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new b211eae06f3 [improve](load) add redshift datasource doc (#2341)
b211eae06f3 is described below
commit b211eae06f304fcc9a3569025b7793d9c4e48616
Author: wudi <w...@selectdb.com>
AuthorDate: Mon Apr 28 17:29:55 2025 +0800
[improve](load) add redshift datasource doc (#2341)
## Versions
- [x] dev
- [x] 3.0
- [x] 2.1
- [ ] 2.0
## Languages
- [x] Chinese
- [x] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
---
docs/data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
.../data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
.../data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
.../data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
sidebars.json | 1 +
static/images/data-operate/redshift_out.png | Bin 0 -> 155865 bytes
static/images/data-operate/redshift_out2.png | Bin 0 -> 156071 bytes
.../data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
.../data-operate/import/data-source/redshift.md | 227 +++++++++++++++++++++
versioned_sidebars/version-2.1-sidebars.json | 1 +
versioned_sidebars/version-3.0-sidebars.json | 1 +
11 files changed, 1365 insertions(+)
diff --git a/docs/data-operate/import/data-source/redshift.md
b/docs/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..c4c63d62b03
--- /dev/null
+++ b/docs/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "en"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+During the process of migrating Redshift, it is usually necessary to use
object storage as an intermediate medium. The core process is as follows:
First, use the
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
statement of Redshift to export data to the object storage; then, use the S3
Load function of Doris to read data from the object storage and import it into
Doris. For details, please refer to [S3 Load](./amazon-s3.md)。
+
+## Considerations
+
+1. Before the migration, it is necessary to select Doris' [Data
Model](../../../table-design/data-model/overview.md), as well as the strategies
for
[Partitioning](../../../table-design/data-partitioning/dynamic-partitioning.md)
and [Bucketing](../../../table-design/data-partitioning/data-bucketing.md)
according to the table structure of Redshift. For more table creation
strategies, please refer to [Load Best Practices](../load-best-practices.md).
+2. When Redshift exports data of the Time type, it is necessary to export it
after casting it to the Varchar type.
+
+
+## Data type mapping
+
+| Redshift | Doris | Comment |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR maximum length is 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. Create Table
+
+To migrate a Redshift table to Doris, first create the Doris table.
+
+Assume we have the following table and data in Redshift:
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+According to this table structure, a Doris primary key partitioned table can
be created. The partition field should be selected according to the business
scenario. Here, the partition field is "order_date", and it is partitioned on a
daily basis.
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. Export Data from Redshift
+
+**2.1 Export to S3 Parquet Files via UNLOAD**
+
+When exporting to S3, export according to the **Partition field of Doris**, as
follows:
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 Verify Exported Files on S3**
+
+Exported files are organized into **subdirectories by partition** on S3:
+
+
+
+
+
+## 3. Load Data to Doris
+
+S3 Load is an asynchronous data load method. After execution, Doris actively
pulls data from the data source. The data source supports object storage
compatible with the S3 protocol, including ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)，etc)
+
+This method is suitable for scenarios involving large volumes of data that
require asynchronous processing in the background. For data imports that need
to be handled synchronously, refer to [TVF Load](./amazon-s3.md#load-with-tvf)。
+
+*Note: For **Parquet/ORC format files that contain complex types
(Struct/Array/Map)**, TVF Load must be used.*
+
+**3.1 Load a Single Partition**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 Check Load Status via SHOW LOAD**
+
+ Since S3 Load import is submitted asynchronously, you can check the status of
a specific label using SHOW LOAD:
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 Handle Load Errors**
+
+ When there are multiple load tasks, you can use the following statement to
query the dates and reasons for data load failures.
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ As shown in the example above, the issue is a **data quality
error**(ETL_QUALITY_UNSATISFIED). To view the detailed error, you need to visit
the URL provided in the result. For example, the data exceeded the defined
length of the country column in the table schema:
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ For data quality errors, if you want to allow skipping erroneous records, you
can set a fault tolerance rate in the Properties section of the S3 Load task.
