HuangXingBo opened a new pull request #11107: [FLINK-16071][python] Optimize 
the cost of the get item of the Row
URL: https://github.com/apache/flink/pull/11107
 
 
   ## What is the purpose of the change
   
   *This pull request will Optimize the cost of the get item of the Row in 
Python UDF*
   
   
   ## Brief change log 
   
     - *Using FlattenRowCoder in Python UDF*
   
   ## Verifying this change
   
   This change added tests and can be verified as follows:
   
     - *It is the perforce improvements without function test*
   
   ## Does this pull request potentially affect one of the following parts:
   
     - Dependencies (does it add or upgrade a dependency): (no)
     - The public API, i.e., is any changed class annotated with 
`@Public(Evolving)`: (no)
     - The serializers: (no)
     - The runtime per-record code paths (performance sensitive): (no)
     - Anything that affects deployment or recovery: JobManager (and its 
components), Checkpointing, Yarn/Mesos, ZooKeeper: (no)
     - The S3 file system connector: (no)
   
   ## Documentation
   
     - Does this pull request introduce a new feature? (no)
     - If yes, how is the feature documented? (not applicable)
   
   ## How does this patch test
   ### Test Code
   
   
       @udf(input_types=[DataTypes.INT(False)], 
result_type=DataTypes.INT(False))
       def inc(x):
           return x
   
       t_env.register_function("inc", inc)
   
       # num_rows = 100000000
       num_rows = 100000
       num_columns = 100
   
       select_list = ["inc(c%s)" % i for i in range(num_columns)]
       t_env.register_table_sink(
           "sink",
           PrintTableSink(
               ["c%s" % i for i in range(num_columns)],
               [DataTypes.INT(False)] * num_columns))
   
       t_env.from_table_source(MultiRowColumnTableSource(num_rows, 
num_columns)) \
           .select(','.join(select_list)) \
           .insert_into("sink")
   
       beg_time = time.time()
       t_env.execute("perf_test")
       print("consume time: " + str(time.time() - beg_time))
   
   
   ## Test Results
   num rows, num colums |  Consume Time(Before) | Consume Time(After)
   10w, 100                         |    53.32s                           |   
22.18s
   100w,10                          |    55.88s                           |   
33.85s
   

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