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From: Filip Janus <fjanus@redhat.com>
Date: Thu, 28 Nov 2024 12:32:15 +0100
Message-ID: <CAFjYY+J7ZcJAoE+K4AoyuiS_N6w8vVCQ6HQ72mPB=i4C+QbAeQ@mail.gmail.com>
Subject: Re: Proposal: Adding compression of temporary files
To: Tomas Vondra <tomas@vondra.me>
Cc: pgsql-hackers@postgresql.org
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I've added a regression test for lz4 compression if the server is compiled
with the "--with-lz4" option.

    -Filip-


ne 24. 11. 2024 v 15:53 odes=C3=ADlatel Filip Janus <fjanus@redhat.com> nap=
sal:

>
>
>     -Filip-
>
>
> st 20. 11. 2024 v 1:35 odes=C3=ADlatel Tomas Vondra <tomas@vondra.me> nap=
sal:
>
>> Hi,
>>
>> On 11/18/24 22:58, Filip Janus wrote:
>> > ...
>> >     Hi all,
>> >     Postgresql supports data compression nowadays, but the compression
>> of
>> >     temporary files has not been implemented yet. The huge queries can
>> >     produce a significant amount of temporary data that needs to
>> >     be stored on disk
>> >     and cause many expensive I/O operations.
>> >     I am attaching a proposal of the patch to enable temporary files
>> >     compression for
>> >     hashjoins for now. Initially, I've chosen the LZ4 compression
>> >     algorithm. It would
>> >     probably make better sense to start with pglz, but I realized it
>> late.
>> >
>>
>> Thanks for the idea & patch. I agree this might be quite useful for
>> workloads generating a lot of temporary files for stuff like sorts etc.
>> I think it will be interesting to think about the trade offs, i.e. how
>> to pick the compression level - at some point the compression ratio
>> stops improving while paying more and more CPU time. Not sure what the
>> right choice is, so using default seems fine.
>>
>> I agree it'd be better to start with pglz, and only then add lz4 etc.
>> Firstly, pglz is simply the built-in compression, supported everywhere.
>> And it's also simpler to implement, I think.
>>
>> >     # Future possible improvements
>> >     Reducing the number of memory allocations within the dumping and
>> >     loading of
>> >     the buffer. I have two ideas for solving this problem. I would
>> >     either add a buffer into
>> >     struct BufFile or provide the buffer as an argument from the calle=
r.
>> >     For the sequential
>> >     execution, I would prefer the second option.
>> >
>>
>> Yes, this would be good. Doing a palloc+pfree for each compression is
>> going to be expensive, especially because these buffers are going to be
>> large - likely larger than 8kB. Which means it's not cached in the
>> memory context, etc.
>>
>> Adding it to the BufFile is not going to fly, because that doubles the
>> amount of memory per file. And we already have major issues with hash
>> joins consuming massive amounts of memory. But at the same time the
>> buffer is only needed during compression, and there's only one at a
>> time. So I agree with passing a single buffer as an argument.
>>
>> >     # Future plan/open questions
>> >     In the future, I would like to add support for pglz and zstd.
>> >     Further, I plan to
>> >     extend the support of the temporary file compression also for
>> >     sorting, gist index creation, etc.
>> >
>> >     Experimenting with the stream mode of compression algorithms. The
>> >     compression
>> >     ratio of LZ4 in block mode seems to be satisfying, but the stream
>> >     mode could
>> >     produce a better ratio, but it would consume more memory due to th=
e
>> >     requirement to store
>> >     context for LZ4 stream compression.
>> >
>>
>> One thing I realized is that this only enables temp file compression for
>> a single place - hash join spill files. AFAIK this is because compressed
>> files don't support random access, and the other places might need that.
>>
>> Is that correct? The patch does not explain this anywhere. If that's
>> correct, the patch probably should mention this in a comment for the
>> 'compress' argument added to BufFileCreateTemp(), so that it's clear
>> when it's legal to set compress=3Dtrue.
>>
>
> I will add the description there.
>
>
>> Which other places might compress temp files? Surely hash joins are not
>> the only place that could benefit from this, right?
>>
>
> Yes, you are definitely right. I have chosen the hash joins as a POC
> because
> there are no seeks besides seeks at the beginning of the buffer.
> I have focused on hashjoins, but there are definitely also other places
> where
> the compression could be used. I want to add support in other places
> in the feature.
