [1]
select pg_prewarm('gin_t');

postgres=# create index on gin_t using gin(a);
INFO:  pid: 145078,  stage 1 took 44476 ms
INFO:  pid: 145177,  stage 1 took 44474 ms
INFO:  pid: 145078,  stage 2 took 2662 ms
INFO:  pid: 145177,  stage 2 took 2611 ms
INFO:  pid: 145177,  stage 3 took 240 ms
INFO:  pid: 145078,  stage 3 took 239 ms

CREATE INDEX
Time: 79472.135 ms (01:19.472)

Then we can see stage 1 take 56% execution time. stage 2 + stage 3 take
3% execution time. and the leader's work takes the rest 41% execution
time. I think that's why we didn't see much performance improvement of
0008 since it improves the "stage 2 and stage 3".

==== Here is my definition for stage 1/2/3. 
stage 1:
         reltuples = table_index_build_scan(heap, index, indexInfo, true, progress,
                                           ginBuildCallbackParallel, state, scan);

        /* write remaining accumulated entries */
        ginFlushBuildState(state, index);
    
stage 2:
        _gin_process_worker_data(state, state->bs_worker_sort)

stage 3:
        tuplesort_performsort(state->bs_sortstate);


But 0008 still does many good stuff:
1. Reduce the IO usage, this would be more useful on some heavily IO
   workload. 
2. Simplify the building logic by removing one stage.
3. Add the 'buffer-writetup' to tuplesort.c, I don't have other user
   case for now, but it looks like a reasonable design.

I think the current blocker is if it is safe to hack the tuplesort.c.
With my current knowledge, It looks good to me, but it would be better
open a dedicated thread to discuss this specially, the review would not
take a long time if a people who is experienced on this area would take
a look. 

> Now, what's the 0010 patch about?
>
> For some indexes (e.g. trgm), the parallel builds help a lot, because
> they produce a lot of temporary data and the parallel sort is a
> substantial part of the work. But for other indexes (especially the
> "smaller" indexes on jsonb headers), it's not that great. For example
> for "jsonb", having 3 workers shaves off only ~25% of the time, not 75%.
>
> Clearly, this happens because a lot of time is spent outside the sort,
> actually inserting data into the index.

You can always foucs on the most important part which inpires me a lot,
even with my simple testing, the "inserting data into index" stage take
40% time.

> So I was wondering if we might
> parallelize that too, and how much time would it save - 0010 is an
> experimental patch doing that. It splits the processing into 3 phases:
>
> 1. workers feeding data into tuplesort
> 2. leader finishes sort and "repartitions" the data
> 3. workers inserting their partition into index
>
> The patch is far from perfect (more a PoC) ..
>
> This does help a little bit, reducing the duration by ~15-25%. I wonder
> if this might be improved by partitioning the data differently - not by
> shuffling everything from the tuplesort into fileset (it increases the
> amount of temporary data in the charts). And also by by distributing the
> data differently - right now it's a bit of a round robin, because it
> wasn't clear we know how many entries are there.

Due to the complexity of the existing code, I would like to foucs on
existing patch first. So I vote for this optimization as a dedeciated
patch. 

>>> and later we called 'tuplesort_performsort(state->bs_sortstate);'.  Even
>>> we have some CTID merges activity in  '....(1)', the tuples are still
>>> ordered, so the sort (in both tuplesort_putgintuple and
>>> 'tuplesort_performsort) are not necessary,
>>> ..
>>> If Matthias's proposal is adopted, my optimization will not be useful
>>> anymore and Matthias's porposal looks like a more natural and effecient
>>> way.
>
> I think they might be complementary. I don't think it's reasonable to
> expect GIN's BuildAccumulator to buffer all the index tuples at the
> same time (as I mentioned upthread: we are or should be limited by
> work memory), but the BuildAccumulator will do a much better job at
> combining tuples than the in-memory sort + merge-write done by
> Tuplesort (because BA will use (much?) less memory for the same number
> of stored values).

