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[GitHub] [flink] wangxlong opened a new pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
wangxlong opened a new pull request #13537:
URL: https://github.com/apache/flink/pull/13537
## What is the purpose of the change
Translate page "intro_to_table_api" into Chinese
## Brief change log
Translate page "intro_to_table_api" into Chinese
## Verifying this change
This change is a trivial rework / code cleanup without any test coverage.
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): (no)
- The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (no)
- The serializers: (no)
- The runtime per-record code paths (performance sensitive): (no)
- Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn/Mesos, ZooKeeper: (no)
- The S3 file system connector: (no)
## Documentation
- Does this pull request introduce a new feature? (no)
- If yes, how is the feature documented? (not applicable / docs / JavaDocs / not documented)
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[GitHub] [flink] flinkbot edited a comment on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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[GitHub] [flink] wangxlong commented on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
wangxlong commented on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-706665786
Hi @dianfu Could you help to have a review in your free time, Thank you~
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[GitHub] [flink] flinkbot edited a comment on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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## CI report:
* fbd56ff6167834a20d8ffcfd801dc2e6f3464657 Azure: [SUCCESS](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=7181)
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[GitHub] [flink] flinkbot commented on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot commented on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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[GitHub] [flink] flinkbot edited a comment on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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## CI report:
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[GitHub] [flink] flinkbot commented on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot commented on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703092256
Thanks a lot for your contribution to the Apache Flink project. I'm the @flinkbot. I help the community
to review your pull request. We will use this comment to track the progress of the review.
## Automated Checks
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## Review Progress
* ❓ 1. The [description] looks good.
* ❓ 2. There is [consensus] that the contribution should go into to Flink.
* ❓ 3. Needs [attention] from.
* ❓ 4. The change fits into the overall [architecture].
* ❓ 5. Overall code [quality] is good.
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[GitHub] [flink] wangxlong commented on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
wangxlong commented on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-707145382
@dianfu Updated.
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[GitHub] [flink] flinkbot edited a comment on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
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flinkbot edited a comment on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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[GitHub] [flink] dianfu commented on a change in pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #13537:
URL: https://github.com/apache/flink/pull/13537#discussion_r503050214
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
source_table = table_env.from_path("datagen")
-# or create a Table from a SQL query:
+# 或者使用 SQL 查询语句创建一张表：
source_table = table_env.sql_query("SELECT * FROM datagen")
result_table = source_table.select(source_table.id + 1, source_table.data)
-# 5. emit query result to sink table
-# emit a Table API result Table to a sink table:
+# 5. 将查询结果发送给 sink 表
+# 将 Table API 结果表数据发送给 sink 表：
result_table.execute_insert("print").wait()
-# or emit results via SQL query:
+# 或者通过 SQL 查询语句来发送：
table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
{% endhighlight %}
{% top %}
-Create a TableEnvironment
+创建 TableEnvironment
---------------------------
-The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment:
+`TableEnvironment` 是 Table API 和 SQL 集成的核心概念。下面代码示例展示了如何创建一个 TableEnvironment:
{% highlight python %}
from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment
-# create a blink streaming TableEnvironment
+# 创建 blink 流 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a blink batch TableEnvironment
+# 创建 blink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
-# create a flink streaming TableEnvironment
+# 创建 flink 流式 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_old_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a flink batch TableEnvironment
+# 创建 flink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
{% endhighlight %}
-For more details about the different ways to create a `TableEnvironment`, please refer to the [TableEnvironment Documentation]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment).
+关于创建 `TableEnvironment` 的更多细节，请查阅 [TableEnvironment 文档]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment)。
-The `TableEnvironment` is responsible for:
+`TableEnvironment` 可以用来:
-* Creating `Table`s
-* Registering `Table`s as a temporary view
-* Executing SQL queries, see [SQL]({% link dev/table/sql/index.zh.md %}) for more details
-* Registering user-defined (scalar, table, or aggregation) functions, see [General User-defined Functions]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) and [Vectorized User-defined Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %}) for more details
-* Configuring the job, see [Python Configuration]({% link dev/python/table-api-users-guide/python_config.zh.md %}) for more details
-* Managing Python dependencies, see [Dependency Management]({% link dev/python/table-api-users-guide/dependency_management.zh.md %}) for more details
-* Submitting the jobs for execution
+* 创建 `Table`
+* 将 `Table` 注册成临时表
+* 执行 SQL 查询，更多细节可查阅 [SQL]({% link dev/table/sql/index.zh.md %})
+* 注册用户自定义的 (标量，表值，或者聚合) 函数, 更多细节可查阅 [普通的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) 和 [向量化的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %})
+* 配置作业，更多细节可查阅 [Python 配置]({% link dev/python/table-api-users-guide/python_config.zh.md %})
+* 管理 Python 依赖，更多细节可查阅 [依赖管理]({% link dev/python/table-api-users-guide/dependency_management.zh.md %})
+* 提交 jobs 执行
-Currently there are 2 planners available: flink planner and blink planner.
