bgeng777 commented on code in PR #27198:
URL: https://github.com/apache/flink/pull/27198#discussion_r2494929420


##########
flink-python/pyflink/examples/datastream/asyncio/remote_model_inference.py:
##########
@@ -0,0 +1,150 @@
+################################################################################
+#  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.
+################################################################################
+import argparse
+import asyncio
+import functools
+import json
+import logging
+import random
+import sys
+from typing import List
+
+from pyflink.common import Encoder, Types, Time, Row
+from pyflink.datastream import StreamExecutionEnvironment, AsyncDataStream, 
AsyncFunction, \
+    RuntimeContext, AsyncRetryStrategy, async_retry_predicates, 
CheckpointingMode
+from pyflink.datastream.connectors.file_system import (FileSink, 
OutputFileConfig, RollingPolicy)
+from pyflink.table import StreamTableEnvironment, TableDescriptor, Schema, 
DataTypes
+
+
+class AsyncLLMRequest(AsyncFunction[Row, str]):
+
+    def __init__(self):
+        self.retried_keys = {}
+
+    def open(self, runtime_context: RuntimeContext):
+        # create model inference client here
+        pass
+
+    def close(self):
+        # close the model inference client here
+        pass
+
+    async def async_invoke(self, value: Row) -> List[str]:
+        # issue the asynchronous request
+        await asyncio.sleep(random.randint(1, 2))
+
+        if value.user_id not in self.retried_keys and random.randint(1, 10) % 
3 == 0:
+            self.retried_keys[value.user_id] = True
+            # remote model inference request may time out
+            raise TimeoutError
+        else:
+            if value.user_id in self.retried_keys:
+                del self.retried_keys[value.user_id]
+            # remote model inference request completes
+            # note that the result should be a collection even there is only 
one result
+            analysis_result = "positive"
+            result = {
+                "user_id": value.user_id,
+                "comments": value.comments,
+                "analysis_result": analysis_result
+            }
+            return [json.dumps(result)]
+
+    def timeout(self, value: Row) -> List[str]:
+        # return a default value in case timeout
+        result = {
+            "user_id": value.user_id,
+            "comments": value.comments,
+            "analysis_result": None
+        }
+        return [json.dumps(result)]

Review Comment:
   As we make `timeout()`  return some None value, I assume that the output is 
something like this 
   ```
   9> {"user_id": 1846187662, "comments": 
"bfde64be5d612b38e45e7c7ce6b6c0036fa0e519176c69aeb780ad14ada24e7523e6ac3ef427180256b33c847b549b34374a",
 "analysis_result": "positive"}
   9> {"user_id": 660541242, "comments": 
"917b1e27f21df14d309a70bd8c169f736cce3888f548304e4ceed10b595e7ee281508d42de93a777ab1742c00ec60eacec53",
 "analysis_result": "positive"}
   9> {"user_id": -980259315, "comments": 
"aa78f20af92b7ce64f6ef7b288d8717e935fdeb8b4ef3545a3514119dcf8c13ddd21f4be00bab851e1bda3b5a017841f835f",
 "analysis_result": null}
   9> {"user_id": -1150764029, "comments": 
"e806355d655d0b01ac8d784a80c57989e0e5711021916ac3a4a27ebda7b3657355540414a2f43b83a04f7e041b632a6d1165",
 "analysis_result": null}
   ```
   
   It works as expected when I use  `AsyncRetryStrategy.no_restart()`. But when 
using the ` AsyncRetryStrategy.fixed_delay()` as this example, I meet something 
strange:
   ```
   ttributeError: ^: 'RetryableResultHandler' object has no attribute 
'_delayed_retry_timer'AttributeError: ^'RetryableResultHandler' object has no 
attribute '_delayed_retry_timer'^^:   ^^^^^^'RetryableResultHandler' object has 
no attribute '_delayed_retry_timer'^^^^^^
       if self._delayed_retry_timer is not None:
   ^^ 
      ^^^^^AttributeError: ^. Did you mean: '_cancel_retry_timer'?
   ^^
   ^^^ AttributeError: 'RetryableResultHandler' object has no attribute 
'_delayed_retry_timer'. Did you mean: '_cancel_retry_timer'?
   . Did you mean: '_cancel_retry_timer'?
   ^'RetryableResultHandler' object has no attribute '_delayed_retry_timer'. 
Did you mean: '_cancel_retry_timer'?
   AttributeError:  ^^^^AttributeError'RetryableResultHandler' object has no 
attribute '_delayed_retry_timer'
   ^^^'RetryableResultHandler' object has no attribute 
'_delayed_retry_timer'^^. Did you mean: '_cancel_retry_timer'?
      : 'RetryableResultHandler' object has no attribute 
'_delayed_retry_timer'. Did you mean: '_cancel_retry_timer'?
   . Did you mean: '_cancel_retry_timer'?
     ^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^. Did you mean: '_cancel_retry_timer'?
   ^
   ```
   