Thank you Matthias for valuing my point! and thanks for highlighting the
benefit that BuildAccumulator can do a better job for sorting in memory
(I think it is mainly because BuildAccumulator can do run-time merge
when accept more tuples).  but I still not willing to go further at this
direction. Reasons are: a). It probably can't make a big difference at
the final result. b). The best implementation of this idea would be
allowing the user of tuplesort.c to insert the pre-sort tuples into tape
directly rather than inserting them into tuplesort's memory and dump
them into tape without a sort. However I can't define a clean API for
the former case. c). create-index is a maintenance work, improving it by
30% would be good, but if we just improve it by <3, it looks not very
charming in practice.

So my option is if we can have agreement on 0008, then we can final
review/test on the existing code (including 0009), and leave further
improvement as a dedicated patch. 

What do you think?

[1] https://www.postgresql.org/message-id/87le0iqrsu.fsf%40163.com

-- 
Best Regards
Andy Fan

On 8/27/24 12:14, Andy Fan wrote:
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> 
>> Hi,
>>
>> I got to do the detailed benchmarking on the latest version of the patch
>> series, so here's the results. My goal was to better understand the
>> impact of each patch individually - especially the two parts introduced
>> by Matthias, but not only - so I ran the test on a build with each fo
>> the 0001-0009 patches.
>>
>> This is the same test I did at the very beginning, but the basic details
>> are that I have a 22GB table with archives of our mailing lists (1.6M
>> messages, roughly), and I build a couple different GIN indexes on
>> that:
> ..
>>
> 
> Very impresive testing!
> 
>> And let's talk about the improvement by Matthias, namely:
>>
>> * 0008 Use a single GIN tuplesort
>> * 0009 Reduce the size of GinTuple by 12 bytes
>>
>> I haven't really seen any impact on duration - it seems more or less
>> within noise. Maybe it would be different on machines with less RAM, but
>> on my two systems it didn't really make a difference.
>>
>> It did significantly reduce the amount of temporary data written, by
>> ~40% or so. This is pretty nicely visible on the "trgm" case, which
>> generates the most temp files of the four indexes. An example from the
>> i5/32MB section looks like this:
>>
>> label    0000 0001  0002  0003  0004  0005  0006  0007  0008  0009  0010
>> ------------------------------------------------------------------------
>> trgm / 3    0 2635  3690  3715  1177  1177  1179  1179   696   682
>> 1016
> 
> After seeing the above data, I want to know where the time is spent and
> why the ~40% IO doesn't make a measurable duration improvement. then I
> did the following test.
> 
> create table gin_t (a int[]);
> insert into gin_t select * from rand_array(30000000, 0, 100, 0, 50); [1]
> select pg_prewarm('gin_t');
> 
> postgres=# create index on gin_t using gin(a);
> INFO:  pid: 145078,  stage 1 took 44476 ms
> INFO:  pid: 145177,  stage 1 took 44474 ms
> INFO:  pid: 145078,  stage 2 took 2662 ms
> INFO:  pid: 145177,  stage 2 took 2611 ms
> INFO:  pid: 145177,  stage 3 took 240 ms
> INFO:  pid: 145078,  stage 3 took 239 ms
> 
> CREATE INDEX
> Time: 79472.135 ms (01:19.472)
> 
> Then we can see stage 1 take 56% execution time. stage 2 + stage 3 take
> 3% execution time. and the leader's work takes the rest 41% execution
> time. I think that's why we didn't see much performance improvement of
> 0008 since it improves the "stage 2 and stage 3".
> 

Yes, that makes sense. It's so small fraction of the computation that it
can't translate to a meaningful speed.