+目前有2个可用的执行器 : flink 执行器 和 blink 执行器。
-You should explicitly set which planner to use in the current program.
-We recommend using the blink planner as much as possible.
+你应该在当前程序中显式地设置使用哪个执行器。
+我们建议尽可能多的使用 blink 执行器。
Review comment:
```suggestion
我们建议尽可能使用 blink 执行器。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
source_table = table_env.from_path("datagen")
-# or create a Table from a SQL query:
+# 或者使用 SQL 查询语句创建一张表：
source_table = table_env.sql_query("SELECT * FROM datagen")
result_table = source_table.select(source_table.id + 1, source_table.data)
-# 5. emit query result to sink table
-# emit a Table API result Table to a sink table:
+# 5. 将查询结果发送给 sink 表
+# 将 Table API 结果表数据发送给 sink 表：
result_table.execute_insert("print").wait()
-# or emit results via SQL query:
+# 或者通过 SQL 查询语句来发送：
table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
{% endhighlight %}
{% top %}
-Create a TableEnvironment
+创建 TableEnvironment
---------------------------
-The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment:
+`TableEnvironment` 是 Table API 和 SQL 集成的核心概念。下面代码示例展示了如何创建一个 TableEnvironment:
{% highlight python %}
from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment
-# create a blink streaming TableEnvironment
+# 创建 blink 流 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a blink batch TableEnvironment
+# 创建 blink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
-# create a flink streaming TableEnvironment
+# 创建 flink 流式 TableEnvironment
Review comment:
keep consistent with the other places?
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -384,32 +384,32 @@ The result is:
8> +U(3,19)
{% endhighlight %}
-In fact, this shows the change logs received by the print sink.
-The output format of a change log is:
+实际上，结果展示了打印接收器所接收到的变更日志。
+变更日志输出的格式为:
{% highlight text %}
-{subtask id}> {message type}{string format of the value}
+{subtask id}> {消息类型}{值的字符串格式}
{% endhighlight %}
-For example, "2> +I(4,11)" means this message comes from the 2nd subtask, and "+I" means it is an insert message. "(4, 11)" is the content of the message.
-In addition, "-U" means a retract record (i.e. update-before), which means this message should be deleted or retracted from the sink.
-"+U" means this is an update record (i.e. update-after), which means this message should be updated or inserted by the sink.
+例如，"2> +I(4,11)" 表示这条消息来自第二个 subtask，同时 "+I" 表示这是一条插入的消息。"(4, 11)" 是这条消息的内容。
+另外，"-U" 表示这是一条回撤的消息 (即更新前)，这意味着应该在 sink 中删除或回撤该消息。
Review comment:
```suggestion
另外，"-U" 表示这是一条撤回消息 (即更新前)，这意味着应该从 sink 中删除或撤回该消息。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -455,18 +455,18 @@ table_env.execute_sql("""
)
""")
-# convert the sql table to Table API table
+# 将 sql 表转化成 Table API 表
Review comment:
```suggestion
# 将 sql 表转换成 Table API 表
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -539,13 +539,13 @@ table_env.execute_sql("INSERT INTO sink_table SELECT * FROM table_source").wait(
{% endhighlight %}
-### Emit Results to Multiple Sink Tables
+### 将数据发送到多张的 Sink 表中
Review comment:
```suggestion
### 将数据发送到多张 Sink 表中
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -565,21 +565,21 @@ table_env.execute_sql("""
)
""")
-# create a statement set
+# 创建 statement set
statement_set = table_env.create_statement_set()
-# emit the "table" object to the "first_sink_table"
+# 发送 "table" 对象到 "first_sink_table"
statement_set.add_insert("first_sink_table", table)
-# emit the "simple_source" to the "second_sink_table" via a insert sql query
+# 通过一条 sql 插入查询语句将数据从 "simple_source" 发送到 "second_sink_table"
Review comment:
```suggestion
# 通过一条 sql 插入语句将数据从 "simple_source" 发送到 "second_sink_table"
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -244,32 +244,32 @@ The result is:
2 3 6
{% endhighlight %}
-### Create using a Catalog
+### 使用 Catalog 创建
-A `TableEnvironment` maintains a map of catalogs of tables which are created with an identifier.
