def from_dict(dct, explicit_partitions=True):
"""
Load dataset metadata from a dictionary.
This must have no external references. Otherwise use ``load_from_dict``
to have them resolved automatically.
"""
# Use the builder class for reconstruction to have a single point for metadata version changes
builder = DatasetMetadataBuilder(
uuid=dct[naming.UUID_KEY],
metadata_version=dct[naming.METADATA_VERSION_KEY],
explicit_partitions=explicit_partitions,
partition_keys=dct.get("partition_keys", None)
if dct[naming.METADATA_VERSION_KEY] >= 4
else None,
table_meta=dct.get("table_meta", None)
if dct[naming.METADATA_VERSION_KEY] >= 4
else None,
)
for key, value in six.iteritems(dct.get("metadata", {})):
builder.add_metadata(key, value)
for partition_label, part_dct in six.iteritems(dct.get("partitions", {})):
builder.add_partition(
partition_label, Partition.from_v2_dict(partition_label, part_dct)
)
for column, index_dct in six.iteritems(dct.get("indices", {})):
if isinstance(index_dct, IndexBase):
builder.add_embedded_index(column, index_dct)
else:
builder.add_embedded_index(
column, ExplicitSecondaryIndex.from_v2(column, index_dct)
)
return builder.to_dataset()
def test_roundtrip():
expected = {"files": {"Queejeb3": "file.parquet"}}
result = Partition.from_v2_dict("partition_label", expected).to_dict()
assert expected == result