> ==== Here is my definition for stage 1/2/3. 
> stage 1:
>          reltuples = table_index_build_scan(heap, index, indexInfo, true, progress,
>                                            ginBuildCallbackParallel, state, scan);
> 
>         /* write remaining accumulated entries */
>         ginFlushBuildState(state, index);
>     
> stage 2:
>         _gin_process_worker_data(state, state->bs_worker_sort)
> 
> stage 3:
>         tuplesort_performsort(state->bs_sortstate);
> 
> 
> But 0008 still does many good stuff:
> 1. Reduce the IO usage, this would be more useful on some heavily IO
>    workload. 
> 2. Simplify the building logic by removing one stage.
> 3. Add the 'buffer-writetup' to tuplesort.c, I don't have other user
>    case for now, but it looks like a reasonable design.
> 
> I think the current blocker is if it is safe to hack the tuplesort.c.
> With my current knowledge, It looks good to me, but it would be better
> open a dedicated thread to discuss this specially, the review would not
> take a long time if a people who is experienced on this area would take
> a look. 
> 

I agree. I expressed the same impression earlier in this thread, IIRC.

>> Now, what's the 0010 patch about?
>>
>> For some indexes (e.g. trgm), the parallel builds help a lot, because
>> they produce a lot of temporary data and the parallel sort is a
>> substantial part of the work. But for other indexes (especially the
>> "smaller" indexes on jsonb headers), it's not that great. For example
>> for "jsonb", having 3 workers shaves off only ~25% of the time, not 75%.
>>
>> Clearly, this happens because a lot of time is spent outside the sort,
>> actually inserting data into the index.
> 
> You can always foucs on the most important part which inpires me a lot,
> even with my simple testing, the "inserting data into index" stage take
> 40% time.
>>> So I was wondering if we might
>> parallelize that too, and how much time would it save - 0010 is an
>> experimental patch doing that. It splits the processing into 3 phases:
>>
>> 1. workers feeding data into tuplesort
>> 2. leader finishes sort and "repartitions" the data
>> 3. workers inserting their partition into index
>>
>> The patch is far from perfect (more a PoC) ..
>>
>> This does help a little bit, reducing the duration by ~15-25%. I wonder
>> if this might be improved by partitioning the data differently - not by
>> shuffling everything from the tuplesort into fileset (it increases the
>> amount of temporary data in the charts). And also by by distributing the
>> data differently - right now it's a bit of a round robin, because it
>> wasn't clear we know how many entries are there.
> 
> Due to the complexity of the existing code, I would like to foucs on
> existing patch first. So I vote for this optimization as a dedeciated
> patch. 
> 

I agree. Even if we decide to do these parallel inserts, it relies on
doing the parallel sort first. So it makes sense to leave that for
later, as an additional improvement.

>>>> and later we called 'tuplesort_performsort(state->bs_sortstate);'.  Even
>>>> we have some CTID merges activity in  '....(1)', the tuples are still
>>>> ordered, so the sort (in both tuplesort_putgintuple and
>>>> 'tuplesort_performsort) are not necessary,
>>>> ..
>>>> If Matthias's proposal is adopted, my optimization will not be useful
>>>> anymore and Matthias's porposal looks like a more natural and effecient
>>>> way.
>>
>> I think they might be complementary. I don't think it's reasonable to
>> expect GIN's BuildAccumulator to buffer all the index tuples at the
>> same time (as I mentioned upthread: we are or should be limited by
>> work memory), but the BuildAccumulator will do a much better job at
>> combining tuples than the in-memory sort + merge-write done by
>> Tuplesort (because BA will use (much?) less memory for the same number
>> of stored values).
> 
> Thank you Matthias for valuing my point! and thanks for highlighting the
> benefit that BuildAccumulator can do a better job for sorting in memory
> (I think it is mainly because BuildAccumulator can do run-time merge
> when accept more tuples).  but I still not willing to go further at this
> direction. Reasons are: a). It probably can't make a big difference at
> the final result. b). The best implementation of this idea would be
> allowing the user of tuplesort.c to insert the pre-sort tuples into tape
> directly rather than inserting them into tuplesort's memory and dump
> them into tape without a sort. However I can't define a clean API for
> the former case. c). create-index is a maintenance work, improving it by
> 30% would be good, but if we just improve it by <3, it looks not very
> charming in practice.
> 
> So my option is if we can have agreement on 0008, then we can final
> review/test on the existing code (including 0009), and leave further
> improvement as a dedicated patch. 
> 
> What do you think?
> 