+`TableEnvironment` 维护了一个使用标识符创建的表的 catalogs 映射。
-The tables in a catalog may either be temporary, and tied to the lifecycle of a single Flink session, or permanent, and visible across multiple Flink sessions.
+Catalog 中的表可以是临时的，并与单个 Flink 会话生命周期相关联，也可以是永久的，跨多个 Flink 会话可见。
Review comment:
```suggestion
Catalog 中的表既可以是临时的，并与单个 Flink 会话生命周期相关联，也可以是永久的，跨多个 Flink 会话可见。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -169,48 +169,48 @@ table.to_pandas()
{% endhighlight %}
-The result is:
+结果为：
{% highlight text %}
id data
0 1 Hi
1 2 Hello
{% endhighlight %}
-By default the table schema is extracted from the data automatically.
+默认情况下，表结构是从数据中自动提取的。
-If the automatically generated table schema isn't satisfactory, you can specify it manually:
+如果自动生成的表模式不符合你的要求，你也可以手动指定：
{% highlight python %}
table_without_schema = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])
-# by default the type of the "id" column is 64 bit int
+# 默认情况下，“id” 列的类型是 64 位整型
default_type = table_without_schema.to_pandas()["id"].dtype
print('By default the type of the "id" column is %s.' % default_type)
from pyflink.table import DataTypes
table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')],
DataTypes.ROW([DataTypes.FIELD("id", DataTypes.TINYINT()),
DataTypes.FIELD("data", DataTypes.STRING())]))
-# now the type of the "id" column is 8 bit int
+# 现在 “id” 列的类型是 8 位整型
type = table.to_pandas()["id"].dtype
print('Now the type of the "id" column is %s.' % type)
{% endhighlight %}
-The result is:
+结果为：
{% highlight text %}
-By default the type of the "id" column is int64.
-Now the type of the "id" column is int8.
+默认情况下，“id” 列的类型是 64 位整型。
+现在 “id” 列的类型是 8 位整型。
{% endhighlight %}
-### Create using a Connector
+### 使用连接器创建
-You can create a Table using connector DDL:
+你可以使用连接器 DDL 创建一张表：
{% highlight python %}
-# create a blink stream TableEnvironment
+# 创建 blink 流式环境
Review comment:
```suggestion
# 创建 blink 流 TableEnvironment
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -244,32 +244,32 @@ The result is:
2 3 6
{% endhighlight %}
-### Create using a Catalog
+### 使用 Catalog 创建
-A `TableEnvironment` maintains a map of catalogs of tables which are created with an identifier.
+`TableEnvironment` 维护了一个使用标识符创建的表的 catalogs 映射。
-The tables in a catalog may either be temporary, and tied to the lifecycle of a single Flink session, or permanent, and visible across multiple Flink sessions.
+Catalog 中的表可以是临时的，并与单个 Flink 会话生命周期相关联，也可以是永久的，跨多个 Flink 会话可见。
-The tables and views created via SQL DDL, e.g. "create table ..." and "create view ..." are also stored in a catalog.
+通过 SQL DDL 创建的表和视图， 例如 "create table ..." 和 "create view ..." 都是被存储在 catalog。
-You can directly access the tables in a catalog via SQL.
