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From: Filip Janus <fjanus@redhat.com>
Date: Thu, 14 Nov 2024 23:13:16 +0100
Message-ID: <CAFjYY+LAxqRpaJpukBx9KjeyhZvMu6O4xcCUU9gw1SFGsUTcDA@mail.gmail.com>
Subject: Proposal: Adding compression of temporary files
To: pgsql-hackers@postgresql.org
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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.

# 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 caller. For
the sequential
execution, I would prefer the second option.

# 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 the
requirement to store
context for LZ4 stream compression.

# 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 every
 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 = 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 = 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 execution time
was reduced
only slightly with the enabled compression.

The second scenario demonstrates no overhead in the case of enabled
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, p.price, SUM(oi.quantity) AS
total_quantity, AVG(oi.price) AS avg_item_price
FROM eshop.products p JOIN eshop.order_items oi ON p.product_id =
oi.product_id JOIN
eshop.orders o ON oi.order_id = o.order_id WHERE o.order_date > '2020-01-01'
AND p.price > 50
GROUP BY p.product_id, 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, but the
execution
time wasn't affected by compression.


+------------+-------------------------+-----------------------+------------------------------+
|  Dataset   | Compression.       | temp_bytes         | Execution Time
(ms)   |
+------------+-------------------------+-----------------------+-----------------------------
+
| A             | Yes                        |  3.09 GiB            |
22s586ms           | work_mem  = 4MB
|                | No                         |  21.89 GiB          |
35s                       | work_mem  = 4MB
+------------+-------------------------+-----------------------+----------------------------------------
| B             | Yes                        |  333 MB               |
1815.545 ms       | work_mem = 4MB
|                 | No                        |  146  MB              |
1500.460 ms        | work_mem = 4MB
|                 | Yes                       |  0 MB
| 3262.305 ms        | work_mem = 80MB
|                 | No                        |  0 MB
| 3174.725 ms         | work_mem = 80MB
+-------------+------------------------+------------------------+-------------------------------------
| C             | Yes                       | 40 MB
| 1011.020 ms        | work_mem = 1MB
|                | No                        |  53 MB                 |
1034.142 ms        | work_mem = 1MB
+------------+------------------------+------------------------+--------------------------------------