Yeah. I think we have agreement on 0001-0007. I'm a bit torn about 0008,
I have not expected changing tuplesort like this when I started working
on the patch, but I can't deny it's a massive speedup for some cases
(where the patch doesn't help otherwise). But then in other cases it
doesn't help at all, and 0010 helps. I wonder if maybe there's a good
way to "flip" between those two approaches, by some heuristics.


regards

-- 
Tomas Vondra

On Tue, 27 Aug 2024 at 12:15, Andy Fan <zhihuifan1213@163.com> wrote:
>
> Tomas Vondra <tomas.vondra@enterprisedb.com> writes:
> > And let's talk about the improvement by Matthias, namely:
> >
> > * 0008 Use a single GIN tuplesort
> > * 0009 Reduce the size of GinTuple by 12 bytes
> >
> > I haven't really seen any impact on duration - it seems more or less
> > within noise. Maybe it would be different on machines with less RAM, but
> > on my two systems it didn't really make a difference.
> >
> > It did significantly reduce the amount of temporary data written, by
> > ~40% or so. This is pretty nicely visible on the "trgm" case, which
> > generates the most temp files of the four indexes. An example from the
> > i5/32MB section looks like this:
> >
> > label    0000 0001  0002  0003  0004  0005  0006  0007  0008  0009  0010
> > ------------------------------------------------------------------------
> > trgm / 3    0 2635  3690  3715  1177  1177  1179  1179   696   682
> > 1016
>
> After seeing the above data, I want to know where the time is spent and
> why the ~40% IO doesn't make a measurable duration improvement. then I
> did the following test.
[...]
> ==== Here is my definition for stage 1/2/3.
> stage 1:
>         reltuples = table_index_build_scan(heap, index, indexInfo, true, progress,
>                                                                                    ginBuildCallbackParallel, state,
scan);
>
>                 /* write remaining accumulated entries */
>                 ginFlushBuildState(state, index);
>
> stage 2:
>                 _gin_process_worker_data(state, state->bs_worker_sort)
>
> stage 3:
>                 tuplesort_performsort(state->bs_sortstate);

Note that tuplesort does most of its sort and IO work while receiving
tuples, which in this case would be during table_index_build_scan.
tuplesort_performsort usually only needs to flush the last elements of
a sort that it still has in memory, which is thus generally a cheap
operation bound by maintenance work memory, and definitely not
representative of the total cost of sorting data. In certain rare
cases it may take a longer time as it may have to merge sorted runs,
but those cases are quite rare in my experience.

> But 0008 still does many good stuff:
> 1. Reduce the IO usage, this would be more useful on some heavily IO
>    workload.
> 2. Simplify the building logic by removing one stage.
> 3. Add the 'buffer-writetup' to tuplesort.c, I don't have other user
>    case for now, but it looks like a reasonable design.

I'd imagine nbtree would like to use this too, for applying some
deduplication in the sort stage. The IO benefits are quite likely to
be worth it; a minimum space saving of 25% on duplicated key values in
tuple sorts sounds real great. And it doesn't even have to merge all
duplicates: even if you only merge 10 tuples at a time, the space
saving on those duplicates would be at least 47% on 64-bit systems.

> I think the current blocker is if it is safe to hack the tuplesort.c.
> With my current knowledge, It looks good to me, but it would be better
> open a dedicated thread to discuss this specially, the review would not
> take a long time if a people who is experienced on this area would take
> a look.

I could adapt the patch for nbtree use, to see if anyone's willing to
review that?

> > Now, what's the 0010 patch about?
> >
> > For some indexes (e.g. trgm), the parallel builds help a lot, because
> > they produce a lot of temporary data and the parallel sort is a
> > substantial part of the work. But for other indexes (especially the
> > "smaller" indexes on jsonb headers), it's not that great. For example
> > for "jsonb", having 3 workers shaves off only ~25% of the time, not 75%.
> >
> > Clearly, this happens because a lot of time is spent outside the sort,
> > actually inserting data into the index.
>
> You can always foucs on the most important part which inpires me a lot,
> even with my simple testing, the "inserting data into index" stage take
> 40% time.

nbtree does sorted insertions into the tree, constructing leaf pages
one at a time and adding separator keys in the page above when the
leaf page was filled, thus removing the need to descend the btree. I
imagine we can save some performance by mirroring that in GIN too,
with as additional bonus that we'd be free to start logging completed
pages before we're done with the full index, reducing max WAL
throughput in GIN index creation.