+你可以通过 SQL 直接访问 catalog 中的表。
-If you want to use tables from a catalog with the Table API, you can use the "from_path" method to create the Table API objects:
+如果你要用 Table API 来使用 catalog 中的表，你可以使用 "from_path" 方法来创建 Table API 对象：
{% highlight python %}
-# prepare the catalog
-# register Table API tables in the catalog
+# 准备 catalog
+# 在 catalog 中注册 Table API 表
Review comment:
```suggestion
# 将 Table API 表注册到 catalog 中
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
source_table = table_env.from_path("datagen")
-# or create a Table from a SQL query:
+# 或者使用 SQL 查询语句创建一张表：
source_table = table_env.sql_query("SELECT * FROM datagen")
result_table = source_table.select(source_table.id + 1, source_table.data)
-# 5. emit query result to sink table
-# emit a Table API result Table to a sink table:
+# 5. 将查询结果发送给 sink 表
+# 将 Table API 结果表数据发送给 sink 表：
result_table.execute_insert("print").wait()
-# or emit results via SQL query:
+# 或者通过 SQL 查询语句来发送：
table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
{% endhighlight %}
{% top %}
-Create a TableEnvironment
+创建 TableEnvironment
---------------------------
-The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment:
+`TableEnvironment` 是 Table API 和 SQL 集成的核心概念。下面代码示例展示了如何创建一个 TableEnvironment:
{% highlight python %}
from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment
-# create a blink streaming TableEnvironment
+# 创建 blink 流 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a blink batch TableEnvironment
+# 创建 blink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
-# create a flink streaming TableEnvironment
+# 创建 flink 流式 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_old_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a flink batch TableEnvironment
+# 创建 flink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
{% endhighlight %}
-For more details about the different ways to create a `TableEnvironment`, please refer to the [TableEnvironment Documentation]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment).
+关于创建 `TableEnvironment` 的更多细节，请查阅 [TableEnvironment 文档]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment)。
-The `TableEnvironment` is responsible for:
+`TableEnvironment` 可以用来:
-* Creating `Table`s
-* Registering `Table`s as a temporary view
-* Executing SQL queries, see [SQL]({% link dev/table/sql/index.zh.md %}) for more details
-* Registering user-defined (scalar, table, or aggregation) functions, see [General User-defined Functions]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) and [Vectorized User-defined Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %}) for more details
-* Configuring the job, see [Python Configuration]({% link dev/python/table-api-users-guide/python_config.zh.md %}) for more details
-* Managing Python dependencies, see [Dependency Management]({% link dev/python/table-api-users-guide/dependency_management.zh.md %}) for more details
-* Submitting the jobs for execution
+* 创建 `Table`
+* 将 `Table` 注册成临时表
+* 执行 SQL 查询，更多细节可查阅 [SQL]({% link dev/table/sql/index.zh.md %})
+* 注册用户自定义的 (标量，表值，或者聚合) 函数, 更多细节可查阅 [普通的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) 和 [向量化的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %})
+* 配置作业，更多细节可查阅 [Python 配置]({% link dev/python/table-api-users-guide/python_config.zh.md %})
+* 管理 Python 依赖，更多细节可查阅 [依赖管理]({% link dev/python/table-api-users-guide/dependency_management.zh.md %})
+* 提交 jobs 执行
-Currently there are 2 planners available: flink planner and blink planner.
+目前有2个可用的执行器 : flink 执行器 和 blink 执行器。
-You should explicitly set which planner to use in the current program.
-We recommend using the blink planner as much as possible.
+你应该在当前程序中显式地设置使用哪个执行器。
+我们建议尽可能多的使用 blink 执行器。
{% top %}
-Create Tables
+创建表
---------------
-`Table` is a core component of the Python Table API. A `Table` is a logical representation of the intermediate result of a Table API Job.
+`Table` 是 Python Table API 的核心组件。一张 `Table` 是 Table API 作业中间结果的逻辑表示。
-A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine tables from different TableEnvironments in same query, e.g., to join or union them.
+一张 `Table` 总是绑定到特定的 `TableEnvironment`。不可能在同一个查询中合并来自不同 TableEnvironments 的表，例如 join 或者 union 它们。
-### Create using a List Object
+### 使用列表对象创建
Review comment:
```suggestion
### 通过列表对象创建
```
翻译成“通过”会不会更通顺一些？
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
source_table = table_env.from_path("datagen")
-# or create a Table from a SQL query:
+# 或者使用 SQL 查询语句创建一张表：
source_table = table_env.sql_query("SELECT * FROM datagen")
result_table = source_table.select(source_table.id + 1, source_table.data)
-# 5. emit query result to sink table
-# emit a Table API result Table to a sink table:
+# 5. 将查询结果发送给 sink 表
+# 将 Table API 结果表数据发送给 sink 表：
result_table.execute_insert("print").wait()
-# or emit results via SQL query:
+# 或者通过 SQL 查询语句来发送：
table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
{% endhighlight %}
{% top %}
-Create a TableEnvironment
+创建 TableEnvironment
---------------------------
-The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment:
+`TableEnvironment` 是 Table API 和 SQL 集成的核心概念。下面代码示例展示了如何创建一个 TableEnvironment:
{% highlight python %}
from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment
-# create a blink streaming TableEnvironment
+# 创建 blink 流 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a blink batch TableEnvironment
+# 创建 blink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
-# create a flink streaming TableEnvironment
+# 创建 flink 流式 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_old_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a flink batch TableEnvironment
+# 创建 flink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
{% endhighlight %}
-For more details about the different ways to create a `TableEnvironment`, please refer to the [TableEnvironment Documentation]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment).