For details, refer to [Import Configuration
Parameters](../../import/import-way/broker-load-manual.md#related-configurations)。
+
+**3.4 Load data for multiple partitions**
+
+ When migrating a large volume of historical data, it is recommended to use a
batch load strategy. Each batch corresponds to one or a few partitions in
Doris. It is recommended to keep the data size under 100GB per batch to reduce
system load and lower the cost of retries in case of load failures.
+
+ You can refer to the script
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh),
which can poll the partition directory on S3 and submit the S3 Load task to
Doris to achieve batch load.
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/data-operate/import/data-source/redshift.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..e2548332174
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "zh-CN"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+在迁移 Redshift 的过程中，通常需要借助对象存储作为中间媒介。核心流程如下：首先通过 Redshift 的
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
语句将数据导出到对象存储；再利用 Doris 的 S3 Load 功能从对象存储中读取数据并导入到 Doris 中，具体可参考 [S3
导入](./amazon-s3.md)。
+
+## 注意事项
+
+1. 在迁移之前，需要根据 Redshift 的表结构选择 Doris
的[数据模型](../../../table-design/data-model/overview.md)，以及[分区](../../../table-design/data-partitioning/dynamic-partitioning.md)和[分桶](../../../table-design/data-partitioning/data-bucketing.md)的策略，更多创建表策略可参考[导入最佳实践](../load-best-practices.md)。
+2. Redshift 导出 Time 类型时，需要 Cast 成 Varchar 类型导出。
+
+
+## 数据类型映射
+
+| Redshift | Doris | 备注 |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR 长度最大 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. 创建表
+
+在迁移 Redshift 表到 Doris 中的时候，需要先创建 Doris 表。
+
+假设我们在 Redshift 中已存在如下表和数据
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+根据这个表结构，可以创建 Doris 主键分区表，分区字段根据业务场景选择，这里分区为 order_date，同时按天分区
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. 导出 Redshift 数据
+
+**2.1 通过 UNLOAD 方式导出到 S3 Parquet 格式的文件**
+
+导出到 S3 时，按照**Doris 的 Partition 字段**进行导出，如下：
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 查看 S3 上的导出文件**
+
+导出后，在 S3 上会按照**分区划分成具体的子目录**，每一个目录是对应的分区数据。如下：
+
+
+
+
+
+## 3. 导入数据到 Doris
+
+导入使用 S3 Load 进行导入，**S3 Load 是一种异步的数据导入方式，执行后 Doris 会主动从数据源拉取数据**，数据源支持兼容 S3
协议的对象存储，包括 ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)等)。
+
+该方式适用于数据量大、需要后台异步处理的场景。对于需要同步处理的数据导入，可以参考 [TVF
导入](./amazon-s3.md#load-with-tvf)。
+
+*注意：对于**含有**复杂类型（Struct/Array/Map）的**Parquet/ORC格式文件**导入，目前必须使用 TVF 导入*
+
+**3.1 导入一个分区的数据**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 通过 Show Load 查看任务运行情况**
+
+ 由于 S3Load 导入是异步提交的，所以需要通过 show load 可以查看指定 label 的导入情况：
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 处理导入过程中的错误**
+
+ 当有多个导入任务时，可以通过以下语句，查询数据导入失败的日期和原因。
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ 如上面的例子是**数据质量错误**(ETL_QUALITY_UNSATISFIED)，具体错误需要通过访问返回的 URL
的链接进行查看，如下是数据超过了表中的 Schema 中 country 列的实际长度：
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ 同时对于数据质量的错误，如果可以允许错误数据跳过的，可以通过在 S3 Load 任务中 Properties
设置容错率，具体可参考[导入配置参数](https://doris.apache.org/zh-CN/docs/dev/data-operate/import/import-way/broker-load-manual#导入配置参数)。
+
+**3.4 导入多个分区的数据**
+
+ 当需要迁移大数据量的存量数据时，建议使用分批导入的策略。每批数据对应 Doris 的一个分区或少量几个分区，数据量建议不超过
100GB，以减轻系统压力并降低导入失败后的重试成本。
+
+ 可参考脚本
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh)，该脚本可以实现了轮询
S3 上的分区目录，同时提交 S3 Load 任务到 Doris 中，实现批量导入的效果。
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/data-operate/import/data-source/redshift.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..e2548332174
