[FLINK-18716][python][docs] Remove the deprecated execute and insert_…

…into calls in PyFlink Table API docs

This closes apache#12992.

docs/dev/python/table_api_tutorial.md

@@ -123,21 +123,17 @@ The table `mySink` has two columns, word and count, and writes data to the file
 
 You can now create a job which reads input from table `mySource`, preforms some transformations, and writes the results to table `mySink`.
 
+Finally you must execute the actual Flink Python Table API job.
+All operations, such as creating sources, transformations and sinks are lazy.
+Only when `execute_insert(sink_name)` is called, the job will be submitted for execution.
+
 {% highlight python %}
 from pyflink.table.expressions import lit
 
 tab = t_env.from_path('mySource')
 tab.group_by(tab.word) \
    .select(tab.word, lit(1).count) \
-   .insert_into('mySink')
-{% endhighlight %}
-
-Finally you must execute the actual Flink Python Table API job.
-All operations, such as creating sources, transformations and sinks are lazy.
-Only when `t_env.execute(job_name)` is called will the job be run.
-
-{% highlight python %}
-t_env.execute("tutorial_job")
+   .execute_insert('mySink').wait()
 {% endhighlight %}
 
 The complete code so far:
@@ -173,9 +169,7 @@ t_env.connect(FileSystem().path('/tmp/output')) \
 tab = t_env.from_path('mySource')
 tab.group_by(tab.word) \
    .select(tab.word, lit(1).count) \
-   .insert_into('mySink')
-
-t_env.execute("tutorial_job")
+   .execute_insert('mySink').wait()
 {% endhighlight %}
 
 ## Executing a Flink Python Table API Program

docs/dev/python/table_api_tutorial.zh.md

@@ -127,21 +127,17 @@ t_env.sql_update(my_sink_ddl)
 
 接下来，我们介绍如何创建一个作业：该作业读取表`mySource`中的数据，进行一些变换，然后将结果写入表`mySink`。
 
+最后，需要做的就是启动Flink Python Table API作业。上面所有的操作，比如创建源表
+进行变换以及写入结果表的操作都只是构建作业逻辑图，只有当`execute_insert(sink_name)`被调用的时候，
+作业才会被真正提交到集群或者本地进行执行。
+
 {% highlight python %}
 from pyflink.table.expressions import lit
 
 tab = t_env.from_path('mySource')
 tab.group_by(tab.word) \
    .select(tab.word, lit(1).count) \
-   .insert_into('mySink')
-{% endhighlight %}
-
-最后，需要做的就是启动Flink Python Table API作业。上面所有的操作，比如创建源表
-进行变换以及写入结果表的操作都只是构建作业逻辑图，只有当`t_env.execute(job_name)`被调用的时候，
-作业才会被真正提交到集群或者本地进行执行。
-
-{% highlight python %}
-t_env.execute("python_job")
+   .execute_insert('mySink').wait()
 {% endhighlight %}
 
 该教程的完整代码如下:
@@ -177,9 +173,7 @@ t_env.connect(FileSystem().path('/tmp/output')) \
 tab = t_env.from_path('mySource')
 tab.group_by(tab.word) \
    .select(tab.word, lit(1).count) \
-   .insert_into('mySink')
-
-t_env.execute("python_job")
+   .execute_insert('mySink').wait()
 {% endhighlight %}
 
 ## 执行一个Flink Python Table API程序

docs/dev/python/user-guide/table/python_table_api_connectors.md

@@ -75,7 +75,7 @@ t_env.execute_sql(source_ddl)
 t_env.execute_sql(sink_ddl)
 
 t_env.sql_query("SELECT a FROM source_table") \
-    .insert_into("sink_table")
+    .execute_insert("sink_table").wait()
 {% endhighlight %}
 
 Below is a complete example of how to use a Kafka source/sink and the JSON format in PyFlink.
@@ -122,9 +122,7 @@ def log_processing():
     t_env.execute_sql(sink_ddl)
 
     t_env.sql_query("SELECT a FROM source_table") \
-        .insert_into("sink_table")
-
-    t_env.execute("payment_demo")
+        .execute_insert("sink_table").wait()
 
 
 if __name__ == '__main__':

docs/dev/python/user-guide/table/python_table_api_connectors.zh.md

@@ -75,7 +75,7 @@ t_env.execute_sql(source_ddl)
 t_env.execute_sql(sink_ddl)
 
 t_env.sql_query("SELECT a FROM source_table") \
-    .insert_into("sink_table")
+    .execute_insert("sink_table").wait()
 {% endhighlight %}
 