> > I think they might be complementary. I don't think it's reasonable to
> > expect GIN's BuildAccumulator to buffer all the index tuples at the
> > same time (as I mentioned upthread: we are or should be limited by
> > work memory), but the BuildAccumulator will do a much better job at
> > combining tuples than the in-memory sort + merge-write done by
> > Tuplesort (because BA will use (much?) less memory for the same number
> > of stored values).
>
> Thank you Matthias for valuing my point! and thanks for highlighting the
> benefit that BuildAccumulator can do a better job for sorting in memory
> (I think it is mainly because BuildAccumulator can do run-time merge
> when accept more tuples).  but I still not willing to go further at this
> direction. Reasons are: a). It probably can't make a big difference at
> the final result. b). The best implementation of this idea would be
> allowing the user of tuplesort.c to insert the pre-sort tuples into tape
> directly rather than inserting them into tuplesort's memory and dump
> them into tape without a sort.

You'd still need to keep track of sorted runs on those tapes, which is what
tuplesort.c does for us.

> However I can't define a clean API for
> the former case.

I imagine a pair of tuplesort_beginsortedrun();
tuplesort_endsortedrun() -functions to help this, but I'm not 100%
sure if we'd want to expose Tuplesort to non-PG sorting algorithms, as
it would be one easy way to create incorrect results if the sort used
in tuplesort isn't exactly equivalent to the sort used by the provider
of the tuples.

>  c). create-index is a maintenance work, improving it by
> 30% would be good, but if we just improve it by <3, it looks not very
> charming in practice.

I think improving 3% on reindex operations can be well worth the effort.

Also, do note that the current patch does (still) not correctly handle
[maintenance_]work_mem: Every backend's BuildAccumulator uses up to
work_mem of memory here, while the launched tuplesorts use an
additional maintenance_work_mem of memory, for a total of (workers +
1) * work_mem + m_w_m of memory usage. The available memory should
instead be allocated between tuplesort and BuildAccumulator, but can
probably mostly be allocated to just BuildAccumulator if we can dump
the data into the tuplesort directly, as it'd reduce the overall
number of operations and memory allocations for the tuplesort. I think
that once we correctly account for memory allocations (and an improved
write path) we'll be able to see a meaningfully larger performance
improvement.

> So my option is if we can have agreement on 0008, then we can final
> review/test on the existing code (including 0009), and leave further
> improvement as a dedicated patch.

As mentioned above, I think I could update the patch for a btree
implementation that also has immediate benefits, if so desired?


Kind regards,

Matthias van de Meent
Neon (https://neon.tech)

Matthias van de Meent <boekewurm+postgres@gmail.com> writes:


> tuplesort_performsort usually only needs to flush the last elements of
> ... In certain rare
> cases it may take a longer time as it may have to merge sorted runs,
> but those cases are quite rare in my experience.

OK, I am expecting such cases are not rare, Suppose we have hundreds of
GB heap tuple, and have the 64MB or 1GB maintenance_work_mem setup, it
probably hit this sistuation. I'm not mean at which experience is the
fact, but I am just to highlights the gap in our minds. and thanks for
sharing this, I can pay more attention in this direction in my future
work. To be clearer, my setup hit the 'mergeruns' case. 

>
>> But 0008 still does many good stuff:
>> 1. Reduce the IO usage, this would be more useful on some heavily IO
>>    workload.
>> 2. Simplify the building logic by removing one stage.
>> 3. Add the 'buffer-writetup' to tuplesort.c, I don't have other user
>>    case for now, but it looks like a reasonable design.
>
> I'd imagine nbtree would like to use this too, for applying some
> deduplication in the sort stage.

