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new ea4234a063 [doc][connector][hive] Add hive chinese doc (#8169)
ea4234a063 is described below
commit ea4234a0632a5fe97eab65c8ffeed19d4eab886a
Author: Jast <[email protected]>
AuthorDate: Fri Nov 29 14:09:16 2024 +0800
[doc][connector][hive] Add hive chinese doc (#8169)
---
docs/en/connector-v2/sink/Hive.md | 74 ++++--
docs/en/connector-v2/source/Hive.md | 79 +++++--
docs/{en => zh}/connector-v2/sink/Hive.md | 246 +++++++++++---------
docs/zh/connector-v2/source/Hive.md | 368 ++++++++++++++++++++++++++++++
4 files changed, 618 insertions(+), 149 deletions(-)
diff --git a/docs/en/connector-v2/sink/Hive.md
b/docs/en/connector-v2/sink/Hive.md
index df5b493884..20f3d22cb8 100644
--- a/docs/en/connector-v2/sink/Hive.md
+++ b/docs/en/connector-v2/sink/Hive.md
@@ -8,7 +8,7 @@ Write data to Hive.
:::tip
-In order to use this connector, You must ensure your spark/flink cluster
already integrated hive. The tested hive version is 2.3.9.
+In order to use this connector, You must ensure your spark/flink cluster
already integrated hive. The tested hive version is 2.3.9 and 3.1.3 .
If you use SeaTunnel Engine, You need put seatunnel-hadoop3-3.1.4-uber.jar and
hive-exec-3.1.3.jar and libfb303-0.9.3.jar in $SEATUNNEL_HOME/lib/ dir.
:::
@@ -204,6 +204,55 @@ Description:
- `kerberos_keytab_path`: The keytab file path for Kerberos authentication.
- `krb5_path`: The path to the `krb5.conf` file used for Kerberos
authentication.
+Run the case:
+
+```bash
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
+
+source {
+ FakeSource {
+ schema = {
+ fields {
+ pk_id = bigint
+ name = string
+ score = int
+ }
+ primaryKey {
+ name = "pk_id"
+ columnNames = [pk_id]
+ }
+ }
+ rows = [
+ {
+ kind = INSERT
+ fields = [1, "A", 100]
+ },
+ {
+ kind = INSERT
+ fields = [2, "B", 100]
+ },
+ {
+ kind = INSERT
+ fields = [3, "C", 100]
+ }
+ ]
+ }
+}
+
+sink {
+ Hive {
+ table_name = "default.test_hive_sink_on_hdfs_with_kerberos"
+ metastore_uri = "thrift://metastore:9083"
+ hive_site_path = "/tmp/hive-site.xml"
+ kerberos_principal = "hive/[email protected]"
+ kerberos_keytab_path = "/tmp/hive.keytab"
+ krb5_path = "/tmp/krb5.conf"
+ }
+}
+```
## Hive on s3
@@ -418,26 +467,3 @@ sink {
}
}
```
-
-## Changelog
-
-### 2.2.0-beta 2022-09-26
-
-- Add Hive Sink Connector
-
-### 2.3.0-beta 2022-10-20
-
-- [Improve] Hive Sink supports automatic partition repair
([3133](https://github.com/apache/seatunnel/pull/3133))
-
-### 2.3.0 2022-12-30
-
-- [BugFix] Fixed the following bugs that failed to write data to files
([3258](https://github.com/apache/seatunnel/pull/3258))
- - When field from upstream is null it will throw NullPointerException
- - Sink columns mapping failed
- - When restore writer from states getting transaction directly failed
-
-### Next version
-
-- [Improve] Support kerberos authentication
([3840](https://github.com/apache/seatunnel/pull/3840))
-- [Improve] Added partition_dir_expression validation logic
([3886](https://github.com/apache/seatunnel/pull/3886))
-
diff --git a/docs/en/connector-v2/source/Hive.md
b/docs/en/connector-v2/source/Hive.md
index af4edc4730..d87739f103 100644
--- a/docs/en/connector-v2/source/Hive.md
+++ b/docs/en/connector-v2/source/Hive.md
@@ -8,7 +8,7 @@ Read data from Hive.
:::tip
-In order to use this connector, You must ensure your spark/flink cluster
already integrated hive. The tested hive version is 2.3.9.
+In order to use this connector, You must ensure your spark/flink cluster
already integrated hive. The tested hive version is 2.3.9 and 3.1.3 .