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "zh-CN"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+在迁移 Redshift 的过程中，通常需要借助对象存储作为中间媒介。核心流程如下：首先通过 Redshift 的
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
语句将数据导出到对象存储；再利用 Doris 的 S3 Load 功能从对象存储中读取数据并导入到 Doris 中，具体可参考 [S3
导入](./amazon-s3.md)。
+
+## 注意事项
+
+1. 在迁移之前，需要根据 Redshift 的表结构选择 Doris
的[数据模型](../../../table-design/data-model/overview.md)，以及[分区](../../../table-design/data-partitioning/dynamic-partitioning.md)和[分桶](../../../table-design/data-partitioning/data-bucketing.md)的策略，更多创建表策略可参考[导入最佳实践](../load-best-practices.md)。
+2. Redshift 导出 Time 类型时，需要 Cast 成 Varchar 类型导出。
+
+
+## 数据类型映射
+
+| Redshift | Doris | 备注 |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR 长度最大 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. 创建表
+
+在迁移 Redshift 表到 Doris 中的时候，需要先创建 Doris 表。
+
+假设我们在 Redshift 中已存在如下表和数据
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+根据这个表结构，可以创建 Doris 主键分区表，分区字段根据业务场景选择，这里分区为 order_date，同时按天分区
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. 导出 Redshift 数据
+
+**2.1 通过 UNLOAD 方式导出到 S3 Parquet 格式的文件**
+
+导出到 S3 时，按照**Doris 的 Partition 字段**进行导出，如下：
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 查看 S3 上的导出文件**
+
+导出后，在 S3 上会按照**分区划分成具体的子目录**，每一个目录是对应的分区数据。如下：
+
+
+
+
+
+## 3. 导入数据到 Doris
+
+导入使用 S3 Load 进行导入，**S3 Load 是一种异步的数据导入方式，执行后 Doris 会主动从数据源拉取数据**，数据源支持兼容 S3
协议的对象存储，包括 ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)等)。
+
+该方式适用于数据量大、需要后台异步处理的场景。对于需要同步处理的数据导入，可以参考 [TVF
导入](./amazon-s3.md#load-with-tvf)。
+
+*注意：对于**含有**复杂类型（Struct/Array/Map）的**Parquet/ORC格式文件**导入，目前必须使用 TVF 导入*
+
+**3.1 导入一个分区的数据**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 通过 Show Load 查看任务运行情况**
+
+ 由于 S3Load 导入是异步提交的，所以需要通过 show load 可以查看指定 label 的导入情况：
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 处理导入过程中的错误**
+
+ 当有多个导入任务时，可以通过以下语句，查询数据导入失败的日期和原因。
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ 如上面的例子是**数据质量错误**(ETL_QUALITY_UNSATISFIED)，具体错误需要通过访问返回的 URL
的链接进行查看，如下是数据超过了表中的 Schema 中 country 列的实际长度：
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ 同时对于数据质量的错误，如果可以允许错误数据跳过的，可以通过在 S3 Load 任务中 Properties
设置容错率，具体可参考[导入配置参数](https://doris.apache.org/zh-CN/docs/dev/data-operate/import/import-way/broker-load-manual#导入配置参数)。
+
+**3.4 导入多个分区的数据**
+
+ 当需要迁移大数据量的存量数据时，建议使用分批导入的策略。每批数据对应 Doris 的一个分区或少量几个分区，数据量建议不超过
100GB，以减轻系统压力并降低导入失败后的重试成本。
+
+ 可参考脚本
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh)，该脚本可以实现了轮询
S3 上的分区目录，同时提交 S3 Load 任务到 Doris 中，实现批量导入的效果。
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/data-operate/import/data-source/redshift.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..e2548332174
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "zh-CN"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+在迁移 Redshift 的过程中，通常需要借助对象存储作为中间媒介。核心流程如下：首先通过 Redshift 的
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
语句将数据导出到对象存储；再利用 Doris 的 S3 Load 功能从对象存储中读取数据并导入到 Doris 中，具体可参考 [S3
导入](./amazon-s3.md)。
+
+## 注意事项
+
+1. 在迁移之前，需要根据 Redshift 的表结构选择 Doris
的[数据模型](../../../table-design/data-model/overview.md)，以及[分区](../../../table-design/data-partitioning/dynamic-partitioning.md)和[分桶](../../../table-design/data-partitioning/data-bucketing.md)的策略，更多创建表策略可参考[导入最佳实践](../load-best-practices.md)。
+2. Redshift 导出 Time 类型时，需要 Cast 成 Varchar 类型导出。
+
+
+## 数据类型映射
+
+| Redshift | Doris | 备注 |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR 长度最大 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. 创建表
+
+在迁移 Redshift 表到 Doris 中的时候，需要先创建 Doris 表。
+
+假设我们在 Redshift 中已存在如下表和数据
