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new b82ef777f62 [doc](glue) modify glue & iceberg s3table (#2717)
b82ef777f62 is described below
commit b82ef777f62a801702f78ffb655f40ff9bab2649
Author: Mingyu Chen (Rayner) <[email protected]>
AuthorDate: Thu Aug 7 18:10:49 2025 -0700
[doc](glue) modify glue & iceberg s3table (#2717)
---
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
.../current/lakehouse/metastores/aws-glue.md | 188 ++++++++-------------
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
.../lakehouse/best-practices/doris-aws-s3tables.md | 49 ++++--
7 files changed, 259 insertions(+), 223 deletions(-)
diff --git a/docs/lakehouse/best-practices/doris-aws-s3tables.md
b/docs/lakehouse/best-practices/doris-aws-s3tables.md
index 4e056f91afb..cb16b6f8bc3 100644
--- a/docs/lakehouse/best-practices/doris-aws-s3tables.md
+++ b/docs/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ Here we create a Table Bucket named doris-s3-table-bucket.
After creation, we wi
### 02 Create Iceberg Catalog
-Create an Iceberg Catalog of type `s3tables`
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- Create an Iceberg Catalog of type `s3tables`
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- Connecting to `s3 tables` using Glue Rest Catalog
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 Access S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-We can insert another batch of data, then use the `iceberg_meta()` function to
view Iceberg Snapshots:
+We can insert another batch of data, then use the `$snapshots` system table to
view Iceberg Snapshots:
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/best-practices/doris-aws-s3tables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/best-practices/doris-aws-s3tables.md
index 39b5408fa24..ee1f9534385 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/best-practices/doris-aws-s3tables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ S3 Table Bucket 是 S3 推出的第三种 Bucket 类型，和之前的 General p
### 02 创建 Iceberg Catalog
-创建一个 `s3tables` 类型的 Iceberg Catalog
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- 创建一个 `s3tables` 类型的 Iceberg Catalog
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- 通过 Glue Rest Catalog 连接 `s3 tables`
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 访问 S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-我们可以再插入一批数据，然后使用 `iceberg_meta()` 函数查看 Iceberg 的 Snapshots：
+我们可以再插入一批数据，然后使用 `$snapshots` 系统表查看 Iceberg 的 Snapshots：
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/metastores/aws-glue.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/metastores/aws-glue.md
index c1fea3813e1..2f550e981df 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/metastores/aws-glue.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/lakehouse/metastores/aws-glue.md
@@ -5,95 +5,37 @@
}
---
-# Glue Catalog 参数文档
-
本文档介绍通过 `CREATE CATALOG` 使用 **AWS Glue Catalog** 访问 **Iceberg 表** 或 **Hive 表**
时的参数配置。
----
-
-## 一、Glue Catalog 支持的类型
+## Glue Catalog 支持的类型
AWS Glue Catalog 当前支持两种类型的 Catalog：
-| Catalog 类型 | 类型标识 (`type`) | 描述 |