The current ntbtree do the deduplication during insert into the nbtree
IIUC, in your new strategy, we can move it the "sort" stage, which looks
good to me [to confirm my understanding].

> The IO benefits are quite likely to
> be worth it; a minimum space saving of 25% on duplicated key values in
> tuple sorts sounds real great.

Just be clearer on the knowledge, the IO benefits can be only gained
when the tuplesort's memory can't hold all the tuples, and in such case,
tuplesort_performsort would run the 'mergeruns', or else we can't get
any benefit?

>
>> I think the current blocker is if it is safe to hack the tuplesort.c.
>> With my current knowledge, It looks good to me, but it would be better
>> open a dedicated thread to discuss this specially, the review would not
>> take a long time if a people who is experienced on this area would take
>> a look.
>
> I could adapt the patch for nbtree use, to see if anyone's willing to
> review that?

I'm interested with it and can do some review & testing. But the
keypoint would be we need some authorities are willing to review it, to
make it happen to a bigger extent, a dedicated thread would be helpful.

>> > Now, what's the 0010 patch about?
>> >
>> > For some indexes (e.g. trgm), the parallel builds help a lot, because
>> > they produce a lot of temporary data and the parallel sort is a
>> > substantial part of the work. But for other indexes (especially the
>> > "smaller" indexes on jsonb headers), it's not that great. For example
>> > for "jsonb", having 3 workers shaves off only ~25% of the time, not 75%.
>> >
>> > Clearly, this happens because a lot of time is spent outside the sort,
>> > actually inserting data into the index.
>>
>> You can always foucs on the most important part which inpires me a lot,
>> even with my simple testing, the "inserting data into index" stage take
>> 40% time.
>
> nbtree does sorted insertions into the tree, constructing leaf pages
> one at a time and adding separator keys in the page above when the
> leaf page was filled, thus removing the need to descend the btree. I
> imagine we can save some performance by mirroring that in GIN too,
> with as additional bonus that we'd be free to start logging completed
> pages before we're done with the full index, reducing max WAL
> throughput in GIN index creation.

I agree this is a promising direction as well.

>> > I think they might be complementary. I don't think it's reasonable to
>> > expect GIN's BuildAccumulator to buffer all the index tuples at the
>> > same time (as I mentioned upthread: we are or should be limited by
>> > work memory), but the BuildAccumulator will do a much better job at
>> > combining tuples than the in-memory sort + merge-write done by
>> > Tuplesort (because BA will use (much?) less memory for the same number
>> > of stored values).
>>
>> Thank you Matthias for valuing my point! and thanks for highlighting the
>> benefit that BuildAccumulator can do a better job for sorting in memory
>> (I think it is mainly because BuildAccumulator can do run-time merge
>> when accept more tuples).  but I still not willing to go further at this
>> direction. Reasons are: a). It probably can't make a big difference at
>> the final result. b). The best implementation of this idea would be
>> allowing the user of tuplesort.c to insert the pre-sort tuples into tape
>> directly rather than inserting them into tuplesort's memory and dump
>> them into tape without a sort.
>
> You'd still need to keep track of sorted runs on those tapes, which is what
> tuplesort.c does for us.
>
>> However I can't define a clean API for
>> the former case.
>
> I imagine a pair of tuplesort_beginsortedrun();
> tuplesort_endsortedrun() -functions to help this.

This APIs do are better than the ones in my mind:) during the range
between tuplesort_beginsortedrun and tuplesort_endsortedrun(), we can
bypass the tuplessort's memory. 

> but I'm not 100%
> sure if we'd want to expose Tuplesort to non-PG sorting algorithms, as
> it would be one easy way to create incorrect results if the sort used
> in tuplesort isn't exactly equivalent to the sort used by the provider
> of the tuples.

OK.

>
>>  c). create-index is a maintenance work, improving it by
>> 30% would be good, but if we just improve it by <3, it looks not very
>> charming in practice.
>
> I think improving 3% on reindex operations can be well worth the effort.