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+根据这个表结构，可以创建 Doris 主键分区表，分区字段根据业务场景选择，这里分区为 order_date，同时按天分区
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. 导出 Redshift 数据
+
+**2.1 通过 UNLOAD 方式导出到 S3 Parquet 格式的文件**
+
+导出到 S3 时，按照**Doris 的 Partition 字段**进行导出，如下：
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 查看 S3 上的导出文件**
+
+导出后，在 S3 上会按照**分区划分成具体的子目录**，每一个目录是对应的分区数据。如下：
+
+
+
+
+
+## 3. 导入数据到 Doris
+
+导入使用 S3 Load 进行导入，**S3 Load 是一种异步的数据导入方式，执行后 Doris 会主动从数据源拉取数据**，数据源支持兼容 S3
协议的对象存储，包括 ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)等)。
+
+该方式适用于数据量大、需要后台异步处理的场景。对于需要同步处理的数据导入，可以参考 [TVF
导入](./amazon-s3.md#load-with-tvf)。
+
+*注意：对于**含有**复杂类型（Struct/Array/Map）的**Parquet/ORC格式文件**导入，目前必须使用 TVF 导入*
+
+**3.1 导入一个分区的数据**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 通过 Show Load 查看任务运行情况**
+
+ 由于 S3Load 导入是异步提交的，所以需要通过 show load 可以查看指定 label 的导入情况：
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 处理导入过程中的错误**
+
+ 当有多个导入任务时，可以通过以下语句，查询数据导入失败的日期和原因。
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ 如上面的例子是**数据质量错误**(ETL_QUALITY_UNSATISFIED)，具体错误需要通过访问返回的 URL
的链接进行查看，如下是数据超过了表中的 Schema 中 country 列的实际长度：
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ 同时对于数据质量的错误，如果可以允许错误数据跳过的，可以通过在 S3 Load 任务中 Properties
设置容错率，具体可参考[导入配置参数](https://doris.apache.org/zh-CN/docs/dev/data-operate/import/import-way/broker-load-manual#导入配置参数)。
+
+**3.4 导入多个分区的数据**
+
+ 当需要迁移大数据量的存量数据时，建议使用分批导入的策略。每批数据对应 Doris 的一个分区或少量几个分区，数据量建议不超过
100GB，以减轻系统压力并降低导入失败后的重试成本。
+
+ 可参考脚本
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh)，该脚本可以实现了轮询
S3 上的分区目录，同时提交 S3 Load 任务到 Doris 中，实现批量导入的效果。
\ No newline at end of file
diff --git a/sidebars.json b/sidebars.json
index 3cdf976da0a..87a9b6b274a 100644
--- a/sidebars.json
+++ b/sidebars.json
@@ -159,6 +159,7 @@
"data-operate/import/data-source/s3-compatible",
"data-operate/import/data-source/snowflake",
"data-operate/import/data-source/bigquery",
+ "data-operate/import/data-source/redshift",
"data-operate/import/data-source/migrate-data-from-other-olap",
"data-operate/import/data-source/migrate-data-from-other-oltp"
]
diff --git a/static/images/data-operate/redshift_out.png
b/static/images/data-operate/redshift_out.png
new file mode 100644
index 00000000000..be3cf6be6ea
Binary files /dev/null and b/static/images/data-operate/redshift_out.png differ
diff --git a/static/images/data-operate/redshift_out2.png
b/static/images/data-operate/redshift_out2.png
new file mode 100644
index 00000000000..50e85bce936
Binary files /dev/null and b/static/images/data-operate/redshift_out2.png differ
diff --git
a/versioned_docs/version-2.1/data-operate/import/data-source/redshift.md
b/versioned_docs/version-2.1/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..c4c63d62b03
--- /dev/null
+++ b/versioned_docs/version-2.1/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "en"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+During the process of migrating Redshift, it is usually necessary to use
object storage as an intermediate medium. The core process is as follows:
First, use the
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
statement of Redshift to export data to the object storage; then, use the S3
Load function of Doris to read data from the object storage and import it into
Doris. For details, please refer to [S3 Load](./amazon-s3.md)。
+
+## Considerations
+
+1. Before the migration, it is necessary to select Doris' [Data
Model](../../../table-design/data-model/overview.md), as well as the strategies