If you use SeaTunnel Engine, You need put seatunnel-hadoop3-3.1.4-uber.jar and
hive-exec-3.1.3.jar and libfb303-0.9.3.jar in $SEATUNNEL_HOME/lib/ dir.
:::
@@ -180,6 +180,71 @@ Description:
- `kerberos_keytab_path`: The keytab file path for Kerberos authentication.
- `krb5_path`: The path to the `krb5.conf` file used for Kerberos
authentication.
+Run the case:
+
+```bash
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
+
+source {
+ Hive {
+ table_name = "default.test_hive_sink_on_hdfs_with_kerberos"
+ metastore_uri = "thrift://metastore:9083"
+ hive.hadoop.conf-path = "/tmp/hadoop"
+ result_table_name = hive_source
+ hive_site_path = "/tmp/hive-site.xml"
+ kerberos_principal = "hive/[email protected]"
+ kerberos_keytab_path = "/tmp/hive.keytab"
+ krb5_path = "/tmp/krb5.conf"
+ }
+}
+
+sink {
+ Assert {
+ source_table_name = hive_source
+ rules {
+ row_rules = [
+ {
+ rule_type = MAX_ROW
+ rule_value = 3
+ }
+ ],
+ field_rules = [
+ {
+ field_name = pk_id
+ field_type = bigint
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ },
+ {
+ field_name = name
+ field_type = string
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ },
+ {
+ field_name = score
+ field_type = int
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ }
+ ]
+ }
+ }
+}
+```
+
## Hive on s3
### Step 1
@@ -307,15 +372,3 @@ sink {
}
}
```
-
-## Changelog
-
-### 2.2.0-beta 2022-09-26
-
-- Add Hive Source Connector
-
-### Next version
-
-- [Improve] Support kerberos authentication
([3840](https://github.com/apache/seatunnel/pull/3840))
-- Support user-defined partitions
([3842](https://github.com/apache/seatunnel/pull/3842))
-
diff --git a/docs/en/connector-v2/sink/Hive.md
b/docs/zh/connector-v2/sink/Hive.md
similarity index 57%
copy from docs/en/connector-v2/sink/Hive.md
copy to docs/zh/connector-v2/sink/Hive.md
index df5b493884..b0741ed0e2 100644
--- a/docs/en/connector-v2/sink/Hive.md
+++ b/docs/zh/connector-v2/sink/Hive.md
@@ -1,111 +1,109 @@
# Hive
-> Hive sink connector
+> Hive Sink 连接器
-## Description
+## 描述
-Write data to Hive.
+将数据写入 Hive。
-:::tip
+:::提示
-In order to use this connector, You must ensure your spark/flink cluster
already integrated hive. The tested hive version is 2.3.9.
+为了使用此连接器，您必须确保您的 Spark/Flink 集群已经集成了 Hive。测试过的 Hive 版本是 2.3.9 和 3.1.3。
-If you use SeaTunnel Engine, You need put seatunnel-hadoop3-3.1.4-uber.jar and
hive-exec-3.1.3.jar and libfb303-0.9.3.jar in $SEATUNNEL_HOME/lib/ dir.