-|--------------|---------------|-------------------------------------|
-| Hive | glue | 对接 Hive Metastore 的 Catalog |
-| Iceberg | glue | 对接 Iceberg 表格式 |
+| Catalog 类型 | 类型标识 (`type`) | 描述 |
+|-------------|------------------|---------------------------------------------|
+| Hive | glue | 对接 Hive Metastore 的 Catalog |
+| Iceberg | glue | 对接 Iceberg 表格式 |
+| Iceberg | rest | 通过 Glue Rest Catalog 对接 Iceberg 表格式 |
+本说明文档分别对这写类型的参数进行详细介绍，便于用户配置。
-本说明文档分别对这两种类型的参数进行详细介绍，便于用户配置。
+## Hive Glue Catalog
----
+Hive Glue Catalog 用于访问 Hive 表，通过 AWS Glue 的 Hive Metastore 兼容接口访问 Glue。配置如下：
-## 二、Iceberg Glue Catalog 参数总揽
+| 参数名称 | 描述
| 是否必须 | 默认值 |
+|---------------------------|-----------------------------------------------------------|----------|--------|
+| `type` | 固定为 `hms`
| 是 | 无 |
+| `hive.metastore.type` | 固定为 `glue`
| 是 | 无 |
+| `glue.region` | AWS Glue 所在区域，例如：`us-east-1`
| 是 | 无 |
+| `glue.endpoint` | AWS Glue
endpoint，例如：`https://glue.us-east-1.amazonaws.com` | 是 | 无 |
+| `glue.access_key` | AWS Access Key ID
| 是 | 空 |
+| `glue.secret_key` | AWS Secret Access Key
| 是 | 空 |
+| `glue.catalog_id` | Glue Catalog ID（暂未支持）
| 否 | 空 |
+| `glue.role_arn` | IAM Role ARN，用于访问 Glue（暂未支持）
| 否 | 空 |
+| `glue.external_id` | IAM External ID，用于访问 Glue（暂未支持）
| 否 | 空 |
-Iceberg Glue Catalog 用于访问 Iceberg 表，必须配置以下参数：
-
-| 参数名称 | 描述
| 是否必须 | 默认值 |
-|---------------------------|-------------------------------------------------------------------------|----------|---------|
-| `type` | 固定为 `iceberg`
| 是 | 无 |
-| `iceberg.catalog.type` | 固定为 `glue`
| 是 | 无 |
-| `warehouse` | Iceberg
数据仓库路径，例如：`s3://my-bucket/iceberg-warehouse/` | 是 | s3://doris
|
-| `glue.region` | AWS Glue 所在区域，例如：`us-east-1`
| 是 | 无 |
-| `glue.endpoint` | AWS Glue
endpoint，例如：`https://glue.us-east-1.amazonaws.com` | 是 | 无 |
-| `glue.access_key` | AWS Access Key ID
| 是 | 空 |
-| `glue.secret_key` | AWS Secret Access Key
| 是 | 空 |
-| `glue.catalog_id` | Glue Catalog ID（暂未支持）
| 否 | 空 |
-| `glue.role_arn` | IAM Role ARN，用于访问 Glue（暂未支持）
| 否 | 空 |
-| `glue.external_id` | IAM External ID，用于访问 Glue（暂未支持）
| 否 | 空 |
-
-### Iceberg Glue Catalog 示例
-
-```sql
-CREATE CATALOG iceberg_glue_catalog WITH (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 'glue',
- 'glue.region' = 'us-east-1',
- 'glue.endpoint' = 'https://glue.us-east-1.amazonaws.com',
- 'glue.access_key' = '<YOUR_ACCESS_KEY>',
- 'glue.secret_key' = '<YOUR_SECRET_KEY>'
-);
-```
-
----
-
-## 三、Hive Glue Catalog 参数总揽
-
-Hive Glue Catalog 用于访问 Hive 表，通过 AWS Glue 作为 Hive Metastore 服务，必须配置以下参数：
-
-| 参数名称 | 描述
| 是否必须 | 默认值 |
-|-----------------------------------|--------------------------------------------------------------------------------------------------|----------|---------|
-| `type` | 固定为 `hms`
| 是 | 无 |
-| `hive.metastore.type` | 固定为 `glue`
| 是 | 无 |
-| `glue.region` | AWS Glue 所在区域，例如：`us-east-1`
| 是 | 无 |
-| `glue.endpoint` | AWS Glue
endpoint，例如：`https://glue.us-east-1.amazonaws.com`
| 是 | 无 |
-| `glue.access_key` | AWS Access Key ID
| 是 | 空 |
-| `glue.secret_key` | AWS Secret Access Key
| 是 | 空 |
-| `glue.catalog_id` | Glue Catalog ID（暂未支持）
| 否 | 空 |