for
[Partitioning](../../../table-design/data-partitioning/dynamic-partitioning.md)
and [Bucketing](../../../table-design/data-partitioning/data-bucketing.md)
according to the table structure of Redshift. For more table creation
strategies, please refer to [Load Best Practices](../load-best-practices.md).
+2. When Redshift exports data of the Time type, it is necessary to export it
after casting it to the Varchar type.
+
+
+## Data type mapping
+
+| Redshift | Doris | Comment |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR maximum length is 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. Create Table
+
+To migrate a Redshift table to Doris, first create the Doris table.
+
+Assume we have the following table and data in Redshift:
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+According to this table structure, a Doris primary key partitioned table can
be created. The partition field should be selected according to the business
scenario. Here, the partition field is "order_date", and it is partitioned on a
daily basis.
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. Export Data from Redshift
+
+**2.1 Export to S3 Parquet Files via UNLOAD**
+
+When exporting to S3, export according to the **Partition field of Doris**, as
follows:
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 Verify Exported Files on S3**
+
+Exported files are organized into **subdirectories by partition** on S3:
+
+
+
+
+
+## 3. Load Data to Doris
+
+S3 Load is an asynchronous data load method. After execution, Doris actively
pulls data from the data source. The data source supports object storage
compatible with the S3 protocol, including ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)，etc)
+
+This method is suitable for scenarios involving large volumes of data that
require asynchronous processing in the background. For data imports that need
to be handled synchronously, refer to [TVF Load](./amazon-s3.md#load-with-tvf)。
+
+*Note: For **Parquet/ORC format files that contain complex types
(Struct/Array/Map)**, TVF Load must be used.*
+
+**3.1 Load a Single Partition**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 Check Load Status via SHOW LOAD**
+
+ Since S3 Load import is submitted asynchronously, you can check the status of
a specific label using SHOW LOAD:
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 Handle Load Errors**
+
+ When there are multiple load tasks, you can use the following statement to
query the dates and reasons for data load failures.
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ As shown in the example above, the issue is a **data quality
error**(ETL_QUALITY_UNSATISFIED). To view the detailed error, you need to visit
the URL provided in the result. For example, the data exceeded the defined
length of the country column in the table schema:
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ For data quality errors, if you want to allow skipping erroneous records, you
can set a fault tolerance rate in the Properties section of the S3 Load task.
For details, refer to [Import Configuration
Parameters](../../import/import-way/broker-load-manual.md#related-configurations)。
+
+**3.4 Load data for multiple partitions**
+
+ When migrating a large volume of historical data, it is recommended to use a
batch load strategy. Each batch corresponds to one or a few partitions in
Doris. It is recommended to keep the data size under 100GB per batch to reduce
system load and lower the cost of retries in case of load failures.