> Also, do note that the current patch does (still) not correctly handle
> [maintenance_]work_mem: Every backend's BuildAccumulator uses up to
> work_mem of memory here, while the launched tuplesorts use an
> additional maintenance_work_mem of memory, for a total of (workers +
> 1) * work_mem + m_w_m of memory usage. The available memory should
> instead be allocated between tuplesort and BuildAccumulator, but can
> probably mostly be allocated to just BuildAccumulator if we can dump
> the data into the tuplesort directly, as it'd reduce the overall
> number of operations and memory allocations for the tuplesort. I think
> that once we correctly account for memory allocations (and an improved
> write path) we'll be able to see a meaningfully larger performance
> improvement.

Personally I am more fans of your "buffer writetup" idea, but not the
same interests with the tuplesort_beginsortedrun /
tuplesort_endsortedrun.  I said the '3%' is for the later one and I
guess you understand it as the former one. 
>
>> So my option is if we can have agreement on 0008, then we can final
>> review/test on the existing code (including 0009), and leave further
>> improvement as a dedicated patch.
>
> As mentioned above, I think I could update the patch for a btree
> implementation that also has immediate benefits, if so desired?

If you are saying about the buffered-writetup in tuplesort, then I think
it is great, and in a dedicated thread for better exposure.

-- 
Best Regards
Andy Fan

On Wed, 28 Aug 2024 at 02:38, Andy Fan <zhihuifan1213@163.com> wrote:
>
> Matthias van de Meent <boekewurm+postgres@gmail.com> writes:
> > tuplesort_performsort usually only needs to flush the last elements of
> > ... In certain rare
> > cases it may take a longer time as it may have to merge sorted runs,
> > but those cases are quite rare in my experience.
>
> OK, I am expecting such cases are not rare, Suppose we have hundreds of
> GB heap tuple, and have the 64MB or 1GB maintenance_work_mem setup, it
> probably hit this sistuation. I'm not mean at which experience is the
> fact, but I am just to highlights the gap in our minds. and thanks for
> sharing this, I can pay more attention in this direction in my future
> work. To be clearer, my setup hit the 'mergeruns' case.

Huh, I've never noticed the performsort phase of btree index creation
(as seen in pg_stat_progress_create_index) take much time if any,
especially when compared to the tuple loading phase, so I assumed it
didn't happen often. Hmm, maybe I've just been quite lucky.

> >
> >> But 0008 still does many good stuff:
> >> 1. Reduce the IO usage, this would be more useful on some heavily IO
> >>    workload.
> >> 2. Simplify the building logic by removing one stage.
> >> 3. Add the 'buffer-writetup' to tuplesort.c, I don't have other user
> >>    case for now, but it looks like a reasonable design.
> >
> > I'd imagine nbtree would like to use this too, for applying some
> > deduplication in the sort stage.
>
> The current ntbtree do the deduplication during insert into the nbtree
> IIUC, in your new strategy, we can move it the "sort" stage, which looks
> good to me [to confirm my understanding].

Correct: We can do at least some deduplication in the sort stage. Not
all, because tuples need to fit on pages and we don't want to make the
tuples so large that we'd cause unnecessary splits while loading the
tree, but merging runs of 10-30 tuples should reduce IO requirements
by some margin for indexes where deduplication is important.

> > The IO benefits are quite likely to
> > be worth it; a minimum space saving of 25% on duplicated key values in
> > tuple sorts sounds real great.
>
> Just be clearer on the knowledge, the IO benefits can be only gained
> when the tuplesort's memory can't hold all the tuples, and in such case,
> tuplesort_performsort would run the 'mergeruns', or else we can't get
> any benefit?

It'd be when the tuplesort's memory can't hold all tuples, but
mergeruns isn't strictly required here, as dumptuples() would already
allow some tuple merging.

> >> I think the current blocker is if it is safe to hack the tuplesort.c.
> >> With my current knowledge, It looks good to me, but it would be better
> >> open a dedicated thread to discuss this specially, the review would not
> >> take a long time if a people who is experienced on this area would take
> >> a look.
> >
> > I could adapt the patch for nbtree use, to see if anyone's willing to
> > review that?
>
> I'm interested with it and can do some review & testing. But the
> keypoint would be we need some authorities are willing to review it, to
> make it happen to a bigger extent, a dedicated thread would be helpful.