   The complete codes to reproduce (note, I use `await 
asyncio.sleep(random.randint(1, 20))` whose largest value is larger than 10s 
timeout:
   ```python
   import argparse
   import asyncio
   import functools
   import json
   import logging
   import random
   import sys
   from typing import List
   
   from pyflink.common import Encoder, Types, Time, Row
   from pyflink.datastream import StreamExecutionEnvironment, AsyncDataStream, 
AsyncFunction, \
       RuntimeContext, AsyncRetryStrategy, async_retry_predicates, 
CheckpointingMode
   from pyflink.datastream.connectors.file_system import (FileSink, 
OutputFileConfig, RollingPolicy)
   from pyflink.table import StreamTableEnvironment, TableDescriptor, Schema, 
DataTypes
   
   
   class AsyncLLMRequest(AsyncFunction[Row, str]):
   
       def __init__(self):
           self.retried_keys = {}
   
       def open(self, runtime_context: RuntimeContext):
           # create model inference client here
           pass
   
       def close(self):
           # close the model inference client here
           pass
   
       async def async_invoke(self, value: Row) -> List[str]:
           # issue the asynchronous request
           await asyncio.sleep(random.randint(1, 20))
   
           if value.user_id not in self.retried_keys and random.randint(1, 10) 
% 3 == 0:
               self.retried_keys[value.user_id] = True
               # remote model inference request may time out
               raise TimeoutError
           else:
               if value.user_id in self.retried_keys:
                   del self.retried_keys[value.user_id]
               # remote model inference request completes
               # note that the result should be a collection even there is only 
one result
               analysis_result = "positive"
               result = {
                   "user_id": value.user_id,
                   "comments": value.comments,
                   "analysis_result": analysis_result
               }
               return [json.dumps(result)]
   
       def timeout(self, value: Row) -> List[str]:
           # return a default value in case timeout
           result = {
               "user_id": value.user_id,
               "comments": value.comments,
               "analysis_result": None
           }
           return [json.dumps(result)]
   
   
   def main(output_path):
       env = StreamExecutionEnvironment.get_execution_environment()
       env.enable_checkpointing(30000, CheckpointingMode.EXACTLY_ONCE)
       t_env = StreamTableEnvironment.create(stream_execution_environment=env)
   
       # source: user_id, comments
       t_env.create_temporary_table(
           'source',
           TableDescriptor.for_connector('datagen')
                          .schema(Schema.new_builder()
                                  .column('user_id', DataTypes.INT())
                                  .column('comments', DataTypes.STRING())
                                  .build())
                          .option('fields.user_id.kind', 'random')
                          .option('fields.comments.kind', 'random')
                          .option('rows-per-second', '100')
                          .build())
   
       table = t_env.from_path('source')
       ds = t_env.to_data_stream(table)
   
       # create an async retry strategy via utility class or a user defined 
strategy
       async_retry_strategy = AsyncRetryStrategy.fixed_delay(
           max_attempts=100,
           backoff_time_millis=1000,
           result_predicate=None,
           
exception_predicate=functools.partial(async_retry_predicates.exception_type_predicate,
                                                 
expected_error_type=TimeoutError))
   
       result_stream = AsyncDataStream.unordered_wait_with_retry(
           data_stream=ds,
           async_function=AsyncLLMRequest(),
           timeout=Time.seconds(10),
           async_retry_strategy=async_retry_strategy,
           capacity=1000,
           output_type=Types.STRING())
   
       # define the sink
       if output_path is not None:
           result_stream.sink_to(
               sink=FileSink.for_row_format(
                   base_path=output_path,
                   encoder=Encoder.simple_string_encoder())
               .with_output_file_config(
                   OutputFileConfig.builder()
                   .with_part_prefix("prefix")
                   .with_part_suffix(".ext")
                   .build())
               .with_rolling_policy(RollingPolicy.default_rolling_policy())
               .build()
           )
       else:
           print("Printing result to stdout. Use --output to specify output 
path.")
           result_stream.print()
   
       # submit for execution
       env.execute()
   
   
   if __name__ == '__main__':
       logging.basicConfig(stream=sys.stdout, level=logging.INFO, 
format="%(message)s")
   
       parser = argparse.ArgumentParser()
       parser.add_argument(
           '--output',
           dest='output',
           required=False,
           help='Output file to write results to.')
   
       argv = sys.argv[1:]
       known_args, _ = parser.parse_known_args(argv)
   
       main(known_args.output)
   
   ```
   
   let me know if it is expected



-- 
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.

To unsubscribe, e-mail: [email protected]

For queries about this service, please contact Infrastructure at:
[email protected]

Reply via email to