+
+ You can refer to the script
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh),
which can poll the partition directory on S3 and submit the S3 Load task to
Doris to achieve batch load.
\ No newline at end of file
diff --git
a/versioned_docs/version-3.0/data-operate/import/data-source/redshift.md
b/versioned_docs/version-3.0/data-operate/import/data-source/redshift.md
new file mode 100644
index 00000000000..c4c63d62b03
--- /dev/null
+++ b/versioned_docs/version-3.0/data-operate/import/data-source/redshift.md
@@ -0,0 +1,227 @@
+---
+{
+ "title": "Redshift",
+ "language": "en"
+}
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+During the process of migrating Redshift, it is usually necessary to use
object storage as an intermediate medium. The core process is as follows:
First, use the
[UNLOAD](https://docs.aws.amazon.com/zh_cn/redshift/latest/dg/r_UNLOAD.html)
statement of Redshift to export data to the object storage; then, use the S3
Load function of Doris to read data from the object storage and import it into
Doris. For details, please refer to [S3 Load](./amazon-s3.md)。
+
+## Considerations
+
+1. Before the migration, it is necessary to select Doris' [Data
Model](../../../table-design/data-model/overview.md), as well as the strategies
for
[Partitioning](../../../table-design/data-partitioning/dynamic-partitioning.md)
and [Bucketing](../../../table-design/data-partitioning/data-bucketing.md)
according to the table structure of Redshift. For more table creation
strategies, please refer to [Load Best Practices](../load-best-practices.md).
+2. When Redshift exports data of the Time type, it is necessary to export it
after casting it to the Varchar type.
+
+
+## Data type mapping
+
+| Redshift | Doris | Comment |
+| ---------------- | -------------- | -------------------- |
+| SMALLINT | SMALLINT | |
+| INTEGER | INT | |
+| BIGINT | BIGINT | |
+| DECIMAL | DECIMAL | |
+| REAL | FLOAT | |
+| DOUBLE PRECISION | DOUBLE | |
+| BOOLEAN | BOOLEAN | |
+| CHAR | CHAR | |
+| VARCHAR | VARCHAR/STRING | VARCHAR maximum length is 65535 |
+| DATE | DATE | |
+| TIMESTAMP | DATETIME | |
+| TIME/TIMEZ | STRING | |
+| SUPER | VARIANT | |
+| OTHER | UNSUPPORTED | |
+
+## 1. Create Table
+
+To migrate a Redshift table to Doris, first create the Doris table.
+
+Assume we have the following table and data in Redshift:
+
+```SQL
+CREATE TABLE sales_data (
+ order_id INTEGER,
+ customer_name VARCHAR(128),
+ order_date DATE,
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+)
+DISTSTYLE AUTO
+
+INSERT INTO sales_data VALUES
+(1, 'Alice', '2025-04-08', 99.99, 'USA'),
+(2, 'Bob', '2025-04-08', 149.50, 'Canada'),
+(3, 'Charlie', '2025-04-09', 75.00, 'UK'),
+(4, 'Diana', '2025-04-10', 200.00, 'Australia');
+```
+
+According to this table structure, a Doris primary key partitioned table can
be created. The partition field should be selected according to the business
scenario. Here, the partition field is "order_date", and it is partitioned on a
daily basis.
