polars_cloud.ExecuteRemote.sink_parquet#

ExecuteRemote.sink_parquet(
uri: str,
*,
compression: ParquetCompression = 'zstd',
compression_level: int | None = None,
statistics: bool | str | dict[str,
bool] = True,
row_group_size: int | None = None,
data_page_size: int | None = None,
maintain_order: bool = True,
storage_options: dict[str,
Any] | None = None,
credential_provider: CredentialProviderFunction | Literal['auto'] | None = 'auto',
metadata: ParquetMetadata | None = None,
field_overwrites: ParquetFieldOverwrites | Sequence[ParquetFieldOverwrites] | Mapping[str,
ParquetFieldOverwrites] | None = None,
sink_to_single_file: None | bool = None,
optimizations: QueryOptFlags = <polars.lazyframe.opt_flags.QueryOptFlags object>,
) → DirectQuery | ProxyQuery#

Start executing the query and write the result to parquet.

Parameters:
uri

Path to which the output should be written. Must be a URI to an accessible object store location.

It is recommended to write to a directory path for example "my-location/", instead of as single file as a single file can only be written from a single node.

If set to "local", the query is executed locally.

compression{‘lz4’, ‘uncompressed’, ‘snappy’, ‘gzip’, ‘lzo’, ‘brotli’, ‘zstd’}

Choose “zstd” for good compression performance. Choose “lz4” for fast compression/decompression. Choose “snappy” for more backwards compatibility guarantees when you deal with older parquet readers.

compression_level

The level of compression to use. Higher compression means smaller files on disk.

  • “gzip” : min-level: 0, max-level: 10.

  • “brotli” : min-level: 0, max-level: 11.

  • “zstd” : min-level: 1, max-level: 22.

statistics

Write statistics to the parquet headers. This is the default behavior.

Possible values:

  • True: enable default set of statistics (default). Some statistics may be disabled.

  • False: disable all statistics

  • “full”: calculate and write all available statistics. Cannot be combined with use_pyarrow.

  • { "statistic-key": True / False, ... }. Cannot be combined with use_pyarrow. Available keys:

    • “min”: column minimum value (default: True)

    • “max”: column maximum value (default: True)

    • “distinct_count”: number of unique column values (default: False)

    • “null_count”: number of null values in column (default: True)

row_group_size

Size of the row groups in number of rows. Defaults to 512^2 rows.

data_page_size

Size of the data page in bytes. Defaults to 1024^2 bytes.

maintain_order

Maintain the order in which data is processed. Setting this to False can be much faster.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

storage_options

Options that indicate how to connect to a cloud provider.

The cloud providers currently supported are AWS, GCP, and Azure. See supported keys here:

  • aws

  • gcp

  • azure

  • Hugging Face (hf://): Accepts an API key under the token parameter: {'token': '...'}, or by setting the HF_TOKEN environment variable.

If storage_options is not provided, Polars will try to infer the information from environment variables.

credential_provider

Provide a function that can be called to provide cloud storage credentials. The function is expected to return a dictionary of credential keys along with an optional credential expiry time.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

metadata

A dictionary or callback to add key-values to the file-level Parquet metadata.

Warning

This functionality is considered experimental. It may be removed or changed at any point without it being considered a breaking change.

field_overwrites

Property overwrites for individual Parquet fields.

This allows more control over the writing process to the granularity of a Parquet field.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

sink_to_single_file

Perform the sink into a single file.

Setting this to True can reduce the amount of work that can be done in a distributed manner and therefore be more memory intensive and slower.

optimizations

The optimization passes done during query optimization.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Examples

>>> query.remote(ctx).sink_parquet("s3://your-bucket/folder/file.parquet")
<polars_cloud.query.query.ProxyQuery at 0x109ca47d0>