Then I'll split it off into a new thread sometime later this week.

> > nbtree does sorted insertions into the tree, constructing leaf pages
> > one at a time and adding separator keys in the page above when the
> > leaf page was filled, thus removing the need to descend the btree. I
> > imagine we can save some performance by mirroring that in GIN too,
> > with as additional bonus that we'd be free to start logging completed
> > pages before we're done with the full index, reducing max WAL
> > throughput in GIN index creation.
>
> I agree this is a promising direction as well.

It'd be valuable to see if the current patch's "parallel sorted"
insertion is faster even than the current GIN insertion path even if
we use only the primary process, as it could be competative.
Btree-like bulk tree loading might even be meaningfully faster than
GIN's current index creation process. However, as I mentioned
significantly upthread, I don't expect that change to happen in this
patch series.

> > I imagine a pair of tuplesort_beginsortedrun();
> > tuplesort_endsortedrun() -functions to help this.
>
> This APIs do are better than the ones in my mind:) during the range
> between tuplesort_beginsortedrun and tuplesort_endsortedrun(), we can
> bypass the tuplessort's memory.

Exactly, we'd have the user call tuplesort_beginsortedrun(); then
iteratively insert its sorted tuples using the usual
tuplesort_putYYY() api, and then call _endsortedrun() when the sorted
run is complete. It does need some work in tuplesort state handling
and internals, but I think that's quite achievable.


Kind regards,

Matthias van de Meent

Tomas Vondra <tomas@vondra.me> writes:

Hi Tomas,

> Yeah. I think we have agreement on 0001-0007.

Yes, the design of 0001-0007 looks good to me and because of the
existing compexitity, I want to foucs on this part for now. I am doing
code review from yesterday, and now my work is done.  Just some small
questions: 

1. In GinBufferStoreTuple,

    /*
     * Check if the last TID in the current list is frozen. This is the case
     * when merging non-overlapping lists, e.g. in each parallel worker.
     */
    if ((buffer->nitems > 0) &&
        (ItemPointerCompare(&buffer->items[buffer->nitems - 1], &tup->first) == 0))
        buffer->nfrozen = buffer->nitems;

should we do (ItemPointerCompare(&buffer->items[buffer->nitems - 1],
&tup->first) "<=" 0), rather than "=="? 

2. Given the "non-overlap" case should be the major case
GinBufferStoreTuple , does it deserve a fastpath for it before calling
ginMergeItemPointers since ginMergeItemPointers have a unconditionally
memory allocation directly, and later we pfree it?

new = ginMergeItemPointers(&buffer->items[buffer->nfrozen], /* first unfronzen */
                   (buffer->nitems - buffer->nfrozen),    /* num of unfrozen */
               items, tup->nitems, &nnew);


3. The following comment in index_build is out-of-date now :)

    /*
     * Determine worker process details for parallel CREATE INDEX.  Currently,
     * only btree has support for parallel builds.
     *

4. Comments - Buffer is not empty and it's storing "a different key"
looks wrong to me. the key may be same and we just need to flush them
because of memory usage. There is the same issue in both
_gin_process_worker_data and _gin_parallel_merge.  

        if (GinBufferShouldTrim(buffer, tup))
        {
            Assert(buffer->nfrozen > 0);

            state->buildStats.nTrims++;

            /*
             * Buffer is not empty and it's storing a different key - flush
             * the data into the insert, and start a new entry for current
             * GinTuple.
             */
            AssertCheckItemPointers(buffer, true);

I also run valgrind testing with some testcase, no memory issue is
found. 

> I'm a bit torn about 0008, I have not expected changing tuplesort like
> this when I started working 
> on the patch, but I can't deny it's a massive speedup for some cases
> (where the patch doesn't help otherwise). But then in other cases it
> doesn't help at all, and 0010 helps.

Yes, I'd like to see these improvements both 0008 and 0010 as a
dedicated improvement. 

-- 
Best Regards
Andy Fan