>
>
>> Another thing is testing. If I run regression tests, it won't use
>> compression at all, because the GUC has "none" by default, right? But we
>> need some testing, so how would we do that? One option would be to add a
>> regression test that explicitly sets the GUC and does a hash join, but
>> that won't work with lz4 (because that may not be enabled).
>
>
> Right, it's "none" by default. My opinion is that we would like to test
> every supported compression method, so I will try to add environment
> variable as
> you recommended.
>
>
>>
>> Another option might be to add a PG_TEST_xxx environment variable that
>> determines compression to use. Something like PG_TEST_USE_UNIX_SOCKETS.
>> But perhaps there's a simpler way.
>>
>> >     # Benchmark
>> >     I prepared three different databases to check expectations. Each
>> >     dataset is described below. My testing demonstrates that my patch
>> >     improves the execution time of huge hash joins.
>> >     Also, my implementation should not
>> >     negatively affect performance within smaller queries.
>> >     The usage of memory needed for temporary files was reduced in ever=
y
>> >      execution without a significant impact on execution time.
>> >
>> >     *## Dataset A:*
>> >     Tables*
>> >     *
>> >     table_a(bigint id,text data_text,integer data_number) - 10000000
>> rows
>> >     table_b(bigint id, integer ref_id, numeric data_value, bytea
>> >     data_blob) - 10000000 rows
>> >     Query:  SELECT *  FROM table_a a JOIN table_b b ON a.id <http://
>> >     a.id> =3D b.id <http://b.id>;
>> >
>> >     The tables contain highly compressible data.
>> >     The query demonstrated a reduction in the usage of the temporary
>> >     files ~20GB -> 3GB, based on this reduction also caused the
>> execution
>> >     time of the query to be reduced by about ~10s.
>> >
>> >
>> >     *## Dataset B:*
>> >     Tables:*
>> >     *
>> >     table_a(integer id, text data_blob) - 1110000 rows
>> >     table_b(integer id, text data_blob) - 10000000 rows
>> >     Query:  SELECT *  FROM table_a a JOIN table_b b ON a.id <http://
>> >     a.id> =3D b.id <http://b.id>;
>> >
>> >     The tables contain less compressible data. data_blob was generated
>> >     by a pseudo-random generator.
>> >     In this case, the data reduction was only ~50%. Also, the executio=
n
>> >     time was reduced
>> >     only slightly with the enabled compression.
>> >
>> >     The second scenario demonstrates no overhead in the case of enable=
d
>> >     compression and extended work_mem to avoid temp file usage.
>> >
>> >     *## Dataset C:*
>> >     Tables
>> >     customers (integer,text,text,text,text)
>> >     order_items(integer,integer,integer,integer,numeric(10,2))
>> >     orders(integer,integer,timestamp,numeric(10,2))
>> >     products(integer,text,text,numeric(10,2),integer)
>> >
>> >     Query: SELECT p.product_id, p.name <http://p.name>, p.price,
>> >     SUM(oi.quantity) AS total_quantity, AVG(oi.price) AS avg_item_pric=
e
>> >     FROM eshop.products p JOIN eshop.order_items oi ON p.product_id =
=3D
>> >     oi.product_id JOIN
>> >     eshop.orders o ON oi.order_id =3D o.order_id WHERE o.order_date >
>> >     '2020-01-01' AND p.price > 50
>> >     GROUP BY p.product_id, p.name <http://p.name>, p.price HAVING
>> >     SUM(oi.quantity) > 1000
>> >     ORDER BY total_quantity DESC LIMIT 100;
>> >
>> >     This scenario should demonstrate a more realistic usage of the
>> database.
>> >     Enabled compression slightly reduced the temporary memory usage, b=
ut
>> >     the execution
>> >     time wasn't affected by compression.