+如果您使用 SeaTunnel 引擎，您需要将
`seatunnel-hadoop3-3.1.4-uber.jar`、`hive-exec-3.1.3.jar` 和 `libfb303-0.9.3.jar`
放在 `$SEATUNNEL_HOME/lib/` 目录中。
:::
-## Key features
-
-- [x] [support multiple table write](../../concept/connector-v2-features.md)
-- [x] [exactly-once](../../concept/connector-v2-features.md)
-
-By default, we use 2PC commit to ensure `exactly-once`
-
-- [x] file format
- - [x] text
- - [x] csv
- - [x] parquet
- - [x] orc
- - [x] json
-- [x] compress codec
- - [x] lzo
-
-## Options
-
-| name | type | required | default value |
-|-------------------------------|---------|----------|----------------|
-| table_name | string | yes | - |
-| metastore_uri | string | yes | - |
-| compress_codec | string | no | none |
-| hdfs_site_path | string | no | - |
-| hive_site_path | string | no | - |
-| hive.hadoop.conf | Map | no | - |
-| hive.hadoop.conf-path | string | no | - |
-| krb5_path | string | no | /etc/krb5.conf |
-| kerberos_principal | string | no | - |
-| kerberos_keytab_path | string | no | - |
-| abort_drop_partition_metadata | boolean | no | true |
-| common-options | | no | - |
+## 关键特性
+
+- [x] [支持多表写入](../../concept/connector-v2-features.md)
+- [x] [精确一次](../../concept/connector-v2-features.md)
+
+默认情况下，我们使用 2PC 提交来确保“精确一次”。
+
+- [x] 文件格式
+ - [x] 文本
+ - [x] CSV
+ - [x] Parquet
+ - [x] ORC
+ - [x] JSON
+- [x] 压缩编解码器
+ - [x] LZO
+
+## 选项
+
+| 名称 | 类型 | 必需 | 默认值 |
+|-------------------------------|---------|------|---------|
+| table_name | string | 是 | - |
+| metastore_uri | string | 是 | - |
+| compress_codec | string | 否 | none |
+| hdfs_site_path | string | 否 | - |
+| hive_site_path | string | 否 | - |
+| hive.hadoop.conf | Map | 否 | - |
+| hive.hadoop.conf-path | string | 否 | - |
+| krb5_path | string | 否 | /etc/krb5.conf |
+| kerberos_principal | string | 否 | - |
+| kerberos_keytab_path | string | 否 | - |
+| abort_drop_partition_metadata | boolean | 否 | true |
+| common-options | | 否 | - |
### table_name [string]
-Target Hive table name eg: db1.table1, and if the source is multiple mode, you
can use `${database_name}.${table_name}` to generate the table name, it will
replace the `${database_name}` and `${table_name}` with the value of the
CatalogTable generate from the source.
+目标 Hive 表名，例如：`db1.table1`。如果源是多模式，您可以使用 `${database_name}.${table_name}`
来生成表名，它将用源生成的 CatalogTable 的值替换 `${database_name}` 和 `${table_name}`。
### metastore_uri [string]
-Hive metastore uri
+Hive 元存储 URI
### hdfs_site_path [string]
-The path of `hdfs-site.xml`, used to load ha configuration of namenodes
+`hdfs-site.xml` 的路径，用于加载 Namenode 的高可用配置
### hive_site_path [string]
-The path of `hive-site.xml`
+`hive-site.xml` 的路径
### hive.hadoop.conf [map]
-Properties in hadoop conf('core-site.xml', 'hdfs-site.xml', 'hive-site.xml')
+Hadoop 配置中的属性（`core-site.xml`、`hdfs-site.xml`、`hive-site.xml`）
### hive.hadoop.conf-path [string]
-The specified loading path for the 'core-site.xml', 'hdfs-site.xml',
'hive-site.xml' files
+指定加载 `core-site.xml`、`hdfs-site.xml`、`hive-site.xml` 文件的路径
### krb5_path [string]
-The path of `krb5.conf`, used to authentication kerberos
+`krb5.conf` 的路径，用于 Kerberos 认证
-The path of `hive-site.xml`, used to authentication hive metastore
+`hive-site.xml` 的路径，用于 Hive 元存储认证
### kerberos_principal [string]
-The principal of kerberos
+Kerberos 的主体
### kerberos_keytab_path [string]
-The keytab path of kerberos
+Kerberos 的 keytab 文件路径
### abort_drop_partition_metadata [boolean]
-Flag to decide whether to drop partition metadata from Hive Metastore during
an abort operation. Note: this only affects the metadata in the metastore, the
data in the partition will always be deleted(data generated during the
synchronization process).