+关于创建 `TableEnvironment` 的更多细节，请查阅 [TableEnvironment 文档]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment)。
-The `TableEnvironment` is responsible for:
+`TableEnvironment` 可以用来:
-* Creating `Table`s
-* Registering `Table`s as a temporary view
-* Executing SQL queries, see [SQL]({% link dev/table/sql/index.zh.md %}) for more details
-* Registering user-defined (scalar, table, or aggregation) functions, see [General User-defined Functions]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) and [Vectorized User-defined Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %}) for more details
-* Configuring the job, see [Python Configuration]({% link dev/python/table-api-users-guide/python_config.zh.md %}) for more details
-* Managing Python dependencies, see [Dependency Management]({% link dev/python/table-api-users-guide/dependency_management.zh.md %}) for more details
-* Submitting the jobs for execution
+* 创建 `Table`
+* 将 `Table` 注册成临时表
+* 执行 SQL 查询，更多细节可查阅 [SQL]({% link dev/table/sql/index.zh.md %})
+* 注册用户自定义的 (标量，表值，或者聚合) 函数, 更多细节可查阅 [普通的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) 和 [向量化的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %})
+* 配置作业，更多细节可查阅 [Python 配置]({% link dev/python/table-api-users-guide/python_config.zh.md %})
+* 管理 Python 依赖，更多细节可查阅 [依赖管理]({% link dev/python/table-api-users-guide/dependency_management.zh.md %})
+* 提交 jobs 执行
-Currently there are 2 planners available: flink planner and blink planner.
+目前有2个可用的执行器 : flink 执行器 和 blink 执行器。
-You should explicitly set which planner to use in the current program.
-We recommend using the blink planner as much as possible.
+你应该在当前程序中显式地设置使用哪个执行器。
+我们建议尽可能多的使用 blink 执行器。
{% top %}
-Create Tables
+创建表
---------------
-`Table` is a core component of the Python Table API. A `Table` is a logical representation of the intermediate result of a Table API Job.
+`Table` 是 Python Table API 的核心组件。一张 `Table` 是 Table API 作业中间结果的逻辑表示。
-A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine tables from different TableEnvironments in same query, e.g., to join or union them.
+一张 `Table` 总是绑定到特定的 `TableEnvironment`。不可能在同一个查询中合并来自不同 TableEnvironments 的表，例如 join 或者 union 它们。
Review comment:
```suggestion
一个`Table` 实例总是与一个特定的 `TableEnvironment`相绑定。不支持在同一个查询中合并来自不同 TableEnvironments 的表，例如 join 或者 union 它们。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -279,22 +279,22 @@ The result is:
{% top %}
-Write Queries
+写查询
---------------
-### Write Table API Queries
+### Table API 查询
-The `Table` object offers many methods for applying relational operations.
-These methods return new `Table` objects representing the result of applying the relational operations on the input `Table`.
-These relational operations may be composed of multiple method calls, such as `table.group_by(...).select(...)`.
+`Table` 对象为应用关系操作提供了许多方法。
Review comment:
```suggestion
`Table` 对象有许多方法，可以用于进行关系操作。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -244,32 +244,32 @@ The result is:
2 3 6
{% endhighlight %}
-### Create using a Catalog
+### 使用 Catalog 创建
-A `TableEnvironment` maintains a map of catalogs of tables which are created with an identifier.
+`TableEnvironment` 维护了一个使用标识符创建的表的 catalogs 映射。
-The tables in a catalog may either be temporary, and tied to the lifecycle of a single Flink session, or permanent, and visible across multiple Flink sessions.
+Catalog 中的表可以是临时的，并与单个 Flink 会话生命周期相关联，也可以是永久的，跨多个 Flink 会话可见。
-The tables and views created via SQL DDL, e.g. "create table ..." and "create view ..." are also stored in a catalog.