-| `glue.role_arn` | IAM Role ARN，用于访问 Glue（暂未支持）
| 否 | 空 |
-| `glue.external_id` | IAM External ID，用于访问 Glue（暂未支持）
| 否 | 空 |
-
-### Hive Glue Catalog 缓存参数（仅 Hive Glue 有效，默认关闭）
-
-#### 表缓存
-
-| 参数名称 | 描述 | 默认值 |
-|----------------------------------|------------------------------|---------|
-| `aws.glue.cache.table.enable` | 是否启用表缓存 | `false` |
-| `aws.glue.cache.table.size` | 表缓存最大条目数 | `1000` |
-| `aws.glue.cache.table.ttl-mins` | 表缓存存活时间（分钟） | `30` |
-
-#### 数据库缓存
-
-| 参数名称 | 描述 | 默认值 |
-|-------------------------------|------------------------------|---------|
-| `aws.glue.cache.db.enable` | 是否启用数据库缓存 | `false` |
-| `aws.glue.cache.db.size` | 数据库缓存最大条目数 | `1000` |
-| `aws.glue.cache.db.ttl-mins` | 数据库缓存存活时间（分钟） | `30` |
-
----
-
-### Hive Glue Catalog 示例
+### 示例
```sql
CREATE CATALOG hive_glue_catalog WITH (
@@ -106,48 +48,64 @@ CREATE CATALOG hive_glue_catalog WITH (
);
```
----
-
-## 四、Glue Catalog 认证方式说明
-
-访问 AWS Glue Catalog 需要进行身份认证，目前支持的两种方式如下（**当前仅支持方式一**）：
+## Iceberg Glue Catalog
-### 方式一：使用 Access Key / Secret Key（已支持 ✅）
+Iceberg Glue Catalog 通过 Glue Client 访问 Glue。配置如下：
-通过设置 `glue.access_key` 和 `glue.secret_key` 来进行静态身份认证。
+| 参数名称 | 描述
| 是否必须 | 默认值 |
+|-------------------------|--------------------------------------------------------------|----------|------------|
+| `type` | 固定为 `iceberg`
| 是 | 无 |
+| `iceberg.catalog.type` | 固定为 `glue`
| 是 | 无 |
+| `warehouse` | Iceberg
数据仓库路径，例如：`s3://my-bucket/iceberg-warehouse/` | 是 | s3://doris |
+| `glue.region` | AWS Glue 所在区域，例如：`us-east-1`
| 是 | 无 |
+| `glue.endpoint` | AWS Glue
endpoint，例如：`https://glue.us-east-1.amazonaws.com` | 是 | 无 |
+| `glue.access_key` | AWS Access Key ID
| 是 | 空 |
+| `glue.secret_key` | AWS Secret Access Key
| 是 | 空 |
+| `glue.catalog_id` | Glue Catalog ID（暂未支持）
| 否 | 空 |
+| `glue.role_arn` | IAM Role ARN，用于访问 Glue（暂未支持）
| 否 | 空 |
+| `glue.external_id` | IAM External ID，用于访问 Glue（暂未支持）
| 否 | 空 |
-| 参数名称 | 描述 | 是否必须 | 示例
|
-|--------------------|--------------------------------------|----------|--------------------------------|
-| `glue.access_key` | AWS Access Key ID，用于身份验证 | 是 |
`AKIA***************` |
-| `glue.secret_key` | AWS Secret Access Key | 是 |
`wJalrXUtnFEMI/K7MDENG/bPxRfi` |
+### 示例
-#### 适用场景：
-- 本地测试、开发环境
-- 运行环境中没有统一的 IAM Role 授权管理
-- 快速集成使用
-
-#### 注意事项：
-- AK/SK 具有权限管理风险，建议避免硬编码到代码或配置文件中。
-- 在生产环境中推荐使用 IAM Role 的方式替代。
-
----
-
-### 方式二：使用 IAM Role（暂未支持 ❌）
+```sql
+CREATE CATALOG iceberg_glue_catalog WITH (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'glue',
+ 'glue.region' = 'us-east-1',
+ 'glue.endpoint' = 'https://glue.us-east-1.amazonaws.com',
+ 'glue.access_key' = '<YOUR_ACCESS_KEY>',
+ 'glue.secret_key' = '<YOUR_SECRET_KEY>'
+);
+```
-通过配置 `glue.role_arn` 和 `glue.external_id`，授权当前程序以某个角色的身份访问 Glue。
+## Iceberg Glue Rest Catalog
-| 参数名称 | 描述 | 是否必须
| 示例 |
-|--------------------|--------------------------------------------------------|----------|----------------------------------------------------------------------|
-| `glue.role_arn` | 目标 IAM Role 的 ARN（Amazon Resource Name） |
是（若启用此方式） | `arn:aws:iam::123456789012:role/MyGlueAccessRole` |
