Received: from malur.postgresql.org ([217.196.149.56]) by arkaria.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 (Exim 4.94.2) (envelope-from ) id 1tq42N-00GQ3P-DT for pgsql-hackers@arkaria.postgresql.org; Thu, 06 Mar 2025 05:44:52 +0000 Received: from localhost ([127.0.0.1] helo=malur.postgresql.org) by malur.postgresql.org with esmtp (Exim 4.94.2) (envelope-from ) id 1tq41N-00GokS-4f for pgsql-hackers@arkaria.postgresql.org; Thu, 06 Mar 2025 05:43:49 +0000 Received: from makus.postgresql.org ([2001:4800:3e1:1::229]) by malur.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 (Exim 4.94.2) (envelope-from ) id 1tq3qK-00GYVP-7Z for pgsql-hackers@lists.postgresql.org; Thu, 06 Mar 2025 05:32:24 +0000 Received: from mail-lj1-x22f.google.com ([2a00:1450:4864:20::22f]) by makus.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 (Exim 4.96) (envelope-from ) id 1tq3qH-001Eyv-1F for pgsql-hackers@postgresql.org; Thu, 06 Mar 2025 05:32:23 +0000 Received: by mail-lj1-x22f.google.com with SMTP id 38308e7fff4ca-307bc125e2eso2676861fa.3 for ; Wed, 05 Mar 2025 21:32:21 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20230601; t=1741239140; x=1741843940; darn=postgresql.org; h=to:subject:message-id:date:from:mime-version:from:to:cc:subject :date:message-id:reply-to; bh=d3N/V8Yo7q5VtGrq9P6akUib9hxQ1ImEcr/1DmIyxkI=; b=EOowtYkFqWA3U/Ppf//UASKWuDSiuwCra+L86xhOV7+O5hFRUONp+AY5WiJJ9FiEMj Dx5mAfsbGWKO5eYRqh8/I0siN6dKPPzA5s0Ee/3JMy9imzgG2W+useRa7Rruwmht/Iyo 4WuGeooqKL2+NCpuHzblaMa6pH4TPO5cKTfnK8ZVw3/4QbhIJjn6fIL+11snyN35NlkN FafFB5Z+cW0Rext6QnjS+CA2yR8izGm4bwbTIwBqVYMxcnFBzvOuB8PzhuhpxNiyozK+ zUAxnYPcKWgqhL8zPW+CNxHtTlHlRHHQT+6ANcU/aF8Mr1yy8s9WxqVuG4eieHmTPtZ5 0u+g== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20230601; t=1741239140; x=1741843940; h=to:subject:message-id:date:from:mime-version:x-gm-message-state :from:to:cc:subject:date:message-id:reply-to; bh=d3N/V8Yo7q5VtGrq9P6akUib9hxQ1ImEcr/1DmIyxkI=; b=WuzZF3taGtQUae0/oeucl6fWlbLvnp37zuXxuMnRt+Z/O40vu5pFNtBuFSxjOY77/f fU4kGhj2Qql7Q00wLYRZ2pti1+nowlJy2TLuV/qSv7QwKkNykfC0n13I6gPSMwrOR3Wy bVNnTCr9+UfxLRQcEmf4Ls3bqJkSuvONHnURJLmkhpaawdPPrV58WvaBptRHkZ4V1oyS Oi9L2Cwk22wBla6dI42K/tL17WFieX1fPG9p9tb9zX9dB66iv8BrVVcD4ETyAtTsqai/ uXzSvpfUHl1A6e2OmyZEwFl/tBBKZypcwH6Rk03pq7sKmEq64KuWAPhUgw9QlqXUuT6A cUFw== X-Gm-Message-State: AOJu0YxRyG0D4scLxeyn3javhX4e/O7S9KcPk+JlTPEwB1VqRGWDGmNm hffRxK/t6sRceXaBaQqU6ZFjyynTAWBvoq2jiGCGeSSrQbdXrCtbZQLFqL3V8FB/UTwJlulaWVn DC4RahOS2bNYrvMyR+FJnEst6YYgvDUxd X-Gm-Gg: ASbGncuU5ttJdn8vUO2myS2TXwkk0aF5pdiG6fTbnFLQE/KK8LK5zDg1eVVy0bileR1 aFRX/TnOj00scoVNP/ctLLNwQjty07laaHxrf+BZocfs2vgYUdWtYCR9mDYScY6/ZFkpuD0tNaK nWBnS81SIhmiaKr4BwCWlO9x9wgPXy X-Google-Smtp-Source: AGHT+IEJw78gf1BDU9fjECSlD83aujncixKJWsBIwe8Fc/RX8MemzmHhrw7OfeksB0Mh81cRGkrNhsPd4orEwj8Tq7c= X-Received: by 2002:a05:651c:198d:b0:30b:a20b:eccc with SMTP id 38308e7fff4ca-30bd7a35e33mr24075171fa.11.1741239139290; Wed, 05 Mar 2025 21:32:19 -0800 (PST) MIME-Version: 1.0 From: Nikhil Kumar Veldanda Date: Wed, 5 Mar 2025 21:32:07 -0800 X-Gm-Features: AQ5f1JoxbD1JmWwW1tPvMupUkX-sBPGaSh3aVTkzDppT7GC3Yn5TPcxGKV00d4E Message-ID: Subject: ZStandard (with dictionaries) compression support for TOAST compression To: pgsql-hackers@postgresql.org Content-Type: multipart/mixed; boundary="0000000000005deec4062fa5d40d" List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Archived-At: Precedence: bulk --0000000000005deec4062fa5d40d Content-Type: multipart/alternative; boundary="0000000000005deec4062fa5d40b" --0000000000005deec4062fa5d40b Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi all, The ZStandard compression algorithm [1][2], though not currently used for TOAST compression in PostgreSQL, offers significantly improved compression ratios compared to lz4/pglz in both dictionary-based and non-dictionary modes. Attached find for review my patch to add ZStandard compression to Postgres. In tests this patch used with a pre-trained dictionary achieved up to four times the compression ratio of LZ4, while ZStandard without a dictionary outperformed LZ4/pglz by about two times during compression of data. Notably, this is the first compression algorithm for Postgres that can make use of a dictionary to provide higher levels of compression, but dictionaries have to be generated and maintained, and so I=E2=80=99ve had t= o break new ground in that regard. To use the dictionary support requires training and storing a dictionary for a given variable-length column type. On a variable-length column, a SQL function will be called. It will sample the column=E2=80=99s data and feed it into the ZStandard training API which wil= l return a dictionary. In the example, the column is of JSONB type. The SQL function takes the table name and the attribute number as inputs. If the training is successful, it will return true; otherwise, it will return false. =E2=80=98=E2=80=99=E2=80=98 