def convert_dir(input_dir: str, output_dir: str, svcschema: SchemaForTable):
"""Convert the data into a single file and write it out"""
defaults = {
pa.string(): "",
pa.int32(): 0,
pa.int64(): 0,
pa.float32(): 0.0,
pa.float64(): 0.0,
pa.date64(): 0.0,
pa.bool_(): False,
pa.list_(pa.string()): ['-'],
pa.list_(pa.int64()): [],
}
df = pd.read_parquet(input_dir, use_legacy_dataset=True)
sqschema = svcschema.get_raw_schema()
arrow_schema = svc_schema.get_arrow_schema()
for column in filter(lambda x: x['name'] not in df.columns, sqschema):
df[column['name']] = column.get('default', defaults[column['type']])
# convert all dtypes to whatever is desired
for column in df.columns:
if column in arrow_schema:
df[column] = df[column].astype(
arrow_schema.field(column).type.to_pandas_dtype())
# If there's the original ifname saved up, then eliminate this unnecessary
# field as this model is no longer necessary
if 'origIfname' in df.columns:
if 'ifname' in df.columns:
df = df.drop(columns=['ifname']) \
.rename(columns={'origIfname': 'ifname'})
elif 'oif' in df.columns:
df = df.drop(columns=['oif']) \
.rename(columns={'origIfname': 'oif'})
table = pa.Table.from_pandas(df, schema=arrow_schema, preserve_index=False)
partition_cols = svcschema.get_partition_columns()
if 'norifcnReason' in df.columns:
df.rename({'notifcnReason': 'notificnReason'}, inplace=True)
pq.write_to_dataset(
table,
root_path=output_dir,
partition_cols=partition_cols,
version="2.0",
compression='ZSTD',
row_group_size=100000,
)
logger.info(f'Wrote converted {input_dir}')
def describe(self, **kwargs):
"""Describes the fields for a given table"""
table = kwargs.get('table', '')
try:
sch = SchemaForTable(table, self.schemas)
except ValueError:
sch = None
if not sch:
df = pd.DataFrame(
{'error': [f'ERROR: incorrect table name {table}']})
return df
entries = [{
'name': x['name'],
'type': x['type'],
'key': x.get('key', ''),
'display': x.get('display', '')
} for x in sch.get_raw_schema()]
df = pd.DataFrame.from_dict(entries).sort_values('name')
return df