-| `glue.external_id` | 外部 ID，通常用于跨账号访问 Glue，防止角色被滥用 | 否 |
`external-glue-id-abc123` |
+Iceberg Glue Rest Catalog 通过 Glue Rest Catalog 接口访问 Glue。目前仅支持存储在 AWS S3 Table
Bucket 中的 Iceberg 表。配置如下：
-#### 适用场景：
-- 生产环境中使用 **IAM Role 访问策略**进行权限管理
-- 支持 **跨 AWS 账号** 的 Glue Catalog 访问
-- 避免将 AK/SK 暴露在配置中，更加安全可靠
+| 参数名称 | 描述
| 是否必须 | 默认值 |
+|----------------------------------|-------------------------------------------------------------------|----------|--------|
+| `type` | 固定为 `iceberg`
| 是 | 无 |
+| `iceberg.catalog.type` | 固定为 `rest`
| 是 | 无 |
+| `iceberg.rest.uri` | Glue Rest
服务端点，例如：`https://glue.ap-east-1.amazonaws.com/iceberg` | 是 | 无 |
+| `warehouse` | Iceberg
数据仓库路径，例如：`<account_id>:s3tablescatalog/<bucket_name>` | 是 | 无 |
+| `iceberg.rest.sigv4-enabled` | 启动 V4 签名格式，固定为 `true`
| 是 | 无 |
+| `iceberg.rest.signing-name` | 签名类型，固定为 `glue`
| 是 | 空 |
+| `iceberg.rest.access-key-id` | 访问 Glue 的 Access Key（同时也用于访问 S3 Bucket）
| 是 | 空 |
+| `iceberg.rest.secret-access-key` | 访问 Glue 的 Secret Key（同时也用于访问 S3 Bucket）
| 是 | 空 |
+| `iceberg.rest.signing-region` | AWS Glue 所在区域，例如：`us-east-1`
| 是 | 空 |
-#### 注意事项：
-- 当前此方式尚未在系统中实现，仅作参数预留。
-- 启用此方式后，将忽略 `glue.access_key` 和 `glue.secret_key` 配置。
-- 未来支持后可作为企业级 Glue 接入的推荐方式。
+### 示例
+```sql
+CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+);
+```
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
index 39b5408fa24..ee1f9534385 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ S3 Table Bucket 是 S3 推出的第三种 Bucket 类型，和之前的 General p
### 02 创建 Iceberg Catalog
-创建一个 `s3tables` 类型的 Iceberg Catalog
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- 创建一个 `s3tables` 类型的 Iceberg Catalog
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- 通过 Glue Rest Catalog 连接 `s3 tables`
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 访问 S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-我们可以再插入一批数据，然后使用 `iceberg_meta()` 函数查看 Iceberg 的 Snapshots：
+我们可以再插入一批数据，然后使用 `$snapshots` 系统表查看 Iceberg 的 Snapshots：
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
index 39b5408fa24..ee1f9534385 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ S3 Table Bucket 是 S3 推出的第三种 Bucket 类型，和之前的 General p
### 02 创建 Iceberg Catalog
-创建一个 `s3tables` 类型的 Iceberg Catalog
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- 创建一个 `s3tables` 类型的 Iceberg Catalog
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- 通过 Glue Rest Catalog 连接 `s3 tables`
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 访问 S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-我们可以再插入一批数据，然后使用 `iceberg_meta()` 函数查看 Iceberg 的 Snapshots：
+我们可以再插入一批数据，然后使用 `$snapshots` 系统表查看 Iceberg 的 Snapshots：
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
diff --git
a/versioned_docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
b/versioned_docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
index 4e056f91afb..cb16b6f8bc3 100644
--- a/versioned_docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
+++ b/versioned_docs/version-2.1/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ Here we create a Table Bucket named doris-s3-table-bucket.
