dianfu commented on a change in pull request #13273:
URL: https://github.com/apache/flink/pull/13273#discussion_r480834034
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File path: docs/dev/python/user-guide/table/10_minutes_to_table_api.zh.md
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@@ -0,0 +1,739 @@
+---
+title: "10分钟入门Table API"
Review comment:
```suggestion
title: "10分钟入门Python Table API"
```
##########
File path: docs/dev/python/user-guide/table/10_minutes_to_table_api.md
##########
@@ -0,0 +1,739 @@
+---
+title: "10 Minutes to Table API"
+nav-parent_id: python_tableapi
+nav-pos: 25
+---
+<!--
+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.
+-->
+
+This document is a short introduction to PyFlink Table API, which is used to
help novice users quickly understand the basic usage of PyFlink Table API.
+For advanced usage, please refer to other documents in this User Guide.
+
+* This will be replaced by the TOC
+{:toc}
+
+Common Structure of Python Table API Program
+--------------------------------------------
+
+All Table API and SQL programs, both batch and streaming, follow the same
pattern. The following code example shows the common structure of Table API and
SQL programs.
+
+{% highlight python %}
+
+from pyflink.table import EnvironmentSettings, StreamTableEnvironment
+
+# 1. create a TableEnvironment
+env_settings =
EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+table_env = StreamTableEnvironment.create(environment_settings=env_settings)
+
+# 2. create source Table
+table_env.execute_sql("""
+ CREATE TABLE datagen (
+ id INT,
+ data STRING
+ ) WITH (
+ 'connector' = 'datagen',
+ 'fields.id.kind' = 'sequence',
+ 'fields.id.start' = '1',
+ 'fields.id.end' = '10'
+ )
+""")
+
+# 3. create sink Table
+table_env.execute_sql("""
+ CREATE TABLE print (
+ id INT,
+ data STRING
+ ) WITH (
+ 'connector' = 'print'
+ )
+""")
+
+# 4. query from source table and perform caculations
+# create a Table from a Table API query:
+source_table = table_env.from_path("datagen")
+# or create a Table from a SQL query:
+source_table = table_env.sql_query("SELECT * FROM datagen")
+
+result_table = source_table.select("id + 1, data")
+
+# 5. emit query result to sink table
+# emit a Table API result Table to a sink table:
+result_table.execute_insert("print").get_job_client().get_job_execution_result().result()
+# or emit results via SQL query:
+table_env.execute_sql("INSERT INTO print SELECT * FROM
datagen").get_job_client().get_job_execution_result().result()
+
+{% endhighlight %}
+
+{% top %}
+
+Create a TableEnvironment
+---------------------------
+
+The `TableEnvironment` is a central concept of the Table API and SQL
integration. The following code example shows how to create a TableEnvironment:
+
+{% highlight python %}
+
+from pyflink.table import EnvironmentSettings, StreamTableEnvironment,
BatchTableEnvironment
+
+# create a blink streaming 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
+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
+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
+env_settings =
EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build()
+table_env = BatchTableEnvironment.create(environment_settings=env_settings)
+
+{% endhighlight %}
+
+The `TableEnvironment` is responsible for:
+
+* Creating `Table`s
+* Registering `Table`s as a temporary view
+* Executing SQL queries, see [SQL]({% link dev/table/sql/index.md %}) for more
details
+* Registering user-defined (scalar, table, or aggregation) functions, see
[General User-defined Functions]({% link
dev/python/user-guide/table/udfs/python_udfs.md %}) and [Vectorized
User-defined Functions]({% link
dev/python/user-guide/table/udfs/vectorized_python_udfs.md %}) for more details
+* Configuring the job, see [Python Configuration]({% link
dev/python/user-guide/table/python_config.md %}) for more details
+* Managing Python dependencies, see [Dependency Management]({% link
dev/python/user-guide/table/dependency_management.md %}) for more details
+* Submitting the jobs for execution
+
+Currently there are 2 planners available: flink planner and blink planner.
+
+You should explicitly set which planner to use in the current program.
+We recommend using the blink planner as much as possible.
+
+{% top %}
+
+Create Tables
+---------------
+
+`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.
+
+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.
+
+### Create using a List Object
+
+You can create a Table from a list object:
+
+{% highlight python %}
+
+# create a blink batch TableEnvironment
+from pyflink.table import EnvironmentSettings, BatchTableEnvironment
+
+env_settings =
EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+table_env = BatchTableEnvironment.create(environment_settings=env_settings)
+
+table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')])
+table.to_pandas()
+
+{% endhighlight %}
+
+The result is:
+
+{% highlight text %}
+ _1 _2
+0 1 Hi
+1 2 Hello
+{% endhighlight %}
+
+You can also create the Table with specified column names:
+
+{% highlight python %}
+
+table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])
+table.to_pandas()
+
+{% endhighlight %}
+
+The result is:
+
+{% highlight text %}
+ id data
+0 1 Hi
+1 2 Hello
+{% endhighlight %}
+
+By default the table schema is extracted from the data automatically.
+
+If the table schema is not as your wish, you can specify it manually:
+
+{% 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
+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
+type = table.to_pandas()["id"].dtype
+print('Now the type of the "id" column is %s.' % type)
+
+{% endhighlight %}
+
+The result is:
+
+{% highlight text %}
+By default the type of the "id" column is int64.
+Now the type of the "id" column is int8.
+{% endhighlight %}
+
+### Create using a Connector
+
+You can create a Table using connector DDL:
+
+{% highlight python %}
+# create a blink stream TableEnvironment
+from pyflink.table import EnvironmentSettings, StreamTableEnvironment
+
+env_settings =
EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+table_env = StreamTableEnvironment.create(environment_settings=env_settings)
+
+table_env.execute_sql("""
+ CREATE TABLE random_source (
+ id BIGINT,
+ data TINYINT
+ ) WITH (
+ 'connector' = 'datagen',
+ 'fields.id.kind'='sequence',
+ 'fields.id.start'='1',
+ 'fields.id.end'='3',
+ 'fields.data.kind'='sequence',
+ 'fields.data.start'='4',
+ 'fields.data.end'='6'
+ )
+""")
+table = table_env.from_path("random_source")
+table.to_pandas()
+
+{% endhighlight %}
+
+The result is:
+
+{% highlight text %}
+ id data
+0 2 5
+1 1 4
+2 3 6
+{% endhighlight %}
+
+### Create using a Catalog
+
+A `TableEnvironment` maintains a map of catalogs of tables which are created
with an identifier.
+
+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 and clusters.
Review comment:
```suggestion
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.
```
##########
File path: docs/dev/python/user-guide/table/10_minutes_to_table_api.md
##########
@@ -0,0 +1,739 @@
+---
+title: "10 Minutes to Table API"
Review comment:
```suggestion
title: "10 Minutes to Python Table API"
```
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