feat: Support Parquet writer options

python/datafusion/__init__.py

@@ -42,7 +42,7 @@
     SessionContext,
     SQLOptions,
 )
-from .dataframe import DataFrame
+from .dataframe import DataFrame, ParquetColumnOptions, ParquetWriterOptions
 from .expr import (
     Expr,
     WindowFrame,
@@ -66,6 +66,8 @@
     "ExecutionPlan",
     "Expr",
     "LogicalPlan",
+    "ParquetColumnOptions",
+    "ParquetWriterOptions",
     "RecordBatch",
     "RecordBatchStream",
     "RuntimeEnvBuilder",

python/datafusion/dataframe.py

@@ -53,6 +53,8 @@
 
 from enum import Enum
 
+from datafusion._internal import ParquetColumnOptions as ParquetColumnOptionsInternal
+from datafusion._internal import ParquetWriterOptions as ParquetWriterOptionsInternal
 from datafusion.expr import Expr, SortExpr, sort_or_default
 
 
@@ -114,6 +116,173 @@ def get_default_level(self) -> Optional[int]:
         return None
 
 
+class ParquetWriterOptions:
+    """Advanced parquet writer options.
+
+    Allows settings the writer options that apply to the entire file. Some options can
+    also be set on a column by column basis, with the field `column_specific_options`
+    (see `ParquetColumnOptions`).
+
+    Attributes:
+        data_pagesize_limit: Sets best effort maximum size of data page in bytes.
+        write_batch_size: Sets write_batch_size in bytes.
+        writer_version: Sets parquet writer version. Valid values are `1.0` and
+            `2.0`.
+        skip_arrow_metadata: Skip encoding the embedded arrow metadata in the
+            KV_meta.
+        compression: Compression type to use. Default is "zstd(3)".
+            Available compression types are
+            - "uncompressed": No compression.
+            - "snappy": Snappy compression.
+            - "gzip(n)": Gzip compression with level n.
+            - "brotli(n)": Brotli compression with level n.
+            - "lz4": LZ4 compression.
+            - "lz4_raw": LZ4_RAW compression.
+            - "zstd(n)": Zstandard compression with level n.
+        dictionary_enabled: Sets if dictionary encoding is enabled. If None, uses
+            the default parquet writer setting.
+        dictionary_page_size_limit: Sets best effort maximum dictionary page size,
+            in bytes.
+        statistics_enabled: Sets if statistics are enabled for any column Valid
+            values are `none`, `chunk`, and `page`. If None, uses the default
+            parquet writer setting.
+        max_row_group_size: Target maximum number of rows in each row group
+            (defaults to 1M rows). Writing larger row groups requires more memory to
+            write, but can get better compression and be faster to read.
+        created_by: Sets "created by" property.
+        column_index_truncate_length: Sets column index truncate length.
+        statistics_truncate_length: Sets statistics truncate length. If None, uses
+            the default parquet writer setting.
+        data_page_row_count_limit: Sets best effort maximum number of rows in a data
+            page.
+        encoding: Sets default encoding for any column. Valid values are `plain`,
+            `plain_dictionary`, `rle`, `bit_packed`, `delta_binary_packed`,
+            `delta_length_byte_array`, `delta_byte_array`, `rle_dictionary`, and
+            `byte_stream_split`. If None, uses the default parquet writer setting.
+        bloom_filter_on_write: Write bloom filters for all columns when creating
+            parquet files.
+        bloom_filter_fpp: Sets bloom filter false positive probability. If None,
+            uses the default parquet writer setting
+        bloom_filter_ndv: Sets bloom filter number of distinct values. If None, uses
+            the default parquet writer setting.
+        allow_single_file_parallelism: Controls whether DataFusion will attempt to
+            speed up writing parquet files by serializing them in parallel. Each
+            column in each row group in each output file are serialized in parallel
+            leveraging a maximum possible core count of n_files * n_row_groups *
+            n_columns.
+        maximum_parallel_row_group_writers: By default parallel parquet writer is
+            tuned for minimum memory usage in a streaming execution plan. You may
+            see a performance benefit when writing large parquet files by increasing
+            `maximum_parallel_row_group_writers` and
+            `maximum_buffered_record_batches_per_stream` if your system has idle
+            cores and can tolerate additional memory usage. Boosting these values is
+            likely worthwhile when writing out already in-memory data, such as from
+            a cached data frame.
+        maximum_buffered_record_batches_per_stream: See
+            `maximum_parallel_row_group_writers`.
+        column_specific_options: Overrides options for specific columns. If a column
+            is not a part of this dictionary, it will use the parameters provided here.
+    """
+
+    def __init__(
+        self,
+        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,
+    ) -> None:
+        """Initialize the ParquetWriterOptions."""
+        self.data_pagesize_limit = data_pagesize_limit
+        self.write_batch_size = write_batch_size
+        self.writer_version = writer_version
+        self.skip_arrow_metadata = skip_arrow_metadata
+        self.compression = compression
+        self.dictionary_enabled = dictionary_enabled
+        self.dictionary_page_size_limit = dictionary_page_size_limit
+        self.statistics_enabled = statistics_enabled
+        self.max_row_group_size = max_row_group_size
+        self.created_by = created_by
+        self.column_index_truncate_length = column_index_truncate_length
+        self.statistics_truncate_length = statistics_truncate_length
+        self.data_page_row_count_limit = data_page_row_count_limit
+        self.encoding = encoding
+        self.bloom_filter_on_write = bloom_filter_on_write
+        self.bloom_filter_fpp = bloom_filter_fpp
+        self.bloom_filter_ndv = bloom_filter_ndv
+        self.allow_single_file_parallelism = allow_single_file_parallelism
+        self.maximum_parallel_row_group_writers = maximum_parallel_row_group_writers
+        self.maximum_buffered_record_batches_per_stream = (
+            maximum_buffered_record_batches_per_stream
+        )
+        self.column_specific_options = column_specific_options
+
+
+class ParquetColumnOptions:
+    """Parquet options for individual columns.
+
+    Contains the available options that can be applied for an individual Parquet column,
+    replacing the global options in `ParquetWriterOptions`.
+
+    Attributes:
+        encoding: Sets encoding for the column path. Valid values are: `plain`,
+            `plain_dictionary`, `rle`, `bit_packed`, `delta_binary_packed`,
+            `delta_length_byte_array`, `delta_byte_array`, `rle_dictionary`, and
+            `byte_stream_split`. These values are not case-sensitive. If `None`, uses
+            the default parquet options
+        dictionary_enabled: Sets if dictionary encoding is enabled for the column path.
+            If `None`, uses the default parquet options
+        compression: Sets default parquet compression codec for the column path. Valid
+            values are `uncompressed`, `snappy`, `gzip(level)`, `lzo`, `brotli(level)`,
+            `lz4`, `zstd(level)`, and `lz4_raw`. These values are not case-sensitive. If
+            `None`, uses the default parquet options.
+        statistics_enabled: Sets if statistics are enabled for the column Valid values
+            are: `none`, `chunk`, and `page` These values are not case sensitive. If
+            `None`, uses the default parquet options.
+        bloom_filter_enabled: Sets if bloom filter is enabled for the column path. If
+            `None`, uses the default parquet options.
+        bloom_filter_fpp: Sets bloom filter false positive probability for the column
+            path. If `None`, uses the default parquet options.
+        bloom_filter_ndv: Sets bloom filter number of distinct values. If `None`, uses
+            the default parquet options.
+    """
+
+    def __init__(
+        self,
+        encoding: Optional[str] = None,
+        dictionary_enabled: Optional[bool] = None,
+        compression: Optional[str] = None,
+        statistics_enabled: Optional[str] = None,
+        bloom_filter_enabled: Optional[bool] = None,
+        bloom_filter_fpp: Optional[float] = None,
+        bloom_filter_ndv: Optional[int] = None,
+    ) -> None:
+        """Initialize the ParquetColumnOptions."""
+        self.encoding = encoding
+        self.dictionary_enabled = dictionary_enabled
+        self.compression = compression
+        self.statistics_enabled = statistics_enabled
+        self.bloom_filter_enabled = bloom_filter_enabled
+        self.bloom_filter_fpp = bloom_filter_fpp
+        self.bloom_filter_ndv = bloom_filter_ndv
+
+
 class DataFrame:
     """Two dimensional table representation of data.
 
