def _to_parquet(tfrecords, file, output_dir, chunks, convert_lists):
out = []
i = 0
w = ParquetWriter(output_dir + file.split("/")[-1].split(".")[0] +
".parquet",
index=False)
for tfrecord in tqdm(tfrecords):
row = {key: val.numpy() for key, val in tfrecord.items()}
out.append(row)
i += 1
if i == chunks:
df = cudf.DataFrame(out)
if convert_lists:
df = _convert_lists(df)
w.write_table(df)
i = 0
out = []
del df
gc.collect()
if len(out) > 0:
df = cudf.DataFrame(out)
if convert_lists:
df = _convert_lists(df)
w.write_table(df)
del df
gc.collect()
w.close()
def test_parquet_writer_bytes_io(simple_gdf):
output = BytesIO()
writer = ParquetWriter(output)
writer.write_table(simple_gdf)
writer.write_table(simple_gdf)
writer.close()
assert_eq(cudf.read_parquet(output), cudf.concat([simple_gdf, simple_gdf]))
def test_parquet_writer_gpu_chunked(tmpdir, simple_pdf, simple_gdf):
gdf_fname = tmpdir.join("gdf.parquet")
writer = ParquetWriter(gdf_fname)
writer.write_table(simple_gdf)
writer.write_table(simple_gdf)
writer.close()
assert_eq(pd.read_parquet(gdf_fname), pd.concat([simple_pdf, simple_pdf]))
def _write_gb_stats(dfs, base_path, col_group, on_host, concat_groups,
name_sep):
if concat_groups and len(col_group) > 1:
col_group = [_make_name(*col_group, sep=name_sep)]
if isinstance(col_group, str):
col_group = [col_group]
rel_path = "cat_stats.%s.parquet" % (_make_name(*col_group, sep=name_sep))
path = os.path.join(base_path, rel_path)
pwriter = None
pa_schema = None
if not on_host:
pwriter = ParquetWriter(path, compression=None)
# Loop over dfs and append to file
# TODO: For high-cardinality columns, should support
# Dask-based to_parquet call here (but would need to
# support directory reading within dependent ops)
n_writes = 0
for df in dfs:
if len(df):
if on_host:
# Use pyarrow
pa_table = pa.Table.from_pandas(df,
schema=pa_schema,
preserve_index=False)
if pwriter is None:
pa_schema = pa_table.schema
pwriter = pq.ParquetWriter(path,
pa_schema,
compression=None)
pwriter.write_table(pa_table)
else:
# Use CuDF
df.reset_index(drop=True, inplace=True)
pwriter.write_table(df)
n_writes += 1
# No data to write
if n_writes == 0:
raise RuntimeError("GroupbyStatistics result is empty.")
# Close writer and return path
pwriter.close()
return path
def test_parquet_writer_chunked_metadata(tmpdir, simple_pdf, simple_gdf):
gdf_fname = tmpdir.join("gdf.parquet")
test_path = "test/path"
writer = ParquetWriter(gdf_fname)
writer.write_table(simple_gdf)
writer.write_table(simple_gdf)
meta_byte_array = writer.close(metadata_file_path=test_path)
fmd = pq.ParquetFile(BytesIO(meta_byte_array)).metadata
assert fmd.num_rows == 2 * len(simple_gdf)
assert fmd.num_row_groups == 2
for r in range(fmd.num_row_groups):
for c in range(fmd.num_columns):
assert fmd.row_group(r).column(c).file_path == test_path
def _convert_file(path, name, out_dir, gpu_mem_frac, fs, cols, dtypes):
fn = f"{name}.parquet"
out_path = fs.sep.join([out_dir, f"{name}.parquet"])
writer = ParquetWriter(out_path, compression=None)
for gdf in nvt.Dataset(
path,
engine="csv",
names=cols,
part_memory_fraction=gpu_mem_frac,
sep='\t',
dtypes=dtypes,
).to_iter():
writer.write_table(gdf)
del gdf
md = writer.close(metadata_file_path=fn)
return md