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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2020/12/04 18:54:28 UTC
[GitHub] [beam] robertwb opened a new pull request #13488: [BEAM-11361] Dynamic splitting of dataframe csv reads.
robertwb opened a new pull request #13488:
URL: https://github.com/apache/beam/pull/13488
Support for headers, but not for escaped newlines (so not yet on by default).
------------------------
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[GitHub] [beam] robertwb commented on pull request #13488: [BEAM-11361] Dynamic splitting of dataframe csv reads.
Posted by GitBox <gi...@apache.org>.
robertwb commented on pull request #13488:
URL: https://github.com/apache/beam/pull/13488#issuecomment-738958324
R: @TheNeuralBit
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[GitHub] [beam] robertwb merged pull request #13488: [BEAM-11361] Dynamic splitting of dataframe csv reads.
Posted by GitBox <gi...@apache.org>.
robertwb merged pull request #13488:
URL: https://github.com/apache/beam/pull/13488
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[GitHub] [beam] TheNeuralBit commented on a change in pull request #13488: [BEAM-11361] Dynamic splitting of dataframe csv reads.
Posted by GitBox <gi...@apache.org>.
TheNeuralBit commented on a change in pull request #13488:
URL: https://github.com/apache/beam/pull/13488#discussion_r542981589
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -192,14 +207,133 @@ def expand(self, root):
self.reader,
self.args,
self.kwargs,
+ self.binary,
self.incremental,
- self.splittable,
- self.binary)))
+ self.splitter)))
from apache_beam.dataframe import convert
return convert.to_dataframe(
pcoll, proxy=_prefix_range_index_with(':', sample[:0]))
+class _Splitter:
+ def empty_buffer(self):
+ raise NotImplementedError(self)
+
+ def read_header(self, handle):
+ raise NotImplementedError(self)
+
+ def read_to_record_boundary(self, buffered, handle):
+ raise NotImplementedError(self)
Review comment:
Could you add docstrings and/or typehints for this class so its clear what implementations should do
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -192,14 +207,134 @@ def expand(self, root):
self.reader,
self.args,
self.kwargs,
+ self.binary,
self.incremental,
- self.splittable,
- self.binary)))
+ self.splitter)))
from apache_beam.dataframe import convert
return convert.to_dataframe(
pcoll, proxy=_prefix_range_index_with(':', sample[:0]))
+class _Splitter:
+ def empty_buffer(self):
+ raise NotImplementedError(self)
+
+ def read_header(self, handle):
+ raise NotImplementedError(self)
+
+ def read_to_record_boundary(self, buffered, handle):
+ raise NotImplementedError(self)
+
+
+class _DelimSplitter(_Splitter):
+ def __init__(self, delim, read_chunk_size=_DEFAULT_BYTES_CHUNKSIZE):
Review comment:
Might be helpful to clarify that this is splitting on delimiters between _records_, it's easy to get mixed up in the CSV case where there's also a field delimiter.
```suggestion
class _RecordDelimSplitter(_Splitter):
""" A _Splitter that splits on delimiters between records. """
def __init__(self, delim, read_chunk_size=_DEFAULT_BYTES_CHUNKSIZE):
```
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -31,16 +33,28 @@
_DEFAULT_BYTES_CHUNKSIZE = 1 << 20
-def read_csv(path, *args, **kwargs):
+def read_csv(path, *args, splittable=False, **kwargs):
"""Emulates `pd.read_csv` from Pandas, but as a Beam PTransform.
Use this as
df = p | beam.dataframe.io.read_csv(...)
to get a deferred Beam dataframe representing the contents of the file.
+
+ If your files are large and records do not contain quoted newlines, you may
+ pass the extra argument splittable=True to enable dynamic splitting for this
+ read.
Review comment:
Can we make this warn that using splittable=True with quoted newlines could lead to dataloss?
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[GitHub] [beam] robertwb commented on a change in pull request #13488: [BEAM-11361] Dynamic splitting of dataframe csv reads.
Posted by GitBox <gi...@apache.org>.
robertwb commented on a change in pull request #13488:
URL: https://github.com/apache/beam/pull/13488#discussion_r543754989
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -31,16 +33,28 @@
_DEFAULT_BYTES_CHUNKSIZE = 1 << 20
-def read_csv(path, *args, **kwargs):
+def read_csv(path, *args, splittable=False, **kwargs):
"""Emulates `pd.read_csv` from Pandas, but as a Beam PTransform.
Use this as
df = p | beam.dataframe.io.read_csv(...)
to get a deferred Beam dataframe representing the contents of the file.
+
+ If your files are large and records do not contain quoted newlines, you may
+ pass the extra argument splittable=True to enable dynamic splitting for this
+ read.
Review comment:
Good point. Done.
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -192,14 +207,134 @@ def expand(self, root):
self.reader,
self.args,
self.kwargs,
+ self.binary,
self.incremental,
- self.splittable,
- self.binary)))
+ self.splitter)))
from apache_beam.dataframe import convert
return convert.to_dataframe(
pcoll, proxy=_prefix_range_index_with(':', sample[:0]))
+class _Splitter:
+ def empty_buffer(self):
+ raise NotImplementedError(self)
+
+ def read_header(self, handle):
+ raise NotImplementedError(self)
+
+ def read_to_record_boundary(self, buffered, handle):
+ raise NotImplementedError(self)
+
+
+class _DelimSplitter(_Splitter):
+ def __init__(self, delim, read_chunk_size=_DEFAULT_BYTES_CHUNKSIZE):
Review comment:
Done.
##########
File path: sdks/python/apache_beam/dataframe/io.py
##########
@@ -192,14 +207,133 @@ def expand(self, root):
self.reader,
self.args,
self.kwargs,
+ self.binary,
self.incremental,
- self.splittable,
- self.binary)))
+ self.splitter)))
from apache_beam.dataframe import convert
return convert.to_dataframe(
pcoll, proxy=_prefix_range_index_with(':', sample[:0]))
+class _Splitter:
+ def empty_buffer(self):
+ raise NotImplementedError(self)
+
+ def read_header(self, handle):
+ raise NotImplementedError(self)
+
+ def read_to_record_boundary(self, buffered, handle):
+ raise NotImplementedError(self)
Review comment:
I actually tried to add typehints initially; they work poorly due to the fact that this works both on bytes and strings (but it's hard for mypy to know that it will be all one or all the other). But I added some comments at least.
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