>> >
>> >
>> >     +------------+-------------------------+-----------------------
>> >     +------------------------------+
>> >     |  Dataset   | Compression.       | temp_bytes         | Execution
>> >     Time (ms)   |
>> >     +------------+-------------------------+-----------------------
>> >     +----------------------------- +
>> >     | A             | Yes                        |  3.09 GiB
>> >     | 22s586ms           | work_mem  =3D 4MB
>> >     |                | No                         |  21.89 GiB
>> >     | 35s                       | work_mem  =3D 4MB
>> >     +------------+-------------------------+-----------------------
>> >     +----------------------------------------
>> >     | B             | Yes                        |  333 MB
>> >     | 1815.545 ms       | work_mem =3D 4MB
>> >     |                 | No                        |  146  MB
>> >       | 1500.460 ms        | work_mem =3D 4MB
>> >     |                 | Yes                       |  0 MB
>> >         | 3262.305 ms        | work_mem =3D 80MB
>> >     |                 | No                        |  0 MB
>> >        | 3174.725 ms         | work_mem =3D 80MB
>> >     +-------------+------------------------+------------------------
>> >     +-------------------------------------
>> >     | C             | Yes                       | 40 MB
>> >     | 1011.020 ms        | work_mem =3D 1MB
>> >     |                | No                        |  53
>> >     MB                 |  1034.142 ms        | work_mem =3D 1MB
>> >     +------------+------------------------+------------------------
>> >     +--------------------------------------
>> >
>> >
>>
>> Thanks. I'll try to do some benchmarks on my own.
>>
>> Are these results fro ma single run, or an average of multiple runs?
>
>
> It is average from multiple runs.
>
> Do
>> you maybe have a script to reproduce this, including the data generation=
?
>
>
> I am attaching my SQL file for database preparation. I also did further
> testing
> with two other machines( see attachment huge_tables.rtf ).
>
>>
>> Also, can you share some information about the machine used for this? I
>> expect the impact to strongly depends on memory pressure - if the temp
>> file fits into page cache (and stays there), it may not benefit from the
>> compression, right?
>>
>
> If it fits into the page cache due to compression, I would consider it as
> a benefit from compression.
> I performed further testing on machines with different memory sizes.
> Both experiments showed that compression was beneficial for execution
> time.
> The execution time reduction was more significant in the case of the
> machine that had
> less memory available.
>
> Tests were performed on:
> MacBook PRO M3 36GB - MacOs
> Virtual machine ARM64 10GB/ 6CPU - Fedora 39
>
>
>>
>> regards
>>
>> --
>> Tomas Vondra
>>
>>

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<div dir=3D"ltr"><div><br clear=3D"all"></div><div><div dir=3D"ltr" class=
=3D"gmail_signature" data-smartmail=3D"gmail_signature"><div dir=3D"ltr"><d=
iv><div>I&#39;ve added a regression test for lz4 compression if the server =
is compiled with the &quot;--with-lz4&quot; option.</div><div><br></div><di=
v dir=3D"ltr">=C2=A0 =C2=A0 -Filip-</div></div></div></div></div><br></div>=
<br><div class=3D"gmail_quote gmail_quote_container"><div dir=3D"ltr" class=
=3D"gmail_attr">ne 24. 11. 2024 v=C2=A015:53 odes=C3=ADlatel Filip Janus &l=
t;<a href=3D"mailto:fjanus@redhat.com">fjanus@redhat.com</a>&gt; napsal:<br=
></div><blockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;=
border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir=3D"ltr"><=
div dir=3D"ltr"><div dir=3D"ltr"><div><br clear=3D"all"></div><div><div dir=
=3D"ltr" class=3D"gmail_signature"><div dir=3D"ltr"><div><div dir=3D"ltr"><=
br></div><div dir=3D"ltr">=C2=A0 =C2=A0 -Filip-</div></div></div></div></di=
v><br></div><br><div class=3D"gmail_quote"><div dir=3D"ltr" class=3D"gmail_=
attr">st 20. 11. 2024 v=C2=A01:35 odes=C3=ADlatel Tomas Vondra &lt;<a href=
=3D"mailto:tomas@vondra.me" target=3D"_blank">tomas@vondra.me</a>&gt; napsa=
l:<br></div><blockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0=
.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Hi,<br>
<br>
On 11/18/24 22:58, Filip Janus wrote:<br>
&gt; ...<br>
&gt;=C2=A0 =C2=A0 =C2=A0Hi all,<br>
