hequn8128 commented on a change in pull request #13203: URL: https://github.com/apache/flink/pull/13203#discussion_r478443841
########## File path: docs/dev/python/getting-started/tutorial/datastream_tutorial.md ########## @@ -0,0 +1,126 @@ +--- +title: "Python DataStream API Tutorial" +nav-parent_id: python_tutorial +nav-pos: 30 +--- +<!-- +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 walkthrough will quickly get you started building a pure Python Flink DataStream project. + +Please refer to the PyFlink [installation guide]({{ site.baseurl }}/dev/python/getting-started/installation.html) on how to set up the Python execution environments. + +* This will be replaced by the TOC +{:toc} + +## Setting up a Python Project + +You can begin by creating a Python project and installing the PyFlink package following the [installation guide]({{ site.baseurl }}/dev/python/getting-started/installation.html#installation-of-pyflink). + +## Writing a Flink Python DataStream API Program + +DataStream API applications begin by declaring a `StreamExecutionEnvironment`. +This is the context in which a streaming program is executed. +It can be used for setting execution parameters such as restart strategy, default parallelism, etc. + +{% highlight python %} +env = StreamExecutionEnvironment.get_execution_environment() +env.set_parallelism(1) +{% endhighlight %} + +Once a `StreamExecutionEnvironment` created, you can declare your source with it. + +{% highlight python %} +ds = env.from_collection( + collection=[(1, 'aaa'), (2, 'bbb')], + type_info=Types.ROW([Types.INT(), Types.STRING()])) +{% endhighlight %} + +This creates a data stream from the given collection. The type is that of the elements in the collection. In this example, the type is a Row type with two fields. The type of the first field is integer type while the second is string type. + +You can now perform transformations on the datastream or writes the data into external system with sink. + +{% highlight python %} +ds.add_sink(StreamingFileSink + .for_row_format('/tmp/output', SimpleStringEncoder()) + .build()) +{% endhighlight %} + +Finally you must execute the actual Flink Python DataStream API job. +All operations, such as creating sources, transformations and sinks are lazy. +Only when `env.execute(job_name)` is called will runs the job. + +{% highlight python %} +env.execute("tutorial_job") +{% endhighlight %} + +The complete code so far: + +{% highlight python %} +from pyflink.common.serialization import SimpleStringEncoder +from pyflink.common.typeinfo import Types +from pyflink.datastream import StreamExecutionEnvironment +from pyflink.datastream.connectors import StreamingFileSink + + +def tutorial(): + env = StreamExecutionEnvironment.get_execution_environment() + env.set_parallelism(1) + ds = env.from_collection( + collection=[(1, 'aaa'), (2, 'bbb')], + type_info=Types.ROW([Types.INT(), Types.STRING()])) + ds.add_sink(StreamingFileSink + .for_row_format('/tmp/output', SimpleStringEncoder()) + .build()) + env.execute("tutorial_job") + + +if __name__ == '__main__': + tutorial() +{% endhighlight %} + +## Executing a Flink Python DataStream API Program Review comment: PyFlink includes Python DataStream API, Python Table API, etc., in Flink. :) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