+通过 SQL DDL 创建的表和视图， 例如 "create table ..." 和 "create view ..." 都是被存储在 catalog。
Review comment:
```suggestion
通过 SQL DDL 创建的表和视图， 例如 "create table ..." 和 "create view ..." 都存储在 catalog中。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -314,24 +314,24 @@ revenue.to_pandas()
{% endhighlight %}
-The result is:
+结果为：
{% highlight text %}
name rev_sum
0 Jack 30
{% endhighlight %}
-### Write SQL Queries
+### SQL 查询
-Flink's SQL integration is based on [Apache Calcite](https://calcite.apache.org), which implements the SQL standard. SQL queries are specified as Strings.
+Flink's SQL 是使用 [Apache Calcite](https://calcite.apache.org) 集成的，它实现了标准的 SQL。SQL 查询语句使用字符串来表达。
Review comment:
```suggestion
Flink 的 SQL 是使用 [Apache Calcite](https://calcite.apache.org) 集成的，它实现了标准的 SQL。SQL 查询语句使用字符串来表达。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
source_table = table_env.from_path("datagen")
-# or create a Table from a SQL query:
+# 或者使用 SQL 查询语句创建一张表：
source_table = table_env.sql_query("SELECT * FROM datagen")
result_table = source_table.select(source_table.id + 1, source_table.data)
-# 5. emit query result to sink table
-# emit a Table API result Table to a sink table:
+# 5. 将查询结果发送给 sink 表
+# 将 Table API 结果表数据发送给 sink 表：
result_table.execute_insert("print").wait()
-# or emit results via SQL query:
+# 或者通过 SQL 查询语句来发送：
table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
{% endhighlight %}
{% top %}
-Create a TableEnvironment
+创建 TableEnvironment
---------------------------
-The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment:
+`TableEnvironment` 是 Table API 和 SQL 集成的核心概念。下面代码示例展示了如何创建一个 TableEnvironment:
{% highlight python %}
from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment
-# create a blink streaming TableEnvironment
+# 创建 blink 流 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a blink batch TableEnvironment
+# 创建 blink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
-# create a flink streaming TableEnvironment
+# 创建 flink 流式 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_old_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=env_settings)
-# create a flink batch TableEnvironment
+# 创建 flink 批 TableEnvironment
env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build()
table_env = BatchTableEnvironment.create(environment_settings=env_settings)
{% endhighlight %}
-For more details about the different ways to create a `TableEnvironment`, please refer to the [TableEnvironment Documentation]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment).
+关于创建 `TableEnvironment` 的更多细节，请查阅 [TableEnvironment 文档]({% link dev/python/table-api-users-guide/table_environment.zh.md %}#create-a-tableenvironment)。
-The `TableEnvironment` is responsible for:
+`TableEnvironment` 可以用来:
-* Creating `Table`s
-* Registering `Table`s as a temporary view
-* Executing SQL queries, see [SQL]({% link dev/table/sql/index.zh.md %}) for more details
-* Registering user-defined (scalar, table, or aggregation) functions, see [General User-defined Functions]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) and [Vectorized User-defined Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %}) for more details
-* Configuring the job, see [Python Configuration]({% link dev/python/table-api-users-guide/python_config.zh.md %}) for more details
-* Managing Python dependencies, see [Dependency Management]({% link dev/python/table-api-users-guide/dependency_management.zh.md %}) for more details
-* Submitting the jobs for execution
+* 创建 `Table`
+* 将 `Table` 注册成临时表
+* 执行 SQL 查询，更多细节可查阅 [SQL]({% link dev/table/sql/index.zh.md %})
+* 注册用户自定义的 (标量，表值，或者聚合) 函数, 更多细节可查阅 [普通的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/python_udfs.zh.md %}) 和 [向量化的用户自定义函数]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.zh.md %})
+* 配置作业，更多细节可查阅 [Python 配置]({% link dev/python/table-api-users-guide/python_config.zh.md %})
+* 管理 Python 依赖，更多细节可查阅 [依赖管理]({% link dev/python/table-api-users-guide/dependency_management.zh.md %})
+* 提交 jobs 执行
-Currently there are 2 planners available: flink planner and blink planner.