test=3D# select build_zstd_dict_for_attribute('"public"."zstd"', 1); build_zstd_dict_for_attribute ------------------------------- t (1 row) =E2=80=98=E2=80=99=E2=80=98 The sampling logic and data to feed to the ZStandard training API can vary by data type. The patch includes an method to write other type-specific training functions and includes a default for JSONB, TEXT and BYTEA. There is a new option called =E2=80=98build_zstd_dict=E2=80=99 that takes a funct= ion name as input in =E2=80=98CREATE TYPE=E2=80=99. In this way anyone can write their = own type-specific training function by handling sampling logic and returning the necessary information for the ZStandard training API in =E2=80=9CZstdTrainingData=E2=80=9D format. ``` typedef struct ZstdTrainingData { char *sample_buffer; /* Pointer to the raw sample buffer */ size_t *sample_sizes; /* Array of sample sizes */ int nitems; /* Number of sample sizes */ } ZstdTrainingData; ``` This information is feed into the ZStandard train API, which generates a dictionary and inserts it into the dictionary catalog table. Additionally, we update the =E2=80=98pg_attribute=E2=80=99 attribute options to include t= he unique dictionary ID for that specific attribute. During compression, based on the available dictionary ID, we retrieve the dictionary and use it to compress the documents. I=E2=80=99ve created standard training function (`zstd_dictionary_builder`) for JSONB, TEXT, and BYTEA. We store dictionary and dictid in the new catalog table =E2=80=98pg_zstd_dictionaries=E2=80=99 ``` test=3D# \d pg_zstd_dictionaries Table "pg_catalog.pg_zstd_dictionaries" Column | Type | Collation | Nullable | Default --------+-------+-----------+----------+--------- dictid | oid | | not null | dict | bytea | | not null | Indexes: "pg_zstd_dictionaries_dictid_index" PRIMARY KEY, btree (dictid) ``` This is the entire ZStandard dictionary infrastructure. A column can have multiple dictionaries. The latest dictionary will be identified by the pg_attribute attoptions. We never delete dictionaries once they are generated. If a dictionary is not provided and attcompression is set to zstd, we compress with ZStandard without dictionary. For decompression, the zstd-compressed frame contains a dictionary identifier (dictid) that indicates the dictionary used for compression. By retrieving this dictid from the zstd frame, we then fetch the corresponding dictionary and perform decompression. ###########################################################################= ## Enter toast compression framework changes, We identify a compressed datum compression algorithm using the top two bits of va_tcinfo (varattrib_4b.va_compressed). It is possible to have four compression methods. However, based on previous community email discussions regarding toast compression changes[3], the idea of using it for a new compression algorithm has been rejected, and a suggestion has been made to extend it which I=E2=80=99ve implemented in thi= s patch. This change necessitates an update to =E2=80=98varattrib_4b=E2=80=99 and = =E2=80=98varatt_external=E2=80=99 on disk structures. I=E2=80=99ve made sure that this changes are backward compatible. ``` typedef union { struct /* Normal varlena (4-byte length) */ { uint32 va_header; char va_data[FLEXIBLE_ARRAY_MEMBER]; } va_4byte; struct /* Compressed-in-line format */ { uint32 va_header; uint32 va_tcinfo; /* Original data size (excludes header) and * compression method; see va_extinfo */ char va_data[FLEXIBLE_ARRAY_MEMBER]; /* Compressed data */ } va_compressed; struct { uint32 va_header; uint32 va_tcinfo; uint32 va_cmp_alg; char va_data[FLEXIBLE_ARRAY_MEMBER]; } va_compressed_ext; } varattrib_4b; typedef struct varatt_external { int32 va_rawsize; /* Original data size (includes header) */ uint32 va_extinfo; /* External saved size (without header) and * compression method */ Oid va_valueid; /* Unique ID of value within TOAST table */ Oid va_toastrelid; /* RelID of TOAST table containing it */ uint32 va_cmp_alg; /* The additional compression algorithms * information. */ } varatt_external; ``` As I need to update this structs, I=E2=80=99ve made changes to the existing= macros. Additionally added compression and decompression routines related to ZStandard as needed. These are major design changes in the patch to incorporate ZStandard with dictionary compression. Please let me know what you think about all this. Are there any concerns with my approach? In particular, I would appreciate your thoughts on the on-disk changes that result from this. kind regards, Nikhil Veldanda Amazon Web Services: https://aws.amazon.com [1] https://facebook.github.io/zstd/ [2] https://github.com/facebook/zstd [3] https://www.postgresql.org/message-id/flat/YoMiNmkztrslDbNS%40paquier.xyz --0000000000005deec4062fa5d40b Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi all,

