the code like this: ```env = StreamExecutionEnviro...
# troubleshooting
h
the code like this:
Copy code
env = StreamExecutionEnvironment.get_execution_environment()
env.set_runtime_mode(RuntimeExecutionMode.BATCH)
env.set_parallelism(1)

my_array = [1, 2, 3, 4, 5]

stream = env.from_collection(collection=my_array)
stream.print()

env.execute()
but job finished, and tm log error. 2023-05-16 054832,411 INFO org.apache.beam.runners.fnexecution.logging.GrpcLoggingService [] - 1 Beam Fn Logging clients still connected during shutdown. 2023-05-16 054832,421 WARN org.apache.beam.sdk.fn.data.BeamFnDataGrpcMultiplexer [] - Hanged up for unknown endpoint. 2023-05-16 054832,426 WARN org.apache.beam.sdk.fn.data.BeamFnDataGrpcMultiplexer [] - Hanged up for unknown endpoint. 2023-05-16 054832,487 WARN org.apache.beam.runners.fnexecution.control.DefaultJobBundleFactory [] - Error cleaning up servers urn: "beamenvprocess:v1" payload: "\032H/usr/local/lib/python3.8/dist-packages/pyflink/bin/pyflink-udf-runner.sh\"\225\002\n\004PATH\022\214\002/root/miniconda3/condabin:/usr/li
d
@HJK nomad Could you share the log file?
πŸ˜— 1
h
sure, tm log file here.
Every time I run a python job, the taskmanager mataspace grows by about 25MB, eventually causing the mataspace OOM,I read the official document, the default process mode is implemented by apache Beam, so I went to study Beam again, I read the source code and found that it should be caused by the close exception when the Close method is executed, but I can't debug it, it may be me caused by configuration issues. By changing to threaded mode, the bottom layer will not have this problem through Pemja.
@Dian Fu my log file here,broπŸ™