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https://issues.apache.org/jira/browse/FLINK-28918?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17580054#comment-17580054
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Luning Wang commented on FLINK-28918:
-------------------------------------
This test can successfully run on an M1 chip. [~hxbks2ks]
> Release Testing: Verify FLIP-206 in Python DataStream API
> ---------------------------------------------------------
>
> Key: FLINK-28918
> URL: https://issues.apache.org/jira/browse/FLINK-28918
> Project: Flink
> Issue Type: Sub-task
> Components: API / Python
> Affects Versions: 1.16.0
> Reporter: Huang Xingbo
> Assignee: Luning Wang
> Priority: Blocker
> Labels: release-testing
> Fix For: 1.16.0
>
>
> * Build flink source code and compile source code
> {code:bash}
> $ cd {flink-source-code}
> $ mvn clean install -DskipTests
> {code}
> * Prepare a Python Virtual Environment
> {code:bash}
> $ cd flink-python/dev
> $ ./lint-python.sh -s basic
> $ source .conda/bin/activate
> {code}
> * Install PyFlink from source code. For more details, you can refer to the
> [doc|https://nightlies.apache.org/flink/flink-docs-master/docs/flinkdev/building/#build-pyflink]
> {code:bash}
> $ cd flink-python/apache-flink-libraries
> $ python setup.py sdist
> $ pip install dist/*.tar.gz
> $ cd ..
> $ pip install -r dev/dev-requirements.txt
> $ python setpy.py sdist
> $ pip install dist/*.tar.gz
> {code}
> h1. Test
> * Write a python datastream job in thread mode
> {code:python}
> from pyflink.common import Configuration
> from pyflink.common.typeinfo import Types
> from pyflink.datastream import StreamExecutionEnvironment
> def main():
> config = Configuration()
> config.set_string("python.execution-mode", "thread")
> env = StreamExecutionEnvironment.get_execution_environment(config)
> ds = env.from_collection(
> [(1, '9', 0), (1, '5', 1), (1, '6', 2), (5, '5', 0), (5, '3', 1)],
> type_info=Types.ROW_NAMED(["v1", "v2", "v3"],
> [Types.INT(), Types.STRING(), Types.INT()]))
> def flat_map_func1(data):
> for i in data:
> yield int(i), 1
> def flat_map_func2(data):
> for i in data:
> yield i
> ds = ds.key_by(lambda x: x[0]) \
> .min_by("v2") \
> .map(lambda x: (x[0], x[1], x[2]),
> output_type=Types.TUPLE([Types.INT(), Types.STRING(),
> Types.INT()])) \
> .key_by(lambda x: x[2]) \
> .max_by(0) \
> .flat_map(flat_map_func1, output_type=Types.TUPLE([Types.INT(),
> Types.INT()])) \
> .key_by(lambda x: [1]) \
> .min_by() \
> .flat_map(flat_map_func2, output_type=Types.INT()) \
> .key_by(lambda x: x) \
> .max_by()
> ds.print()
> env.execute("key_by_min_by_max_by_test_batch")
> if __name__ == '__main__':
> main()
> {code}
> * run the python datastream job and watch the result
> {code:bash}
> $ python demo.py
> {code}
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