The ZStandard compression algorithm [1][2= ], though not currently used for TOAST compression in PostgreSQL, offers si= gnificantly improved compression ratios compared to lz4/pglz in both dictio= nary-based and non-dictionary modes. Attached find for review my patch to a= dd ZStandard compression to Postgres. In tests this patch used with a pre-t= rained dictionary achieved up to four times the compression ratio of LZ4, w= hile ZStandard without a dictionary outperformed LZ4/pglz by about two time= s during compression of data.

Notably, this is the first compression algorithm = for Postgres that can make use of a dictionary to provide higher levels of = compression, but dictionaries have to be generated and maintained, and so I= =E2=80=99ve had to break new ground in that regard. To use the dictionary s= upport requires training and storing a dictionary for a given variable-leng= th column type. On a variable-length column, a SQL function will be called.= It will sample the column=E2=80=99s data and feed it into the ZStandard tr= aining API which will return a dictionary. In the example, the column is of= JSONB type. The SQL function takes the table name and the attribute number= as inputs. If the training is successful, it will return true; otherwise, = it will return false.

=E2=80=98=E2=80=99=E2=80=98
test=3D# select build_zstd_dic= t_for_attribute('"public"."zstd"', 1);build_zst= d_dict_for_attribute
-------------------------------
t
(1 row)
=E2=80=98=E2=80= =99=E2=80=98

The sampling logic and data to feed to the ZStandard training API = can vary by data type. The patch includes an method to write other type-spe= cific training functions and includes a default for JSONB, TEXT and BYTEA. = There is a new option called =E2=80=98build_zstd_dict=E2=80=99 that takes a= function name as input in =E2=80=98CREATE TYPE=E2=80=99. In this way anyon= e can write their own type-specific training function by handling sampling = logic and returning the necessary information for the ZStandard training AP= I in =E2=80=9CZstdTrainingData=E2=80=9D format.

```
typedef struct ZstdTrainingD= ata
{
char *sample_buffer; /* Pointer to the raw sample buffer */
size_t *samp= le_sizes; /* Array of sample sizes */
int nitems; /* Number of sample sizes = */
```
This information is feed into the ZStandard train A= PI, which generates a dictionary and inserts it into the dictionary catalog= table. Additionally, we update the =E2=80=98pg_attribute=E2=80=99 attribut= e options to include the unique dictionary ID for that specific attribute. = During compression, based on the available dictionary ID, we retrieve the d= ictionary and use it to compress the documents. I=E2=80=99ve created standa= rd training function (`zstd_dictionary_builder`) for JSONB, TEXT, and BYTEA= .=C2=A0