+
+```sql
+CREATE TABLE `sales_data` (
+ order_id INT,
+ order_date DATE NOT NULL,
+ customer_name VARCHAR(128),
+ amount DECIMAL(10,2),
+ country VARCHAR(48)
+) ENGINE=OLAP
+UNIQUE KEY(`order_id`,`order_date`)
+PARTITION BY RANGE(`order_date`) (
+PARTITION p20250408 VALUES [('2025-04-08'), ('2025-04-09')),
+PARTITION p20250409 VALUES [('2025-04-09'), ('2025-04-10')),
+PARTITION p20250410 VALUES [('2025-04-10'), ('2025-04-11'))
+)
+DISTRIBUTED BY HASH(`order_id`) BUCKETS 16
+PROPERTIES (
+ "dynamic_partition.enable" = "true",
+ "dynamic_partition.time_unit" = "DAY",
+ "dynamic_partition.end" = "5",
+ "dynamic_partition.prefix" = "p",
+ "dynamic_partition.buckets" = "16",
+ "replication_num" = "1"
+);
+```
+
+## 2. Export Data from Redshift
+
+**2.1 Export to S3 Parquet Files via UNLOAD**
+
+When exporting to S3, export according to the **Partition field of Doris**, as
follows:
+
+```sql
+unload ('select * from sales_data')
+to 's3://mybucket/redshift/sales_data/'
+iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
+PARQUET
+PARTITION BY (order_date) INCLUDE
+```
+
+**2.2 Verify Exported Files on S3**
+
+Exported files are organized into **subdirectories by partition** on S3:
+
+
+
+
+
+## 3. Load Data to Doris
+
+S3 Load is an asynchronous data load method. After execution, Doris actively
pulls data from the data source. The data source supports object storage
compatible with the S3 protocol, including ([AWS
S3](./amazon-s3.md)，[GCS](./google-cloud-storage.md)，[AZURE](./azure-storage.md)，etc)
+
+This method is suitable for scenarios involving large volumes of data that
require asynchronous processing in the background. For data imports that need
to be handled synchronously, refer to [TVF Load](./amazon-s3.md#load-with-tvf)。
+
+*Note: For **Parquet/ORC format files that contain complex types
(Struct/Array/Map)**, TVF Load must be used.*
+
+**3.1 Load a Single Partition**
+
+```sql
+LOAD LABEL sales_data_2025_04_08
+(
+ DATA INFILE("s3://mybucket/redshift/sales_data/order_date=2025-04-08/*")
+ INTO TABLE sales_data
+ FORMAT AS "parquet"
+ (order_id, order_date, customer_name, amount, country)
+)
+WITH S3
+(
+ "provider" = "S3",
+ "s3.endpoint" = "s3.ap-southeast-1.amazonaws.com",
+ "s3.access_key" = "<ak>",
+ "s3.secret_key"="<sk>",
+ "s3.region" = "ap-southeast-1"
+);
+```
+
+**3.2 Check Load Status via SHOW LOAD**
+
+ Since S3 Load import is submitted asynchronously, you can check the status of
a specific label using SHOW LOAD:
+
+```yaml
+mysql> show load where label = "label_sales_data_2025_04_08"\G
+*************************** 1. row ***************************
+ JobId: 17956078
+ Label: label_sales_data_2025_04_08
+ State: FINISHED
+ Progress: 100.00% (1/1)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=0; dpp.norm.ALL=2
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):3600;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: NULL
+ CreateTime: 2025-04-10 17:50:53
+ EtlStartTime: 2025-04-10 17:50:54
+ EtlFinishTime: 2025-04-10 17:50:54
+ LoadStartTime: 2025-04-10 17:50:54
+LoadFinishTime: 2025-04-10 17:50:54
+ URL: NULL
+ JobDetails: {"Unfinished
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[]},"ScannedRows":2,"TaskNumber":1,"LoadBytes":91,"All
backends":{"5eec1be8612d4872-91040ff1e7208a4f":[10022]},"FileNumber":1,"FileSize":1620}
+ TransactionId: 766228
+ ErrorTablets: {}
+ User: root
+ Comment:
+1 row in set (0.00 sec)
+```
+
+**3.3 Handle Load Errors**
+
+ When there are multiple load tasks, you can use the following statement to
query the dates and reasons for data load failures.