 下面是如何在 PyFlink 中使用 Kafka source/sink 和 JSON 格式的完整示例。
@@ -122,9 +122,7 @@ def log_processing():
     t_env.execute_sql(sink_ddl)
 
     t_env.sql_query("SELECT a FROM source_table") \
-        .insert_into("sink_table")
-
-    t_env.execute("payment_demo")
+        .execute_insert("sink_table").wait()
 
 
 if __name__ == '__main__':

docs/dev/table/connect.md

@@ -2122,7 +2122,7 @@ table_env.register_table_sink(
 )
 
 table = ...
-table.insert_into("csvOutputTable")
+table.execute_insert("csvOutputTable").wait()
 {% endhighlight %}
 </div>
 </div>

docs/dev/table/connect.zh.md

@@ -2118,7 +2118,7 @@ table_env.register_table_sink(
 )
 
 table = ...
-table.insert_into("csvOutputTable")
+table.execute_insert("csvOutputTable").wait()
 {% endhighlight %}
 </div>
 </div>

docs/dev/table/streaming/query_configuration.md

@@ -110,7 +110,7 @@ table_env.register_table_sink("outputTable",  # table name
                               sink)  # table sink
 
 # emit result Table via a TableSink
-result.execute_insert("outputTable")
+result.execute_insert("outputTable").wait()
 
 {% endhighlight %}
 </div>

docs/dev/table/streaming/query_configuration.zh.md

@@ -110,7 +110,7 @@ table_env.register_table_sink("outputTable",  # table name
                               sink)  # table sink
 
 # emit result Table via a TableSink
-result.insert_into("outputTable")
+result.execute_insert("outputTable").wait()
 
 {% endhighlight %}
 </div>

docs/dev/table/tableApi.md

@@ -143,9 +143,7 @@ t_env.execute_sql(sink_ddl)
 # specify table program
 orders = t_env.from_path("Orders")  # schema (a, b, c, rowtime)
 
-orders.group_by(orders.a).select(orders.a, orders.b.count.alias('cnt')).insert_into("Result")
-
-t_env.execute("python_job")
+orders.group_by("a").select(orders.a, orders.b.count.alias('cnt')).execute_insert("result").wait()
 
 {% endhighlight %}
 

docs/dev/table/tableApi.zh.md

@@ -143,9 +143,7 @@ t_env.execute_sql(sink_ddl)
 # specify table program
 orders = t_env.from_path("Orders")  # schema (a, b, c, rowtime)
 
-orders.group_by(orders.a).select(orders.a, orders.b.count.alias('cnt')).insert_into("Result")
-
-t_env.execute("python_job")
+orders.group_by("a").select(orders.a, orders.b.count.alias('cnt')).execute_insert("result").wait()
 
 {% endhighlight %}
 

docs/ops/python_shell.md

@@ -75,8 +75,7 @@ The example below is a simple program in the Python shell:
 ...         .field("c", DataTypes.STRING()))\
 ...     .create_temporary_table("stream_sink")
 >>> t.select("a + 1, b, c")\
-...     .insert_into("stream_sink")
->>> st_env.execute("stream_job")
+...     .execute_insert("stream_sink").wait()
 >>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
 >>> with open(sink_path, 'r') as f:
 ...     print(f.read())
@@ -107,8 +106,7 @@ The example below is a simple program in the Python shell:
 ...         .field("c", DataTypes.STRING()))\
 ...     .create_temporary_table("batch_sink")
 >>> t.select("a + 1, b, c")\
-...     .insert_into("batch_sink")
->>> bt_env.execute("batch_job")
+...     .execute_insert("batch_sink").wait()
 >>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
 >>> with open(sink_path, 'r') as f:
 ...     print(f.read())

docs/ops/python_shell.zh.md

@@ -75,8 +75,7 @@ $ pyflink-shell.sh local
 ...         .field("c", DataTypes.STRING()))\
 ...     .create_temporary_table("stream_sink")
 >>> t.select("a + 1, b, c")\
-...     .insert_into("stream_sink")
->>> st_env.execute("stream_job")
+...     .execute_insert("stream_sink").wait()
 >>> # 如果作业运行在local模式, 你可以执行以下代码查看结果:
 >>> with open(sink_path, 'r') as f:
 ...     print(f.read())
@@ -107,8 +106,7 @@ $ pyflink-shell.sh local
 ...         .field("c", DataTypes.STRING()))\
 ...     .create_temporary_table("batch_sink")
 >>> t.select("a + 1, b, c")\
-...     .insert_into("batch_sink")
->>> bt_env.execute("batch_job")
+...     .execute_insert("batch_sink").wait()
 >>> # 如果作业运行在local模式, 你可以执行以下代码查看结果:
 >>> with open(sink_path, 'r') as f:
 ...     print(f.read())