&gt;=C2=A0 =C2=A0 =C2=A0Postgresql supports data compression nowadays, but =
the compression of<br>
&gt;=C2=A0 =C2=A0 =C2=A0temporary files has not been implemented yet. The h=
uge queries can=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0produce a significant amount of temporary data that=
 needs to<br>
&gt;=C2=A0 =C2=A0 =C2=A0be=C2=A0stored on disk=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0and cause many expensive I/O operations.<br>
&gt;=C2=A0 =C2=A0 =C2=A0I am attaching a proposal of the patch to enable te=
mporary files<br>
&gt;=C2=A0 =C2=A0 =C2=A0compression for<br>
&gt;=C2=A0 =C2=A0 =C2=A0hashjoins for now. Initially, I&#39;ve chosen the L=
Z4 compression<br>
&gt;=C2=A0 =C2=A0 =C2=A0algorithm. It would<br>
&gt;=C2=A0 =C2=A0 =C2=A0probably make better sense to start with pglz, but =
I realized it late.<br>
&gt; <br>
<br>
Thanks for the idea &amp; patch. I agree this might be quite useful for<br>
workloads generating a lot of temporary files for stuff like sorts etc.<br>
I think it will be interesting to think about the trade offs, i.e. how<br>
to pick the compression level - at some point the compression ratio<br>
stops improving while paying more and more CPU time. Not sure what the<br>
right choice is, so using default seems fine.<br>
<br>
I agree it&#39;d be better to start with pglz, and only then add lz4 etc.<b=
r>
Firstly, pglz is simply the built-in compression, supported everywhere.<br>
And it&#39;s also simpler to implement, I think.<br>
<br>
&gt;=C2=A0 =C2=A0 =C2=A0# Future possible improvements<br>
&gt;=C2=A0 =C2=A0 =C2=A0Reducing the number of memory allocations within th=
e dumping and<br>
&gt;=C2=A0 =C2=A0 =C2=A0loading of<br>
&gt;=C2=A0 =C2=A0 =C2=A0the buffer. I have two ideas for solving this probl=
em. I would<br>
&gt;=C2=A0 =C2=A0 =C2=A0either add a buffer into<br>
&gt;=C2=A0 =C2=A0 =C2=A0struct BufFile or provide the buffer as an argument=
 from the caller.<br>
&gt;=C2=A0 =C2=A0 =C2=A0For the sequential=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0execution, I would prefer the second option.<br>
&gt; <br>
<br>
Yes, this would be good. Doing a palloc+pfree for each compression is<br>
going to be expensive, especially because these buffers are going to be<br>
large - likely larger than 8kB. Which means it&#39;s not cached in the<br>
memory context, etc.<br>
<br>
Adding it to the BufFile is not going to fly, because that doubles the<br>
amount of memory per file. And we already have major issues with hash<br>
joins consuming massive amounts of memory. But at the same time the<br>
buffer is only needed during compression, and there&#39;s only one at a<br>
time. So I agree with passing a single buffer as an argument.<br>
<br>
&gt;=C2=A0 =C2=A0 =C2=A0# Future plan/open questions<br>
&gt;=C2=A0 =C2=A0 =C2=A0In the future, I would like to add support for pglz=
 and zstd.<br>
&gt;=C2=A0 =C2=A0 =C2=A0Further, I plan to<br>
&gt;=C2=A0 =C2=A0 =C2=A0extend the support of the temporary file compressio=
n also for<br>
&gt;=C2=A0 =C2=A0 =C2=A0sorting, gist index creation, etc.<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0Experimenting with the stream mode of compression a=
lgorithms. The<br>
&gt;=C2=A0 =C2=A0 =C2=A0compression=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0ratio of LZ4 in block mode seems to be satisfying, =
but the stream<br>
&gt;=C2=A0 =C2=A0 =C2=A0mode could=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0produce a better ratio, but it would consume more m=
emory due to the<br>
&gt;=C2=A0 =C2=A0 =C2=A0requirement to store<br>
&gt;=C2=A0 =C2=A0 =C2=A0context for LZ4 stream compression.<br>
&gt; <br>
<br>
One thing I realized is that this only enables temp file compression for<br=
>
a single place - hash join spill files. AFAIK this is because compressed<br=
>
files don&#39;t support random access, and the other places might need that=
.<br>
<br>
Is that correct? The patch does not explain this anywhere. If that&#39;s<br=
>
correct, the patch probably should mention this in a comment for the<br>
&#39;compress&#39; argument added to BufFileCreateTemp(), so that it&#39;s =
clear<br>
when it&#39;s legal to set compress=3Dtrue.<br></blockquote><div>=C2=A0</di=
v><div>I will add the description there.<br></div><div>=C2=A0</div><blockqu=
ote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;border-left:1px=
