timsaucer commented on code in PR #1123:
URL:
https://github.com/apache/datafusion-python/pull/1123#discussion_r2145455758
##########
python/datafusion/dataframe.py:
##########
@@ -704,38 +694,135 @@ def write_csv(self, path: str | pathlib.Path,
with_header: bool = False) -> None
def write_parquet(
self,
path: str | pathlib.Path,
- compression: Union[str, Compression] = Compression.ZSTD,
- compression_level: int | None = None,
+ data_pagesize_limit: int = 1024 * 1024,
+ write_batch_size: int = 1024,
+ writer_version: str = "1.0",
+ skip_arrow_metadata: bool = False,
+ compression: Optional[str] = "zstd(3)",
+ dictionary_enabled: Optional[bool] = True,
+ dictionary_page_size_limit: int = 1024 * 1024,
+ statistics_enabled: Optional[str] = "page",
+ max_row_group_size: int = 1024 * 1024,
+ created_by: str = "datafusion-python",
+ column_index_truncate_length: Optional[int] = 64,
+ statistics_truncate_length: Optional[int] = None,
+ data_page_row_count_limit: int = 20_000,
+ encoding: Optional[str] = None,
+ bloom_filter_on_write: bool = False,
+ bloom_filter_fpp: Optional[float] = None,
+ bloom_filter_ndv: Optional[int] = None,
+ allow_single_file_parallelism: bool = True,
+ maximum_parallel_row_group_writers: int = 1,
+ maximum_buffered_record_batches_per_stream: int = 2,
+ column_specific_options: Optional[dict[str, ParquetColumnOptions]] =
None,
Review Comment:
I think the full parquet writer options are going to be less commonly used
for most users. I would suggest we want to have two different versions of the
`write_parquet` signature, one that is the simple existing compression
parameters and on that looks like
```
def write_parquet(self, path: str | pathlib.Path, options:
ParquetWriterOptions)
```
And then we have a `ParquetWriterOptions` class that has this large
initialize options.
##########
python/tests/test_dataframe.py:
##########
@@ -1560,40 +1588,323 @@ def test_write_compressed_parquet(df, tmp_path,
compression, compression_level):
@pytest.mark.parametrize(
- ("compression", "compression_level"),
- [("gzip", 12), ("brotli", 15), ("zstd", 23), ("wrong", 12)],
+ "compression",
+ ["gzip(12)", "brotli(15)", "zstd(23)"],
)
-def test_write_compressed_parquet_wrong_compression_level(
- df, tmp_path, compression, compression_level
-):
+def test_write_compressed_parquet_wrong_compression_level(df, tmp_path,
compression):
path = tmp_path
- with pytest.raises(ValueError):
- df.write_parquet(
- str(path),
- compression=compression,
- compression_level=compression_level,
- )
+ with pytest.raises(Exception, match=r"valid compression range .*?
exceeded."):
+ df.write_parquet(str(path), compression=compression)
-@pytest.mark.parametrize("compression", ["wrong"])
+@pytest.mark.parametrize("compression", ["wrong", "wrong(12)"])
def test_write_compressed_parquet_invalid_compression(df, tmp_path,
compression):
path = tmp_path
- with pytest.raises(ValueError):
+ with pytest.raises(Exception, match="Unknown or unsupported parquet
compression"):
df.write_parquet(str(path), compression=compression)
-# not testing lzo because it it not implemented yet
-# https://github.com/apache/arrow-rs/issues/6970
-@pytest.mark.parametrize("compression", ["zstd", "brotli", "gzip"])
-def test_write_compressed_parquet_default_compression_level(df, tmp_path,
compression):
- # Test write_parquet with zstd, brotli, gzip default compression level,
- # ie don't specify compression level
- # should complete without error
- path = tmp_path
+@pytest.mark.parametrize(
+ ("writer_version", "format_version"),
+ [("1.0", "1.0"), ("2.0", "2.6"), (None, "1.0")],
+)
+def test_write_parquet_writer_version(df, tmp_path, writer_version,
format_version):
+ """Test the Parquet writer version. Note that writer_version=2.0 results in
+ format_version=2.6"""
+ if writer_version is None:
+ df.write_parquet(tmp_path)
+ else:
+ df.write_parquet(tmp_path, writer_version=writer_version)
- df.write_parquet(str(path), compression=compression)
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+ assert metadata["format_version"] == format_version
+
+
+@pytest.mark.parametrize("writer_version", ["1.2.3", "custom-version", "0"])
+def test_write_parquet_wrong_writer_version(df, tmp_path, writer_version):
+ """Test that invalid writer versions in Parquet throw an exception."""
+ with pytest.raises(
+ Exception, match="Unknown or unsupported parquet writer version"
+ ):
+ df.write_parquet(tmp_path, writer_version=writer_version)
+
+
+@pytest.mark.parametrize("dictionary_enabled", [True, False, None])
+def test_write_parquet_dictionary_enabled(df, tmp_path, dictionary_enabled):
+ """Test enabling/disabling the dictionaries in Parquet."""