+目前有2个可用的执行器 : flink 执行器 和 blink 执行器。
-You should explicitly set which planner to use in the current program.
-We recommend using the blink planner as much as possible.
+你应该在当前程序中显式地设置使用哪个执行器。
+我们建议尽可能多的使用 blink 执行器。
{% top %}
-Create Tables
+创建表
---------------
-`Table` is a core component of the Python Table API. A `Table` is a logical representation of the intermediate result of a Table API Job.
+`Table` 是 Python Table API 的核心组件。一张 `Table` 是 Table API 作业中间结果的逻辑表示。
-A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine tables from different TableEnvironments in same query, e.g., to join or union them.
+一张 `Table` 总是绑定到特定的 `TableEnvironment`。不可能在同一个查询中合并来自不同 TableEnvironments 的表，例如 join 或者 union 它们。
-### Create using a List Object
+### 使用列表对象创建
-You can create a Table from a list object:
+你可以使用一个列表对象创建一张表：
{% highlight python %}
-# create a blink batch TableEnvironment
+# 创建 blink 批处理环境
Review comment:
```suggestion
# 创建 blink 批 TableEnvironment
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -384,32 +384,32 @@ The result is:
8> +U(3,19)
{% endhighlight %}
-In fact, this shows the change logs received by the print sink.
-The output format of a change log is:
+实际上，结果展示了打印接收器所接收到的变更日志。
+变更日志输出的格式为:
{% highlight text %}
-{subtask id}> {message type}{string format of the value}
+{subtask id}> {消息类型}{值的字符串格式}
{% endhighlight %}
-For example, "2> +I(4,11)" means this message comes from the 2nd subtask, and "+I" means it is an insert message. "(4, 11)" is the content of the message.
-In addition, "-U" means a retract record (i.e. update-before), which means this message should be deleted or retracted from the sink.
-"+U" means this is an update record (i.e. update-after), which means this message should be updated or inserted by the sink.
+例如，"2> +I(4,11)" 表示这条消息来自第二个 subtask，同时 "+I" 表示这是一条插入的消息。"(4, 11)" 是这条消息的内容。
+另外，"-U" 表示这是一条回撤的消息 (即更新前)，这意味着应该在 sink 中删除或回撤该消息。
+"+U" 表示这是一条更新的记录 (即更新后)，这意味着应该在 sink 中更新或插入该消息。
-So, we get this result from the change logs above:
+所以，我们从上面的变更日志中得到以下结果：
{% highlight text %}
(4, 11)
(2, 15)
(3, 19)
{% endhighlight %}
-### Mix the Table API and SQL
+### Table API 和 SQL 的混合使用
-The `Table` objects used in Table API and the tables used in SQL can be freely converted to each other.
+Table API 中的 `Table` 对象和 SQL 中的 tables 可以自由地相互转换。
-The following example shows how to use a `Table` object in SQL:
+下面例子展示了如何在 SQL 中使用 `Table` 对象：
{% highlight python %}
-# create a sink table to emit results
+# 创建一张 sink 表来发送数据
Review comment:
```suggestion
# 创建一张 sink 表来接收结果数据
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -455,18 +455,18 @@ table_env.execute_sql("""
)
""")
-# convert the sql table to Table API table
+# 将 sql 表转化成 Table API 表
table = table_env.from_path("sql_source")
-# or create the table from a sql query
+# 或者从 sql 查询语句中创建表
table = table_env.sql_query("SELECT * FROM sql_source")
-# emit the table
+# 将数据发送到该表中
Review comment:
```suggestion
# 将表中的数据写出
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -419,27 +419,27 @@ table_env.execute_sql("""
)
""")
-# convert the Table API table to a SQL view
+# 将 Table API 表转换成 SQL 的视图
table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])
table_env.create_temporary_view('table_api_table', table)
-# emit the Table API table
+# 将数据写入 Table API 表
Review comment:
```suggestion
# 将 Table API 表的数据写入结果表
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -492,20 +492,20 @@ table.to_pandas()
{% endhighlight %}
-The result is:
+结果为：
{% highlight text %}
id data
0 1 Hi
1 2 Hello
{% endhighlight %}
-<span class="label label-info">Note</span> "to_pandas" will trigger the materialization of the table and collect table content to the memory of the client, it's good practice to limit the number of rows collected via <a href="{{ site.pythondocs_baseurl }}/api/python/pyflink.table.html#pyflink.table.Table.limit">Table.limit</a>.
