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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2020/08/21 13:34:00 UTC
[GitHub] [flink] morsapaes commented on a change in pull request #13193: [FLINK-18918][python][docs] Add dedicated connector documentation for Python Table API
morsapaes commented on a change in pull request #13193:
URL: https://github.com/apache/flink/pull/13193#discussion_r474666933
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
Review comment:
Here, it would be nice to add a short sentence explaining that Flink is a Java/Scala-based project, just for the sake of completeness and so users who are not familiar with Flink understand why they have to deal with JARs in a Python program.
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+t_env.execute_sql(source_ddl)
+t_env.execute_sql(sink_ddl)
+
+t_env.sql_query("select a from source_table") \
Review comment:
```suggestion
t_env.sql_query("SELECT a FROM source_table") \
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ 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")
+
+ t_env.execute("payment_demo")
+
+
+if __name__ == '__main__':
+ log_processing()
+{% endhighlight %}
+
+
+## Predefined Sources and Sinks
+
+A few basic data sources and sinks are built into Flink and are always available. The predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
+
+### from/to Pandas
+
+PyFlink Tables support conversion to and from Pandas DataFrame.
+
+{% highlight python %}
+
+import pandas as pd
+import numpy as np
+
+# Create a PyFlink Table
+pdf = pd.DataFrame(np.random.rand(1000, 2))
+table = t_env.from_pandas(pdf, ["a", "b"]).filter("a > 0.5")
+
+# Convert the PyFlink Table to a Pandas DataFrame
+pdf = table.to_pandas()
+{% endhighlight %}
+
+### from_elements()
+
+`from_elements()` is used to creates a table from a collection of elements. The elements types must be acceptable atomic types or acceptable composite types.
+
+{% highlight python %}
+
+table_env.from_elements([(1, 'Hi'), (2, 'Hello')])
+
+# use the second parameter to specify custom field names
+table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['a', 'b'])
+
+# use the second parameter to specify custom table schema
+table_env.from_elements([(1, 'Hi'), (2, 'Hello')],
+ DataTypes.ROW([DataTypes.FIELD("a", DataTypes.INT()),
+ DataTypes.FIELD("b", DataTypes.STRING())]))
+{% endhighlight %}
+
+The above query returns a Table like:
+
+{% highlight python %}
++----+-------+
+| a | b |
++====+=======+
+| 1 | Hi |
++----+-------+
+| 2 | Hello |
++----+-------+
+{% endhighlight %}
+
+## User-defined sources & sinks
+
+In some cases, you may want to define custom sources and sinks. Currently, sources and sinks must be implemented in Java/Scala but you can define a TableFactory to support their use via DDL. More details can be found in the [Java/Scala document]({{ site.baseurl }}/dev/table/sourceSinks.html).
Review comment:
```suggestion
In some cases, you may want to define custom sources and sinks. Currently, sources and sinks must be implemented in Java/Scala, but you can define a `TableFactory` to support their use via DDL. More details can be found in the [Java/Scala documentation]({{ site.baseurl }}/dev/table/sourceSinks.html).
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ 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")
+
+ t_env.execute("payment_demo")
+
+
+if __name__ == '__main__':
+ log_processing()
+{% endhighlight %}
+
+
+## Predefined Sources and Sinks
+
+A few basic data sources and sinks are built into Flink and are always available. The predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
Review comment:
```suggestion
Some data sources and sinks are built into Flink and are available out-of-the-box. These predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
Review comment:
```suggestion
This page describes how to use connectors in PyFlink and highlights the details to be aware of when using Flink connectors in Python programs.
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ 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")
+
+ t_env.execute("payment_demo")
+
+
+if __name__ == '__main__':
+ log_processing()
+{% endhighlight %}
+
+
+## Predefined Sources and Sinks
+
+A few basic data sources and sinks are built into Flink and are always available. The predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
+
+### from/to Pandas
+
+PyFlink Tables support conversion to and from Pandas DataFrame.
+
+{% highlight python %}
+
+import pandas as pd
+import numpy as np
+
+# Create a PyFlink Table
+pdf = pd.DataFrame(np.random.rand(1000, 2))
+table = t_env.from_pandas(pdf, ["a", "b"]).filter("a > 0.5")
+
+# Convert the PyFlink Table to a Pandas DataFrame
+pdf = table.to_pandas()
+{% endhighlight %}
+
+### from_elements()
+
+`from_elements()` is used to creates a table from a collection of elements. The elements types must be acceptable atomic types or acceptable composite types.
Review comment:
```suggestion
`from_elements()` is used to create a table from a collection of elements. The element types must be acceptable atomic types or acceptable composite types.
