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ak_to_parquet.py
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ak_to_parquet.py
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# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
from collections.abc import Iterable, Sized, Mapping, Sequence
import numpy as np
import awkward as ak
def to_parquet(
data,
destination,
list_to32=False,
string_to32=True,
bytestring_to32=True,
emptyarray_to=None,
categorical_as_dictionary=False,
extensionarray=True,
count_nulls=True,
compression="zstd",
compression_level=None,
row_group_size=64 * 1024 * 1024,
data_page_size=None,
parquet_flavor=None,
parquet_version="1.0",
parquet_page_version="1.0",
parquet_metadata_statistics=True,
parquet_coerce_timestamps=None,
parquet_old_int96_timestamps=None,
parquet_compliant_nested=False,
parquet_extra_options=None,
hook_after_write=None,
):
import awkward._v2._connect.pyarrow
pyarrow_parquet = awkward._v2._connect.pyarrow.import_pyarrow_parquet(
"ak.to_parquet"
)
fsspec = awkward._v2._connect.pyarrow.import_fsspec("ak.to_parquet")
if isinstance(data, Iterable) and not isinstance(data, Sized):
iterator = iter(data)
elif isinstance(data, Iterable):
iterator = iter([data])
else:
raise ak._v2._util.error(
TypeError(
"'data' must be an array (one row group) or iterable of arrays (row group per array)"
)
)
row_group = 0
array = next(iterator)
layout = ak._v2.operations.convert.ak_to_layout.to_layout(
array, allow_record=False, allow_other=False
)
table = ak._v2.operations.convert.ak_to_arrow_table._impl(
layout,
list_to32,
string_to32,
bytestring_to32,
emptyarray_to,
categorical_as_dictionary,
extensionarray,
count_nulls,
)
if parquet_compliant_nested:
list_indicator = "list.element"
else:
list_indicator = "list.item"
if table.column_names == [""]:
column_prefix = ("",)
else:
column_prefix = ()
form = layout.form
def parquet_columns(specifier, only=None):
if specifier is None:
selected_form = form
else:
selected_form = form.select_columns(specifier)
parquet_column_names = selected_form.columns(
list_indicator=list_indicator, column_prefix=column_prefix
)
if only is not None:
column_types = selected_form.column_types()
assert len(parquet_column_names) == len(column_types)
if only == "string":
return [
x
for x, y in zip(parquet_column_names, column_types)
if y == "string"
]
elif only == "floating":
return [
x
for x, y in zip(parquet_column_names, column_types)
if isinstance(y, np.dtype) and issubclass(y.type, np.floating)
]
else:
return parquet_column_names
if compression is True:
compression = "zstd"
elif compression is False or compression is None:
compression = "none"
elif isinstance(compression, Mapping):
replacement = {}
for specifier, value in compression.items():
replacement.update({x: value for x in parquet_columns(specifier)})
compression = replacement
if isinstance(compression_level, Mapping):
replacement = {}
for specifier, value in compression_level.items():
replacement.update({x: value for x in parquet_columns(specifier)})
compression_level = replacement
# if compression_categorical is True:
# compression_categorical = parquet_columns(None, only="string")
# elif compression_categorical is False or compression_categorical is None:
# compression_categorical = False
# elif isinstance(compression_categorical, Mapping):
# replacement = {}
# for specifier, value in compression_categorical.items():
# replacement.update(
# {x: value for x in parquet_columns(specifier, only="string")}
# )
# compression_categorical = [x for x, value in replacement.items() if value]
# if compression_floating is True:
# compression_floating = parquet_columns(None, only="floating")
# elif compression_floating is False or compression_floating is None:
# compression_floating = False
# elif isinstance(compression_floating, Mapping):
# replacement = {}
# for specifier, value in compression_floating.items():
# replacement.update(
# {x: value for x in parquet_columns(specifier, only="floating")}
# )
# compression_floating = [x for x, value in replacement.items() if value]
if parquet_metadata_statistics is True:
parquet_metadata_statistics = True
elif parquet_metadata_statistics is False or parquet_metadata_statistics is None:
parquet_metadata_statistics = False
elif isinstance(parquet_metadata_statistics, Mapping):
replacement = {}
for specifier, value in parquet_metadata_statistics.items():
replacement.update({x: value for x in parquet_columns(specifier)})
parquet_metadata_statistics = [x for x, value in replacement.items() if value]
elif isinstance(parquet_metadata_statistics, Sequence):
replacement = []
for specifier in parquet_metadata_statistics:
replacement.extend([x for x in parquet_columns(specifier)])
parquet_metadata_statistics = replacement
if parquet_extra_options is None:
parquet_extra_options = {}
with fsspec.open(destination, "wb") as file:
with pyarrow_parquet.ParquetWriter(
destination,
table.schema,
filesystem=file.fs,
flavor=parquet_flavor,
version=parquet_version,
# use_dictionary=compression_categorical,
compression=compression,
write_statistics=parquet_metadata_statistics,
use_deprecated_int96_timestamps=parquet_old_int96_timestamps,
compression_level=compression_level,
# use_byte_stream_split=compression_floating,
data_page_version=parquet_page_version,
use_compliant_nested_type=parquet_compliant_nested,
data_page_size=data_page_size,
coerce_timestamps=parquet_coerce_timestamps,
**parquet_extra_options,
) as writer:
while True:
writer.write_table(table, row_group_size=row_group_size)
if hook_after_write is not None:
hook_after_write(
row_group=row_group,
array=array,
layout=layout,
table=table,
writer=writer,
)
row_group += 1
try:
array = next(iterator)
except StopIteration:
break
layout = ak._v2.operations.convert.ak_to_layout.to_layout(
array, allow_record=False, allow_other=False
)
table = ak._v2.operations.convert.ak_to_arrow_table._impl(
layout,
list_to32,
string_to32,
bytestring_to32,
emptyarray_to,
categorical_as_dictionary,
extensionarray,
count_nulls,
)