@@ -737,6 +906,58 @@ def write_parquet(
 
         self.df.write_parquet(str(path), compression.value, compression_level)
 
+    def write_parquet_options(
+        self, path: str | pathlib.Path, options: ParquetWriterOptions
+    ) -> None:
+        """Execute the :py:class:`DataFrame` and write the results to a Parquet file.
+
+        Allows advanced writer options to be set with `ParquetWriterOptions`.
+
+        Args:
+            path: Path of the Parquet file to write.
+            options: Sets the writer parquet options (see `ParquetWriterOptions`).
+        """
+        options_internal = ParquetWriterOptionsInternal(
+            options.data_pagesize_limit,
+            options.write_batch_size,
+            options.writer_version,
+            options.skip_arrow_metadata,
+            options.compression,
+            options.dictionary_enabled,
+            options.dictionary_page_size_limit,
+            options.statistics_enabled,
+            options.max_row_group_size,
+            options.created_by,
+            options.column_index_truncate_length,
+            options.statistics_truncate_length,
+            options.data_page_row_count_limit,
+            options.encoding,
+            options.bloom_filter_on_write,
+            options.bloom_filter_fpp,
+            options.bloom_filter_ndv,
+            options.allow_single_file_parallelism,
+            options.maximum_parallel_row_group_writers,
+            options.maximum_buffered_record_batches_per_stream,
+        )
+
+        column_specific_options_internal = {}
+        for column, opts in (options.column_specific_options or {}).items():
+            column_specific_options_internal[column] = ParquetColumnOptionsInternal(
+                bloom_filter_enabled=opts.bloom_filter_enabled,
+                encoding=opts.encoding,
+                dictionary_enabled=opts.dictionary_enabled,
+                compression=opts.compression,
+                statistics_enabled=opts.statistics_enabled,
+                bloom_filter_fpp=opts.bloom_filter_fpp,
+                bloom_filter_ndv=opts.bloom_filter_ndv,
+            )
+
+        self.df.write_parquet_options(
+            str(path),
+            options_internal,
+            column_specific_options_internal,
+        )
+
     def write_json(self, path: str | pathlib.Path) -> None:
         """Execute the :py:class:`DataFrame` and write the results to a JSON file.