+
+```SQL
+mysql> show load where state='CANCELLED' and label like "label_test%"\G
+*************************** 1. row ***************************
+ JobId: 18312384
+ Label: label_test123
+ State: CANCELLED
+ Progress: 100.00% (3/3)
+ Type: BROKER
+ EtlInfo: unselected.rows=0; dpp.abnorm.ALL=4; dpp.norm.ALL=0
+ TaskInfo: cluster:s3.ap-southeast-1.amazonaws.com; timeout(s):14400;
max_filter_ratio:0.0; priority:NORMAL
+ ErrorMsg: type:ETL_QUALITY_UNSATISFIED; msg:quality not good enough to
cancel
+ CreateTime: 2025-04-15 17:32:59
+ EtlStartTime: 2025-04-15 17:33:02
+ EtlFinishTime: 2025-04-15 17:33:02
+ LoadStartTime: 2025-04-15 17:33:02
+LoadFinishTime: 2025-04-15 17:33:02
+ URL:
http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342
+ JobDetails: {"Unfinished
backends":{"7602ccd7c3a4854-95307efca7bfe341":[]},"ScannedRows":4,"TaskNumber":1,"LoadBytes":188,"All
backends":{"7602ccd7c3a4854-95307efca7bfe341":[10022]},"FileNumber":3,"FileSize":4839}
+ TransactionId: 769213
+ ErrorTablets: {}
+ User: root
+ Comment:
+```
+
+ As shown in the example above, the issue is a **data quality
error**(ETL_QUALITY_UNSATISFIED). To view the detailed error, you need to visit
the URL provided in the result. For example, the data exceeded the defined
length of the country column in the table schema:
+
+```python
+[root@VM-10-6-centos ~]$ curl
"http://10.16.10.6:28747/api/_load_error_log?file=__shard_2/error_log_insert_stmt_7602ccd7c3a4854-95307efca7bfe342_7602ccd7c3a4854_95307efca7bfe342";
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [USA] schema length: 1; actual length: 3; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Canada] schema length: 1; actual length: 6; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [UK] schema length: 1; actual length: 2; . src
line [];
+Reason: column_name[country], the length of input is too long than schema.
first 32 bytes of input str: [Australia] schema length: 1; actual length: 9; .
src line [];
+```
+
+ For data quality errors, if you want to allow skipping erroneous records, you
can set a fault tolerance rate in the Properties section of the S3 Load task.
For details, refer to [Import Configuration
Parameters](../../import/import-way/broker-load-manual.md#related-configurations)。
+
+**3.4 Load data for multiple partitions**
+
+ When migrating a large volume of historical data, it is recommended to use a
batch load strategy. Each batch corresponds to one or a few partitions in
Doris. It is recommended to keep the data size under 100GB per batch to reduce
system load and lower the cost of retries in case of load failures.
+
+ You can refer to the script
[s3_load_demo.sh](https://github.com/apache/doris/blob/master/samples/load/shell/s3_load_demo.sh),
which can poll the partition directory on S3 and submit the S3 Load task to
Doris to achieve batch load.
\ No newline at end of file
diff --git a/versioned_sidebars/version-2.1-sidebars.json
b/versioned_sidebars/version-2.1-sidebars.json
index c8453959e32..83ac7c6ec6d 100644
--- a/versioned_sidebars/version-2.1-sidebars.json
+++ b/versioned_sidebars/version-2.1-sidebars.json
@@ -135,6 +135,7 @@
"data-operate/import/data-source/s3-compatible",
"data-operate/import/data-source/snowflake",
"data-operate/import/data-source/bigquery",
+ "data-operate/import/data-source/redshift",
"data-operate/import/data-source/migrate-data-from-other-olap",
"data-operate/import/data-source/migrate-data-from-other-oltp"
]
diff --git a/versioned_sidebars/version-3.0-sidebars.json
b/versioned_sidebars/version-3.0-sidebars.json
index 26b17f163b8..4d80dc9d719 100644
--- a/versioned_sidebars/version-3.0-sidebars.json
+++ b/versioned_sidebars/version-3.0-sidebars.json
@@ -159,6 +159,7 @@
"data-operate/import/data-source/s3-compatible",
"data-operate/import/data-source/snowflake",
"data-operate/import/data-source/bigquery",
+ "data-operate/import/data-source/redshift",
"data-operate/import/data-source/migrate-data-from-other-olap",
"data-operate/import/data-source/migrate-data-from-other-oltp"
]
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