+在中止操作期间是否从 Hive Metastore 中删除分区元数据的标志。注意：这只影响元存储中的元数据，分区中的数据将始终被删除（同步过程中生成的数据）。
-### common options
+### 通用选项
-Sink plugin common parameters, please refer to [Sink Common
Options](../sink-common-options.md) for details
+Sink 插件的通用参数，请参阅 [Sink Common Options](../sink-common-options.md) 了解详细信息。
-## Example
+## 示例
```bash
-
Hive {
table_name = "default.seatunnel_orc"
metastore_uri = "thrift://namenode001:9083"
}
-
```
-### example 1
+### 示例 1
-We have a source table like this:
+我们有一个源表如下：
```bash
create table test_hive_source(
@@ -128,10 +126,9 @@ create table test_hive_source(
test_struct STRUCT<street:STRING, city:STRING,
state:STRING, zip:INT>
)
PARTITIONED BY (test_par1 STRING, test_par2 STRING);
-
```
-We need read data from the source table and write to another table:
+我们需要从源表读取数据并写入另一个表：
```bash
create table test_hive_sink_text_simple(
@@ -151,10 +148,9 @@ create table test_hive_sink_text_simple(
test_date DATE
)
PARTITIONED BY (test_par1 STRING, test_par2 STRING);
-
```
-The job config file can like this:
+作业配置文件可以如下：
```
env {
@@ -170,7 +166,7 @@ source {
}
sink {
- # choose stdout output plugin to output data to console
+ # 选择 stdout 输出插件将数据输出到控制台
Hive {
table_name = "test_hive.test_hive_sink_text_simple"
@@ -182,7 +178,7 @@ sink {
}
```
-### example2: Kerberos
+### 示例 2：Kerberos
```bash
sink {
@@ -197,27 +193,76 @@ sink {
}
```
-Description:
+描述：
+
+- `hive_site_path`：`hive-site.xml` 文件的路径。
+- `kerberos_principal`：Kerberos 认证的主体。
+- `kerberos_keytab_path`：Kerberos 认证的 keytab 文件路径。
+- `krb5_path`：用于 Kerberos 认证的 `krb5.conf` 文件路径。
+
+运行案例：
+
+```bash
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
-- `hive_site_path`: The path to the `hive-site.xml` file.
-- `kerberos_principal`: The principal for Kerberos authentication.
-- `kerberos_keytab_path`: The keytab file path for Kerberos authentication.
-- `krb5_path`: The path to the `krb5.conf` file used for Kerberos
authentication.
+source {
+ FakeSource {
+ schema = {
+ fields {
+ pk_id = bigint
+ name = string
+ score = int
+ }
+ primaryKey {
+ name = "pk_id"
+ columnNames = [pk_id]
+ }
+ }
+ rows = [
+ {
+ kind = INSERT
+ fields = [1, "A", 100]
+ },
+ {
+ kind = INSERT
+ fields = [2, "B", 100]
+ },
+ {
+ kind = INSERT
+ fields = [3, "C", 100]
+ }
+ ]
+ }
+}
+sink {
+ Hive {
+ table_name = "default.test_hive_sink_on_hdfs_with_kerberos"
+ metastore_uri = "thrift://metastore:9083"
+ hive_site_path = "/tmp/hive-site.xml"
+ kerberos_principal = "hive/[email protected]"
+ kerberos_keytab_path = "/tmp/hive.keytab"
+ krb5_path = "/tmp/krb5.conf"
+ }
+}
+```
## Hive on s3
-### Step 1
+### 步骤 1
-Create the lib dir for hive of emr.
+为 EMR 的 Hive 创建 lib 目录。
```shell
mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib
```
-### Step 2
+### 步骤 2
-Get the jars from maven center to the lib.
+从 Maven 中心获取 jar 文件到 lib。
```shell
cd ${SEATUNNEL_HOME}/plugins/Hive/lib
@@ -225,9 +270,9 @@ wget
https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.6.5/hadoop-aw
wget
https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar
```
-### Step 3
+### 步骤 3
-Copy the jars from your environment on emr to the lib dir.
+从您的 EMR 环境中复制 jar 文件到 lib 目录。
```shell
cp /usr/share/aws/emr/emrfs/lib/emrfs-hadoop-assembly-2.60.0.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
@@ -236,9 +281,9 @@ cp
/usr/share/aws/emr/hadoop-state-pusher/lib/javax.inject-1.jar ${SEATUNNEL_HOM
cp /usr/share/aws/emr/hadoop-state-pusher/lib/aopalliance-1.0.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
```
-### Step 4
+### 步骤 4
-Run the case.
+运行案例。
```shell
env {
@@ -291,35 +336,35 @@ sink {
## Hive on oss
-### Step 1
+### 步骤 1
-Create the lib dir for hive of emr.
+为 EMR 的 Hive 创建 lib 目录。
```shell
mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib
```
-### Step 2
+### 步骤 2
-Get the jars from maven center to the lib.
+从 Maven 中心获取 jar 文件到 lib。
```shell
cd ${SEATUNNEL_HOME}/plugins/Hive/lib
wget
https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar
```
-### Step 3
+### 步骤 3
-Copy the jars from your environment on emr to the lib dir and delete the
conflicting jar.