 solid rgb(204,204,204);padding-left:1ex">Which other places might compress=
 temp files? Surely hash joins are not<br>
the only place that could benefit from this, right?<br></blockquote><div><b=
r></div><div>Yes, you are definitely right. I have=C2=A0chosen the hash joi=
ns as a POC because=C2=A0</div><div>there are no seeks besides seeks at the=
 beginning of the buffer.=C2=A0</div><div>I have focused on hashjoins, but =
there are definitely also other places where</div><div>the compression coul=
d be used. I want to add support in other places</div><div>in the feature.<=
br></div><div>=C2=A0</div><blockquote class=3D"gmail_quote" style=3D"margin=
:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"=
>
Another thing is testing. If I run regression tests, it won&#39;t use<br>
compression at all, because the GUC has &quot;none&quot; by default, right?=
 But we<br>
need some testing, so how would we do that? One option would be to add a<br=
>
regression test that explicitly sets the GUC and does a hash join, but<br>
that won&#39;t work with lz4 (because that may not be enabled).</blockquote=
><div>=C2=A0</div><div>Right, it&#39;s &quot;none&quot; by default. My opin=
ion is that we would like to test</div><div>every supported compression met=
hod, so I will try to add environment variable as</div><div>you recommended=
.<br></div><div>=C2=A0<br></div><blockquote class=3D"gmail_quote" style=3D"=
margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-lef=
t:1ex">
<br>
Another option might be to add a PG_TEST_xxx environment variable that<br>
determines compression to use. Something like PG_TEST_USE_UNIX_SOCKETS.<br>
But perhaps there&#39;s a simpler way.<br>
<br>
&gt;=C2=A0 =C2=A0 =C2=A0# Benchmark<br>
&gt;=C2=A0 =C2=A0 =C2=A0I prepared three different databases to check expec=
tations. Each=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0dataset is described below. My testing demonstrates=
 that my patch=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0improves the execution time of huge hash joins.=C2=
=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0Also, my implementation should not<br>
&gt;=C2=A0 =C2=A0 =C2=A0negatively affect performance within smaller querie=
s.=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0The usage of memory needed for temporary files was =
reduced in every<br>
&gt;=C2=A0 =C2=A0 =C2=A0=C2=A0execution without a significant impact on exe=
cution time.<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0*## Dataset A:*<br>
&gt;=C2=A0 =C2=A0 =C2=A0Tables*<br>
&gt;=C2=A0 =C2=A0 =C2=A0*<br>
&gt;=C2=A0 =C2=A0 =C2=A0table_a(bigint id,text data_text,integer data_numbe=
r) - 10000000 rows<br>
&gt;=C2=A0 =C2=A0 =C2=A0table_b(bigint id, integer ref_id, numeric data_val=
ue, bytea<br>
&gt;=C2=A0 =C2=A0 =C2=A0data_blob) - 10000000 rows<br>
&gt;=C2=A0 =C2=A0 =C2=A0Query:=C2=A0 SELECT *=C2=A0 FROM table_a a JOIN tab=
le_b b ON <a href=3D"http://a.id" rel=3D"noreferrer" target=3D"_blank">a.id=
</a> &lt;http://<br>
&gt;=C2=A0 =C2=A0 =C2=A0<a href=3D"http://a.id" rel=3D"noreferrer" target=
=3D"_blank">a.id</a>&gt; =3D <a href=3D"http://b.id" rel=3D"noreferrer" tar=
get=3D"_blank">b.id</a> &lt;<a href=3D"http://b.id" rel=3D"noreferrer" targ=
et=3D"_blank">http://b.id</a>&gt;;<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0The tables contain highly compressible data.<br>
&gt;=C2=A0 =C2=A0 =C2=A0The query demonstrated a reduction in the usage of =
the temporary=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0files ~20GB -&gt; 3GB, based on this reduction also=
 caused the execution=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0time of the query to be reduced by about ~10s.<br>
&gt; <br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0*## Dataset B:*<br>
&gt;=C2=A0 =C2=A0 =C2=A0Tables:*<br>
&gt;=C2=A0 =C2=A0 =C2=A0*<br>
&gt;=C2=A0 =C2=A0 =C2=A0table_a(integer id, text data_blob) - 1110000 rows<=
br>
&gt;=C2=A0 =C2=A0 =C2=A0table_b(integer id, text data_blob) - 10000000 rows=
<br>
&gt;=C2=A0 =C2=A0 =C2=A0Query:=C2=A0 SELECT *=C2=A0 FROM table_a a JOIN tab=
le_b b ON <a href=3D"http://a.id" rel=3D"noreferrer" target=3D"_blank">a.id=
</a> &lt;http://<br>
&gt;=C2=A0 =C2=A0 =C2=A0<a href=3D"http://a.id" rel=3D"noreferrer" target=
=3D"_blank">a.id</a>&gt; =3D <a href=3D"http://b.id" rel=3D"noreferrer" tar=
