def test_slice_zero_row_groups(self):
table = pa.Table.from_batches([],
schema=pa.schema([("A", pa.string())]))
with parquet_file(table) as path:
self.assertEqual(
parquet.read_slice_as_text(path, "csv", range(1), range(0)),
"A")
self.assertEqual(
parquet.read_slice_as_text(path, "json", range(1), range(0)),
"[]")
def read_cached_render_result_slice_as_text(crr: CachedRenderResult,
format: str, only_columns: range,
only_rows: range) -> str:
"""
Call `parquet-to-text-stream` and return its output.
Ignore out-of-range rows and columns.
Raise CorruptCacheError if the cached data does not match `crr`. That can
mean:
* The cached Parquet file is corrupt
* The cached Parquet file is missing
* `crr` is stale -- the cached result is for a different delta. This
could be detected by a `Workflow.cooperative_lock()`, too, should the
caller want to distinguish this error from the others.
To limit the amount of text stored in RAM, use relatively small ranges for
`only_columns` and `only_rows`.
This uses `parquet-to-text-stream` with `--row-range` and `--column-range`.
Read `parquet-to-text-stream` documentation to see how nulls and floats are
handled in your chosen format (`csv` or `json`). (In a nutshell: it's
mostly non-lossy, though CSV can't represent `null`.)
"""
if not crr.table_metadata.columns:
# Zero-column tables aren't written to cache
return {}
try:
with downloaded_parquet_file(crr) as parquet_path:
return parquet.read_slice_as_text(parquet_path, format,
only_columns, only_rows)
except (pyarrow.ArrowIOError, FileNotFoundError): # FIXME unit-test
raise CorruptCacheError
def test_slice_zero_rows(self):
with tempfile_context() as path:
# ensure at least 1 row group
parquet.write(
path,
pa.table({
"A":
pa.array([], pa.string()),
"B":
pa.DictionaryArray.from_arrays(pa.array([], pa.int32()),
pa.array([], pa.string())),
"C":
pa.array([], pa.timestamp("ns")),
"D":
pa.array([], pa.float64()),
}),
)
self.assertEqual(
parquet.read_slice_as_text(path, "csv", range(4), range(0)),
"A,B,C,D")
self.assertEqual(
parquet.read_slice_as_text(path, "json", range(4), range(0)),
"[]")