+从您的 EMR 环境中复制 jar 文件到 lib 目录并删除冲突的 jar。
```shell
cp -r /opt/apps/JINDOSDK/jindosdk-current/lib/jindo-*.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
rm -f ${SEATUNNEL_HOME}/lib/hadoop-aliyun-*.jar
```
-### Step 4
+### 步骤 4
-Run the case.
+运行案例。
```shell
env {
@@ -369,9 +414,9 @@ sink {
}
```
-### example 2
+### 示例 2
-We have multiple source table like this:
+我们有多个源表如下：
```bash
create table test_1(
@@ -384,13 +429,13 @@ PARTITIONED BY (xx);
...
```
-We need read data from these source tables and write to another tables:
+我们需要从这些源表读取数据并写入其他表：
-The job config file can like this:
+作业配置文件可以如下：
```
env {
- # You can set flink configuration here
+ # 您可以在此处设置 Flink 配置
parallelism = 3
job.name="test_hive_source_to_hive"
}
@@ -411,33 +456,10 @@ source {
}
sink {
- # choose stdout output plugin to output data to console
+ # 选择 stdout 输出插件将数据输出到控制台
Hive {
table_name = "${database_name}.${table_name}"
metastore_uri = "thrift://ctyun7:9083"
}
}
```
-
-## Changelog
-
-### 2.2.0-beta 2022-09-26
-
-- Add Hive Sink Connector
-
-### 2.3.0-beta 2022-10-20
-
-- [Improve] Hive Sink supports automatic partition repair
([3133](https://github.com/apache/seatunnel/pull/3133))
-
-### 2.3.0 2022-12-30
-
-- [BugFix] Fixed the following bugs that failed to write data to files
([3258](https://github.com/apache/seatunnel/pull/3258))
- - When field from upstream is null it will throw NullPointerException
- - Sink columns mapping failed
- - When restore writer from states getting transaction directly failed
-
-### Next version
-
-- [Improve] Support kerberos authentication
([3840](https://github.com/apache/seatunnel/pull/3840))
-- [Improve] Added partition_dir_expression validation logic
([3886](https://github.com/apache/seatunnel/pull/3886))
-
diff --git a/docs/zh/connector-v2/source/Hive.md
b/docs/zh/connector-v2/source/Hive.md
new file mode 100644
index 0000000000..00a322cc40
--- /dev/null
+++ b/docs/zh/connector-v2/source/Hive.md
@@ -0,0 +1,368 @@
+# Hive
+
+> Hive 源连接器
+
+## 描述
+
+从 Hive 读取数据。
+
+:::提示
+
+为了使用此连接器，您必须确保您的 Spark/Flink 集群已经集成了 Hive。测试过的 Hive 版本是 2.3.9 和 3.1.3。
+
+如果您使用 SeaTunnel 引擎，您需要将
`seatunnel-hadoop3-3.1.4-uber.jar`、`hive-exec-3.1.3.jar` 和 `libfb303-0.9.3.jar`
放在 `$SEATUNNEL_HOME/lib/` 目录中。
+:::
+
+## 关键特性
+
+- [x] [批处理](../../concept/connector-v2-features.md)
+- [ ] [流处理](../../concept/connector-v2-features.md)
+- [x] [精确一次](../../concept/connector-v2-features.md)
+
+在 `pollNext` 调用中读取分片中的所有数据。读取的分片将保存在快照中。
+
+- [x] [schema 投影](../../concept/connector-v2-features.md)
+- [x] [并行度](../../concept/connector-v2-features.md)
+- [ ] [支持用户定义的分片](../../concept/connector-v2-features.md)
+- [x] 文件格式
+ - [x] 文本
+ - [x] CSV
+ - [x] Parquet
+ - [x] ORC
+ - [x] JSON
+
+## 选项
+
+| 名称 | 类型 | 必需 | 默认值 |
+|-----------------------|--------|------|---------|
+| table_name | string | 是 | - |
+| metastore_uri | string | 是 | - |
+| krb5_path | string | 否 | /etc/krb5.conf |
+| kerberos_principal | string | 否 | - |
+| kerberos_keytab_path | string | 否 | - |
+| hdfs_site_path | string | 否 | - |
+| hive_site_path | string | 否 | - |
+| hive.hadoop.conf | Map | 否 | - |
+| hive.hadoop.conf-path | string | 否 | - |
+| read_partitions | list | 否 | - |