get=3D"_blank">b.id</a> &lt;<a href=3D"http://b.id" rel=3D"noreferrer" targ=
et=3D"_blank">http://b.id</a>&gt;;<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0The tables contain less compressible data. data_blo=
b was generated<br>
&gt;=C2=A0 =C2=A0 =C2=A0by a pseudo-random generator.<br>
&gt;=C2=A0 =C2=A0 =C2=A0In this case, the data reduction was only ~50%. Als=
o, the execution<br>
&gt;=C2=A0 =C2=A0 =C2=A0time was reduced=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0only slightly with the enabled compression.<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0The second scenario demonstrates no overhead in the=
 case of enabled=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0compression and extended work_mem to avoid temp fil=
e usage.<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0*## Dataset C:*<br>
&gt;=C2=A0 =C2=A0 =C2=A0Tables<br>
&gt;=C2=A0 =C2=A0 =C2=A0customers (integer,text,text,text,text)<br>
&gt;=C2=A0 =C2=A0 =C2=A0order_items(integer,integer,integer,integer,numeric=
(10,2))<br>
&gt;=C2=A0 =C2=A0 =C2=A0orders(integer,integer,timestamp,numeric(10,2))<br>
&gt;=C2=A0 =C2=A0 =C2=A0products(integer,text,text,numeric(10,2),integer)<b=
r>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0Query: SELECT p.product_id, <a href=3D"http://p.nam=
e" rel=3D"noreferrer" target=3D"_blank">p.name</a> &lt;<a href=3D"http://p.=
name" rel=3D"noreferrer" target=3D"_blank">http://p.name</a>&gt;, p.price,<=
br>
&gt;=C2=A0 =C2=A0 =C2=A0SUM(oi.quantity) AS total_quantity, AVG(oi.price) A=
S avg_item_price<br>
&gt;=C2=A0 =C2=A0 =C2=A0FROM eshop.products p JOIN eshop.order_items oi ON =
p.product_id =3D<br>
&gt;=C2=A0 =C2=A0 =C2=A0oi.product_id JOIN=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0eshop.orders o ON oi.order_id =3D o.order_id WHERE =
o.order_date &gt;<br>
&gt;=C2=A0 =C2=A0 =C2=A0&#39;2020-01-01&#39; AND p.price &gt; 50<br>
&gt;=C2=A0 =C2=A0 =C2=A0GROUP BY p.product_id, <a href=3D"http://p.name" re=
l=3D"noreferrer" target=3D"_blank">p.name</a> &lt;<a href=3D"http://p.name"=
 rel=3D"noreferrer" target=3D"_blank">http://p.name</a>&gt;, p.price HAVING=
<br>
&gt;=C2=A0 =C2=A0 =C2=A0SUM(oi.quantity) &gt; 1000<br>
&gt;=C2=A0 =C2=A0 =C2=A0ORDER BY total_quantity DESC LIMIT 100;<br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0This scenario should demonstrate a more realistic u=
sage of the database.<br>
&gt;=C2=A0 =C2=A0 =C2=A0Enabled compression slightly reduced the temporary =
memory usage, but<br>
&gt;=C2=A0 =C2=A0 =C2=A0the execution<br>
&gt;=C2=A0 =C2=A0 =C2=A0time wasn&#39;t affected by compression.<br>
&gt; <br>
&gt; <br>
&gt;=C2=A0 =C2=A0 =C2=A0+------------+-------------------------+-----------=
------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0+------------------------------+<br>
&gt;=C2=A0 =C2=A0 =C2=A0| =C2=A0Dataset =C2=A0 | Compression. =C2=A0 =C2=A0=
 =C2=A0 | temp_bytes =C2=A0 =C2=A0 =C2=A0=C2=A0=C2=A0 | Execution<br>
&gt;=C2=A0 =C2=A0 =C2=A0Time (ms)=C2=A0=C2=A0 | =C2=A0 =C2=A0 =C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0+------------+-------------------------+-----------=
------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0+----------------------------- +<br>
&gt;=C2=A0 =C2=A0 =C2=A0| A =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 | Yes=
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A0 |=C2=A0 3.09 GiB =C2=A0 =C2=A0 =C2=A0 =C2=A0=C2=A0=C2=A0=C2=A0<b=
r>
&gt;=C2=A0 =C2=A0 =C2=A0| 22s586ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0 | work_mem=C2=A0 =3D 4MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0| =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0=
=C2=A0 | No=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0=C2=A0 =C2=A0 |=C2=A0 21.89 GiB =C2=A0 =C2=A0 =C2=A0 =C2=A0=C2=
=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0| 35s=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0 | work_mem=C2=A0 =3D 4MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0+------------+-------------------------+-----------=
------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0+----------------------------------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0| B=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0 | Yes=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0 |=C2=A0=C2=A0333 MB =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0| 1815.545 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 |=
