dagster-polars 0.1.0

Dagster integration library for Polars

dagster-polars

Polars integration library for Dagster.

Installation

pip install dagster-polars

IOManagers

All IOManagers log various metadata about the DataFrame - size, schema, sample, stats. For all IOManagers the columns input metadata can be used to select a subset of columns to load.

BasePolarsUPathIOManager

Is a base class for IO managers that store Polars DataFrames in filesystem - local or remote. Shouldn't be used directly unless you want to implement your own IOManager. It has the following features (which are inherited by all UPath-based IOManagers in this library):

• inherits all the features of the UPathIOManager - works with local and remote filesystems (like S3), supports loading multiple partitions (use dict[str, DataFrame] type annotation), ...
• sensitive to type annotations. Will load eager or lazy DataFrame based on the type annotation: polars.DataFrame or LazyFrame.
• Optional type annotations are supported. If the input annotation is Optional and is missing in the filesystem, the IOManager will skip loading the input and return None instead. If the output annotation is Optional and the output is None, the IOManager will skip writing the output to the filesystem.
• Supports reading/writing arbitrary metadata dict into storage (in contrast to saving Dagster metadata into Dagster's postgres). This metadata can be then accessed outside Dagster. Tuple[DataFrame/LazyFrame, Dict[str, Any]] type annotation must be used on the input/output to trigger metadata read/write. This feature is supported in:
  • PolarsParquetIOManager - metadata is saved in the Parquet file's schema metadata as json-serialized bytes at "dagster_polars_storage_metadata" key.

The following typing aliases are provided for convenience:

• StorageMetadata = Dict[str, Any]
• DataFramePartitions = Dict[str, DataFrame]
• LazyFramePartitions = Dict[str, LazyFrame]
• DataFrameWithMetadata = Tuple[DataFrame, StorageMetadata]
• LazyFrameWithMetadata = Tuple[LazyFrame, StorageMetadata]
• DataFramePartitionsWithMetadata = Dict[str, DataFrameWithMetadata]
• LazyFramePartitionsWithMetadata = Dict[str, LazyFrameWithMetadata]

Complete description of dagster_polars behavior for all supported type annotations:

Type annotation	Behavior
DataFrame	read/write DataFrame. Raise error if it's not found in storage.
LazyFrame	read LazyFrame. Raise error if it's not found in storage.
Optional[DataFrame]	read/write DataFrame. Skip if it's not found in storage or the output is None.
Optional[LazyFrame]	read LazyFrame. Skip if it's not found in storage
DataFrameWithMetadata	read/write DataFrame and metadata. Raise error if it's not found in storage.
LazyFrameWithMetadata	read LazyFrame and metadata. Raise error if it's not found in storage.
Optional[DataFrameWithMetadata]	read/write DataFrame and metadata. Skip if it's not found in storage or the output is None.
Optional[LazyFrameWithMetadata]	read LazyFrame and metadata. Skip if it's not found in storage.
DataFramePartitions	read multiple DataFrames as Dict[str, DataFrame]. Raise an error if any of thems is not found in storage, unlesee "allow_missing_partitions" input metadata is set to True
LazyFramePartitions	read multiple LazyFrames as Dict[str, LazyFrame]. Raise an error if any of thems is not found in storage, unlesee "allow_missing_partitions" input metadata is set to True
DataFramePartitionsWithMetadata	read multiple DataFrames and metadata as Dict[str, Tuple[DataFrame, StorageMetadata]]. Raise an error if any of thems is not found in storage, unlesee "allow_missing_partitions" input metadata is set to True
LazyFramePartitionsWithMetadata	read multiple LazyFrames and metadata as Dict[str, Tuple[LazyFrame, StorageMetadata]]. Raise an error if any of thems is not found in storage, unlesee "allow_missing_partitions" input metadata is set to True

Generic builtins (like tuple[...] instead of Tuple[...]) are supported for Python >= 3.9.

PolarsParquetIOManager

Implements reading and writing files in Apache Parquet format. Supports reading partitioned Parquet datasets (for example, often produced by Spark). All read/write options can be set via Dagster metadata values. Supports writing/reading custom metadata into the Parquet file's schema metadata.

PolarsDeltaIOManager

• PolarsDeltaIOManager - for reading and writing Delta Lake. All read/write options can be set via Dagster metadata values. mode, overwrite_schema and version can be set via config parameters. partition_by can be set to use native Delta Lake partitioning (it's passed to delta_write_options of write_delta). The IOManager won't manage partitioning in this case, and all the asset partitions will be stored in the same Delta Table directory. You are responsible for filtering correct partitions when reading the data in the downstream assets. Required dependencies can be installed with pip install 'dagster-polars[deltalake]'.

BigQueryPolarsIOManager

Implements reading and writing data from/to BigQuery. Supports writing partitioned tables ("partition_expr" input metadata key must be specified). Required dependencies can be installed with pip install 'dagster-polars[gcp]'.

Examples

See examples.

Development

Installation

poetry install
poetry run pre-commit install

Testing

poetry run pytest

Ideas

• Data validation like in dagster-pandas
• Maybe use DagsterTypeLoader ?