+| read_columns | list | 否 | - |
+| compress_codec | string | 否 | none |
+| common-options | | 否 | - |
+
+### table_name [string]
+
+目标 Hive 表名，例如：`db1.table1`
+
+### metastore_uri [string]
+
+Hive 元存储 URI
+
+### hdfs_site_path [string]
+
+`hdfs-site.xml` 的路径，用于加载 Namenode 的高可用配置
+
+### hive.hadoop.conf [map]
+
+Hadoop 配置中的属性（`core-site.xml`、`hdfs-site.xml`、`hive-site.xml`）
+
+### hive.hadoop.conf-path [string]
+
+指定加载 `core-site.xml`、`hdfs-site.xml`、`hive-site.xml` 文件的路径
+
+### read_partitions [list]
+
+用户希望从 Hive 表中读取的目标分区，如果用户未设置此参数，将读取 Hive 表中的所有数据。
+
+**提示：分区列表中的每个分区应具有相同的目录层级。例如，一个 Hive 表有两个分区：`par1` 和 `par2`，如果用户设置如下：**
+**`read_partitions = [par1=xxx, par1=yyy/par2=zzz]`，这是不合法的**
+
+### krb5_path [string]
+
+`krb5.conf` 的路径，用于 Kerberos 认证
+
+### kerberos_principal [string]
+
+Kerberos 认证的主体
+
+### kerberos_keytab_path [string]
+
+Kerberos 认证的 keytab 文件路径
+
+### read_columns [list]
+
+数据源的读取列列表，用户可以使用它来实现字段投影。
+
+### compress_codec [string]
+
+文件的压缩编解码器，支持的详细信息如下所示：
+
+- txt: `lzo` `none`
+- json: `lzo` `none`
+- csv: `lzo` `none`
+- orc/parquet:
+ 自动识别压缩类型，无需额外设置。
+
+### 通用选项
+
+源插件的通用参数，请参阅 [Source Common Options](../source-common-options.md) 了解详细信息。
+
+## 示例
+
+### 示例 1：单表
+
+```bash
+ Hive {
+ table_name = "default.seatunnel_orc"
+ metastore_uri = "thrift://namenode001:9083"
+ }
+```
+
+### 示例 2：多表
+> 注意：Hive 是结构化数据源，应使用 `table_list`，`tables_configs` 将在未来移除。
+
+```bash
+ Hive {
+ table_list = [
+ {
+ table_name = "default.seatunnel_orc_1"
+ metastore_uri = "thrift://namenode001:9083"
+ },
+ {
+ table_name = "default.seatunnel_orc_2"
+ metastore_uri = "thrift://namenode001:9083"
+ }
+ ]
+ }
+```
+
+```bash
+ Hive {
+ tables_configs = [
+ {
+ table_name = "default.seatunnel_orc_1"
+ metastore_uri = "thrift://namenode001:9083"
+ },
+ {
+ table_name = "default.seatunnel_orc_2"
+ metastore_uri = "thrift://namenode001:9083"
+ }
+ ]
+ }
+```
+
+### 示例 3：Kerberos
+
+```bash
+source {
+ Hive {
+ table_name = "default.test_hive_sink_on_hdfs_with_kerberos"
+ metastore_uri = "thrift://metastore:9083"
+ hive.hadoop.conf-path = "/tmp/hadoop"
+ result_table_name = hive_source
+ hive_site_path = "/tmp/hive-site.xml"
+ kerberos_principal = "hive/[email protected]"
+ kerberos_keytab_path = "/tmp/hive.keytab"
+ krb5_path = "/tmp/krb5.conf"
+ }
+}
+```
+
+描述：
+
+- `hive_site_path`：`hive-site.xml` 文件的路径。
+- `kerberos_principal`：Kerberos 认证的主体。
+- `kerberos_keytab_path`：Kerberos 认证的 keytab 文件路径。
+- `krb5_path`：用于 Kerberos 认证的 `krb5.conf` 文件路径。
+
+运行案例：
+
+```bash
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
+
+source {
+ Hive {
+ table_name = "default.test_hive_sink_on_hdfs_with_kerberos"
+ metastore_uri = "thrift://metastore:9083"
+ hive.hadoop.conf-path = "/tmp/hadoop"
+ result_table_name = hive_source
+ hive_site_path = "/tmp/hive-site.xml"
+ kerberos_principal = "hive/[email protected]"
+ kerberos_keytab_path = "/tmp/hive.keytab"
+ krb5_path = "/tmp/krb5.conf"
+ }
+}
+
+sink {
+ Assert {
+ source_table_name = hive_source
+ rules {
+ row_rules = [
+ {
+ rule_type = MAX_ROW
+ rule_value = 3
+ }
+ ],
+ field_rules = [
+ {
+ field_name = pk_id
+ field_type = bigint