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
Review comment:
"and highlights the different parts between using connectors in PyFlink vs Java/Scala."
I think this doesn't really reflect what is being explained in this page and would suggest:
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
Review comment:
```suggestion
Below is a complete example of how to use a Kafka source and the JSON format in PyFlink.
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+t_env.execute_sql(source_ddl)
+t_env.execute_sql(sink_ddl)
+
+t_env.sql_query("select a from source_table") \
Review comment:
Just a personal preference/suggestion, for readability.
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ 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")
+
+ t_env.execute("payment_demo")
+
+
+if __name__ == '__main__':
+ log_processing()
+{% endhighlight %}
+
+
+## Predefined Sources and Sinks
+
+A few basic data sources and sinks are built into Flink and are always available. The predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
+
+### from/to Pandas
+
+PyFlink Tables support conversion to and from Pandas DataFrame.
+
+{% highlight python %}
+
+import pandas as pd
+import numpy as np
+
+# Create a PyFlink Table
+pdf = pd.DataFrame(np.random.rand(1000, 2))
+table = t_env.from_pandas(pdf, ["a", "b"]).filter("a > 0.5")
+
+# Convert the PyFlink Table to a Pandas DataFrame
+pdf = table.to_pandas()
+{% endhighlight %}
+
+### from_elements()
+
+`from_elements()` is used to creates a table from a collection of elements. The elements types must be acceptable atomic types or acceptable composite types.
+
+{% highlight python %}
+
+table_env.from_elements([(1, 'Hi'), (2, 'Hello')])
+
+# use the second parameter to specify custom field names
+table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['a', 'b'])
+
+# use the second parameter to specify custom table schema
Review comment:
```suggestion
# use the second parameter to specify a custom table schema
```
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ 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")
+
+ t_env.execute("payment_demo")
+
+
+if __name__ == '__main__':
+ log_processing()
+{% endhighlight %}
+
+
+## Predefined Sources and Sinks
+
+A few basic data sources and sinks are built into Flink and are always available. The predefined data sources include reading from Pandas DataFrame, or ingesting data from collections. The predefined data sinks support writing to Pandas DataFrame.
Review comment:
IMO, using "basic" and such words may downplay the technology to the user.
##########
File path: docs/dev/python/user-guide/table/python_table_api_connectors.md
##########
@@ -0,0 +1,194 @@
+---
+title: "Connectors"
+nav-parent_id: python_tableapi
+nav-pos: 130
+---
+<!--
+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 page describes how to use connectors in PyFlink and highlights the different parts between using connectors in PyFlink vs Java/Scala.
+
+* This will be replaced by the TOC
+{:toc}
+
+<span class="label label-info">Note</span>For general connector information and common configuration, please refer to the corresponding [Java/Scala documentation]({{ site.baseurl }}/dev/table/connectors/index.html).
+
+## Download connector and format jars
+
+For both connectors and formats, implementations are available as jars that need to be specified as job [dependencies]({{ site.baseurl }}/dev/python/user-guide/table/dependency_management.html).
+
+{% highlight python %}
+
+table_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+{% endhighlight %}
+
+## How to use connectors
+
+In PyFink's Table API, DDL is the recommended way to define sources and sinks, executed via the `execute_sql()` method on the `TableEnvironment`. This makes the table available for use by the application.
+
+{% highlight python %}
+
+source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+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")
+
+{% endhighlight %}
+
+Below is a complete example of how to use the Kafka and Json format in PyFlink.
+
+{% highlight python %}
+
+from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
+from pyflink.table import StreamTableEnvironment, EnvironmentSettings
+
+
+def log_processing():
+ env = StreamExecutionEnvironment.get_execution_environment()
+ env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
+ t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
+ t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)
+ # specify connector and format jars
+ t_env.get_config().get_configuration().set_string("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")
+
+ source_ddl = """
+ CREATE TABLE source_table(
+ a VARCHAR,
+ b INT
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'source_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'connector.properties.group.id' = 'test_3',
+ 'connector.startup-mode' = 'latest-offset',
+ 'format.type' = 'json'
+ )
+ """
+
+ sink_ddl = """
+ CREATE TABLE sink_table(
+ a VARCHAR
+ ) WITH (
+ 'connector.type' = 'kafka',
+ 'connector.version' = 'universal',
+ 'connector.topic' = 'sink_topic',
+ 'connector.properties.bootstrap.servers' = 'kafka:9092',
+ 'format.type' = 'json'
+ )
+ """
+
+ t_env.execute_sql(source_ddl)
+ t_env.execute_sql(sink_ddl)
+
+ t_env.sql_query("select a from source_table") \
Review comment:
```suggestion
t_env.sql_query("SELECT a FROM source_table") \
```
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