+ df.write_parquet(tmp_path, dictionary_enabled=dictionary_enabled)
+ # by default, the dictionary is enabled, so None results in True
+ result = dictionary_enabled if dictionary_enabled is not None else True
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+
+ for row_group in metadata["row_groups"]:
+ for col in row_group["columns"]:
+ assert col["has_dictionary_page"] == result
+
+
+@pytest.mark.parametrize(
+ ("statistics_enabled", "has_statistics"),
+ [("page", True), ("chunk", True), ("none", False), (None, True)],
+)
+def test_write_parquet_statistics_enabled(
+ df, tmp_path, statistics_enabled, has_statistics
+):
+ """Test configuring the statistics in Parquet. In pyarrow we can only
check for
+ column-level statistics, so "page" and "chunk" are tested in the same
way."""
+ df.write_parquet(tmp_path, statistics_enabled=statistics_enabled)
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+
+ for row_group in metadata["row_groups"]:
+ for col in row_group["columns"]:
+ if has_statistics:
+ assert col["statistics"] is not None
+ else:
+ assert col["statistics"] is None
+
+
+@pytest.mark.parametrize("max_row_group_size", [1000, 5000, 10000, 100000])
+def test_write_parquet_max_row_group_size(large_df, tmp_path,
max_row_group_size):
+ """Test configuring the max number of rows per group in Parquet. These
test cases
+ guarantee that the number of rows for each row group is
max_row_group_size, given
+ the total number of rows is a multiple of max_row_group_size."""
+ large_df.write_parquet(tmp_path, max_row_group_size=max_row_group_size)
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+ for row_group in metadata["row_groups"]:
+ assert row_group["num_rows"] == max_row_group_size
+
+
+@pytest.mark.parametrize("created_by", ["datafusion", "datafusion-python",
"custom"])
+def test_write_parquet_created_by(df, tmp_path, created_by):
+ """Test configuring the created by metadata in Parquet."""
+ df.write_parquet(tmp_path, created_by=created_by)
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+ assert metadata["created_by"] == created_by
+
+
+@pytest.mark.parametrize("statistics_truncate_length", [5, 25, 50])
+def test_write_parquet_statistics_truncate_length(
+ df, tmp_path, statistics_truncate_length
+):
+ """Test configuring the truncate limit in Parquet's row-group-level
statistics."""
+ ctx = SessionContext()
+ data = {
+ "a": [
+ "a_the_quick_brown_fox_jumps_over_the_lazy_dog",
+ "m_the_quick_brown_fox_jumps_over_the_lazy_dog",
+ "z_the_quick_brown_fox_jumps_over_the_lazy_dog",
+ ],
+ "b": ["a_smaller", "m_smaller", "z_smaller"],
+ }
+ df = ctx.from_arrow(pa.record_batch(data))
+ df.write_parquet(tmp_path,
statistics_truncate_length=statistics_truncate_length)
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+
+ for row_group in metadata["row_groups"]:
+ for col in row_group["columns"]:
+ statistics = col["statistics"]
+ assert len(statistics["min"]) <= statistics_truncate_length
+ assert len(statistics["max"]) <= statistics_truncate_length
+
+
+def test_write_parquet_default_encoding(tmp_path):
+ """Test that, by default, Parquet files are written with dictionary
encoding.
+ Note that dictionary encoding is not used for boolean values, so it is not
tested
+ here."""
+ ctx = SessionContext()
+ data = {
+ "a": [1, 2, 3],
+ "b": ["1", "2", "3"],
+ "c": [1.01, 2.02, 3.03],
+ }
+ df = ctx.from_arrow(pa.record_batch(data))
+ df.write_parquet(tmp_path)
+
+ for file in tmp_path.rglob("*.parquet"):
+ parquet = pq.ParquetFile(file)
+ metadata = parquet.metadata.to_dict()
+
+ for row_group in metadata["row_groups"]:
+ for col in row_group["columns"]:
+ assert col["encodings"] == ("PLAIN", "RLE", "RLE_DICTIONARY")
+
+
+@pytest.mark.parametrize(
+ ("encoding", "data_types", "result"),
+ [
+ ("plain", ["int", "float", "str", "bool"], ("PLAIN", "RLE")),
+ ("rle", ["bool"], ("RLE",)),
+ ("delta_binary_packed", ["int"], ("RLE", "DELTA_BINARY_PACKED")),
+ ("delta_length_byte_array", ["str"], ("RLE",
"DELTA_LENGTH_BYTE_ARRAY")),
+ ("delta_byte_array", ["str"], ("RLE", "DELTA_BYTE_ARRAY")),
+ ("byte_stream_split", ["int", "float"], ("RLE", "BYTE_STREAM_SPLIT")),
+ ],
+)
+def test_write_parquet_encoding(tmp_path, encoding, data_types, result):
+ """Test different encodings in Parquet in their respective support column
types."""
+ ctx = SessionContext()
+
+ data = {}
+ for data_type in data_types:
+ match data_type:
+ case "int":
+ data["int"] = [1, 2, 3]
+ case "float":
+ data["float"] = [1.01, 2.02, 3.03]
+ case "str":
+ data["str"] = ["a", "b", "c"]
+ case "bool":
+ data["bool"] = [True, False, True]
Review Comment:
This code failed for me because our minimum supported python is 3.9 and
match was introduced in 3.10. 3.9 doesn't reach end of life until Oct 2025.
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