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ },
+ {
+ field_name = name
+ field_type = string
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ },
+ {
+ field_name = score
+ field_type = int
+ field_value = [
+ {
+ rule_type = NOT_NULL
+ }
+ ]
+ }
+ ]
+ }
+ }
+}
+```
+
+## Hive on s3
+
+### 步骤 1
+
+为 EMR 的 Hive 创建 lib 目录。
+
+```shell
+mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib
+```
+
+### 步骤 2
+
+从 Maven 中心获取 jar 文件到 lib。
+
+```shell
+cd ${SEATUNNEL_HOME}/plugins/Hive/lib
+wget
https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.6.5/hadoop-aws-2.6.5.jar
+wget
https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar
+```
+
+### 步骤 3
+
+从您的 EMR 环境中复制 jar 文件到 lib 目录。
+
+```shell
+cp /usr/share/aws/emr/emrfs/lib/emrfs-hadoop-assembly-2.60.0.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
+cp /usr/share/aws/emr/hadoop-state-pusher/lib/hadoop-common-3.3.6-amzn-1.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
+cp /usr/share/aws/emr/hadoop-state-pusher/lib/javax.inject-1.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
+cp /usr/share/aws/emr/hadoop-state-pusher/lib/aopalliance-1.0.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
+```
+
+### 步骤 4
+
+运行案例。
+
+```shell
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
+
+source {
+ Hive {
+ table_name = "test_hive.test_hive_sink_on_s3"
+ metastore_uri =
"thrift://ip-192-168-0-202.cn-north-1.compute.internal:9083"
+ hive.hadoop.conf-path = "/home/ec2-user/hadoop-conf"
+ hive.hadoop.conf = {
+ bucket="s3://ws-package"
+
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
+ }
+ read_columns = ["pk_id", "name", "score"]
+ }
+}
+
+sink {
+ Hive {
+ table_name = "test_hive.test_hive_sink_on_s3_sink"
+ metastore_uri =
"thrift://ip-192-168-0-202.cn-north-1.compute.internal:9083"
+ hive.hadoop.conf-path = "/home/ec2-user/hadoop-conf"
+ hive.hadoop.conf = {
+ bucket="s3://ws-package"
+
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
+ }
+ }
+}
+```
+
+## Hive on oss
+
+### 步骤 1
+
+为 EMR 的 Hive 创建 lib 目录。
+
+```shell
+mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib
+```
+
+### 步骤 2
+
+从 Maven 中心获取 jar 文件到 lib。
+
+```shell
+cd ${SEATUNNEL_HOME}/plugins/Hive/lib
+wget
https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar
+```
+
+### 步骤 3
+
+从您的 EMR 环境中复制 jar 文件到 lib 目录并删除冲突的 jar。
+
+```shell
+cp -r /opt/apps/JINDOSDK/jindosdk-current/lib/jindo-*.jar
${SEATUNNEL_HOME}/plugins/Hive/lib
+rm -f ${SEATUNNEL_HOME}/lib/hadoop-aliyun-*.jar
+```
+
+### 步骤 4
+
+运行案例。
+
+```shell
+env {
+ parallelism = 1
+ job.mode = "BATCH"
+}
+
+source {
+ Hive {
+ table_name = "test_hive.test_hive_sink_on_oss"
+ metastore_uri =
"thrift://master-1-1.c-1009b01725b501f2.cn-wulanchabu.emr.aliyuncs.com:9083"
+ hive.hadoop.conf-path = "/tmp/hadoop"
+ hive.hadoop.conf = {
+ bucket="oss://emr-osshdfs.cn-wulanchabu.oss-dls.aliyuncs.com"
+ }
+ }
+}
+
+sink {
+ Hive {
+ table_name = "test_hive.test_hive_sink_on_oss_sink"
+ metastore_uri =
"thrift://master-1-1.c-1009b01725b501f2.cn-wulanchabu.emr.aliyuncs.com:9083"
+ hive.hadoop.conf-path = "/tmp/hadoop"
+ hive.hadoop.conf = {
+ bucket="oss://emr-osshdfs.cn-wulanchabu.oss-dls.aliyuncs.com"
+ }
+ }
+}
+```