After creation, we wi
### 02 Create Iceberg Catalog
-Create an Iceberg Catalog of type `s3tables`
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- Create an Iceberg Catalog of type `s3tables`
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- Connecting to `s3 tables` using Glue Rest Catalog
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 Access S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-We can insert another batch of data, then use the `iceberg_meta()` function to
view Iceberg Snapshots:
+We can insert another batch of data, then use the `$snapshots` system table to
view Iceberg Snapshots:
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
diff --git
a/versioned_docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
b/versioned_docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
index 4e056f91afb..cb16b6f8bc3 100644
--- a/versioned_docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
+++ b/versioned_docs/version-3.0/lakehouse/best-practices/doris-aws-s3tables.md
@@ -33,19 +33,35 @@ Here we create a Table Bucket named doris-s3-table-bucket.
After creation, we wi
### 02 Create Iceberg Catalog
-Create an Iceberg Catalog of type `s3tables`
-
-```sql
-CREATE CATALOG iceberg_s3 PROPERTIES (
- 'type' = 'iceberg',
- 'iceberg.catalog.type' = 's3tables',
- 'warehouse' =
'arn:aws:s3tables:us-east-1:169698000000:bucket/doris-s3-table-bucket',
- 's3.region' = 'us-east-1',
- 's3.endpoint' = 's3.us-east-1.amazonaws.com',
- 's3.access_key' = 'AKIASPAWQE3ITEXAMPLE',
- 's3.secret_key' = 'l4rVnn3hCmwEXAMPLE/lht4rMIfbhVfEXAMPLE'
-);
-```
+- Create an Iceberg Catalog of type `s3tables`
+
+ ```sql
+ CREATE CATALOG iceberg_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 's3tables',
+ 'warehouse' =
'arn:aws:s3tables:<region>:<acount_id>:bucket/<s3_table_bucket_name>',
+ 's3.region' = '<region>',
+ 's3.endpoint' = 's3.<region>.amazonaws.com',
+ 's3.access_key' = '<ak>',
+ 's3.secret_key' = '<sk>'
+ );
+ ```
+
+- Connecting to `s3 tables` using Glue Rest Catalog
+
+ ```sql
+ CREATE CATALOG glue_s3 PROPERTIES (
+ 'type' = 'iceberg',
+ 'iceberg.catalog.type' = 'rest',
+ 'iceberg.rest.uri' = 'https://glue.<region>.amazonaws.com/iceberg',
+ 'iceberg.rest.warehouse' =
'<acount_id>:s3tablescatalog/<s3_table_bucket_name>',
+ 'iceberg.rest.sigv4-enabled' = 'true',
+ 'iceberg.rest.signing-name' = 'glue',
+ 'iceberg.rest.access-key-id' = '<ak>',
+ 'iceberg.rest.secret-access-key' = '<sk>',
+ 'iceberg.rest.signing-region' = '<region>'
+ );
+ ```
### 03 Access S3Tables
@@ -107,7 +123,7 @@ Doris > SELECT * FROM partition_table;
### 05 Time Travel
-We can insert another batch of data, then use the `iceberg_meta()` function to
view Iceberg Snapshots:
+We can insert another batch of data, then use the `$snapshots` system table to
view Iceberg Snapshots:
```sql
Doris > INSERT INTO partition_table VALUES
@@ -118,10 +134,7 @@ Query OK, 2 rows affected (9.76 sec)
```
```
-Doris > SELECT * FROM iceberg_meta(
- -> 'table' = 'iceberg_s3.my_namespace.partition_table',
- -> 'query_type' = 'snapshots'
- -> )\G
+Doris > SELECT * FROM partition_table$snapshots\G
*************************** 1. row ***************************
committed_at: 2025-01-15 23:27:01
snapshot_id: 6834769222601914216
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