-<span class="label label-info">Note</span> "to_pandas" is not supported by the flink planner, and not all data types can be emitted to pandas DataFrames.
+<span class="label label-info">Note</span> "to_pandas" 将会触发表的物化同时将表的内容收集到客户端内存中，所以通过 <a href="{{ site.pythondocs_baseurl }}/api/python/pyflink.table.html#pyflink.table.Table.limit">Table.limit</a> 来限制收集数据的条数是一种很好的做法。
Review comment:
```suggestion
<span class="label label-info">Note</span> "to_pandas" 将会触发表的物化，同时将表的内容收集到客户端内存中，所以通过 <a href="{{ site.pythondocs_baseurl }}/api/python/pyflink.table.html#pyflink.table.Table.limit">Table.limit</a> 来限制收集数据的条数是一种很好的做法。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -565,21 +565,21 @@ table_env.execute_sql("""
)
""")
-# create a statement set
+# 创建 statement set
statement_set = table_env.create_statement_set()
-# emit the "table" object to the "first_sink_table"
+# 发送 "table" 对象到 "first_sink_table"
Review comment:
```suggestion
# 将 "table" 的数据写入 "first_sink_table"
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
Review comment:
```suggestion
# 通过 Table API 查询语句创建一张表：
```
使用 -> 通过？其他地方也看一下是否有必要改动。
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -588,23 +588,23 @@ The result is:
7> +I(2,Hello)
{% endhighlight %}
-Explain Tables
+Explain 表
-----------------
-The Table API provides a mechanism to explain the logical and optimized query plans used to compute a `Table`.
-This is done through the `Table.explain()` or `StatementSet.explain()` methods. `Table.explain()`returns the plan of a `Table`. `StatementSet.explain()` returns the plan for multiple sinks. These methods return a string describing three things:
+Table API 提供了一种机制来翻译 `Table` 的逻辑和优化查询计划。
Review comment:
```suggestion
Table API 提供了一种机制来解释 `Table` 的逻辑查询计划和优化后的查询计划。
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -588,23 +588,23 @@ The result is:
7> +I(2,Hello)
{% endhighlight %}
-Explain Tables
+Explain 表
-----------------
-The Table API provides a mechanism to explain the logical and optimized query plans used to compute a `Table`.
-This is done through the `Table.explain()` or `StatementSet.explain()` methods. `Table.explain()`returns the plan of a `Table`. `StatementSet.explain()` returns the plan for multiple sinks. These methods return a string describing three things:
+Table API 提供了一种机制来翻译 `Table` 的逻辑和优化查询计划。
+这个通过 `Table.explain()` 或者 `StatementSet.explain()` 方法来完成。`Table.explain()` 返回 `Table` 的执行计划。`StatementSet.explain()` 返回多个 sink 的执行计划。这些方法返回一个字符串来描述三个方面：
Review comment:
```suggestion
这是通过 `Table.explain()` 或者 `StatementSet.explain()` 方法来完成的。`Table.explain()` 可以返回一个 `Table` 的执行计划。`StatementSet.explain()` 则可以返回含有多个 sink 的作业的执行计划。这些方法会返回一个字符串，字符串描述了以下三个方面的信息：
```
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
Review comment:
```suggestion
# 4. 查询 source 表，同时执行计算
```
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[GitHub] [flink] wangxlong commented on a change in pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
wangxlong commented on a change in pull request #13537:
URL: https://github.com/apache/flink/pull/13537#discussion_r503322986
##########
File path: docs/dev/python/table-api-users-guide/intro_to_table_api.zh.md
##########
@@ -64,84 +64,84 @@ table_env.execute_sql("""
)
""")
-# 4. query from source table and perform caculations
-# create a Table from a Table API query:
+# 4. 从 source 表中查询同时执行计算
+# 使用 Table API 查询语句创建一张表：
Review comment:
`使用`改为`通过`是会通顺些，我看下下其它地方，可以改动的都改动了。
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[GitHub] [flink] dianfu closed pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
dianfu closed pull request #13537:
URL: https://github.com/apache/flink/pull/13537
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[GitHub] [flink] flinkbot edited a comment on pull request #13537: [FLINK-19478][docs-zh][python] Translate page "intro_to_table_api" into Chinese
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #13537:
URL: https://github.com/apache/flink/pull/13537#issuecomment-703094638
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