[jira] [Work logged] (BEAM-7886) Make row coder a standard coder and implement in python

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/BEAM-7886?focusedWorklogId=319237&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-319237 ]

ASF GitHub Bot logged work on BEAM-7886:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 26/Sep/19 21:32
            Start Date: 26/Sep/19 21:32
    Worklog Time Spent: 10m 
      Work Description: TheNeuralBit commented on pull request #9188: [BEAM-7886] Make row coder a standard coder and implement in Python
URL: https://github.com/apache/beam/pull/9188#discussion_r328835963
 
 

 ##########
 File path: sdks/python/apache_beam/coders/row_coder.py
 ##########
 @@ -0,0 +1,162 @@
+#
+# 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.
+#
+
+from __future__ import absolute_import
+
+import itertools
+from array import array
+
+from apache_beam.coders.coder_impl import StreamCoderImpl
+from apache_beam.coders.coders import BytesCoder
+from apache_beam.coders.coders import Coder
+from apache_beam.coders.coders import FastCoder
+from apache_beam.coders.coders import FloatCoder
+from apache_beam.coders.coders import IterableCoder
+from apache_beam.coders.coders import StrUtf8Coder
+from apache_beam.coders.coders import TupleCoder
+from apache_beam.coders.coders import VarIntCoder
+from apache_beam.portability import common_urns
+from apache_beam.portability.api import schema_pb2
+from apache_beam.typehints.schemas import named_tuple_from_schema
+from apache_beam.typehints.schemas import named_tuple_to_schema
+
+__all__ = ["RowCoder"]
+
+
+class RowCoder(FastCoder):
+  """ Coder for `typing.NamedTuple` instances.
+
+  Implements the beam:coder:row:v1 standard coder spec.
+  """
+
+  def __init__(self, schema):
+    self.schema = schema
+    self.components = [
+        coder_from_type(field.type) for field in self.schema.fields
+    ]
+
+  def _create_impl(self):
+    return RowCoderImpl(self.schema, self.components)
+
+  def is_deterministic(self):
+    return all(c.is_deterministic() for c in self.components)
+
+  def to_type_hint(self):
+    return named_tuple_from_schema(self.schema)
+
+  def as_cloud_object(self, coders_context=None):
+    raise NotImplementedError("TODO")
+
+  def __eq__(self, other):
+    return type(self) == type(other) and self.schema == other.schema
+
+  def __hash__(self):
 
 Review comment:
   Did some reading on this and it looks like I was wrong. From (SO)[https://stackoverflow.com/a/53519136]:
   > A class that overrides `__eq__()` and does not define `__hash__()` will have its `__hash__()` implicitly set to None.
   
   I went ahead and removed __hash__
   
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 319237)
    Time Spent: 10h 20m  (was: 10h 10m)

> Make row coder a standard coder and implement in python
> -------------------------------------------------------
>
>                 Key: BEAM-7886
>                 URL: https://issues.apache.org/jira/browse/BEAM-7886
>             Project: Beam
>          Issue Type: Improvement
>          Components: beam-model, sdk-java-core, sdk-py-core
>            Reporter: Brian Hulette
>            Assignee: Brian Hulette
>            Priority: Major
>          Time Spent: 10h 20m
>  Remaining Estimate: 0h
>




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