We store dictionary= and dictid in the new catalog table =E2=80=98pg_zstd_dictionaries=E2=80=99=

```=
= test=3D# \d pg_zstd_dictionaries
Table "pg_catalog.pg_zstd_dictionaries= "
Column | Type | Collation | Nullable | Default
--------+-------+------= -----+----------+---------
dictid | oid | | not null |
dict | bytea | | not n= ull |
Indexes:
"pg_zstd_dictionaries_dictid_index" PRIMARY KEY, btr= ee (dictid)
```=C2=A0
=
This is= the entire ZStandard dictionary infrastructure. A column can have multiple= dictionaries. The latest dictionary will be identified by the pg_attribute= attoptions. We never delete dictionaries once they are generated. If a dic= tionary is not provided and attcompression is set to zstd, we compress with= ZStandard without dictionary. For decompression, the zstd-compressed frame= contains a dictionary identifier (dictid) that indicates the dictionary us= ed for compression. By retrieving this dictid from the zstd frame, we then = fetch the corresponding dictionary and perform decompression.

###############= ##############################################################

Enter toast com= pression framework changes,

We identify a compressed datum compression algorith= m using the top two bits of va_tcinfo (varattrib_4b.va_compressed).=C2=A0
It is possible to have fou= r compression methods. However, based on previous community email discussio= ns regarding toast compression changes[3], the idea of using it for a new c= ompression algorithm has been rejected, and a suggestion has been made to e= xtend it which I=E2=80=99ve implemented in this patch. This change necessit= ates an update to =E2=80=98varattrib_4b=E2=80=99 and =E2=80=98varatt_extern= al=E2=80=99 on disk structures. I=E2=80=99ve made sure that this changes ar= e backward compatible.=C2=A0

```
typedef union
{
struct /* Normal varlena (4-byte length) */
{
uint32 va_hea= der;
char va_data[FLEXIBLE_ARRAY_MEMBER];
} va_4byte;
struct /* Compressed-in-= line format */
{
uint32 va_header;
uint32 va_tcinfo; /* Original data size (ex= cludes header) and
* compression method; see va_extinfo */
char va_data[FL= EXIBLE_ARRAY_MEMBER]; /* Compressed data */
} va_compressed;
struct{<= br style=3D"color:rgb(0,0,0);font-style:normal;font-variant-caps:normal;fon= t-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-tr= ansform:none;white-space:normal;word-spacing:0px;text-decoration:none">uint32 v= a_header;
uint32 va_tcinfo;
uint32 va_cmp_alg;
char va_data[FLEXIBLE_ARRAY_MEM= BER];
} va_compressed_ext;
} varattrib_4b;

typedef struct varatt_external<= br style=3D"color:rgb(0,0,0);font-style:normal;font-variant-caps:normal;fon= t-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-tr= ansform:none;white-space:normal;word-spacing:0px;text-decoration:none">{=
int32 v= a_rawsize; /* Original data size (includes header) */
uint32 va_extinfo; /* = External saved size (without header) and
* compression method */
Oid va_value= id; /* Unique ID of value within TOAST table */
Oid va_toastrelid; /* RelID = of TOAST table containing it */
uint32 va_cmp_alg; /* The additional compres= sion algorithms
* information. */
} varatt_external;
```

As I need to update this = structs, I=E2=80=99ve made changes to the existing macros. Additionally add= ed compression and decompression routines related to ZStandard as needed. T= hese are major design changes in the patch to incorporate ZStandard with di= ctionary compression.=C2=A0

= Please let me know what you think about all this. Are there any concerns wi= th my approach? In particular, I would appreciate your thoughts on the on-d= isk changes that result from this.

kind regards,

Nikhil Veldanda
Amazon Web Services= :=C2=A0https://aws.amazon.= com

[1]= =C2=A0https://fac= ebook.github.io/zstd/
[2]=C2=A0https://github.com/facebook/zstd
[3]=C2=A0htt= ps://www.postgresql.org/message-id/flat/YoMiNmkztrslDbNS%40paquier.xyz<= br style=3D"color:rgb(0,0,0);font-style:normal;font-variant-caps:normal;fon= t-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-tr= ansform:none;white-space:normal;word-spacing:0px;text-decoration:none">
=
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