Regards,

    -Filip-

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<div dir=3D"ltr"><div dir=3D"ltr"><div dir=3D"ltr">Hi all,<br>Postgresql su=
pports data compression nowadays, but the compression of <br>temporary file=
s has not been implemented yet. The huge queries can=C2=A0</div><div dir=3D=
"ltr">produce a significant amount of temporary data that needs to be=C2=A0=
stored on disk=C2=A0</div><div dir=3D"ltr">and cause many expensive I/O ope=
rations.<br>I am attaching a proposal of the patch to enable temporary file=
s compression for <br>hashjoins for now. Initially, I&#39;ve chosen the LZ4=
 compression algorithm. It would</div><div dir=3D"ltr">probably make better=
 sense to start with pglz, but I realized it late. <br><br># Future possibl=
e improvements<br>Reducing the number of memory allocations within the dump=
ing and loading of</div><div dir=3D"ltr">the buffer. I have two ideas for s=
olving this problem. I would either add a buffer into</div><div dir=3D"ltr"=
>struct BufFile or provide the buffer as an argument from the caller. For t=
he sequential=C2=A0</div><div dir=3D"ltr">execution, I would prefer the sec=
ond option.<br><br># Future plan/open questions<br>In the future, I would l=
ike to add support for pglz and zstd. Further, I plan to<br>extend the supp=
ort of the temporary file compression also for sorting, gist index creation=
, etc.<br><br>Experimenting with the stream mode of compression algorithms.=
 The compression=C2=A0</div><div dir=3D"ltr">ratio of LZ4 in block mode see=
ms to be satisfying, but the stream mode could=C2=A0</div><div dir=3D"ltr">=
produce a better ratio, but it would consume more memory due to the require=
ment to store</div><div dir=3D"ltr">context for LZ4 stream compression.<br>=
<br># Benchmark<br>I prepared three different databases to check expectatio=
ns. Each=C2=A0</div><div dir=3D"ltr">dataset is described below. My testing=
 demonstrates that my patch=C2=A0</div><div dir=3D"ltr">improves the execut=
ion time of huge hash joins.=C2=A0</div><div dir=3D"ltr">Also, my implement=
ation should not <br>negatively affect performance within smaller queries.=
=C2=A0</div><div dir=3D"ltr">The usage of memory needed for temporary files=
 was reduced in every</div><div dir=3D"ltr">=C2=A0execution without a signi=
ficant impact on execution time.<br><div><br></div><div dir=3D"ltr"><b>## D=
ataset A:</b></div><div>Tables<b><br></b></div><div>table_a(bigint id,text =
data_text,integer data_number) - 10000000 rows<br></div><div>table_b(bigint=
 id, integer ref_id, numeric data_value, bytea data_blob) - 10000000 rows</=
div><div>Query:=C2=A0 SELECT *=C2=A0 FROM table_a a JOIN table_b b ON <a hr=
ef=3D"http://a.id" target=3D"_blank">a.id</a> =3D <a href=3D"http://b.id" t=
arget=3D"_blank">b.id</a>;</div><div><br></div><div>The tables contain high=
ly compressible data.</div><div>The query demonstrated a reduction in the u=
sage of the temporary=C2=A0</div><div>files ~20GB -&gt; 3GB, based on this =
reduction also caused the execution=C2=A0</div><div>time of the query to be=
 reduced by about ~10s.</div><div><br></div><div><br></div><div dir=3D"ltr"=
><div><b>## Dataset B:</b></div><div>Tables:<b><br></b></div><div>table_a(i=
nteger id, text data_blob) - 1110000 rows<br></div><div>table_b(integer id,=
 text data_blob) - 10000000 rows<br></div><div>Query:=C2=A0 SELECT *=C2=A0 =
FROM table_a a JOIN table_b b ON <a href=3D"http://a.id" target=3D"_blank">=
a.id</a> =3D <a href=3D"http://b.id" target=3D"_blank">b.id</a>;</div><div>=
<br></div><div>The tables contain less compressible data. data_blob was gen=
erated by a pseudo-random generator. <br></div><div>In this case, the data =
reduction was only ~50%. Also, the execution time was reduced=C2=A0</div><d=
iv>only slightly with the enabled compression.</div><div><br></div><div><di=
v>The second scenario demonstrates no overhead in the case of enabled=C2=A0=
</div><div>compression and extended work_mem to avoid temp file usage.</div=
></div><div><br></div><div><b>## Dataset C:</b></div><div>Tables<br></div><=
div>customers (integer,text,text,text,text)</div><div>order_items(integer,i=
nteger,integer,integer,numeric(10,2))</div><div>orders(integer,integer,time=
stamp,numeric(10,2))</div><div>products(integer,text,text,numeric(10,2),int=
eger) <br></div><div><br></div><div>Query: <span>SELECT</span>=20
    p.product_id,
    <a href=3D"http://p.name" target=3D"_blank">p.name</a>,
    p.price,
    <span>SUM</span>(oi.quantity) <span>AS</span> total_quantity,
    <span>AVG</span>(oi.price) <span>AS</span> avg_item_price
<span><br></span></div><div><span>FROM</span> eshop.products p
<span>JOIN</span> eshop.order_items oi <span>ON</span> p.product_id <span>=
=3D</span> oi.product_id
<span>JOIN</span>=C2=A0</div><div>eshop.orders o <span>ON</span> oi.order_i=
d <span>=3D</span> o.order_id
<span>WHERE</span> o.order_date <span>&gt;</span> <span>&#39;2020-01-01&#39=
;</span>
<span>AND</span> p.price <span>&gt;</span> <span>50</span> <br></div><div><=
span>GROUP</span> <span>BY</span> p.product_id, <a href=3D"http://p.name" t=
arget=3D"_blank">p.name</a>, p.price
<span>HAVING</span> <span>SUM</span>(oi.quantity) <span>&gt;</span> <span>1=
000</span>
<span><br></span></div><div><span>ORDER</span> <span>BY</span> total_quanti=
ty <span>DESC</span>
LIMIT <span>100</span>;</div><div><br></div><div>This scenario should demon=
strate a more realistic usage of the database.</div><div>Enabled compressio=
n slightly reduced the temporary memory usage, but the execution</div><div>=
time wasn&#39;t affected by compression. <br></div><div><br></div></div><di=
v dir=3D"ltr"><br clear=3D"all"><div><div dir=3D"ltr" class=3D"gmail_signat=
ure" data-smartmail=3D"gmail_signature"><div dir=3D"ltr"><div><div dir=3D"l=
tr">+------------+-------------------------+-----------------------+-------=
-----------------------+<br>| =C2=A0Dataset =C2=A0 | Compression. =C2=A0 =
=C2=A0 =C2=A0 | temp_bytes =C2=A0 =C2=A0 =C2=A0=C2=A0=C2=A0 | Execution Tim=
e (ms)=C2=A0=C2=A0 | =C2=A0 =C2=A0 =C2=A0 <br>+------------+---------------=
----------+-----------------------+----------------------------- +<br>| 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 | 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>| =
=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 | 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>+------=
------+-------------------------+-----------------------+------------------=
----------------------</div><div>| 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 | 1815.545 ms=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0 | work_mem =3D 4MB</div><div>|=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 =C2=A0 | 1500.460 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 | =
work_mem =3D 4MB</div><div><div>| =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 =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></div><div>|=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 =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</div></div><div>+-------------+--=
----------------------+------------------------+---------------------------=
----------</div><div>| C=C2=A0=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 | Y=
es=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 |=C2=A01011.020 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0 | work_mem =3D 1MB</div><div>|=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 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 1034.142 ms=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =
| work_mem =3D 1MB</div><div><a class=3D"gmail_plusreply" id=3D"m_-25938994=
52949839955m_-7586772034955316253plusReplyChip-1">+------------+-----------=
-------------+------------------------+------------------------------------=
--</a><br>
</div><div><a class=3D"gmail_plusreply" id=3D"m_-2593899452949839955m_-7586=
772034955316253plusReplyChip-0"><br></a></div><div><a class=3D"gmail_plusre=
ply" id=3D"m_-2593899452949839955m_-7586772034955316253m_504224926142981623=
1plusReplyChip-1"><br></a></div><div>Regards,<br></div><div><br></div><div =
dir=3D"ltr">=C2=A0 =C2=A0 -Filip-</div></div></div></div></div></div>
</div>
</div>
</div>

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