 work_mem =3D 4MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0|=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | No=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 |=C2=A0 146=C2=A0 MB =C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0=C2=A0 | 1500.460 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0 | work_mem =3D 4MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0| =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0=C2=A0 =
=C2=A0=C2=A0 | Yes=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
 |=C2=A0 0 MB =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0 =C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0=C2=A0 =C2=A0 |=C2=A03262.305 ms=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0 | work_mem =3D 80MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0|=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | No=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 |=C2=A0 0 MB =C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =C2=A0 =C2=A0=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0=C2=A0=C2=A0 |=C2=A03174.725 ms=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | work_mem =3D 80MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0+-------------+------------------------+-----------=
-------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0+-------------------------------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0| C=C2=A0=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =
| Yes=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | 40 MB=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0<br>
&gt;=C2=A0 =C2=A0 =C2=A0|=C2=A01011.020 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0 | work_mem =3D 1MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0|=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | No=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 |=C2=A0 53<br>
&gt;=C2=A0 =C2=A0 =C2=A0MB=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 |=C2=A0 1034.142 ms=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | work_mem =3D 1MB<br>
&gt;=C2=A0 =C2=A0 =C2=A0+------------+------------------------+------------=
------------<br>
&gt;=C2=A0 =C2=A0 =C2=A0+--------------------------------------<br>
&gt; <br>
&gt; <br>
<br>
Thanks. I&#39;ll try to do some benchmarks on my own.<br>
<br>
Are these results fro ma single run, or an average of multiple runs?</block=
quote><div>=C2=A0</div><div>It is average from multiple runs. <br></div><di=
v><br></div><blockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0=
.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"> Do<br>
you maybe have a script to reproduce this, including the data generation?</=
blockquote><div>=C2=A0</div><div>I am attaching my SQL file for database pr=
eparation. I also did further testing</div><div>with two other machines( se=
e attachment huge_tables.rtf ). <br></div><blockquote class=3D"gmail_quote"=
 style=3D"margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);p=
adding-left:1ex">
<br>
Also, can you share some information about the machine used for this? I<br>
expect the impact to strongly depends on memory pressure - if the temp<br>
file fits into page cache (and stays there), it may not benefit from the<br=
>
compression, right?<br></blockquote><div>=C2=A0</div><div>If it fits=C2=A0i=
nto the page cache due to compression, I would consider it as a benefit fro=
m compression.</div><div>I performed further testing on machines with diffe=
rent memory sizes.</div><div>Both experiments showed that compression was b=
eneficial for execution time.=C2=A0</div><div>The execution time reduction =
was more significant in the case of the machine that had</div><div>less mem=
ory available.</div><div><br></div><div>Tests were performed on:</div><div>=
MacBook PRO M3 36GB - MacOs</div><div>Virtual machine ARM64 10GB/ 6CPU - Fe=
dora 39</div><div><br></div><blockquote class=3D"gmail_quote" style=3D"marg=
in:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1e=
x">
<br>
<br>
regards<br>
<br>
-- <br>
Tomas Vondra<br>
<br>
</blockquote></div></div>
</div>
</blockquote></div>

--000000000000da7efd0627f76f8d--

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